VISUAL NAVIGATION in ANTS Antoine Wystrach

TTHHÈÈSSEE

En vue de l'obtention du

DOCTORAT DE L’UNIVERSITÉ DE TOULOUSE

Délivré par l'Université Paul Sabatier - Toulouse III Discipline ou spécialité : Neurosciences, Comportement et Cognition

Présentée et soutenue par Antoine Wystrach Le 28 Novembre 2011

Titre : Visual navigation in ants

JURY

Pr Barbara Webb (IPAB, University of Edinburgh) Rapporteur Dr Brunot Poucet (LNC, Université de Provence) Rapporteur Pr Martin Giurfa (CRCA, UPS Toulouse III) Directeur d'équipe Dr Guy Beugnon (CRCA, UPS Toulouse III) Directeur de thèse

Ecole doctorale : CLESCO Unité de recherche : Centre de Recherches sur la Cognition Animale, UMR 5169 Directeur(s) de Thèse : Pr Ken Cheng et Dr Guy Beugnon Rapporteurs : Pr Barbara Webb et Dr Brunot Poucet

VISUAL NAVIGATION IN ANTS

Antoine Wystrach

Centre de Recherche sur la Cognition Animale Université Paul Sabatier

Submitted October 2011

Thèse en vue de l’obtention du Doctorat de l’Université de Toulouse

TABLE OF CONTENTS

PREFACE SUMMARY i

RÉSUMÉ iii

ACKNOWLEDGEMENTS vii

INTRODUCTION 1

CHAPTER I Ants learn geometry and features 13

CHAPTER II Geometry, features, and panoramic views: ants in 29 rectangular arenas.

CHAPTER III Views, landmarks, and routes: how do desert ants 71 negotiate an obstacle course?

CHAPTER IV Landmarks or panoramas: what do navigating ants 95 attend to for guidance?

CHAPTER V Ants might use different view-matching strategies 123 on and off the route.

CHAPTER VI Ant visual memories: multiple snapshots or holistic 151 encoding?

CHAPTER VII Why we study insect navigation – Ants, Umwelts 179 and Morgan's Canon.

CONCLUSION 195

REFERENCES 205

APPENDIX I Formatted publication of chapters 1,2,3 and 4. 221

APPENDIX II Other publications related to insect navigation. 271

SUMMARY

Navigating efficiently in the outside world requires many cognitive abilities like extracting, memorising, and processing information. The remarkable navigational abilities of insects are an existence proof of how small brains can produce exquisitely efficient, robust behaviour in complex environments. During their foraging trips, insects, like ants or bees, are known to rely on both path integration and learnt visual cues to recapitulate a route or reach familiar places like the nest. The strategy of path integration is well understood, but much less is known about how insects acquire and use visual information. Field studies give good descriptions of visually guided routes, but our understanding of the underlying mechanisms comes mainly from simplified laboratory conditions using artificial, geometrically simple landmarks.

My thesis proposes an integrative approach that combines 1– field and lab experiments on two visually guided ant species (Melophorus bagoti and Gigantiops destructor) and 2– an analysis of panoramic pictures recorded along the animal’s route. The use of panoramic pictures allows an objective quantification of the visual information available to the animal. Results from both species, in the lab and the field, converged, showing that ants do not segregate their visual world into objects, such as landmarks or discrete features, as a human observers might assume. Instead, efficient navigation seems to arise from the use of cues widespread on the ants’ panoramic visual field, encompassing both proximal and distal objects together. Such relatively unprocessed panoramic views, even at low resolution, provide remarkably unambiguous spatial information in natural environment. Using such a simple but efficient panoramic visual input, rather than focusing on isolated landmarks, seems an appropriate strategy to cope with the complexity of natural scenes and the poor resolution of insects’ eyes.

Also, panoramic pictures can serve as a basis for running analytical models of navigation. The predictions of these models can be directly compared with the actual behaviour of real ants, allowing the iterative tuning and testing of different hypotheses. This integrative approach led me to the conclusion that ants do not rely on a single navigational technique, but might switch between strategies according to whether they are on or off their familiar terrain. For example,

ants can recapitulate robustly a familiar route by simply aligning their body in a way that the current view matches best their memory. However, this strategy becomes ineffective when displaced away from the familiar route. In such a case, ants appear to head instead towards the regions where the skyline appears lower than the height recorded in their memory, which generally leads them closer to a familiar location. How ants choose between strategies at a given time might be simply based on the degree of familiarity of the panoramic scene currently perceived.

Finally, this thesis raises questions about the nature of ant memories. Past studies proposed that ants memorise a succession of discrete 2D ‘snapshots’ of their surroundings. Contrastingly, results obtained here show that knowledge from the end of a foraging route (15 m) impacts strongly on the behaviour at the beginning of the route, suggesting that the visual knowledge of a whole foraging route may be compacted into a single holistic memory. Accordingly, repetitive training on the exact same route clearly affects the ants’ behaviour, suggesting that the memorised information is processed and not ‘obtained at once’. While navigating along their familiar route, ants’ visual system is continually stimulated by a slowly evolving scene, and learning a general pattern of stimulation rather than storing independent but very similar snapshots appears a reasonable hypothesis to explain navigation on a natural scale; such learning works remarkably well with neural networks. Nonetheless, what the precise nature of ants’ visual memories is and how elaborated they are remain wide open questions.

Overall, my thesis tackles the nature of ants’ perception and memory as well as how both are processed together to output an appropriate navigational response. These results are discussed in the light of comparative cognition. Both vertebrates and insects have resolved the same problem of navigating efficiently in the world. In light of Darwin’s theory of evolution, there is no a priori reason to think that there is a clear division between cognitive mechanisms of different species. The actual gap between insect and vertebrate cognitive sciences may result more from different approaches rather than real differences. Research on insect navigation has been approached with a bottom-up philosophy, one that examines how simple mechanisms can produce seemingly complex behaviour. Such parsimonious solutions, like the ones explored in the present thesis, can provide useful baseline hypotheses for navigation in other larger-brained animals, and thus contribute to a more truly comparative cognition.

RÉSUMÉ

Les remarquables capacités de navigation des insectes nous prouvent à quel point ces « mini- cerveaux » peuvent produire des comportements admirablement robustes et efficaces dans des environnements complexes. En effet, être capable de naviguer de façon efficace et autonome dans un environnement parfois hostile (désert, forêt tropicale) sollicite l’intervention de nombreux processus cognitifs impliquant l’extraction, la mémorisation et le traitement de l’information spatiale préalables à une prise de décision locomotrice orientée dans l’espace. Lors de leurs excursions hors du nid, les insectes tels que les abeilles, guêpes ou fourmis, se fient à un processus d’intégration du trajet, mais également à des indices visuels qui leur permettent de mémoriser des routes et de retrouver certains sites alimentaires familiers et leur nid. L’étude des mécanismes d’intégration du trajet a fait l’objet de nombreux travaux, par contre, nos connaissances à propos de l’utilisation d’indices visuels sont beaucoup plus limitées et proviennent principalement d’études menées dans des environnements artificiellement simplifiés, dont les conclusions sont parfois difficilement transposables aux conditions naturelles.

Cette thèse propose une approche intégrative, combinant 1- des études de terrains et de laboratoire conduites sur deux espèces de fourmis spécialistes de la navigation visuelle (Melophorus bagoti et Gigantiops destructor) et 2- des analyses de photos panoramiques prisent aux endroits où les fourmis naviguent qui permettent de quantifier objectivement l’information visuelle accessible à l’insecte. Les résultats convergents obtenus sur le terrain et au laboratoire permettent de montrer que, chez ces deux espèces, les fourmis ne fragmentent pas leur monde visuel en multiples objets indépendants, et donc ne mémorisent pas de ‘repères visuels’ ou de balises particuliers comme le ferait un être humain. En fait, l’efficacité de leur navigation émergerait de l’utilisation de paramètres visuels étendus sur l’ensemble de leur champ visuel panoramique, incluant repères proximaux comme distaux, sans les individualiser. Contre-intuitivement, de telles images panoramiques, même à basse résolution, fournissent une information spatiale précise et non ambiguë dans les environnements naturels. Plutôt qu’une focalisation sur des repères isolés, l’utilisation de vues dans leur globalité semble être plus efficace pour représenter la complexité des scènes naturelles et être mieux adaptée à la basse résolution du système visuel des insectes.

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Les photos panoramiques enregistrées peuvent également servir à l’élaboration de modèles navigationnels. Les prédictions de ces modèles sont ici directement comparées au comportement des fourmis, permettant ainsi de tester et d’améliorer les différentes hypothèses envisagées. Cette approche m’a conduit à la conclusion selon laquelle les fourmis utilisent leurs vues panoramiques de façons différentes suivant qu’elles se déplacent en terrain familier ou non. Par exemple, aligner son corps de manière à ce que la vue perçue reproduise au mieux l’information mémorisée est une stratégie très efficace pour naviguer le long d’une route bien connue ; mais n’est d’aucune efficacité si l’insecte se retrouve en territoire nouveau, écarté du chemin familier. Dans ces cas critiques, les fourmis semblent recourir à une seconde stratégie qui consiste à se déplacer vers les régions présentant une ligne d’horizon plus basse que celle mémorisée, ce qui généralement conduit vers le terrain familier. Afin de choisir parmi ces deux différentes stratégies, les fourmis semblent tout simplement se fier au degré de familiarisation avec le panorama perçu.

Cette thèse soulève aussi la question de la nature de l’information visuelle mémorisée par les insectes. Le modèle du « snapshot » qui prédomine dans la littérature suppose que les fourmis mémorisent une séquence d’instantanés photographiques placés à différents points le long de leurs routes. A l’inverse, les résultats obtenus dans le présent travail montrent que l’information visuelle mémorisée au bout d’une route (15 mètres) modifie l’information mémorisée à l’autre extrémité de cette même route, ce qui suggère que la connaissance visuelle de l’ensemble de la route soit compactée en une seule et même représentation mémorisée. Cette hypothèse s’accorde aussi avec d’autres de nos résultats montrant que la mémoire visuelle ne s’acquiert pas instantanément, mais se développe et s’affine avec l’expérience répétée. Lorsqu’une fourmi navigue le long de sa route, ses récepteurs visuels sont stimulés de façon continue par une scène évoluant doucement et régulièrement au fur et à mesure du déplacement. Mémoriser un pattern général de stimulations, plutôt qu’une série de « snapshots » indépendants et très ressemblants les uns aux autres, constitue une hypothèse parcimonieuse. Cette hypothèse s’applique en outre particulièrement bien aux modèles en réseaux de neurones, suggérant sa pertinence biologique.

Dans l’ensemble, cette thèse s’intéresse à la nature des perceptions et de la mémoire visuelle des fourmis, ainsi qu’à la manière dont elles sont intégrées et traitées afin de produire une réponse navigationnelle appropriée. Nos résultats sont aussi discutés dans le cadre de la cognition comparée. Insectes comme vertébrés ont résolu le même problème qui consiste à iv

naviguer de façon efficace sur terre. A la lumière de la théorie de l’évolution de Darwin, il n’y a ‘a priori’ aucune raison de penser qu’il existe une forme de transition brutale entre les mécanismes cognitifs des différentes espèces animales. Le fossé marqué entre insectes et vertébrés au sein des sciences cognitives pourrait bien être dû à des approches différentes plutôt qu’à de vraies différences ontologiques. Historiquement, l’étude de la navigation de l’insecte a suivi une approche de type ‘bottom-up’ qui recherche comment des comportements apparemment complexes peuvent découler de mécanismes simples. Ces solutions parcimonieuses, comme celles explorées dans cette thèse, peuvent fournir de remarquables hypothèses de base pour expliquer la navigation chez d’autres espèces animales aux cerveaux et comportements apparemment plus complexes, contribuant ainsi à une véritable cognition comparée.

ACKNOWLEDGEMENTS

While doing a PhD in Co-tutelle, everything is multiplied by two, except the time, which results in a pretty dense life and the double amount of people to be grateful to. I first would like to thank both hemispheres for cooperating so well to provide me with a continual summer over 4 years, the only constant thing during my PhD. Otherwise, every half a year my life switched completely, although I always found wonderful support that enabled me to keep balance. Regarding the abiotic environment, I substituted French wine and good cheese in France for a wonderful, pristine nature in Australia. Regarding work, I switched from a desk in a mostly neuro-scientific lab (CRCA) and doing experiments indoors to a cottage in a forested behavioural lab (BBE) and outdoors fieldwork.

Regarding my supervisors, I switched from Guy Beugnon to Ken Cheng. I first would like to thank Ken Cheng, for enabling this PhD and field work in Alice Springs, for his open mind, his remarkably acute understanding of my sometimes tricky problems, his wise solutions, his confidence in me and his amazing celerity in reviewing manuscripts. I am also grateful to my other supervisor Guy Beugnon for enabling me to go to French Guyana, for his extreme tolerance and absolute trust in my ideas and projects. I think it would be appropriate to add Paul Graham here, who has been my main source of guidance and inspiration, and with whom I will have the chance to continue my academic life as a post-doc. If researchers were Jedi, I think I would have been his padawan. Thanks Paul, may the ant navigation be with you. Finally, I would like to thanks Jochen Zeil, who invited me as a speaker in a prestigious international conference and whom I suspect has been a recurrent reviewer of my publications, one with always good suggestions. Thanks.

Regarding my friends, there are five categories: 1-My friend from Toulouse: John and Deborah, who have been of vital support as my soldier models in the war against French administration. Gérard and Maud, for their effective help in the CRCA jungle. Jeremy, who doesn’t need any public compliments. I am very grateful to Quentin, Florian, Skan, Caro, Alba, Anais, Auré, Julien and Jano for bringing me so much warmth and showing me how to be happy in Toulouse (I am also sometimes grateful to Marco for asking me extremely interesting questions about animal behaviour and never listening to my answers).

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2- My friends from Caen: before all, my indestructible childhood friends Jimmy, Nico and Thibo, scaffold of my life, as well as Marion, Nathan and Jeanno who made a wonderful comeback as great friends. I also thank Thomas Lebrette and Virginie Jean, simply because they will always be there for me. Also Manu, who gave me both great advices and time. 3- My friends from Sydney: firstly, massive thanks to Diala, Madeleine and Nikki who cheerfully taught me most of my English as well as how to be cool in Sydney. Patrick, which must be the best office mate one can hope for always being available and providing a remarkable ‘English filter’. I am also extremely grateful to the more recently arrived lizard gang: Dan, Dani, Yianyian and Pau, which undeniably made the lab (during the day) and our house (during the night) permanently jubilant and shining places. I loved those moments. 4- The outsiders, that is, the ones that are always there but never here (because they were doing a PhD like me). Anai, Uriel and Erik with whom I continually shared – via modern ways of communication – both my emotional and intellectual life. Cimer. 5- The specials, that is, the ones who manage to be part of my life for more than 6 months in a row. Laurence, whose permanent hearty laughs enlightened my days and made my happiest year during this PhD. Merci beaucoup. Fernando, whose constant connexion and permanent understanding substituted what Santa Clause did not bring to me. And Seppi, without whom I would have had: no home, no delicious food, lost 10 times my bag or pullover, lived in a tent for 4 month in Alice Spring, no cosy evenings, a bit less GnT, no awesome weekends and no Christmas… without whom this adventure wouldn’t have been the same at all. Thanks.

Regarding girlfriends, the Co-tutelle gave me a hard time, as I did not find one who could follow me across the globe. I am not sure it is appropriate to mention names here as some might still be a bit jealous (I dare that because they all have a priceless sense of humour). I am indefinitely grateful that you accepted me even if you knew it would inevitably lead to a terrible goodbye. I regret deeply I couldn’t offer you a better stability. Thanks.

Finally my family, my grand-parents Moutti, Oma, Opa and particularly my parents Matthias and Bernadette, for which there was no substitute in Australia. Since I understand a bit about the mechanisms of ontogeny, I realised how much I should be grateful to them, and I am. For their constant and absolute support, I dedicate this work to them.

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Enfin, ma famille, mes grands-parents Moutti, Oma, Opa et particulièrement mes parents Matthias et Bernadette, pour qui il n’y a pas de substitut possible en Australie. Depuis que je comprends un peu les mécanismes de l’ontogenèse, je réalise à quel point je devrais leur être reconnaissant, et je le suis. Pour leur soutien constant et absolu, je leur dédicace ce travail.

To my Parents

ix Preface

Introduction

INTRODUCTION

Why navigation?

By suggesting that only humans have souls and other animals are machines, Descartes offered a formal framework for segregating human from other animals in the quest of understanding animal intelligence (Descartes, 1637). Today, many biologists and psychologists are focused on investigating the development and precursors of seemingly unique human intellectual capacities (de Waal and Ferrari, 2010). But in light of Darwin’s theory of evolution, the modern study of comparative cognition often takes the point of view that differences between human and animal cognition are likely to be of a continuous nature (Shettleworth, 2010b). From Aplysia to humans, many similarities in how the neural hardware works may be found, from the nature of action potentials to the range of neurotransmitters used in communication between neurons (Kandel et al., 2000). Nevertheless, natural selection might produce different ‘cognitive phenotypes’ suited to different niches (Dukas, 1998). By understanding similarities and differences between species in their cognitive solutions to day-to-day tasks, one can explore how natural intelligence depends on factors such as environment, social structure, evolutionary history and brain size, and thus unravel functional and mechanistic principles underlying animal cognition. For comparisons between species to be most meaningful, it helps to look at how animals solve similar problems. In this context, navigation provides an ideal ground. Indeed, most animals have to solve the problem of navigating to important locations and comparisons across taxa are often facilitated by the similarity of environments within which different species navigate. Research suggests that to return to important locations, such as a nest or food sources, animals rely on multiple cognitive processes (Shettleworth, 2010, ch. 8). Animals must filter and learn the environmental information that is appropriate for navigation, they must organise those memories robustly and then, when navigating, convert those memories into spatial decisions. Moreover, the goal of a navigating animal – getting from A to B – is often clearly defined and its movements provide an easily recordable read- out of the work of its brain. Taken together, these things make navigation a particularly useful and valuable behavioural domain for the study of comparative cognition.

Introduction

Why insects?

There is ever-growing evidence that complex ‘clever-looking’ animal behaviour may arise from simple mechanisms rather than ‘higher’ processes (Shettleworth, 2010a). The remarkable navigational abilities of social insects are proof that small brains can produce exquisitely efficient, robust navigation in complex environments (Wehner, 2003; Srinivasan, 2010). Consequently, studies of the smaller-brained navigational specialists, such as social hymenopterans, give us an opportunity to understand the minimum cognitive requirements for sophisticated navigation. Explanations of insects’ spatial behaviour are usually parsimonious and unburdened by assumptions about higher-level cognitive processes and thus provide useful baseline hypotheses for robotics (Srinivasan, Mandyam V., 2011) and other larger- brained animals (Wang and Spelke, 2002; Cheng, 2008; Platt and Spelke, 2009). Insects provide a diverse repertoire of innate and learned behaviours (Srinivasan, 2010) that can be tackled at different levels. The tractability of their neural circuits, often possessing individually identifiable and physiologically accessible neurons, has produced a remarkable understanding of sensori-motor integration at the cellular level (Huston and Jayaraman, 2011), and their fast development and short lifespans allow experimental control of an individual’s experience and a colony’s development for behavioural studies (see for example (Tautz et al., 2003). Insect spatial behaviours have been tackled on a range of different levels: from the arrangement of photo-receptors within ommatidia (Rossel and Wehner, 1986; Wehner, 1994), to the neural sensory-motor integration in their brain (Dyer et al., 2011; Huston and Jayaraman, 2011), to the motor reorientation response and development of individual routes (Wehner et al., 2004; Lent et al., 2010; Kraft et al., 2011), to the learning and maintenance of those individual routes (Judd and Collett, 1998; Collett et al., 2003; Kohler and Wehner, 2005; Macquart et al., 2006; Collett, 2010), to the social interactions between foragers that underlie emergent collective behaviour, to the resulting efficient exploitation of the environment at the colony level (Fresneau, 1994; Seeley, 2010). Such knowledge across mechanistic levels enables a holistic understanding of the question, a feat that is harder to achieve in vertebrates. Solitary foraging ants in particular are a superb model system for studies of navigation as they can be easily tracked over their entire spatial range (and lifetime) (Wehner et al., 2004; Muser et al., 2005) in environments where we can assess the available sensory information (Zeil et al., 2003; Stürzl and Zeil, 2007; Philippides et al., 2011). Ants have

Introduction colonised almost all habitats of the world (Hölldobler and Wilson, 1990). Species of the same genus can often be found in many different habitats and different subfamilies and tribes can be found in equivalent habitats. This provides an ideal ground for direct interspecific comparisons designed to address how structures and cognitive mechanisms have evolved to meet specific niche requirements (Greiner et al., 2007; Schwarz and Cheng, 2010; Bühlmann et al., 2011; Narendra et al., 2011). These advantages make ant navigation an ideal candidate system for studying both proximate and ultimate processes that underlie natural cognition.

Current understanding of insect navigation

The remarkable ability of ants, bees and wasps to recapitulate routes and accurately pinpoint their nest or burrow has interested scientists for centuries (Santschi, 1913). The first recorded experimental displacements or manipulation of cues are a century old (Santschi, 1913; Cornetz, 1914; Sobel, 1990). These seminal works led to an ever-growing interest in experiments designed to explore the mechanisms underlying insects’ navigation (Tinbergen and Kruyt, 1938; Von Frisch, 1967; Wehner and Räber, 1979; Cartwright and Collett, 1983). Nowadays, our current understanding is that efficient navigation in insects arises from the combined use of two major strategies: path integration and information learnt about the visual surroundings. The first strategy, Path Integration (PI), is a mechanism in which distance and direction of travel are continuously integrated to keep track of the direct path home (Wehner and Srinivasan, 2003). Insects obtain directional information from a celestial compass (Wehner and Menzel, 1969), and distance information is provided by a step-counter (Thielin- Bescond and Beugnon, 2005; Wittlinger et al., 2007; Wolf, 2011) and ventral optic flow (Ronacher and Wehner, 1995) in ants or optic flow in flying insects (Srinivasan et al., 2000). PI in desert ants can be computed along two- or three-dimensional paths (Wohlgemuth et al., 2001, 2002) and can be remarkably accurate compared to vertebrates (Etienne et al., 1996; Klatzky et al., 1997). The second strategy that underpins navigation in insect relies on vision to learn and use information from the surrounding scenes. Ants, like all insects (Land, 1997), have a large visual field (>300deg) but a very low visual acuity (1 deg in Gigantiops, 4 deg in Melophorus (Schwarz et al., 2011)). Nevertheless, such a modest visual input provides remarkably reliable

Introduction information for navigating in the outdoor world (Zeil et al., 2003; Philippides et al., 2011). With experience, each foraging ant develops her personal knowledge of the visual environment, as perceived during her foraging life. After a few trials, ants can readily recognise whether they are on familiar or unfamiliar terrain, and use their knowledge of the surrounding scene to recapitulate their well-known routes and relocate their nest entrance independently of path integration (e.g., Collett et al., 1992; Kohler and Wehner, 2005; Wehner et al., 2006; Sommer et al., 2008) and of any chemical information. The pros and cons of the two strategies are complementary. The egocentric strategy of path integration requires no information from past journeys but is prone to cumulative errors (Müller and Wehner, 1988; Sommer and Wehner, 2004) and cannot account for any accidental passive displacement. In contrast, relying on the visual scene provides a geocentric reference for relocating a goal even after passive displacements (Wehner et al., 1996; Kohler and Wehner, 2005; Collett et al., 2007), but requires several foraging trips to learn (Narendra et al., 2007b). Thus, path integration can lead naïve ants back to the nest during their first trips, while the process of visual learning is still developing (Wehner et al., 1996). Over trials, ants increase their visual knowledge further by extending the length of their foraging trips. In experienced ants, the information provided by visual scenes dominates the information given by the PI in a familiar environment (Andel and Wehner, 2004; Kohler and Wehner, 2005) but path integration still helps the insect to head in the correct nest direction when on unfamiliar terrain or when leaving newly discovered feeding sites (Wehner and Srinivasan, 2003; Beugnon et al., 2005; Narendra, 2007a). Ants weight the two strategies according to the richness of their visual environment (Bühlmann et al., 2011). For instance, when displaced to unfamiliar terrain, Cataglyphis fortis — which forages on the visually poor Saharan salt pans — ran its whole route according to its PI (Bühlmann et al., 2011) whereas Melophorus bagoti — which lives in the semi-cluttered Australian desert — cover only half of the distance dictated by its PI (Narendra, 2007a) and Gigantiops destructor —which lives in the very cluttered Amazonian rain forest — stops following its PI after only 50 cm (Beugnon et al., 2005). Evolution has also shaped the accuracy of both strategies according to the environmental constraints. For example, C. fortis possess a remarkably accurate odometer (compare Cheng et al., 2006; Narendra et al., 2007a) for PI; whereas M. bagoti are quicker at learning visual cues (Schwarz and Cheng, 2010).

Introduction

PI and visual learning are also used by other animals for navigation, including humans (Etienne et al., 1996). However, insects may differ from mammals on a critical point. Insect, contrary to mammals (O’Keefe and Nadel, 1978; Jacobs and Schenk, 2003; Poucet and Save, 2009), may not combine visual knowledge and metric positions provided by PI in order to build a ‘map-like’ representation of the familiar space in their brain (Wehner, 2009). Surely, PI helps the insect acquire relevant visual information (Müller and Wehner, 2010) but then, guidance by vision works independently of PI and appears to be the results of procedural (i.e., what to do) rather than a positional (i.e., where am I) knowledge. Although the question of a cognitive map is still open (Menzel et al., 2005), all data gathered so far in insects can be explained by PI and visual navigation working as two separate modules in the brain (Cruse and Wehner, 2011). In the following thesis, I therefore assumed that insects do not build a map-like representation of the world, but respond to familiar views procedurally.

The coalface of insect visual navigation

The major strategy of Path Integration is well understood (Wehner and Srinivasan, 2003; Ronacher, 2008) but much less is known about how insects acquire and use visual information, especially in complex natural environments. Field studies give good descriptions of visually guided routes (Fresneau, 1994; Baader, 1996; Collett et al., 2003; Kohler and Wehner, 2005; Wehner et al., 2006; Cheng et al., 2009; Wystrach et al., 2011c), but our understanding of the underlying mechanisms comes mainly from simple laboratory experiments using artificial, simple objects (e.g., Cartwright and Collett, 1983; Lent et al., 2010; Review: Collett and Rees, 1997). From the first seminal works onward (Wehner, 1972; Collett and Land, 1975) all experiments conducted converged on the idea that insects are able to memorise visual information and can later to compare this information with their current view by applying some type of matching procedure. But despite years of beautiful and painstaking works, some questions remain widely open. Notably:

1- What visual cues do insects use for guidance? Laboratory experiments suggest that insects can store a pallet of features/parameters like strong boundaries (Harris et al., 2007), spots of light, centre of gravity, and colour of areas (Ernst and Heisenberg, 1999; Horridge, 2005). Bees and wasps also have access to dynamic

Introduction cues such as translational optic flow (Lehrer et al., 1988; Sobel, 1990; Zeil, 1993b; Dittmar et al., 2010; Srinivasan, 2011). But knowledge about which cues insects use specifically for guidance is largely absent, especially in complex natural environments. Recent findings have started to give some clues about the way ants see natural scenes. For example, the skyline, the 1D boundary between terrestrial objects and the sky, is an important source of visual information (Graham and Cheng, 2009b; Reid et al., 2011). However, it is still debated whether insect memories are best represented as 2D unprocessed panoramic images (Zeil et al., 2003), by a labelled set of discrete landmarks (Cartwright and Collett, 1983) or, at the other extreme, by quantitative distributions of visual parameters without any retinotopic spatial information (Möller, 2001; Horridge, 2005). Although it concerns the very first level of sensory encoding, such uncertainties reveal how little is known about navigating insects’ visual ecology.

2- How are visual memories organised and how do they develop? Evidence suggests that under some circumstances, an ant’s visual memory can be constituted of several discrete snapshots taken at different locations along its familiar route (Judd and Collett, 1998). However, such evidence comes from very short routes (40 cm) displayed in laboratory condition with artificial, geometrically simple objects as landmarks. Contrastingly, experiments (Harris et al., 2007) and robot models (Baddeley et al., 2011) suggest that routes may be encoded holistically (i.e., a single representation for the whole route) not as waypoints. Whether large-scale routes (i.e., tens of meters) displayed by ants in complex natural scenes are encoded as a succession of snapshots or compacted into a single holistic memory is yet unanswered. This reveals how little is known about the nature of insects’ visual memories.

3- How are visual memories and current views processed to output an appropriate navigational response? Several hypotheses have been suggested to explain how insects may process visual information to recapitulate a route or pinpoint a goal (Möller and Vardy, 2006). For example, insects could return to the target place by minimising the difference between their current view and a stored view at the goal (Cartwright and Collett, 1983; Zeil et al., 2003). Insect may also simply rotate their body to align memorised and currently seen features; and thus walk in the direction that presents the best retinotopic overlapping (Collett, 2010; Graham et al., 2010; Lent et al., 2010). Views could also be associated with a particular action such as 6

Introduction moving in a particular compass direction (Collett and Collett, 2009) or following a pure motor routine (Bisch-Knaden and Wehner, 2001; Macquart et al., 2006; Lent et al., 2009). Overall, the complex abundance of those hypotheses is unlikely to reflect the usually simple cognitive solution found in insects. Whether insects use a single or several such strategies and what is the actual nature of the one used in complex natural environments remains unanswered.

Thesis Aims and Outline

Current knowledge about the cognitive mechanisms underlying insect visual navigation comes from various approaches, notably, behavioural experiments (Collett et al., 2006; Srinivasan, 2010); modelling – often inspired by autonomous robotics research (Möller, 2000; Webb, 2002; Smith et al., 2007; Webb, 2008, 2009; Baddeley et al., 2011) – and physical analysis of environments which provide some understanding of where useful information is in visual scenes (Möller, 2002; Zeil et al., 2003; Stürzl and Zeil, 2007; Stürzl et al., 2008; Philippides et al., 2011). In the present thesis, I tried an integrative approach that combined all three techniques. Behavioural experiments on ants – both in the lab and in the field – are here systematically combined with panoramic photographs recorded from an ant’s perspective. The analysis of the pictures enables an objective quantification of the visual information available to the animal. Also, the images can serve as a basis to develop models of the ant’s cognitive processes with clear predictions. The advantage of such an integrative approach is that the output of the models can be directly compared with the recorded behaviour of the real ants, allowing the iterative tuning of the hypotheses. Such a bottom-up approach enabled me to put forward simple explanations of the ant behaviours. This thesis thus contributes to our understanding of the cognitive mechanisms underlying insect navigation by providing elements of responses for the 3 questions mentioned above. In addition, I tried to expand these ideas within the context of comparative cognition by discussing the implications of the present approach and results for vertebrate navigation.

More specifically,

Introduction

Chapter I explores the behaviour of the ant Gigantiops destructor in a spatial learning assay frequently used with vertebrates. The ants displayed similar behaviour to vertebrates, yet we were able to put forward a simple explanation in which ants did not have to segregate their visual world into objects, as often assumed for vertebrates, but instead might use unprocessed panoramic views. This simple mechanism is gaining traction as a plausible hypothesis of visual navigation in many species. This chapter has been published in Current Biology and has already had a strong impact on the field of comparative cognition.

Chapter II further investigates the behaviour of the ant Gigantiops destructor in rectangular arenas. A detailed analysis of the ant paths and panoramic pictures allowed us to refute the hypothesis that ants were segregating their visual world into features and geometry of space, as often assumed for vertebrates. The results support instead a simple matching process (i.e., a visual compass) based on panoramic visual input; and reveal how flexibility can arise from this simple strategy. This chapter has been published in the Journal of Experimental Psychology: Animal Behavior Processes.

Chapter III investigates the mechanisms of route following in the ant Melophorus bagoti. This study provides evidence that ants can memorise and ‘recognise’ natural objects such as logs, branches and tussocks along their familiar route, although removing them does not prevent the ants homing efficiently. Further manipulations revealed a strong link between the use of route-landmarks and the rest of the visual panorama, which suggests the use of panoramic views, encompassing both route-landmarks and distal panorama. This chapter has been published in the Journal of Comparative Physiology A.

Chapter IV tackles directly the question of what visual cues ants use for guidance. The results, obtained with Melophorus bagoti in the field, reveal that contrary to what has been commonly thought, ants do not functionally segregate their natural visual world into landmarks (for guidance) and distal panorama (as contextual cue); but appear to be guided by panoramic cues encompassing both. This chapter has been published in Frontiers in Zoology.

Chapter V tests different navigational models to explain Melophorus bagoti ants’ responses to passive displacement in the field. The results refute some models and suggest that ants switch between two different visual strategies according to whether they are on or off their familiar route. In this work, we also presented a new navigational strategy based on 8

Introduction skyline height comparison to explain the ant behaviours when released at novel locations. This chapter has been accepted in the Journal of Experimental Biology (in press).

Chapter VI investigates the nature of ants’ visual memory. The results, obtained with Melophorus bagoti in the field, refute the idea that ants’ visual memories are constituted of a series of snapshots stored independently and favoured instead the hypotheses that route are encoded holistically, that is, compacted into a single representation that drives the behaviour along the whole route. This chapter is intended to be submitted to the Journal of Experimental Biology.

Chapter VII is an essay in which I attempted to stress the benefits of a bottom-up approach within the context of comparative cognition. The point is exemplified with cases from the history of the field of insect navigation. This essay cautions against deleterious ‘top- down’ biases such as off-hand anthropomorphism and offers solutions to them. This chapter has been submitted as an essay to Animal Behaviour.

Conclusion. I conclude this thesis by integrating the different elements of responses provided by the presented chapters and current knowledge into a general discussion about insect navigation and its importance in comparative cognition.

Appendix I contain the formatted publications which have been published as part of the thesis: chapters 1,2,3,4 (chapter 5 is in press):  Wystrach, A. and Beugnon, G. (2009). Ants Learn Geometry and Features. Curr. Biol. 19, 61-66.  Wystrach, A., Sosa, S., Cheng, K. and Beugnon, G. (2011). Geometry, features and panoramic views: ants in rectangular arenas. J. Exp. Psychol. Anim. Behav. Process.Advance online publication doi: 10.1037/a0023886.)  Wystrach, A., Schwarz, S., Schultheiss, P., Beugnon, G. and Cheng, K. (2011). Views, landmarks, and routes: how do desert ants negotiate an obstacle course? J. Comp. Physiol. A -Neuroethol. Sens. Neural Behav. Physiol. 197, 167-179.  Wystrach, A., Beugnon, G. and Cheng, K. (2011). Landmarks or panoramas: what do navigating ants attend to for guidance? Front. in Zool. 8, 21.

Introduction

Appendix II contains publications related to insect navigation to which I contributed during my PhD.  Wystrach, A. (2009). Ants in rectangular arenas: A support for the global matching theory. Commun Integr Biol. 2, 388 - 390.  Schultheiss, P., Schwarz, S. and Wystrach, A. (2010). Nest Relocation and Colony Founding in the Australian Desert Ant, Melophorus bagoti Lubbock (Hymenoptera: Formicidae). Psyche 2010, 4pp.  Schwarz, S., Albert, L., Wystrach, A. and Cheng, K. (2011). Ocelli contribute to the encoding of celestial compass information in the Australian desert ant Melophorus bagoti. J. Exp. Biol. 214, 901-906.  Schwarz, S., Wystrach, A. and Cheng, K. (2011). A new navigational mechanism mediated by ant ocelli. Biology Letters. (doi:10.1098/rsbl.2011.0489)  Schwarz, S. and Wystrach, A. (2011). Visual input and path stabilisation in walking ants. Commun Integr Biol. 4.

Introduction

Chapter I

CHAPTER I

Ants Learn Geometry and Features

This chapter is published in Current Biology

Current Biology 19, 1–6, January 13, 2009

Chapter I

Ants Learn Geometry and Features

Antoine Wystrach1 and Guy Beugnon1

1Department of Biological Sciences, Macquarie University, Sydney, NSW 2109 Australia

Abstract

Rats trained to relocate a particular corner in a rectangular arena systematically confound the correct corner and the diametrically opposite one—this rotational error demonstrates the use of the geometry of space (i.e., the spatial arrangement of the different components of a visual scene). In many cases, geometric information is preferentially used over other spatial cues, suggesting the presence of a dedicated geometric module located in the parahippocampus (Epstein and Kanwisher, 1998) and processing only geometric information. Since rotational errors were first demonstrated in 1986 (Cheng, 1986), the use of the geometry of space has attracted great interest and now seems to be widespread in vertebrate species, including humans (Cheng and Newcombe, 2005). Until now, rotational errors have only been considered in vertebrate species. Here, for the first time, rotational errors are demonstrated in an insect. Our results, similar to those obtained with vertebrates, can be parsimoniously explained by a view-based matching strategy well known in insects, thereby challenging the hypothesis of a ‘‘geometric module’’ located in the animal’s brain. While introducing a new concept of flexibility in the view-based matching theory, this study creates a link between two major topics of animal navigation: rotational errors in vertebrates and view-based navigation in insects.

Chapter I

1) Experimental Procedures

Species Colonies of Gigantiops destructor were captured in the rain forest of French Guyana and were reared in the experimental room in Toulouse (temperature, 29C; humidity, 50%). All the experiments were performed with individually marked foragers of Gigantiops destructor from four different colonies.

Experimental Set-Up and Protocol Foragers climbed freely on a piece of paper and were transferred from their nest to a 50-cm- long plastic channel, where they performed their outbound trip toward the feeder site. Once the single Drosophila provided by the experimenter was captured and killed, the homing ant transporting its prey was gently carried via an opaque plastic tube directly underneath the experimental arena. The rectangular arena was 80 cm long and 40 cm wide, with 20-cm-high white walls, corresponding to the size proportions of the experimental arenas used in vertebrate studies. To leave the opaque tube, disorientated ants had to go through a hole drilled in the table leading them to the center of the arena. One exit hole was pierced in each corner and connected via a 10-cm-long plastic tube to a plastic box placed outside the arena. The four exits potentially allowed ants to return to their nest except in experiment 4, where three of the four plastic tubes were obstructed at the end. Once the ant arrived inside an exit box, it was immediately replaced in its nest. After a few minutes spent inside the nest, the ant was generally ready to start again. The first five trips were considered as training, and the 30 following trips were recorded. In experiments 2, 3, and 4, the arena was covered by an opaque plastic dome (from the first trip of each ant onward) in order to avoid the use of external or lighting cues. The dome was lit up diffusely by a white circline fluorescent lighting (32W).

Data Collection and Statistics The trips of the ants inside the arena were recorded with a Sony Black & White CCD video camera suspended from the ceiling via a pole and adjusted to ﬁt the circular hole (5 cm diameter) at the top of the dome. The video image of the arena was recorded with a S-VHS Panasonic tape recorder (frame rate 25 frames/s). We measured on the recorded paths the two following data for each homing trip: (1) the initial approach to a corner and (2) the ﬁrst exit chosen among the four available. Once the ant went through an exit hole, the exit choice was

Chapter I considered to have been performed. The initial approach to a corner was recorded when the ants crossed for the first time one of the four fictive arcs of circle centered at each corner (see Figure 1B). For each ant, binomial statistical tests were used both for the exit data and for the initial-approach data. (1) To determine the use of geometry information, we compared the ratio of the number of rotational errors (i.e., choices of the corner diagonally opposite to the preferred one) to the number of the two other errors (i.e., choice of the corners located along the wrong diagonal). (2) To determine the use of featural information, we did the following: (a) for ants with significant rotational-error results, we compared the number of choices for the preferred corner to the number of rotational errors; and (b) for ants with no significant rotational errors results, we compared the number of choices for the preferred corner to the number for the three other corners. To ensure that the sequence of choices (between the correct corner and the rotational-error corner) is randomly distributed along the trials, we performed a ‘‘runtest’’ of the program SATA for each ant displaying systematic rotational errors during experiments 2 and 3 (two-tailed p > 0.12).

2) Results and Discussion

As with Vertebrates, Ants Can Rely on the Geometry of Space The ﬁrst demonstration that animals can use geometric information provided by the shape of their environment to ﬁnd a hidden goal came from a set of reorientation tasks in rats (Cheng, 1986) placed in a rectangular arena, a paradigm still much used today. A key property of such a rectangular environment is that each corner stands in the same geometric relation with the entire arena as the corner diametrically opposite to it (Figure 1). Disorientated in the center of the rectangular arena, some vertebrates trained to relocate a particular corner systematically display rotational errors (i.e., they confound the correct corner with the diametrically opposite one), showing that they rely on the geometric information of their surroundings for reorientation. We adapted Cheng’s paradigm for testing rats (Cheng, 1986) to the neotropical ant, Gigantiops destructor. This species, which has the largest eyes of any ant species (Gronenberg and Holldobler, 1999), is known for its remarkable view-based navigational capacities. On their natural solitary foraging excursions, workers of Gigantiops do not use chemical trails and can cover distances up to 20 m through the extremely cluttered environment of the rain forest (Beugnon et al., 2001). In our laboratory experiments, the ants performed their

Chapter I

Figure 1. Schematic Representation of the Geometric Information Available in a Rectangular-Shaped Environment. (A) The correct exit hole (black dot) shared exactly the same geometric relation as the diagonally opposite one (white dot), which corresponds to the rotational error. The geometric information (i.e., angular and metric relation between the corners and sense of left-right relations) is sufficient to distinguish between corners 1–3 and corners 2–4, but not to distinguish between 1 and 3 (or between 2 and 4). (B) Two dependent measures were taken. The initial approach to a corner was defined as the first fictive arc (19 cm in radius) around a corner that the ant crossed. The exit choice was defined as the first hole at a corner that the ant entered into. In this hypothetical example, the virtual ant first headed toward corner 4 but chose exit 1 in the end. The cross indicates the entry hole of the arena.

Chapter I outbound trip in a channel toward a foraging site where a living Drosophila was provided. When their prey had been caught and killed, loaded ants were disorientated in the center of a rectangular arena and had to reach one of the four exit holes drilled in the corners to return to their remote nest. In this first set of experiments, whichever exit the ant chose led back to the nest: this procedure is called nondifferential conditioning. Each ant performed 35 successive trips: the first five trips were not recorded and were considered as ‘‘training,’’ whereas two measures were collected for each of the 30 following trips: (1) their initial approach to a corner and (2) the exit chosen (Figure 1B). In the first experiment, the rectangular arena was simply put on a table in the experimental room, allowing the ants to see distal cues beyond the arena. Ants managed to systematically return to one preferred corner (preferred / three others: p % 3.85075E-12), peculiar to each individual, among the four available, and displayed no significant rotational errors (rotational errors / errors: p R 0.5) (Figure 2A). Because no internal cue from the arena allowed them to distinguish between a corner and the diametrically opposite one, these systematic choices could only be explained by the use of extra-arena visual cues or some compass cue such as a magnetic compass. In any case, these results indicate that Gigantiops ants spontaneously tended to always return to an individual preferred corner, although all four exits led them back to the nest. Indeed, some visual ant species are solitary central-place foragers, and although they do not use chemical trails, they have been shown to repeatedly follow the same two-way individual routes when navigating back and forth between their nest and a foraging place (Collett et al., 1992; Collett and Collett, 2002; Macquart et al., 2006; Wehner et al., 2006). This first experiment did not reveal whether the ants use geometry or not. In a second experiment, all four exits still led to the nest (nondifferential conditioning), but the arena was covered by an opaque plastic dome throughout experimentation, preventing ants from seeing any extra-arena cues in the experimental room. The dome was lit up by white circline fluorescent lighting to create diffuse isotropic illumination in the whole arena. In these visually controlled conditions, ants reached their preferred exit as often as the diametrically opposite one (rotational errors / errors: p % 0.0023; rotational errors / preferred: p R 0.5716) (Figure 2B). Two conclusions can be drawn from these results. First, the extra-arena cues used by ants in the first experiment were visual and not some internal inertial or geocentric cue such as a magnetic compass. Second, Gigantiops ants displayed systematic rotational errors in much the same way as many vertebrates in rectangular experimental spaces. 18

Chapter I

Figure 2: Mean Percentages of Choices for Each Corner for Each Experiment ± Standard Deviation. Data of the different individuals are pooled with their preferred corner in the top-left corner for each experiment. External numbers correspond to the ‘‘exits choices’’ results and internal numbers to the ‘‘initial approach’’ results. The top-left corner corresponds to the preferred corner, the bottom-right corresponds to the rotational error, and the two others indicate other errors. The big circles symbolize the presence of an opaque plastic dome over the arena preventing the use of extra-arena cues. (A) Rectangular arena simply put onto a table, each corner leading to the nest (n = 150: 5 ants, 30 trials each). (B) Arena covered by an opaque plastic dome, each corner leading ant to the nest (n = 210: 7 ants, 30 trials each). (C) Arena covered by an opaque plastic dome with distinct black shapes added in the corners of the arena. Each corner led to the nest (n = 270: 9 ants, 30 trials each). (D) Same visual conditions as in (C) but with a differential conditioning procedure: only the cross-shaped corner led to the nest (n = 210: 7 ants, 30 trials each).

Chapter I

The Use of Features and Geometry Could Be Explained by a View-Based Matching Strategy. To find out whether nongeometric or featural cues can be used, we then added featural information to the geometric shape of the space by providing from the first trip of each ant a distinct black shape in each corner of the arena. Ants and bees are well known for recognizing patterns or landmarks, especially when they are close to the nest entrance, as in our experiments (Cheng et al., 1987; Collett, 1996; Bisch-Knaden and Wehner, 2003). The shapes used in the present work have been chosen in order to be easily distinguishable by the foragers as seen from the center of the arena (45 cm away). These shapes have an approximate angular size of 12 degrees at a distance of 45 cm, and foragers of Gigantiops have been shown to perceive and move toward small circular black targets having an angular size of 1 degree at a distance of 50 cm (see Materials and Methods in (Macquart et al., 2006)). These ants can also distinguish and memorize vertical black targets of 2 degrees angular width on a white background (Macquart, 2008). Thus, the presence of these four distinctive black shapes should have allowed ants to distinguish between the arena’s two geometrically equivalent locations that the extra-arena cues allowed them to distinguish between during the course of the first experiment. But, surprisingly, ants still displayed systematic rotational errors (rotational errors / errors: p % 0.0145; preferred / rotational errors: p R 0.0987) (Figure 2C) in the presence of the conspicuous shapes placed directly above the exit holes, revealing that they preferentially used the global shape of the arena to get back home. Although a nondifferential conditioning procedure was used in the present experiment (i.e., all corners were rewarding because they all led ants back to the nest), the fact that ants displayed rotational errors but avoided the two other errors implies that they were relocating a particular place. So why have they used only geometric information and not the conspicuous targeting features? Would a differential conditioning procedure favor the learning of featural information? We then tested other ants in the same visual conditions with a differential conditioning procedure (Giurfa et al., 1999; Dyer and Chittka, 2004). In that case, three of the four exits led to a closed tube and only one exit led back to the nest. Thus, only the use of featural information could allow ants to find the correct route home and avoid dead ends. Here, in contrast to the results obtained from non-differential conditioning, the ants displayed no significant rotational errors and largely succeeded in choosing the correct exit hole (rotational errors / errors: p R 0.125; preferred / three others: p % 6.00717E-11) (Figure 2D).

Chapter I

These results confirm that ants can recognize and use the black shapes in the corners as navigational cues. Interestingly, the ants displayed systematic rotational errors in their initial approach to a corner (rotational errors / errors: p % 0.0123) (Figure 2D). Although results show a slight preference for the correct corner in that initial approach, individual statistics show no significant differences between the correct corner and the rotational error (rotational errors / preferred: p > 0.2295). Thus, in these particular conditions, the ants seem to rely only on geometry during their initial approach to a corner. However, once having arrived about 10 cm from the wrong visual pattern, they systematically executed a U-turn and went back toward the opposite correct corner (Figure 3D). During the nondifferential conditioning experiment (Figure 2C), ants relied on the shape of the arena but did not use the visual features. Analogous surprising results have also been found in vertebrate species (Cheng and Newcombe, 2005). To explain both the use of geometry and the absence of use of featural information, several interesting studies suggest the presence of a geometric module in the animal’s brain (Epstein and Kanwisher, 1998; Cheng, 2005; Jones et al., 2007; Doeller and Burgess, 2008; Doeller et al., 2008). However, this process, supposed to extract and work solely on the geometry (Cheng, 1986), is very complex from a neurophysiological point of view, and it is presumed to be located in the vertebrate’s hippocampus (Burgess et al., 2000; Hartley et al., 2000). Should we invoke such a complex system in an insect minibrain? Our results can be more parsimoniously explained. Since Cartwright and Collett’s seminal work (Cartwright and Collett, 1982, 1983), researchers working on insect navigation have agreed that insects in general and ants in particular preferentially use a view-based matching strategy to recognize landmarks and to relocate a target (Judd and Collett, 1998; Harris et al., 2007). In order to return to a crucial place, like the nest, insects rely on a memorized view previously taken at that goal location. They achieve their displacement by comparing their current view with the memorized target view, moving from higher levels of mismatch to lower levels of mismatch. When the two views are perfectly matching, the goal is reached. Interestingly, recent simulations and tests in virtual reality show that following such a global image-matching gradient-descent algorithm (with the reference image taken at the target corner) could produce rotational errors (Stürzl et al., 2008). From the center of the rectangular arena, the visual weight of its global shape, covering most of the visual field, is much more important than the visual weight of the features located in the corners, creating prominent local minima in mismatch at each of the two diagonally opposite corners. In other words, there is no need to extract the geometry of the environment via a geometric module to 21

Chapter I exploit the shape of the rectangular arenas. The image matching model can explain rotational errors even in the presence of distinct shape in the corner (Figure 2 in (Stürzl et al., 2008)). But in that case, because of the presence of these features, the match is slightly better at the correct corner. This may explain the slight bias observed in the ants toward a correct corner (Figures 2C and 2D). The results obtained with these computational models, similar to the data with ants, strongly rein-force the hypothesis that ants’ rotational errors result from the use of a global view-based matching strategy rather than from the use of a geometric module. Indeed, the brain of insects has no hippocampus, and insects are well known for using a view- matching strategy, which is parsimonious from a neural point of view (Möller et al., 1999). Our results are quite similar to those obtained in vertebrates. They provide the ﬁrst experimental biological support for the idea (Cheung et al., 2008) that the use of what has been called ‘‘the geometry of space’’ in the literature arises from a view-based matching strategy without the extraction of any geometric properties. But how can we explain from a view-based-matching point of view the ants’ use of the information provided by the black shapes in the corner during the differential conditioning procedure (Figure 2D)?

A Second View-Based Matching Process Is Involved Contrary to the nondifferential conditioning procedure (Figure 2C), ants used the featural information in the differential conditioning procedure, although they determined their initial approach to a corner by relying on the global shape of the arena (Figure 2D). For those that performed the rotational error and thus arrived in front of the wrong black feature, they clearly used featural information to reject that corner without entering into the blocked exit hole. They made a U-turn at this location and headed back directly to the opposite, correct corner (Figure 3D). Provided with a simple global matching gradient-descent algorithm, an agent approaching the rotational error corner would get stuck. First, it would be repelled from that corner because of the increased mismatch caused by the wrong feature; second, it will be pushed back toward it because of the rectangular shape of the arena. The rotational-error corner is indeed located at a local minimum in mismatch [26]. However, in our experiment, the ants do not get stuck in the vicinity of such a corner but make a U-turn and head to the correct corner. Therefore, there must be a ‘‘change of state.’’ In order for an ant to leave that local minimum and avoid being stuck, its standard gradient-descent view-based matching process should be temporarily inhibited, and be replaced with a visuo-motor routine executing a U-turn. The trigger for the visuo-motor routine could be the view at the rotational error 22

Chapter I

Figure 3: Example of Ants’ Trajectories from the Different Experiments. The top-left corner corresponds to the preferred corner, the bottom-right corresponds to the rotational error, and the two other corners indicate other errors. The big circles symbolize the presence of an opaque plastic dome over the arena preventing the use of extra-arena cues; the black dots indicate the entry hole at the center of the arena. (A) Rectangular arena simply put onto a table without any opaque plastic dome, with each corner leading to the nest. (B) Arena covered by an opaque plastic dome, with each corner leading ant to the nest. (C) Arena covered by an opaque plastic dome with distinct black shapes added in the corners of the arena. Each corner led to the nest. (D) Same visual conditions as in (C) but with a differential conditioning procedure: only the cross-shaped corner led to the nest. Two paths illustrate runs aimed directly toward the correct corner, and two others illustrate U-turns executed just in front of the shape located at the rotational error corner. 23

Chapter I corner, which has been associated with a negative outcome (an obstructed pipe). Alternatively, it could be based on the level of mismatch at the rotational-error corner. If the mismatch value is still too high (because of the presence of a wrong black shape), the gradient-descent matching process is inhibited and the U-turn triggered. These alternative hypotheses were tested in the next experiment. After having performed their 35 paths (five unrecorded and 30 recorded) in the differential conditioning procedure, each of these ants (n = 7) was tested once with a new condition: the four black shapes in the corners were all transposed clockwise (Figure 4A). In conformity with the gradient-descent matching process, all the ants first used the global shape of the arena and thus approached one of the two geometrically correct corners during their test (Figure 4A). But once they arrived at the local minimum, instead of the black shapes they usually experienced, the ants faced one of the shapes previously located in the geometrically incorrect corners, the two shapes that had been encountered the least. In that test, six of the seven ants made a U-turn, that is, behaved in the same way as when they were facing the familiar wrong black shape located in the rotational-error corner during the normal condition (Figure 4B). If the U-turn was triggered by a memorized view taken at the rotational-error corner (stored negative pattern), the ants should have attempted to go through the hole because the trigger for the U-turn (the familiar feature at the rotational-error corner) was missing. Results do not support this claim (% U-turn correct / other p = 0.014). The hypothesis of a supplementary view memorized at the rotational-error corner (stored negative pattern) can therefore be rejected. Consequently, it seems that when they arrived at a local minimum, the ants assessed the quality of the match between the current view and the memorized target view and executed a U-turn because the mismatch was too high. Further supporting this hypothesis is the result that the ants spent more time in the arena before attempting to enter a hole (Figure 4B). Thus, the apparent distinction between the ‘‘geometry of space’’ on the one hand and the ‘‘features’’ on the other hand could simply result from two successive processes of view- based matching. Ants do not first use geometry and, in a second stage, extract and consider the features in a separate way. Instead, they start by following a global matching gradient- descent algorithm, and second, they consider the quality of the match at a local minimum to decide whether to go through or inhibit the gradient-descent matching process and execute a U-turn.

Chapter I

Figure 4: Comparison between the Normal and the Test Condition of the Differential Conditioning Procedure Experiment. (A) Schematic representation of the black shapes positions during the normal condition (first 35 trials) and the test condition (36th trial). During the test condition, the black shapes located in the geometrically correct corners (gray dots) are different from those experienced during the normal condition at the correct corner (black dot) and at the rotational error corner (white dot). (B) Number of ants out of seven displaying a U-turn and mean time spent (the error bar indicates standard deviation) in the arena before entering a hole when approaching (1) the correct corner during the normal condition; (2) the rotational-error corner during the normal condition; and (3) the geometrically correct corner during the test condition. For each ant, only the last trial of the normal condition for both the approach to the correct corner and the approach to the rotational-error corner are considered here.

Chapter I

Flexibility in the View-Based Matching Processes Involved Interestingly, different training conditions generate different patterns of results. U-turns were triggered mostly in the differential conditioning procedure (Figure 2D) and rarely during the nondifferential conditioning procedure (Figure 2C). Ants spontaneously behave as though they are following a global matching gradient-descent algorithm, but the presence of a negative outcome, like an obstructed pipe, seems to trigger an additional process judging the degree of mismatch at the local minima. These results imply that ants are able to adapt view- based matching processes according to need. This ﬂexibility may help ants to reduce the cognitive load of spatial information processing during navigation. Overall, these results raise the hypothesis that insects are guided more by global views than by individual landmarks. Within a cluttered environment like the rainforest, relying on the global view instead of focusing on particular landmarks leads to continuous use of the relationships between large, conspicuous, and extended objects. Contrary to a landmark extraction strategy, such a global strategy avoids the risk of confusion between similar landmarks and is thus more robust against the natural changes of the scenery. However, we have shown that ants can also resort to proximal information when necessary. This is because once the ants arrive in the neighborhood of the target, the contribution of the ‘‘features’’ to the global view increases. In natural conditions, furthermore, insects use and memorize proximal landmarks precisely when approaching the nest entrance (Cheng et al., 1987; Collett, 1996; Bisch-Knaden and Wehner, 2003).

3) Conclusions

Our study shows for the first time that an insect makes similar errors as vertebrates when relocating a goal in the corner of a rectangular arena. In addition, our results strongly support the recent modeling and empirical studies (Cheng, 2008; Cheung et al., 2008; Stürzl et al., 2008) that cast doubt on a ‘‘geometrical module’’ and thus stress the view-based-matching hypothesis that is widely accepted in the insect literature. Studies on the use of landmarks in a variety of animals have found differences between species. Some results in pigeons or humans seem difficult to explain with a pixel-by- pixel image-matching strategy (Cheng, 1988; Doeller and Burgess, 2008). In invertebrates, our study has shown flexibility in the use of view-based matching: U-turns are triggered when

Chapter I a poor match leads to adverse consequences. This behavior has not been described yet in vertebrate animals moving in rectangular arenas, although most of these studies have not looked closely at the paths taken by animals. It would be instructive to compare such paths across species; such analyses would help constrain models for explaining behavior.

Acknowledgments

We thank Emmanuel Lecoutey and Ken Cheng for their help and comments on the manuscript and Gerard Latil for his help with construction of the experimental apparatus.

Supplementary Information

Table S1: Distributions of ants according to the four following profiles: Pref only: significant preference for one corner and no significant rotational error. Rot: significant rotational error and no significant difference in the number of choices for the rotational error and for the prefered corner. Pref + Rot: significant rotational error but significant preference for the correct corner. Random: no significant preferences.

Differential Nondifferential conditioning conditioning Geometry + Extra Geometry only Geometry + Featural cues arena cues Experiment #1 Experiment #2 Experiment #3 Experiment #4 (n = 5) (n = 13) (n = 11) (n = 7) Exit Heading Exit Heading Exit Heading Exit Heading Pref only 5 5 0 0 0 0 7 0 Rot 0 0 7 7 9 8 0 5 Pref + Rot 0 0 0 0 0 0 0 0 Random 0 0 6 6 2 3 0 2

Chapter I

Chapter II

CHAPTER II

Geometry, features, and panoramic views: ants in rectangular arenas

This chapter is published in Journal of Experimental Psychology: Animal Behavior Processes

Doi: 10.1037/a0023886

29 Chapter II

Geometry, features, and panoramic views: ants in rectangular arenas

Antoine Wystrach1,2, Sosa Sebastian2, Ken Cheng1, Guy Beugnon2

1Department of Biological Sciences, Macquarie University, Sydney, NSW 2109 Australia

2Centre de Recherches sur la Cognition Animale, CNRS, UMR 5169, Université Paul- Sabatier, Toulouse F-31062, France

Abstract

When tested in rectangular arenas, the navigational behavior of the ant Gigantiops destructor can produce results similar to vertebrates. Such results are usually interpreted as supporting the ability of animals to segregate spatial geometry and features. Here, we combine a detailed analysis of ants’ paths with panoramic images taken from the ant’s perspective that can serve as a basis for developing view-based matching models. The corner choices observed in ants were better predicted by the use of panoramic views along with a simple matching process [rotational image difference function (rIDF)] than by models assuming segregation of geometry and features (G/F). Our view-based matching model could also explain some aspects of the ants’ path (i.e., initial direction, length) resulting from the different visual conditions, suggesting that ants were using such a taxon-like strategy. Analyzed at the individual level, the results show that ants’ idiosyncratic paths tend to evolve gradually from trial to trial, revealing that the ants were partially updating their route memory after each trial. This study illustrates the remarkable flexibilities that can arise from the use of taxon-like strategies and stresses the importance of considering them in vertebrates.

Keywords: ant navigation, Gigantiops destructor, view-based matching, visual compass, geometry

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1) Introduction

Rats trained to find food in a particular corner of a rectangular arena systematically confound the rewarded corner with its diagonally opposite one. These “rotational errors,” or more precisely the fact that rats can avoid the two corners of the other diagonal, demonstrate their ability to rely on the rectangular shape of the arena, also called the geometry of their surroundings (Cheng, 1986). Surprisingly, rotational errors persist even when distinct features disambiguate the different corners of the arena, suggesting the presence of a dedicated “geometric module” in the brain (Cheng, 1986). This discovery stimulated a great deal of research in many vertebrate species, and several theories arose from those 25 years of research to explain the use of geometry (summarized by (Cheng and Newcombe, 2005). The theories range from the use of various global geometric axes (Gallistel, 1990; Cheng, 2005), to local geometry (Pearce et al., 2004), to boundary-based analysis (Doeller and Burgess, 2008). Those theories, along with some models (Lee et al., 2006; Newcombe and Huttenlocher, 2006; Miller and Shettleworth, 2007; Dawson et al., 2009; Lee and Spelke, 2010b) assume that features and geometry of space are somehow segregated. Although this distinction is well supported by neurophysiological and behavioral data in vertebrates (Lee and Spelke, 2010a), how such segregation applies in natural conditions outside of laboratory arenas is debated (Sutton, 2009). In contrast, other authors have proposed view-based models that operate without assumptions about representation of geometry but rely purely on the input provided by the visual scene (Cheung et al., 2008; Sheynikhovich et al., 2009; Ponticorvo and Miglino, 2010). Parsimonious view-based mechanisms can explain the results obtained in rectangular arenas because the geometry of space is implicitly contained in panoramic views and does not require any explicit computation (Stürzl et al., 2008). In the model of Sheynikhovich et al. (2009), edge-based views drive both a direct view based matching system (the taxon system of (O’Keefe and Nadel, 1978))and an allocentric system based on well-known types of neurons identified from single-cell recording: grid cells (Hafting et al., 2005), head direction cells (Taube et al., 1990), and place cells (O’Keefe and Dostrovsky, 1971; Muller and Kubie, 1987), often called the locale strategy (O’Keefe and Nadel, 1978). While geometry and features (G/F) models flow from the results obtained with vertebrates in artificial arenas, view-based models arise from seminal works conducted on insects where explanations of spatial behavior have eschewed assumptions about higher-level cognitive processes (Tinbergen and Kruyt, 1938; Wehner and Räber, 1979; Cartwright and

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Collett, 1983). The current understanding of ant navigation is that ants do not learn “cognitive-maps” (Wehner et al., 2006). Rather, efficient navigation results from multiple taxon-like strategies based on the interaction of path integration (Wehner and Srinivasan, 2003) and learnt visuo-motor routines (Wehner et al., 1996) that can be triggered by different contextual cues (Collett et al., 2006). For instances, views can be memorized along familiar routes in association with particular motor actions (Knaden et al., 2006) or compass direction (Collett et al., 1999). Memorized views can also be compared with the currently perceived view to compute the goal direction (Judd and Collett, 1998; Harris et al., 2005; Collett, 2010; Graham et al., 2010) or position (Zeil et al., 2003; Graham et al., 2004). Such taxon-like strategies have been repeatedly tested in hymenoptera and robustly explain the results on insect navigation obtained indoors (Collett and Collett, 2002) as well as outdoors (Collett et al., 2007; Graham and Cheng, 2009b). The results obtained do not necessitate the assumption that insects segregate their visual world into landmarks and panorama, or geometry and features. Instead, insect guidance might be based on parametric cues widespread on their panoramic visual input (Pastergue Ruiz et al., 1995; Wehner and Müller, 2010; Wystrach et al., 2011c). Recently, rectangular arenas have hosted for the first time an invertebrate, the ant Gigantiops destructor (Wystrach and Beugnon, 2009). Homing ants loaded with their reward entered the center of an arena with various cues on the wall. They had to find any one of the corners to exit the arena and go home. The ants displayed rotational errors in the absence as well as in the presence of distinguishable featural cues in the corners. However, when a conditioning procedure was applied, such that only one corner allowed the ants to return to their nest, all the ants learned to use the featural cues and hence avoided rotational errors. Those results were quite similar to those obtained with vertebrates, yet the insect behavior could be parsimoniously interpreted as resulting from the matching of panoramic views (Wystrach, 2009; Wystrach and Beugnon, 2009). A view-based strategy may not be restricted to insects only. It has also been suggested to explain certain aspects of vertebrate navigation (Benhamou, 1997) in mice (Alyan, 2004), rats (Cheung et al., 2008; Sheynikhovich et al., 2009), and humans (Wang and Spelke, 2002). In a recent study, Pecchia and Vallortigara found clear evidence that geometry learning in chicks arise from the use of a view-based strategy (Pecchia and Vallortigara, 2010). However, whether “geometry” or “features” or “panoramic snapshots” drive animal search in rectangular arenas could not be clearly demonstrated in most experiments published to date

32 Chapter II and the topic of geometry remains influential and much debated among experimental psychologists (Cheng and Newcombe, 2005; Cheng, 2008; Twyman and Newcombe, 2010). The present study follows the basic design of these kinds of experiments: Gigantiops destructor ants were trained in rectangular arenas containing a conspicuous feature on one of the long walls and then tested with the feature switched to a short wall. In addition to the usual analysis of the corner choice frequencies displayed by the animals, we combined a detailed recording of the ants’ paths with panoramic images taken from the ant’s perspective. This approach can be used to elaborate models of the ant’s cognitive processes with clear predictions while quantifying objectively the information available to animals and therefore rules out presumptions about the salience of different cues (Stürzl et al., 2008). We modeled here a particular view-based matching hypothesis where the memorized views facing the goal is matched with the current view to retrieve the goal direction. This hypothesis, which can be called ‘visual compass,’ is based on the rotational image difference function (rIDF) obtained while comparing the stored view with each possible orientation of the current view (Zeil et al., 2003; Graham et al., 2010). In addition to explaining some characteristics of the paths taken by the ants, the corner choice frequencies predicted by the rIDF hypothesis could be compared with the predictions of the G/F hypothesis. In other words, our experimental manipulations show whether ants used panoramic views or segregated the so-called “geometry” from the “features” of the arena in a way that can be readily tested in vertebrates.

2) Experiment

2.1) Method

Rectangular arena and features. The rectangular arena was 80 40 cm with 20-cm- high white walls. An entrance hole was drilled at the middle of the arena, and four exit holes were drilled in the corners, allowing ants to reach via a 10-cm long plastic tube one of the four exit boxes located outside of the arena. To prevent the use of external cues by the ants, the whole arena was covered by an opaque plastic dome lit up diffusely by white circular fluorescent lighting (32W). Four bands of heavy paper were applied on the surface of the walls, covering the bottom 6 cm of the panorama. By varying the paper’s pattern and color, three different conditions were carried out. In the first one, the four walls were covered by

33 Chapter II black paper. In the second one, three walls had black paper while one wall had striped paper (black and white strips of 2.5 cm width). In the third condition, three walls had black paper while one wall had white paper. For the two last conditions, the striped and the white paper respectively are called features. During the training trials the feature covered a long wall. During the tests, however, the feature was shifted to a short wall. Animals. Colonies of Gigantiops destructor captured in the rain forest of French Guiana were reared in the experimental room in Toulouse (temperature 29°C; humidity, 50%). The experiments were carried out with individually marked foragers from 10 different colonies. Procedure. Foragers first had to perform their outbound trip through a 50-cm-long plastic channel before reaching a feeder site where a single Drosophila was provided. Once the prey was captured and killed, the loaded homing ant was carried via an opaque plastic tube to the central entrance hole underneath the rectangular arena. The disoriented ant thus entered the arena in its center and had to reach one of the four exit holes located in the corners to get back to her nest. Each exit hole led to an exit box outside of the arena. After whichever corner was chosen, the ant was immediately transferred back to its nest. After a few minutes spent inside the nest, the same ant was generally ready for the next trial. The ants were confronted with a training situation (i.e., with a feature on a long wall) for a least the first 10 trials. From the 11th trial onward, the ants could be confronted with a test situation (i.e., with a feature on a short wall) only if she had displayed at least six consecutive successful training trials (i.e., chose the same corner and made no U-turns in doing so). A test trial was always followed by training trials. Afterward, depending on the motivation and constancy of the ant, supplemental test trials could be performed after at least three consecutive successful training trials (5.6 trials on average). The number of training and test trials performed depended on the training success of the ant, varying from 15 trials with one test to 45 trials with seven tests. In the 4-black-walls condition, because of the absence of any feature walls, no test was given. Table 1 presents the average number of training and tests trials displayed by the ants. Table1. Group size and average number of trials (SD).

Groups No. of ants No. training No. successful No. test trials trials training trials 4 bl 14 31  18.6 19.4  14.7 No tests bl/bl 6 18.8  4.1 13.7  7.0 3.2  3.1 st/bl 10 16.9  6.9 15.7  9.3 2.7  1.4 wh/bl 11 14.1  6.7 14.7  9.6 2.3  1.5

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Preferred corner: Group allocation. In the 4-black-walls condition (n 14), all ants were considered as a single group. However, among the 3-black-1-striped-walls (n 16) and the 3-black-1-white-walls (n 11) conditions, the ants were split into two groups depending on the location of their preferred corner (i.e., the most chosen corner during training trials). The ants preferring a corner adjacent to the feature wall (striped/black or white/black) and the ants that preferred a corner at the intersection of two black-walls (black/black) were pooled separately for both conditions. In the 3-black-1-white-walls condition, however, all the ants preferred a corner adjacent to the feature wall. Thus, the ants were split into four groups: the 4-black-walls group (4bl, n 14); the black/black group (bl/bl) from the 3-black-1striped-walls condition (n 6); the striped/black group (st/bl) from the 3-black1striped-walls condition (n 10); and the white/black group (wh/bl) from the 3-black-1-white-walls condition (n 11).

Analysis of corner choices. The first five trials of each ant were considered as familiarization trials and were not analyzed. From the sixth trial onward, the corner choice was recorded when the ant entered an exit hole. For each individual, training and test trials were pooled and analyzed separately. The statistics were performed at the group level, each individual contributing to its group with the proportions of its corner choices as dependent measures. The following statistical tests were performed for both training and test trials. First, the nonrandomness of corner choices was tested by comparing the proportion of choices for the preferred corner in each group against the chance expectation of 0.25 using a binomial test. Then, potential differences between the choice proportions for the other three corners (called errors) were tested in each group using a nonparametric Kruskal Wallis H test. Finally Mann–Whitney U tests were also used to investigate potential intergroup differences in the proportion of choices for the preferred corner.

Recording of trajectories and selection of the paths. The trips of the ants inside the arena were recorded with a Sony DCR PC-10E video camera suspended from the ceiling via a pole and adjusted to fit the circular hole (5 cm diameter) at the top of the dome. The trajectories were extracted with the software Noldus Ethovision and transformed into (x, y) coordinates at a rate of five times per second. The first five trials of each ant were considered as familiarization trials and were not analyzed. Some paths were unusually long because of some unforeseen circumstances such as an ant losing the Drosophila or losing motivation inexplicably. The paths longer than 250 cm were ignored in the path analysis. The percentage

35 Chapter II of paths longer than 250 cm was similar across groups (average of 14%; standard deviation across groups of 4%).

Monte Carlo analysis of stereotypicality and variability of training paths. This analysis was restricted to the training paths that were successful (ending in the preferred corner or the two geometrically correct corners for the 4bl group). The aim was to compare variability found within different sets of paths: paths of different individuals in different groups, paths of different individuals in the same group, and paths of the same individual across trials (sequentially or chosen at random). To allow such comparisons, all those paths were reflected if necessary to end at the top left corner. For the calculation of differences between paths, pairs were chosen at random according to restrictions to be described below, making the analysis Monte Carlo in nature. Each chosen path was divided into 100 points at 1%, 2%, . . . 100% of its path length. We assessed the distances separating the two paths at each of these 100 corresponding points. The “inter-individual (inter-group)” path variability was calculated by comparing pairs of paths chosen randomly with replacement. One path came from some individual in one group, while the other path always came from some individual from a second group. The individual might vary across draws as well as the paths. For each pair of groups, a Monte Carlo draw of 1000 pairs of paths produced a very stable mean varying very little from one draw to another. The “inter-individual (intra-group)” path variability was calculated by comparing pairs of paths chosen randomly with replacement between individuals of the same group. One path came from one individual, while the second path came from a different individual in the group. The individuals and the paths varied from one sampling to the next. Again, a Monte Carlo draw of 1000 pairs of paths produced a very stable mean for each group. The “intra-individual (random inter-trial)” path variability was calculated by comparing pairs of paths chosen randomly with replacement from the same individual. Again, a Monte Carlo draw of 1000 pairs of paths produced a very stable mean for each individual and therefore for each group. Finally, the “intra-individual (sequential inter-trial)” path variability was calculated by comparing pairs of successive paths in chronological sequence for each individual. This calculation is not Monte Carlo, as all successive pairs of paths for each individual were calculated. An averaged value was obtained for each group.

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To test for potential differences between those four different levels of interpath variability (i.e., “inter-individual (inter-group)”; “inter-individual (intra-group)”; “intra- individual (random inter-trial)”; “intra-individual (sequential inter-trial)”) the average distance between paths over the 100 points of comparison was used as a dependant variable, reducing each interpath comparison to one value. A potential “group effect” (whether paths of individuals from the same group tend to be more similar than paths of individuals from different groups) was tested by comparing the 1000 “interindividual (inter-group)” values with the 1000 “inter-individual (intra-group)” values (independent samples t test). Potential individual “path signature” (i.e., individual idiosyncrasy or stereotypy (Macquart et al., 2006)) was tested by comparing for each group the 1000 “inter-individual (intra-group)” values with the 1000 “intra-individual (random inter-trial)” values (independent samples t test). To test whether the “path signature” of an ant evolved over successive trials (concept called here “sequential evolution”), we compared for each individual its “intra-individual (sequential inter-trial)” value with its 1000 “intra-individual (random inter-trial)” values. A good estimation of the position of that “Sequential inter-trial” value among the distribution of the 1000 “Random inter-trial” values is to look at the percentage of the 1000 “Random inter- trial” values which are smaller than the “Sequential inter-trial” value.

Analysis of distance traveled. The distance traveled by the ants was calculated for each trial using a Matlab program processing the (x, y) coordinates obtained from Ethovision. Potential intergroup differences in the distances traveled were tested using ANOVA with the Tukey-Kramer post hoc test on all possible pairs of groups. This analysis was carried out for the successful trials only (i.e., those ending in the preferred corner or the two geometrically correct corners for the 4bl group).

Analysis of the initial directions. The initial directions taken by the ants were recorded at 10 cm from the central entrance of the arena. Even though they were disoriented, the ants did not stop when entering the arena at the center but tended to orient their body while moving. As a consequence, the noise in the initial direction is considerable across trials. Therefore, we used the initial direction of the mean path of each individual to carry out this analysis. Using a Matlab program, we determined the point at which each individual mean path crosses for the first time a fictive circle of 10-cm radius around the center of the arena. A

37 Chapter II potential intergroup difference was tested using the Watson–Williams test for circular data (Batschelet, 1981). Analysis of randomness of errors along the trial sequences. We analyzed the sequence of corner choice during the training condition for each individual using a runs test to determine whether the errors tended to occur randomly or successively. Only the individuals that displayed more than two errors were included in the analysis (n 22 ants).

2.2) Results and Discussion

Ants’ corner choices We call here “error” any choice that does not end up in the preferred corner of the ant. Figure 1 shows the corner choice frequencies displayed by the ants in each condition. It is important to note that the patterns for different individuals have been suitably transformed so that the preferred corner of each individual is presented at the top-left for each condition.

Training in 4-black-walls rectangle (Group 4bl). No individual chose its preferred corner significantly more than its diagonally opposite one (p .1, Binomial test). This confirms that the ants could not distinguish between a corner and its diagonally opposite one. As a group, the ants chose significantly more often the two geometrically correct corners (the preferred corner and its diagonally opposite one) than the two errors (the two other corners) (p .001, one-sample t test) (Figure 1A).

Training in featured rectangle (groups bl/bl; st/bl; wh/bl). The ants showed a spontaneous preference for having their preferred corner adjacent to the featured wall (Table 1, No. of ants). This preference is significant for the 3-black-1white-walls condition (Figure 1D; n 11 vs. n 0; p .001, Binomial test) but not for the 3-black-1-striped-walls condition (Figure 1B, C; n 6 vs. n 10; p .454, Binomial test). The three groups (bl/bl; st/bl; wh/bl) each showed a significant preference for their preferred corner (for each group: p .001, one- sample t test) and a significant bias for one particular error (bl/bl: p .005; st/bl: p .001; wh/bl: p .022, Kruskal Wallis H; Figure 1B, C, D).

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Figure 1: Mean percentage of corner choices for each group during training trials (left) and tests (right). Patterns for different individuals have been suitably transformed so that the preferred corners are all presented at the top-left for each condition. Results highlighted in black indicate a significantly preferred corner. Results highlighted in light gray indicate a significant bias toward an error over and above other error corners. The 4-black-walls group underwent no tests. The circular histograms illustrate the goodness of the matching for a given orientation resulting from the pixel-by-pixel panoramic image comparison with a Reference picture, following the procedure in Figure 6. The locations of the circular histograms represent the location where the compared pictures were taken. Predictions are given for the G/F model (i.e., assuming that geometry and features are segregated) (white rectangles) and the rIDF model (open circles).

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Intergroup analysis in training. Regarding the frequency of choices for the preferred corner, no significant differences were found between the three featured-rectangle groups (p .113, Mann–Whitney U). However, the 4bl ants chose their two preferred geometrically identical corners (pooled together) significantly less often than the st/bl and wh/bl ants chose their one preferred corner (respectively, p .001 and p .001, Mann– Whitney U). There was no such significant difference between the 4bl and the bl/bl groups (p .409, Mann–Whitney U).

Intragroup analysis in tests. For the test condition, each of the three groups displayed a significant preference for a particular corner. The bl/bl ants chose significantly more the corner now adjacent to the feature but which maintains the correct geometry with respect to the rectangular shape of the arena (p .001, one-sample t test). In fact, Figure 1B shows that this corner was chosen almost exclusively. Interestingly, they hardly chose the diagonally opposite wall, which also stands in the same geometric relation to the arena as the corner preferred in training, and furthermore has the same local features, namely black walls around the corner. The st/bl and wh/bl ants, however, preferred the corner that maintained the local features, although it was now incorrect relative to the rectangular shape of the arena (respectively, p .021 and p .001, one-sample t test) (Figure 1C, D).

Intergroup analysis in tests. During the tests, the st/bl ants chose their preferred corner significantly less often than the bl/bl and wh/bl ants did (respectively p .014 and p .014, Mann–Whitney U). The bl/bl and wh/bl ants’ corner choices were indeed extremely constant during the tests, hence the st/bl group is the only group to present a significant asymmetry between the three other corners ( p .026 Kruskal–Wallis H): when not choosing the corner that maintained the local features (preferred corner) the st/bl ants tended to prefer the geometrically correct corners, and especially the one having black walls between pairs of paths over the 100 points of comparison was on both side rather than the one presenting the correct local used as a dependant variable for the following analyses. feature, but on the wrong side.

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Figure 2: Monte Carlo results showing mean distances separating pairs of successful paths. (A) The distance separating the paths (y axis) is plotted as a function of the percentage of path traveled (x axis). (B) The distance separating each pair of paths compared has been averaged to one value. Boxes show median, upper and lower quartiles, whiskers extend to the upper and lower deciles. (A and B) The results from different groups or different pairs of groups have been combined. Inter-individual (inter-group): 1000 randomly chosen pairs of paths from pairs of individuals from different groups were used for the computation. Inter-individual (intra-group): 1000 randomly chosen pairs of paths from pairs of individuals of the same group were used. Intra-individual (random inter-trial): 1000 randomly chosen pairs of paths within individuals were used. Intra-individual (sequential inter-trial): all successive pairs of paths of each individual were used for computation (this calculation is thus not Monte Carlo in nature). The stars indicate a significant difference (p % .001) between the different types of interpath distances.

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Four levels of interpath variability Only successful paths (i.e., those ending in the preferred corner or the two geometrically correct corners for the 4bl group) of the training trials were analyzed. All the paths analyzed were reflected if necessary to end at the top left corner. Because the different groups show similar patterns, the results of the different conditions have been plotted together in Figure 2. The average distance between pairs of paths over the 100 points of comparison was used as a dependant variable for the following analyses.

Intergroup versus intragroup: A group effect? Overall, the variability between the paths of individuals from different groups ((Figure 2 “inter-individual [inter-group]”) is significantly higher than that between the paths of individuals from the same groups (Figure 2 “inter-individual (intra-group)”) (p .001, independent samples t test). This reveals that the visual panorama influences the route taken by the ants traveling from the same starting point to the same endpoint. That influence can be seen in considering the differences between the mean paths from each group shown in Figure 3A.

Intragroup analysis of inter-individual versus intra-individual: Individual idiosyncrasy? For each group, the mean of path differences is significantly lower within individuals [Figure 2 “intra-individual (random inter-trial)”] than between individuals [Figure 2 “inter-individual (intra-group)”] (4bl: 11.61 cm vs. 13.72 cm, p .001; bl/bl: 8.08 cm vs. 9.88 cm, p .001; st/bl: 7.60 cm vs. 9.73 cm, p .001; wh/bl: 8.16 cm vs. 10.55 cm, p .001;). This reveals idiosyncrasies in individual ants, or individual path signatures. Some examples of individual paths are shown in Figure 4.

Intraindividual analysis of random versus sequential: Sequential evolution? For each group, the mean intraindividual interpath difference is lower for successive pairs of paths [Figure 2 “intra-individual (sequential inter-trial)”] than for random pairs of paths from the same individual [Figure 2 “intra-individual (random inter-trial)”]. The percentage of the 1000 “random inter-trial” values that are smaller than the “sequential inter-trial” value is very low for each group (4bl: 10.12 cm, 0%; bl/bl: 7.25 cm, 6.7%; st/bl: 7.04 cm, 4.5%; wh/bl: 7.67 cm, 0%). Although this is not true for all individuals, these average results at the group level show a strong tendency for an individual’s path to evolve gradually, changing a little from trial to trial (examples in Figures 4B, C).

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Figure 3: Intergroup differences in the successful paths (i.e., those ending in the preferred corner or the two geometrically correct corners for the 4bl group). (A) Mean paths of the successful trials for each individual (gray paths) and group (black paths). The mean paths of the groups (black paths) have been computed with all individuals equally weighted. All the successful paths have been reflected if necessary to end in the top-left corner before mean computations. (B) Initial direction recorded at 10 cm from the center (for the 4bl group, paths ending in the rotational error have been rotated to end in the top left corner). Each open gray dot represents the average path of one individual. The circular histograms illustrate the goodness of the matching for a given orientation resulting from the pixel-by-pixel panoramic image comparison with a Reference picture. For each group, mean directions are given for the picture-based-model (open arrow head) and the ants’ initial direction (filled gray dot). The pointy black arrows indicate the direction of the preferred corner (N for nest direction). Intergroup comparison in the ants’ initial direction shows significant differences (Watson- Williams test, p % .05). (C) Average distance traveled by the ants per trial for the four different groups. Error bars represent the standard deviation across individuals. Stars indicate significant intergroup mean differences p % .05. Gray numbers in brackets indicates the percentage of agents that behaved as “remnants” in the rIDF model (see Figure 6).

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Successive errors during training trials. Among the ants having displayed more than two errors, half of them show a pattern where those errors did not occur randomly (11/22, p .001, run-tests). The errors tended to follow one another in successive trials. An example of an individual sequence of paths is shown in Figure 4C. The fact that the errors tend to be grouped in the sequence of trials is consistent with the presence of sequential evolution. If paths tend to be similar to the previous one, a path resulting in an error might be followed by another error.

Intergroup differences in paths Initial direction. Figure 3B presents the average initial direction of each individual. Although all the paths regarded here start and end at the same point, the ants’ initial direction recorded at 10 cm varies significantly between the groups (Watson–Williams test, p .030). This result at the group level reveals the impact of the visual environment on the ants’ initial direction.

Distance moved. Figure 3C shows the distance traveled by the ants in the different visual conditions. During the training trials, the 4bl ants traveled significantly longer paths than the ants of the featured-wall groups (ANOVA, p .038, all paired comparisons by the Tukey-Kramer test). Indeed most of the ants of the 4-black-walls group displayed a long curve at the beginning of their path before keeping a constant bearing toward a corner (example in Figure 4A ‘ant 6’ and 6B ‘ant 11,’ Trials 30–55). This pattern is predictable from the hypothesis of view-based matching. The presence of a distinctive wall adds visual information, which creates a stronger visual heterogeneity and thus facilitates a better discrimination between the different places of the arena, thus channeling ants on the correct way. There is no significant difference in the distance moved among the three featured- rectangle groups (Tukey-Kramer test).

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Figure 4: Inter and intraindividual differences. (A) Examples of the successful (i.e., nonerror) paths from three individuals illustrating interindividual idiosyncrasies. For the 4- black- walls condition (ant 6), the rotational errors are considered as successful trials and have therefore been rotated and plotted with the paths resulting in the preferred corner. (B) Examples of training paths plotted in sequential order from two individuals depicting the sequential evolution of the path. Like most of the ants, ant 25 displayed several errors in a row (Trials 20–24). (C) Examples of a sequence of five successive training paths from two individuals showing the evolution of the path across trials.

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3) Modeling

3.1) Method

G/F corner predictions We modeled the G/F hypothesis in two different ways. In the first model, each corner possessed a geometrical cue (G) and two featural cues (F) that corresponded to the left and right wall pattern. The preferred corner of the training condition (top-left in Figure 1) served as the reference for the correct geometry (G) and features (Fs). G and F points were attributed to each corner if they shared the same G or Fs as the preferred corner. The predicted corner choices were distributed according to the proportion of points (G & F) of each corner. The relative weight between G and F points was adjusted (as a free parameter) to obtain the best fit with the ants’ corner-choice data. In the second model, we added the possibility of differential weighting between the features. The model was otherwise the same as in the first model. The difference is that different featural cues (black, white, or striped) might be given different weights (free parameters) relative to geometric cues.

View-based matching model View-based matching theory can be modeled using a pixel-by-pixel comparison of panoramic pictures: a reference picture at the goal represents the supposed memorized view and any supplemental pictures can represent the current view at any chosen location (Zeil et al., 2003; Stürzl et al., 2008). In theory, the insect is supposed to move to reduce the mismatch between its current and memorized view. Here, views are used as visual compass: the “oriented reference picture” (i.e., memorized view) is taken on the way toward the goal and the nonoriented picture at a particular location (i.e., current view) is compared to the ‘oriented reference picture’ for each possible orientation. The best matching orientation thus predict the direction taken by the insect.

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Figure 5: Reference picture of each group used for the rotational IDF model: the reference picture represents the supposed view memorized by the ants. They were taken in front of the ant’s preferred corner. Each reference picture encompasses the 360° of the panorama but faces the preferred corner exit hole (i.e., exit hole at the middle of the horizontal axis of the picture). Each picture has been sized at 360 x 118 pixels (angular resolution " 1°) thus matching approximately the visual acuity of Gigantiops ants.

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360 degrees pictures. The 360 degrees panoramic images of the rectangular arena were recorded with a Canon G10 camera viewing a convex mirror (GoPano). The pictures were recorded with viewpoint 2.5 cm above floor level in the same visual condition that the ants experienced. The vertical field of view of the imaging system covers 118° (68° above to 50° below horizon). The pictures were first unwarped with the software Photo Warp, then converted into black and white (grayscale) and finally resized at 360 118 pixels (angular resolution 1°), thus matching approximately the visual acuity of Gigantiops ants.

rIDF (rotational image difference function). For each training and test condition, five pictures were taken: one at the center of the rectangle and four at the midway point between the center and each corner. The picture taken at the midpoint between the center and the ants’ preferred corner during training was considered as the reference picture (Iref). Iref was rotated to “face” the preferred corner (i.e., the exit hole of the preferred corner is located at the center of the horizontal field of the picture). The reference picture of each group is shown in Figure 5, whereas Figure 6 illustrates how the pictures are compared in our model. Each picture of a condition (Ic) was compared with the oriented Iref (Figure 6A). The difference between two pictures was calculated as the mean squared pixel difference (MSPD) over all corresponding pairs of pixels (as in (Zeil et al., 2003; Stürzl et al., 2008)). The MSPD between Iref and each Ic was calculated for all possible orientations of Ic (in steps of 1°). At that stage, the MSPD function describes the extent of the mismatch for each orientation of Ic. To reflect the relative goodness of the matching of each possible orientation of Ic, the MSPD function was inverted and the lowest value (orientation with the worst matching) set to zero (Figure 6B). Circular histograms were then used to illustrate the relative goodness of the matching for each orientation of Ic pooled in 30° bins (Figure 6C).

rIDF corner predictions. The predictions of the distribution of corner choices were calculated from the five rotational IDF histograms of each condition. First, each rotational IDF histogram was divided into four sectors of 90° associated with a value of goodness of matching (Figure 6D). To emphasize the relative importance of the good matching directions, the matching value of each 90° sector was raised to the power of 4 (x  x4). One hundred agents were released at the center histograms (CH) and distributed between the four midway histograms (MH) according to the distribution of goodness of matching to each sector from the center (the distribution of the central histogram in Figure 6D). Then, agents “arriving” at

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Figure 6: Rotational IDF (rIDF): from 360° panoramic pictures to a circular histogram of matching. (A) The oriented Reference picture is taken at the midway point between the center and the ant’s preferred corner during training (here in the white-featured rectangle). This reference picture represents the view memorized by the ant and “faces” the ant’s preferred corner (black arrow). The nonoriented Tested picture represents the current view perceived by an ant at a particular position (here at the center of the arena during a test condition, circular arrow). The Tested picture is compared pixel by pixel with the oriented Reference picture (MSPD: mean square pixels difference) for each possible orientation (the two examples given here are for the best (235°) and worst (315°) matching orientations). (B) The goodness of the matching can be plotted as a function of the orientation of the Tested picture; the similarity value for the worst matching orientation is set to 0. (C) The plot is converted into a circular histogram (bins of 30°) and shown in the rectangular arena at the position where the Tested picture was taken. (D) Illustration of the steps from the circular histograms to the distribution of corner choice. The agents are released at the center and distributed to the midways histograms according to the matching distribution in each 90° sector. The gray arrows indicate movement toward intermediate midway histograms. The black arrows indicate a final movement toward a corner. The agents displaying U-turn or turning at least two times without ending in a corner are considered as “remnants” (R) and are distributed randomly in the four corners.

49 Chapter II the midway points were distributed according to the distribution of goodness of matching found at each the MH sectors. The agents were redistributed either to the associated corner (one sector), or to the two adjacent midway points (two sectors), or back to the center (one sector), this last group of agents being considered as “remnant.” In a last step, the agents “arriving” at a second midway point were redistributed according to the goodness of matching found at that midway histogram. The pool of “remnant” agents corresponds to ants that displayed U-turns or “turned around” without selecting a corner. To prevent a large number of iterations, we assumed that remnant ants would end up choosing any corner and tolerating the potential mismatch. Thus, in our model, the remnant ants were distributed evenly between the four corners. This parameter-free model is hardly likely to correspond to the actual mechanism that the ants actually use but is useful for generating predictions based on the information available to the ants with few assumptions.

rIDFs and initial direction. We attempted to explain the group differences in the initial direction using our “visual compass” hypothesis of view-based navigation based on rIDFs (Figure 3B). The 4-black-walls condition is a particular case. The paths of the ants of that group ending in the rotational error (bottom right corner) have been rotated by 180° to make all paths end in one corner (the top left). Therefore, a half of the modeled circular histogram of that group should be rotated by 180° as well. The rotational IDF predicts a rotational error if the initial orientation is toward the bottom of the rectangle (from 180° to 359°) (Figure 1A). Consequently, this part of the circular histograms has been rotated by 180° for the 4-black-walls (Figure 3B).

3.2) Results and Discussion

Models comparisons. We compared models using the Akaike Information Criterion (AIC). The AIC provides a ranking of models relative to one another based on the residual error and the number of parameters used to fit the data (the lower the value the better) (Burnham & Anderson, 2002). The residual error and AIC of the models are shown in Table 2. The rIDF model obtained the best AIC because it fits better the ants’ data (lowest mean squared error) while using a lower number of free parameters than the G/F models. However, a closer look is required as none of the models predict the data without systematic errors.

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Table 2. The performance of three models predicting all the data on corner choices. Note. The Akaike information criterion (AIC) is based on the residual error (M error2), and provides a ranking of model relative to one another (the lower the value the better). Model performance is based on the error relative to the number of free parameters (r) calibrated to fit the data. AIC = nlog(error) + 2(r+2), where n is the number of data points, and r is the number of free parameters. Error = M error2(n-r-1)/n. For the Rotational IDF model, the distribution of the agents corresponds to the percentage of matching of each 90 sector multiplied by 4. For the Geometry/Features models the weighting of the cues is given for G (geometry); F (features); Fbl (Black feature); Fst (Striped feature); Fwh (white feature). A corner possesses 2 features. The results of the rotational IDF model and the best Geometry/Features model obtained are presented in Fig.3.

Model Weightings r M error2 AIC Rotational IDF % of matching for 90 ^4 0 253.0 70.8 Geometry/Features G=8; Fbl=1; Fst=37; Fwh=175 3 311.3 77.9 Geometry/Feature G=8.5; F=25 1 736.6 85.4

G/F models. The Geometry/Feature models presented here are based on the concept found in the literature that “features” and “geometry” are somehow segregated one from the other. Accordingly, the agent trained at a given corner is potentially able to memorize both the geometry (i.e., the relation of the trained corner to the rectangular shape of the arena) and the feature (i.e., the color of the adjacent walls of the trained corner). The AIC was better for the model with a differential weighting between the features, thus only those results are presented in detail in Figure 1 (white rectangle numbers).

G/F models in training condition. The G/F model explains the systematic rotational errors observed in ants in the 4bl condition. According to the theory, the absence of different features on the walls makes the rotational error identical to the reference corner. Indeed, both corners share the correct geometry and local features (i.e., two black walls). The G/F model explains as well the preference for one corner observed during the training condition of the featured rectangle. Indeed only the reference corner (top-left) itself possesses both the correct local features and geometry and is thus mostly chosen (Figure 1 “Training”). Regarding the errors, however, the predicted bias is in favor of the rotational error (bottom-right): although the rotational error does not preserve the correct local feature, it preserves at least the correct geometry. For the bl/bl group, the top-right corner could as well be a preferred error because although it does not preserve the correct geometry it does preserve the correct local features. Nevertheless, the G/F model cannot account for the bias observed in ants toward the bottom- left corner.

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G/F models in test condition. The G/F hypotheses predict a potentially perfect solution for the test condition of the bl/bl group. Indeed the bottom-right corner possesses both the correct geometry and local features (Figure 1B). However, the ants did not choose this predicted corner (2.5%) but displayed instead a strong preference for the top-left corner (97.5%) although the local feature of this corner is wrong. The only way for the model to reduce the error of this misprediction is to weight the geometry considerably more than the black feature, thus favoring the top-left corner almost as much as the bottomright corner. Conversely, the ants’ corner choices in st/bl and wh/bl tests demand a stronger weight on the features than on geometry. The weight has to be stronger for the white feature than for the striped feature. This leads to large differences between different feature weights. Indeed, relative to the weight given to the geometry, the models need to lower as much as possible the weight of the black feature while increasing as much as possible the weight of the other features. The best fit is obtained with a weighting 175 times stronger for the white feature than for the black feature, a pattern that makes little sense except that it helps to reduce mispredictions. Any other attempt with reduced weight differences would worsen the predictions of the model. Favoring the features would improve the st/bl and wh/bl predictions but worsen the bl/bl prediction. Conversely, favoring geometry would improve the bl/bl prediction but worsen the st/bl and wh/bl predictions.

rIDF model. Comparisons of matching between panoramic pictures as a function of their orientation resulted mostly in smooth curves with a unique and clear maximum of matching at a particular orientation (Figure 6B). Some pictures taken at the center, however, led to a bimodal distribution, revealing an ambiguity in the best matching direction. The predictions of the rIDF model are shown in white circles in Figure 1.

rIDF in training condition. The rotational IDF explains most of the ants’ corner- choice frequencies in the training condition. It describes the presence of a systematic rotational error in the 4bl group as well as the absence of it in the featured rectangles. It also predicts the bias for the error located at the bottom left corner during training in featured rectangles (Figure 1B, C, D). The rIDF model fails badly in the st/bl training condition. Indeed, contrary to what is predicted, the ants never confounded their preferred corner with the top-right and bottom right ones (Figure 1C). This failure might have arisen from some unrealistic modeling procedures rather than from a more fundamental problem with the viewmatching strategy. The central histogram of the st/bl condition is the only case of a

52 Chapter II bimodal distribution of matching values found in featured rectangles. This bimodal distribution results from comparing the striped features in the pixel-by-pixel way proposed by the model. In this st/bl training condition, the stripes need to line up correctly to minimize mismatch. The matching of perceived and target striped walls over a range of orientations (the way the model calculates mismatch) means that the goodness of matching alternates between very good values (when the stripes are superimposed white on white and black on black) and very bad values (when white stripes are superimposed on black stripes and black stripes superimposed on white stripes). This means the match between two striped walls is no better on average than the match between a striped wall and a black wall. This feature of the model is probably unrealistic, and it leads to poor predictions.

rIDF in test condition. Remarkably, the rIDF model also describes the surprising results obtained with ants in the test conditions. For the bl/bl group test condition (Figure 1B), the corner located at the bottom right possesses both the correct local feature (black/black) and the correct geometry of the trained corner and indeed matches well with the reference picture. However, the center picture of that test condition matches better when oriented toward the top, and both top right and top left pictures match better when oriented toward the left, explaining why ants massively preferred the top left corner (97.5%) and avoided the bottom right one (2.5%). The significant difference obtained in the test condition between the st/bl and wh/bl groups are also predicted by the rotational IDF. Contrary to the constancy of the wh/bl ants for the bottom-left corner (90.6%), the ants of the st/bl group displayed as well a sizable attraction to the bottom right corner (22.8%). Indeed, in the wh/bl test condition, both the center and the bottom-right pictures match better when oriented toward the bottom- left corner, whereas in the st/bl condition, the two analogous pictures match for both the bottom-left and bottom-right corner (Figure 1C, D). rIDF and paths. Initial direction. The ants’ initial direction recorded at 10 cm varies significantly between the groups, revealing the impact of the visual environment on the ants’ early decision. The rotational IDF model, which is based on comparing 360° pictures, accounts for such an impact of the visual environment. The better matching orientations at the center of the arena predicted by the rIDF model vary also across conditions. This model does not describe the ants’ results precisely, but the predicted intergroup differences at the center of the arena fit qualitatively with the ant’s initial directions (Figure 3B). Indeed, both ants and model show a

53 Chapter II bias toward the top in the 4-black-walls and black/black conditions and a bias toward the bottom for the striped/black and white/black conditions. In cases of a bimodal distribution of the goodness of the matching (in the 4-black-walls and striped/black conditions), it seems that most of the ants were able to select the direction that matched better and avoid the opposite “second best-matching” direction.

Distance moved. Another aspect of the paths accounted for by our rIDF model is the percentage of “remnant” agents. These are the agents that turn around rather than selecting a corner directly (see Figure 6D). The percentage of remnants was quite low for all the featural conditions (M 5.86% 4.09 SD) but much higher in the 4bl condition (51.44%). These results are in line with the significantly longer paths displayed by ants in the 4bl condition (Figure 3C). Indeed, in that condition, ants often aimed first at a wrong corner before curving and heading at the preferred one (example in Figure 4A ‘ant 6’ and Figure 4B ‘ant 11,’ Trials 30– 55), although this effect is less perceptible when averaging the paths.

4) General Discussion

In their natural habitat, forager ants have been selected for running back to their nest as soon as they have found a desirable food item. In the rectangular arenas used for this study, the loaded ant was immediately replaced to its nest once it reached a corner. Because in nature the nest is always at the same position, each ant, in our rectangular arenas, tends to return to the same corner from trial to trial. This spontaneous tendency in regimes of nondifferential conditioning reflects the natural behavior of the ants in homing. While all corners led ants home, their choice of corners was far from random and depended on the visual surroundings.

Spontaneous Rotational Errors and Nature of the Features Corner choices were nonrandom in all conditions, but especially so when a feature wall was present. When all the walls of the rectangular arena were black, the ants could not distinguish between their preferred corner and the diagonally opposite one. Nevertheless, each ant showed corner preferences by avoiding the two corners from the other, nonpreferred diagonal (Figure 1A). Such a selective preference for a diagonal is often interpreted as the use of geometric information in vertebrates (Cheng & New-combe, 2005). But such results can also

54 Chapter II be interpreted in the framework of view based matching. The corners of different diagonals differ in terms of where edges are positioned in a panoramic view of the arena, edges formed from the meeting of the ground, the walls, and the “sky” (Stürzl et al., 2008). In our previous study (Wystrach & Beugnon, 2009), only individual ants displaying nonrandom distributions were shown in the figure, whereas in the present work, all the individuals were pooled no matter how they performed. However, although the proportion of “random ants” is similar across the previous and present studies (respectively, 6/13 and 5/14), the frequency of errors among the “successful ants” was lower in the first study than here (respectively, 9.5% and 20.45%, p .0237). This difference might result from the variation of the layout of the arenas used. In the first study, the arena had pure white walls, but each edge was contrasted with a black line, making the rectangular shape of the arena more apparent than in the present study. This difference is consistent with a strategy of view-based matching: the more manifest the rectangular shape of the arena, the more the mismatch between the geometrically correct and incorrect corners, and therefore, the fewer the number of errors displayed (Wystrach, 2009). When a featural wall was present, most ants (21 of 27) spontaneously preferred a corner where the feature wall abutted a black wall. At these corners, the edge between the walls is strongly contrasted. This bias was the strongest (all 11 ants) when the featural wall was completely white, making these corners the only high-contrast vertical edges. Such an attraction to edges is consistent with previous studies on ant navigation that presented the insects with high-contrast stimuli in the lab (Judd and Collett, 1998; Harris et al., 2005; Harris et al., 2007) and may also reflect a natural tendency to approach a landmark (Graham et al., 2003). Furthermore, whichever corner an ant preferred, she displayed constancy in sticking to it, while committing very few rotational errors (Figure 1B, C, D). The corner constancy was very strong (over 90%) in ants that preferred an intersection of the feature wall with the black wall. This constancy also contrasts with results in Wystrach and Beugnon’s study (Figure 1C of (Wystrach and Beugnon, 2009)). Ants in that study were presented with features not on a wall but in the corners, and contrary to the present study, they committed systematic rotational errors. This difference illustrates that the size of the features matters in guiding view-based matching, as pointed out by Stürzl et al. (2008). In our previous study, from the center of the arena, the four features in the corners covered less than 7% of the azimuth. The mismatch between facing one corner and the diagonally opposite one was thus minor and ignored by the ants (Wystrach, 2009). In the present study, however, the feature wall covered 35% of the azimuth, resulting in strong mismatches between the different corners,

55 Chapter II mismatches large enough for all the ants to avoid systematic errors. This sizedependent power of the features is not inherent in the G/F hypothesis.

G/F or View-Based Matching? The G/F hypothesis start with the assumption that features and geometry of space are somehow segregated (for a review, see Cheng, 2005). This includes several recent models attempting to explain results obtained in rectangular arenas (reviewed by (Twyman and Newcombe, 2010)). Those G/F models cannot account for the results observed here in ants. They fail in two main points. (1) In the training condition in featured rectangles, they explain very well the preference for the main chosen corner but fail to explain the principal error displayed by the ant: the G/F models predict a bias for the rotational error in all training conditions but the ants shown instead a significant bias for the bottom-left corner (Figures 1B, C, D); and (2) In test condition, they fail to explain the strong preference of the bl/bl group for the top-left corner (see Figure 1). The rIDF model presented here succeeds better in explaining the corner choice results observed in ants (see Table 2). In two of four training conditions, the predictions are quantitatively worse than the G/F models (Figure 1C, D). However, contrary to the G/F models, the predictions are qualitatively correct and the significant bias observed for the bottom-left corner is well predicted (Figure 1B, C, D). The rIDF model has no free parameters calibrated to fit the data. It is therefore not surprising that the model presents some quantitative imprecision. All the predictions of the test conditions are remarkably accurate. Worth mentioning as well is that the higher percentage of “remnants” (agents turning around rather than selecting corner directly) predicted in the 4 black walls condition (51.44% in contrast to the featured rectangle having M 5.86% 4.09 SD) is consistent with the significantly longer paths displayed by the ants in that condition (Figure 3C). Of course we do not believe that our rIDF model has captured in detail the mechanisms of view based matching in ants, a topic of ongoing research in ants(Collett, 2010; Graham et al., 2010; Lent et al., 2010; Wehner and Müller, 2010; Wystrach et al., 2011c). Ants in general probably do not process views in a purely pixel-by-pixel fashion, and our ants did not always take a route through the midway point between center and corner as the model proposes. However, this simple view-based model based on panoramic views performed better than the different G/F models that we devised. The G/F models had serious deficiencies that seem difficult to correct while assuming that geometry and features are segregated. Our

56 Chapter II modeling efforts suggest that working on improving view-based models might prove more fruitful.

The Nature of the Visual Environment Influences the Path Our results show that the nature of the visual environment influences the path. This claim is supported by the intergroup differences obtained here: paths of distinct individuals are more different when performed in different visual conditions (see Figure 2). Consequently, paths of distinct individuals are more similar when displayed in the same visual conditions (i.e., same arena configuration). Therefore, the nature of the visual environment somehow “shapes” the path (see in Figure 3A). The visual conditions had an effect as early as on the initial direction taken by the ants. With the goal located in the top-left corner, the ants familiar with the distinctive wall at the top (i.e., the st/bl and wh/bl groups) showed a bias toward the bottom, whereas the ants familiar with the distinctive wall at the bottom (bl/bl group) showed a bias toward the top (Figure 3A, B). Remarkably, our simple rIDF model is consistent with those biases (Figure 3B). To take a concrete example, consider the black/black group. Those ants might have memorized a view while facing their preferred corner, that is, a view containing mostly black walls, with the featured wall perceived only far on the rear of the left eye (Figure 5B). When entering the arena at the center, the best matching view should have the feature wall toward the rear of the left eye. Such a view is to be found when the ant orients toward the top of the arena. It may be important for the ants to keep the perceived features on the same eye during travel (Graham and Collett, 2002; Harris et al., 2005). This could explain the tendency of ants to confound their preferred corner (top left in Figure 1B, C, D) with the closest adjacent corner (bottom left in Figure 1B, C, D). As shown in Figure 1C, D for example, in traveling from the center to both the preferred corner and the most frequently visited error corner, the featural wall would project to the right eye during the journey, and the left eye would perceive mostly black walls. This idea can also explain the results obtained in tests. Referring to Figure 1C, D again, the ants kept the feature wall on their right and the black walls on the left while traveling to their preferred corner in tests as in training. The rule of keeping the features on the appropriate eyes finds some support from earlier research. It has been shown in desert ants that no interocular transfer (IOT) occurs when they use terrestrial cues for navigation (Wehner & Müller, 1985). In the present paradigm, however, this hypothesis systematically predicts the selection of two of the four

57 Chapter II corners and cannot account for the preference of one particular corner as observed in ants. A supplementary explanation is therefore required. It has been shown that wood ants move toward landmarks that subtend a smaller angular size than that encoded in the memorized view (Durier et al., 2003). In the training condition of the present study, this would explain why ants preferred one corner of the two resulting from the rule of keeping features on the correct eye. For instance, suppose that the ants from the st/bl and wh/bl groups have memorized a view close to the featured wall. While the ant en route is keeping the features on the correct eye, the perceived featured wall appears smaller than its memorized size and consequently attracts the ant. Conversely, the ants from the bl/bl group would have memorized a view in which the featured wall appears smaller and the black walls larger. En route, the featured wall thus appears too big, and the black walls too small, resulting in an attraction toward the black walls, away from the featured wall. This concept accounts as well for the results obtained in tests. Indeed, a consequence of the test condition is that, from the center of the arena, the feature, which has been switched to a short wall, always appears too small compared with what was experienced during the training condition, and therefore tends to attract the ants.

Individual Flexibilities Although the visual environment “shapes” the paths of the ants, it does not dictate exact paths, leaving room for substantial variation between and within individuals. In our study, the ants’ paths were more similar within individuals than between individuals (see Figure 2). This reveals an individual idiosyncrasy, also called path signature (Macquart et al., 2006; Harris et al., 2007). Such individual path signatures have already been shown several times in ants slaloming between arrays of landmarks in artificial and natural environments (Kohler and Wehner, 2005; Macquart et al., 2006; Wystrach et al., 2011c). In the present study, however, no objects forced the ants to make any detours. Individual idiosyncrasies can be attributed to interindividual divergences in the parameters used to do the task. Those parameters consist in stored views (Judd and Collett, 1998; Graham et al., 2007; Harris et al., 2007) and also motor routines (Macquart et al., 2008; Lent et al., 2009). Beyond this generality, details linking view and motor parameters have been recently investigated with Cataglyphis fortis ants following a curved route around a single conspicuous landmark (Collett, 2010) but remain to be specified in complex natural environment.

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At the individual scale, ants displayed variability in their own paths across trials. Even after 50 trials, the paths can still vary substantially (example in Figure 4B, ant 11). This reflects some slack in the learned visuomotor processes. As ants do not seem to continuously match views as they walk along habitual route (Lent et al., 2009; Collett, 2010), this variability could result from irregularity in the motor routine displayed while views are matched perfectly at particular points along the route. However, some paths can be entirely different between two consecutive trials (i.e., no superimposition) showing that ants are to some extent also tolerant to view mismatch. Such tolerance to mismatch also ex-plains why ants do not behave randomly during artificial test conditions where the visual environment is suddenly altered, as is the case with Gigantiops ants (Macquart et al., 2006; Wystrach & Beugnon, 2009) and also other ants species (Wehner and Räber, 1979; Judd and Collett, 1998; Fukushi, 2001; Graham and Collett, 2002; Durier et al., 2003; Graham et al., 2003; Graham et al., 2004; Narendra et al., 2007b). In Wystrach (2009) this concept is called the Mismatch Tolerance Threshold (MTT). As the tolerance in view matching cannot be disentangled here from the tolerance in reproducing motor actions, MTT encompass both view and motor action mismatch and can be measured in term of differences in the paths displayed by a same individual. Such MTT differs from individual to individual, and can be lowered (i.e., made less tolerant to mismatch) with differential conditioning if a more precise matching is necessary to reach the target (Wystrach, 2009).

Sequential Evolution of the Path Remarkably, the comparison of paths within individuals reveals that the paths tend to be the most similar across successive trials (see Figure 2). In other words, an individual’s paths tend to evolve gradually from trial to trial (example in Figure 4B, C). This can be true even after 50 trials and explains why errors in corner choices were significantly clumped (example in Figure 4B, ant 11). Drifting of the parameters used to do the task (i.e., stored views and motor routines) would seem to capture the idea here. Such parameters can drift because there is no “selection pressure” (reinforcement contingency) to enforce rigidity. Nonetheless, this sequential evolution of the paths reveals that views and motor routines are not stored rigidly after a critical period of learning, but are updated with the foraging experience. The simulations that we ran in the present study (available online as supplemental material) suggest that the extent to which the memory of the last trial overrides the previous averaged memory is around 25%. This kind of updating of memories, with a substantial weight given to recent experience, is not a new idea in learning theory (Bouton and Moody, 2004; Lent et al.,

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2009; Collett, 2010). But such gradual updating is not found in some contexts of navigation. In path integration for instance, only the most recent trip completely dictates behavior (Cheng et al., 2006; Narendra et al., 2007a). At the opposite extreme, Gigantiops ants foraging in a familiar area in the wild place most weight on a long-term stored vector memory (Beugnon et al., 2005). Updating is not a universal phenomenon in spatial learning.

Insights on View-Based Matching View-based matching is nowadays much more than a single hypothesis, and it harbors many different subtleties and flexibilities that must vary across species. Difference may already arise at the sensory level. Contrary to pixel-by-pixel image comparison, the success of ants in matching the striped feature shows that ants do not store views purely retinotopically (see also (Lent et al., 2010)). What is encoded in a view is likely to be a combination of both static cues like edges, center of gravity, color of areas (Collett and Collett, 2002), skyline elevations (Graham and Cheng, 2009b), light distribution (de Ibarra et al., 2009), and dynamic cues like optic flow (Zeil, 1993b; Dittmar et al., 2010). How such cues are then memorized and processed to output behaviors may also vary across species. Even a single individual may well rely on different view-based strategies within a single trip. The present results support the idea that ants may somehow use their view as a visual compass (see also (Graham et al., 2010; Müller and Wehner, 2010). In contrast to matching by gradient descent (Zeil et al., 2003), the visual compass gives a best direction for any given current view without the need to sample the goodness of the matching of all neighboring locations. This would be remarkably well suited for route navigation as it would allow an ant to retrieve and stick to the correct direction while requiring a minimal number of memorized views. In contrast, moving to lower the mismatch between current and memorized views (i.e., matching gradient descent) appears unsuited to route behavior as it might result in tortuous paths. However, when the task is to relocate a precise location like the nest entrance, matching gradient descent requires a single memorized view at the goal and therefore provides a more parsimonious explanation than visual compass does (Zeil et al., 2003). Indeed, the use of a visual compass has difficulty in explaining the homing success observed in ants and bees trained to a route and released at novel locations on the other side of the goal (Wehner et al., 1996; Capaldi and Dyer, 1999), as the best matched rotation of the current views would lead the insect in the usual route direction and therefore away from the goal. For the purpose of homing from such new locations, relying on the apparent size of the memorized and perceived elements (Graham and Collett, 2002; Durier et al., 2003; Graham et

60 Chapter II al., 2004) appears more effective. For instance, being attracted by the elements of skyline that appear smaller (lower in elevation) than encoded in the memorized view would enable an ant to head in the home direction from a new location in natural condition (Wystrach et al., in press). The question of how to match the different elements of the skyline can be simply achieved by using the celestial compass as reference or by visual compass matching (Zeil et al., 2003). Finally, our results, along with others (Macquart et al., 2006; Lent et al., 2009; Collett, 2010) stress the importance of motor components as a part of the view-based strategy, as well as the flexibility and adaptability of insect memories. Future explicit modeling of performance, taking into account the inter-and intraindividual variabilities as well as the whole experience of the insect, is required to better understand insects’ taxon-like strategies.

5) Conclusion

Taken together, our results refute the models assuming segregation of spatial geometry and features in ants. Instead, the use of panoramic views along with a simple matching process makes a more likely theory because it can explain the impact of the different visual conditions on the ants’ paths and corner choices. The taxon-like strategies of hymenopterans encompass more than beacon-like strategies that form one major plank of the mammalian taxon system (O’Keefe and Nadel, 1978). Indeed, even when the goal is in sight, ants do not go in a straight line toward it. The present results illustrate the flexibility and adaptability of such taxon-like strategies, and the long-distance navigation of insects [hundreds of meters in desert ants (Wehner, 2003), more than 20 km in orchid bees (Janzen, 1971), and 3600 km for Monarch butterflies (Brower, 1995)] is an existence proof of how such strategies can be exquisitely adapted to produce efficient and robust behavior in complex environments. In vertebrates, most of the literature emphasizes a segregation of geometry and features, and a recent study clearly refutes a static image-matching hypothesis in three-year- old children (Lee and Spelke, 2010a). However, the debate is still open as view-based models succeed in explaining some results obtained in artificial arenas (Cheung et al., 2008; Sheynikhovich et al., 2009; Ponticorvo and Miglino, 2010), and a recent study has neatly demonstrated the use of a view-based strategy in chicks (Pecchia and Vallortigara, 2010). Evidently, both vertebrates and insects process visual information from views and, interestingly, the neural circuits that underlie their vision show striking similarities (Sanes and

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Zipursky). As the problem of being able to navigate in the world is also shared between vertebrates and insects, there might therefore be some evolutionary convergence in their navigational mechanisms, the extent of which would be valuable to determine.

Acknowledgements

We thank Julien Robert and Gérard Latil for their precious help in designing and constructing the experimental apparatus. We are also grateful to Maud Combe for her time and programming skills that enabled the interpath comparisons.

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Supplemental information

Correlation across intra-individual’s differences. The “intra-individual” path variability across successful trials measured here might reflect the ant’s tolerance to mismatch (MTT). The higher the individual’s tolerance to mismatch is, the higher her path variability across trials should be. A high tolerance to mismatch should result in a high propensity of displaying errors. As predicted by the MTT concept, the “Intra- individual” path variability (measured from the non-error trials only) is significantly positively correlated with the frequencies of corner choice errors (r=0.342; p=0.039)(Fig. S1A). The intra-individual path variability tends to be smaller when calculated between successive trials rather than between random trials. This could be explained by two hypotheses. The ants could have displayed either sudden switches or a progressive evolution in their path layout. We observed a significant positive correlation between sequential and random variability across individuals (r = 0.895 ; p < 0.001 ) (Fig. S1B). This correlation supports the second hypothesis (i.e., progressive evolution of the path) but not the first one (i.e., sudden switches at particular trials). Indeed, if an ant would display sudden switches at particular trials only, the extent of their random path variability would be strongly increased while the sequential variability would keep lower. In contrast, if an ant displays a progressive evolution in her path layout, the extent of her random path variability would reflect directly the extent of its sequential variability, causing a strong correlation between the two variables. Such a progressive evolution of the path layout indicates that ants updated their memory from trial to trial. Interestingly, the best-fit slope of this correlation (Fig. S1B) is less than 1 (slope = 0.763). This indicates that it is the individuals having a high sequential variability that displayed a more apparent path evolution. This makes sense according to the MTT concept and the fact that individuals update their memory on each trial. Indeed, the higher the individual’s MTT, the more its paths should vary from trial to trial (high sequential variability), and thus the more its memory is altered, and therefore, the quicker its path layout should evolve (lower ratio sequential/random variability).

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Figure S1. Plots of the individual ants according to their intra-individual path variability calculated across random trials (x axis) and two other variables (y axis). The random inter-trial path variability measures the non-constancy of the path layouts across the successful (i.e., nonerror) trials within individuals. It is an indirect measure of the ant’s tolerance to mismatch. Each dot represents an individual ant; the lines represent the linear best-fit trends. A) Results obtained with real ants. The percentage of errors (y axis) refers to the frequency of choice for other corners than the preferred one. B) The sequential variability (y axis) results from the comparison of paths from successive trials. The results obtained in real ants (top left) show that the random inter-trial variability increases quicker than the sequential inter-trial variability (i.e., the path layout tends to evolve from trial to trial). Simulations of 1000 ants shows that the extent to which the memory of the last trial overrides the previous average memory modifies the slope of the correlation. The results of the simulations are shown for 3 possible memory updating extent: no updating of the memory at all, with only the first trial used (bottom-left: memory override value = 0); complete replacement of the memory, with only the previous trial used (bottom-right: memory override value = 1); the slope best matching the real ants’ data has been obtained for a partial updating of the memory (top-right: memory override value = 0.25).

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The actual slope of this correlation (Fig. S1B) depends on the extent to which the memory of the last trial overrides the previous memory. As shown by the simulations, a “memory override” value of 0 (no updating of the memory) leads roughly to a slope of 1 (simulated slope = 0.9379), which means no path evolution. A “memory override” value of 1 (only the last trial is used), however, leads to a smaller slope (simulated slope = 0.2060) than the one observed in ants (observed slope = 0.7630). Therefore, the ants might refresh only partially their memory at each trial. The simulated slope that matches best the observed slope was obtained for a “memory override” value of 0.25 (simulated slope = 0.7315). The range of MTT values attributed to the population of simulated ants is directly reflected by the distribution of the individual’s sequential variability. The best matching simulated distribution of individuals’ sequential variability (Gaussian distribution: mean = 7.96, SD = 2.92), compared with the observed distribution (Gaussian distribution: mean = 8.02, std = 2.78), has individuals’ MTT values ranging from 5 cm to 30 cm (uniform distribution). The MTT value corresponds here to the maximum variation tolerated by the individual between its memorized path and the path it actually takes (See Fig. S2), encompassing both visual and motor mismatch tolerance.

Memory override computer simulation methods We used the software Matlab to run simulations and find out to what extent the last trial overrides the memory. We ran twenty simulations with different “memory override” values: from 0 (no override or memory fixed) to 1 (complete override of the memory, with only the previous trial used) by steps of 0.05. In order to get precise best-fit lines, each simulation processed 1000 model ants. A MTT value was randomly chosen for each simulated ant (best fit simulation obtained for MTT values uniformly distributed between 5 and 30 cm) (Figure.S2). Each simulated ants displayed 40 trials (as the real ants on average did). The path taken on a trial was represented by a simple value. The variability between two paths was simply calculated as the difference between the two paths’ values. We thus obtained one value for each pair of paths compared (as we did with the real ants’ data).

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Figure S2. Examples of the inaccuracy (gray areas) to which a path memory (black paths) can lead to as a function of the individual’s MTT (Mismatch Tolerance Threshold). The MTT is not expressed here in terms of visual mismatch tolerated but in terms of distance away from the memorized path tolerated (includes thus both visual and motor mismatch tolerances). The simulations’ results matching the best the real ants’ data were obtained for MTT values ranging from 5 cm (left panel) to 30 cm (right panel) in the population of simulated ants.

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All the simulated ants started with a “memorized path” of 0. The first path taken was equal to the “memorized path” plus or minus a random “variation”. This “variation” value was randomly chosen in the range given by the MTT value of the individual (uniform distribution). Thus,

path takenn = memorized pathn–1 + variationn

Then the “memorized path” value is updated as a function of the “memory override” value of the simulation:

memorized pathn = memorized pathn–1 + (variationn  memory override)

If memory override = 1 then memorized pathn = path takenn–1.

The next trial (n+1) started with memorized pathn. As a result, the path taken by an individual drifts from trial to trial; the extent of this drifting depends of both the individual’s MTT value and the memory override value (fixed across all individuals for a given simulation). In the arena (80 cm  40 cm), the real ants’ paths were constrained by the walls. The biggest interpath values theoretically possible to obtain from a pair of successful paths (i.e., ending in the same corner and shorter than 250 cm) reach approximately 80 cm. To simulate the presence of the walls of the arena, the “paths taken” values were thus constrained between –40 to +40, setting the biggest inter-path value possible at 80).

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CHAPTER III

Views, landmarks, and routes: how do desert ants negotiate an obstacle course?

This chapter has been published in the Journal of Comparative Physiology A.

J Comp Physiol A (2011) 197:167–179 DOI 10.1007/s00359-010-0597-2

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Views, landmarks, and routes: How do desert ants negotiate an obstacle course?

Antoine Wystrach1,2, Sebastian Schwarz1, Patrick Schultheiss1, Guy Beugnon2, Ken Cheng1

1Department of Biological Sciences, Macquarie University, Sydney, NSW 2109 Australia

2Centre de Recherches sur la Cognition Animale, CNRS, UMR 5169, Université Paul- Sabatier, Toulouse F-31062, France

Abstract

The Australian desert ant Melophorus bagoti often follows stereotypical routes through a cluttered landscape containing both distant panoramic views and obstacles (plants) to navigate around. We created an artificial obstacle course for the ants between a feeder and their nest. Landmarks comprised natural objects in the landscape such as logs, branches, and tussocks. Many ants travelled stereotypical routes home through the obstacle course in training, threading repeatedly the same gaps in the landmarks. Manipulations altering the relations between the landmarks and the surrounding panorama, however, affected the routes in two major ways. Both interchanging the positions of landmarks (transpositions) and displacing the entire landmark set along with the starting position of the ants (translations) (1) reduced the stereotypicality of the route, and (2) increased turns and meanders during travel. The ants might have used the entire panorama in view-based travel, or the distal panorama might prime the identification and use of landmarks en route. Despite the large data set, both options (not mutually exclusive) remain viable.

Keywords: Landmark – Route – Navigation – Panorama – Desert ant

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1) Introduction

Many animals use terrestrial objects — landmarks — for navigation (Shettleworth 2010). Landmarks may be encoded and represented in a map-like fashion, although this idea of ‘cognitive mapping’ is fraught with controversy in both vertebrate (Benhamou 1996; Bennett 1996) and invertebrate animals (Gould 1986; Dyer 1991; Wehner and Menzel 1990; Menzel et al. 2005; review: Shettleworth 2010). Just as often, landmarks are used to chart routes, which are typically stereotypical paths through a landscape dotted with objects. Route following in vertebrate animals has been little studied (but see Calhoun 1963), but in insect navigation, the topic has received much attention (Rosengren 1971; Collett et al. 1992; Collett et al. 1993; Zhang et al. 1996; Kohler and Wehner 2005). We here begin to examine the mechanisms of route following in a species known for the behaviour, the Australian red honey ant Melophorus bagoti. M. bagoti lives in a wide range over semi-arid Central Australia, where the habitat is typically filled with plants, from trees and bushes to grass tussocks (Muser et al. 2005), where mated queens attempt to dig nests in the ground to start colonies (Schultheiss et al. 2010). The ant is the most thermophilic on the continent (Christian and Morton 1992), and forages in the heat of the day in the few hot months of summer. The heat of the ground surface makes it too volatile for chemical trails, so that the ants forage individually, relying on both path integration (Wehner et al. 2006; Narendra 2007; Narendra et al. 2007) and, more commonly, landmarks, through which they run stereotypical routes (Kohler and Wehner 2005; review: Cheng et al. 2009). While M. bagoti’s stereotypical routes through natural terrain have been plotted (Kohler and Wehner 2005), many details of its route following behaviour have yet to be characterised. We investigated the interaction between the use of individual landmarks along a route and the broad context of the panoramic view around the ant as she travels. The ants had to travel an artificial obstacle course between a feeder and their nest. Using natural materials found in the landscape to create landmarks along the route, objects such as logs, tussocks, and branches, we manipulated these movable but stationary landmarks on tests. After sufficient training, we effected various transformations, including the removal of the landmarks, transpositions of landmarks, in which two or more landmarks switched positions, and displacements, in which the whole array of landmarks was translated and/or rotated.

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Based on much past research on the importance of contextual and panoramic cues, we expected that a mismatch between the usual route landmarks and the panoramic context would affect the route following behaviour: the more the mismatch, the less the usual route would be followed. Panoramic cues might prime the retrieval of memories of landmarks and how to negotiate them (Collett et al. 2003), or they might also be used directly for guidance (Graham and Cheng 2009a). We also expected that a mismatch between the positions of the route objects and the panoramic cues would affect path characteristics, such as how much the ants meandered during travel, and whether and where the ants spent the most time searching, as they often did.

2) Materials and Methods

Animals Desert ants Melophorus bagoti, the red honey ant, from two nests were tested in situ near their nest from December, 2008 through February, 2009. Experimentation was carried out in the mornings until ~12:00 and afternoons from 14:30 onwards. The midday period was not conducive to experimentation as the ants often spend large amounts of time taking refuge on a plant (Christian and Morton 1992; pers. obs.).

Field site The experiments took place on the grounds of the CSIRO Centre for Arid Zone Research, ~10 km south of Alice Springs, in a setting filled with trees (Acacia and Hakea), bushes, grass tussocks (mostly the invasive buffel grass), and buildings. Views associated with the two nests can be seen in Fig. 1.

Experimental set up Two experiments with different set ups were conducted on two different nests. Each nest was provided with free access to a feeder (a square plastic tub, 20 cm  20 cm  15 cm deep, sunk into the ground) 10 m North (Experiment 1) or West (Experiment 2) from their nest. During training, small pieces of cookies were scattered in the feeder for the ants to pick up, and sticks placed in the feeder allowed the ants to climb out, the walls of the plastic tub being extremely difficult for ants to climb. Between the nest and the feeder, we set up obstacles using natural materials, grass tussocks, leaves, branches, logs, and rocks, for the ants to navigate through. A grid of 1-m square units consisting of string wound around tent pegs stuck into the ground

74 Chapter III provided reference for recording an ant’s trajectory on gridded paper. The strings were off the ground and did not interfere with ant movements. Such a grid was also set up for tests on a distant test field. Experiment 1 was set up with an ‘obstacle course’ of six landmarks placed directly on the ground (Fig. 1a). The landmark nearest the feeder was 2.5 m away, with each successive landmark 1 m farther; the farthest landmark thus lay 7.5 m from the feeder. Throughout training, the landmarks remained stationary in the same configuration. Experiment 2 was set up with four rows of landmarks between feeder and nest, the nearest row to the feeder 3.5 m away and the farthest row 7.5 m away (Fig. 1b). The rows were evenly spaced, with three objects in row 1, two in row 2, three in row 3, and two in row 4. For easy cue manipulations such as displacements, we placed all the objects in white half cylinders (80 cm long, 20 cm wide) sunk into the ground. Between the cylinders lay a gap of 40 cm. Throughout training, these landmarks also remained stationary in the same configuration. At the start of the journey home, we created a short stretch of alley to start the ants off in the correct general direction and prevent them from scattering in random directions. The alley was a gap 120 cm long and 40 cm wide with 10 cm high white plastic walls on the sides formed with segments of ‘channels’. It was aligned in the feeder-nest direction except for one test in which the orientation of the landmark array was rotated (Distal Rotated, described below). On that test, the starting alley was also rotated to face the landmarks. The ants were forced to start their homeward journeys down this alley during training and on tests.

Procedure On her first arrival at the feeder, each ant was painted for individual identification. Painted ants were free to travel back and forth between feeder and nest. After at least one full day of training, a painted ant might have her training return journey recorded and then tested. On these occasions, the ant was allowed to travel home with a piece of cookie (as during training). We recorded only the runs of ants that carried a piece of cookie the entire journey. Her path was recorded on a gridded piece of paper. We trapped the ant and picked her up when she reached the vicinity of the nest and began to make searching loops. Such an ant has run off her homebound vector based on path integration, so that the vector to run is now the zero vector. She is called a zero-vector ant. Zero-vector ants are used to test how ants use landmarks for navigation because they cannot derive a direction of travel based on path integration, and this because the zero vector does not specify a direction. We then brought the zero-vector ant back to the rim of the feeder and placed on the ground to run home again. Her

75 Chapter IV path was recorded again, and this time she was allowed to enter her nest. When the ant returned again to the feeder, she was tested again under zero-vector conditions. Typically, this was a matter of minutes, but the timing varied as we had typically other trained ants to test as well. If the three runs, one full-vector and two zero-vector runs, showed the same pattern in negotiating the obstacles, the ant would then be tested in a manipulated condition the next time she appeared at the feeder. The same pattern meant that an ant went between the obstacles in the same manner; technically, she crossed the same line segments connecting the landmarks in the same order. All tests were on zero-vector ants, each preceded by a normal full-vector homebound training run, which was recorded. The ant was captured in the vicinity of the nest, and then allowed to run home again under zero-vector conditions, this time with the manipulations effected on the landmarks. During manipulated tests, the sticks in the feeder were removed to prevent any ants in the feeder from exiting, this to prevent ants from being trained with a different set up.

Manipulated tests In Experiment 1 (Fig. 1a), the only manipulation consisted of transposing the landmarks while keeping one landmark in each of the landmark positions used in training (Fig. 1c). In Experiment 2, we effected the transposition on all rows, within each row. For rows 2 and 4, with only 2 landmarks, only one transposition is possible. Rows 1 and 3 have 3 landmarks each, and two transpositions are possible in which the positions of all three landmarks are switched. We picked the transposition for each ant that preserved the pair of landmarks between which the ant usually travelled. For example, suppose that the landmarks are aligned A, B, C, from left to right, and the ant habitually travelled between B and C (Fig. 1d). The transposition would change the arrangement to B, C, A, allowing the continued possibility of travelling between B and C. This same transposition was effected if the ant habitually travelled to the right of C. A second set of transformations consisting of landmark translations was conducted in Experiment 2 only. For these transformations, the entire array of landmarks was rigidly translated to the right, looking from the feeder to the nest. The tested ant’s starting point was likewise translated. Geographically, it was only possible to translate to the right, where open space was found. In a translation, the direction of the array was preserved; geometrically, no rotation was effected. We effected translations in increments measured in landmark units, with a one-unit move placing one landmark to its neighbour’s position 1.2 m to the right. The four translations effected were: Translation 1 (1.2 m to the right), Translation 3 (3.6 m to the

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Figure 1: experimental settings and set ups. a, b Photos of experimental settings and artificial landmarks used in Experiment 1 (a) and Experiment 2 (b), looking from the feeder towards the nest. c The transposition effected in Experiment 1, with the grey line showing an example of a training run. The same transposition was effected for all ants. d An example of a transposition used in Experiment 2, and a training run. The stars indicate nest location in c and d

77 Chapter IV right), Translation 8 (9.6 m to the right), and Translation Distal (to a distant, completely unfamiliar test field ~100 m away). We tested ants in a number of other conditions in Experiment 2, some to gather data on other manipulations, others as controls. In the Panorama test, ants were allowed to return home from the feeder on the training field, but all the experimental landmarks were removed, leaving solely the distant panoramic cues for orientation. The Distal Rotated test probed the significance of the compass direction of the landmarks on a distal test field. Thus, the test took place on the distal test field as in the Translation Distal test, but the landmark array was rotated by 80 clockwise, as was the starting alley. On the Distal No Landmark test, the ants were again tested on the distal unfamiliar test field, but without any of the experimental landmarks present. Finally, a control group was tested in the Translation Distal condition (called Distal Control). The Distal Control group was trained to home from the same feeder without any landmarks. They were tested on the distal test field with the landmarks in place that their experimental counterparts had been trained with. This condition tested whether the ants had any untrained tendency to approach the landmarks as beacons, as ants sometimes do (Graham et al. 2003).

Data analysis We present the gist of data analysis here, leaving details for the Results section to minimise repetitiveness. Paths were digitised using GraphClick software into coordinates with (0, 0) being the start of the journey, the x-axis representing left-right travel (negative to the left), and the y-axis representing homeward travel in the positive direction. For Experiment 1, we noted whether the sides chosen corresponded with the same side (left or right) of a landmark or with the Earth-based position (to the same side irrespective of which landmark was at the position); depending on the ant’s habitual path, these predictions sometimes coincided. For Experiment 2, we also noted whether an ant on a transposition test followed the landmarks (going through the gap defined by the same pair of landmarks irrespective of their position) or the Earth-based position (going through the same Earth-based gap irrespective of the landmarks on either side), or travelled another way (all others). On other tests in Experiment 2, we also classified travel through each row exhaustively, as described in Results.

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Indirectness and curvature of paths. When the visual cues were transformed, ants often hesitated, turned left and right as they travelled, and sometimes appeared to scan the visual surround (a behaviour that we are analysing in another work). We calculated a measure of such ‘wiggling’, called Meander, and compared it across all test conditions, including training runs.

Effect of nest entrance relocation. A natural, unplanned event allowed us to analyse the effect of nest location on training paths. During the course of experimentation, the nest entrance relocated from a point slightly to the right of the y-axis to a location slightly to the left. We compared the training runs of ants trained before and after this transition (details in Results).

Unless otherwise stated, results of statistical tests were considered significant at alpha = 0.05.

3) Results

Our observations showed that the ants readily solved the obstacle course between the feeder and the nest during training. Many ants established stereotypical routes through the same gaps spontaneously. Experiment 2 had larger obstacles, making a larger deviation for ants to go through a non-habitual gap. A higher proportion of ants made 3 consecutive runs through the same gaps in Experiment 2 (192 of 210 or 91.4%) than in Experiment 1 (66 of 112 or 58.9%; p < 0.001, Fisher’s exact test).

Transposition tests Transposing landmarks clearly affected the stereotypical routes that the ants took to head home, in both Experiment 1 and Experiment 2 (Fig. 2). At the time of testing, all ants (100%) had traversed the same pattern of gaps back to their nest for 3 consecutive homing journeys. On the transposition test, only 56 of 72 ants (77.8%) in Experiment 1 and 33 of 72 ants (45.8%) in Experiment 2 charted the habitual route home through the same gaps in Earth- based coordinates (Earth-based or Both in Figs. 2c,d), a higher proportion in Experiment 1 (p < 0.001, Fisher’s exact test). In Experiment 1, the ants were more likely to follow the Earth-based route, striking the habitual route through what were now ‘wrong’ landmarks, than they were to steer around

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Figure 2: routes in the transposition test. a, b Paths from training runs (top) and from transposition tests (bottom) in Experiment 1 (a, n=12) and Experiment 2 (b, n=18). The training runs are from the runs preceding the transposition test. The rectangles in b demarcate the starting alley. c Choice of side at each row (with a single landmark) on transposition tests in Experiment 1. An Earth-based choice means choosing the usual Earth-based side at that row, irrespective of what landmark was at that row. A landmark choice means choosing the side of the displaced landmark that an ant habitually took in training. Sometimes both these criteria were satisfied. d Choice of gaps at each of the 4 rows (see panel b) on transposition tests in Experiment 2. An Earth-based choice means choosing the usual Earth-based gap irrespective of what landmarks make up the gap. A landmark choice means choosing the gap through the displaced landmarks.

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Figure 3: routes in the panorama test: a Gap choices on the Panorama test in Experiment 2, in which the usual landmarks along the route had been removed (n=18). The now non- existent ‘gaps’ were defined by Earth-based positions (Earth-based choice). Crossing the row anywhere other than the Earth-based gap was called “Else”. b Runs on the Panorama test, with the dotted lines indicating the usual positions of the landmarks missing on the test. The star indicates the nest position, and the rectangles demarcate the starting alley

81 Chapter IV the side of a transposed landmark that they habitually headed for in training (Figs. 2a,c). In fact, the ratio was 3 : 1. Ignoring choices categorised as Both or Other, 10 ants had at least 1 Earth-based or Landmark choice. Averaging across individual ants, the mean proportion of Earth-based choices (Earth-based / (Earth-based + Landmark)) was 0.75. The 95% confidence interval exceeded 0.5. In Experiment 2, ants followed the Earth-based routes (flanked by ‘wrong’ landmarks) more in rows 1, 2, and 4, and the landmarks more in row 3 (Fig. 2d). Considering only Earth-based and Landmark choices (and ignoring 9 Other choices), the average proportion of Earth-based choices across individual ants was 0.57, not significantly different from 0.5.

Panorama test On the Panorama test, the ants travelled home successfully from the feeder on the training field with the training landmarks removed (Fig. 3). Only one of 18 ants failed the test, not reaching rows 3 and 4 (Never Reached choices). Even without the habitual landmark obstacles, the ants mostly went through the habitual landmarkless ‘gap’ at row 1 (Earth-based choices, Fig. 3a). Thereafter, this tendency diminished somewhat, with more ants travelling past the row somewhere else (Else choices).

Displacement tests On translation tests in Experiment 2, the entire array of landmarks was shifted to the right without rotation, with the ant’s starting point likewise shifted. For each row in each translation, we defined four categories of travel. The ant could travel through its habitual gap, taking the Usual Route. She could choose another gap (Wrong Gap). Or she could use the Panorama. This was defined as a combination of travelling through a gap to the left of the usual one, on the row in question and all subsequent rows, and eventually heading towards the nest. Determining whether an ant headed to the nest proved completely unambiguous. On exiting the landmark array, an ant either headed towards the nest and entered it or else she searched around in loops. Finally, she might never reach a row. The unsurprising strong influence of the usual panorama encountered on training runs is indicated by marked differences in the nature of routes as a function of the distance of translation (Fig. 4). The proportion of ants going through the habitual gap (Usual route choices) diminished with distance of translation. The Panorama category increased at distances of 3.6 m (Translation 3) and 9.6 m (Translation 8) translation, but at 9.6 m, the number of ants that never reached the last row (and never found the nest) increased. Of course, at the distal test field at which no

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Figure 4: gap choices and path trajectories on tests with landmark displacements in Experiment 2. The ants’ starting position was displaced along with the landmarks. a Row- by-row gap choices. The usual route indicates an ant going through the same gap defined by the displaced landmarks that she took in training. A Panorama choice was defined as a choice to the left of the usual gap, followed by the ant eventually heading home. b-e Paths on translation tests, from Translation 1 (b, n=18), Translation 3 (c, n=20), Translation 8 (d, n=16), and Translation Distal (e, n=11). Solid black lines indicate paths defined as following the panoramic cues. (Paths of the Distal Rotated test appear in Fig. 5.) The star indicates the nest position, and the rectangles demarcate the starting alley. f Summary of gap choices in different displacement tests averaging all rows.

83 Chapter IV familiar panoramic cues were found, the use of any familiar panoramic cues was impossible. A statistical comparison can be made by considering only row 1 choices, such that the data are independent, with one choice from each ant across all test conditions. Excluding the Never Reached category (just one ant in the Translation Distal test), the proportions of categories of choices differed across translation conditions (2 (6) = 29.2, p < 0.001).

Navigational behaviour changed row by row as well (Fig. 4a). The choice of the usual route diminished row by row. Except for one tie in Translation Distal, the monotonically decreasing pattern held for all translation tests. The exact probability of a monotonically decreasing sequence of 4 is 1/(4!) = 0.042. The exact probability for 3 such sequences (in Translation 1, Translation 3, and Translation 8) is 0.0423 = 7.2–5.

We found the opposite trend with the Panorama choice: this increased row by row, monotonically with a tie in each of Translation 1, Translation 3, and Translation 8. (The Translation Distal and Distal Rotated tests produced no Panorama choices for obvious reasons.) We refrain from conducting inferential statistics here because the pattern is dictated by the definition of the Panorama choice. If the choice of one row is Panorama, the choice on all subsequent rows must be Panorama as well. More informative is the row at which the ant first chose the Panorama option. Combining only Translation 3 and Translation 8, in which most of the Panorama choices were made, Panorama was chosen first 10 times in row 1, 8 times in row 3 in 8, and once each in row 2 and row 4.

A higher proportion of ants never reached a row before turning around in search loops as translation distance increased, with the Never Reached category comprising a majority of rows 3 and 4 on the distal test field when the landmarks were not rotated (Translation Distal), and comprising 100% of the runs when the landmarks were rotated by 80 (Distal Rotated). In the Translation Distal test, the vast majority of ants reached the first row, but less than 20% of the ants chose the usual gap in row 1, and a majority of ants never reached row 3 (Fig. 4a). In the Distal Rotated test, only a minority reached the first row. Most ants searched in loops (Figs. 4a, 5b), as did ants in the Distal No Landmark test (Fig. 5c) and the Distal Control test (Fig 5d). The landmarks on the Distal Control test, never encountered before by the ants, did not attract the ants to them.

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Figure 5: meander in all tests in Experiment 2 and paths of the three remaining conditions. a Meander in all conditions, computed from the orientations of 30-cm line segments calculated from the digitised (x, y) coordinates of the path. Each successive segment was formed by drawing a circle of 30 cm radius around the end of the previous segment and noting where the path crossed this circle. The circle was placed at the starting point for segment 1. Meander was defined as the average absolute angular difference between successive segments in radians. Conditions sharing the same letter are not significantly different by Tukey’s post hoc tests. Individual points in the training condition cannot be readily identified because there were 499 data points. LM stands for landmark. b-d Paths in the Distal Rotated (b, n=15), Distal No Landmark (c, n=29) and Distal Control (d, n=15) conditions. In c, the dotted lines represent the positions of the landmark obstacles encountered during training, now absent on the test. The star indicates the nest position, and the rectangles demarcate the starting alley. 85 Chapter IV

Meander of paths On all the tests in Experiment 2, we devised a measure of Meander by dividing the path into 30-cm line segments. A circle of 30 cm radius was placed at the start of a route and a straight line segment drawn to where the route crossed this circle to deliver segment 1. The circle was then centred at the end of segment 1, and where the route crossed the circle delivered the end of segment 2, etc. The Meander index measures how much the path changes direction from segment to segment. The absolute angular deviation in radians from one segment to the next was averaged over all segments. Thus, 0 radians meant that the two segments were collinear, while  radians meant that the ant turned straight back. Variances of Meander differed significantly across conditions by O’Brien’s test (O’Brien 1979) (Fig. 5a, p < 0.001). We then used Tukey’s post hoc test to compare all pairs of conditions (Fig. 5a). The training condition produced the least Meander. Even a translation of 1.2 m (Translation 1) produced more Meander than training runs. Transposition produced more Meander than Translation 1. The Panorama test, in which the landmarks on the training field were removed for the test, also produced more Meander than training runs. This shows that the removal of the training landmarks made the ants turn back and forth more. Nevertheless, the powerful role of the distant panorama is revealed by the fact that paths on this test had less Meander than tests on the distal test field (conditions 7-10 in Fig. 5a). Finally, rotation of the landmarks on the distal test field did not produce any noticeable effects on Meander (compare conditions 7 and 8).

Distribution in feeder-nest direction Another informative path characteristic is how much of each path is distributed across the length of the journey. Because in some conditions, ants initiated searching loops from the start, we extended the range along the feeder-nest direction to 4 m behind the feeder (–4 m to 10 m). The training condition, the Panorama test, and tests on the distal test field were analysed. For each ant, we calculated the median point, spatially, of the path, and the interquartile spread. Tukey’s posthoc comparisons were then used to compare the conditions pairwise (Table 1). Runs from training and Panorama tests (Figs. 2b and 3b, respectively) had medians near the halfway point (5 m) and wide spreads, reflecting straight runs. Groups with no relevant landmark information of any kind (Distal No Landmark, Fig. 5c, and Distal Control, Fig. 5d), either because no landmarks were provided (Distal No Landmark) or because they were not trained with landmarks (Distal Control), showed peaks near the starting

86 Chapter III point (0 m) with lower interquartile ranges than training runs, reflecting searching centred on the starting point. Thus, ants not trained with landmarks were not attracted to them. Ants that were trained with landmarks headed towards the training landmarks on the distal test to some extent (Translation Distal and Distal Rotated groups, Figs 4e and 5b, respectively). On this measure, the directional orientation of the landmarks seemed to matter: when the landmarks were rotated by 80, the manipulation induced the ants (in the Distal Rotated group) to exhibit a median at a shorter distance from the start.

Table 1. Median point of path along the y-axis and the interquartile range, calculated in each individual ant and then averaged across all ants in each condition. Conditions sharing the same letter do not differ from each other significantly by Tukey’s post hoc test.

Mean Interquartile Mean Median (m) SD N SD Condition Range (m) Training 4.6 a 0.40 499 4.4 a 0.42 Panorama 4.5 a 0.99 19 3.8 b 0.99 Translation Distal 3.2 b 1.01 13 2.3 c 1.49 Distal Rotated 2.1 c 1.32 20 2.3 c 1.59 Distal No Landmarks 0.3 d 0.87 29 2.5 c 0.65 Distal Control 0.3 d 0.72 15 2.1 c 0.80

Effect of nest location on paths Our final analysis was based on a natural manipulation that the ants themselves effected. During the course of experimentation, the ants shifted their nest entrance, from very slightly to the right of our y-axis to slightly to the left of the y-axis. Considering ants with at least three consecutive training runs through the same gaps, the gap choices differed as a function of nest location (Fig. 6), biased more to the right when the nest was to the right. Considering each row separately, and only choices in which at least one group had 5 ants making that choice, 2 tests of independence showed that the distributions differed significantly between the two groups at each row (p’s < 0.005). This pattern shows that the ants tended to weave an obstacle course around the vector direction that pointed to their nest.

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Figure 6: spontaneous gap choices. The choice of gaps on training runs before and after the nest entrance moved from very slightly to the right of the y-axis (left panel) to slightly to the left of the y-axis (right panel) in the course of experimentation. The thickness of arrows represents the proportion of choices through that gap. Dashed lines represent the straight line connecting the feeder and the nest, represented by the black dots.

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4) Discussion

The results show in some detail the important link between the distant panorama and route following behaviour through an obstacle course. What is still not clear from the results is whether the distant panorama functions more as a navigational cue directly guiding route following, or more as contextual cues triggering appropriate behaviour with respect to the individual landmarks forming the obstacle course. We focus on these two major points in this section.

Link between the distant panorama and route following In training, many ants developed stereotypical courses through the obstacles between feeder and nest. All the ants that participated in tests had followed a habitual route through the same gaps a number of times before being tested. Changing the visual scene in any way changed the habitual behaviour, whether landmarks exchanged positions, shifted locations, or were absent altogether. The ants were then less likely to take a route through the habitual gaps defined by the positions of the landmarks. And they turned back and forth (meandered) more in their travel. In the case of shifting the entire set of landmarks along with the starting position of the ants (translations), a ‘dose dependent response curve’ was evident: the larger the translation, the bigger the effect (Fig. 4 for gap choices, Fig. 5 for Meander). These ‘dose response’ characteristics might indicate probabilistic contextual modulation in individual ants, meaning that the probability that the context will trigger the usual path decreases gradually with increasing change of context. But contextual modulation might also be all-or-none, with each ant having a sharp threshold of tolerance for change of context, beyond which the context simply does not trigger the usual behaviour and ‘all bets are off’. While the group data show a ‘dose response curve’ and not a step function, we tested each ant only once. It is possible to obtain a dose response curve for a group if individual ants vary in step-like tolerance thresholds. As an analogy, individual animals might learn in an all- or-none basis as a function of trials of training while the group data show a smooth learning acquisition curve (Gallistel et al. 2004). On the transposition test, more ants charted the habitual route in Earth-based coordinates in Experiment 1 than in Experiment 2. Several reasons might account for this difference. In Experiment 1, the ants encountered one landmark at a time as they traversed the y-axis towards the nest, whereas in Experiment 2, multiple landmarks needed to be negotiated at each row. The obstacle avoidance requirements were thus simpler in Experiment 1.

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Transpositions in Experiment 1 also meant that the same gap would often be chosen on the basis of either the landmark or the Earth-based route. In addition, Experiment 2 contained larger landmarks dominating more of the view on the homeward route. Some weak evidence from this study suggests that the ants might have also encoded the compass direction to travel to the first gap, presumably based on a sky compass. This is called a local vector (Collett et al. 1998; Collett and Collett 2002; Cheng 2006). On the Panorama test in Experiment 2, with the training landmarks missing, many ants still headed to the usual landmarkless ‘gap’ at the first row. Also in Experiment 2, more ants in the Translation 1 test followed the usual landmarks than ants in the Transposition test, and they also exhibited less Meander. The transposition switched landmark positions and required the ants to travel in a different compass direction to the usual landmarks, creating a possible conflict between landmarks and the local vector. On translation tests, the starting position was also translated, preserving the compass direction and local vector to the usual gap. Some ants reached the usual gap at the first row of landmarks under diverse conditions, even on the distal test field (Translation Distal test, Fig. 4a). But no ants reached the usual gap at the first row when the array of landmarks was rotated on the distal test field (Distal rotated test, Fig. 4a), and few reached the first row at all, despite the fact that the starting alley pointed towards the landmarks. A local vector on emerging from the starting alley might have oriented the ants towards the ‘correct’ landmarks when they were in the direction found during training. Definitive evidence for the use of local vectors was found recently by testing the ants in a round arena devoid of skyline cues (Legge et al. online). Even with conflicting landmark cues, the ants showed a strong tendency to head in the trained compass direction to find an exit out of the arena. Other ants too can learn local vectors and motor sequences (Collett et al. 1998; Macquart et al. 2006, 2008).

Panorama and/or contextual cues Our results confirm once again that M. bagoti follows stereotypical routes, in our case through an artificially constructed obstacle course. A common view of this route following behaviour is that what to do with respect to a landmark object is conditioned upon or triggered by contextual cues (Collett et al. 1998; Collett and Collett 2002; Cheng 2006). The panoramic cues function as contextual cues to facilitate the use of particular landmarks, helping the animal to identify landmarks, providing signposts for behaviour (Collett and Collett 2002), or setting the occasion for the use of servomechanisms based on particular landmarks (Cheng 2006). But contextual cues do not control behaviour directly. That control

90 Chapter III is placed in the landmark object around which the insect is navigating. Plenty of evidence links contextual cues of all kinds to memory retrieval and behaviour (review: Collett et al. 2003). The physical setting in which the animal is navigating can serve as a contextual cue (Collett and Kelber 1988; Colborn et al. 1999; Cheng 2005), in one case even after the animal has entered a test apparatus that blocked the view of the surroundings (Collett et al. 1997). Other contextual cues include the motivation to travel (for example, having food to take home vs. going out to seek forage; Dyer et al. 2002; Beugnon et al. 2005), the time of day (Koltermann 1971), the encounter of a particular familiar landmark (Collett et al. 2002), and possibly the distance the insect has already travelled (Srinivasan et al. 1999) and sequential cues (Chameron et al. 1998; Zhang et al. 1999), in which a step in the sequence provides the context for appropriate memory retrieval. Recent evidence shows that M. bagoti sometimes uses a panoramic snapshot directly for orientation (Graham and Cheng 2009a, 2009b; for suggestive results on honeybees, see Towne and Moscrip 2008). Replacing the natural skyline (a record of how elevated the tops of terrestrial objects are, without identifying the objects) with an artificial skyline made of black cloth forming an arena, is sufficient for the ants to chart an initial direction home (Graham and Cheng 2009a). This suggests that the context/landmark separation may not be necessary for explaining all route following behaviour, although some contextual cues would still play a role in guiding navigation (for example, providing the motivation to home). Individual landmarks need not be identified at all (see also Macquart et al. 2006). Instead, the entire panorama, encompassing near and far landmark cues, drives behaviour. Matching of global panoramic views can explain both route following and homing from new release points (Zeil et al. 2003; Wystrach 2009; Wystrach and Beugnon 2009). Might a route actually consist of a series of matches to panoramic skylines, without segregation of distant contextual cues and nearby signposts? Despite the sizeable set of manipulations, the data presented here can be accommodated equally well on either view. A dose dependent degradation in following the usual route through the obstacle course after translations of the landmark array (and the ant’s starting position) may reflect an increasing probability of failure of the degraded context to trigger the usual route. Or else it may reflect a degradation in the overall match with the panorama, a panorama including both the landmarks and the distant trees and bushes. The transposition likewise changes both the overall panorama and the link between the panoramic context and individual landmarks. The success of the ants on the Panorama test, with all experimental landmarks absent, should not be seen as a triumph for the hypothesis of the

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Figure 7: panoramic views taken between row 1 and row 2 in the training condition (a) and in the transposition condition (b, c) in Experiment 2. a Panoramic view on a training run. The photo was taken with a curved mirror (GoPano Plus) attached to the front of the lens of a digital camera placed lens down on the ground, and then unwarped using software (PhotoWarp 2.5) to form a cylindrical view, in which the right edge is coincident with the left edge. The camera location was along the usual training route of one ant, between row 1 and row 2. The photo has been blurred to simulate approximately a 5 visual resolution. b A panoramic photo from a transposition test, taken at the same location as in a. The photo was obtained and treated in the same fashion. c A panoramic photo from a transposition test, taken at a location 1.2 m to the left of the camera location in a and b, the location of the best matching gap based on the landmarks along the route. Otherwise, the photo was obtained and treated in the same fashion.

92 Chapter III direct control by the panorama. It is possible that beyond row 4, the ants had identified and used other landmarks for homing. To take two specific outcomes that both major hypotheses support, consider the transposition and Translation 1. With a transposition, the panorama appears to match the training conditions better on the landmark-based route (Fig. 7c compared with Fig. 7a) rather than the Earth-based route (Fig. 7b compared with Fig. 7a). But while Fig. 7c looks to match the training view better, the advantage of following this landmark-based route may well be balanced by the tendency of the ants to head to row 1 in a habitual compass direction. For Translation 1, the match in heading towards the usual route is much better than any other (data not shown), the distant panoramic cues having little changed with the 1.2 m translation. Both hypotheses would predict a high degree of adherence to the usual route, which is what the ants did (Fig. 4). We do not believe that the two broad hypotheses are indistinguishable. Nor do we think that they are equivalent models formulated in different words. More detailed modelling of a range of experimentally transformed conditions might well provide discriminating evidence. It is likely that both models are correct, but in different circumstances. Characterising these different circumstances forms an important research agenda.

Acknowledgements

The research was supported by grants from the Australian Research Council (DP0770300) and Macquarie University (graduate research funds and scholarships to SS, PS, and AW). We thank the CSIRO Centre for Arid Zone Research for letting us use their grounds for research, and two anonymous reviewers for helpful comments on the manuscript. The experiments reported in this article were conducted in compliance with the laws of Australia and the Northern Territory. The authors declare no conflict of interest.

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CHAPTER IV

Landmarks or panoramas: what do navigating ants attend to for guidance?

This chapter is published in Frontiers in Zoology

Frontiers in Zoology 2011, 8:21 doi: 10.1186/1742-9994-8-21 (Melophorus bagoti picture: Patrick Schultheiss)

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Landmarks or panoramas: what do navigating ants attend to for guidance?

Antoine Wystrach1,2, Guy Beugnon2, Ken Cheng1

1Department of Biological Sciences, Macquarie University, Sydney, NSW 2109 Australia

2Centre de Recherches sur la Cognition Animale, CNRS, UMR 5169, Université Paul- Sabatier, Toulouse F-31062, France

Abstract

Background: Insects are known to rely on terrestrial landmarks for navigation. Landmarks are used to chart a route or pinpoint a goal. The distant panorama, however, is often thought not to guide navigation directly during a familiar journey, but to act as a contextual cue that primes the correct memory of the landmarks. Results: We provided Melophorus bagoti ants with a huge artificial landmark located right near the nest entrance to find out whether navigating ants focus on such a prominent visual landmark for homing guidance. When the landmark was displaced by small or large distances, ant routes were affected differently. Certain behaviours appeared inconsistent with the hypothesis that guidance was based on the landmark only. Instead, comparisons of panoramic images recorded on the field, encompassing both landmark and distal panorama, could explain most aspects of the ant behaviours. Conclusion: Ants navigating along a familiar route do not focus on obvious landmarks or filter out distal panoramic cues, but appear to be guided by cues covering a large area of their panoramic visual field, including both landmarks and distal panorama. Using panoramic views seems an appropriate strategy to cope with the complexity of natural scenes and the poor resolution of insects’ eyes. The ability to isolate landmarks from the rest of a scene may be beyond the capacity of animals that do not possess a dedicated object-perception visual stream like primates.

Key words: ant – insect navigation – panoramic views – landmark – route learning

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1) Introduction

Many insects use terrestrial objects — landmarks — for navigation. Ants and bees in particular are known to rely on landmarks both to pinpoint a goal (Tinbergen and Kruyt, 1938; Wehner and Räber, 1979; Cartwright and Collett, 1983; Durier et al., 2003), and also to chart routes, which are typically idiosyncratic paths through a landscape dotted with landmarks (Collett, 1992, 1996; Kohler and Wehner, 2005; Macquart et al., 2006; Wystrach et al., 2011c). Knowledge about how insects exploit such landmark information comes mostly from studies conducted in visually controlled and impoverished conditions, like experimental rooms or deserts, where the salience of many potential cues is minimal and only experimental landmarks are made prominent (Wehner and Räber, 1979; Cartwright and Collett, 1983; Judd and Collett, 1998; Åkesson and Wehner, 2002; Durier et al., 2003; Graham et al., 2003; Narendra, 2007b; Narendra et al., 2007b). Concurrently, studies conducted in visually rich environments suggested that ants and bees ignore the features of familiar landmarks if they are presented within a wrong panoramic context (Collett and Kelber, 1988). This led to the idea that panoramas and landmarks are different cues that have different functions: a class of theories claims that the panorama serves as a contextual cue that triggers the recall of the appropriate landmark memory, on which guidance is based (Collett and Collett, 2002; Cheng, 2006; Collett et al., 2006). The segregation between landmark and panorama seems striking in these experimental conditions. This class of theories, however, faces the question of how insects segregate contextual cues and landmarks in natural environments, with complex depth structures. One theoretical proposal is that the amount of motion parallax is used as a depth cue to filter out distant landmarks (Cartwright and Collett, 1987). Insects are known to use motion parallax as a depth cue (Cheng et al., 1987; Lehrer et al., 1988; Sobel, 1990; Zeil, 1993b; Dittmar et al., 2010). But whether insects use such depth information to segregate out landmarks has not been determined empirically. It has also been suggested that insects may not segregate landmarks from the panorama at all but are guided instead by cues widespread on their panoramic visual field, which encompass both landmarks and panorama (Zeil et al., 2003; Graham and Cheng, 2009b; Wystrach, 2009; Wystrach and Beugnon, 2009; Wehner and Müller, 2010; Reid et al., 2011; Wystrach et al., 2011b). Some results support this class of theories. For instance, multiple landmarks (Cartwright and Collett, 1983; Pastergue Ruiz et al., 1995; Durier et al., 2003) but

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also landmarks and panorama (Graham et al., 2004; Wystrach et al., 2011c) seem to be “bound together” in insect memories. An imitation of the skyline (elevations of surrounding terrestrial objects) is sufficient for orientation in one species of desert ants (Graham and Cheng, 2009b). In some cases (Graham et al., 2003; Wystrach and Beugnon, 2009), the ants proved able to navigate robustly using pretty much plain white walls or curtains. Such performance can be readily explained by the use of panoramic views that encompass the global shape of the arena (Stürzl et al., 2008). Yet it is difficult to create experimental conditions in which the two classes of theories make different predictions. For example, in a previous study, many manipulations on ants’ home routes were conducted (Wystrach et al., 2011c). But both classes of theories could account equally well for the large body of results. We here investigate the effect of displacing a prominent landmark within natural surroundings. A huge black landmark was placed immediately behind a nest entrance of Melophorus bagoti ants (Figure 1). Standing in a flat area devoid of proximal trees, the landmark was designed to stick out from the rest of the panorama, and thus to be as easy as possible for an insect to learn, memorise, and extract from the rest of the scenery. We analysed the paths displayed by the ants in response to displacements of the landmark. In parallel, we recorded panoramic ‘ant’s eye’ images in order to quantify the panoramic alteration of the scenery caused by the landmark displacements. This approach allowed us to relate the ants’ behaviour not only to the landmark, but to the whole panoramic scene, providing us with insight on whether navigating ants were focusing on the landmark or using cues widespread on their panoramic visual field.

2) Methods

Nest area and Landmark. We chose a nest located in an area devoid of any proximal trees and provided the ants with a feeder 10 m away from their nest. The area between the nest and feeder, where the ants navigated, was open, flat, and cleared of any natural debris. The artificial landmark consisted of a huge black sheet (3 m wide and 2 m high) stretched between two poles 90 cm behind the nest entrance (Figure 1). The landmark width subtended an angular size of 118° at the nest location and 15° at the most distant location (i.e., feeder). Melophorus bagoti acuity being about 4° (Schwarz et al., 2011), the landmark could be perceived all along their homeward route. To the

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Figure 1: Photos of the experimental set up with the landmark in training position.

A. Picture taken 5 m from the nest. B. Ant’s-eye picture (300°, resolution of 4° (Schwarz et al., 2010) taken 5 m from the nest (bottom) or at the nest position (top). The dashed lines delimit 180°. The landmark is located 90 cm behind the nest while the closest tree (on the left of the picture A) was located 14 m away from the nest. The panorama was thus providing very little dynamic change compared to the landmark for ants approaching their nest.

99 Chapter IV ants, the landmark presented a strong dynamic change in size (increasing in retinal angle of 64° (from 54° to 118°) along the azimuth in the last 2 m of the route). In contrast, the rest of the panorama presented very little apparent displacement, the closest tree being located roughly 14 m away behind the nest. All in all, our artificial landmark stood as an obvious beacon for the nest entrance.

Protocol. M. bagoti lives in the semi-arid terrain of central Australia, which is typically filled with bushes, grass tussocks and trees. The ants were given food ad libitum in a fixed feeder 10 m from the nest entrance, and painted at their first visit to the feeder with a colour that marked the day of arrival. After 2 days of spontaneous shuttling between the nest and the feeder, with the artificial landmark immediately behind the nest, the marked foragers were tested. An ant returning from the feeder was captured near the nest and released again at the feeder location with the artificial landmark either left at its original position, removed or displaced by 0°, ±16°, or ±32° relative to the feeder-nest direction. Another test consisted of releasing the ants 10 m or 2 m in front of an identical landmark located in a distant test field roughly 100 m away in the same absolute orientation as in the training condition. Ants were tested singly, and each ant was only tested once.

Path analysis. The training and test fields were covered by a grid of 1-m squares made out of strings stretched between tent pegs that allowed the recording of paths by hand. The recorded paths were digitised into (x,y) coordinates with the software Graphclick® (http://www.arizona- software.ch) and processed using Matlab® (Math Works, Natick, MA, USA) programs. Paths were analysed for U-turns and tortuosity. U-turns were defined as walking back at least 20 cm along the Y axis within 50 cm of displacement. The maximum angle away from the training direction that can be travelled for more than 50 cm without being considered as U- turn was thus 113 degrees. The tortuosity index of a path corresponded to the averaged absolute turn angle (in radians) between successive chunks of 20-cm line segments connecting points on the path. A circle of 20 cm radius was placed at the starting point of the digitised path, and where the circle intersected the path defined the first segment. The circle was then placed at the end of segment 1 to define segment 2, etc.

Panoramic images analysis.

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To quantify changes in the visual panorama generated by displacing the artificial landmark, 5 reference panoramic pictures (black and white 360*40 pixels) were taken along the trained route, from the feeder to the nest, with the landmark in the training position. For each test condition, with the landmark displaced or removed, we mapped the area explored by the ants with 17 pictures (see Additional file 1). The test pictures were compared to the reference picture that corresponded to the same distance from the feeder. This was always the reference picture that best matched the tested picture. To compare each image to a reference image, we calculated the pixel-wise RMS (route mean square) error for all possible orientations of the reference image. The pixel-wise RMS gives us a value for the mismatch, or image difference, between two images. The RMS of the best matching orientation was recorded for each of the 17 tests pictures and used for the construction of the image difference map (see Figure 2). Interpolation of image differences values using the 17 pictures provided an estimate of mismatch across the whole terrain of travel.

3) Results

Panoramic pictures: image difference distribution We quantified the alteration of the visual panorama created by the different displacements of the landmark (Figure 2). Across multiple positions, we recorded and compared panoramic images taken with the landmark either in the training position (reference scenery) or displaced (test scenery) (see Additional file 1). The panoramic image difference between reference and test scenery across the field can then be calculated (Zeil et al., 2003). At first, the landmark was shifted into a distant area (roughly 100 m away). The picture comparisons revealed high image differences between the training and distant test field. Indeed, even in front of the landmark, a great part of the panoramic view is very different from that found at an equivalent position on the training field. Within the training area, removing the landmark does not significantly alter the view at the beginning of the route but results in high image differences near the nest position (Figure 2A).

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Indeed, the visual area covered by the landmark (or here, absence of landmark) is negligible at the feeder but increases as a tangent function as one approaches the nest (Figure 1, see also Additional file 1). Similarly, shifting the landmark by 16° or 32° does not significantly alter the view at the beginning of the route but creates high image differences at the real nest position. The 16° displacement creates a region of high image difference in the area opposite to the displaced landmark. This results in a valley of lower image differences between the feeder and the landmark. Within this valley, a zone of higher mismatch is located around 7-8 m on the way towards the 16° displaced landmark (Figure 2B). The presence of this latter zone of mismatch is easily explained. As one moves from the feeder towards the displaced landmark, the image differences result from two competing factors: the landmark and the rest of the panorama. As a result of moving away from the training direction, the perceived distant panorama (i.e., all the scenery except the landmark) becomes more and more altered, thus steadily increasing the mismatch. The landmark, however, matches its target counterpart perfectly, and although very small at the beginning of the route (filling <5% of the azimuth), it increases in size sharply with distance, thereby minimizing the global mismatch. In combining landmark and panorama, the panoramic image differences grows as an ant travels from the feeder towards the landmark until a point of maximum mismatch (around 7-8 m along the feeder nest axis), beyond which the increasing size of the matching landmark diminishes the global mismatch (Figure 2B). It is important to emphasise that the distributions of image differences presented here are not intended to model a particular homing strategy such as matching gradient descent, but simply allowed us to quantify the modification of the panoramic scenery the ants were subjected to during the tests. Whatever the actual process involved, any guidance strategy based on panoramic input should lead to disrupted behaviour if the global scenery is too much altered. Therefore, if ants are guided by panoramic views, they should not be able to reach the nest position in any of the tests conditions, as all of them present substantial panoramic image differences around the nest location. With 16° displacements of the landmark, however, the ants may end up searching in front of the displaced landmark, but their approaching route should then be altered while crossing the hill of high mismatch located at around 7-8 m.

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Figure 2: Map of panoramic mismatches and first U-turn locations. The maps result from comparing panoramic pictures taken during Removal of the landmark (A), Rotation 16° (B) and Rotation 32° (C) tests with reference pictures from the training condition. ‘% of panoramic mismatch’ indicates the percentage of mismatching pixel across the image. Locations for comparisons are shown in the Additional file 1. Mismatch levels were then interpolated between those locations (triangle-based cubic interpolation). The darker the shade, the lower the mismatch between views. Each cross represents the location of the first U-turn (walking at least 20 cm back towards the feeder within 50 cm of displacement) of the ants. Red crosses: first U-turns of ants that never searched densely in front of the landmark. Yellow crosses: first U-turn of ants that displayed a U-turn before searching in front of the landmark. Blue crosses: first U-turn of ants that displayed no U-turn before searching in front of the landmark (Blue crosses thus correspond to the beginning of the search). Bar: landmark position during test. Dashed line: landmark position during training. Stars: nest position. White circle: fictive nest position relative to the landmark.

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Figure 3: Rotational image differences. Images from the test conditions (i.e., current view) are compared with the ‘references images’ from the training condition (i.e., memorised view) for every possible rotation. The ‘reference images’ have been taken along the feeder-nest line and are all facing towards the nest. A. Example of the image differences distribution between a test image and the ‘reference image’ as a function of the rotation of the test image. The two lowest choices of image differences are indicated by the black (best choice) and grey (second best choice) arrow. ‘% of panoramic mismatch’ indicates the percentage of mismatching pixel across the image. B,C. Black and grey arrow of a given location represents respectively the best and second best matching rotation of a given location when the landmark is displaced by 16° (B) and 32° (C). The length of the arrows is proportional to the matching value.

Panoramic pictures: rotational image difference.

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Panoramic images can be rotated until they produce the best matching to the reference image. Here, rotational IDFs (i.e., Image Difference in Function of the rotation) presented often two distinct best choices for matching (Figure 3A). One choice (distant panorama choice) was generally obtained while facing the same direction as during training, because the distant panorama of both images overlap well. The other choice (landmark choice) was generally obtained while facing roughly towards the displaced landmark, because the landmarks of both images are superimposed. At the beginning of the route, the ‘distant panorama choice’ provides a better matching value than the ‘landmark choice’ (Figure 3 B,C) because the landmark appears very small and the distant panorama covers most of the view. However, as one travels towards the nest, the apparent size of the landmark increases and the part of the field of view covered by the distant panorama decreases. Therefore, the ‘landmark choice’ matching quality grows and the ‘distant panorama choice’ decreases in importance. Ants travelling towards their nest in the training direction may thus suddenly switch in orientation when the image difference along the ‘distant panorama choice’ becomes too bad or when the ‘landmark choice’ direction becomes better. Such a switch towards the landmark direction does not imply that the ant is now attending to the landmark, but just that the panoramic image difference is lower while facing in that new direction. Ants may also ‘hesitate’ between the two directions when they provide equivalent matching quality, leading potentially to wiggling paths for that part of the route (see examples figure 4G). Interestingly, a side difference arises in the 16° conditions. When the landmark is displaced to the left, facing the landmark becomes the best matching rotation at earlier locations than when the landmark is displaced to the right (Figure 3B). Some ants might thus continue to walk in the training direction longer when the landmark is displaced to the right. Following purely the strategy of walking in the best matching direction should nonetheless lead the ant to the displaced landmark in the 16° condition (Figure 3B) and to the nest or the landmark in the 32° condition (Figure 3C). However, the actual image difference value of the selected direction might be important too. Ants might stop following the best matching direction if the image difference is considered too high (Figure 2 displays the distribution of the actual best matching values).

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Ant responses: control condition. Ants homing from the feeder were captured just before reaching their nest in front of the landmark, and released again at the feeder location. When the landmark was left at its original position, the ants ran their route home again readily (Figure 4A) showing that they were guided by the perceived scenery and were not affected by potentially conflicting information provided by their path integrator. However, changing either the presence or the position of the landmark affected homing performance adversely, showing that the ants were affected by such alteration of the scenery.

Ant responses: distant area, 32° displacements and removal of the landmark. When the landmark was translated to a distant area presenting an unfamiliar panorama, the ants released at 10 m (i.e., fictive feeder) or 2 m in front of the fictive nest engaged immediately in a search pattern at the release point and none of them (0 out of 16 and 0 out of 15 respectively) went searching at the fictive nest relative to the landmark (Figure 4B). On the training field, with the landmark removed (Figure 4C) or displaced by 32° (Figure 4D), the ants tended to run a first relatively straight segment but then displayed a U-turn on average half way from the nest and started searching. None of these ants (No-landmark: 0 out of 23; Left and Right 32° displacements: 0 out of 31) found the nest or reached the fictive nest in front of the displaced landmark (within 3 min). Interestingly, the approaches were on average centred along the feeder-nest axis in the no-landmark condition, but were a little bit skewed towards the displaced landmark in the 32° condition (Additional file 2). The searches appeared centred on the first U-turn, but, interestingly, showed a larger spread than the searches displayed on the distant area (Additional file 2).

Ant responses: 16° displacements of the landmark. With a smaller displacement of the landmark (16°), the ants displayed different behaviours, which we categorised into 3 groups (Figure 4E,F,G). Some ants (13 out 49) never reached the nest or searched for it in front of the displaced landmark (Figure 4E). The others (36 out of 49 individuals) eventually aimed at the landmark and displayed a dense search for the nest in front of it (Figure 4F,G). Determining whether (Figure 4F,G) or not (Figure 4E) an ant searched at the goal proved completely unambiguous, as two independent judges could agree completely: the ‘nest-search’ pattern would suddenly get much denser and the ants would not leave the area in front of the landmark for several minutes.

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Figure 4: Test paths of individual ants. Ant were captured at the nest and released at the feeder (10 m away from the nest), with (A) the landmark in the same position as during training; (B) the landmark placed in a distant area (ant released either 2 m or 10 m in front of the fictive nest entrance); (C) the landmark removed; (D) the landmark rotated 32° away from the feeder- nest line (centred on the feeder) to the left or the right; (E,F,G) the landmark rotated 16° for 3 categories of ants: (E) ants that never display a dense ‘nest-search’ in front of the landmark; (F) ants that displayed a nest-search in front of the landmark but showed U- turns during their approach; (G) ants that displayed a nest-search in front of the landmark but showed no U-turn during their approach. Each path represents an individual ant, with one of them chosen at random highlighted in black. Bar: landmark position on the test (3 m wide). Dashed line: landmark position during training. Stars: nest position. White circle: fictive nest position relative to the landmark. Diamond: release point on the distant test field.

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The 36 ants that searched for the nest (i.e., dense nest-search) in front of the landmark were categorised in two groups depending on whether or not they displayed U-turns while approaching the landmark. U-turns consisted of more than a sharp turn, but also the stipulation that the ant walks back in a direction at least 113 degrees away from the training direction (i.e., went at least 20 cm down along the Y axis within 50 cm of travel). As a result, an ant could display very sharp turns to the left and to the right without being considered as displaying U-turns. Around half of these ants (19 out of 36) displayed at least one U-turn before reaching the displaced landmark (Figure 4F), a much higher proportion than in the control group returning under training conditions (Fisher’s exact test: 19/36 vs. 1/25, odds ratio 25.56, p <0.0001). Interestingly, their first U-turns were not located randomly along the route (Chi-square against random distribution across categories of 1 m: χ2=30, df=9, p<0.0001) but occurred mostly between 7 m and 8 m away from the feeder (first U-turn average distance from the feeder ± sd: 7.43 ± 1.48 m) (Figure 2B yellow crosses, Figure 4F). The other half (17 out of 36) approached the landmark without displaying any U- turns. The first U-turn of these 17 individuals occurred right in front of the landmark (Figure 2B blue crosses, Figure 4G) and, rather than showing uncertainty en route, corresponds to the beginning of the characteristic dense search for the nest entrance. However, a closer look at the approach of those individuals revealed an increasing tortuosity that reaches its maximum around 7 to 8 m away from the feeder, a pattern that was not observed in the control group (ANOVA groups*distances: n=17+25, F=9.008, p = 0.0001; between groups: F=12.971, p=0.0009) (Figure 5). Overall, even though most ants searched for the nest in front of the 16°-displaced landmark, its displacement notably affected their approach. Their paths were more tortuous than in the control condition: wiggles and U-turns were strongest around 7 to 8 m away from the feeder.

Path tortuosity and compass direction. To test whether or not this degradation was due to the fact that the ants in the 16° condition were led in a slightly different compass direction than during training, we focused on individuals that displayed long segments oriented towards the displaced landmark. Some ant paths (17 out of 48) presented a neat transition in the direction of travel, with a first segment oriented towards the nest and a second segment oriented towards the landmark (see Additional file 3 for examples). The switch in direction occurred on average around 5 m

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Figure 5: Tortuosity along the path. Index of path tortuosity at different distances away from the feeder (M±sem) for ants from the control group (in black) and from the Rotation 16° condition (in grey) that displayed no U-turn before searching in front of the landmark. The tortuosity index corresponds to the averaged absolute angle (in radians) between the directions of successive chunks of 20-cm line segments connecting points on the path. A circle of 20 cm radius was placed at the starting point of the digitised path, and where the circle intersected the path defined the first segment. The circle was then placed at the end of segment 1 to define segment 2, etc.

109 Chapter IV away from the feeder (average distance from the feeder ± sd: 5.1 ± 1.3 m). Around half of those ants (8 out of 17) displayed a first U-turn while approaching the landmark. Those first U-turns did not occur immediately after the switch towards the landmark as it would be expected if the path disruption was due to the new compass direction of travel, but several meters thereafter (average distance between switch and first Uturn ± sd: 3.8 ± 0.9 m), that is, around 7-8 m away from the feeder (average distance of the first U-turns from the feeder ± sd: 7.9 ± 1.1 m).Other ants (8 out of 48) headed towards the 16°-displaced landmark from the start (see Additional file 3 for examples). Although the direction of travel was similarly oriented towards the landmark all along their approach (heading direction: paired sample t test: 0-4 meter vs. 4-8 meter, t= –0.508, p=0.627), the tortuosity of their paths increased significantly in the second half of the journey (tortuosity: paired sample t test: 0-4 meter vs. 4- 8 meter, t= –4.635, p=0.002) and half of them (4 out of 8) also displayed a first U-turn after 5 m of travel towards the landmark (first U-turn average distance from the feeder ± sd: 6.5 ± 1.3). Overall, ants were not equally perturbed everywhere along their way towards the landmark. Their paths were disrupted mostly around 7 m away from the feeder, independently of the ant’s compass direction of travel. It seems therefore unlikely that the observed degradation of the path results from a discrepancy between the landmark direction and a memorised celestial compass information.

Side differences. In both 16° and 32° conditions, displacing the landmark to the left or to the right had different effects on the ants’ first U-turn location. In 16° conditions, U-turn location differences appeared along the x-axis. When the landmark was displaced to the right, U-turns occurred closer to the feeder-nest axis and further away from the landmark side than when the landmark was displaced to the left (t-test independent samples (values mirrored for one side): t=4.254, p=0.0002). Remarkably, such a difference was predicted by the panoramic image comparisons (see “rotational matching of panoramic views”). Along the y-axis, the distribution of first U-turns were similar on average (t-test independent samples: t=1.587, p=0.1234) but were more spread in the 16°Right condition (Levene’s test: F=6.376, p=0.0170). With 32° displacement of the landmark, no side differences in U-turn distribution appeared along the x-axis (t-test independent samples: t=–1.208, p=0.2368). Along the y- axis, however, U-turns occurred significantly earlier (t-test independent samples: t=14.047, p<0.0001) and were significantly more scattered (Levene’s test: F=6.128, p=0.0190) when the landmark was displaced to the right.

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4) Discussion

Melophorus bagoti lives a habitat full of landmark information such as bush, trees or distant cliffs, and evolution has tuned those ants to learn quickly (Schwarz and Cheng, 2010)and rely heavily on the so called ‘landmark information’ (Kohler and Wehner, 2005). We here investigate whether navigating ants functionally segregate the perceived scenery into landmarks for guidance and the panorama as contextual cue. Such theories infer that, for the animal, the given landmark is somewhat isolated from the rest of the panorama. For this purpose, we gave ants every incentive to isolate a landmark from the panorama by choosing an area devoid of proximal trees, by clearing that area of any proximal clutter and providing them with a particularly prominent artificial landmark at the nest entrance.

Initial segments and searches. The ants accustomed to the landmark behind their nest were captured as zero-vector ants (i.e., just before reaching their nest entrance) and released again at the feeder position. Because the ‘zero state’ of their path integrator cannot provide them with a homing direction, zero-vector ants have to rely on the visual surroundings to home. In a landmark rich habitat, the recognition of the surrounding overrides completely the information given by the path integrator (Andel and Wehner, 2004; Kohler and Wehner, 2005; Wystrach et al., 2011c). It is therefore not surprising that when the landmark was left at its original position, the recognition of familiar surroundings led zero-vector M.bagoti ants to run their home route again readily and thoroughly (Fig. 4A). When the landmark was displaced from the training position, however, the ant routes were notably altered, revealing that such modification of the scenery affected their homing. When released on the distant test field, the large landmark was not used: ants engaged immediately in a systematic search around the release point (Figure 4B) as they typically do when released in an unfamiliar environment (Schultheiss and Cheng, 2011). When the landmark was removed from the training field or displaced by 16° or 32° to the sides, most of the ants ran first a relatively straight segment, showing that they recognised the scenery at the beginning of the route (Figure 4C, D, E, F, G). Whether the ants recalled a local vector (i.e., segment of travel based on compass information) or used a view based matching strategy to achieve this first segment cannot be properly disentangled here. But previous work on this species showed that panorama can be matched and used independently of the compass

111 Chapter IV direction (Graham and Cheng, 2009b) stressing the use of a view based matching strategy rather than a local vector. Moreover, some approaches were here skewed towards the landmark in both 16° and 32° conditions (see Additional file 2), contesting the hypothesis of a pure local vector. The ants from the 32° displacements (Figure 4D) or no-landmark conditions (Figure 4C) engaged in winding search loops on average half-way to the nest. None searched at the real nest or at the fictive nest position in front of the landmark. Interestingly, these searches were more spread than the systematic search displayed on unfamiliar terrain, revealing that other factors, possibly view based matching or compass information, were influencing the search pattern (Additional file 2).

Guidance is not focused on the landmark. The hypothesis assuming that the distant panorama is not used for guidance but as a contextual cue provides an explanation for the behaviours described above. The panoramic context could be seen as delivering a negative verdict, rendering the landmark not worth approaching, and triggering the observed search behaviours. However, two pieces of evidence show that guidance was not purely based on the landmark, and that ants were attending simultaneously to other cues from the panorama. Firstly, displacing the landmark to the left or to the right had different effects on the ants. A 16° displacement to the left led the ants to meander more towards the landmark and less towards the feeder-nest middle line than a 16° displacement to the right. And a 32° displacement to the left had an earlier impact on the ants’ paths than a 32° displacement to the right. Since the landmark presented highly contrasted edges against the background, such side differences should not have arisen if guidance was purely based on the landmark. Secondly, in the 16° displacement condition, some ants ended up searching for their nest in front of the landmark, but their approach did not resemble the straight approach found in control ants (compare Figure 4A with Figures 4F,G). Instead, they exhibited behaviours indicative of ‘uncertainty’: they U-turned or showed more tortuous paths during their approach (Figure 4F,G). The presence of this uncertainty in the ant paths was independent of their direction of travel, rejecting the hypothesis that uncertainty was resulting purely from a conflict with a stored local vector (i.e., a memorised compass direction) pointing along the training direction. Consistent with this, previous work showed that M.bagoti can readily match and use familiar panorama presented in a wrong compass direction (Graham and Cheng, 2009b). As ants reached — and therefore used — the displaced landmark, the path

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Figure 6: Panoramic mismatches and search distribution along the Y-axis. A. Panoramic mismatch along the feeder-landmark line for the Rotation 16° and 32° tests and along the feeder-nest line for the no-landmark test. The vertical arrows represent the average position of the first U- turn displayed by each ant (counting only ants that displayed U-turns before reaching the landmark) (ANOVA between groups: F=11.096; p<0.0001; all pairs Tukey’s post hoc: Rotation16° a, Rotation32° b, no-landmark b). The horizontal arrows represent the average value of mismatch where the first U-turns were displayed (inferred from the 2D distribution of image difference, Figure 2) (ANOVA between groups: F=0.3823; p=0.6835). The open circles on the side of each arrow indicate the inter- individual standard deviation. B. Search distribution of the ants along the Y-axis. Paths were limited to the first 30 m travelled. The relative distribution was first calculated for each individual and then pooled for each condition so that each individual ant contributed equally to the distribution. The arrows indicate the position of the nest (for the no-landmark condition) or the fictive nest in front of the landmark (for Rotation 16° and 32° conditions).

113 Chapter IV uncertainty observed cannot be attributed to a negative verdict of a hypothetical contextual cue either. Such path uncertainty must therefore result from an alteration of the terrestrial cues the ants were using for guidance. As the highly contrasted landmark was not altered in itself, we can conclude that guidance was simultaneously based on other terrestrial cues.

Functional segregation landmarks/panorama or panoramic views? As guidance was not focused on the landmark only, the class of theories assuming a functional segregation between panorama (as context) and landmarks (as guidance cues) needs to invoke other processes like the simultaneous use of other landmarks extracted from the distant landscape for guidance and not for context. But then the process of deciding which landmarks are to be used for guidance and which ones are used as contextual cues appears complex and cannot be based on a simplistic distinction between proximal landmarks and distal panorama. We find it most parsimonious to account for the results by proposing that the ants in our experiment were using guidance strategies based on large panoramic views, without summoning the need to segregate such panoramic views into context and landmarks. By comparing panoramic images in simple ways, we could explain here the sharp transitions in the direction of travel observed, the presence of wiggling paths at particular locations, some of the differences observed when displacing the landmark to the left or to right (see ‘Rotational matching of panoramic views’ in Results), as well as why the ant routes were disrupted at different locations across groups (U-turns and searching) (see ‘Panoramic image difference distribution’ in Results). The correspondence between the regions where the ants’ travel was disrupted and the regions of high panoramic image differences (on average ~15% in this case, but with individual variation) (Figure 6) suggests that guidance cues must be widespread on the ants’ panoramic visual field.

How to match panoramic views? Despite a great amount of work (Möller et al., 1999; Zeil et al., 2003; Möller and Vardy, 2006; Stürzl et al., 2008; Collett, 2010; Lent et al., 2010; Wystrach et al., 2011b) how insects match memorised and current views to produce such efficient navigational behaviours is far from fully understood. Recent evidence shows that ants are able to align their body in order to match the retinal position of the features memorized along a familiar route (Lent et al., 2010). Such a simple mechanism based on panoramic images also explains spontaneous biases in ant routes observed in an artificial arena (Wystrach et al., 2011b).

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The present work also supports this hypothesis for route following (see ‘Rotational matching of panoramic views’ in Results), but suggests that ants may not always follow that strategy. Indeed, in the 32° condition, the ants U-turned and started searching on average half-way to the nest although our analysis of rotational image differences shows that walking in the best matching directions should lead the ants all the way towards the nest or the landmark. We suggest that individual ants may possess a mismatch tolerance threshold that allows them to switch between navigational strategies. If the mismatch is considered too bad, ants might stop such a route strategy (i.e., walking along the best matching direction) and start another strategy. Due to the artificial alteration of the scenery in this situation, this second strategy led the ants to meander in loops, but in a different fashion than the systematic search displayed in a totally unknown area (see Additional file 2). In more naturalistic situations, such a strategy might be adapted for navigation in less familiar environments that do not show excessive mismatch, such as situations in which the ant has been led astray or blown away from her familiar route corridor by a small distance (work in preparation). Although analysing panoramic images explains a lot, other puzzles remained. We did not manage to explain the early U-turns observed in the 32° left condition. A better knowledge of the nature of insects’ perceptions and memories would be precious for further illuminating guidance mechanisms.

Nature of the insect views? Do insects use landmarks for guidance? Yes. Much evidence in bees, wasps and ants shows that insects are highly influenced by landmarks (for a review (Collett et al., 2006); in Melophorus bagoti (Narendra et al., 2007b; Wystrach et al., 2011c). Do insects focus on individual landmarks only, filtering out the distant panorama from guidance mechanisms? We think not. The present work shows that, even when the dichotomy between proximal landmark and distal panorama is artificially emphasised, guidance is not focused solely on the landmark. But then, are insects’ panoramic views constituted of an ensemble of individual landmarks? Probably not. Evidence shows that insects store a pallet of features/parameters like strong boundaries (Harris et al., 2007), spots of light, centre of gravity, and colour of areas (Ernst and Heisenberg, 1999) and appear to do so without reconstructing the actual pattern (Horridge, 2005). Insects also have access to landmark distance information based on motion parallax (Lehrer et al., 1988; Sobel, 1990; Zeil, 1993b; Lehrer and Collett, 1994; Dittmar et al., 2010). The motion parallax creates a pattern of optic flow that can be used to pinpoint a target location (Dittmar et al., 2010). As with static cues, such dynamic cues can

115 Chapter IV potentially be matched across the whole panoramic view (Dittmar, 2011) and the insect may not be using them exclusively on isolated landmarks. Rather than isolated landmarks, encoding such a pallet of static and dynamic parameters simultaneously across a large part of the retina seems an appropriate strategy to cope with the complexity of natural scenes and the poor resolution of insects’ eyes.

5) Conclusion

We have created conditions in which a landmark seemed prominent, easy to extract, and very useful, at least to our primate visual system. But we (primates) have high acuity frontal foveal vision that can be focused on individual objects. Added to that is an entire specialised stream, the so-called ventral stream that is dedicated to object perception (Mishkin et al., 1983; Goodale and Milner, 1992). To those humans who have seen this landmark, it seemed the obvious one to use. Yet the evidence suggests that the ants did not focus only on the landmark but relied simultaneously on the distant panorama for guidance. Is this pattern peculiar to our experimental situation? We have reasons to think that the use of panoramas as a whole would be more widespread in insect navigation. Using cues that are encoded and processed simultaneously across a large part of the retina can well explain present and past results obtained in ants and seems an appropriate strategy to cope with the complexity of natural scenes, the poor resolution of insects’ eyes, and the lack of dedicated object-perception visual streams. It is still unclear what the nature of the parameters is that comprise insects’ perceptions and memories, but future studies should not assume that insects functionally segregate landmarks and distal panorama without evidence for such a dichotomy.

Acknowledgements

We thank Sebastian Schwarz, Patrick Schultheiss and Madeleine Tempé for their help on the field, and Paul Graham for helping, lending his panoramic camera, and providing comments on the manuscript. We also wanted to thank two reviewers for many thoughtful remarks and suggestions. This research was supported by a Macquarie University research excellence scholarship (iMQRES) to AW and an Australian Research Council Discovery Project grant (DP110100608). We thank the CSIRO Centre for Arid Zone Research for letting us use their grounds for research.

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Additional file 1

Additional figure 1: Panoramic picture comparisons. A. The grey circles indicate the locations where the 360° pictures were recorded in training and test conditions. Each picture from the test condition was compared (MSPD = mean squared pixel difference (see (Zeil et al., 2003) for more details)) with the most similar training picture. The picture resolution was set to 4 pixel/degree (B) in order to approach the insects’ visual acuity, and then transformed into black and white (C) to avoid bias due to variations in overall light levels. Such a transformation picks out the skyline (top elevations of terrestrial objects) as key information, consistent with recent findings on this species (Graham and Cheng, 2009a).

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Additional file 2

With large displacements (Rotation 32 Right and 32 Left) or removal of the landmark (No landmark), ants ran a rather straight initial segment and then displayed a U-turn on average half way from the nest and started searching. We considered here the first U-turn displayed as marking the end of the initial approach and the beginning of the search. Initial approaches tended to be a little bit skewed towards the displaced landmark (t- test against midline significant only for Rotation 32 Right at 5 m: t=–4.2479, p<0.001,) (A). The subsequent searches appeared on average centred on the first U-turn in all three groups (paired t-test: search centre of gravity vs. U-turn position: along x_axis: p’s>0.253 (B); along y-axis: p’s>0.431). Interestingly, the search scatter varied significantly across condition (ANOVA F=8.7, p<0.001). A displacement of the landmark to the left (Rotation 32 Left) led to slightly more scattered searches than the removal of the landmark (No landmark). Strikingly, searches displayed on the modified training field (Rotation 32 Right, 32 Left and No landmark) were much more scattered than searches displayed on the unfamiliar test field (Distant field) (C). This shows that ants’ search behaviour is not a rigid strategy but is adjusted according to the view perceived. The view perceived on the modified training field must provide a better match to the route memorised during training than the view perceived on the distant test field does. Such a better match might induce the ants to carry on further away before turning, leading to a more widely spread search. Within the training field conditions, displacement of the landmark results in a slightly flatter gradient of image differences on the landmark side. This may explain why searches with the displaced landmark were slightly more spread than when the landmark was removed. In contrast, on the distant field, strong mismatches may induce the ants to turn and thus display tighter loops. Interestingly, the search patterns displayed on the distant field appeared slightly skewed towards the bottom left (see Figure 4.B), suggesting that such characteristic systematic searches displayed in unfamiliar environment (Schultheiss and Cheng, 2011) might also be in part driven by vision.

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Additional figure 2: Large displacements and removal of the landmark: approaches and searches. A. Lateral deviation of the individual ants at 2 m, 5 m, 8 m, and 10 m away from the feeder during their initial approach (i.e., before the first U-turn). The big grey circles indicate the average direction and the small black dots indicate the 95% confidence interval. The numbers on the side indicate the percentage of ants that reached that distance before displaying a U-turn. Bar: beacon position during test. Dashed bar: beacon position during training. Stars: nest position. Open circle: fictive nest position relative to the beacon. B. Distribution of the lateral positions of the ants’ first U-turn (black) and subsequent search centre of gravity (grey). C. Distribution of the individuals’ search spread. The spread was measured as the average distance of the search from its centre of gravity. Test groups with identical letters are not significantly different by Tukey’s post hoc test. B,C) Whiskers of the boxplot extend to the most extreme data points not considered outliers, and outliers (i.e., individuals further than 1.5 x interquartile-range away from the closest quartile) are plotted individually (crosses).

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Additional file 3

Additional figure 3: Directional switch and first U-turn. Paths from individuals that headed towards the 16° displaced landmark (black bar) from the beginning or after a neat transition in the direction of travel. Grey arrows indicate the beginning of the segment oriented towards the landmark and crosses indicate the presence of a first U-turn along the approach to the landmark. Behaviours indicative of ‘uncertainty’ such as U-turn or high meandering do not occur immediately after the switch towards the landmark as it would be expected if the path disruption was due to the new compass direction of travel. Previous work showed that M. bagoti ants can readily match and use familiar panorama presented in a wrong compass direction (Graham and Cheng, 2009a). Dashed line indicates the position of the landmark during training.

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CHAPTER V

Ants might use different view-matching strategies on and off the route

This chapter has been accepted in the Journal of Experimental Biology

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Ants might use different view-matching strategies on and off the route

Antoine Wystrach1,2, Guy Beugnon2, Ken Cheng1

1Department of Biological Sciences, Macquarie University, Sydney, NSW 2109 Australia

2Centre de Recherches sur la Cognition Animale, CNRS, UMR 5169, Université Paul- Sabatier, Toulouse F-31062, France

Abstract

Individual foraging ants are known to rely on views of their surroundings for route learning and for pinpointing goals. Different strategies have been proposed to explain how ants might process visual information for navigation but little is known about the actual development and nature of the view-based strategies used by ants in complex natural environments. Here we constrained the knowledge of Melophorus bagoti ants to either the nest vicinity or a 10 m long curved route and analysed their initial direction when released at both novel and familiar locations. In parallel, we used 360 pictures of the scene as a basis for modelling different navigational strategies. We propose here a new hypothesis based on skyline height comparison to explain how ants home from novel locations. Interestingly, this strategy succeeded well at novel locations but failed on familiar terrain. In contrast, the use of a visual compass strategy failed at novel locations but could explain the results on familiar routes. We suggest that ants may switch between skyline height comparison and a visual compass strategy depending on whether they are on familiar terrain or not. How ants could switch between strategies and how their memories develop are discussed in turn.

Key words: insect navigation - skyline height comparison - visual compass - mismatch gradient descent - route learning – memory – Melophorus bagoti.

List of symbols and abbreviations: RP: release point. Nexp: nest experienced ants; Rexp: route experienced ants, rIDF: rotational Image Difference Function.

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1) Introduction

Individual foraging ants show remarkable navigational abilities. Our current understanding is that such efficient navigation arises from the combined use of path integration and information learnt about the visual surroundings. The major strategy of Path Integration is well understood (Ronacher, 2008). Animals combine directional information from compass cues with distance information (e.g., from step-counting) to perform a continuous calculation of the direct path home (Müller and Wehner, 1988; Wehner and Srinivasan, 2003). However, much less is known about how insects acquire and use visual information, especially in complex natural environments. In landmark rich environments, the recognition of familiar surrounds completely overrides the information given by the path integrator (Andel and Wehner, 2004; Kohler and Wehner, 2005; Narendra, 2007b; Wystrach et al., 2011c). The robustness of the strategies underpinning the use of visual information can be observed in the field by capturing homing ants just before they reach their nest (i.e., zero-vector ants) and displacing them to different locations (Wehner et al., 1996, Fukushi and Wehner, 2004; Kohler and Wehner, 2005; Collett et al., 2007; Narendra, 2007b; Graham and Cheng, 2009a). By using information from the whole surrounding natural scene – and not only individual landmarks (Wystrach et al., 2011a) – such zero-vector ants can home robustly not only when released on their habitual route, but also from novel locations after displacements sideways from their habitual route (Fukushi and Wehner, 2004; Kohler and Wehner, 2005; Narendra, 2007b; Wystrach et al., 2011c) or to the opposite side of the nest from their habitual route (Wehner et al., 1996). Several hypotheses have been suggested to explain how insects may process visual information to recapitulate a route or pinpoint a goal (Cartwright and Collett, 1983; Möller, 2001; Möller and Vardy, 2006; Harris et al., 2007; Collett, 2010; Graham et al., 2010; Lent et al., 2010; Baddeley et al., 2011). However, little is known about the actual development and nature of the view-based strategies used by ants on and off familiar routes in complex natural environments. In the present work, we trained Melophorus bagoti ants in the field to travel within predetermined areas and released them as zero-vector ants at familiar or novel locations (as in Fukushi and Wehner, 2004; Narendra, 2007b). In addition, we recorded panoramic pictures in the field that allowed us to develop, test and thus adjudicate between several navigational strategies. We focused mainly on three kinds of view-based strategies: mismatch gradient descent (Zeil et al., 2003); visual compass (Graham et al., 2010; Lent et al., 2010; Wystrach et

125 Chapter V al., 2011b); and a new hypothesis based on skyline height comparisons. Finally, we tested ants with different experiences (in duration and spatial extent), which provided us with insight into the development of their navigational memories.

2) Materials and Methods

Study site and species The study was conducted in the semi-arid desert of central Australia, 10 km south of Alice Springs, Northern Territory. The landscape is typically dotted by Buffel grass (Cenchrus ciliaris) but also marked by larger trees (Acacia estrophiolata, Hakea eyreana, and Eucalyptus species) providing distal landmarks for navigation. The thermophilic red honey ant Melophorus bagoti forages during hot summer days, mainly on dead insects, seeds and sugary plant exudates (Muser et al., 2005; Schultheiss et al., 2010). Foragers navigate outdoors individually relying heavily on vision and without the help of chemical trails (Cheng et al., 2009).

Experimental set up We restricted the ants’ foraging area by erecting a barrier without occluding the 360° panoramic view (Fig. 1B). The barrier either restricted the ants to a 1.1-m radius around the nest or to a 10-m curved route that ended at a feeder (Fig. 1). The width of the curved route was approximately 1.5 m. The experiment was replicated on two different nests approximately 200 m apart from each other and with a 90° difference in the compass orientation of the route. The outbound route curved towards the right for nest 1 and towards the left for nest 2 (as in Fig. 1). All the data (ants’ initial direction and panoramic pictures) from nest 1 were mirror reflected to enable comparison with the data from nest 2.

Groups and procedure Training. The ants were constrained by the barrier to the immediate vicinity of the nest (circle of 1.1 m radius). We marked ants over 5 consecutive days. These ants were excluded from the study. Unmarked ants that emerged from the nest after 5 days were

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Figure 1: (A) Scheme of the experimental set up. Continuous and dashed line represents the barrier that can constrain the foragers to a designated area without hindering their view of the scene. Naïve and Nexp where constrained to the immediate surroundings of the nest (N) (circle with dash line, feeder Fn) while Rexp ants could extend their knowledge to a 10 m long curved route (without dashed line, feeder Fr). Black dots indicate the three release points. Open circles indicate the positions where the 360 pictures of the route were recorded for modelling. (B) Schematic cross section of the barrier. We dug a groove in which long coated wooden planks (12 cm high; white rectangle) were placed to surround the area without sticking above the surface level. The slippery coated side faced inwards, preventing the ant from climbing out of the enclosed area. The groove was filled with twigs, making it hard for ants to walk there and thus inducing the foragers to keep away from the groove and stay at the surface level of the designated area, hence preserving their view of the surroundings. (C) Picture of the experimental set up. White arrows indicate the release points.

127 Chapter V considered naïve (Muser et al., 2005). We waited for these naïve ants to arrive at a feeder placed within the restricted area (Fn in Fig 1A) and captured them either upon their immediate arrival (naïve group) or marked them with a distinctive colour and allowed them to forage for 2 days (nest experienced group). While ants from the ‘naïve group’ experienced the nest surroundings a couple of times at most (on learning walks) before reaching the feeder for the first time and being captured, individuals from the ‘nest experienced group’ did roughly on average 27±11 trips over these 2 days (estimated from ants from the same nests after the experiments were completed). We also developed a third group of ants, the route experienced ants. For this, we removed part of the barrier surrounding the nest, to open up the curved route (see Fig. 1). The feeder with cookie crumbs was removed from the vicinity of the nest and placed instead at the end of the foraging route, labelled Fr in Fig. 1. We scattered a few cookie crumbs along the route to induce the ants to forage along the route and discover the feeder. Unmarked ants that emerged from the nest were considered naïve. We waited for these naïve ants to arrive at the feeder placed at the end of the route, marked them with a distinctive colour and let them forage along the route for 2 days (route experienced group). A good way of assessing the experience of the ants was to look at their homing paths along the route. Inexperienced ants that left the feeder followed the direction according to their path integrator and therefore aimed in the nest direction, crashed into the barrier, and struggled among the twigs along the groove until they reached the end of the curve. In contrast, most of the ‘route experienced’ ants proved able to suppress the direction dictated by their path integrator and ran efficiently along the route, following the curve and avoiding the groove. Only such experienced ants were tested and called here “route experienced” ants. Overall, we implemented three training conditions for each of the two nests: 1- the ‘naïve ants’ that experienced the nest vicinity in a single excursion to the feeder (Naïve); 2- the ‘nest experienced’ ants that experienced the nest vicinity during two consecutive days (Nexp) and 3- the ‘route experienced’ ants that experienced the whole route during two consecutive days (Rexp).

Tests. Ants from all training conditions were tested in the same manner. A test consisted in capturing a zero-vector ant, releasing it and recording its headings after 60 cm of travel at the three different release points consecutively. Zero-vector ants are homing ants that have been captured just before reaching their nest. Thus, their path integrator is set to zero but the captured ants can rely on terrestrial visual information to home from the release locations

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(e.g., Wehner et al., 1996; Fukushi, 2001; Wang and Spelke, 2002; Fukushi and Wehner, 2004; Kohler and Wehner, 2005; Narendra, 2007b; Graham and Cheng, 2009a). During a test, the ants were released, singly, at each of three different release points (RP, with coordinates in metres along the x- and y-axes shown in Fig. 1), the “route RP”, the “sideway RP” and the “opposite RP”. At each RP, a wooden 360° goniometer (1.2 m  1.2 m) with 24 sectors of 15° each was placed onto the ground to enable the recording of the ants’ initial direction of travel at 60 cm. In order to test each ant consecutively at all three release points (RP), the ants were re- captured just after leaving the goniometer of the first RP (e.g., opposite RP), then placed at a second RP (e.g., route RP), and finally at the last RP (e.g., sideway RP). The order of the RPs was counterbalanced across individuals and had no significant effect on the accuracy of the direction chosen (ANOVA: n=1178; F=0.82; p=0.441). Each ant was tested only once at each of the three release points.

Data analysis We used circular statistics to analyse the distribution of directions taken by the ants at each RP (Batschelet, 1981).

Intra-group analysis. The intra-group analysis was conducted for each RP for the three groups of both nests (total of 18 circular distributions). We used a V-test to test whether the Naïve and Nexp were significantly oriented towards the nest at each of the RPs. As we did not want to assume any predicted direction for the route experienced group (Rexp) we proceeded in three steps. First, we used a Rayleigh test to test whether the directions were non-randomly distributed. For the significantly oriented distributions, we then checked whether the nest direction was within the 95% confidence interval of the distribution or not. If not, we finally checked whether the mean vector of the distribution pointed instead towards the route.

Inter-group comparisons. The inter-group comparisons were conducted separately for the three RPs. We tested three potential effects: the experience effect, the knowledge effect, and the nest effect. As dependent measures, we analysed both the direction taken (i.e., the mean orientation of the distribution) with a Watson and Williams test; and the group accuracy (i.e., the variance of the distribution) with a K-test (Batschelet, 1981).

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The experience effect was tested by comparing Naïve vs. Nexp for each nest separately. The knowledge effect was tested by comparing Nexp vs. Rexp for each nest separately. The nest effect was tested by comparing nest1 vs. nest2 in each of the group conditions. Such a nest effect may arise from differences in the scenery between the two nest locations. This way of analysing the data led to multiple comparisons between groups. Nexp distributions were compared three times and Naïve and Rexp distributions were compared twice. Hence we used the Bonferonni correction, in which the p-values involving Nexp were multiplied by 3 and those involving Naïve and Rexp groups were multiplied by 2.

Panoramic images and models Recording. We recorded 360 panoramic pictures of the natural scene experienced by the ants. One picture was taken at each release point, one at the nest, and 19 along the curved route (every 50 cm from the route RP to the nest, Fig. 1) of each nest. These panoramic pictures were recorded with a Canon G10 camera mounted on a convex mirror (GoPano) placed on the ground. We took great care to keep the imaging system horizontal by using a thick wooden board and a spirit level. The field of view of the imaging system covers 360° horizontally and 120° vertically (70° above to 50° below the horizon). The pictures were unwarped with the software Photo Warp, trimmed in order to remove the floor (i.e., bottom 30°), converted into binary black and white to avoid any illumination artefacts and resized at 120*40 pixels (angular resolution = 3°) in order to match approximately the visual acuity of M. bagoti (Schwarz et al., 2011). The recorded panoramic pictures served as a basis to test different navigational models. The heading direction predicted by the models could thus be directly compared with the heading direction observed in the real ants.

Skyline height model. We tried to predict the direction chosen by the ants at each release point with a model based on the perceived skyline height. The skyline is the elevation of the tops of terrestrial objects, a cue these ants, like wood ants (Fukushi, 2001) are known to use. The model assumes that the ants have memorised how the skyline looks from the nest location. At an unknown release point, the ant will then be attracted by regions of the skyline that appear lower than in the memorised skyline. In other words, regions of skyline that are too high imply that the ant is too close to that part of the scene, whereas regions that are too low imply that the ant is too far away from those regions.

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Before comparing the skyline heights, both memorised and perceived views need to be aligned correctly with respect to each other. We tried two possible ways of aligning the pictures. Either 1) both pictures were aligned along the same absolute orientation, or 2) one picture was rotated in order to produce the best possible pixel-by-pixel matching with the other. The skyline heights were extracted from the picture as in Philippides et al. (2011). We set the resolution of our model in order to match the resolution of the recorded data (i.e., 24 sectors of 15° each): the 360° images were divided into 24 sectors of 15° each. The average skyline height of each sector was then calculated, thus converting the skyline into 24 height values. Graham and Cheng (2009b) presented skylines at this resolution to the ants, and found that it was sufficient for orientation. We used a two-valued code for comparing the heights between the nest and release point skylines: the model assigned a value of +1 to the sectors for which the skyline appears lower at the release point than at the nest (grey circles in Fig. 3). The resulting predicted direction for the release point is calculated as the circular average of the +1 sectors (grey arrowhead in Fig. 3).

Memory retrieval and image matching. The ants that experienced the curved route may have memorised several views along that route (Judd and Collett, 1998; Kohler and Wehner, 2005). Being released at a novel release point may trigger the retrieval of the memorised view that is the most similar to the view perceived at the location. Testing this hypothesis requires a quantification of the difference between the view at the release point and all the potential memorised views. We thus compared the images recorded at each RP with all the ones taken along the route. The difference between two pictures was calculated as the mean squared pixel difference (MSPD) over all corresponding pairs of pixels (as in Zeil et al., 2003). The MSPD between two pictures was calculated for all possible rotations of the images, and only the value of the best matching rotation was kept.

Visual compass model. To enable homing from novel locations, the visual compass hypothesis assumes that ants stored several views around the nest while facing the nest (Graham et al., 2010; Müller and Wehner, 2010). We thus recorded 32 supplemental panoramic pictures within the constrained nest 1 area (8 pictures at each of the distances of 15 cm, 30 cm, 70 cm, and 110 cm along 8 radial directions centred on the nest). The pictures were transformed in the same way as the pictures taken on the route at the RPs (i.e., black and white, 3° angular resolution). When released at a RP, the ants are supposed to be able to

131 Chapter V retrieve the memorised view that best matches the current location. We thus calculated the best MSPD of the 32 nest pictures for each RP (route RP, sideway RP, opposite RP). The best matching rotation of the current view, when compared to the best matching retrieved view, can in theory lead the ants towards their nest (Graham et al., 2010). These calculations led to a model that can predict mean directions, and whose performance can be evaluated and compared with the skyline models by using the Akaike Information Criterion (AIC) (Burnham and Anderson, 2002) (see Note of Table 1). The visual compass model can also be adapted to travel on a route. In that case, we assumed that the views memorised along the route are oriented along the familiar walking direction. At a given release point, the agent recalls the memorised view that best matches the current location and heads along the direction that matches best the recalled view.

3) Results

When released on the goniometer at the different locations, the vast majority of the ants in all conditions did not meander on the board, but paused, scanned the world by rotating on the spot, and then dashed straight in the selected direction.

Intra-group data Naïve ants. Remarkably, Naïve ants proved able to head towards the nest from both the sideway RP and opposite RP (nest1: n=44 ; nest2: n=55; V-test for nest direction: p’s<0.001) but showed less accuracy at the route RP (V-test for nest direction: nest1 p=0.046; nest2 p=0.105) (Fig. 2B). Naïve ants from nest2 showed a bimodal distribution at the route RP: some ants aimed towards the nest, but others were attracted instead towards the east (towards the bottom on Fig. 2B), away from the nest. Interestingly, this alternative attraction is noticeable at the sideway RP as well (Fig. 2A).

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Figure.2: Distribution of the initial directions (at 60 cm) taken by the ants at the three release points. (A) The black dots indicate the locations of the release points in each experimental set up. (B) Naïve ants had experienced the nest surrounding a couple of trials at most. (C) Nexp ants had experienced the nest surrounding for two consecutive days. (D) Rexp ants had experienced the whole curved route for two consecutive days. Black arcs represent the directions that point towards the route. (BCD) The arrows represent the circular average vectors of the distributions. Results are presented for both nest1 (black dots and arrows) and nest2 (grey dots and arrows). The open arrowheads indicate the nest direction. The ‘n1 n2’ at the top indicate the number of ants tested for nest1 and nest2, respectively.

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Nest experienced ants. The ants that experienced the nest surroundings for 2 days (Nexp) were significantly oriented towards the nest direction from all the tested release points (nest1: n=70; nest2: n=65; V-test for nest direction: p’s<0.001) (Fig. 2B). Interestingly, the Nexp ants from nest2, as the Naïve ants, showed also in addition an attraction towards the east at the sideway RP (i.e., towards the bottom, grey dots in the middle row of Figs. 2A,C).

Route experienced ants. At the route RP, ants that had experienced the route (Rexp) presented a non-random distribution (nest1: n=33; nest2: n=45; Rayleigh test: p<0.001; Fig. 2C). They did not aim towards the nest but towards the route (the 95% CI excludes the nest and includes the route; Fig. 2D). Similarly, at the sideway RP, these ants did not aim towards the nest, but headed instead on average towards the half-way mark of the route (Rayleigh test: p<0.001; the 95% CI excludes the nest and includes the route). The opposite RP led to clearly oriented choices for nest2 (Rayleigh test: p<0.001; Fig. 2D), but not for nest1 (Rayleigh test: p=0.119).

Inter-group comparisons Experience effect. For both nests, experiencing the nest surroundings for two days (Nexp) rather than a couple of trials (Naïve) had no significant impact on the direction taken at the release points (Watson Williams test: Naïve vs. Nexp p’s>0.464), but had an effect on the group accuracy. Indeed, at all RPs, the distribution was more concentrated for Nexp ants than Naïve ants (Fig. 2; compare B and C) but this was significant only at the route and sideway RPs of nest1 (K-test, route RP: p=0.001; sideway RP: p=0.044).

Knowledge effect. For both nests, experiencing the curved route rather than only the nest surroundings led to a significantly better accuracy at the route RP (K-test: p’s<0.001), and to a different heading direction at both the route RP (Watson Williams test: p’s<0.001) and sideway RP (Watson Williams test: nest 1 p<0.001, nest 2 p=0.006) (Fig. 2, compare C and D). Surprisingly, experiencing the curved route rather than only the nest surroundings resulted in a significantly lower accuracy at the opposite RP for nest1 (K-test: p=0.031), but not for nest2 (K-test: p=0.794) (Fig. 2, compare C and D).

Nest effect. Two significant differences arose between the nests. Firstly, the Nexp ants released on the route RP were significantly more accurate at nest1 than at nest2 (K-test: p=0.033). Secondly, Rexp ants released at the opposite point were significantly less accurate

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Figure 3: Illustration of the skyline height comparisons. The black and white 360° pictures represent the views at the nest (top pictures) and at the different release points. The grey dots indicate the regions where the skyline appears lower than at the nest and the grey arrowheads indicate the circular average of those regions. Circular histograms show the Naive ants’ heading after 0.6 m in sectors of 15°. The black arrows and arrowheads indicate the circular average vector of the ants’ distributions. The open arrowheads indicate the nest position. The systematic errors displayed by Naïve ants at some release point (nest 2 route and sideway RPs) seem to correspond to regions where the skyline appeared lower than at the nest.

135 Chapter V at nest1 than at nest2 (K-test: p=0.029). As the two replicates of the experiments occurred at different locations, such dissimilarities in the ants’ behaviour probably result from differences in the scene perceived at the two nest locations; innate differences between the two ant colonies seem less likely as an explanation.

Comparison of models for naïve and nest experienced ants. According to the AICs, the ‘skyline height with absolute alignment’ model explains the ants’ data much better than the visual compass or the ‘skyline height with best rotation alignment’ (Table 1, ‘nest knowledge’). The overall values of AICs, however, hide the pattern of successes and failures of the different models, which we next clarify. Table 1. The evaluation of different models. Note. The Akaike information criterion (AIC) is based on the residual error (M error2), and provides a ranking of model relative to one another (the lower the value the better). Model performance is based on the error relative to the number of free parameters (r) calibrated to fit the data. AIC = n.ln(M error2/n) + r, where n is the number of data points, and r is the number of free parameters. The mixed model assumes that the agent relies on the ‘visual compass’ when on the familiar route, where the recalled memory and the current view are very similar (route RP in Fig. 3), and relies on ‘skyline height’ comparisons when released at novel locations, where the recalled memory and the current view mismatch (parallel and opposite RPs in Fig.3). model M error2 (rad) AIC Skyline height (absolute alignment) 0.24 -16.96 Nest knowledge Skyline height (best rotation alignment) 1.84 7.35 (Naïve and Nexp) Visual compass 2.77 6.10 Route experience Skyline height (absolute alignment) 2.77 6.11 (best matching Visual compass 2.90 6.38 memory recalled) Mixed 0.36 -6.22

Skyline height models. The predictions fit the ants’ data much better when memorised and perceived skylines are compared after being aligned along the same absolute orientation rather than aligned in order to produce the best matching (Table 1). Indeed, aligning the two skylines along the same absolute orientation increases the chance of keeping the corresponding parts of the scene superimposed, which leads to a comparison of heights between corresponding parts of the scene. On the contrary, rotating the pictures may produce spurious matches and result in comparing heights between unrelated parts of the scene.

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The model predicts the results of the Naïve ants better than those of the Nexp (M error2 = 0.16 and 0.32 rad respectively). Indeed, the model’s predicted directions are sometimes ambiguous and reflect the uncertainty of the Naïve ants. Remarkably, the bimodal distributions observed in Naïve ants of the nest2 at the route and sideway RPs are predicted by the model (see circular histograms in Fig. 3). It seems that Naïve ants released at those novel locations headed towards the region where the perceived skylines appeared lower than memorised at the nest, and therefore, sometimes confounded the nest direction with other regions of the scene that also appeared too low.

Visual compass model. The visual compass hypothesis (Graham et al., 2010; Wystrach et al., 2011b) failed to predict the directions taken by the ants having experienced only the nest surroundings (Table 1, ‘nest knowledge’). This failure is not due to an ambiguous rotational IDF, but due to the incapacity of our model at retrieving a correct memory from the nest-views collection. We next explain why. This hypothesis assumes that ants stored several views around the nest while facing the nest, and when released at a RP, are able to retrieve a view that has been stored at the same side of the nest as the current location. Finding the compass direction that best matches this retrieved view should indicate approximately the nest direction. The best matching view from the memory collection should in theory be the one that has been stored at the closest location from the current position, and which is thus located on the same side of the nest as the current location. To retrieve the nest direction, it is crucial that views located on the correct side of the nest match views at the current location better than views on other sides of the nest do. We tested if the pictures taken at the correct side of the nest matched better than the pictures taken at the opposite side of the nest. This was true for the opposite RP (t=3.8, p=0.01) but not for the sideway RP (t=1.9, p=0.73). At the route RP, the matching was on average even better for the views located at the opposite side of the nest. These failures to retrieve the pictures located on the correct side of the nest are due to the very high similarity between the nest-pictures (all taken within a 1.1 m radius circle) and the very distinct views at the RP’s (7 m or 8.9 m away). At that distance, the relative difference in matching between the best and the worst nest-pictures is very small (route RP: worst=79.04%, best=82.31%; sideway RP: worst=82.10%, best=85.5%; opposite RP: worst=82.84%, best=86.96%). Basically, as compared to the scene at the distant RP’s, the differences in matching due to the changes of the scene around the nest (the signal) are swamped by the differences in matching resulting from noise in our pictures (perhaps due to the uneven ground that, despite our care,

137 Chapter V may have led to small deviations from the vertical axis of the imaging system). As a result, to explain homing from distant novel release points, the visual compass hypothesis needs to assume that ants are able to cope with very small differences in levels of matching. Pixel by pixel picture comparisons could not usually detect such differences robustly.

Model comparison for route experienced ants. Both skyline height and visual compass models can theoretically operate with route memories. The literature suggests that route memories can be constituted by a succession of individual views taken along the route (Judd and Collett, 1998; Harris et al., 2007). In that case, the agent needs to retrieve the appropriate memory from its memory collection. We thus attempted to explain the results obtained with Rexp ants, as evidence shows that they memorised information along the route.

Memory retrieval. We quantified the matching of the scenes along the route when compared with the scene at each RP. In general, the closer to the release point a location on the route is, the better the matching of the route view from that location with that obtained at the release point. As expected, the scene at the beginning of the route (close to the feeder and the route RP) matches very well the route RP, and the scene becomes increasingly different as it progresses along the route towards the nest (Fig. 4A). The best memory recalled at the route RP would therefore be a view memorised at the beginning of the route (very close to the route RP itself) (Fig. 4A). At the sideway RP, the scene perceived is most similar to the scene found at both the beginning (close to the feeder) and at the end (close to the nest) of the route, and most different from the scene around the middle of the route (Fig. 4B). The route picture that best matches the sideway RP happened to be at the beginning of the route for nest1, and at the end for nest2 (Fig. 4B, bold circles). For nest2, at the opposite RP, the scene is most similar to the perceived view at the nest and similarity decreases when comparing it with the scenes along the outbound route (Fig. 4C). The results for nest1 are surprising. The scene is similar to the opposite RP at the nest location, then the match deteriorates as one progresses along the route, and unexpectedly improves again as one approaches the feeder location (Fig. 4C). Such a good matching between the opposite RP and the beginning of the route must be due to coincidental similarities between the sceneries. As a result, views memorised at the nest and at beginning of the route provide very similar matching values (MSPD: 1.25×106 and 1.17×106 respectively) and nest1 ants released at the opposite RP may recall incorrectly a

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Figure 4: Route experienced ants and models’ results for the (A) route RP, (B) sideway RP and (C) opposite RP. (ABC) Circular histograms show the Rexp ants’ heading after 0.6 m in sectors of 15°. Coloured dots indicate the mismatch of the different route pictures (route memories) when compared to the current release point. Bold circles indicate the best matching route picture (memory), which is recalled and processed by the models at the different release points. Skyline height comparison: the grey dots indicate the regions where the skyline appears lower than on the recalled memory and the grey arrowheads indicate the circular average of those regions. Visual compass: the black circular plot corresponds to the inverted rIDF (goodness of matching for each possible orientation) between the view at the release point and the recalled memory. The closer the line is to the exterior circle the better is the matching (relative for each plot). The pointy black arrowheads indicate the best matching orientation, which is the direction predicted by the visual compass model. Interestingly, the ants’ data are better explained by the skyline height comparison at novel locations (B, C), and by the visual compass at familiar locations (A). White ‘N’s indicate the nest position.

139 Chapter V memory from the beginning of the route (Fig. 4C) which may explain their lower accuracy compared with ants from nest2.

Skyline height model. Because the ‘skyline height model with best rotation alignment’ failed to explain homing in the nest knowledge conditions (i.e., Naïve and Nexp) (Table 1, nest knowledge), we tested only the ‘skyline height model with absolute alignment’ for the route knowledge group. Overall, this model failed to explain the results observed in Rexp ants (Table 1). A closer look reveals that this failure is mostly due to mispredictions at the route RP (Fig. 4A, grey arrowheads). On the contrary, the skyline model predicts well the ants’ heading at the sideway and opposite RPs (Fig. 4B,C, grey arrowheads). At those RPs, current and retrieved images were further apart, and therefore produced more reliable information on skyline height differences.

Visual compass. The visual compass model did not predict the behaviour of Rexp ants (Table 1). This model had difficulty explaining the homing observed in ants from the opposite RP (Fig. 4C, black arrowheads). At the route RP, however, the visual compass provides a neat minimum of matching and thus predicts well the ants’ remarkable accuracy at heading along the route (Fig. 4A, black line and arrowheads). Indeed, at the route RP, because current and memorised views are very close and similar, the visual compass retrieves very robustly the heading direction learned during training.

Mixed model. We created another model to capitalise on the different successes of the skyline height and visual compass models. The mixed model incorporates both skyline height and visual compass strategies. The model uses a visual compass strategy when the recalled (i.e., best matching) memory matches the current view extremely well (i.e., on familiar terrain, with the parameter of threshold set at MSPD < 0.5106). When the best memorised view mismatches the current view above this tiny threshold (i.e., at novel locations), however, the mixed model uses the skyline height method. In the present case, this mixed model used the visual compass strategy only at the route RP (memory mismatch MSPD < 0.2x106 for both nests) and the skyline height strategy at both sideway and opposite RPs (memory mismatch MSPD > 1.1x106 for both RPs of both nests). These sizeable differences in mismatch levels, between the match at the route RP and the matches at the sideway and opposite RPs, mean that the threshold value can vary over quite a range. This mixed model resulted in a much better overall fit to the ants’ data (Table 1).

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4) Discussion

We investigated the mechanisms of navigational behaviour and the experience-dependent use of cues in a desert ant by devising a set up that allowed us to control the experience and the information available to the ants during training. Some ants had access only to the immediate surroundings of their nest (circle of 1.1 m radius) while others could extend their knowledge to a 10 m long curved route. Tests consisted in releasing zero-vector ants (i.e., homing ants captured just before reaching their nest and therefore deprived of information from Path Integration) at three key locations: at the start of the homing route (route RP) beside the route (sideway RP) or opposite to it relative to the nest (opposite RP) (see Fig. 1). The advantage of using a curved route is that route following can be distinguished from direct nest homing, allowing us to differentiate between different navigational strategies. Which navigational strategies the ants used and how their visual memories developed are discussed in turn.

Different strategies for different purposes Ants having experienced only the immediate surroundings of their nest proved able to extrapolate their knowledge and headed towards the nest from the three novel test locations (Fig. 1 and Fig. 2B, C). After 2 days of training along the curved route, the ants released at the same locations behaved differently from ants with nest knowledge only, revealing that they had memorised new information along the route. Both ants (Wehner et al., 1996) and bees (Becker, 1958; Capaldi and Dyer, 1999) have been shown to home successfully from novel release points at a much larger scale, although the information available to the insects was not quantified in these studies. Using panoramic pictures recorded on the field, we tested here different hypotheses to explain these performances.

Mismatch gradient descent. Under some conditions, a single stored view acquired at the goal location can allow an agent to return to where the view was acquired using a gradient-descent matching strategy (Zeil et al., 2003; Stürzl et al., 2008; Pahl et al., 2011). In gradient-descent matching, the agent moves in order to reduce the mismatch between the currently perceived view and the memorised goal-view. When the two views match perfectly, the agent has reached the goal. Although this strategy can explain homing from novel release points (Zeil et al., 2003), it cannot output a relevant initial direction of travel from a particular point. Mismatch gradient descent is a move-and-compare strategy, and any novel direction taken is chosen at random. To output a correct initial direction, the agent would need to assess

141 Chapter V first the matching quality of neighbouring locations or at least perform translatory movements (Möller and Vardy, 2006). This idea can explain remarkably well and parsimoniously how homing ants guide their tortuous search when they have arrived near the nest (Wehner and Räber, 1979; Narendra et al., 2007b). However, such a homing strategy appears unlikely to explain the neat directional decisions taken after ‘rotating on the spot’, which we observed in ants released at more distant novel locations. Another explanation is thus required.

Skyline height. An alternative, novel hypothesis is based on comparing the skyline heights between the memorised view at the goal and the current view at the novel location. Regions of the scene where the skyline appears lower than at the goal location might indicate that the agent is too far away from that region, and therefore attract it. Conversely, regions of the scene where the skyline appears higher than at the goal location might indicate that the agent is too close to that area, and therefore repel it. Our results show that such a model is robust only when the skylines are compared when aligned in the same absolute orientation. Insects can in principle achieve that by memorising views in association with a geomagnetic or a celestial compass reference (Collett and Baron, 1994; Dickinson, 1994; Åkesson and Wehner, 2002). The skyline height model succeeded well at explaining the ant’s heading from novel locations (Fig. 3 and Fig. 4B,C). It would be valuable to test that hypothesis by experiments explicitly manipulating the heights of different parts of the skyline. In contrast, the skyline height model failed badly in explaining the headings of the ants released on their familiar route (Fig. 4A). This is because on familiar terrain, like at the route RP, the retrieved memory had been acquired at a location very close to the current position, and thus the current and memorised views present similar skylines. Obtaining navigational information from the small differences of similar skyline heights would require very accurate measurements of skyline heights that our way of recording pictures could not achieve. Skyline height comparison depends on large differences and therefore does not work if views are similar. It seems therefore unlikely that the robust navigation displayed by ants on familiar routes involves such detailed and error-prone skyline height comparisons.

Visual compass. As two views taken in the same area match best when aligned parallel to each other (Zeil et al., 2003), views can serve as a visual compass (Graham et al., 2010; Wystrach et al., 2011b). Using views as visual compasses can in theory explain homing

142 Chapter V from novel locations (Graham et al., 2010). This requires the memorisation of several views taken around the nest, with each view memorised while facing the nest. When released further away at a novel location, the currently perceived view has to be compared with each of the memorised views in order to retrieve the best matching one. The best matching memorised view is hopefully the one taken at the closest position to the current location and therefore, the one located between the nest and the current position. As this memorised view has been stored while facing the nest, the nest direction can be retrieved by aligning the current view along the best matching direction. This hypothesis is supported by recent evidence that Ocymyrmex ants performed well choreographed learning walks in order to memorise views while facing the nest (Müller and Wehner, 2010).

Aligning an agent’s body according to a reference view can operate successfully across large distances in the natural environment (Philippides et al., 2011). However, when it comes to homing from a novel location, the crucial point is to be able to retrieve the correct memory. Any retrieval error would lead the insect in a wrong direction, as our model frequently did (Table 1). In the present case, the memorised views were too close and similar to each other to allow robust retrieval of the correct memory from the distant RPs. It is thus doubtful that ants would use such an error-prone strategy to home from novel locations. Although our visual compass model failed to explain homing from novel distant locations (Table 1 and Fig. 4 B,C) it succeeded remarkably well when the release point was along the familiar route (Fig. 4A). The closer match between current and memorised views (because they are obtained at locations close to one another) facilitates the retrieval of the closest memory, and guarantees a correct alignment. Moreover, the cost of retrieving a memory that is not the one taken at the closest location is minimal as all route memories would face roughly the same direction. Evidence shows that ants are able to align their body in order to match the retinal position of the features memorized along a familiar route (Collett, 2010; Lent et al., 2010). The extent of the rotation needed for aligning their body correctly is even calculated before the actual turn (Lent et al., 2010) revealing how ants excel in aligning views. Whether or not the visual compass is helped by the use of celestial compass information cannot be disentangled here. It is probable that ants learn views in association with celestial compass information. An alternative is that the view is linked to a heading associated with the celestial compass, a local vector (Collett and Collett, 2009). The use of a visual compass vs. a local vector on a familiar route cannot be disambiguated in the present

143 Chapter V work. Nonetheless, when released within an artificial reproduction of the skyline perceived at the feeder, M. bagoti ants head according to the skyline itself and not according to the celestial compass (Graham and Cheng, 2009b), favouring the visual compass hypothesis over the local vector hypothesis.

Switching between strategies. Homing from distant novel locations or recapitulating a well-known route are two very different tasks and therefore might require different strategies. The successes and failures of the two models we tested here complement one another. Comparing skyline heights is risky when locations of the current view and the retrieved memory are close to each other (i.e., on familiar terrain) but works well when homing from novel locations, more distant from familiar terrain. On the contrary, the visual compass provides a parsimonious and robust strategy for heading along the familiar route, but appears unreliable for homing from novel distant locations. Our results suggest that ants might use the visual compass on familiar terrain and skylight height comparisons at novel locations. Deciding which strategy to use could be achieved by assessing the mismatch between the current view and the best matching memory. A very low mismatch indicates that the insect is very close to its familiar route (or on it) and would trigger the strategy of using the visual compass. Higher mismatch levels, however, indicate that the insect is on novel terrain, and trigger the comparison of skyline heights. Our modelling revealed a sizeable gap in goodness of matching between locations, meaning that the threshold for switching between these strategies does not have to be very exact. We chose here arbitrarily a threshold of mismatch to make a difference between familiar (route RP) and novel terrain (sideway and opposite RPs). We do not know how ants may assess the level of unfamiliarity of a scene, but past work also suggests that they do possess such mismatch thresholds (Wystrach, 2009; Wystrach and Beugnon, 2009; Wystrach et al., 2011a; Wystrach et al., 2011b). Examination of published ants’ paths in the field also suggests the presence of two such strategies. Zero-vector ants released at novel locations several metres away from their route display indecisive headings, but usually roughly oriented in the correct direction (Fig. 6 of Narendra, 2007b). Such uncertainty in their headings reflects the wide and sometimes ambiguous directions computed from skyline height comparison (see Figs. 3 and 4, grey circles). In contrast, when the ants hit their route corridor, they display a dramatic switch of behaviour by suddenly heading unhesitantly straight along their route (Fig. 5; see also: Wehner et al., 1996; Kohler and Wehner, 2005; Narendra, 2007b). Such resolute headings

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Figure 5: Inbound runs of three zero-vector ants after displacement from the nest N to a location (black dot) sideways from the ants’ habitual routes (grey lines) and illustrating the switch between navigational strategies. Ants may use 3 distinct ways of processing views: 1- at novel locations that are not totally unfamiliar, skyline height comparison provides rough and sometimes ambiguous directional information; 2- on a familiar route, or close enough to it, a visual compass provides a robust and unambiguous direction parallel to it; 3- close to the nest, a mismatch gradient descent strategy drives a convoluted search for the nest entrance. (A) Ant H6: five runs shown in blue, (B) ant F7: two runs shown in black, (C) ant P8: two runs shown in green. In run 1, although the ant was still off her familiar route, she suddenly started to run parallel to it, following the turn angles of the route, as if she had estimated the mismatch to be low enough to trigger a visual compass strategy. Once she arrived in the vicinity of the nest, however, her behaviour changed radically again to a more tortuous nest- search path, as would be predicted by the use of a mismatch gradient descent strategy. From Kohler and Wehner (2005).

145 Chapter V reflect the unique and unambiguous direction given by a visual compass strategy on familiar terrain. In addition to using skyline-height comparisons and the visual compass, the mismatch-gradient-descent strategy seems to provide a parsimonious explanation for the tortuous search displayed by ants after they have arrived within the nest vicinity, although it has to be kept in mind that visual compass might well be able to explain nest pinpointing behaviour. Once again, the nest search strategy could be triggered when the perceived scene matches sufficiently well the view memorised at the nest. Remarkable examples of such switches in behaviour are found in Kohler and Wehner (2005) (Fig. 5). Finally, when M. bagoti ants are displaced to very distant locations where the view mismatch is extremely high, they perform a systematic search (Schultheiss and Cheng, 2011), apparently abandoning attempts at view matching altogether. The search patterns in M. bagoti on a test field distant from the training site share some characteristics with those found in the North African Cataglyphis fortis (Wehner and Srinivasan, 1981; Merkle et al., 2006), ants that live on open saltpans with little or nothing by way of views usable for matching. The size of the search distribution increases with increases in the feeder-nest distance, and searches proceed in loops that increase in size as the search duration increases.

Development of navigational memories Ants that have experienced the nest vicinity for a different number of times (a couple of trials vs. 2 full days) or experienced different areas (i.e., nest vicinity or route) for the same amount of time showed distinct headings at the release points. This shows that the content of ants’ memory is modified by both knowledge of a new area and repeated training.

Nest memory. Remarkably, naïve ants having experienced the nest surroundings a couple of trials at most were able to head towards the nest from novel distant locations (Fig. 2B). Nonetheless, some individuals displayed systematic errors indicated by headings away from the nest at some RPs (Fig. 3, ‘nest 2, route and sideway RPs’). This alternative direction also points to regions where the perceived skyline appears lower than at the nest, suggesting that following a skyline height comparison strategy may have induced some naïve ants to head in such a wrong direction (Fig. 3). Interestingly, the ants that have experienced the nest vicinity for two days were more accurate at finding the home direction and avoided some of the mistakes observed in naïve ants (Fig. 2C). Our model fit data from naïve ants better, and did not match the success achieved by nest experienced ants. However, some nest

146 Chapter V experienced ants were also attracted by those wrong alternative directions (Fig. 2C) and the skyline height comparison model was still by far the best we found to explain their behaviour. Thus, we suggest that the differences in behaviour observed between naïve and experienced ants stem from better memory in experienced ants, rather than different ways of processing views. It could be supposed that the experienced ants had time to memorise several views around the nest that enable better homing. This is unlikely. Using several goal-pictures from the nest vicinity did not improve the model because the pictures were highly similar and thus led to similar outputs. Another hypothesis would be that experienced ants had memorised and used more detailed views than naïve ants. However, increasing the resolution of the images added mostly noise to our model and did not improve its performance (data not shown). The present model, however, is based on black and white images, and it is possible that experienced ants have learned useful information from a potential light or colour gradient that improved their performance. A second viable hypothesis would be that the experienced ants had learned to filter relevant information from the scene, resulting in a more efficient processing of the views. For instance, relying on distal rather than proximal cues for retrieving a direction from a novel distant release point would be useful. Such ‘proximal noise’ could be detected and filtered out by relying on translational optic flow while experiencing the nest surroundings (Cartwright and Collett, 1987; Zeil, 1993a, b; Dittmar et al., 2010). Ants might have learned to filter the memory for the most informative cues.

Route memories. Experiencing the curved route rather than only the nest surroundings had a significant impact on the ants’ behaviour at the release points. Rexp ants headed towards the route and not towards the nest at both sideway and route RP’s (Fig. 2D). This indicates that experiencing the curved route for two days led the ants to learn and recall information memorised along the route. The stored information could consist of a series of views taken along the route. If this is the case, an ant released at a RP would need to be able to retrieve the appropriate view from its memory collection. Ants can achieve this by storing and retrieving views sequentially (Chameron et al., 1998). But because zero-vector ants have already run the route and were about to reach the nest when captured, such a sequential retrieval of views would imply the recall of a nest-view. This hypothesis is refuted by the attraction towards the route observed in our ants as well as the differences in the direction taken by nest-knowledge vs. route-experienced ants. Another hypothesis is that the view at the release point triggers the recall of the memorised view that matches best the current location (Collett et al., 2006). Hence, zero-vector ants released on their familiar route

147 Chapter V normally resume their course to the nest (Kohler and Wehner, 2005). Such a hypothesis fits the present ant data well and also indicates how ants could choose between a visual compass strategy (when the best recalled memory matches the current scene very well, as at the route RP) or a skyline comparison strategy (when the best recalled memory presents a higher level of mismatch, as at sideway and opposite RPs) (see Fig. 4). An alternative to the ‘multiple-views’ hypothesis is that the route might be learned more holistically, resulting in a single memory for homing along the whole route (Baddeley et al., 2011). Such a holistic memory could be built up by selecting and integrating the information from the scene that is relevant for the whole route, or by memorising how the view changes rather than storing several different but similar views independently. A holistic memory would provide ants with a more compact way of storing the information required to recognise familiar views. A robot using this approach can successfully recapitulate a non- trivial S-shaped route through a real-world environment by using its holistic memory as a visual compass (Baddeley et al., 2011). In the present case, this model could explain the behaviour observed at the route RP (i.e., on familiar terrain) but failed badly at the other RPs (i.e., at novel locations) (Baddeley personal communication), consistent with what we found for our model based on the visual compass. A holistic memory could also be combined with skyline height comparisons to explain most of the present results. The use of a holistic memory has proved useful for route navigation in robots. It would be valuable to develop such a model for ants, based realistically on their sensory processes, and test its predictions on ant navigation in their natural habitats.

5) Conclusion

The present work suggests that ants process memorised and perceived views differently according to the navigational task to achieve. If they are on their familiar route, using memorised views as visual compasses (Graham et al., 2010; Wystrach et al., 2011b) offers a remarkably simple solution to keeping a correct heading and recapitulating the route. However, if the ants have been displaced by several metres to novel locations – perhaps by gusts of wind (personal observations) – using a visual compass becomes very error-prone but comparing skyline heights becomes robust enough for charting a heading, and such a model could explain the ants’ mistakes and successes. At the end of the route, when it comes to pinpoint the precise entrance of the nest, using a strategy of descending the gradient of image

148 Chapter V difference may become the best strategy (Zeil et al., 2003). Finally, being displaced to very distant and totally unfamiliar area triggers systematic searches in zero-vector ants (Wehner and Srinivasan, 1981; Schultheiss and Cheng, 2011). Selecting the appropriate strategy can be simply achieved by assessing the goodness of the matching between the memory and the current view. This work also provides insight into the development of the ants’ navigational memories. A couple of trials (i.e. potentially learning walks) is enough for ants to store relevant information that enables them to home from neighbouring novel locations. However, evidence shows that M. bagoti improves its memory of the nest surroundings after repeated experience. Our model probably fits the performance of naïve ants better because ants’ memory is closer to a raw image (like our model) at first, but then gets filtered to become more efficient with repeated training. When it comes to learning a route, the precise nature and development of the insects’ memories are still unknown. Notably, the use of multiple stored views as well as a single holistic memory of the route can both potentially explain past and present results. Storing a single holistic memory, rather than multiple independent views at different stages of the route, appears more parsimonious, but has never been shown in insects. A better understanding of the nature of insects’ memories should form a goal for future research on insect navigation.

Acknowledgements

We thank Sebastian Schwarz, Patrick Schultheiss and Laurence Albert for their help on the field, and Eric Legge for the dotted circular histograms. We also thank Bart Baddeley for agreeing to run his ‘holistic memory’ model with our pictures. This research was supported by a Macquarie University research excellence scholarship (iMQRES) to AW and an Australian Research Council Discovery Project grant (DP110100608). We thank the CSIRO Centre for Arid Zone Research for letting us use their grounds for research.

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CHAPTER VI

Ant visual memories: multiple snapshots or holistic encoding?

This chapter is intended to be submitted to the Journal of Experimental Biology

(Picture: Nicolas Thomas)

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Ant visual memories: multiple snapshots or holistic encoding?

Antoine Wystrach1,2, Guy Beugnon2, Ken Cheng1

1Department of Biological Sciences, Macquarie University, Sydney, NSW 2109 Australia

2Centre de Recherches sur la Cognition Animale, CNRS, UMR 5169, Université Paul- Sabatier, Toulouse F-31062, France Abstract

The long stereotypical routes displayed by solitary foraging hymenopterans in complex environments provide an ideal ground to understand how minimal cognitive mechanisms generate efficient and robust visually-guided navigation. It is often assumed that insect routes are encoded as a succession of discrete snapshots, with actions associated with them. Contrastingly, a recent model suggests that routes may be encoded holistically, that is, compacted into a single representation. Here, we attempted to disentangle between both hypotheses by training Melophorus bagoti ants along different lengths of a same route, and recording their behaviour when released on a test field that presented some resemblance with the familiar nest surroundings. Our results show that the knowledge of the scene perceived at the end of the route (i.e., further than 10m away from the nest) had an impact on the recognition of the nest surroundings. Such results are hard to reconcile with the multiple snapshots hypothesis, as they imply interactions between information stored at the beginning and end of the route. Instead, assuming a holistic encoding of the route appears a parsimonious explanation. It naturally predicts that supplemental knowledge of the end of the route modifies the whole memory, and thus impacts on the recognition of the beginning of the route, as observed here. Further results suggest that repeated training at early stage allow ants to improve their memory by filtering relevant information from the scene, which also fits the hypothesis of building up a holistic memory. This work stresses the importance of considering the holistic memory hypothesis to explain insect visual navigation.

Key words: insect navigation - ant - memory - route learning – view based homing – snapshot – holistic memory.

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1) Introduction

The remarkable navigational skills of social insects have fascinated scientists for more than a century and there is an ever growing appreciation that studies of such smaller-brained navigational specialists provide a remarkable opportunity to understand ‘minimal intelligence’ at work in solving important problems. An experienced foraging ant finds her way home expertly, and those living in habitat with usable visual cues are known to use visual landmarks in homing (for reviews: Collett, 1996; Wehner et al., 1996; Cheng et al., 2009). During the last decades, several hypotheses have been suggested to explain how insects may process visual information to recapitulate a route or pinpoint a goal (Wehner and Räber, 1979; Cartwright and Collett, 1983; Möller, 2001; Zeil et al., 2003; Möller and Vardy, 2006; Harris et al., 2007; Basten and Mallot, 2010; Collett, 2010; Graham et al., 2010; Lent et al., 2010; Baddeley et al., 2011). The basic idea is that insects store visual information, and then process current views and memory together in order to output an appropriate behavioural response. Models differ as to what is stored and how views are compared. To elucidate such mechanisms, knowledge about the nature of insect visual memory is essential. Current navigational models can be improved if information about what insects encode, when they store it, and when they use it can be better specified. For example, it is still debated whether insect memories are best represented as 2D unprocessed images (Zeil et al., 2003), as a labelled set of discrete landmarks (Cartwright and Collett, 1983) or, at the other extreme, as a quantitative distribution of visual parameters without any retinotopic spatial information (Möller, 2001). Evidence suggests strongly that under some circumstances, an ant’s visual memories can be constituted of several discrete snapshots taken at different locations along their familiar route (Judd and Collett, 1998). However, such evidence comes from a very short route (40 cm) displayed in laboratory condition with artificial, geometrically simple objects as landmarks. To our knowledge, whether or not the large-scale routes (i.e., tens of meters) displayed by ants in complex natural scenes (Fresneau, 1994; Baader, 1996; Wehner et al., 1996; Collett et al., 2003; Kohler and Wehner, 2005; Wystrach et al., 2011c) are encoded as such a series of discrete snapshots is still an open question. It has also been suggested that insects may memorise not only static representations of the world (Campan and Beugnon, 1989). Several experiments showed the ability of insects to store dynamic cues such as translational optic flow patterns (Lehrer et al., 1988; Zeil, 1993b; Dittmar et al., 2010). Also, robotic models suggest that routes may be encoded holistically — and not as a series of snapshots stored independently — as a single memory for homing

153 Chapter VI successfully along a whole route (Baddeley et al., 2011). The most diagnostic features that define views encountered on the route are encoded and used. In the present work, we attempted to investigate whether ants in natural conditions encode their stereotypic route as a series of discrete snapshots or more holistically. For this purpose, we constrained the visual knowledge of Melophorus bagoti ants to different lengths of a route (5 m, 10 m, or 16 m) and asked whether knowledge of the end of a route (i.e., near the foraging site) had an impact on the memory of the beginning of the route (i.e., near the nest). We also investigated the development of visual memories by comparing ants with different amounts of experience (1 trial, 2 days, or 4 days) of the same route. Our results clearly refute the idea that routes are encoded as a series of purely independent snapshot memories and support tentatively the newly emerging hypothesis of a more holistic encoding of views.

2) Materials and Methods

Study site and species The present work was conducted in the semi-arid desert of central Australia, 10 km south of Alice Springs, Northern Territory. The landscape varies across areas from semi-cluttered to relatively open. Some parts are typically dotted by Buffel grass (Cenchrus ciliaris) and marked by several trees (Acacia estrophiolata, Hakea eyreana, and Eucalyptus species) while other areas present much more open scenery with bare, red soil. Foragers of Melophorus bagoti — the red honeypot ants — are active during hot summer days, seeking mainly roasted dead insects, seeds and sugary plant exudates (Muser et al., 2005; Schultheiss et al., 2010). Melophorus bagoti ants navigate outdoors individually without the help of chemical trails and present remarkable navigational skills, relying heavily on their visual environment (Cheng et al., 2009).

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Figure 1: Experimental set up. The training conditions were replicated on two different nests and all ants were tested on the same distant test field. A huge (4 m wide and 2.5 m high) black landmark (black bar) was erected 50 cm behind the nests (N) and on the test field in the same compass direction. Continuous and dashed line represents the barrier that can constrain the foragers to the designated route without hindering their view of the scene. Ants were trained to forage at 5 m, 10 m, or 16 m away from the nest. Dashed lines indicate the parts of the barrier that could be removed for the 10-m or 16-m training conditions. Grey lines on the test field represent the grid enabling the recording of the ant paths by hand. N indicates the nests; FN indicates the fictive nest relative to the landmark on the test field; F indicates the feeder (only the feeder at the end of the constrained route was present in all training condition). Black circle indicate the release point on the test field.

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Experimental set up We erected a huge artificial landmark made out of a black sheet (4 m wide and 2.5 m high) stretched between two poles 50 cm behind a Melophorus bagoti nest entrance. We provided the ants with a feeder full of cookie crumbs located either at 5 m, 10 m, or 16 m away from the nest in the direction opposite to the artificial landmark (Fig. 1). The foragers were constrained to forage between the nest and the feeder along a predetermined straight route. The route was roughly 2 m wide, flat, cleared of any natural debris, and bordered by a low barrier sunk into the ground that prevents the ants from foraging elsewhere without hindering their view of the scenery (see Fig. 1 of (Wystrach et al., in press) for detailed information). The set up was replicated on two nests ~250 m apart from each other. Nest 1 presented a rather open scenery with a few little trees while nest 2 was surrounded by closer, bigger and denser trees (Fig. 2). The routes and artificial landmark of both nests were aligned along the same absolute orientation. We erected a third similar artificial landmark in a test field located in a different area approximately 150 m away from both nests. This landmark was also aligned along the same compass direction as the landmarks at the two nests. The test field area (8 m × 7 m) in front of the landmark was also cleared of any natural debris and covered by a grid of 1-m squares made out of strings stretched between tent pegs that allowed the recording of paths by hand.

Training and groups It was crucial in this study to control the experience and spatial knowledge of each ant tested. Once the route barrier was set up, we marked ants over 5 consecutive days. Those ants were excluded from the study. Unmarked ants that emerged from the nest after 5 days were considered naïve (Muser et al., 2005). We provided them with a feeder full of cookie crumbs at the end of the route (at 5 m, 10 m, or 16 m depending on the group). Such naïve ants were marked with a different colour and allowed to forage along the designated route for two consecutive days (5m-2days, 10m-2days and ‘16m-2days’ groups). We defined different groups according to the length of the route the ant had access to during the two days of training. A first cohort of ants were constrained to a 5 m long route (‘5m-2days’), another cohort had access to extend their foraging trip to a 10 m long route (‘10m-2days’), and a last cohort of ants had to travel 16 m along the route to reach the feeder (with the ‘16m-2days’ group implemented only for nest 1). We also created two supplemental groups at nest 1. One consisted of ants that just reached the feeder at 5 m for the first time

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Figure 2: Panoramic pictures recorded 5 m in front of the nests (or fictive nest on the test field). Pictures are presented at an angular resolution of 3° in order to match approximately the visual acuity of M. bagoti (Schwarz et al., 2011).

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(‘5m-1trial’). The other consisted of ants trained for 4 consecutive days along the 10 m long route (‘10m-4days’).

Tests All ants from all groups were tested the same way. After training, the marked ants were captured individually at the end of their way home just before reaching their nest. Such ants are called zero-vector ants because they have run off their path-integrator defined home vector, and therefore, cannot rely on path integration to home when subsequently released. The captured ants were carried in a dark tube and released individually on the test field, 2.5 m in front of the landmark, that is, 2 m in front of the fictive nest location relative to the landmark. We made sure that the ants were still holding their cookie crumb, which guaranteed their motivation to home. The homing search of the released ants was recorded for roughly 4 min (±1 min depending on the velocity of the individual). Each ant was only tested once.

Paths analysis The test condition was designed to find out whether the presence of the familiar landmark on the test field would lure the homing ants into searching for their nest in front of it or not. Lured ants would display a dense search for the nest entrance in front of the landmark whereas ‘not lured’ ants would display the typical search pattern centred on the release point observed in ants released on unfamiliar terrain (as in Schultheiss and Cheng, 2011) (Fig. 3A). We asked two different judges, blind to the conditions, to determine for each path whether the ant had been lured into searching for its nest in front of the test field landmark or not. The criterion was that a ‘lured’ ant would present increasing meanderings around the fictive nest area and stick to that region rather than the region around the release point. A ‘non-lured’ ant, however, would neither skew its search towards the fictive nest nor show any obvious increase of meanderings. The judges also had the possibility to rate an ant path as ‘ambiguous’. The judges were very consistent across all 224 paths (% of agreement = 98.89; Cohen’s Kappa value: 0.9772; Cohen’s Kappa test: z=15.1735, p<0.0001). The extremely high inter-rater reliability in classifying paths made a more exact definition of a criterion irrelevant. Importantly, the judges never disagreed oppositely between ‘lured’ and ‘not lured’. The only rare disagreements were about whether a path had been rated as ‘ambiguous’ or not.

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Figure 3: Ants’ results on the test field. After training, ants were captured as zero-vector ants and released singly on the test field where their paths were recorded. The ant paths were categorised into two groups according to whether they displayed a dense search at the fictive nest (‘Lured’) or only a loose search pattern centred on the release point typical of their searches in an unfamiliar environment (‘Not lured’). A) Example of a ‘Not lured’ and a ‘Lured’ ant path. Black bars represent the landmark on the test field. Open circle indicates the fictive nest. Black dots indicate the release point. B) Percentage of ‘Lured’ (in black) and ‘Not lured’ ants (in grey) in different training conditions. The numbers at the top indicates the number of ants tested in each condition. The numbers of meters at the bottom indicate the length of the training route. All group were trained for 2 days before being released on the test field, except ‘5m-1trial’ (1 trial along the route only) and ‘10m 4 days’ (trained for 4 days). Horizontal lines at the top indicate the relevant comparisons done with those two particular groups.

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Overall, only 14 paths (6.2%) distributed across the conditions were qualified as ‘ambiguous’ by at least one judge. We adopted here the most conservative approach by discarding all those ambiguous paths and focusing the analysis only on the paths that were rated ‘lured’ or ‘not lured’ by both judges. Thus, the set that made up the presented data had 100% inter-rater agreement. Frequencies of ‘lured’ and ‘not lured’ ants were compared between groups with Fisher’s exact tests and the p values were Bonferonni corrected when necessary.

Panoramic pictures comparison In parallel, we recorded 360 panoramic pictures of the natural scenery experienced by the ants. We recorded one picture every meter along the midline of the training route of both nests. On the test field, we recorded 35 pictures (one every meter) distributed evenly over an area 7 m wide and 5 m long in front of the landmark. These panoramic pictures were recorded with a Canon G10 camera mounted on a convex mirror (GoPano) placed on the ground. The field of view of the imaging system covers 360° horizontally and 120° vertically (63° above to 57° below horizon). The pictures were unwarped with the software Photo Warp, trimmed in order to remove the floor (i.e., bottom 57°), converted into binary black and white to avoid illuminations biases and resized at 120*21 pixels (angular resolution = 3°) in order to match approximately the visual acuity of M. bagoti (Schwarz et al., 2011). We compared the images recorded at each location of the test field with all the ones taken along the route for both nests. The difference between two pictures was calculated as the mean squared pixel difference (MSPD) over all corresponding pairs of pixels as in (Zeil et al., 2003). All pictures were oriented along the same absolute orientation before being compared.

3) Results

Picture comparisons Test field vs. individual training images. Overall, the scenery of the test field matches better the training route of nest 1 than the training route of nest 2 (paired t-test across the 35 test field positions: t=–21.36, p<0.0001) (Fig. 4). This is because test field and nest 1 both present rather open sceneries, while nest 2 is surrounded by several conspicuous trees (Fig. 2). The test field scenery matches best the training routes in a corridor area in front of the landmark (Fig. 4). Indeed, along this corridor, the landmark is perceived from the same

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Figure 4: Test field panoramic mismatches distribution. The maps of mismatch result from comparing panoramic pictures taken on the test field (1 picture every meter on an area 7 m wide and 5 m long) with panoramic pictures recorded along the training route (memories) of both nests. Numbers indicate which training route picture (in meters away from the nest) was best matching — and thus recalled — at the locations delineated by the black lines. For instance, ‘3’ indicates the training route picture taken 3 m away from the nest; ‘0’ indicates the training route picture taken at the nest entrance. For each test field picture, only the mismatch value of the best matching training route picture (best memory recalled) was taken. Mismatch levels were then interpolated between test field locations (triangle-based cubic interpolation). Numbers are in bold or not according to whether the training route picture recalled presented a significantly better matching than the other route pictures or not. Black bars represent the landmark.

161 Chapter VI vantage point as along the training routes. The best matching is found at the fictive nest position because this is where test and training landmarks occupy the biggest part of the panoramic pictures. Conversely, going further away from the landmark induces a reduction of its perceived size and therefore increases the relative importance of the mismatching scenery. Interestingly, within the matching corridor in front of the landmark, each view on the test field matches best the memorised route-views taken at the corresponding distance away from the landmark (bold numbers in Fig. 4). For example, 2 m in front of the test field landmark, the best matching route-view is the one memorised 2 m in front of the landmark. The areas on both sides of the test field, however, match badly because such ‘sideways’ views of the landmark have never been experienced along the straight training routes (Fig. 4). The route- views that best match the sides of the test field (non-bold numbers in Fig. 4) are not particularly relevant in the sense that any route-view would produce similarly bad matching. Overall, only the first 4 meters of the training routes (i.e., in front of the landmark) present meaningful similarities with the test field. The other memorised route-views, taken further away on the training routes, always present high mismatches with the test field because of the diminishing perceptual size of the training landmark and increasing importance of the mismatching scenery (see example at the fictive nest in Fig. 5).

Average training images. We created an average image of the route experienced for each group. Average images were simply obtained by averaging the pixel intensity between the individual images of the portion of the route experienced by the different groups. For instance, the 5 m group average image corresponds to the average of the images recorded from 0 m to 5 m away from the nest (1 image every meter), and for the 16 m group, the average image encompasses the images recorded from 0 m to 16 m. Those average images are not intended to represent what could be a holistic memory, but are here only to illustrate the relative importance of the landmark and other features when experiencing different lengths of the training route. The longer the route, the less the landmark is represented, and the more it is overshadowed in salience by other features (Fig. 6A). Interestingly, image differences between the average image and the test field reveal that the scenery at nest 1 matches the test field better than the scenery at nest 2; and that the shorter the portion of the route involved, the better the average image matches the test field (Fig. 6B). The more the landmark is represented, the more the average view matches the test field.

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Figure 5: Fictive nest mismatch. Panoramic mismatch of the scene perceived at the fictive nest location on the test field according to the training route panoramic picture recalled for nest 1 (black) and nest 2 (grey). Only the first metres of the training route present decent matching with scene at the fictive nest. Vertical dashed bars represent from left to right the end of the training route for the 5m, 10m and 16m groups respectively (the 10m picture of nest 2 has been lost). When at the fictive nest on the test field, the recall of the best matching snapshot should be non-ambiguous whatever the length of the training route experienced.

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Figure 6: Averaged images. A) Averaged images were obtained by averaging the pixel intensity between the individual images of the portion of the route experienced by the different groups. B) Average of the image differences obtained between the averaged image of the group and images recorded on the test field in front of the test-landmark (from 0 to 5 m away from the test-landmark). Error bars represent the standard deviation of the mismatch across those different locations of the test field.

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Ants on the test field Lured or not lured? During training, ants usually discovered the feeder in a couple of hours and started shuttling hastily between feeder and nest. From the first trial on, the ants proved able to home unhesitantly along the route in a straight path and relocated their nest entrance in few seconds. After training, the ants were captured at the end of a homing run, just before reaching their nest (i.e., as zero-vector ants).

When released on the test field, some ants displayed a search pattern centred on the release point with no particular attraction towards the landmark (example in Fig. 3A ‘Not lured’). Such a search pattern is typically displayed when ants are released on totally unfamiliar terrain (Schultheiss and Cheng, 2011) and indicate that, although they passed just in front of it, the presence of the familiar landmark did not lure those ants into confounding the test field with the familiar training scenery. Other ants, however, showed an attraction towards the landmark and displayed a dense search for their nest in front of it (example in Fig. 3A ‘Lured’). Such behaviour reveals that the presence of the landmark has lured the ant into confounding the test field with its real-nest surroundings and led the ant to trigger its familiar- view-based guidance mechanisms. Some ants went searching in front of the landmark immediately after being released. Other ants, however, displayed some loops around the release point at first, and then went searching at the fictive nest location. At which given place and moment the ants got lured and triggered the nest approach and search on the test field could not be determined precisely here, but informal inspection of the paths suggested that the switch tended to occur more when the ants were close to the landmark, suggesting that the landmark played an important role in scene recognition mechanisms. This is not surprising in our situation because the rest of the scenery was in general different between the test site and the training sites.

The high inter-individual variability in paths, the low number of paths recorded, and the lack of knowledge of the ant’s body orientation make any attempt to correlate in detail the ants’ paths with the scenery mismatch inappropriate. However, the presence or not of an obvious search for the nest entrance at the fictive nest position allowed us to determine unambiguously whether or not the ants discriminated between the test field and the training field.

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In categorising the ants’ search patterns into ‘Lured’ and ‘Not lured’ we do not imply anything about the ecological relevance of their behaviour. Indeed, in nature, ants are very unlikely to get passively displaced by hundreds of metres only to end up right in front of such a distinctive and familiar feature. We would not want to speculate on whether being lured vs. not lured is ‘better’ in such a strange situation. The test, however, did allow us to examine the effects of different kinds of experiences on search behaviour.

Inter-nest differences. The results showed striking differences between the two nests. Ants trained at nest 2 almost never got lured when released on the test field, and displayed the search pattern characteristic of ants on unfamiliar surroundings (Fig. 3B). In contrast, the proportion of ants from nest 1 that got lured and searched at the fictive nest position was much higher (Fisher’s exact tests of nest 1 vs. nest 2 at 5 m and 10 m training distances: p’s<0.0001) (Fig. 3).

Nest 1: effect of the length of the route experienced. Interestingly, the proportion of lured ants from nest 1 varied significantly as a function of the length of the training route the ants had experienced (Fisher’s exact test: p<0.0001) (Fig. 3B). The ants having experienced the first 5 m of the route only were more likely to search at the fictive nest than the ants having experienced a longer route. The ants having experienced the whole 16 m of the route were least likely to search at the fictive nest (Fig. 3B). Thus, the proportion of lured ants differed despite the facts that the last portion of the route (i.e. the 5 m before the nest) must have been very similar across all the groups, and that the test distance from the landmark was within this ‘shared’ portion (i.e., less than 5 m).

Nest 1: effect of repeated training. The single trial ants having experienced homing along the 5 m route only once were less likely to get lured into searching at the fictive nest on the test field than ants having experienced the same 5 m of the route for two days (Fisher’s exact test of ‘5m-1trial’ vs. ‘5m-2days’: p=0.0002). This result is not due to a lack of ability of the naïve ants to relocate accurately their nest. Indeed, after having been tested, such naïve ants were released again on the familiar training route and proved able to relocate their real nest readily (data not presented). This result reveals that the nature of visually guided behaviour is somehow modified during the first two days of training, with either the nature of the visual memory, the nature of the comparison/decision processes, or both changing.

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We also tested whether additional training beyond two days would impact on the ants’ behaviour. The answer was a clear no. Ants trained to the 10 m long route for 4 days rather than 2 days did not improve their probability of discriminating between the test and training field (Fisher’s exact test of ‘10m-2days’ vs. ‘10m-4days’: p=0.7694), suggesting that the development of the visual memory of the given route had reached a steady state after 2 days of training. The chances of obtaining the observed ‘lured’ rate of the 10m-4days group may be compared assuming different expected (in the statistical sense) ‘lured’ rates. If they had the expected ‘lured’ rate derived from the observed ‘lured’ rate of the ‘10m-2days’ group (null hypothesis), the chance of obtaining the observed ‘lured’ rate was 0.158. In comparison, the chance of obtaining the observed ‘10m-4days’ ‘lured’ rate (15/22) was only 0.003 (less than 50 times as likely) assuming expected ‘lured’ rate of the ‘5m-1trial’ group (alternative hypothesis 1); only 0.051 (less than 3 times as likely) assuming the expected ‘lured’ rate of the 5m-2days group (alternative hypothesis 2) and only >0.0001 (less than 105 times as likely). Against these three alternative hypotheses, the null hypothesis has the most support from the observed data. It thus appears that the ants trained for 4 days along 10 m of the route behaved like ants trained for 2 days along 10 m of the route rather than like any other group.

4) Discussion

We attempted here to investigate the nature of ant visual memories. For this purpose, we placed a huge landmark just behind a Melophorus bagoti nest and controlled their knowledge of the surroundings by constraining the ants to forage along a predetermined route only. After training, the individuals were captured as zero-vector ants and released on a distant test field presenting a different scenery (test-scene), but where the same huge landmark created some resemblance to the familiarised training-scene. The response of the ants could be classified clearly in a binary fashion. Some ants went searching densely for their nest in front of the test-landmark as if on familiar terrain (Fig. 3A ‘lured’), while others displayed a systematic search as if on totally unfamiliar terrain (Fig. 3A ‘not lured’). The main results showed that only one nest (nest 1) was lured to any extent. Within nest 1, different training experiences led to different rates of being lured: the shorter the training distance, the more the ants were lured.

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Past results obtained with this species provide a framework to interpret those behaviours. We have shown recently in similar conditions that ants would not focus solely on such an obvious landmark, but would rely simultaneously on distant panorama cues as well for guidance (Wystrach et al., 2011a). Also, ants appear to switch between navigational strategies according to whether they are on familiar terrain or not (Wystrach et al., in press). We suggested that ants are able to quantify the degree of mismatch between the view at their current location and a remembered view, and use the mismatch level to implement one or another navigational strategy. If the mismatch between the perceived scene and their memory is too bad, ants would stop following their route strategy and start behaving as if on unfamiliar terrain. In short, they possess a mismatch tolerance threshold (‘how to match panoramic view’ in Wystrach et al., 2011a). Interestingly, the level of such a mismatch tolerance threshold (MTT) appears to vary among individual ants, at least in Gigantiops destructor (Wystrach, 2009; Wystrach et al., 2011b), with some ants pursuing a ‘familiar terrain strategy’ at a higher degree of mismatch than others. Such inter-individual variation in MTT might account as well for inter-individual differences in the behaviour on the test field of nest 1 ants given the same training condition. In the present study, ants released on the test field might have assessed the degree of familiarity of the new surrounding scene. Some individuals considered at some point — often when close to the landmark — the test-scene familiar enough to trigger a ‘familiar terrain strategy’ and ended up searching for their nest in front of the landmark (Fig. 3A ‘lured’); while other ants consistently considered the mismatch of the test-scene too high and thus adopted and maintained a systematic search strategy typically used for searching on unfamiliar terrain (Schultheiss and Cheng, 2011) (Fig. 3A ‘not lured’).

Nest 1 and nest 2: different memorised scenes We replicated the experiment on two nests that presented very different sceneries. The scenery at nest 2 was quite different than the test field (Fig. 2). Despite the presence of the landmark, almost all the ants familiarised with nest 2 scenery considered the test field as unfamiliar. This confirms that ants did not attend to the landmark only, but accounted for the high mismatch perceived in the rest of their panoramic visual field at the test field (Wystrach et al., 2011a). The scenery at nest 1, however, was more similar to the test field and a much higher percentage of ants triggered a ‘familiar-scene navigational strategy’ (Fig. 3B). The different results obtained between nest 1 and 2 confirm that the responses observed on the test

168 Chapter VI field depend on the degree of visual matching between the view perceived on the test field and the view that the ants memorised during training. Interestingly, in both cases, following a mismatch gradient descent strategy (Zeil et al., 2003) should have led all individuals to the fictive nest in front of the landmark as it presents a minimum of mismatch (Fig. 4). However, this minimum of mismatch is quite high, and many ants were not attracted to it. This confirms that ants do not always follow a mismatch gradient strategy but take the level of mismatch into account, and use the threshold to select and implement different strategies.

Knowledge of the far end of a route impacts on the behaviour near the nest. Ants with differing spatial knowledge acquired during training assessed familiarity of the test field differently, opening a window into the nature of their view-based matching. Fortunately, the similarity between the test field and nest 1 sceneries was just right for demonstrating this (unlike nest 2, whose scenery was too different from the test field). Some ants considered the test field as unfamiliar while others engaged in a ‘familiar-scene navigational strategy’. Interestingly, the different training regimes led to different results on the test field. The longer the training route experienced by the ants, the higher the probability that the test field was considered as unfamiliar (Fig. 3B, nest 1, ‘5m-2days’, ‘10m-2days’ and ‘16m-2days’). The interesting point is that such additional knowledge had been acquired further away along the route, and resulted in a lower likelihood of ants getting lured into searching for their nest entrance on the test field. In other words, we can conclude that the knowledge of the scene perceived at the end of the route (i.e., near the feeder, further than 5 m or 10 m away from the nest), increased the discrimination between the test scene and the scene near the nest. To discriminate is defined here as giving a different behavioural response, that is, searching around the release point (not lured) rather than searching at the fictive nest (lured).

Holistic memory hypothesis? An alternative to the ‘multiple-snapshots’ hypothesis is that the route might be learned more holistically, resulting in a single memory for homing along the whole route. A robot using such a single holistic memory has been shown to successfully recapitulate a non-trivial S- shaped route through a real-world environment (Baddeley et al., 2011). Such a holistic memory can be built up by selecting and integrating the information from the scene that is most relevant for the whole route, or by memorising some elements of how the view changes along with the displacement. Compacting views encountered along the route into a single

169 Chapter VI representation naturally highlight the most relevant features, which allow to differentiate views encountered along the route vs. views off the route. The idea is parsimonious in the sense that a holistic memory would provide ants with a more compact way of storing the information required to recognise familiar views. The present results are consistent with this hypothesis and thus offer some initial support. Crucially, a holistic memory, as with snapshots, can be compared to the current scene and provides at any time a measure of the degree of familiarity of the current location (Baddeley et al., 2011), thus enabling the selection between navigational strategies for familiar terrain vs. unfamiliar terrain. Assuming that ants build up and update a single memory based on the whole foraging route would mean that the ants having experienced 5 m, 10 m, or 16 m of the route would possess different memories. The landmark, which covers a great part of the scene near the nest, would be very well represented in the memory of the ants having experienced only the first 5 m of the route (Fig. 6A and Fig. 7 ‘nest 1’). In contrast, ants having experienced up to 16 m of the route, where the landmark is barely perceptible and other features are much more prominent (Fig. 6A and Fig. 7 ‘nest 1’), would build up a memory in which the relative importance of the landmark is diminished and overshadowed in salience by other features. As a result, due to the presence of the same landmark on the test field, a holistic memory of the first 5 m of the training route would provide a better match to the test-scene than a holistic memory encompassing further portions of the route (as observed with averaged images: Fig. 6B), leading the ants that have experienced a longer route to have a higher probability of considering the test field as unfamiliar, as we observed here (Fig. 3B, nest 1, ‘5m-2days’, ‘10m-2days’, ‘16m-2days’). The recorded pictures reveal how smoothly a natural skyline evolves along a route (Fig. 7). Even in highly cluttered environment, some features of skylines provide consistency along the route (Philippides et al., 2011) which could facilitate the efficacy of a holistic encoding. While navigating along their familiar route, ants’ visual input is continually stimulated by such an evolving scene, and it seems adequate that such visual input feeds and updates a same neural circuit rather than is stored as a series of independent snapshots presenting high redundancy of information. A holistic encoding of the route does not necessitate a continuous learning. It could also be built from a series of discrete views, with each new views feeding and updating the unique neural network supporting the memory. The more the number of views used to develop the holistic memory, the more robust might be the

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Figure 7: Skylines along the routes. Stacked skylines, showing the skylines for every meter along the training route and in front of the landmark in the test field. The region highlighted in grey show sequences of locations where a visual feature is persistent and moving smoothly across the visual field. The feature highlighted with a darker grey is the landmark. The numbers on the side indicate the distance in meters away from the nest (or fictive nest for the test field). The skylines in bold mark the end of the route experienced by the ants in the 5m, 10m, or 16m condition (the 10m picture of nest 2 has been lost). The vertical dashed lines represent the frontal 180° field of view.

171 Chapter VI recognition of any point of the route. Past research has revealed the ability of insects to rely on the shape of the skyline (Graham and Cheng, 2009b) and their aptitude for memorising dynamic cues in addition to static cues (Lehrer et al., 1988; Sobel, 1990; Zeil, 1993b; Dittmar et al., 2010). Both kind of filtered visual input could in theory serve such a holistic encoding.

Multiple-snapshots hypothesis? Alternatives to a holistic memory, however, might also account for the pattern of data. The idea that ants’ visual memories are constituted of several discrete snapshots taken at different locations along their familiar route is based on solid evidence (Judd and Collett, 1998). Wood ants approaching the silhouette of an upright triangle fixated the edge at a series of discrete locations on their retina. However, this evidence comes from a very short route (40 cm) displayed in laboratory conditions with a geometrically simple object as the sole distinctive landmark. To our knowledge, parallel results have not yet been obtained when it comes to recapitulating a large-scale route (10-100 m) in complex natural scenes. Results in the field showed that ants captured and released on a different section of their route can readily resume their familiar route; in Melophorus bagoti: (Kohler and Wehner, 2005); in Cataglyphis fortis: (Andel and Wehner, 2004). These results can be accounted for by assuming that ants recall the appropriate memory from their memory collection according to the scene currently perceived (Collett et al., 2006). If ants store multiple memories for a route, however, the ants from nest 1 released on the test field in our study would surely recall, if any, a memory which has been stored close to their real nest because only the first four meters of the training route provide a decent match with the test field (Fig. 4) and any snapshot taken further away along the route would be highly mismatching given the ant’s viewpoint on the test field (Fig. 5). The strong effect exerted by different training experiences must mean that the bank of snapshots interact in some way, affecting either the comparison process, the retrieval process, the memory, or some combination of these. We discuss each in turn. It could be that having experienced a longer route led the ants to have a lower mismatch threshold. This would cause the ‘10m-2days’ and ‘16m-2days’ ants to be lured less often even if the same ‘correct’ memory was retrieved. It is not clear, however, what the function of a lower mismatch threshold would be, given that, as we have argued, the ants should have had little trouble retrieving the correct memory or at least some snapshot similar to the correct memory. Also, this hypothesis appears hard to reconcile with the lower ‘lured’ rate observed in 5 m single-trial ants. Surely more experience, possibly resulting in better

172 Chapter VI memories, ought to result in a lower mismatch threshold, and lower the proportion of ‘lured’ ants. The results were the other way around (Fig.3; nest 1 ‘5m-2days’ vs. 5m-1trial ants). It could be that having a larger bank of memories affects the retrieval process, making the ants less likely to retrieve any memory at all for matching. For functional reasons, this possibility seems unlikely to us. Ants of this species often have to negotiate far longer and more complex routes than what they had here (Kohler and Wehner, 2005; Wehner et al., 2006; Sommer et al., 2008). They thus really need to retrieve from a large bank of memories. Their success in the just cited papers makes a bottleneck in memory retrieval unlikely. A viable hypothesis is that the acquisition of supplemental snapshots obtained from traversing a longer route modifies the nature of the snapshots obtained nearer the nest. Given the uncertainties in any remembered view, an advantage may be gained by averaging the specific view with a prototypical view: some ‘average’ view on the route. Examples of arithmetically average images can be seen in Fig. 6A, but the prototypical view for an animal may not be the arithmetically average view. While this causes systematic errors in memory, it might serve to reduce overall errors. Such averaging of categorical prototypes has been demonstrated in humans, among other things in human recall of spatial locations (Huttenlocher et al., 1991), and has been argued to reduce overall errors. Such ‘Bayesian’ use of information maximises information entering into decisions in combining both specific and categorical information (Cheng et al., 2007). The different ‘average’ views of ants with different lengths of training routes would then cause differences in behaviour. This scenario would imply in any case that snapshot memories are not stored independently. Finally, it could be argued that snapshots are stored independently, but always recalled simultaneously, with the outputted behaviour resulting from the processing of the current view with all the different snapshots. Such a strategy appears to us less parsimonious than a single holistic memory as it implies both a much higher memory load (with redundant information) and a higher processing power.

Foraging experience hypothesis Another alternative hypothesis is that differences between groups arose purely from differences in the amount of foraging experience, irrespective of the nature of the experience obtained from different parts of the route. Single-trial ants, having little experience of the first 5 m of the route only, showed a lower probability of being lured by the landmark on the test field than ants given 2 days of training. It could be thus supposed that increasing experience of the landmark along the training route increases the chance of being lured by the landmark

173 Chapter VI on the test field. Somehow, an amount of encountering the landmark on the route home is necessary for matching on the basis of this landmark on the test field. This hypothesis would explain the data pattern by claiming that within 2 days of training, ‘5m-2days’ ants may have performed more foraging trips than ‘10m-2days’ or ‘16m-2days’ ants, and have thus more experience of the landmark. The hypothesis, however, is untenable given a number of inconsistent results in which a less trained group showed a higher ‘lured’ rate. Thus, the 5 m group was lured more often than the ‘10m-4days’ group, and the ‘5m-1trial’ group was lured more often than the ‘16m-2days’ group, as well as more often than the two groups from nest 2, whose ants also encountered the same landmark.

Development of memories Previous work on the same species showed that repeated route experience improved the ants’ visual memories and homing accuracy (Narendra et al., 2007b; Wystrach et al., in press). Here, however, ants having experienced the 5 m route only once discriminated more between test and training fields than ants having experienced the same route for two consecutive days (Fig. 3 nest 1 ‘5m-2days’ vs. ‘5m-1trial’). Repeated experienced improves the efficiency of the ant memories but, at the same time, diminishes here their ability or willingness to discriminate between training and test field scenes. One viable explanation would be that repeated training allows ants to improve their memory by filtering relevant information from the scene. Naïve ants’ memory might be closer to a raw image at first, but then, with repeated training, the ants discard inappropriate information (e.g., proximal noise or other unreliable features present in the training scene) and highlight useful information. Some such process is a natural assumption of the holistic memory hypothesis (Baddeley et al., 2011). Ecologically, it makes sense that such a process would require multiple trials, and past research on this species supports this claim (Narendra et al., 2007b). M. bagoti were trained for different numbers of trials to find their nest in the middle of four black cylinders; the feeder-nest distance was 10 m. They were then tested with the configuration of cylinders on a distant test field. Although no panoramic images were taken, it took the ants up to 15 trials to show substantial search density at the centre of the array of landmarks, and search density was highest when the training experience was spread over two or more days. The learning of views was also examined in a situation in which what the ants could see was controlled by borders similar to those used in this study (Wystrach et al., in press). In this study, ants constrained to forage within the nest immediate surrounding were better at heading towards their nest from novel distant locations after two days of

174 Chapter VI foraging experience, showing here also that multiple trials allowed them to refine their memories. Because a single trial cannot inform the ant of the temporal reliability of a cue, it is safer to rely first on a great variety of cues widespread on the whole panoramic visual field. A learning process over several trials increases the chance that the highlighted cues are stable ones. In the present case, the landmark surely is a useful cue. Focusing on the edges of the landmark would be of great help to pinpoint the nest accurately. Such a process of refining the memory by learning would explain why the memory of experienced ants, in which relevant cues such as the landmark have been emphasised, matches better the test field than the raw memory of naïve ants, in which irrelevant cues from the training scene are still weighted (Fig. 3 nest 1 ‘5m-1trial’ vs. ‘5m-2days’)

5) Conclusion

We attempted to investigate whether ants in natural conditions encode their stereotypic route as a series of discrete way points or more holistically. Our results show clearly that knowledge of the scene at the end of the route (i.e., near the feeder) has an impact on the memory of the beginning of the route, near the nest. This result seems hard to reconcile with the idea that ants’ visual memories are constituted of several snapshots taken along the route and stored independently, because the acquisition of snapshots at the end of the route should not impact on the one memorised and recalled near the nest. Assuming that ants encode a route as a succession of individually stored snapshot requires thus additional explanations, such as that acquisition of supplemental snapshots modifies the nature of the ones previously memorised. Instead, the hypothesis of a holistic encoding of the route, although speculative, appears to be the most parsimonious to explain our results. It naturally predicts that supplemental knowledge of the end of the route modifies the whole memory, and thus impact on the recognition of the beginning of the route, as observed here. Moreover, building up a holistic encoding of the route imply than a same memory is updated with learning, an idea consistent with the results observed here in naïve ants (see ‘Development of memories’). Ants were here navigating within a relatively open landscape, leading to a smooth dynamic change of the scene perceived while progressing along the route (Fig.7). Insect routes, which can be very long and pass by great discontinuities of landscapes, might perhaps

175 Chapter VI still be constituted of several segments. But navigation along those segments might not be driven by multiple and independent snapshots memories, and future studies should consider the hypothesis of a holistic visual encoding, which appears parsimonious and would be valuable to investigate.

Acknowledgements

We thank Sebastian Schwarz and Alice Baniel for their help on the field. This research was supported by a Macquarie University research excellence scholarship (iMQRES) to AW and an Australian Research Council Discovery Project grant (DP110100608). We thank CAT Centre for Appropriate Research for letting us use their grounds for research.

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CHAPTER VII

Why we study insect navigation –

Ants, Umwelts and Morgan's Canon

This chapter is submitted as an essay in Animal Behaviour (Image from the movie ‘Matinee’)

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Why we study insect navigation – Ants, Umwelts and Morgan's Canon

Antoine Wystrach1,2 and Paul Graham3

1Department of Biological Sciences, Macquarie University, Sydney, NSW 2109 Australia 2Centre de Recherches sur la Cognition Animale, CNRS, UMR 5169, Université Paul- Sabatier, Toulouse F-31062, France 3School of Life Sciences, University of Sussex Brighton, UK

Abstract

There is ever-growing evidence that complex ‘clever-looking’ animal behaviour may arise from simple mechanisms rather than ‘higher’ processes. The remarkable navigational abilities of social insects are proof that small brains can produce exquisitely efficient, robust navigation in complex environments. Due to strong wariness of anthropomorphism stemming from the ethological tradition, research on insect navigation has been approached with a bottom-up philosophy and can thus provide useful baseline hypotheses for navigation in other larger-brained animals. Yet despite the wariness, unhelpful anthropomorphic beliefs still penetrate the field of insect navigation, revealing how careful all researchers in animal cognition must be. We explain here how the use of panoramic views, without the need of landmark extraction, can provide parsimonious explanations for insect navigation. The use of panoramic views can also explain insects’ behaviours that have been showcased as revealing allocentric place navigation, or the use of a cognitive map in the vertebrate literature. For instance, insects’ ability to rely on geometrical layout and configural arrangement of cues, or the use of distal cues to reach a goal from novel locations over trajectories that have not been previously travelled can be parsimoniously explained by panoramic-view-based navigation. Even in humans, navigation may well be based in part on panoramic visual input in addition to or instead of the spatial relations of individual objects. Differences between animals in cognition are likely to be of a continuous nature, and the simple mechanisms invoked in the insect literature to explain these apparently complex behaviours would be worth considering for vertebrate navigation. Keywords: comparative cognition – navigation – panoramic views – bottom-up approach

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1) Introduction

Comparative cognition A major goal within biology and psychology is to understand the natural intelligence of animals. For many, this field of animal cognition is concerned with investigating the development and precursors of seemingly unique human intelligences (de Waal and Ferrari, 2010). However, a more general project is to examine similarities and differences between species in their cognitive solutions to day-to-day tasks, to inform us about how natural intelligence depends on factors such as environment, social structure, evolutionary history and brain size. From Aplysia to humans, many similarities in how the neural hardware works may be found, from the nature of action potentials to the range of neurotransmitters used in communication between neurons (Kandel et al., 2000). Such neural similarities might lead to some common cognitive strategies that underpin behaviour in different species or even taxa (Shettleworth, 2010b). For comparisons between species to be most meaningful, it helps to look at how animals solve similar problems. In this context, navigation provides an ideal ground for comparative cognition.

Why Navigation? Using learnt information to navigate within a familiar environment undoubtedly solicits several cognitive processes. Animals must learn the environmental information that will be appropriate for navigation, they must organise those memories robustly and then, when navigating, convert those memories into spatial decisions. Regarding the animal cognition project, navigation has two significant advantages as a model system for comparison. Firstly, most animals have to solve the problem of navigating to important locations. Comparisons across taxa are often facilitated by the similarity of environments within which different species navigate. Secondly, the goal of a navigating animal — getting from A to B — is often clearly defined and its movements provide an easily recordable read-out of the work of its brain. Consider for comparison the study of communication. In this case, both the intention of the signaller and the nature of the communication channel are opaque to a third-party observer. It takes considerable efforts to decipher what the function of a signal is. Spatial behaviour produces a low-dimensional behavioural output (movement in space and time) which can be objectively quantified by scientists. Furthermore, these movements are a faithful proxy for the outputs of the neurophysiological processes that produced them. Morgan’s canon and bottom up approach.

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The comparative cognition project needs sound principles for interpreting animal intelligence. In our attempts to understand the mechanisms underpinning spatial behaviour, we have to bear in mind one of the central tenets of animal cognition research, namely, Morgan’s Canon. C. Lloyd Morgan was a psychologist, who in response to over-enthusiastic anthropomorphic interpretations of animal behaviour, stated:

In no case is an animal activity to be interpreted in terms of higher psychological processes, if it can be fairly interpreted in terms of processes which stand lower in the scale of psychological evolution and development (Morgan, 1903, p59).

The vast majority of the scientific community nowadays agree with and apply the principle of parsimony. In our view, however, selecting a parsimonious hypothesis among the ones available is not sufficient. There is ever-growing evidence that complex ‘clever-looking’ animal behaviour may arise from simple mechanisms rather than ‘higher’ processes (Shettleworth, 2010a). These simple mechanisms might be hard to fathom while investigating complex behaviour and favouring parsimonious hypotheses among complex ones will not help in unravelling them. One lesson we draw from Lloyd Morgan is that we should try and explain behaviour from a bottom-up perspective (de Waal and Ferrari, 2010; Shettleworth, 2010a), aimed at understanding the basal mechanisms of animal cognition first.

2) A bottom up approach of insect navigation

The remarkable navigational abilities of social insects are proof that small brains can produce exquisitely efficient, robust navigation in complex environments (Wehner, 2003; Srinivasan, 2010). Perhaps because of implicit assumptions about the limits of small brains or perhaps because of its heritage in sensory physiology, the study of insect navigation has been approached with a bottom-up philosophy, one that examines how simple mechanisms can produce seemingly complex behaviour.

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The natural life of an insect navigator “Nothing in biology makes sense except in the light of evolution (Dobzhansky, 1951)”. Because cognitive abilities have been shaped by natural selection, animal behaviour has been selected to function well in the natural ecological niche of the study species. To understand the problems and constraints that shaped the animal’s mind, it thus helps to observe the spontaneous behaviours displayed in its natural environment. Social insects are particularly easy to study when it comes to navigation. The eusocial organisation within a colony means that there are individual specialist foragers, who have to return home repeatedly to be successful. With these dedicated foragers, it is easy to assign the correct motivation to their behaviour, and they can be easily tracked outdoors (Santschi, 1913; Von Frisch, 1967; Wehner, 1972; Collett and Land, 1975), where we can assess the available sensory information (Zeil et al., 2003; Stürzl and Zeil, 2007; Philippides et al., 2011). Solitary foraging ants are particularly useful in this regard as we can observe the lifetime’s work of an individually labelled forager in her natural habitat over the natural scale of foraging. Let us sketch the life history of an individual ant forager. Upon first leaving the nest, a new forager performs a “learning walk” where a carefully orchestrated series of loops and turns allows her to explore the visual surroundings from close to the nest entrance (Wehner et al., 2004; Müller and Wehner, 2010). Simple manipulations or displacements of the individuals reveal that the knowledge gained during these special manoeuvres allows her to use information from the visual scene to pin-point the nest entrance on future foraging trips. As with the learning flights of bees and wasps (Zeil, 1993a, b), the manoeuvres are designed to provide ample opportunity to view and memorise the surroundings from perspectives that will be useful on subsequent return journeys. This exploratory behaviour is an example of ‘forward looking’ learning — learning in advance of the requirement for the information. Similar ‘forward looking’ learning is found in longer- distance travellers: thus, indigo buntings learn a star compass in their first autumn of life ahead of their migration south (Emlen, 1970). When the forager finally leaves the vicinity of the nest to forage, she is safely connected to it because of her path integration (PI) system. PI is an idiothetic mechanism where odometry and compass information are continuously combined such that at all times during a foraging journey the ant has the approximate direction and distance information required to take a direct path home (Wehner and Srinivasan, 2003; Ronacher, 2008). PI can be used to guide homeward runs and, by remembering the co-ordinates of food sources, ants can also use PI to chart outbound routes to locations where they had previously found food

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(Wehner et al., 1983; Schmid-Hempel, 1984; Collett et al., 1999). During these very early foraging runs the information provided by PI, in conjunction with ants’ innate responses to visual stimuli, determines the shape of routes. However, because PI is an idiothetic mechanism it is subject to cumulative error and does not register passive displacements, such as those caused by a gust of wind. To mitigate these risks, ants learn the visual information required to guide routes between the nest and foraging grounds. This learning is made easier because the ‘default’ strategy of PI provides consistent route shapes over a series of foraging runs, thus simplifying the problem of learning the appropriate visual information for subsequent route guidance. Over trials, ants increase their visual knowledge further by extending the length of their foraging trips (Fresneau, 1994; Wehner et al., 2004). In experienced ants, the information provided by visual scenes dominates the information given by PI in a familiar environment in cases of conflict (Andel and Wehner, 2004; Kohler and Wehner, 2005), but path integration continues to operate in the background (Andel and Wehner, 2004), and helps the insect to head in the correct nest direction when on unfamiliar terrain or when leaving newly discovered feeding sites (Wehner and Srinivasan, 2003; Beugnon et al., 2005; Narendra, 2007a). How ants weight the two strategies is affected by the richness of the typical visual environment in which they have evolved (Bühlmann et al., 2011). For instance, when displaced to unfamiliar terrain, Cataglyphis fortis — which forages mostly on the visually poor Saharan salt pans — ran its whole route according to its PI (Bühlmann et al., 2011) whereas Melophorus bagoti — which lives in the moderately cluttered Australian desert — covers only a fraction of the distance dictated by its PI (Narendra, 2007a), and Gigantiops destructor —which lives in the very cluttered Amazonian rain forest — stops following its PI after only 50 cm (Beugnon et al., 2005). Evolution has also shaped the efficacy of both strategies according to their expected importance in the natural habitats. For example, Cataglyphis fortis, which often must rely solely on path integration, possesses a remarkably accurate odometer for PI (compare (Cheng et al., 2006), and (Narendra et al., 2007a)); whereas Melophorus bagoti are quicker at learning visual cues (Schwarz and Cheng, 2010). Many studies of ants in natural conditions have provided a remarkable understanding of insect navigation. This approach in the field has the advantage of offering ecologically relevant answers that can serve as an appropriate scaffold on which to investigate cognitive mechanisms. When approached from a bottom-up perspective, the richness of the natural habitats helps to safeguard us from potentially artificial and misleading concepts that do not apply to the animals’ world.

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Figure 1: Images of the landmark. Both images have been recorded from the same location (10.9 m away from the black sheet). A) Image recorded with a standard camera field of view (66° horizontally). B) Picture presenting the horizontal 360 ° panorama. The image resolution has been lowered to fit roughly the visual acuity of ants (i.e., 3°). The vertical black lines delimitate the area presented by the image in A.

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How do ants use vision for navigation? Answering this question gives a good example of how a bottom-up approach can help to unravel simple solutions to complex behaviour. The bottom-up approach requires an objective quantification of the information available to the animal, which can be achieved, in the case of vision, by recording and analysing 360° pictures of the environment. Having pictures of what the ants actually see guards the experimenter from his/her assumptions about the nature of the visual cues present in the environment. Indeed, even in artificially simplified situations in natural environments, assumptions about the salience of cues can be surprisingly misleading. One of our recent studies illustrates well how our human visual representations can deceive us. Ants were trained to find their nest in front of a huge highly contrasted landmark (3 m wide and 2 m high). The landmark stood in an area cleared of natural clutter and devoid of other proximal landmarks so that it seemed prominent, and very useful, at least to our primate visual system. To those humans who have seen this landmark, it seemed the obvious one to use (Fig. 1A). Yet, small displacements of the landmark revealed that the ants were not beaconing at it, but were also relying strongly on the overall panorama for guidance. Consistent with this, the analysis of panoramic pictures recorded on the field revealed that the landmark was far less significant than it appeared to us (Fig. 1B). Simple comparisons of recorded panoramic images allowed us to explain most aspects of the ants’ responses, providing us with parsimonious explanations of their behaviour. Thus, ants may not focus on individual landmarks, even obvious ones, but appear to be guided instead by cues covering a large area of their panoramic visual field, encompassing both proximal landmarks and distant panorama, without segregation into individual objects or landmarks vs. background. The use of such panoramic views, rather than individual landmarks, appears consistent with past results obtained in ants. It explains naturally the link between different elements of the scenery (Pastergue Ruiz et al., 1995; Graham et al., 2004) as those elements might not even be segregated as such in the ant’s memory. In natural conditions, as panoramic views also encompass the most distant cues like hills or dominating trees, such visual inputs explain the ability of ants and bees to home from novel release points over novel routes (Santschi, 1913; Fourcassié, 1991; Capaldi and Dyer, 1999; Collett et al., 2007; Narendra, 2007b; Wystrach et al., in press), to extract information from the skyline (Fukushi, 2001; Graham and Cheng, 2009b), or a low-contrast natural scene during twilight (Reid et al., 2011).

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Even in experiments where artificial landmarks are made prominent and other cues minimal, some results hint at the use of panoramic views. For example, wood ants trained to follow a single conspicuous black bar moving on a LCD screen presented a small bias revealing that they were also guided by other static visual features of the arena (Lent et al., 2009). Similarly, ants used to aiming first at a conspicuous black cylindrical landmark before turning towards the food source, continued to display such a detour when the landmark was removed and their starting position displaced (Graham et al., 2003). In this case, as in others (Wystrach, 2009; Wystrach and Beugnon, 2009), the ants proved able to navigate robustly using pretty much plain white walls or curtains. Such performances appear unlikely to be the result of using only some inconspicuous landmarks, but can be readily explained by the use of panoramic images that encompass the global shape of the arena (Stürzl et al., 2008). Using panoramic views, rather than focusing on isolated landmarks, seems after all an appropriate strategy to cope with the complexity of natural scenes and the poor resolution of insects’ eyes.

3) Debating sexy hypotheses from other studies

Top-down assumptions may arise when considering hypotheses that have come from studies of vertebrate animals, in situations quite different from the natural habitats of ants. Such hypotheses should be treated cautiously. We chose three examples where insect navigation studies have considered hypothesis emerging from research on vertebrates.

Do ants learn visual sequences? The concept of visual sequence learning, also called serial or ordered recall (i.e. the ability to link visual stimuli according to their temporal sequence) arose first in human (Ebbinghaus, 1964) and was then replicated in other vertebrates (Terrace, 1993). Later, it was tested in bees using two juxtaposed decision boxes: the correct visual stimulus among the two presented in the second decision box depended on the identity of a priming stimulus presented in the first decision box. With extensive learning, bees could learn the task (Zhang et al., 1999). This ability was assumed to ease the retrieval of the navigational memories acquired sequentially along routes. The first experimental replicates in ants appeared to support this ability (Riabinina et al., 2011). But despite apparent evidence, assuming that ants learn a visual sequence may have been an erroneous conclusion. In line with the bottom-up approach, previous work in ants investigating the chromatic adaptation in the retinula cells in ants

187 Chapter VII revealed a specific colour adaptation of the visual receptors (Menzel and Knaut, 1973). This allowed the authors to propose a more parsimonious hypothesis than sequential learning: a negative after effect due to colour adaptation of the visual receptors. The presentation of the priming coloured cue in the first box was strong enough to adapt the ants’ colour channels and, consequently, the two cues presented in the second decision box must have appeared tinged with a negative afterimage, allowing the ants to learn the task without memorising the priming cue. Subsequent tests actually confirmed the bias and showed that ants usually fail to learn a sequence of visual stimuli (Riabinina et al., 2011). Indeed, nothing in their natural navigating environment seems to require such an ability: field studies shown that a visual sequence is not essential for route following (Kohler and Wehner, 2005), which can actually be explained without the need of any memory retrieval mechanisms (Baddeley et al., 2011). The ability of honey bees (Zhang et al., 1999) and bumble bees (Dale et al., 2005) to link sequences of visual stimuli after extensive training may have resulted from mechanisms that have evolved for tasks such as visiting flowers in turn rather than navigating along a route.

Do ants learn geometry and features? Vertebrates have been assumed to functionally segregate the geometrical layout of the environment from the features that compose it (Cheng and Newcombe, 2005). Such claims flow from the seminal work of Cheng (Cheng, 1986), showing that disorientated rats reorient by using the shape of a rectangular arena rather than its features. Interestingly, ants tested in the same condition displayed similar behaviours as vertebrates (Wystrach and Beugnon, 2009), but the ability of segregating geometry and features was harder to accept given the background of view-based explanations in insects. Here also, a bottom-up approach provided a more parsimonious explanation. By quantifying for the first time the available visual information in rectangular environments, Stürzl et al (Stürzl et al., 2008) showed that the shape of such arenas is implicitly contained in panoramic views and that simple view-based strategies could explain the results obtained with vertebrates (Cheung et al., 2008) as well as ants (Wystrach and Beugnon, 2009). A subsequent study in rectangular arenas confirmed that ants do not segregate feature and geometry (Wystrach et al., 2011b), with models invoking the use of simple panoramic view-matching strategies faring much better than models based on segregating geometric and featural cues.

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Do insects need cognitive maps? Spatial cognitive maps (Tolman, 1948) are an important concept supported nowadays by neurobiological evidence in vertebrates (O’Keefe and Nadel, 1978; Jacobs and Schenk, 2003; Poucet and Save, 2009). At first glance, insects appear to present the abilities necessary for invoking ‘allocentric place navigation’, which supposedly results from a cognitive map: Insects can reorient using the geometrical layout of an environment (Wystrach and Beugnon, 2009); use distal cues to pinpoint locations (Collett et al., 2007) even after removal of familiar proximal cues (Wystrach et al., 2011c); and can also reach a goal from a variety of novel locations, including the charting of trajectories that have not been previously used (Wehner et al., 1996; Capaldi and Dyer, 1999; Wystrach et al., in press). Gould interpreted honeybees’ success in homing from new release point with the vertebrate-inspired hypothesis of a cognitive map (Gould, 1986). In two failures to replicate Gould’s results (Wehner and Menzel, 1990; Dyer, 1991), the explanation was put forward that bees could have simply used distal landmarks for view-based navigation. The use of panoramic views, together with the vector-based strategy of path integration, can explain all the behaviours mentioned above (Wystrach et al., 2011a, in press). Although the question of a cognitive map is still open (Menzel et al., 2005), all data gathered so far in insects can be explained by PI and visual strategies working as separate modules in the brain (Cruse and Wehner, 2011). In Cruse and Wehner’s (2011) model, the modules only ‘interact’ in competing to gain control of command signals to direct behaviour, and thus do not come up with a newly computed representation. They further suggest that a sensible definition of cognitive mapping is the ability to integrate such separate modules to arrive at new representations.

4) Taking the human out of studies of animal navigation

The human experimenter’s visual Umwelt (and his assumptions). The concept of a landmark is a good example of how our representation of the world can influence the way experiments in animal navigation are designed and interpreted. Humans find it elementary and obvious to identify landmarks and use them, hardly having to bother with the rest of the panorama. But primates have evolved an entire specialized stream dedicated to object recognition (Mishkin et al., 1983; Goodale and Milner, 1992). For us, a scene is naturally constituted of definite elements that can be individually identified and labelled with names such as “tree”, “black cylinder” or “wall”. Each of those objects can thus

189 Chapter VII be used as a landmark for navigation. Such categorisation, however, results from filtered visual information in good part supplied by a high-resolution fovea, on top of which are added multiple steps of ‘early’ and ‘late’ visual and cognitive processing. All this processing delivers our visual Umwelt: our personal way of seeing the world resulting from such filters developed during our evolutionary and personal ontogenetic history (von Uexküll, 1957). Reality is only available through an animal’s Umwelt. There is no ‘direct path’ to the real physical world. But in executing experiments, we need to try guarding against biases arising from our human Umwelt. When Tinbergen discovered that displacing a circle of cone pines surrounding a digger wasp burrow would lure the wasp into searching for its burrow within the displaced cone pines rather than at its real position, it appeared reasonable to conclude that the wasps’ orientation was controlled by these “nearby landmarks” (i.e., the cone pines). This conclusion is indeed true, but our Umwelt may also deceive us into thinking that the wasp was using those “nearby landmarks” as landmarks, whereas the insect might well have been guided purely by panoramic views, part and parcel of which include the nearby landmarks, but without functionally segregating them from the rest of the scenery. Indeed, the large retinal sizes projected by proximal objects leads to strong panoramic mismatches if they are misplaced on the retina. Moreover, the perceived distant scenery does not vary much with small displacements. Thus, comparing panoramic images would have most likely revealed that the view at the centre of the displaced circle of pine cones presents a much better overall match the training condition than does the view at the real nest position, now but without the cone pines. Following Tinbergen’s seminal experiments, multiple studies showed clearly that insects are strongly influenced by landmarks such as a trees (Santschi, 1913; Fourcassié, 1991), bushes and stones (Wystrach et al., 2011c), black cylinders (Wehner and Räber, 1979; Cartwright and Collett, 1983; Graham et al., 2003; Narendra, 2007b; Collett, 2010), or walls (Graham and Collett, 2002; Harris et al., 2005) (Review (Collett et al., 2006; Collett et al., 2007)). But, as we explained earlier, those results can be explained parsimoniously by the use of whole views, and do not require us to invoke the ability of extracting the landmarks from the rest of the scenery. We now know that ants do not focus guidance on such landmarks even when they are prominent (Wystrach et al., 2011a). Given the current absence of evidence for landmark extraction in insects, the use of the word “landmark” in referring to the cues that insects are using should be avoided. It biases us in a top-down fashion to assume that insects extract and use individual landmarks, a description stemming from our way of seeing rather

190 Chapter VII than from direct evidence. Following Morgan’s canon, we should favour the simplest explanation. Bottom-up approaches, like the use of objective panoramic pictures, and measurements of the insect’s visual anatomy, provide a more solid plank for characterising the animal’s Umwelt, better than starting with our own Umwelt.

Landmarks or scenes for vertebrates? Research on insect cognition has been wary of anthropomorphism, yet unhelpful anthropomorphic assumptions still penetrate the field (see previous section). This reveals how careful researchers of animal cognition must be when working with any animal, including vertebrates. For example, our human concept of “landmark” is often used in studies on rats (Greene and Cook, 1997; Benhamou and Poucet, 1998; Stackman and Herbert, 2002). Indeed, rats may well use the objects they are provided within experiments as individually labelled landmarks, extracted from the rest of their view (Brown et al., 2010). But we should take heed because to our knowledge, no neural machinery dedicated to object recognition, analogous (or homologous) to the visual ventral stream in primates, has yet been described in those animals; and some behavioural studies found that rats rely on landmark configuration rather than landmark identity for navigation (Benhamou and Poucet, 1998). Interestingly, object and place memory seems to be dissociated in both rats’ (Winters et al., 2004) and primates’ (Alvarado and Bachevalier, 2005; Bachevalier and Nemanic, 2008) brain. The ventral stream that allows object recognition in primates appears to have evolved for reasons other than spatial navigation. For example, human patients with visual agnosia following brain damage in the ventral stream are often unable to recognise objects, even though they can navigate through the world with considerable skill (Farah, 1990). In contrast, spatial navigation can be strongly impaired when lesions occurred in the parahippocampal place area (PPA), a region involved primarily in scene recognition, based on wide-field visual input, which treats the entire scene as a unified object and independently from its component elements (Epstein, 2008). Thus, even human navigation may well be dependent on global encoding of panoramic scenes to a good extent, surprisingly converging (in an evolutionary sense) on the strategies found in insects. Overall, the use of panoramic views together with vector-based strategies using path integration can explain how insects accomplish most behavioural tasks attributed to place navigation in the vertebrate literature. Insect-like egocentric strategies and the possible uses of panoramic views in vertebrate navigation deserve to be considered seriously (Wang and Spelke, 2002; Platt and Spelke, 2009). Moreover, any higher order process is likely based on

191 Chapter VII early simpler ‘insect-like’ egocentric mechanisms, as shown in regard to the potential construction of geometrical models of the world in birds (Pecchia and Vallortigara, 2010; Pecchia et al., 2011). One elaborate recent model using panoramic views has been proposed to account for map-like ‘locale’ learning in rats (Sheynikhovich et al., 2009). The views are ‘early’ in nature, consisting of oriented contour edges in the visual panorama. The locale system (O’Keefe and Nadel, 1978) is explicitly built on taxon components – views and path integration – with some integration that insects might lack for building map-like representations.

5) Conclusion

The actual differences between insect and vertebrate navigation may result in part from different approaches rather than real differences in the cognitive systems of the animals. We have shown how our human subjective Umwelt can adversely lead to dubious assumptions, even in studying insect cognition. But anthropomorphism is more likely to appear when studying animals that are phylogenetically closer to us, and this may have played a part in leading researchers of insects and vertebrates to different conclusions during the last century. Perhaps because of implicit assumptions about the limits of small brains, research on insect navigation has been approached with a bottom-up philosophy, one that examines how simple mechanisms can produce seemingly complex behaviour. This bottom-up approach has revealed how a taxon-like system, such as path integration or the use of panoramic views, without extracting landmarks nor building a cognitive map, can produce efficient, robust and much more flexible navigational systems than previously thought. As the problem of being able to navigate in the world is shared by both vertebrates and insects, there may therefore be some evolutionary convergence in their navigational mechanisms. Evidently, both vertebrates and insects process visual information from views and, interestingly, the neural circuits that underlie their vision show striking similarities (Sanes and Zipursky, 2010). Any higher order process is likely based on early, simpler egocentric mechanisms, so insect-like egocentric strategies and the possible uses of panoramic views in vertebrate navigation deserve to be considered seriously, and in this way can contribute to a more truly comparative cognition.

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194 Conclusion

CONCLUSION

The problem faced by a navigating insect

To appreciate the present work, one needs to understand the problem faced by a navigating insect. Foragers of some ant species, like many other hymenopterans, venture out solitarily in the outside world in quest for food that they need to bring back to their colony. By relying mostly on vision, the lone foragers travel along their own familiar route that leads them to a favourite foraging area. Individuals can develop several such routes and decide flexibly along which one to go (Collett et al., 2006). These foraging routes can be surprisingly long: the record is 20 km in Orchids bees (Janzen, 1971), but an ant such as Melophorus bagoti can travel a hundred meters easily (personal observation), which, at a human scale, would correspond to something like 10 km. Remarkably, many ants such as Melophorus bagoti or Gigantiops destructor navigate in visually extremely complex and cluttered environments (Fig 1). For a travelling ant, grasses are the size of trees, tussocks the size of buildings, and clutter such as dead leaves or twigs becomes really conspicuous and thus add further visual complexity. Along its routes, an ant will thus experience a vast multiplicity of different places which often appear very similar for us (Fig. 1 shows that nothing is more similar to tussocks than other tussocks) but that the insect needs to recognise robustly to avoid getting lost. Amazingly, during 12 months spent on the field observing ants navigating in clutter, I never saw a single individual getting lost naturally. What is baffling is that insects achieve this despite a brain smaller than a pin-head and a very poor eye-sight. Visual acuity in ants and bees is less than 1 unit of information per degree (less than 1 unit of information every 4 degrees in M. bagoti (Schwarz et al., 2011), every 2 degrees in Bees (Srinivasan and Lehrer, 1988) and perhaps reaching 1 degree in G. destructor (the best sighted of all ants)), which is roughly a hundred times poorer than in humans. Lowering the resolution of pictures to match insects’ acuity leads to very blurry images (see examples in chapters 3, 4 and 7). So how can insects navigate by vision so well despite their mini-brain and such a poor sight? The present thesis reviews this question and provides some answers.

195 Conclusion

Figure 1: Typical natural environments where ants such as Melophorus bagoti (top image: central Australia) and Gigantiops destructor (bottom image: Amazonian rain forest) navigate robustly by vision.

196 Conclusion

Visual input

Our human way of seeing the world deceives us into thinking that visual scenes are constituted of objects. More objectively, visual scenes are constituted of photons with different wavelengths coming from different directions. Other animals may not extract any objects from the environment, and rely instead on cues that are unfathomable for us human (von Uexküll, 1957). Nonetheless, the literature often assumes that animals segregate their visual world as we do, and use the different objects or features as “landmarks” for navigation. For example, it has been suggested that vertebrates segregate the geometrical layout of the environment from the individual features that it’s composed of (Cheng and Newcombe, 2005), or that insects segregate proximal landmarks from the distal panorama (Camlitepe and Stradling, 1995; Collett and Collett, 2002; Cheng, 2006; Collett et al., 2006). Such segregations have been justified in the literature because local cues (such as proximal landmarks or features) and global cues (such as geometrical layout or distal panorama) are best used for different functions. Local cues produce accurate positional information useful for guidance and global cues provide a non-ambiguous context to reorient or ensure that the present local cues are the right ones to use (Cheng, 2005). However, these assumptions about how animals segregate visual scenes might well result from our human way of seeing the world rather than direct evidence (chapter 7). The work presented in this thesis shows that ants do not perform such functional segregations, but appear to be guided by the complete and relatively unprocessed panoramic scene they perceive, encompassing both local and global cues simultaneously. By combining analyses of panoramic pictures and ant paths, I designed experiments where the predictions of the use of panoramic views diverged from those of functional segregations such as geometry/features (chapter 2) or landmarks/distal panorama (chapter 4). The behaviour of both species of ants (chapter 2: G. destructor and chapter 4: M. bagoti) were inconsistent with the global/local cue segregation hypotheses. Instead, most aspects of their paths were correlated with predictions resulting from panoramic image comparisons, suggesting that ants were guided by cues that are spread out over their panoramic visual field. Indeed, modelling work has revealed that raw panoramic pictures, even at low resolution, provide non-ambiguous information about precise locations outdoors and indoors and can thus explain navigation without the need to segregate local and global cues (Zeil et al., 2003; Stürzl and Zeil, 2007; Philippides et al., 2011). Such a hypothesis could also explain

197 Conclusion the navigational behaviours observed in past research (see chapter 7) and is parsimonious in the sense that it does not require the neural machinery necessary to extract objects from a scene. Using panoramic views, rather than focusing on isolated landmarks, seems after all an appropriate strategy to cope with the complexity of natural scenes and the poor resolution of insects’ eyes. Now we face the question of the nature of panoramic cues used by insects in natural environments. Experiments showed that the skyline — the boundary between terrestrial cues and sky — is enough for M. bagoti ants to retrieve a correct heading direction (Graham and Cheng, 2009b). These ants could for instance simply exploit the UV-Green contrast for delineating the skyline robustly (Möller, 2002). But we need to keep in mind that such a visual input would be inefficient in forests, as no skyline cue is available. Evolution must have driven each species to favour cues that are useful given their particular environment. For instance, rain forest ants G. destructor appear to be particularly sensitive to light gradients (personal observation). Future research should consider the visual ecology of each species in trying to unravel which visual cues insects attend to for navigation. A comparative approach (Shettleworth, 2010b) may yield important insights in this regard. Interestingly, solitary foraging ants usually display exploratory behaviours called learning walks when leaving their nest for the first time (Wehner et al., 2004). Pauses that are observed during these learning walks are likely to correspond to key moments where ants memorise visual information (Müller and Wehner, 2010), the investigation of which promises great insight into what information different ants extract from their respective environments.

Visual memory

Early experiments suggested that insect store 2D retinotopical views — ‘snapshots’— at places of interest such as the nest, and later compare them with the currently perceived view to return at the goal (Wehner and Räber, 1979; Cartwright and Collett, 1983). But a single snapshot was insufficient to guide an insect from start to goal and later studies suggested that ants memorise several snapshots from different vantage points along their route (Judd and Collett, 1998). The idea was that ants recall the snapshot that best matches the current location, and trigger a motor action associated to it (review: (Collett et al., 2006)). Such a flexible recall of memories could explain the ability of ants to recapitulate their familiar route even when shifted to an arbitrary place along it (Kohler and Wehner, 2005).

198 Conclusion

Some results in the present thesis, however, contest this multiple-snapshots hypothesis. Here we show that the knowledge acquired at the end of the route (15 m) has an effect on the memory of the beginning of the route (chapter 6). One can supposed that the different memorised snapshots interfere with each other, but we favour a more parsimonious hypothesis. The idea is that routes might be encoded holistically, that is, compacted into a single representation that allows the recognition of any point of the route, and discrimination of on-route and off-route directions, thereby considerably simplifying the problem of memory retrieval. Such a holistic memory has been shown to enable a robot to successfully recapitulate a non-trivial route through a real environment (Baddeley et al., 2011). While navigating along their familiar route, ants’ visual input is continually stimulated by a slowly evolving scene, and it seems adequate that this visual input feeds into and updates the same neural circuit rather than being stored as a series of independent snapshots which have high redundancy of information. The concept of such a holistic representation may be hard to fathom, but works remarkably well with neural networks (Baddeley et al. in preparation). The visual input that feeds into the neural networks which in turn support the holistic memory could be of any kind, encompassing both static features and parameters, and dynamic cues like optic flow. Results presented here also provided first insights into the development of ants’ visual memories. In two of the present experiments, ants that have experienced the exact same terrain a different number of times (a few trials vs. 2 days of experience) responded differently in the same tests (chapters 5 and 6). These differences could not be accounted for by the memorisation of a different number of snapshots, and therefore implied that the nature of ant memories was somehow modified with experience. Results from both studies converged on the idea that early memories of naïve ants resemble more a relatively unprocessed view of the surroundings at first, and that these memories then improved with experience by highlighting or retaining only information from the scene that is relevant for navigation (chapters 5 and 6). Interestingly, the holistic memory hypothesis implies that the stored visual representation is built up and updated step by step, which naturally predicts a pattern of memory development. Ecologically, it makes sense that this process requires multiple trials. Because a single trial cannot inform the ant of the temporal reliability of a cue, it is safer to rely first on a great variety of cues which are spread out over the whole panoramic visual field. A learning process over several trials increases the chance that the retained cues are stable ones.

199 Conclusion

Surely, insects’ visual memories are more sophisticated than 2D retinotopical snapshots of the world. In my view, future work should further investigate the hypothesis of a holistic memory and focus on neural networks to model and assess the nature of ant visual memory.

Visual processing

Ants can thus memorise some relevant visual information along their familiar route, and compare it to what they perceive at a given moment in order to navigate. In doing so, ants are able to successfully recapitulate their familiar route (Collett et al., 2003; Kohler and Wehner, 2005; Wystrach et al., 2011c), pinpoint their nest (Tinbergen and Kruyt, 1938; Wehner and Räber, 1979; Durier et al., 2003; Dacke and Srinivasan, 2007; Narendra et al., 2007b), but also home from novel locations over trajectories that have not been previously used (Wehner et al., 1996; Collett et al., 2007) chapter 5). Here we are interested in how ants actually compare memory and current perceptions to output an appropriate navigational response. The work done in this thesis provides evidence that ants do not always follow the same strategy, but use at least two different ways of comparing memory and current perception: one when on a familiar route, and another one when off the route, on novel terrain. When on a familiar route, ants may use what can be called a visual compass strategy. The idea is that ants align their body to the direction where the perceived view provides the best retinotopic match to their memory (Graham et al., 2010) (chapter 2). Even though memory and perceived view do not match perfectly, this process results robustly in a direction parallel to the one faced during learning (Zeil et al., 2003), even with an holistic memory (Baddeley et al., 2011), and thus enables the ant to walk along its route. Evidence shows that the extent of the rotation needed for aligning their body correctly is even calculated before the actual turn (Lent et al., 2010) revealing how ants excel in aligning views. By rotating pictures in the same way, we could predict the direction taken by the ants both under natural conditions (chapter 5) and in an artificial arena (chapter 2).

However, this visual compass strategy becomes ineffective when the ant is at novel locations because the perceived view is too different from the memory. Gusts of wind often blow ants several meters away from their familiar route (personal observations). In those cases the insect shows no problem in returning to its route across novel terrain. Our visual

200 Conclusion compass model failed to explain the success of ants released several meters away from their familiar route (chapter 5) suggesting the use of another strategy when on novel locations. To explain the ability of ants to home successfully from novel locations, we here proposed a new hypothesis based on skyline height comparisons (chapter 5). In the model, the agent compares the memorised skyline heights to those perceived at the novel location, and heads towards the regions where the skyline appears too low, as they might indicate that the ant is too far away from those regions. This simple model could explain the ants’ headings when released several meters away from their familiar route. Interestingly, this strategy is efficient only when perceived view and memory present substantial differences. When the familiar route is reached, the perceived view becomes too similar to the memory and comparing skyline of similar heights becomes ineffective, giving a functional reason to switch to a visual compass strategy (chapter 5). How can ants choose the appropriate strategy? Ants might well be able to assess the degree of familiarity of their current location and select between strategies accordingly. Their ability to align their body to obtain the best match between current visual input and memory proves that they can somehow assess the congruence between perceived and stored visual patterns. In terms of neural networks, it is easy to imagine how integration across perceived and stored patterns can result in a quantitative measure of congruence — even with an holistic memory (Baddeley et al., in preparation) — and how summation of such integrative neurons can lead to the qualitative selection of a given strategy. Some results of the present work support such a switch in strategy according to the degree of familiarity. If the familiarity is gradually altered along the route, ants’ responses are binary: they run along their route readily at the beginning but then suddenly stop following a compass strategy and switch to meanderings (chapter 4). Similarly, ants released to moderately mismatching terrain also behaved binarily, with some ants resuming their route strategy and others behaving as if on unfamiliar terrain (chapter 6). This idea of switching between strategies is also supported by previous works which suggest that each ant possesses a personal Mismatch Tolerance Threshold (MTT) (Wystrach, 2009), which could thus, when reached, inhibit the familiar- terrain strategy — likely a visual compass — and trigger an unfamiliar-terrain strategy such as skyline height comparison. The use of a visual compass strategy on a familiar route is supported by multiple lines of evidence (Collett, 2010; Lent et al., 2010) (chapters 2 and 5). Many questions remain, however, about how ants behave on unfamiliar terrain. The skyline height comparison hypothesis proposed here now needs to be tested experimentally in ants. Besides, ants could

201 Conclusion also use, in addition to skyline height comparison, the gradient of familiarity in a dynamic way by favouring directions which provide increasing familiarity as a strategy for returning to familiar terrain (Zeil et al., 2003). Also, ants are famous for displaying a well-structured systematic search on totally unfamiliar terrain (Wehner and Srinivasan, 1981; Merkle et al., 2006; Schultheiss and Cheng, 2011). Whether this systematic search corresponds to a third strategy triggered only when the current view mismatches the memory exceedingly, or corresponds to an always-triggered parallel motor strategy, weighted according to the unfamiliarity of the current view, is unknown. Finally, it would be valuable to investigate whether ants switch to a specific pinpointing strategy when approaching the nest entrance. A mismatch gradient descent strategy can explain successfully nest pinpointing with a single view memorised at the nest (Zeil et al., 2003), but so does a visual-compass strategy with knowledge acquired from different vantage points while facing the nest (Graham et al., 2010); Baddeley et al., in preparation), which is likely to be experienced during learning walks (Müller and Wehner, 2010).

Insights into comparative cognition

Overall, this thesis explains how a simple taxon-like system, using panoramic views together with low-level visual processing, without extracting landmarks nor building a cognitive map, can allow insects to produce efficient, robust and remarkably flexible navigation in complex environments. At first sight, it may seem that such simple ‘insect-like strategies’ are strikingly different from human navigation. Our subjective idea is that we humans recognise places because of identified objects, such as a train station or a favourite bookshop, and navigate from place to place because of a map-like representation. But this might reflect ‘higher processes’ which are accessible to our consciousness and are made salient by cultural constructs such as our language and physical maps (Landau and Lakusta, 2009), rather than the true basal mechanisms supporting human navigation (Wang and Spelke, 2002; Platt and Spelke, 2009). Actually, our high-resolution fovea that can focus on objects and the visual ventral- stream allowing object recognition in primates appears to have evolved for reasons other than spatial navigation (chapter 7). Human patients with visual agnosia following brain damage in the ventral stream are often unable to recognise objects, even though they can navigate through the world with considerable skill (Farah, 1990). In contrast, spatial navigation can be

202 Conclusion strongly impaired when lesions occurred in the parahippocampal place area (PPA), a region involved in scene recognition only. Based on wide-field retinal input, the entire scene is treated as a unified object, independently from its component elements (Epstein, 2008). Thus, human navigation appears to be dependent on global encoding of panoramic scenes rather than the recognition of individual objects, surprisingly converging to what we argue for insects. As the problem of being able to navigate in the world is shared between vertebrates and insects, we can suspect some evolutionary convergence in their navigational mechanisms (Shettleworth, 2010b). The actual differences between insect and vertebrate navigation may result in part from different approaches on the part of researchers rather than real differences in the cognitive systems of the animals. Both vertebrates and insects process visual information from views and, interestingly, the neural circuits that underlie their vision show striking similarities (Sanes and Zipursky, 2010). Some results (Pecchia and Vallortigara, 2010; Pecchia et al., 2011) and models (Sheynikhovich et al., 2009) are starting to reveal how ‘higher level’ processing which occurs in vertebrates might depend on egocentric encoding of panoramic views. Due to strong wariness of anthropomorphism stemming from the ethological tradition, or perhaps because of implicit assumptions about the limits of small brains, research on insect navigation has been approached with a bottom-up philosophy, one that examines how simple mechanisms can produce seemingly complex behaviour. The insect navigation literature is full of insights on how simple strategies can explain most behavioural tasks attributed to higher processes in the vertebrate literature. Such parsimonious solutions, like the ones explored in the present thesis, can thus provide useful baseline hypotheses for navigation in other larger-brained animals, and in this way contribute to a truly comparative cognition.

203 Conclusion

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220 Appendix I

APPENDIX I

Appendix I contain the formatted publications which have been published as part of the thesis:

Chapter 1: Wystrach, A. and Beugnon, G. (2009). Ants Learn Geometry and Features. Curr. Biol. 19, 61-66.

Chapter 2: Wystrach, A., Sosa, S., Cheng, K. and Beugnon, G. (2011). Geometry, features and panoramic views: ants in rectangular arenas. J. Exp. Psychol. Anim. Behav. Process.Advance online publication doi: 10.1037/a0023886.)

Chapter 3: Wystrach, A., Schwarz, S., Schultheiss, P., Beugnon, G. and Cheng, K. (2011). Views, landmarks, and routes: how do desert ants negotiate an obstacle course? J. Comp. Physiol. A -Neuroethol. Sens. Neural Behav. Physiol. 197, 167-179.

Chapter 4: Wystrach, A., Beugnon, G. and Cheng, K. (2011). Landmarks or panoramas: what do navigating ants attend to for guidance? Front. in Zool. 8, 21.

Chapter 5 is in press.

221 Appendix I

222 ` Please cite this article in press as: Wystrach and Beugnon, Ants Learn Geometry and Features, Current Biology (2009), doi:10.1016/ j.cub.2008.11.054 Current Biology 19, 1–6, January 13, 2009 ª2009 Elsevier Ltd All rights reserved DOI 10.1016/j.cub.2008.11.054 Report Ants Learn Geometry and Features

Antoine Wystrach1,* and Guy Beugnon1 natural solitary foraging excursions, workers of Gigantiops 1Universite´ de Toulouse do not use chemical trails and can cover distances up to CNRS – UPS (UMR 5161) 20 m through the extremely cluttered environment of the rain Centre de Recherches sur la Cognition Animale forest [5]. In our laboratory experiments, the ants performed F-31062 Toulouse cedex 9 their outbound trip in a channel toward a foraging site where France a living Drosophila was provided. When their prey had been caught and killed, loaded ants were disorientated in the center of a rectangular arena and had to reach one of the four exit Summary holes drilled in the corners to return to their remote nest. In this first set of experiments, whichever exit the ant chose led Rats trained to relocate a particular corner in a rectangular back to the nest: this procedure is called nondifferential condi- arena systematically confound the correct corner and the tioning. Each ant performed 35 successive trips: the first five diametrically opposite one—this rotational error demon- trips were not recorded and were considered as ‘‘training,’’ strates the use of the geometry of space (i.e., the spatial whereas two measures were collected for each of the 30 arrangement of the different components of a visual scene). following trips: (1) their initial approach to a corner and (2) In many cases, geometric information is preferentially used the exit chosen (Figure 1B). over other spatial cues, suggesting the presence of a dedi- In the first experiment, the rectangular arena was simply put cated geometric module located in the parahippocampus on a table in the experimental room, allowing the ants to see [1] and processing only geometric information. Since rota- distal cues beyond the arena. Ants managed to systematically tional errors were first demonstrated in 1986 [2], the use of return to one preferred corner (preferred / three others: p % the geometry of space has attracted great interest and now 3.85075E-12), peculiar to each individual, among the four seems to be widespread in vertebrate species, including hu- available, and displayed no significant rotational errors mans [3]. Until now, rotational errors have only been consid- (rotational errors / errors: p R 0.5) (Figure 2A). Because no ered in vertebrate species. Here, for the first time, rotational internal cue from the arena allowed them to distinguish errors are demonstrated in an insect. Our results, similar to between a corner and the diametrically opposite one, these those obtained with vertebrates, can be parsimoniously ex- systematic choices could only be explained by the use of plained by a view-based matching strategy well known in extra-arena visual cues or some compass cue such as insects, thereby challenging the hypothesis of a ‘‘geometric a magnetic compass. In any case, these results indicate module’’ located in the animal’s brain. While introducing that Gigantiops ants spontaneously tended to always return a new concept of flexibility in the view-based matching to an individual preferred corner, although all four exits led theory, this study creates a link between two major topics them back to the nest. Indeed, some visual ant species are of animal navigation: rotational errors in vertebrates and solitary central-place foragers, and although they do not use view-based navigation in insects. chemical trails, they have been shown to repeatedly follow the same two-way individual routes when navigating back Results and Discussion and forth between their nest and a foraging place [6–9]. This first experiment did not reveal whether the ants use geometry As with Vertebrates, Ants Can Rely on the Geometry or not. of Space In a second experiment, all four exits still led to the nest (non- The first demonstration that animals can use geometric infor- differential conditioning), but the arena was covered by an opa- mation provided by the shape of their environment to find que plastic dome throughout experimentation, preventing ants a hidden goal came from a set of reorientation tasks in rats from seeing any extra-arena cues in the experimental room. [2] placed in a rectangular arena, a paradigm still much used The dome was lit up by white circline fluorescent lighting to today. A key property of such a rectangular environment is create diffuse isotropic illumination in the whole arena. In these that each corner stands in the same geometric relation with visually controlled conditions, ants reached their preferred exit the entire arena as the corner diametrically opposite to it as often as the diametrically opposite one (rotational errors / (Figure 1). Disorientated in the center of the rectangular arena, errors: p % 0.0023; rotational errors / preferred: p R 0.5716) some vertebrates trained to relocate a particular corner (Figure 2B). Two conclusions can be drawn from these results. systematically display rotational errors (i.e., they confound First, the extra-arena cues used by ants in the first experiment the correct corner with the diametrically opposite one), were visual and not some internal inertial or geocentric showing that they rely on the geometric information of their cue such as a magnetic compass. Second, Gigantiops ants surroundings for reorientation. displayed systematic rotational errors in much the same way We adapted Cheng’s paradigm for testing rats [2] to the as many vertebrates in rectangular experimental spaces. neotropical ant, Gigantiops destructor. This species, which has the largest eyes of any ant species [4], is known for its The Use of Features and Geometry Could Be Explained remarkable view-based navigational capacities. On their by a View-Based Matching Strategy To find out whether nongeometric or featural cues can be used, we then added featural information to the geometric *Correspondence: [email protected] shape of the space by providing from the first trip of each Please cite this article in press as: Wystrach and Beugnon, Ants Learn Geometry and Features, Current Biology (2009), doi:10.1016/ j.cub.2008.11.054 Current Biology Vol 19 No 1 2

ABFigure 1. A Schematic Representation of the Geometric Information Available in a Rectan- gular-Shaped Environment (A) The correct exit hole (black dot) shared exactly the same geometric relation as the diagonally opposite one (white dot), which corresponds to the rotational error. The geometric information (i.e., angular and metric relation between the corners and sense of left-right relations) is sufficient to distinguish between corners 1–3 and corners 2–4, but not to distinguish between 1 and 3 (or between 2 and 4). (B) Two dependent measures were taken. The initial approach to a corner was defined as the first fictive arc (19 cm in radius) around a corner that the ant crossed. The exit choice was defined as the first hole at a corner that the ant entered into. In this hypothetical example, the virtual ant first headed toward corner 4 but chose exit 1 in the end. The cross indicates the entry hole of the arena. ant a distinct black shape in each corner of the arena. Ants and experiment. But, surprisingly, ants still displayed systematic bees are well known for recognizing patterns or landmarks, rotational errors (rotational errors / errors: p % 0.0145; especially when they are close to the nest entrance, as in our preferred / rotational errors: p R 0.0987) (Figure 2C) in the experiments [10–12]. The shapes used in the present work presence of the conspicuous shapes placed directly above have been chosen in order to be easily distinguishable by the the exit holes, revealing that they preferentially used the global foragers as seen from the center of the arena (45 cm away). shape of the arena to get back home. Although a nondifferen- These shapes have an approximate angular size of 12 degrees tial conditioning procedure was used in the present experi- at a distance of 45 cm, and foragers of Gigantiops have been ment (i.e., all corners were rewarding because they all led shown to perceive and move toward small circular black ants back to the nest), the fact that ants displayed rotational targets having an angular size of 1 degree at a distance of 50 errors but avoided the two other errors implies that they cm (see Materials and Methods in [6]). These ants can also were relocating a particular place. So why have they used distinguish and memorize vertical black targets of 2 degrees only geometric information and not the conspicuous targeting angular width on a white background [13]. Thus, the presence features? Would a differential conditioning procedure favor the of these four distinctive black shapes should have allowed learning of featural information? ants to distinguish between the arena’s two geometrically We then tested other ants in the same visual conditions with equivalent locations that the extra-arena cues allowed them a differential conditioning procedure [14, 15]. In that case, to distinguish between during the course of the first three of the four exits led to a closed tube and only one exit

Figure 2. Mean Percentages of Choices for Each Corner for Each Experiment, 6 Standard Devia- A B tion Data of the different individuals are pooled with their preferred corner in the top-left corner for each experiment. External numbers correspond to the ‘‘exits choices’’ results and internal numbers to the ‘‘initial approach’’ results. The top-left corner corresponds to the preferred corner, the bottom-right corresponds to the rotational error, and the two others indicate other errors. The big circles symbolize the presence of an opaque plastic dome over the arena preventing the use of extra-arena cues. (A) Rectangular arena simply put onto a table, each corner leading to the nest (n = 150: 5 ants, 30 trials each). (B) Arena covered by an opaque plastic dome, each corner leading ant to the nest (n = 210: 7 ants, 30 trials each). C D (C) Arena covered by an opaque plastic dome with distinct black shapes added in the corners of the arena. Each corner led to the nest (n = 270: 9 ants, 30 trials each). (D) Same visual conditions as in (C) but with a differential conditioning procedure: only the cross-shaped corner led to the nest (n = 210: 7 ants, 30 trials each). Please cite this article in press as: Wystrach and Beugnon, Ants Learn Geometry and Features, Current Biology (2009), doi:10.1016/ j.cub.2008.11.054 Ants Learn Geometry and Features 3

Figure 3. Example of Ants’ Trajectories from the Different Experiments A B The top-left corner corresponds to the preferred corner, the bottom-right corresponds to the rotational error, and the two other corners indicate other errors. The big circles symbolize the presence of an opaque plastic dome over the arena preventing the use of extra-arena cues; the black dots indicate the entry hole at the center of the arena. (A) Rectangular arena simply put onto a table without any opaque plastic dome, with each corner leading to the nest. (B) Arena covered by an opaque plastic dome, with each corner leading ant to the nest. (C) Arena covered by an opaque plastic dome with distinct black shapes added in the corners of the arena. Each corner led to the nest. (D) Same visual conditions as in (C) but with a differential conditioning procedure: only the CDcross-shaped corner led to the nest. Two paths illustrate runs aimed directly toward the correct corner, and two others illustrate U-turns executed just in front of the shape located at the rotational error corner.

Our results can be more parsimoniously explained. Since Cartwright and Collett’s seminal work [22, 23], researchers working on insect navigation have agreed that insects in general and ants in particular preferentially use a view-based matching strategy to recognize landmarks and to relocate led back to the nest. Thus, only the use of featural information a target [24, 25]. In order to return to a crucial place, like the could allow ants to find the correct route home and avoid dead nest, insects rely on a memorized view previously taken at ends. Here, in contrast to the results obtained from nondiffer- that goal location. They achieve their displacement by ential conditioning, the ants displayed no significant rotational comparing their current view with the memorized target errors and largely succeeded in choosing the correct exit hole view, moving from higher levels of mismatch to lower levels (rotational errors / errors: p R 0.125; preferred / three others: of mismatch. When the two views are perfectly matching, p % 6.00717E-11) (Figure 2D). These results confirm that ants the goal is reached. Interestingly, recent simulations and can recognize and use the black shapes in the corners as tests in virtual reality show that following such a global navigational cues. Interestingly, the ants displayed systematic image-matching gradient-descent algorithm (with the refer- rotational errors in their initial approach to a corner (rotational ence image taken at the target corner) could produce rota- errors / errors: p % 0.0123) (Figure 2D). Although results show tional errors [26]. From the center of the rectangular arena, a slight preference for the correct corner in that initial approach, the visual weight of its global shape, covering most of the individual statistics show no significant differences between the visual field, is much more important than the visual weight correct corner and the rotational error (rotational errors / of the features located in the corners, creating prominent preferred: p > 0.2295). Thus, in these particular conditions, the local minima in mismatch at each of the two diagonally oppo- ants seem to rely only on geometry during their initial approach site corners. In other words, there is no need to extract the to a corner. However, once having arrived about 10 cm from the geometry of the environment via a geometric module to wrong visual pattern, they systematically executed a U-turn and exploit the shape of the rectangular arenas. The image- went back toward the opposite correct corner (Figure 3D). matching model can explain rotational errors even in the pres- During the nondifferential conditioning experiment (Fig- ence of distinct shape in the corner (Figure 2 in [26]). But in ure 2C), ants relied on the shape of the arena but did not use that case, because of the presence of these features, the the visual features. Analogous surprising results have also match is slightly better at the correct corner. This may explain been found in vertebrate species [3]. To explain both the use the slight bias observed in the ants toward a correct corner of geometry and the absence of use of featural information, (Figures 2C and 2D). The results obtained with these compu- several interesting studies suggest the presence of a tational models, similar to the data with ants, strongly rein- geometric module in the animal’s brain [1, 16–19]. However, force the hypothesis that ants’ rotational errors result from this process, supposed to extract and work solely on the the use of a global view-based matching strategy rather geometry [2], is very complex from a neurophysiological point than from the use of a geometric module. Indeed, the brain of view, and it is presumed to be located in the vertebrate’s of insects has no hippocampus, and insects are well known hippocampus [20, 21]. Should we invoke such a complex for using a view-matching strategy, which is parsimonious system in an insect minibrain? from a neural point of view [27]. Please cite this article in press as: Wystrach and Beugnon, Ants Learn Geometry and Features, Current Biology (2009), doi:10.1016/ j.cub.2008.11.054 Current Biology Vol 19 No 1 4

Our results are quite similar to those obtained in vertebrates. They provide the ﬁrst experimental biological support for the idea [28] that the use of what has been called ‘‘the geometry of space’’ in the literature arises from a view-based matching strategy without the extraction of any geometric properties. But how can we explain from a view-based-matching point of view the ants’ use of the information provided by the black shapes in the corner during the differential conditioning procedure (Figure 2D)?

A Second View-Based Matching Process Is Involved Contrary to the nondifferential conditioning procedure (Fig- ure 2C), ants used the featural information in the differential conditioning procedure, although they determined their initial approach to a corner by relying on the global shape of the arena (Figure 2D). For those that performed the rotational error and thus arrived in front of the wrong black feature, they clearly used featural information to reject that corner without entering into the blocked exit hole. They made a U-turn at this location and headed back directly to the opposite, correct corner (Figure 3D). Provided with a simple global matching gradient-descent algorithm, an agent approaching the rotational error corner would get stuck. First, it would be repelled from that corner because of the increased mismatch caused by the wrong feature; second, it will be pushed back toward it because of the rectangular shape of the arena. The rotational-error corner is indeed located at a local minimum in mismatch [26]. However, in our experiment, the ants do not get stuck in the vicinity of such a corner but make a U-turn and head to the Figure 4. Comparison between the Normal and the Test Condition of the correct corner. Therefore, there must be a ‘‘change of state.’’ Differential Conditioning Procedure Experiment In order for an ant to leave that local minimum and avoid being (A) Schematic representation of the black shapes positions during the th stuck, its standard gradient-descent view-based matching normal condition (first 35 trials) and the test condition (36 trial). During the test condition, the black shapes located in the geometrically correct process should be temporarily inhibited, and be replaced corners (gray dots) are different from those experienced during the normal with a visuo-motor routine executing a U-turn. The trigger for condition at the correct corner (black dot) and at the rotational error corner the visuo-motor routine could be the view at the rotational- (white dot). error corner, which has been associated with a negative (B) Number of ants out of seven displaying a U-turn and mean time spent outcome (an obstructed pipe). Alternatively, it could be based (the error bar indicates standard deviation) in the arena before entering on the level of mismatch at the rotational-error corner. If the a hole when approaching (1) the correct corner during the normal condition; (2) the rotational-error corner during the normal condition; and (3) the mismatch value is still too high (because of the presence of geometrically correct corner during the test condition. For each ant, only a wrong black shape), the gradient-descent matching process the last trial of the normal condition for both the approach to the correct is inhibited and the U-turn triggered. corner and the approach to the rotational-error corner are considered here. These alternative hypotheses were tested in the next experiment. After having performed their 35 paths (five unrecorded and 30 recorded) in the differential conditioning procedure, each of these ants (n = 7) was tested once with a new condi- hypothesis of a supplementary view memorized at the tion: the four black shapes in the corners were all transposed rotational-error corner (stored negative pattern) can therefore clockwise (Figure 4A). In conformity with the gradient-descent be rejected. Consequently, it seems that when they arrived at matching process, all the ants first used the global shape of a local minimum, the ants assessed the quality of the match the arena and thus approached one of the two geometrically between the current view and the memorized target view correct corners during their test (Figure 4A). But once they and executed a U-turn because the mismatch was too high. arrived at the local minimum, instead of the black shapes Further supporting this hypothesis is the result that the ants they usually experienced, the ants faced one of the shapes spent more time in the arena before attempting to enter previously located in the geometrically incorrect corners, the a hole (Figure 4B). two shapes that had been encountered the least. In that Thus, the apparent distinction between the ‘‘geometry of test, six of the seven ants made a U-turn, that is, behaved in space’’ on the one hand and the ‘‘features’’ on the other the same way as when they were facing the familiar wrong hand could simply result from two successive processes of black shape located in the rotational-error corner during the view-based matching. Ants do not first use geometry and, in normal condition (Figure 4B). If the U-turn was triggered by a second stage, extract and consider the features in a separate a memorized view taken at the rotational-error corner (stored way. Instead, they start by following a global matching negative pattern), the ants should have attempted to go gradient-descent algorithm, and second, they consider the through the hole because the trigger for the U-turn (the familiar quality of the match at a local minimum to decide whether to feature at the rotational-error corner) was missing. Results do go through or inhibit the gradient-descent matching process not support this claim (% U-turn correct / other p = 0.014). The and execute a U-turn. Please cite this article in press as: Wystrach and Beugnon, Ants Learn Geometry and Features, Current Biology (2009), doi:10.1016/ j.cub.2008.11.054 Ants Learn Geometry and Features 5

Flexibility in the View-Based Matching Processes Involved wide, with 20-cm-high white walls, corresponding to the size proportions Interestingly, different training conditions generate different of the experimental arenas used in vertebrate studies. To leave the opaque patterns of results. U-turns were triggered mostly in the differ- tube, disorientated ants had to go through a hole drilled in the table leading them to the center of the arena. One exit hole was pierced in each corner ential conditioning procedure (Figure 2D) and rarely during the and connected via a 10-cm-long plastic tube to a plastic box placed nondifferential conditioning procedure (Figure 2C). Ants spon- outside the arena. The four exits potentially allowed ants to return to their taneously behave as though they are following a global match- nest except in experiment 4, where three of the four plastic tubes were ob- ing gradient-descent algorithm, but the presence of a negative structed at the end. Once the ant arrived inside an exit box, it was imme- outcome, like an obstructed pipe, seems to trigger an addi- diately replaced in its nest. After a few minutes spent inside the nest, the tional process judging the degree of mismatch at the local ant was generally ready to start again. The first five trips were considered as training, and the 30 following trips were recorded. In experiments 2, 3, minima. These results imply that ants are able to adapt view- and 4, the arena was covered by an opaque plastic dome (from the first trip based matching processes according to need. This flexibility of each ant onward) in order to avoid the use of external or lighting cues. may help ants to reduce the cognitive load of spatial informa- The dome was lit up diffusely by a white circline fluorescent lighting (32W). tion processing during navigation. Overall, these results raise the hypothesis that insects are Data Collection and Statistics The trips of the ants inside the arena were recorded with a Sony Black & guided more by global views than by individual landmarks. White CCD video camera suspended from the ceiling via a pole and Within a cluttered environment like the rainforest, relying on adjusted to fit the circular hole (5 cm diameter) at the top of the dome. the global view instead of focusing on particular landmarks The video image of the arena was recorded with a S-VHS Panasonic tape leads to continuous use of the relationships between large, recorder (frame rate 25 frames/s). We measured on the recorded paths conspicuous, and extended objects. Contrary to a landmark- the two following data for each homing trip: (1) the initial approach to extraction strategy, such a global strategy avoids the risk of a corner and (2) the first exit chosen among the four available. Once the ant went through an exit hole, the exit choice was considered to have confusion between similar landmarks and is thus more robust been performed. The initial approach to a corner was recorded when the against the natural changes of the scenery. However, we have ants crossed for the first time one of the four fictive arcs of circle centered shown that ants can also resort to proximal information when at each corner (see Figure 1B). For each ant, binomial statistical tests were necessary. This is because once the ants arrive in the neigh- used both for the exit data and for the initial-approach data. (1) To deter- borhood of the target, the contribution of the ‘‘features’’ to mine the use of geometry information, we compared the ratio of the the global view increases. In natural conditions, furthermore, number of rotational errors (i.e., choices of the corner diagonally opposite to the preferred one) to the number of the two other errors (i.e., choice of insects use and memorize proximal landmarks precisely the corners located along the wrong diagonal). (2) To determine the use of when approaching the nest entrance [10–12]. featural information, we did the following: (a) for ants with significant rotational-error results, we compared the number of choices for the preferred Conclusions corner to the number of rotational errors; and (b) for ants with no signifi- Our study shows for the first time that an insect makes similar cant rotational errors results, we compared the number of choices for the preferred corner to the number for the three other corners. To ensure errors as vertebrates when relocating a goal in the corner of that the sequence of choices (between the correct corner and the rota- a rectangular arena. In addition, our results strongly support tional-error corner) is randomly distributed along the trials, we performed the recent modeling and empirical studies [26, 28, 29] that a ‘‘runtest’’ of the program SATA for each ant displaying systematic rota- cast doubt on a ‘‘geometrical module’’ and thus stress the tional errors during experiments 2 and 3 (two-tailed p > 0.12). view-based-matching hypothesis that is widely accepted in the insect literature. Supplemental Data Studies on the use of landmarks in a variety of animals have Supplemental Data include one table and can be found with this article found differences between species. Some results in pigeons online at http://www.current-biology.com/supplemental/S0960-9822(08) or humans seem difficult to explain with a pixel-by-pixel 01571-6. image-matching strategy [18, 30]. In invertebrates, our study has shown flexibility in the use of view-based matching: Acknowledgments U-turns are triggered when a poor match leads to adverse consequences. This behavior has not been described yet in We thank Emmanuel Lecoutey and Ken Cheng for their help and comments on the manuscript and Gerard Latil for his help with construction of the vertebrate animals moving in rectangular arenas, although experimental apparatus. most of these studies have not looked closely at the paths taken by animals. It would be instructive to compare such Received: September 8, 2008 paths across species; such analyses would help constrain Revised: November 21, 2008 models for explaining behavior. Accepted: November 21, 2008 Published online: December 31, 2008 Experimental Procedures References Species Colonies of Gigantiops destructor were captured in the rain forest of 1. Epstein, R., and Kanwisher, N. (1998). 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Once the single Drosophila provided 229–240. by the experimenter was captured and killed, the homing ant transporting 5. Beugnon, G., Chagne, P., and Dejean, A. (2001). Colony structure and its prey was gently carried via an opaque plastic tube directly underneath foraging behavior in the tropical formicine ant, Gigantiops destructor. the experimental arena. The rectangular arena was 80 cm long and 40 cm Insectes Soc. 48, 347–351. Please cite this article in press as: Wystrach and Beugnon, Ants Learn Geometry and Features, Current Biology (2009), doi:10.1016/ j.cub.2008.11.054 Current Biology Vol 19 No 1 6

6. Macquart, D., Garnier, L., Combe, M., and Beugnon, G. (2006). Ant navigation en route to the goal: Signature routes facilitate way-finding of Gigantiops destructor. J. Comp. Physiol. [A] 192, 221–234. 7. Collett, T.S., and Collett, M. (2002). Memory use in insect visual navigation. Nat. Rev. Neurosci. 3, 542–552. 8. Collett, T.S., Dillmann, E., Giger, A., and Wehner, R. (1992). Visual landmarks and route following in desert ants. J. Comp. Physiol. [A] 170, 435–442. 9. Wehner, R., Boyer, M., Loertscher, F., Sommer, S., and Menzi, U. (2006). Ant navigation: One-way routes rather than maps. Curr. Biol. 16, 75–79. 10. Cheng, K., Collett, T.S., Pickhard, A., and Wehner, R. (1987). The use of visual landmarks by honeybees - Bees weight landmarks according to their distance from the goal. J. Comp. Physiol. [A] 161, 469–475. 11. Collett, T.S. (1996). Insect navigation en route to the goal: Multiple strategies for the use of landmarks. J. Exp. Biol. 199, 227–235. 12. Bisch-Knaden, S., and Wehner, R. (2003). Landmark memories are more robust when acquired at the nest site than en route: Experiments in desert ants. Naturwissenschaften 90, 127–130. 13. Macquart, D. (2008). Cognition spatiale chez la fourmi Gigantiops destructor (Hymenoptera, Formicidae): E´ tude expe´ rimentale du suivi de route. PhD thesis, Universite´ Toulouse III, Touloue, France, 127 pp. 14. Giurfa, M., Hammer, M., Stach, S., Stollhoff, S., Muller-Deisig, N., and Mizyrycki, C. (1999). Pattern learning by honeybees: Conditioning procedure and recognition strategy. Anim. Behav. 57, 315–324. 15. Dyer, A.G., and Chittka, L. (2004). Fine colour discrimination requires differential conditioning in bumblebees. Naturwissenschaften 91, 224–227. 16. Cheng, K. (2005). Reflections on geometry and navigation. Connect. Sci. 17, 5–21. 17. Jones, P.M., Pearce, J.M., Davies, V.J., Good, M.A., and McGregor, A. (2007). Impaired processing of local geometric features during navigation in a water maze following hippocampal lesions in rats. Behav. Neurosci. 121, 1258–1271. 18. Doeller, C.F., and Burgess, N. (2008). Distinct error-correcting and incidental learning of location relative to landmarks and boundaries. Proc. Natl. Acad. Sci. USA 105, 5909–5914. 19. Doeller, C.F., King, J.A., and Burgess, N. (2008). Parallel striatal and hippocampal systems for landmarks and boundaries in spatial memory. Proc. Natl. Acad. Sci. USA 105, 5915–5920. 20. Burgess, N., Jackson, A., Hartley, T., and O’Keefe, J. (2000). Predictions derived from modelling the hippocampal role in navigation. Biol. Cybern. 83, 301–312. 21. Hartley, T., Burgess, N., Lever, C., Cacucci, F., and O’Keefe, J. (2000). Modeling place fields in terms of the cortical inputs to the hippocampus. Hippocampus 10, 369–379. 22. Cartwright, B.A., and Collett, T.S. (1982). How honey bees use landmarks to guide their return to a food source. Nature 295, 560–564. 23. Cartwright, B.A., and Collett, T.S. (1983). Landmark learning in bees - Experiments and models. J. Comp. Physiol. 151, 521–543. 24. Judd, S.P.D., and Collett, T.S. (1998). Multiple stored views and landmark guidance in ants. Nature 392, 710–714. 25. Harris, R.A., Graham, P., and Collett, T.S. (2007). Visual cues for the retrieval of landmark memories by navigating wood ants. Curr. Biol. 17, 93–102. 26. Sturzl, W., Cheung, A., Cheng, K., and Zeil, J. (2008). The information content of panoramic images I: The rotational errors and the similarity of views in rectangular experimental arenas. J. Exp. Psychol. Anim. Behav. Process. 34, 1–14. 27. Moller, R., Maris, M., and Lambrinos, D. (1999). A neural model of landmark navigation in insects. Neurocomputing 26–7, 801–808. 28. Cheung, A., Stuerzl, W., Zeil, J., and Cheng, K. (2008). The information content of panoramic images II: View-based navigation in nonrectangular experimental arenas. J. Exp. Psychol. Anim. Behav. Process. 34, 15–30. 29. Cheng, K. (2008). Whither geometry? Troubles of the geometric module. Trends Cogn. Sci. 12, 355–361. 30. Cheng, K. (1988). Some psychophysics of the pigeons use of landmarks. J. Comp. Physiol. [A] 162, 815–826.

Geometry, Features, and Panoramic Views: Ants in Rectangular Arenas

Antoine Wystrach Ken Cheng Universite«de Toulouse UPS, and Macquarie University Macquarie University

Sebastian Sosa Guy Beugnon Universite«de Toulouse UPS Universite«de Toulouse UPS, and Centre National de la Recherche Scientifique, Toulouse, France

Keywords: ant navigation, Gigantiops destructor, view-based matching, visual compass, geometry

Supplemental materials: http://dx.doi.org/10.1037/a0023886.supp

Rats trained to find food in a particular corner of a rectangular 25 years of research to explain the use of geometry (summarized arena systematically confound the rewarded corner with its diag- by Cheng & Newcombe, 2005). The theories range from the use of onally opposite one. These “rotational errors,” or more precisely various global geometric axes (Gallistel, 1990; Cheng, 2005), to the fact that rats can avoid the two corners of the other diagonal, local geometry (Pearce, Good, Jones, & McGregor, 2004), to demonstrate their ability to rely on the rectangular shape of the boundary-based analysis (Doeller & Burgess, 2008). Those theo- arena, also called the geometry of their surroundings (Cheng, ries, along with some models (Lee, Shusterman, & Spelke, 2006; 1986). Surprisingly, rotational errors persist even when distinct Lee & Spelke, 2010a; Newcombe & Huttenlocher, 2006; Miller & features disambiguate the different corners of the arena, suggesting Shettleworth, 2007; Dawson, Kelly, Spetch, & Dupuis, 2009) the presence of a dedicated “geometric module” in the brain assume that features and geometry of space are somehow segre- (Cheng, 1986). This discovery stimulated a great deal of research gated. Although this distinction is well supported by neurophysi- in many vertebrate species, and several theories arose from those ological and behavioral data in vertebrates (Lee & Spelke, 2010b), how such segregation applies in natural conditions outside of laboratory arenas is debated (Sutton, 2009). In contrast, other authors have proposed view-based models that operate without assumptions about representation of geometry but rely purely on Antoine Wystrach, Universite«de Toulouse UPS, Centre de Recherches sur la Cognition Animale, Toulouse, France and Department of Biological the input provided by the visual scene (Cheung, Stu¬rzl, Zeil, & Sciences, Macquarie University, Sydney, New South Wales, Australia; Cheng, 2008; Sheynikhovich, Chavarriaga, Strösslin, Arleo, & Sebastian Sosa, Centre de Recherches sur la Cognition Animale, Universite« Gerstner, 2009; Ponticorvo & Miglino, 2010). Parsimonious view- de Toulouse UPS; Ken Cheng, Department of Biological Sciences, Mac- based mechanisms can explain the results obtained in rectangular quarie University; Guy Beugnon, Centre de Recherches sur la Cognition arenas because the geometry of space is implicitly contained in Animale, Universite«de Toulouse UPS, and Centre National de la Recher- panoramic views and does not require any explicit computation che Scientifique, Toulouse, France. (Stu¬rzl, Cheung, Cheng, & Zeil, 2008). In the model of Sheynik- We thank Julien Robert and Ge«rard Latil for their precious help in hovich et al. (2009), edge-based views drive both a direct view designing and constructing the experimental apparatus. We are also grate- based matching system (the taxon system of O’Keefe & Nadel, ful to Maud Combe for her time and programming skills that enabled the interpath comparisons. 1978) and an allocentric system based on well-known types of Correspondence concerning this article should be addressed to Antoine neurons identified from single-cell recording: grid cells (Hafting, Wystrach, Universite«de Toulouse Paul-Sabatier, Centre de Recherches sur Fyhn, Molden, Moser, & Moser, 2005), head direction cells la Cognition Animale, Toulouse, France. E-mail: [email protected] (Taube, Muller, & Ranck, 1990), and place cells (Muller & Kubie,

1 2 WYSTRACH, SOSA, CHENG, AND BEUGNON

1987; O’Keefe & Dostrovsky, 1971), often called the locale strat- much debated among experimental psychologists (Cheng & New- egy (O’Keefe & Nadel, 1978). combe, 2005; Cheng, 2008; Twyman & Newcombe, 2010). While geometry and features (G/F) models flow from the results The present study follows the basic design of these kinds of obtained with vertebrates in artificial arenas, view-based models experiments: Gigantiops destructor ants were trained in rectangu- arise from seminal works conducted on insects where explanations lar arenas containing a conspicuous feature on one of the long of spatial behavior have eschewed assumptions about higher-level walls and then tested with the feature switched to a short wall. In cognitive processes (Cartwright & Collett, 1983; Tinbergen & addition to the usual analysis of the corner choice frequencies Kruyt, 1938; Wehner & Ra¬ber, 1979). The current understanding displayed by the animals, we combined a detailed recording of the of ant navigation is that ants do not learn “cognitive-maps” (Weh- ants’ paths with panoramic images taken from the ant’s perspec- ner, Boyer, Loertscher, Sommer, & Menzi, 2006). Rather, efficient tive. This approach can be used to elaborate models of the ant’s navigation results from multiple taxon-like strategies based on the cognitive processes with clear predictions while quantifying ob- interaction of path integration (Wehner & Srinivasan, 2003) and jectively the information available to animals and therefore rules learnt visuomotor routines (Wehner, Michel, & Antonsen, 1996) out presumptions about the salience of different cues (Stu¬rzl & that can be triggered by different contextual cues (T. S. Collett, Zeil, 2007). We modeled here a particular view-based matching Graham, Harris, & Hempel de Ibarra, 2006). For instances, views hypothesis where the memorized views facing the goal is matched can be memorized along familiar routes in association with par- with the current view to retrieve the goal direction. This hypoth- ticular motor actions (Knaden, Lange, & Wehner, 2006) or com- esis, which can be called ‘visual compass,’ is based on the rota- pass direction (M. Collett, Collett, & Wehner, 1999). Memorized tional image difference function (rIDF) obtained while comparing views can also be compared with the currently perceived view to the stored view with each possible orientation of the current view compute the goal direction (Graham, Philippides, & Baddeley, (Zeil, Hofmann, & Chahl, 2003; Graham et al., 2010). In addition 2010; Harris, Graham, & Collett, 2007; M. Collett, 2010; Judd & to explaining some characteristics of the paths taken by the ants, Collett, 1998) or position (Graham, Durier, & Collett, 2004; Zeil, the corner choice frequencies predicted by the rIDF hypothesis Hofmann, & Chahl, 2003). Such taxon-like strategies have been could be compared with the predictions of the G/F hypothesis. In repeatedly tested in hymenoptera and robustly explain the results other words, our experimental manipulations show whether ants used panoramic views or segregated the so-called “geometry” on insect navigation obtained indoors (T. S. Collett & Collett, from the “features” of the arena in a way that can be readily tested 2002) as well as outdoors (T. S. Collett, Graham, & Harris, 2007; in vertebrates. Graham & Cheng, 2009). The results obtained do not necessitate the assumption that insects segregate their visual world into landmarks and panorama, or geometry and features. Instead, insect Experiment guidance might be based on parametric cues widespread on their panoramic visual input (Pastergue-Ruiz, Beugnon, & Lachaud, Method 1995; Mu¬ller & Wehner, 2010; Wystrach, Schwarz, Schultheiss, Rectangular arena and features. The rectangular arena was Beugnon, & Cheng, 2011). 80 ϫ 40 cm with 20-cm-high white walls. An entrance hole was Recently, rectangular arenas have hosted for the first time an drilled at the middle of the arena, and four exit holes were drilled invertebrate, the ant Gigantiops destructor (Wystrach & Beugnon, in the corners, allowing ants to reach via a 10-cm long plastic tube 2009). Homing ants loaded with their reward entered the center of one of the four exit boxes located outside of the arena. To prevent an arena with various cues on the wall. They had to find any one the use of external cues by the ants, the whole arena was covered of the corners to exit the arena and go home. The ants displayed by an opaque plastic dome lit up diffusely by white circular rotational errors in the absence as well as in the presence of fluorescent lighting (32W). Four bands of heavy paper were ap- distinguishable featural cues in the corners. However, when a plied on the surface of the walls, covering the bottom 6 cm of the conditioning procedure was applied, such that only one corner panorama. By varying the paper’s pattern and color, three different allowed the ants to return to their nest, all the ants learned to use conditions were carried out. In the first one, the four walls were the featural cues and hence avoided rotational errors. Those results covered by black paper. In the second one, three walls had black were quite similar to those obtained with vertebrates, yet the insect paper while one wall had striped paper (black and white strips of behavior could be parsimoniously interpreted as resulting from the 2.5 cm width). In the third condition, three walls had black paper matching of panoramic views (Wystrach, 2009; Wystrach & Beu- while one wall had white paper. For the two last conditions, the gnon, 2009). striped and the white paper respectively are called features. During A view-based strategy may not be restricted to insects only. It the training trials the feature covered a long wall. During the tests, has also been suggested to explain certain aspects of vertebrate however, the feature was shifted to a short wall. navigation (Benhamou, 1997) in mice (Alyan, 2004), rats (Cheung Animals. Colonies of Gigantiops destructor captured in the et al., 2008; Stu¬rzl et al., 2008; Sheynikhovich et al., 2009), and rain forest of French Guiana were reared in the experimental room humans (Wang & Spelke, 2002). In a recent study, Pecchia and in Toulouse (temperature 29¡C; humidity, 50%). The experiments Vallortigara (2010) found clear evidence that geometry learning in were carried out with individually marked foragers from 10 dif- chicks arise from the use of a view-based strategy (Pecchia & ferent colonies. Vallortigara, 2010). However, whether “geometry” or “features” Procedure. Foragers first had to perform their outbound trip or “panoramic snapshots” drive animal search in rectangular are- through a 50-cm-long plastic channel before reaching a feeder site nas could not be clearly demonstrated in most experiments pub- where a single Drosophila was provided. Once the prey was lished to date and the topic of geometry remains influential and captured and killed, the loaded homing ant was carried via an ANTS IN RECTANGULAR ARENAS 3 opaque plastic tube to the central entrance hole underneath the trials were pooled and analyzed separately. The statistics were rectangular arena. The disoriented ant thus entered the arena in its performed at the group level, each individual contributing to its center and had to reach one of the four exit holes located in the group with the proportions of its corner choices as dependent corners to get back to her nest. Each exit hole led to an exit box measures. The following statistical tests were performed for both outside of the arena. After whichever corner was chosen, the ant training and test trials. was immediately transferred back to its nest. After a few minutes First, the nonrandomness of corner choices was tested by com- spent inside the nest, the same ant was generally ready for the next paring the proportion of choices for the preferred corner in each trial. group against the chance expectation of 0.25 using a binomial test. The ants were confronted with a training situation (i.e., with a Then, potential differences between the choice proportions for the feature on a long wall) for a least the first 10 trials. From the 11th other three corners (called errors) were tested in each group using trial onward, the ants could be confronted with a test situation (i.e., a nonparametric Kruskal Wallis H test. Finally MannÐWhitney U with a feature on a short wall) only if she had displayed at least six tests were also used to investigate potential intergroup differences consecutive successful training trials (i.e., chose the same corner in the proportion of choices for the preferred corner. and made no U-turns in doing so). A test trial was always followed Recording of trajectories and selection of the paths. The by training trials. Afterward, depending on the motivation and trips of the ants inside the arena were recorded with a Sony DCR constancy of the ant, supplemental test trials could be performed PC-10E video camera suspended from the ceiling via a pole and after at least three consecutive successful training trials (5.6 trials adjusted to fit the circular hole (5 cm diameter) at the top of the on average). The number of training and test trials performed dome. The trajectories were extracted with the software Noldus depended on the training success of the ant, varying from 15 trials Ethovision and transformed into (x, y) coordinates at a rate of five with one test to 45 trials with seven tests. In the 4-black-walls times per second. The first five trials of each ant were considered condition, because of the absence of any feature walls, no test as familiarization trials and were not analyzed. Some paths were was given. Table 1 presents the average number of training and unusually long because of some unforeseen circumstances such as tests trials displayed by the ants. an ant losing the Drosophila or losing motivation inexplicably. Preferred corner: Group allocation. In the 4-black-walls The paths longer than 250 cm were ignored in the path analysis. condition (n ϭ 14), all ants were considered as a single group. The percentage of paths longer than 250 cm was similar across However, among the 3-black-1-striped-walls (n ϭ 16) and the groups (average of 14%; standard deviation across groups of 4%). 3-black-1-white-walls (n ϭ 11) conditions, the ants were split into Monte Carlo analysis of stereotypicality and variability of two groups depending on the location of their preferred corner training paths. This analysis was restricted to the training paths (i.e., the most chosen corner during training trials). The ants that were successful (ending in the preferred corner or the two preferring a corner adjacent to the feature wall (striped/black or geometrically correct corners for the 4bl group). The aim was to white/black) and the ants that preferred a corner at the intersection compare variability found within different sets of paths: paths of of two black-walls (black/black) were pooled separately for both different individuals in different groups, paths of different individ- conditions. In the 3-black-1-white-walls condition, however, all uals in the same group, and paths of the same individual across the ants preferred a corner adjacent to the feature wall. Thus, the trials (sequentially or chosen at random). To allow such compar- ants were split into four groups: the 4-black-walls group (4bl, n ϭ isons, all those paths were reflected if necessary to end at the top 14); the black/black group (bl/bl) from the 3-black-1striped-walls left corner. For the calculation of differences between paths, pairs condition (n ϭ 6); the striped/black group (st/bl) from the 3-black- were chosen at random according to restrictions to be described 1striped-walls condition (n ϭ 10); and the white/black group below, making the analysis Monte Carlo in nature. Each chosen (wh/bl) from the 3-black-1-white-walls condition (n ϭ 11). path was divided into 100 points at 1%, 2%, . . . 100% of its path Analysis of corner choices. The first five trials of each ant length. We assessed the distances separating the two paths at each were considered as familiarization trials and were not analyzed. of these 100 corresponding points. From the sixth trial onward, the corner choice was recorded when The “inter-individual (inter-group)” path variability was calcu- the ant entered an exit hole. For each individual, training and test lated by comparing pairs of paths chosen randomly with replacement. One path came from some individual in one group, while the other path always came from some individual from a second group. The individual might vary across draws as well as the paths. Table 1 For each pair of groups, a Monte Carlo draw of 1000 pairs of paths Group Size and Average Number of Trials (ϮSD) produced a very stable mean varying very little from one draw to No. training No. successful No. test another. Groups No. of ants trials/ants training trials/ants trials/ants The “inter-individual (intra-group)” path variability was calculated by comparing pairs of paths chosen randomly with replace- 4 bl 14 31.0 Ϯ 18.6 19.4 Ϯ 14.7 No tests ment between individuals of the same group. One path came from bl/bl 6 18.8 Ϯ 4.1 13.7 Ϯ 7.0 3.2 Ϯ 3.1 st/bl 10 16.9 Ϯ 6.9 15.7 Ϯ 9.3 2.7 Ϯ 1.4 one individual, while the second path came from a different wh/bl 11 14.1 Ϯ 6.7 14.7 Ϯ 9.6 2.3 Ϯ 1.5 individual in the group. The individuals and the paths varied from one sampling to the next. Again, a Monte Carlo draw of 1000 pairs Note. The successful training trials are those ending in the preferred of paths produced a very stable mean for each group. corner (or the two geometrically correct corners for the 4bl group). Only the successful training trials have been taken into account for the inter- The “intra-individual (random inter-trial)” path variability was paths variability measurements and analysis. bl ϭ black; st ϭ striped; calculated by comparing pairs of paths chosen randomly with wh ϭ white. replacement from the same individual. Again, a Monte Carlo draw 4 WYSTRACH, SOSA, CHENG, AND BEUGNON of 1000 pairs of paths produced a very stable mean for each Results and Discussion individual and therefore for each group. Finally, the “intra-individual (sequential inter-trial)” path vari- Ants’ corner choices. We call here “error” any choice that ability was calculated by comparing pairs of successive paths in does not end up in the preferred corner of the ant. Figure 1 shows chronological sequence for each individual. This calculation is not the corner choice frequencies displayed by the ants in each con- Monte Carlo, as all successive pairs of paths for each individual dition. It is important to note that the patterns for different indi- were calculated. An averaged value was obtained for each group. viduals have been suitably transformed so that the preferred corner To test for potential differences between those four different of each individual is presented at the top-left for each condition. levels of interpath variability (i.e., “inter-individual (inter-group)”; Training in 4-black-walls rectangle (Group 4bl). No indi- “inter-individual (intra-group)”; “intra-individual (random inter- vidual chose its preferred corner significantly more than its diag- trial)”; “intra-individual (sequential inter-trial)”) the average dis- onally opposite one (p Ͼ .1, Binomial test). This confirms that the tance between paths over the 100 points of comparison was used ants could not distinguish between a corner and its diagonally as a dependant variable, reducing each interpath comparison to one opposite one. As a group, the ants chose significantly more often value. the two geometrically correct corners (the preferred corner and its A potential “group effect” (whether paths of individuals from diagonally opposite one) than the two errors (the two other cor- the same group tend to be more similar than paths of individuals ners) (p Ͻ .001, one-sample t test) (Figure 1A). from different groups) was tested by comparing the 1000 “inter- Training in featured rectangle (groups bl/bl; st/bl; wh/bl). individual (inter-group)” values with the 1000 “inter-individual The ants showed a spontaneous preference for having their pre- (intra-group)” values (independent samples t test). ferred corner adjacent to the featured wall (Table 1, No. of ants). Potential individual “path signature” (i.e., individual idiosyn- This preference is significant for the 3-black-1white-walls condi- crasy or stereotypy; Macquart, Garnier, Combe & Beugnon, 2006) tion (Figure 1D; n ϭ 11 vs. n ϭ 0; p Ͻ .001, Binomial test) but not was tested by comparing for each group the 1000 “inter-individual for the 3-black-1-striped-walls condition (Figure 1B, C; n ϭ 6 vs. (intra-group)” values with the 1000 “intra-individual (random n ϭ 10; p ϭ .454, Binomial test). The three groups (bl/bl; st/bl; inter-trial)” values (independent samples t test). wh/bl) each showed a significant preference for their preferred To test whether the “path signature” of an ant evolved over corner (for each group: p Ͻ .001, one-sample t test) and a signif- successive trials (concept called here “sequential evolution”), we icant bias for one particular error (bl/bl: p ϭ .005; st/bl: p Ͻ .001; compared for each individual its “intra-individual (sequential wh/bl: p ϭ .022, Kruskal Wallis H; Figure 1B, C, D). inter-trial)” value with its 1000 “intra-individual (random inter- Intergroup analysis in training. Regarding the frequency of trial)” values. A good estimation of the position of that “Sequential choices for the preferred corner, no significant differences were inter-trial” value among the distribution of the 1000 “Random found between the three featured-rectangle groups (p Ͼ .113, inter-trial” values is to look at the percentage of the 1000 MannÐWhitney U). However, the 4bl ants chose their two pre- “Random inter-trial” values which are smaller than the “Se- ferred geometrically identical corners (pooled together) signifi- quential inter-trial” value. cantly less often than the st/bl and wh/bl ants chose their one Analysis of distance traveled. The distance traveled by the preferred corner (respectively, p ϭ .001 and p Ͻ .001, MannÐ ants was calculated for each trial using a Matlab program process- Whitney U). There was no such significant difference between the ing the (x, y) coordinates obtained from Ethovision. Potential 4bl and the bl/bl groups (p ϭ .409, MannÐWhitney U). intergroup differences in the distances traveled were tested using Intragroup analysis in tests. For the test condition, each of ANOVA with the Tukey-Kramer post hoc test on all possible pairs the three groups displayed a significant preference for a particular of groups. This analysis was carried out for the successful trials corner. The bl/bl ants chose significantly more the corner now only (i.e., those ending in the preferred corner or the two geomet- adjacent to the feature but which maintains the correct geometry rically correct corners for the 4bl group). with respect to the rectangular shape of the arena (p Ͻ .001, Analysis of the initial directions. The initial directions taken one-sample t test). In fact, Figure 1B shows that this corner was by the ants were recorded at 10 cm from the central entrance of the chosen almost exclusively. Interestingly, they hardly chose the arena. Even though they were disoriented, the ants did not stop diagonally opposite wall, which also stands in the same geometric when entering the arena at the center but tended to orient their relation to the arena as the corner preferred in training, and body while moving. As a consequence, the noise in the initial furthermore has the same local features, namely black walls direction is considerable across trials. Therefore, we used the around the corner. The st/bl and wh/bl ants, however, preferred the initial direction of the mean path of each individual to carry out corner that maintained the local features, although it was now this analysis. Using a Matlab program, we determined the point at incorrect relative to the rectangular shape of the arena (respec- which each individual mean path crosses for the first time a fictive tively, p ϭ .021 and p Ͻ .001, one-sample t test) (Figure 1C, D). circle of 10-cm radius around the center of the arena. A potential Intergroup analysis in tests. During the tests, the st/bl ants intergroup difference was tested using the WatsonÐWilliams test chose their preferred corner significantly less often than the for circular data (Batschelet, 1981). bl/bl and wh/bl ants did (respectively p ϭ .014 and p ϭ .014, Analysis of randomness of errors along the trial sequences. MannÐWhitney U). The bl/bl and wh/bl ants’ corner choices We analyzed the sequence of corner choice during the training were indeed extremely constant during the tests, hence the st/bl condition for each individual using a runs test to determine group is the only group to present a significant asymmetry whether the errors tended to occur randomly or successively. Only between the three other corners (p ϭ .026 KruskalÐWallis H): the individuals that displayed more than two errors were included when not choosing the corner that maintained the local features in the analysis (n ϭ 22 ants). (preferred corner) the st/bl ants tended to prefer the geometri- ANTS IN RECTANGULAR ARENAS 5

Figure 1. Mean percentage of corner choices for each group during training trials (left) and tests (right). Patterns for different individuals have been suitably transformed so that the preferred corners are all presented at the top-left for each condition. Results highlighted in black indicate a significantly preferred corner. Results highlighted in light gray indicate a significant bias toward an error over and above other error corners. The 4-black-walls group underwent no tests. The circular histograms illustrate the goodness of the matching for a given orientation resulting from the pixel-by-pixel panoramic image comparison with a Reference picture, following the procedure in Figure 6. The locations of the circular histograms represent the location where the compared pictures were taken. Predictions are given for the G/F model (i.e., assuming that geometry and features are segregated) (white rectangles) and the rIDF model (open circles).

cally correct corners, and especially the one having black walls between pairs of paths over the 100 points of comparison was on both side rather than the one presenting the correct local used as a dependant variable for the following analyses. feature, but on the wrong side. Intergroup versus intragroup: A group effect? Overall, the Four levels of interpath variability. Only successful paths variability between the paths of individuals from different groups (i.e., those ending in the preferred corner or the two geometri- (Figure 2 “inter-individual [inter-group]”) is significantly higher cally correct corners for the 4bl group) of the training trials than that between the paths of individuals from the same groups were analyzed. All the paths analyzed were reflected if neces- [Figure 2 “inter-individual (intra-group)”] (p Ͻ .001, independent- sary to end at the top left corner. Because the different groups samples t test). This reveals that the visual panorama influences show similar patterns, the results of the different conditions the route taken by the ants traveling from the same starting point have been plotted together in Figure 2. The average distance to the same endpoint. That influence can be seen in considering the 6 WYSTRACH, SOSA, CHENG, AND BEUGNON

Figure 2. Monte Carlo results showing mean distances separating pairs of successful paths. (A) The distance separating the paths (y axis) is plotted as a function of the percentage of path traveled (x axis). (B) The distance separating each pair of paths compared has been averaged to one value. Boxes show median, upper and lower quartiles, whiskers extend to the upper and lower deciles. (A and B) The results from different groups or different pairs of groups have been combined. Inter-individual (inter-group): 1000 randomly chosen pairs of paths from pairs of individuals from different groups were used for the computation. Inter-individual (intra-group): 1000 randomly chosen pairs of paths from pairs of individuals of the same group were used. Intra-individual (random inter-trial): 1000 randomly chosen pairs of paths within individuals were used. Intra-individual (sequential inter-trial): all successive pairs of paths of each individual were used for computation (this calculation is thus not Monte Carlo in nature). The stars indicate a significant difference (p Ͻ .001) between the different types of interpath distances.

differences between the mean paths from each group shown in evolution. If paths tend to be similar to the previous one, a path Figure 3A. resulting in an error might be followed by another error. Intragroup analysis of inter-individual versus intra-individ- Intergroup differences in paths. ual: Individual idiosyncrasy? For each group, the mean of path Initial direction. Figure 3B presents the average initial direc- differences is significantly lower within individuals [Figure 2 tion of each individual. Although all the paths regarded here start “intra-individual (random inter-trial)”] than between individuals and end at the same point, the ants’ initial direction recorded at 10 [Figure 2 “inter-individual (intra-group)”] (4bl: 11.61 cm vs. 13.72 cm varies significantly between the groups (WatsonÐWilliams test, cm, p Ͻ .001; bl/bl: 8.08 cm vs. 9.88 cm, p Ͻ .001; st/bl: 7.60 cm p ϭ .030). This result at the group level reveals the impact of the vs. 9.73 cm, p Ͻ .001; wh/bl: 8.16 cm vs. 10.55 cm, p Ͻ .001;). visual environment on the ants’ initial direction. This reveals idiosyncrasies in individual ants, or individual path Distance moved. Figure 3C shows the distance traveled by signatures. Some examples of individual paths are shown in the ants in the different visual conditions. During the training Figure 4. trials, the 4bl ants traveled significantly longer paths than the ants Intraindividual analysis of random versus sequential: Sequen- of the featured-wall groups (ANOVA, p Ͻ .038, all paired com- tial evolution? For each group, the mean intraindividual inter- parisons by the Tukey-Kramer test). Indeed most of the ants of the path difference is lower for successive pairs of paths [Figure 2 4-black-walls group displayed a long curve at the beginning of “intra-individual (sequential inter-trial)”] than for random pairs of their path before keeping a constant bearing toward a corner paths from the same individual [Figure 2 “intra-individual (random (example in Figure 4A ‘ant 6’ and 6B ‘ant 11,’ Trials 30Ð55). This inter-trial)”]. The percentage of the 1000 “random inter-trial” pattern is predictable from the hypothesis of view-based matching. values that are smaller than the “sequential inter-trial” value is The presence of a distinctive wall adds visual information, which very low for each group (4bl: 10.12 cm, 0%; bl/bl: 7.25 cm, 6.7%; creates a stronger visual heterogeneity and thus facilitates a better st/bl: 7.04 cm, 4.5%; wh/bl: 7.67 cm, 0%). Although this is not true discrimination between the different places of the arena, thus for all individuals, these average results at the group level show a channeling ants on the correct way. There is no significant differ- strong tendency for an individual’s path to evolve gradually, ence in the distance moved among the three featured-rectangle changing a little from trial to trial (examples in Figures 4B, C). groups (Tukey-Kramer test). Successive errors during training trials. Among the ants having displayed more than two errors, half of them show a pattern Modeling where those errors did not occur randomly (11/22, p Ͻ .001, run-tests). The errors tended to follow one another in successive Method trials. An example of an individual sequence of paths is shown in Figure 4C. The fact that the errors tend to be grouped in the G/F corner predictions. We modeled the G/F hypothesis in sequence of trials is consistent with the presence of sequential two different ways. In the first model, each corner possessed a ANTS IN RECTANGULAR ARENAS 7

Figure 3. Intergroup differences in the successful paths (i.e., those ending in the preferred corner or the two geometrically correct corners for the 4bl group). (A) Mean paths of the successful trials for each individual (gray paths) and group (black paths). The mean paths of the groups (black paths) have been computed with all individuals equally weighted. All the successful paths have been reflected if necessary to end in the top-left corner before mean computations. (B) Initial direction recorded at 10 cm from the center (for the 4bl group, paths ending in the rotational error have been rotated to end in the top left corner). Each open gray dot represents the average path of one individual. The circular histograms illustrate the goodness of the matching for a given orientation resulting from the pixel-by-pixel panoramic image comparison with a Reference picture. For each group, mean directions are given for the picture-based-model (open arrow head) and the ants’ initial direction (filled gray dot). The pointy black arrows indicate the direction of the preferred corner (N for nest direction). Intergroup comparison in the ants’ initial direction shows significant differences (Watson-Williams test, p Ͻ .05). (C) Average distance traveled by the ants per trial for the four different groups. Error bars represent the standard deviation across individuals. Stars indicate significant intergroup mean differences p Ͻ .05. Gray numbers in brackets indicates the percentage of agents that behaved as “remnants” in the rIDF model (see Figure 6). geometrical cue (G) and two featural cues (F) that corresponded to pictures: a reference picture at the goal represents the supposed the left and right wall pattern. The preferred corner of the training memorized view and any supplemental pictures can represent the condition (top-left in Figure 1) served as the reference for the current view at any chosen location (Zeil et al., 2003; Stürzl et al., correct geometry (G) and features (Fs). G and F points were 2008). In theory, the insect is supposed to move to reduce the attributed to each corner if they shared the same G or Fs as the mismatch between its current and memorized view. Here, views preferred corner. The predicted corner choices were distributed are used as visual compass: the “oriented reference picture” (i.e., according to the proportion of points (G ϩ F) of each corner. The memorized view) is taken on the way toward the goal and the relative weight between G and F points was adjusted (as a free nonoriented picture at a particular location (i.e., current view) is parameter) to obtain the best fit with the ants’ corner-choice data. compared to the ‘oriented reference picture’ for each possible In the second model, we added the possibility of differential orientation. The best matching orientation thus predict the direc- weighting between the features. The model was otherwise the tion taken by the insect. same as in the first model. The difference is that different featural 360 degrees pictures. The 360 degrees panoramic images of cues (black, white, or striped) might be given different weights the rectangular arena were recorded with a Canon G10 camera (free parameters) relative to geometric cues. viewing a convex mirror (GoPano). The pictures were recorded View-based matching model. View-based matching theory with viewpoint 2.5 cm above floor level in the same visual con- can be modeled using a pixel-by-pixel comparison of panoramic dition that the ants experienced. The vertical field of view of the 8 WYSTRACH, SOSA, CHENG, AND BEUGNON

Figure 4. Inter and intraindividual differences. (A) Examples of the successful (i.e., nonerror) paths from three individuals illustrating interindividual idiosyncrasies. For the 4-black-walls condition (ant 6), the rotational errors are considered as successful trials and have therefore been rotated and plotted with the paths resulting in the preferred corner. (B) Examples of training paths plotted in sequential order from two individuals depicting the sequential evolution of the path. Like most of the ants, ant 25 displayed several errors in a row (Trials 20Ð24). (C) Examples of a sequence of five successive training paths from two individuals showing the evolution of the path across trials. imaging system covers 118¡ (68¡ above to 50¡ below horizon). reflect the relative goodness of the matching of each possible The pictures were first unwarped with the software Photo Warp, orientation of Ic, the MSPD function was inverted and the lowest then converted into black and white (grayscale) and finally resized value (orientation with the worst matching) set to zero (Figure 6B). at 360 ϫ 118 pixels (angular resolution ϭ 1¡), thus matching Circular histograms were then used to illustrate the relative good- approximately the visual acuity of Gigantiops ants. ness of the matching for each orientation of Ic pooled in 30¡ bins rIDF (rotational image difference function). For each train- (Figure 6C). ing and test condition, five pictures were taken: one at the center rIDF corner predictions. The predictions of the distribution of the rectangle and four at the midway point between the center of corner choices were calculated from the five rotational IDF and each corner. The picture taken at the midpoint between the histograms of each condition. First, each rotational IDF histogram center and the ants’ preferred corner during training was consid- was divided into four sectors of 90¡ associated with a value of ered as the reference picture (Iref). Iref was rotated to “face” the goodness of matching (Figure 6D). To emphasize the relative preferred corner (i.e., the exit hole of the preferred corner is importance of the good matching directions, the matching value of located at the center of the horizontal field of the picture). The each 90¡ sector was raised to the power of 4 (x 3 x4). One hundred reference picture of each group is shown in Figure 5, whereas agents were released at the center histograms (CH) and distributed Figure 6 illustrates how the pictures are compared in our model. between the four midway histograms (MH) according to the dis- Each picture of a condition (Ic) was compared with the oriented Iref tribution of goodness of matching to each sector from the center (Figure 6A). The difference between two pictures was calculated (the distribution of the central histogram in Figure 6D). Then, as the mean squared pixel difference (MSPD) over all correspond- agents “arriving” at the midway points were distributed according ing pairs of pixels (as in Zeil et al., 2003; Stu¬rzl et al., 2008). The to the distribution of goodness of matching found at each the MH MSPD between Iref and each Ic was calculated for all possible sectors. The agents were redistributed either to the associated orientations of Ic (in steps of 1¡). At that stage, the MSPD function corner (one sector), or to the two adjacent midway points (two describes the extent of the mismatch for each orientation of Ic. To sectors), or back to the center (one sector), this last group of agents ANTS IN RECTANGULAR ARENAS 9

of models relative to one another based on the residual error and the number of parameters used to fit the data (the lower the value the better) (Burnham & Anderson, 2002). The residual error and AIC of the models are shown in Table 2. The rIDF model obtained the best AIC because it fits better the ants’ data (lowest mean squared error) while using a lower number of free parameters than the G/F models. However, a closer look is required as none of the models predict the data without systematic errors. G/F models. The Geometry/Feature models presented here are based on the concept found in the literature that “features” and “geometry” are somehow segregated one from the other. Accord- ingly, the agent trained at a given corner is potentially able to memorize both the geometry (i.e., the relation of the trained corner to the rectangular shape of the arena) and the feature (i.e., the color of the adjacent walls of the trained corner). The AIC was better for the model with a differential weighting between the features, thus only those results are presented in detail in Figure 1 (white rectangle numbers). G/F models in training condition. The G/F model explains the systematic rotational errors observed in ants in the 4bl condition. According to the theory, the absence of different features on the walls makes the rotational error identical to the Figure 5. Reference picture of each group used for the rotational IDF reference corner. Indeed, both corners share the correct geom- model: the reference picture represents the supposed view memorized by etry and local features (i.e., two black walls). The G/F model the ants. They were taken in front of the ant’s preferred corner. Each explains as well the preference for one corner observed during reference picture encompasses the 360¡ of the panorama but faces the the training condition of the featured rectangle. Indeed only the preferred corner exit hole (i.e., exit hole at the middle of the horizontal axis reference corner (top-left) itself possesses both the correct local of the picture). Each picture has been sized at 360 ϫ 118 pixels (angular features and geometry and is thus mostly chosen (Figure 1 resolution ϭ 1¡) thus matching approximately the visual acuity of Gigan- “Training”). Regarding the errors, however, the predicted bias tiops ants. is in favor of the rotational error (bottom-right): although the rotational error does not preserve the correct local feature, it being considered as “remnant.” In a last step, the agents “arriving” preserves at least the correct geometry. For the bl/bl group, the at a second midway point were redistributed according to the top-right corner could as well be a preferred error because goodness of matching found at that midway histogram. The pool of although it does not preserve the correct geometry it does “remnant” agents correspond to ants that displayed U-turns or preserve the correct local features. Nevertheless, the G/F model “turned around” without selecting a corner. To prevent a large cannot account for the bias observed in ants toward the bottom- number of iterations, we assumed that remnant ants would end up left corner. choosing any corner and tolerating the potential mismatch. Thus, G/F models in test condition. The G/F hypotheses predict a in our model, the remnant ants were distributed evenly between the potentially perfect solution for the test condition of the bl/bl four corners. This parameter-free model is hardly likely to corre- group. Indeed the bottom-right corner possesses both the cor- spond to the actual mechanism that the ants actually use but is rect geometry and local features (Figure 1B). However, the ants useful for generating predictions based on the information avail- did not choose this predicted corner (2.5%) but displayed in- able to the ants with few assumptions. stead a strong preference for the top-left corner (97.5%) al- rIDFs and initial direction. We attempted to explain the though the local feature of this corner is wrong. The only way group differences in the initial direction using our “visual com- for the model to reduce the error of this misprediction is to pass” hypothesis of view-based navigation based on rIDFs (Figure weight the geometry considerably more than the black feature, 3B). The 4-black-walls condition is a particular case. The paths of thus favoring the top-left corner almost as much as the bottom- the ants of that group ending in the rotational error (bottom right right corner. Conversely, the ants’ corner choices in st/bl and corner) have been rotated by 180¡ to make all paths end in one wh/bl tests demand a stronger weight on the features than on corner (the top left). Therefore, a half of the modeled circular geometry. The weight has to be stronger for the white feature histogram of that group should be rotated by 180¡ as well. The than for the striped feature. This leads to large differences rotational IDF predicts a rotational error if the initial orientation is between different feature weights. Indeed, relative to the weight toward the bottom of the rectangle (from 180¡ to 359¡) (Figure given to the geometry, the models need to lower as much as 1A). Consequently, this part of the circular histograms has been possible the weight of the black feature while increasing as rotated by 180¡ for the 4-black-walls (Figure 3B). much as possible the weight of the other features. The best fit Results and Discussion is obtained with a weighting 175 times stronger for the white feature than for the black feature, a pattern that makes little Models comparisons. We compared models using the sense except that it helps to reduce mispredictions. Any other Akaike Information Criterion (AIC). The AIC provides a ranking attempt with reduced weight differences would worsen the 10 WYSTRACH, SOSA, CHENG, AND BEUGNON

Figure 6. Rotational IDF (rIDF): from 360¡ panoramic pictures to a circular histogram of matching. (A) The oriented Reference picture is taken at the midway point between the center and the ant’s preferred corner during training (here in the white-featured rectangle). This reference picture represents the view memorized by the ant and “faces” the ant’s preferred corner (black arrow). The nonoriented Tested picture represents the current view perceived by an ant at a particular position (here at the center of the arena during a test condition, circular arrow). The Tested picture is compared pixel by pixel with the oriented Reference picture (MSPD: mean square pixels difference) for each possible orientation (the two examples given here are for the best (235¡) and worst (315¡) matching orientations). (B) The goodness of the matching can be plotted as a function of the orientation of the Tested picture; the similarity value for the worst matching orientation is set to 0. (C) The plot is converted into a circular histogram (bins of 30¡) and shown in the rectangular arena at the position where the Tested picture was taken. (D) Illustration of the steps from the circular histograms to the distribution of corner choice. The agents are released at the center and distributed to the midways histograms according to the matching distribution in each 90¡ sector. The gray arrows indicate movement toward intermediate midway histograms. The black arrows indicate a final movement toward a corner. The agents displaying U-turn or turning at least two times without ending in a corner are considered as “remnants” (R) and are distributed randomly in the four corners. predictions of the model. Favoring the features would improve rIDF model. Comparisons of matching between panoramic the st/bl and wh/bl predictions but worsen the bl/bl prediction. pictures as a function of their orientation resulted mostly in smooth Conversely, favoring geometry would improve the bl/bl predic- curves with a unique and clear maximum of matching at a particular tion but worsen the st/bl and wh/bl predictions. orientation (Figure 6B). Some pictures taken at the center, however,

Table 2 The Performance of Three Models Predicting All the Data on Corner Choices

Model Weightings rMerror2 AIC

Rotational IDF % of matching for 90¡ ٙ4 0 253.0 70.8 Geometry/features G ϭ 8; Fbl ϭ 1; Fst ϭ 37; Fwh ϭ 175 3 311.3 77.9 Geometry/feature G ϭ 8.5; F ϭ 25 1 736.6 85.4

Note. The Akaike information criterion (AIC) is based on the residual error (M error2) and provides a ranking of model relative to one another (the lower the value the better). Model performance is based on the error relative to the number of free parameters (r) calibrated to fit the data. AIC ϭ nlog(error) ϩ 2(r ϩ 2), where n is the number of data points, and r is the number of free parameters. Error ϭ M error2(n Ϫ r Ϫ 1)/n. For the Rotational IDF model, the distribution of the agents at a given circular histogram corresponds to the percentage of matching of each 90¡ sector powered by 4 (see Figure 6D). For the Geometry/Features models the weighting of the cues is given for G (geometry); F(features); Fbl (Black feature); Fst (Striped feature); Fwh (white feature). A corner possesses two features. The results of the rotational IDF model and the best Geometry/Features model obtained are presented in Figure 1. ANTS IN RECTANGULAR ARENAS 11 led to a bimodal distribution, revealing an ambiguity in the best top in the 4-black-walls and black/black conditions and a bias matching direction. The predictions of the rIDF model are shown in toward the bottom for the striped/black and white/black conditions. white circles in Figure 1. In cases of a bimodal distribution of the goodness of the matching rIDF in training condition. The rotational IDF explains most (in the 4-black-walls and striped/black conditions), it seems that of the ants’ corner-choice frequencies in the training condition. It most of the ants were able to select the direction that matched describes the presence of a systematic rotational error in the 4bl better and avoid the opposite “second best-matching” direction. group as well as the absence of it in the featured rectangles. It also Distance moved. Another aspect of the paths accounted for by predicts the bias for the error located at the bottom left corner our rIDF model is the percentage of “remnant” agents. These are during training in featured rectangles (Figure 1B, C, D). The rIDF the agents that turn around rather than selecting a corner directly model fails badly in the st/bl training condition. Indeed, contrary to (see Figure 6D). The percentage of remnants was quite low for all what is predicted, the ants never confounded their preferred corner the featural conditions (M ϭ 5.86% Ϯ 4.09 SD) but much higher with the top-right and bottom right ones (Figure 1C). This failure in the 4bl condition (51.44%). These results are in line with the might have arisen from some unrealistic modeling procedures significantly longer paths displayed by ants in the 4bl condition rather than from a more fundamental problem with the view- (Figure 3C). Indeed, in that condition, ants often aimed first at a matching strategy. The central histogram of the st/bl condition is wrong corner before curving and heading at the preferred one the only case of a bimodal distribution of matching values found in (example in Figure 4A ‘ant 6’ and Figure 4B ‘ant 11,’ Trials featured rectangles. This bimodal distribution results from com- 30Ð55), although this effect is less perceptible when averaging the paring the striped features in the pixel-by-pixel way proposed by paths. the model. In this st/bl training condition, the stripes need to line up correctly to minimize mismatch. The matching of perceived and General Discussion target striped walls over a range of orientations (the way the model calculates mismatch) means that the goodness of matching alter- In their natural habitat, forager ants have been selected for nates between very good values (when the stripes are superim- running back to their nest as soon as they have found a desirable posed white on white and black on black) and very bad values food item. In the rectangular arenas used for this study, the loaded (when white stripes are superimposed on black stripes and black ant was immediately replaced to its nest once it reached a corner. stripes superimposed on white stripes). This means the match Because in nature the nest is always at the same position, each ant, between two striped walls is no better on average than the match in our rectangular arenas, tends to return to the same corner from between a striped wall and a black wall. This feature of the model trial to trial. This spontaneous tendency in regimes of nondiffer- is probably unrealistic, and it leads to poor predictions. ential conditioning reflects the natural behavior of the ants in rIDF in test condition. Remarkably, the rIDF model also homing. While all corners led ants home, their choice of corners describes the surprising results obtained with ants in the test was far from random and depended on the visual surroundings. conditions. For the bl/bl group test condition (Figure 1B), the corner located at the bottom right possesses both the correct local Spontaneous Rotational Errors and Nature of the feature (black/black) and the correct geometry of the trained corner Features and indeed matches well with the reference picture. However, the center picture of that test condition matches better when oriented Corner choices were nonrandom in all conditions, but especially toward the top, and both top right and top left pictures match better so when a feature wall was present. When all the walls of the when oriented toward the left, explaining why ants massively rectangular arena were black, the ants could not distinguish be- preferred the top left corner (97.5%) and avoided the bottom right tween their preferred corner and the diagonally opposite one. one (2.5%). The significant difference obtained in the test condi- Nevertheless, each ant showed corner preferences by avoiding the tion between the st/bl and wh/bl groups are also predicted by the two corners from the other, nonpreferred diagonal (Figure 1A). rotational IDF. Contrary to the constancy of the wh/bl ants for the Such a selective preference for a diagonal is often interpreted as bottom-left corner (90.6%), the ants of the st/bl group displayed as the use of geometric information in vertebrates (Cheng & New- well a sizable attraction to the bottom right corner (22.8%). Indeed, combe, 2005). But such results can also be interpreted in the in the wh/bl test condition, both the center and the bottom-right framework of view based matching. The corners of different pictures match better when oriented toward the bottom-left corner, diagonals differ in terms of where edges are positioned in a whereas in the st/bl condition, the two analogous pictures match panoramic view of the arena, edges formed from the meeting of the for both the bottom-left and bottom-right corner (Figure 1C, D). ground, the walls, and the “sky” (Stu¬rzl et al., 2008). In our rIDF and paths. previous study (Wystrach & Beugnon, 2009), only individual ants Initial direction. The ants’ initial direction recorded at 10 cm displaying nonrandom distributions were shown in the figure, varies significantly between the groups, revealing the impact of the whereas in the present work, all the individuals were pooled no visual environment on the ants’ early decision. The rotational IDF matter how they performed. However, although the proportion of model, which is based on comparing 360¡ pictures, accounts for “random ants” is similar across the previous and present studies such an impact of the visual environment. The better matching (respectively, 6/13 and 5/14), the frequency of errors among the orientations at the center of the arena predicted by the rIDF model “successful ants” was lower in the first study than here (respec- vary also across conditions. This model does not describe the ants’ tively, 9.5% and 20.45%, p ϭ .0237). This difference might result results precisely, but the predicted intergroup differences at the from the variation of the layout of the arenas used. In the first center of the arena fit qualitatively with the ant’s initial directions study, the arena had pure white walls, but each edge was con- (Figure 3B). Indeed, both ants and model show a bias toward the trasted with a black line, making the rectangular shape of the arena 12 WYSTRACH, SOSA, CHENG, AND BEUGNON more apparent than in the present study. This difference is consis- the data. It is therefore not surprising that the model presents some tent with a strategy of view-based matching: the more manifest the quantitative imprecision. All the predictions of the test conditions rectangular shape of the arena, the more the mismatch between are remarkably accurate. Worth mentioning as well is that the the geometrically correct and incorrect corners, and therefore, the higher percentage of “remnants” (agents turning around rather than fewer the number of errors displayed (Wystrach, 2009). selecting corner directly) predicted in the 4 black walls condition When a featural wall was present, most ants (21 of 27) sponta- (51.44% in contrast to the featured rectangle having M ϭ 5.86% Ϯ neously preferred a corner where the feature wall abutted a black 4.09 SD) is consistent with the significantly longer paths displayed wall. At these corners, the edge between the walls is strongly by the ants in that condition (Figure 3C). contrasted. This bias was the strongest (all 11 ants) when the Of course we do not believe that our rIDF model has captured featural wall was completely white, making these corners the only in detail the mechanisms of view based matching in ants, a topic high-contrast vertical edges. Such an attraction to edges is consis- of ongoing research in ants (Lent, Graham, & Collett, 2010; tent with previous studies on ant navigation that presented the Graham et al., 2010; Mu¬ller & Wehner, 2010; M. Collett, 2010; insects with high-contrast stimuli in the lab (Harris, Hempel de Wystrach et al., 2011). Ants in general probably do not process Ibarra, Graham, & Collett, 2005; Harris et al., 2007; Judd & views in a purely pixel-by-pixel fashion, and our ants did not Collett, 1998) and may also reflect a natural tendency to approach always take a route through the midway point between center and a landmark (Graham, Fauria, & Collett, 2003). corner as the model proposes. However, this simple view-based Furthermore, whichever corner an ant preferred, she displayed model based on panoramic views performed better than the dif- constancy in sticking to it, while committing very few rotational ferent G/F models that we devised. The G/F models had serious errors (Figure 1B, C, D). The corner constancy was very strong deficiencies that seem difficult to correct while assuming that (over 90%) in ants that preferred an intersection of the feature wall geometry and features are segregated. Our modeling efforts sug- with the black wall. This constancy also contrasts with results in gest that working on improving view-based models might prove Wystrach and Beugnon’s study (Figure 1C of Wystrach & Beu- more fruitful. gnon, 2009). Ants in that study were presented with features not on a wall but in the corners, and contrary to the present study, they The Nature of the Visual Environment Influences committed systematic rotational errors. This difference illustrates the Path that the size of the features matters in guiding view-based matching, as pointed out by Stu¬rzl et al. (2008). In our previous study, Our results show that the nature of the visual environment from the center of the arena, the four features in the corners influences the path. This claim is supported by the intergroup covered less than 7% of the azimuth. The mismatch between differences obtained here: paths of distinct individuals are more facing one corner and the diagonally opposite one was thus minor different when performed in different visual conditions (see Figure and ignored by the ants (Wystrach, 2009). In the present study, 2). Consequently, paths of distinct individuals are more similar however, the feature wall covered 35% of the azimuth, resulting in when displayed in the same visual conditions (i.e., same arena strong mismatches between the different corners, mismatches large configuration). Therefore, the nature of the visual environment enough for all the ants to avoid systematic errors. This size- somehow “shapes” the path (see in Figure 3A). The visual condi- dependent power of the features is not inherent in the G/F hypoth- tions had an effect as early as on the initial direction taken by the esis. ants. With the goal located in the top-left corner, the ants familiar with the distinctive wall at the top (i.e., the st/bl and wh/bl groups) G/F or View-Based Matching? showed a bias toward the bottom, whereas the ants familiar with the distinctive wall at the bottom (bl/bl group) showed a bias The G/F hypothesis start with the assumption that features and toward the top (Figure 3A, B). Remarkably, our simple rIDF geometry of space are somehow segregated (for a review, see model is consistent with those biases (Figure 3B). To take a Cheng, 2005). This includes several recent models attempting to concrete example, consider the black/black group. Those ants explain results obtained in rectangular arenas (reviewed by Twy- might have memorized a view while facing their preferred corner, man & Newcombe, 2010). Those G/F models cannot account for that is, a view containing mostly black walls, with the featured the results observed here in ants. They fail in two main points. (1) wall perceived only far on the rear of the left eye (Figure 5B). In the training condition in featured rectangles, they explain very When entering the arena at the center, the best matching view well the preference for the main chosen corner but fail to explain should have the feature wall toward the rear of the left eye. Such the principal error displayed by the ant: the G/F models predict a a view is to be found when the ant orients toward the top of the bias for the rotational error in all training conditions but the ants arena. shown instead a significant bias for the bottom-left corner (Figures It may be important for the ants to keep the perceived features 1B, C, D); and (2) In test condition, they fail to explain the strong on the same eye during travel (Graham & Collett, 2002; Harris et preference of the bl/bl group for the top-left corner (see Figure 1). al., 2005). This could explain the tendency of ants to confound The rIDF model presented here succeeds better in explaining the their preferred corner (top left in Figure 1B, C, D) with the closest corner choice results observed in ants (see Table 2). In two of four adjacent corner (bottom left in Figure 1B, C, D). As shown in training conditions, the predictions are quantitatively worse than Figure 1C, D for example, in traveling from the center to both the the G/F models (Figure 1C, D). However, contrary to the G/F preferred corner and the most frequently visited error corner, the models, the predictions are qualitatively correct and the significant featural wall would project to the right eye during the journey, and bias observed for the bottom-left corner is well predicted (Figure the left eye would perceive mostly black walls. This idea can also 1B, C, D). The rIDF model has no free parameters calibrated to fit explain the results obtained in tests. Referring to Figure 1C, D ANTS IN RECTANGULAR ARENAS 13 again, the ants kept the feature wall on their right and the black Collett, 2010; Lent et al., 2009), this variability could result from walls on the left while traveling to their preferred corner in tests as irregularity in the motor routine displayed while views are in training. matched perfectly at particular points along the route. However, The rule of keeping the features on the appropriate eyes finds some paths can be entirely different between two consecutive trials some support from earlier research. It has been shown in desert (i.e., no superimposition) showing that ants are to some extent also ants that no interocular transfer (IOT) occurs when they use tolerant to view mismatch. Such tolerance to mismatch also ex- terrestrial cues for navigation (Wehner & Mu¬ller, 1985). In the plains why ants do not behave randomly during artificial test present paradigm, however, this hypothesis systematically predicts conditions where the visual environment is suddenly altered, as is the selection of two of the four corners and cannot account for the the case with Gigantiops ants (Macquart et al., 2006; Wystrach & preference of one particular corner as observed in ants. A supple- Beugnon, 2009) and also other ants species (Durier et al., 2003; mentary explanation is therefore required. Fukushi, 2001; Graham & Collett, 2002; Graham et al., 2003; It has been shown that wood ants move toward landmarks that Graham et al., 2004; Judd & Collett, 1998; Narendra, Si, Su- subtend a smaller angular size than that encoded in the memorized likowski, & Cheng, 2007; Wehner & Ra¬ber, 1979). In Wystrach view (Durier, Graham, & Collett, 2003). In the training condition (2009) this concept is called the Mismatch Tolerance Threshold of the present study, this would explain why ants preferred one (MTT). As the tolerance in view matching cannot be disentangled corner of the two resulting from the rule of keeping features on the here from the tolerance in reproducing motor actions, MTT en- correct eye. For instance, suppose that the ants from the st/bl and compass both view and motor action mismatch and can be mea- wh/bl groups have memorized a view close to the featured wall. sured in term of differences in the paths displayed by a same While the ant en route is keeping the features on the correct eye, individual. Such MTT differs from individual to individual, and the perceived featured wall appears smaller than its memorized can be lowered (i.e., made less tolerant to mismatch) with differ- size and consequently attracts the ant. Conversely, the ants from ential conditioning if a more precise matching is necessary to reach the bl/bl group would have memorized a view in which the the target (Wystrach, 2009). featured wall appears smaller and the black walls larger. En route, the featured wall thus appears too big, and the black walls too Sequential Evolution of the Path small, resulting in an attraction toward the black walls, away from the featured wall. This concept accounts as well for the results Remarkably, the comparison of paths within individuals reveals obtained in tests. Indeed, a consequence of the test condition is that the paths tend to be the most similar across successive trials that, from the center of the arena, the feature, which has been (see Figure 2). In other words, an individual’s paths tend to evolve switched to a short wall, always appears too small compared with gradually from trial to trial (example in Figure 4B, C). This can be what was experienced during the training condition, and therefore true even after 50 trials and explains why errors in corner choices tends to attract the ants. were significantly clumped (example in Figure 4B, ant 11). Drift- ing of the parameters used to do the task (i.e., stored views and Individual Flexibilities motor routines) would seem to capture the idea here. Such parameters can drift because there is no “selection pressure” (reinforce- Although the visual environment “shapes” the paths of the ants, ment contingency) to enforce rigidity. Nonetheless, this sequential it does not dictate exact paths, leaving room for substantial vari- evolution of the paths reveals that views and motor routines are not ation between and within individuals. In our study, the ants’ paths stored rigidly after a critical period of learning, but are updated were more similar within individuals than between individuals (see with the foraging experience. The simulations that we ran in the Figure 2). This reveals an individual idiosyncrasy, also called path present study (available online as supplemental material) suggest signature (Macquart et al., 2006; Harris et al., 2007). Such indi- that the extent to which the memory of the last trial overrides the vidual path signatures have already been shown several times in previous averaged memory is around 25%. This kind of updating ants slaloming between arrays of landmarks in artificial and nat- of memories, with a substantial weight given to recent experience, ural environments (Kohler & Wehner, 2005; Macquart et al., 2006; is not a new idea in learning theory (Bouton & Moody, 2004; M. Wystrach et al., 2011). In the present study, however, no objects Collett, 2010; Lent et al., 2009). But such gradual updating is not forced the ants to make any detours. Individual idiosyncrasies can found in some contexts of navigation. In path integration for be attributed to interindividual divergences in the parameters used instance, only the most recent trip completely dictates behavior to do the task. Those parameters consist in stored views (Graham, (Cheng, Narendra, & Wehner, 2006; Narendra, Cheng, & Wehner, Durier, & Collett, 2007; Harris et al., 2007; Judd & Collett, 1998) 2007). At the opposite extreme, Gigantiops ants foraging in a and also motor routines (Lent, Graham, & Collett, 2009; Macquart, familiar area in the wild place most weight on a long-term stored Latil, & Beugnon, 2008). Beyond this generality, details linking vector memory (Beugnon, Lachaud, & Chagne«, 2005). Updating is view and motor parameters have been recently investigated with not a universal phenomenon in spatial learning. Cataglyphis fortis ants following a curved route around a single conspicuous landmark (M. Collett, 2010) but remain to be speci- Insights on View-Based Matching fied in complex natural environment. At the individual scale, ants displayed variability in their own View-based matching is nowadays much more than a single paths across trials. Even after 50 trials, the paths can still vary hypothesis, and it harbors many different subtleties and flexibili- substantially (example in Figure 4B, ant 11). This reflects some ties that must vary across species. Difference may already arise at slack in the learned visuomotor processes. As ants do not seem to the sensory level. Contrary to pixel-by-pixel image comparison, continuously match views as they walk along habitual route (M. the success of ants in matching the striped feature shows that ants 14 WYSTRACH, SOSA, CHENG, AND BEUGNON do not store views purely retinotopically (see also Lent et al., The taxon-like strategies of hymenopterans encompass more 2010). What is encoded in a view is likely to be a combination of than beacon-like strategies that form one major plank of the both static cues like edges, center of gravity, color of areas (T. S. mammalian taxon system (O’Keefe & Nadel, 1978). Indeed, even Collett & Collett, 2002), skyline elevations (Graham & Cheng, when the goal is in sight, ants do not go in a straight line toward 2009), light distribution (Hempel de Ibarra, Philippides, Riabinina, it. The present results illustrate the flexibility and adaptability of & Collett, 2009), and dynamic cues like optic flow (Campan & such taxon-like strategies, and the long-distance navigation of Beugnon, 1989; Dittmar, Stu¬rzl, Baird, Boeddeker, & Egelhaaf, insects [hundreds of meters in desert ants (Wehner, 2003), more 2010; Zeil, 1993). than 20 km in orchid bees (Janzen, 1971), and 3600 km for How such cues are then memorized and processed to output Monarch butterflies (Brower, 1995)] is an existence proof of how behaviors may also vary across species. Even a single individual such strategies can be exquisitely adapted to produce efficient and may well rely on different view-based strategies within a single robust behavior in complex environments. trip. The present results support the idea that ants may somehow In vertebrates, most of the literature emphasizes a segregation of use their view as a visual compass (see also Graham et al., 2010; geometry and features, and a recent study clearly refutes a static Mu¬ller & Wehner, 2010). In contrast to matching by gradient image-matching hypothesis in three-year-old children (Lee & descent (Zeil et al., 2003), the visual compass gives a best direction Spelke, 2010a). However, the debate is still open as view-based for any given current view without the need to sample the good- models succeed in explaining some results obtained in artificial ness of the matching of all neighboring locations. This would be arenas (Cheung et al., 2008; Sheynikhovich et al., 2009; Ponti- remarkably well suited for route navigation as it would allow an corvo & Miglino, 2010), and a recent study has neatly demon- ant to retrieve and stick to the correct direction while requiring a strated the use of a view-based strategy in chicks (Pecchia & minimal number of memorized views. In contrast, moving to lower Vallortigara, 2010). Evidently, both vertebrates and insects process the mismatch between current and memorized views (i.e., match- visual information from views and, interestingly, the neural cir- ing gradient descent) appears unsuited to route behavior as it might cuits that underlie their vision show striking similarities (Sanes & result in tortuous paths. However, when the task is to relocate a Zipursky, 2010). As the problem of being able to navigate in the precise location like the nest entrance, matching gradient descent world is also shared between vertebrates and insects, there might requires a single memorized view at the goal and therefore pro- therefore be some evolutionary convergence in their navigational vides a more parsimonious explanation than visual compass does mechanisms, the extent of which would be valuable to determine. (Zeil et al., 2003). Indeed, the use of a visual compass has difficulty in explaining the homing success observed in ants and bees trained to a route and released at novel locations on the other side References of the goal (Wehner et al., 1996; Capaldi & Dyer, 1999), as the best matched rotation of the current views would lead the insect in Alyan, S. H. (2004). Conditions for landmark-based navigation in the the usual route direction and therefore away from the goal. For the house mouse, Mus musculus. Animal Behaviour, 67, 171Ð175. Batschelet, E. (1981). Circular statistics in biology. New York, NY: purpose of homing from such new locations, relying on the appar- Academic Press. ent size of the memorized and perceived elements (Durier et al., Benhamou, S. (1997). On systems of reference involved in spatial memory. 2003; Graham & Collett, 2002; Graham et al., 2004) appears more Behavioural Processes, 40, 149Ð163. effective. For instance, being attracted by the elements of skyline Beugnon, G., Lachaud, J. P., & Chagne«, P. (2005). Use of long-term stored that appear smaller (lower in elevation) than encoded in the mem- vector information in the neotropical ant Gigantiops destructor. Journal orized view would enable an ant to head in the home direction of Insect Behavior, 18, 415Ð432. from a new location in natural condition (Wystrach, Beugnon, & Bouton, M. E., & Moody, E. W. (2004). Memory processes in classical Cheng, in preparation). The question of how to match the different conditioning. Neuroscience and Biobehavioral Reviews, 28, 663Ð674. elements of the skyline can be simply achieved by using the Brower, L. S. (1995). Understanding and misunderstanding the migration celestial compass as reference or by visual compass matching (Zeil of the monarch butterfly (Nymphalidae) in North America: 1857Ð1995. Journal of the Lepidopterists Society, 49, 304Ð385. et al., 2003). Burnham, K. P., & Anderson, D. R. (2002). Model selection and multi- Finally, our results, along with others (M. Collett, 2010; Lent et model inference: A practical information-theoretic approach (2nd ed.). al., 2009; Macquart et al., 2006) stress the importance of motor Springer-Verlag. components as a part of the view-based strategy, as well as the Campan, R., & Beugnon, G. (1989). Spatial memories and cognition in flexibility and adaptability of insect memories. Future explicit insects. Etologia, 1, 63Ð86. modeling of performance, taking into account the inter- and intra- Capaldi, E. A., & Dyer, F. C. (1999). The role of orientation flights on individual variabilities as well as the whole experience of the homing performance in honeybees. Journal of Experimental Biology, insect, is required to better understand insects’ taxon-like strate- 202, 1655Ð1666. gies. Cartwright, B. A., & Collett, T. S. (1983). Landmark learning in bees: Experiments and models. Journal of Comparative Physiology A, 151, 521Ð543. Conclusion Cheng, K. (1986). A purely geometric module in the rats spatial representation. Cognition, 23, 149Ð178. Taken together, our results refute the models assuming segre- Cheng, K. (2005). Reflections on geometry and navigation. Connection gation of spatial geometry and features in ants. Instead, the use of Science, 17, 5Ð21. panoramic views along with a simple matching process makes a Cheng, K. (2008). Whither geometry? Troubles of the geometric module. more likely theory because it can explain the impact of the differ- Trends in Cognitive Sciences, 12, 355Ð361. ent visual conditions on the ants’ paths and corner choices. Cheng, K., Narendra, A., & Wehner, R. (2006). Behavioral ecology of ANTS IN RECTANGULAR ARENAS 15

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Author's personal copy

J Comp Physiol A (2011) 197:167–179 DOI 10.1007/s00359-010-0597-2

ORIGINAL PAPER

Views, landmarks, and routes: how do desert ants negotiate an obstacle course?

Antoine Wystrach • Sebastian Schwarz • Patrick Schultheiss • Guy Beugnon • Ken Cheng

Received: 13 July 2010 / Revised: 5 October 2010 / Accepted: 8 October 2010 / Published online: 23 October 2010 Ó Springer-Verlag 2010

Abstract The Australian desert ant Melophorus bagoti Introduction often follows stereotypical routes through a cluttered landscape containing both distant panoramic views and Many animals use terrestrial objects, landmarks, for navi- obstacles (plants) to navigate around. We created an arti- gation (Shettleworth 2010). Landmarks may be encoded ficial obstacle course for the ants between a feeder and their and represented in a map-like fashion, although this idea of nest. Landmarks comprised natural objects in the landscape ‘cognitive mapping’ is fraught with controversy in both such as logs, branches, and tussocks. Many ants travelled vertebrate (Benhamou 1996; Bennett 1996) and inverte- stereotypical routes home through the obstacle course in brate animals (Gould 1986; Wehner and Menzel 1990; Dyer training, threading repeatedly the same gaps in the land- 1991; Menzel et al. 2005; review, Shettleworth 2010). Just marks. Manipulations altering the relations between the as often, landmarks are used to chart routes, which are landmarks and the surrounding panorama, however, typically stereotypical paths through a landscape dotted affected the routes in two major ways. Both interchanging with objects. Route-following in vertebrate animals has the positions of landmarks (transpositions) and displacing been little studied (but see Calhoun 1963), but in insect the entire landmark set along with the starting position navigation, the topic has received much attention (Rosen- of the ants (translations) (1) reduced the stereotypicality of gren 1971; Collett et al. 1992, 1993; Zhang et al. 1996; the route, and (2) increased turns and meanders during Kohler and Wehner 2005). We here begin to examine the travel. The ants might have used the entire panorama in mechanisms of route-following in a species known for the view-based travel, or the distal panorama might prime the behaviour, the Australian red honey ant Melophorus bagoti. identification and use of landmarks en route. Despite the M. bagoti lives in a wide range over semi-arid Central large data set, both options (not mutually exclusive) remain Australia, where the habitat is typically filled with plants, viable. from trees and bushes to grass tussocks (Muser et al. 2005), where mated queens attempt to dig nests in the ground to Keywords Landmark Á Route Á Navigation Á Panorama Á start colonies (Schultheiss et al. 2010). The ant is the most Desert ant thermophilic on the continent (Christian and Morton 1992), and forages in the heat of the day in the few hot months of summer. The heat of the ground surface makes it too volatile for chemical trails, so that the ants forage individually, relying on both path integration (Wehner et al. 2006; Narendra 2007; Narendra et al. 2007) and, more commonly, A. Wystrach Á S. Schwarz Á P. Schultheiss Á K. Cheng (&) Department of Biological Sciences, Macquarie University, landmarks, through which they run stereotypical routes Sydney, NSW 2109, Australia (Kohler and Wehner 2005; review, Cheng et al. 2009). e-mail: [email protected] While M. bagoti’s stereotypical routes through natural terrain have been plotted (Kohler and Wehner 2005), many A. Wystrach Á G. Beugnon Centre de Recherches sur la Cognition Animale, CNRS, details of its route-following behaviour have yet to be Universite´ Paul Sabatier, 31062 Toulouse, France characterised. We investigated the interaction between the 123 Author's personal copy

168 J Comp Physiol A (2011) 197:167–179 use of individual landmarks along a route and the broad to a feeder (a square plastic tub, 20 cm 9 20 cm 9 15 cm context of the panoramic view around the ant as she trav- deep, sunk into the ground) 10 m North (Experiment 1) or els. The ants had to travel an artificial obstacle course West (Experiment 2) from their nest. During training, small between a feeder and their nest. Using natural materials pieces of cookies were scattered in the feeder for the ants to found in the landscape to create landmarks along the route, pick up, and sticks placed in the feeder allowed the ants to objects such as logs, tussocks, and branches, we manipu- climb out, the walls of the plastic tub being extremely dif- lated these movable but stationary landmarks on tests. ficult for ants to climb. Between the nest and the feeder, we After sufficient training, we effected various transforma- set up obstacles using natural materials, grass tussocks, tions, including the removal of the landmarks, transposi- leaves, branches, logs, and rocks, for the ants to navigate tions of landmarks, in which two or more landmarks through. A grid of 1-m square units consisting of string switched positions, and displacements, in which the whole wound around tent pegs stuck into the ground provided array of landmarks was translated and/or rotated. reference for recording an ant’s trajectory on gridded paper. Based on much past research on the importance of con- The strings were off the ground and did not interfere with textual and panoramic cues, we expected that a mismatch ant movements. Such a grid was also set up for tests on a between the usual route landmarks and the panoramic con- distant test field. text would affect the route-following behaviour: the more Experiment 1 was set up with an ‘obstacle course’ of six the mismatch, the less the usual route would be followed. landmarks placed directly on the ground (Fig. 1a). The Panoramic cues might prime the retrieval of memories of landmark nearest the feeder was 2.5 m away, with each landmarks and how to negotiate them (Collett et al. 2003), or successive landmark 1 m farther; the farthest landmark they might also be used directly for guidance (Graham and thus lay 7.5 m from the feeder. Throughout training, the Cheng 2009a). We also expected that a mismatch between landmarks remained stationary in the same configuration. the positions of the route objects and the panoramic cues Experiment 2 was set up with four rows of landmarks would affect path characteristics, such as how much the ants between feeder and nest, the nearest row to the feeder meandered during travel, and whether and where the ants 3.5 m away and the farthest row 7.5 m away (Fig. 1b). The spent the most time searching, as they often did. rows were evenly spaced, with three objects in row 1, two in row 2, three in row 3, and two in row 4. For easy cue manipulations such as displacements, we placed all the Materials and methods objects in white half cylinders (80 cm long, 20 cm wide) sunk into the ground. Between the cylinders lay a gap Animals of 40 cm. Throughout training, these landmarks also remained stationary in the same configuration. At the start Desert ants Melophorus bagoti, the red honey ant, from of the journey home, we created a short stretch of alley to two nests were tested in situ near their nest from December start the ants off in the correct general direction and pre- 2008 through February 2009. Experimentation was carried vent them from scattering in random directions. The alley out in the mornings until *12:00 and afternoons from was a gap 120 cm long and 40 cm wide with 10-cm-high 14:30 onwards. The midday period was not conducive to white plastic walls on the sides formed with segments of experimentation as the ants often spend large amounts of ‘channels’. It was aligned in the feeder-nest direction time taking refuge on a plant (Christian and Morton 1992, except for one test in which the orientation of the landmark personal observation). array was rotated (Distal Rotated, described below). On that test, the starting alley was also rotated to face the Field site landmarks. The ants were forced to start their homeward journeys down this alley during training and on tests. The experiments took place on the grounds of the CSIRO Centre for Arid Zone Research, *10 km south of Alice Procedure Springs, in a setting filled with trees (Acacia and Hakea), bushes, grass tussocks (mostly the invasive buffel grass), On her first arrival at the feeder, each ant was painted for and buildings. Views associated with the two nests can be individual identification. Painted ants were free to travel seen in Fig. 1. back and forth between feeder and nest. After at least one full day of training, a painted ant might have her training Experimental setup return journey recorded and then tested. On these occasions, the ant was allowed to travel home with a piece of Two experiments with different setups were conducted on cookie (as during training). We recorded only the runs of two different nests. Each nest was provided with free access ants that carried a piece of cookie the entire journey. Her 123 Author's personal copy

J Comp Physiol A (2011) 197:167–179 169

a b

c d 10 10 10 10 gniniarT noitisopsnarT tset gniniarT noitisopsnarT tset 9 9 9 9

8 8 8 8 F D 7 7 7 I J 7 JI E C 6 6 6 F HG 6 HG F D B 5 5 5 5 C A DE DE 4 4 4 4 B F 3 3 3 ABC 3 BCA A E 2 2 2 2 distance from feeder to nest (m) distance from feeder to nest (m)

1 1 1 1

0 0 0 0 3 2 1 0 1 2 3 3 2 1 0 1 2 3 3 2 1 0 1 2 3 3 2 1 0 1 2 3 ( redeef morf noitaived laretal noitaived morf redeef )m( laretal noitaived morf redeef )m(

Fig. 1 Experimental settings and setups. a, b Photos of experimental example of a training run. The same transposition was effected for all settings and artiﬁcial landmarks used in Experiment 1 (a) and ants. d An example of a transposition used in Experiment 2, and a Experiment 2 (b), looking from the feeder towards the nest. c The training run. The stars indicate nest location in c and d transposition effected in Experiment 1, with the grey line showing an path was recorded on a gridded piece of paper. We trap- trained ants to test as well. If the three runs, one full- ped the ant and picked her up when she reached the vector and two zero-vector runs, showed the same pattern vicinity of the nest and began to make searching loops. in negotiating the obstacles, the ant would then be tested Such an ant has run off her homebound vector based on in a manipulated condition the next time she appeared at path integration, so that the vector to run is now the zero the feeder. The same pattern meant that an ant went vector. She is called a zero-vector ant. Zero-vector ants between the obstacles in the same manner; technically, she are used to test how ants use landmarks for navigation crossed the same line segments connecting the landmarks because they cannot derive a direction of travel based on in the same order. All tests were on zero-vector ants, each path integration, and this because the zero vector does not preceded by a normal full-vector homebound training run, specify a direction. We then brought the zero-vector ant which was recorded. The ant was captured in the vicinity back to the rim of the feeder and placed her on the ground of the nest, and then allowed to run home again under to run home again. Her path was recorded again, and this zero-vector conditions, this time with the manipulations time she was allowed to enter her nest. When the ant effected on the landmarks. During manipulated tests, the returned again to the feeder, she was tested again under sticks in the feeder were removed to prevent any ants in zero-vector conditions. Typically, this was a matter of the feeder from exiting, this to prevent ants from being minutes, but the timing varied as we had typically other trained with a different setup. 123 Author's personal copy

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Manipulated tests experimental counterparts had been trained with. This condition tested whether the ants had any untrained In Experiment 1 (Fig. 1a), the only manipulation consisted tendency to approach the landmarks as beacons, as ants of transposing the landmarks while keeping one landmark sometimes do (Graham et al. 2003). in each of the landmark positions used in training (Fig. 1c). In Experiment 2, we effected the transposition on all rows, Data analysis within each row. For rows 2 and 4, with only 2 landmarks, only one transposition is possible. Rows 1 and 3 have 3 We present the gist of data analysis here, leaving details for landmarks each, and two transpositions are possible in the ‘‘Results’’ section to minimise repetitiveness. Paths which the positions of all three landmarks are switched. were digitised using GraphClickTM software into coordi- We picked the transposition for each ant that preserved the nates with (0, 0) being the start of the journey, the x-axis pair of landmarks between which the ant usually travelled. representing left–right travel (negative to the left), and the For example, suppose that the landmarks are aligned A, B, y-axis representing homeward travel in the positive direc- C, from left to right, and the ant habitually travelled tion. For Experiment 1, we noted whether the sides chosen between B and C (Fig. 1d). The transposition would corresponded with the same side (left or right) of a land- change the arrangement to B, C, A, allowing the continued mark or with the Earth-based position (to the same side possibility of travelling between B and C. This same irrespective of which landmark was at the position); transposition was effected if the ant habitually travelled to depending on the ant’s habitual path, these predictions the right of C. sometimes coincided. For Experiment 2, we also noted A second set of transformations consisting of landmark whether an ant on a transposition test followed the land- translations was conducted in Experiment 2 only. For these marks (going through the gap defined by the same pair of transformations, the entire array of landmarks was rigidly landmarks irrespective of their position) or the Earth-based translated to the right, looking from the feeder to the nest. position (going through the same Earth-based gap irre- The tested ant’s starting point was likewise translated. spective of the landmarks on either side), or travelled Geographically, it was only possible to translate to the another way (all others). On other tests in Experiment 2, we right, where open space was found. In a translation, the also classified travel through each row exhaustively, as direction of the array was preserved; geometrically, no described in ‘‘Results’’. rotation was effected. We effected translations in increments measured in landmark units, with a 1-unit move Indirectness and curvature of paths placing one landmark to its neighbour’s position 1.2 m to the right. The four translations effected were: Translation 1 When the visual cues were transformed, ants often hesi- (1.2 m to the right), Translation 3 (3.6 m to the right), tated, turned left and right as they travelled, and sometimes Translation 8 (9.6 m to the right), and Translation Distal appeared to scan the visual surround (a behaviour that we (to a distant, completely unfamiliar test field *100 m are analysing in another work). We calculated a measure of away). such ‘wiggling’, called Meander, and compared it across We tested ants in a number of other conditions in all test conditions, including training runs. Experiment 2, some to gather data on other manipulations, others as controls. In the Panorama test, ants were allowed Effect of nest entrance relocation to return home from the feeder on the training field, but all the experimental landmarks were removed, leaving solely A natural, unplanned event allowed us to analyse the effect the distant panoramic cues for orientation. The Distal of nest location on training paths. During the course of Rotated test probed the significance of the compass direc- experimentation, the nest entrance relocated from a point tion of the landmarks on a distal test field. Thus, the test slightly to the right of the y-axis to a location slightly to the took place on the distal test field as in the Translation left. We compared the training runs of ants trained before Distal test, but the landmark array was rotated by 80° and after this transition (details in ‘‘Results’’). clockwise, as was the starting alley. On the Distal No Unless otherwise stated, results of statistical tests were Landmark test, the ants were again tested on the distal considered significant at alpha = 0.05. unfamiliar test field, but without any of the experimental landmarks present. Finally, a control group was tested in the Translation Distal condition (called Distal Control). Results The Distal Control group was trained to home from the same feeder without any landmarks. They were tested on Our observations showed that the ants readily solved the the distal test field with the landmarks in place that their obstacle course between the feeder and the nest during 123 Author's personal copy

J Comp Physiol A (2011) 197:167–179 171 training. Many ants established stereotypical routes the ant’s starting point likewise shifted. For each row in through the same gaps spontaneously. Experiment 2 had each translation, we defined four categories of travel. The larger obstacles, making a larger deviation for ants to go ant could travel through its habitual gap, taking the Usual through a non-habitual gap. A higher proportion of ants Route. She could choose another gap (Wrong Gap). Or she made three consecutive runs through the same gaps in could use the Panorama. This was defined as a combination Experiment 2 (192 of 210 or 91.4%) than in Experiment 1 of travelling through a gap to the left of the usual one, on (66 of 112 or 58.9%; p \ 0.001, Fisher’s exact test). the row in question and all subsequent rows, and eventually heading towards the nest. Determining whether an ant Transposition tests headed to the nest proved completely unambiguous. On exiting the landmark array, an ant either headed towards Transposing landmarks clearly affected the stereotypical the nest and entered it or else she searched around in loops. routes that the ants took to head home, in both Experiment Finally, she might never reach a row. The unsurprising 1 and Experiment 2 (Fig. 2). At the time of testing, all ants strong influence of the usual panorama encountered on (100%) had traversed the same pattern of gaps back to their training runs is indicated by marked differences in the nest for three consecutive homing journeys. On the trans- nature of routes as a function of the distance of translation position test, only 56 of 72 choices (77.8%) in Experiment (Fig. 4). The proportion of ants going through the habitual 1 and 33 of 72 choices (45.8%) in Experiment 2 followed gap (Usual route choices) diminished with distance of the habitual gap in Earth-based coordinates (Earth-based or translation. The Panorama category increased at distances Both in Fig. 2c, d), a higher proportion in Experiment 1 of 3.6 m (Translation 3) and 9.6 m (Translation 8) trans- (p \ 0.001, Fisher’s exact test). lation, but at 9.6 m, the number of ants that never reached In Experiment 1, the ants were more likely to follow the the last row (and never found the nest) increased. Of Earth-based route, striking the habitual route through what course, at the distal test field at which no familiar pano- were now ‘wrong’ landmarks, than they were to steer around ramic cues were found, the use of any familiar panoramic the side of a transposed landmark that they habitually headed cues was impossible. A statistical comparison can be made for in training (Fig. 2a, c). In fact, the ratio was 3:1. Ignoring by considering only row 1 choices, such that the data are choices categorised as Both or Other, 10 ants had at least 1 independent, with one choice from each ant across all test Earth-based or Landmark choice. Averaging across indi- conditions. Excluding the Never Reached category (just vidual ants, the mean proportion of Earth-based choices one ant in the Translation Distal test), the proportions of [Earth-based/(Earth-based ? Landmark)] was 0.75. The categories of choices differed across translation conditions 95% confidence interval exceeded 0.5. In Experiment 2, ants (v2 (6) = 29.2, p \ 0.001). followed the Earth-based routes (flanked by ‘wrong’ land- Navigational behaviour changed row by row as well marks) more in rows 1, 2, and 4, and the landmarks more in (Fig. 4a). The choice of the usual route diminished row by row 3 (Fig. 2d). Considering only Earth-based and Land- row. Except for one tie in Translation Distal, the mono- mark choices (and ignoring 9 Other choices), the average tonically decreasing pattern held for all translation tests. proportion of Earth-based choices across individual ants was The exact probability of a monotonically decreasing 0.57, not significantly different from 0.5. sequence of 4 is 1/(4!) = 0.042. The exact probability for 3 such sequences (in Translation 1, Translation 3, and Panorama test Translation 8) is 0.0423 = 7.2-5. We found the opposite trend with the Panorama On the Panorama test, the ants travelled home successfully choice: this increased row by row, monotonically with a from the feeder on the training field with the training tie in each of Translation 1, Translation 3, and Trans- landmarks removed (Fig. 3). Only one of 18 ants failed the lation 8. (The Translation Distal and Distal Rotated tests test, not reaching rows 3 and 4 (Never Reached choices). produced no Panorama choices for obvious reasons.) We Even without the habitual landmark obstacles, the ants refrain from conducting inferential statistics here because mostly went through the habitual landmarkless ‘gap’ at row the pattern is dictated by the definition of the Panorama 1 (Earth-based choices, Fig. 3a). Thereafter, this tendency choice. If the choice of one row is Panorama, the choice diminished somewhat, with more ants travelling past the on all subsequent rows must be Panorama as well. More row somewhere else (Else choices). informative is the row at which the ant first chose the Panorama option. Combining only Translation 3 and Displacement tests Translation 8, in which most of the Panorama choices were made, Panorama was chosen first 10 times On translation tests in Experiment 2, the entire array of in row 1, 8 times in row 3, and once each in row 2 and landmarks was shifted to the right without rotation, with row 4. 123 Author's personal copy

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Fig. 2 Routes in the ab transposition test. a, b Paths 10 10 from training runs (top) and from transposition tests 8 8 (bottom) in Experiment 1 (a, n = 12) and Experiment 2 (b, n = 18). The training runs 6 6 are from the runs preceding the transposition test. The 4 4 rectangles in b demarcate the starting alley. c Choice of side 2 2 at each row (with a single landmark) on transposition tests distance from feeder to nest (m) 0 0 in Experiment 1. An Earth- −6 −4 −2 0 2 4 6 −6 −4 −2 0 2 4 6 based choice means choosing lateral deviation from feeder (m) the usual Earth-based side at that row, irrespective of what 10 10 landmark was at that row. A landmark choice means 8 8 choosing the side of the displaced landmark that an ant 6 habitually took in training. 6 Sometimes both these criteria were satisﬁed. d Choice of 4 4 gaps at each of the 4 rows (see panel b) on transposition 2 2 tests in Experiment 2. An Earth-

based choice means choosing distance from feeder to nest (m) 0 0 the usual Earth-based gap −6 −4 −2 0 2 4 6 −6 −4 −2 0 2 4 6 irrespective of what landmarks lateral deviation from feeder (m) make up the gap. A landmark choice means choosing the gap c 1 through the displaced landmarks 0.9 Other

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A higher proportion of ants never reached a row before a Never reached turning around in search loops as translation distance increased, with the Never Reached category comprising a Else majority of rows 3 and 4 on the distal test field when the landmarks were not rotated (Translation Distal), and com- Earth-based prising 100% of the runs when the landmarks were rotated 1 by 80° (Distal Rotated). In the Translation Distal test, the vast majority of ants reached the first row, but less than 20% 0.9 of the ants chose the usual gap in row 1, and a majority of 0.8 ants never reached row 3 (Fig. 4a). In the Distal Rotated test, 0.7 only a minority reached the first row. Most ants searched in loops (Figs. 4a, 5b), as did ants in the Distal No Landmark 0.6 test (Fig. 5c) and the Distal Control test (Fig. 5d). The 0.5 landmarks on the Distal Control test, never encountered 0.4 before by the ants, did not attract the ants to them.

Proportion of runs 0.3 Meander of paths 0.2

0.1 On all the tests in Experiment 2, we devised a measure of 0 Meander by dividing the path into 30-cm line segments. A Row 1 Row 2 Row 3 Row 4 circle of 30 cm radius was placed at the start of a route and a straight line segment drawn to where the route crossed this b10 circle to deliver segment 1. The circle was then centred at the end of segment 1, and where the route crossed the circle 9 delivered the end of segment 2, etc. The Meander index measures how much the path changes direction from segment 8 to segment. The absolute angular deviation in radians from one segment to the next was averaged over all segments. 7 Thus, 0 radians meant that the two segments were collinear, while p radians meant that the ant turned straight back. 6 Variances of Meander differed signiﬁcantly across conditions by O’Brien’s test (O’Brien 1979) (Fig. 5a, 5 p \ 0.001). We then used Tukey’s post hoc test to compare

4 all pairs of conditions (Fig. 5a). The training condition produced the least Meander. Even a translation of 1.2 m 3 (Translation 1) produced more Meander than training runs. Transposition produced more Meander than Translation 1.

distance from feeder to nest (m) 2 The Panorama test, in which the landmarks on the training field were removed for the test, also produced more 1 Meander than training runs. This shows that the removal of the training landmarks made the ants turn back and forth 0 more. Nevertheless, the powerful role of the distant panorama is revealed by the fact that paths on this test had less −1 −3 −2 −1 0 1 2 3 Meander than tests on the distal test field (conditions 7–10 lateral deviation from feeder (m) in Fig. 5a). Finally, rotation of the landmarks on the distal test field did not produce any noticeable effects on Mean- Fig. 3 a Gap choices on the Panorama test in Experiment 2, in which der (compare conditions 7 and 8). the usual landmarks along the route had been removed (n = 18). The now non-existent ‘gaps’ were defined by Earth-based positions (Earth-based choice). Crossing the row anywhere other than the Distribution in feeder-nest direction Earth-based gap was called ‘‘Else’’. b Runs on the Panorama test, with the dotted lines indicating the usual positions of the landmarks missing on the test. The star indicates the nest position, and the Another informative path characteristic is how much of rectangles demarcate the starting alley each path is distributed across the length of the journey.

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bcde 10 10 10 10 9 9 9 9 8 8 8 8 7 7 7 7 6 6 6 6 5 5 5 5 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1

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Fig. 4 Gap choices and path trajectories on tests with landmark Translation 1 (b, n = 18), Translation 3 (c, n = 20), Translation displacements in Experiment 2. The ants’ starting position was 8(d, n = 16), and Translation Distal (e, n = 11). Solid black displaced along with the landmarks. a Row-by-row gap choices. The lines indicate paths defined as following the panoramic cues. (Paths usual route indicates an ant going through the same gap defined by the of the Distal Rotated test appear in Fig. 5.) The star indicates the displaced landmarks that she took in training. A Panorama choice was nest position, and the rectangles demarcate the starting alley. defined as a choice to the left of the usual gap, followed by the ant f Summary of gap choices in different displacement tests averaging eventually heading home. b–e Paths on translation tests, from all rows

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Fig. 5 Meander in all tests in Experiment 2 and paths of the three different by Tukey’s post hoc tests. Individual points in the training remaining conditions. a Meander in all conditions, computed from the condition cannot be readily identified because there were 499 data orientations of 30-cm line segments calculated from the digitised points. LM stands for landmark. b–d Paths in the Distal Rotated (x, y) coordinates of the path. Each successive segment was formed by (b, n = 15), Distal No Landmark (c, n = 29) and Distal Control drawing a circle of 30 cm radius around the end of the previous (d, n = 15) conditions. In c, the dotted lines represent the positions of segment and noting where the path crossed this circle. The circle was the landmark obstacles encountered during training, now absent on placed at the starting point for segment 1. Meander was defined as the the test. The star indicates the nest position, and the rectangles average absolute angular difference between successive segments in demarcate the starting alley radians. Conditions sharing the same letter are not significantly

Because in some conditions, ants initiated searching loops reflecting straight runs. Groups with no relevant landmark from the start, we extended the range along the feeder-nest information of any kind (Distal No Landmark, Fig. 5c; and direction to 4 m behind the feeder (-4 to 10 m). The Distal Control, Fig. 5d), either because no landmarks were training condition, the Panorama test, and tests on the distal provided (Distal No Landmark) or because they were not test field were analysed. For each ant, we calculated the trained with landmarks (Distal Control), showed peaks near median point, spatially, of the path, and the interquartile the starting point (0 m) with lower interquartile ranges than spread. Tukey’s post hoc comparisons were then used to training runs, reflecting searching centred on the starting compare the conditions pairwise (Table 1). Runs from point. Thus, ants not trained with landmarks were not training and Panorama tests (Figs. 2b, 3b, respectively) had attracted to them. Ants that were trained with landmarks medians near the halfway point (5 m) and wide spreads, headed towards the training landmarks on the distal test to 123 Author's personal copy

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Table 1 Median point of path Condition Mean median (m) SD N Mean interquartile SD along the y-axis and the range (m) interquartile range, calculated in each individual ant and then Training 4.6 a 0.40 499 4.4 a 0.42 averaged across all ants in each condition Panorama 4.5 a 0.99 19 3.8 b 0.99 Translation Distal 3.2 b 1.01 13 2.3 c 1.49 Conditions sharing the same Distal Rotated 2.1 c 1.32 20 2.3 c 1.59 letter do not differ from each Distal No Landmarks 0.3 d 0.87 29 2.5 c 0.65 other signiﬁcantly by Tukey’s Distal Control 0.3 d 0.72 15 2.1 c 0.80 post hoc test some extent (Translation Distal and Distal Rotated groups, the right when the nest was to the right. Considering each Figs. 4e and 5b, respectively). On this measure, the row separately, and only choices in which at least one group directional orientation of the landmarks seemed to matter: had C5 ants making that choice, v2 tests of independence when the landmarks were rotated by 80°, the manipulation showed that the distributions differed signiﬁcantly between induced the ants (in the Distal Rotated group) to exhibit a the two groups at each row (ps \ 0.005). This pattern shows median at a shorter distance from the start. that the ants tended to weave an obstacle course around the vector direction that pointed to their nest. Effect of nest location on paths

Our ﬁnal analysis was based on a natural manipulation that Discussion the ants themselves effected. During the course of experimentation, the ants shifted their nest entrance, from very The results show in some detail the important link between slightly to the right of our y-axis to slightly to the left of the the distant panorama and route-following behaviour y-axis. Considering ants with at least three consecutive through an obstacle course. What is still not clear from the training runs through the same gaps, the gap choices dif- results is whether the distant panorama functions more as a fered as a function of nest location (Fig. 6), biased more to navigational cue directly guiding route-following, or more

Fig. 6 The choice of gaps on training runs before and after the nest entrance moved from very slightly to the right of the y-axis (left panel) to slightly to the left of the y-axis (right panel) in the course of experimentation. The thickness of arrows represents the proportion of choices through that gap. Dashed lines represent the straight line connecting the feeder and the nest, represented by the black dots

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J Comp Physiol A (2011) 197:167–179 177 as contextual cues triggering appropriate behaviour with Collett 2002; Cheng 2006). On the Panorama test in respect to the individual landmarks forming the obstacle Experiment 2, with the training landmarks missing, many course. We focus on these two major points in this section. ants still headed to the usual landmarkless ‘gap’ at the first row. Also in Experiment 2, more ants in the Translation 1 Link between the distant panorama and route-following test followed the usual landmarks than ants in the Trans- position test, and they also exhibited less Meander. The In training, many ants developed stereotypical courses transposition switched landmark positions and required the through the obstacles between feeder and nest. All the ants ants to travel in a different compass direction to the usual that participated in tests had followed a habitual route landmarks, creating a possible conflict between landmarks through the same gaps a number of times before being and the local vector. On translation tests, the starting tested. Changing the visual scene in any way changed the position was also translated, preserving the compass habitual behaviour, whether landmarks exchanged posi- direction and local vector to the usual gap. Some ants tions, shifted locations, or were absent altogether. The ants reached the usual gap at the first row of landmarks under were then less likely to take a route through the habitual diverse conditions, even on the distal test field (Translation gaps defined by the positions of the landmarks. And they Distal test, Fig. 4a). But no ants reached the usual gap at turned back and forth (meandered) more in their travel. In the first row when the array of landmarks was rotated on the case of shifting the entire set of landmarks along with the distal test field (Distal rotated test, Fig. 4a), and few the starting position of the ants (translations), a ‘dose- reached the first row at all, despite the fact that the starting dependent response curve’ was evident: the larger the alley pointed towards the landmarks. A local vector on translation, the bigger the effect (Fig. 4 for gap choices, emerging from the starting alley might have oriented the Fig. 5 for Meander). ants towards the ‘correct’ landmarks when they were in These ‘dose response’ characteristics might indicate the direction found during training. Definitive evidence for probabilistic contextual modulation in individual ants, the use of local vectors was found recently by testing the meaning that the probability that the context will trigger ants in a round arena devoid of skyline cues (Legge et al. the usual path decreases gradually with increasing change 2010). Even with conflicting landmark cues, the ants of context. But contextual modulation might also be all-or- showed a strong tendency to head in the trained compass none, with each ant having a sharp threshold of tolerance direction to find an exit out of the arena. Other ants too can for change of context, beyond which the context simply learn local vectors and motor sequences (Collett et al. does not trigger the usual behaviour and ‘all bets are off’. 1998; Macquart et al. 2006, 2008). While the group data show a ‘dose response curve’ and not a step function, we tested each ant only once. It is possible Panorama and/or contextual cues to obtain a dose-response curve for a group if individual ants vary in step-like tolerance thresholds. As an analogy, Our results confirm once again that M. bagoti follows individual animals might learn in an all-or-none basis as a stereotypical routes, in our case through an artificially function of trials of training while the group data show a constructed obstacle course. A common view of this route- smooth learning acquisition curve (Gallistel et al. 2004). following behaviour is that what to do with respect to a On the transposition test, more ants charted the habitual landmark object is conditioned upon or triggered by con- route in Earth-based coordinates in Experiment 1 than in textual cues (Collett et al. 1998; Collett and Collett 2002; Experiment 2. Several reasons might account for this dif- Cheng 2006). The panoramic cues function as contextual ference. In Experiment 1, the ants encountered one land- cues to facilitate the use of particular landmarks, helping mark at a time as they traversed the y-axis towards the nest, the animal to identify landmarks, providing signposts for whereas in Experiment 2, multiple landmarks needed to be behaviour (Collett and Collett 2002), or setting the occa- negotiated at each row. The obstacle avoidance require- sion for the use of servomechanisms based on particular ments were thus simpler in Experiment 1. Transpositions in landmarks (Cheng 2006). But contextual cues do not con- Experiment 1 also meant that the same gap would often be trol behaviour directly. That control is placed in the land- chosen on the basis of either the landmark or the Earth- mark object around which the insect is navigating. Plenty based route. In addition, Experiment 2 contained larger of evidence links contextual cues of all kinds to memory landmarks dominating more of the view on the homeward retrieval and behaviour (review, Collett et al. 2003). The route. physical setting in which the animal is navigating can serve Some weak evidence from this study suggests that the as a contextual cue (Collett and Kelber 1988; Colborn et al. ants might have also encoded the compass direction to 1999; Cheng 2005), in one case even after the animal has travel to the first gap, presumably based on a sky compass. entered a test apparatus that blocked the view of the sur- This is called a local vector (Collett et al. 1998; Collett and roundings (Collett et al. 1997). Other contextual cues 123 Author's personal copy

178 J Comp Physiol A (2011) 197:167–179 include the motivation to travel (for example, having food to take home vs. going out to seek forage; Dyer et al. 2002; Beugnon et al. 2005), the time of the day (Koltermann 1971), the encounter of a particular familiar landmark (Collett et al. 2002), and possibly the distance the insect has already travelled (Srinivasan et al. 1999) and sequential cues (Chameron et al. 1998; Zhang et al. 1999), in which a step in the sequence provides the context for appropriate memory retrieval. Recent evidence shows that M. bagoti sometimes uses a panoramic snapshot directly for orientation (Graham and Cheng 2009a, b; for suggestive results on honeybees, see Towne and Moscrip 2008). Replacing the natural skyline (a record of how elevated the tops of terrestrial objects are, without identifying the objects) with an artificial skyline made of black cloth forming an arena, is sufficient for the ants to chart an initial direction home (Graham and Cheng Fig. 7 Panoramic views taken between row 1 and row 2 in the 2009a). This suggests that the context/landmark separation training condition (a) and in the transposition condition (b, c)in may not be necessary for explaining all route-following Experiment 2. a Panoramic view on a training run. The photo was behaviour, although some contextual cues would still play taken with a curved mirror (GoPano Plus) attached to the front of the lens of a digital camera placed lens down on the ground, and then a role in guiding navigation (for example, providing the unwarped using software (PhotoWarp 2.5) to form a cylindrical view, motivation to home). Individual landmarks need not be in which the right edge is coincident with the left edge. The camera identified at all (see also Macquart et al. 2006). Instead, the location was along the usual training route of one ant, between row 1 entire panorama, encompassing near and far landmark and row 2. The photo has been blurred to simulate approximately a 5° visual resolution. b A panoramic photo from a transposition test, cues, drives behaviour. Matching of global panoramic taken at the same location as in a. The photo was obtained and treated views can explain both route-following and homing from in the same fashion. c A panoramic photo from a transposition test, new release points (Zeil et al. 2003; Wystrach 2009; taken at a location 1.2 m to the left of the camera location in a and b, Wystrach and Beugnon 2009). Might a route actually the location of the best matching gap based on the landmarks along the route. Otherwise, the photo was obtained and treated in the same consist of a series of matches to panoramic skylines, fashion without segregation of distant contextual cues and nearby signposts? Despite the sizeable set of manipulations, the data pre- route (Fig. 7b compared with Fig. 7a). But while Fig. 7c sented here can be accommodated equally well on either looks to match the training view better, the advantage of view. A dose-dependent degradation in following the usual following this landmark-based route may well be balanced route through the obstacle course after translations of the by the tendency of the ants to head to row 1 in a habitual landmark array (and the ant’s starting position) may reflect compass direction. For Translation 1, the match in heading an increasing probability of failure of the degraded context towards the usual route is much better than any other (data to trigger the usual route. Or else it may reflect a degra- not shown), the distant panoramic cues having little dation in the overall match with the panorama, a panorama changed with the 1.2 m translation. Both hypotheses would including both the landmarks and the distant trees and predict a high degree of adherence to the usual route, which bushes. The transposition likewise changes both the overall is what the ants did (Fig. 4). panorama and the link between the panoramic context and We do not believe that the two broad hypotheses are individual landmarks. The success of the ants on the Pan- indistinguishable. Nor do we think that they are equivalent orama test, with all experimental landmarks absent, should models formulated in different words. More detailed not be seen as a triumph for the hypothesis of the direct modelling of a range of experimentally transformed con- control by the panorama. It is possible that beyond row 4, ditions might well provide discriminating evidence. It is the ants had identified and used other landmarks for likely that both models are correct, but in different cir- homing. cumstances. Characterising these different circumstances To take two specific outcomes that both major hypo- forms an important research agenda. theses support, consider the transposition and Translation 1. With a transposition, the panorama appears to match the Acknowledgments The research was supported by grants from the Australian Research Council (DP0770300) and Macquarie University training conditions better on the landmark-based route (graduate research funds and scholarships to S.S., P.S., and A.W.). (Fig. 7c compared with Fig. 7a) rather than the Earth-based We thank the CSIRO Centre for Arid Zone Research for letting us use 123 Author's personal copy

J Comp Physiol A (2011) 197:167–179 179 their grounds for research, and two anonymous reviewers for helpful Graham P, Cheng K (2009b) Which portion of the natural panorama comments on the manuscript. The experiments reported in this article is used for view based navigation in the Australian desert ant? J were conducted in compliance with the laws of Australia and the Comp Physiol A 195:681–689 Northern Territory. The authors declare no conflict of interest. Graham P, Fauria K, Collett TS (2003) The influence of beacon- aiming on the routes of wood ants. J Exp Biol 206:535–541 Kohler M, Wehner R (2005) Idiosyncratic route memories in desert References ants, Melophorus bagoti: how do they interact with path integration vectors? Neurobiol Learn Mem 83:1–12 Koltermann R (1971) 24-Std-Periodik in der Langzeiterinnerung an Benhamou S (1996) No evidence for cognitive mapping in rats. 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RESEARCH Open Access Landmarks or panoramas: what do navigating ants attend to for guidance? Antoine Wystrach1,2*, Guy Beugnon2 and Ken Cheng1

Abstract Background: Insects are known to rely on terrestrial landmarks for navigation. Landmarks are used to chart a route or pinpoint a goal. The distant panorama, however, is often thought not to guide navigation directly during a familiar journey, but to act as a contextual cue that primes the correct memory of the landmarks. Results: We provided Melophorus bagoti ants with a huge artificial landmark located right near the nest entrance to find out whether navigating ants focus on such a prominent visual landmark for homing guidance. When the landmark was displaced by small or large distances, ant routes were affected differently. Certain behaviours appeared inconsistent with the hypothesis that guidance was based on the landmark only. Instead, comparisons of panoramic images recorded on the field, encompassing both landmark and distal panorama, could explain most aspects of the ant behaviours. Conclusion: Ants navigating along a familiar route do not focus on obvious landmarks or filter out distal panoramic cues, but appear to be guided by cues covering a large area of their panoramic visual field, including both landmarks and distal panorama. Using panoramic views seems an appropriate strategy to cope with the complexity of natural scenes and the poor resolution of insects’ eyes. The ability to isolate landmarks from the rest of a scene may be beyond the capacity of animals that do not possess a dedicated object-perception visual stream like primates. Keywords: ant, insect navigation, panoramic views, landmark, route learning

Introduction that panoramas and landmarks are different cues that Many insects use terrestrial objects – landmarks – for have different functions: a class of theories claims that navigation. Ants and bees in particular are known to the panorama serves as a contextual cue that triggers rely on landmarks both to pinpoint a goal [1-4], and the recall of the appropriate landmark memory, on also to chart routes, which are typically idiosyncratic which guidance is based [16-18]. The segregation paths through a landscape dotted with landmarks [5-9]. between landmark and panorama seems striking in Knowledge about how insects exploit such landmark these experimental conditions. This class of theories, information comes mostly from studies conducted in however, faces the question of how insects segregate visually controlled and impoverished conditions, like contextual cues and landmarks in natural environments, experimental rooms or deserts, where the salience of with complex depth structures. One theoretical proposal many potential cues is minimal and only experimental is that the amount of motion parallax is used as a depth landmarks are made prominent [1,2,4,10-14]. cue to filter out distant landmarks [19]. Insects are Concurrently, studies conducted in visually rich envir- known to use motion parallax as a depth cue [20-25]. onments suggested that ants and bees ignore the fea- But whether insects use such depth information to tures of familiar landmarks if they are presented within segregate out landmarks has not been determined a wrong panoramic context [15]. This led to the idea empirically. It has also been suggested that insects may not segre- * Correspondence: [email protected] gate landmarks from the panorama at all but are guided 1 Department of Biological Sciences, Macquarie University, Sydney, NSW 2109 instead by cues widespread on their panoramic visual Australia Full list of author information is available at the end of the article field, which encompass both landmarks and panorama

© 2011 Wystrach et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Wystrach et al. Frontiers in Zoology 2011, 8:21 Page 2 of 11 http://www.frontiersinzoology.com/content/8/1/21

[26-32]. Some results support this class of theories. For panoramic ‘ant’s eye’ images in order to quantify the instance, multiple landmarks [1,2,33] but also landmarks panoramic alteration of the scenery caused by the land- and panorama [9,34] seem to be “bound together” in mark displacements. This approach allowed us to relate insect memories. An imitation of the skyline (elevations the ants’ behaviour not only to the landmark, but to the of surrounding terrestrial objects) is sufficient for orien- whole panoramic scene, providing us with insight on tation in one species of desert ants [26]. In some cases whether navigating ants were focusing on the landmark [14,30], the ants proved able to navigate robustly using or using cues widespread on their panoramic visual pretty much plain white walls or curtains. Such perfor- field. mance can be readily explained by the use of panoramic views that encompass the global shape of the arena [35]. Results Yet it is difficult to create experimental conditions in Panoramic pictures: image difference distribution which the two classes of theories make different predic- We quantified the alteration of the visual panorama cre- tions. For example, in a previous study, many manipula- ated by the different displacements of the landmark tions on ants’ home routes were conducted [9]. But (Figure 2). Across multiple positions, we recorded and both classes of theories could account equally well for compared panoramic images taken with the landmark the large body of results. either in the training position (reference scenery) or dis- We here investigate the effect of displacing a promi- placed (test scenery) (see Additional file 1). The panora- nent landmark within natural surroundings. A huge mic image difference between reference and test scenery black landmark was placed immediately behind a nest across the field can then be calculated [32]. At first, the entrance of Melophorus bagoti ants (Figure 1). Standing landmark was shifted into a distant area (roughly 100 m in a flat area devoid of proximal trees, the landmark was away). The picture comparisons revealed high image dif- designed to stick out from the rest of the panorama, ferences between the training and distant test field. and thus to be as easy as possible for an insect to learn, Indeed, even in front of the landmark, a great part of memorise, and extract from the rest of the scenery. We the panoramic view is very different from that found at analysed the paths displayed by the ants in response to an equivalent position on the training field. Within the displacements of the landmark. In parallel, we recorded training area, removing the landmark does not significantly alter the view at the beginning of the route but results in high image differences near the nest position (Figure 2A). Indeed, the visual area covered by the landmark (or here, absence of landmark) is negligible at the feeder but increases as a tangent function as one approaches the nest (Figure 1, see also Additional file 1). Similarly, shifting the landmark by 16° or 32° does not significantly alter the view at the beginning of the route but creates high image differences at the real nest position. The 16° displacement creates a region of high image difference in the area opposite to the displaced landmark. This results in a valley of lower image differences between the feeder and the landmark. Within this valley, a zone of higher mismatch is located around 7-8 m on the way towards the 16° displaced landmark (Figure 2B). Thepresenceofthislatterzone of mismatch is easily explained. As one moves from the feeder towards the displaced landmark, the image differences result from two competing factors: the landmark and the rest of the panorama. As a result of moving away from the training Figure 1 Photos of the experimental set up with the landmark direction, the perceived distant panorama (i.e., all the in training position. A. Picture taken 5 m from the nest. B. Ant’s- eye picture (300°, resolution of 4° [49]) taken 5 m from the nest scenery except the landmark) becomes more and more (bottom) or at the nest position (top). The dashed lines delimit 180°. altered, thus steadily increasing the mismatch. The land- The landmark was located 90 cm behind the nest while the closest mark, however, matches its target counterpart perfectly, tree (on the left of the picture A) was located 14 m away from the and although very small at the beginning of the route nest. The panorama was thus providing very little dynamic change (filling < 5% of the azimuth), it increases in size sharply compared to the landmark for ants approaching their nest. with distance, thereby minimizing the global mismatch. Wystrach et al. Frontiers in Zoology 2011, 8:21 Page 3 of 11 http://www.frontiersinzoology.com/content/8/1/21

A B

distance from feeder (m) 2

0 í 02 í 02 í 02

C % panoramic mismatch 10

19 8 18

17 6 16

4 15

14 distance from feeder (m) 2 13

12 0 í í í 0 2 í 0 2 4 6 distance from feeder (m) Figure 2 Maps of panoramic mismatches and first U-turn locations. The maps result from comparing panoramic pictures taken during Removal of the landmark (A), Rotation 16° (B) and Rotation 32° (C) tests with reference pictures from the training condition. ‘% of panoramic mismatch’ indicates the percentage of mismatching pixel across the image. Locations for comparisons are shown in the Additional file 1. Mismatch levels were then interpolated between those locations (triangle-based cubic interpolation). The darker the shade, the lower the mismatch between views. Each cross represents the location of the first U-turn (walking at least 20 cm back towards the feeder within 50 cm of displacement) of the ants. Red crosses: first U-turns of ants that never searched densely in front of the landmark. Yellow crosses: first U-turn of ants that displayed a U-turn before searching in front of the landmark. Blue crosses: first U-turn of ants that displayed no U-turn before searching in front of the landmark (Blue crosses thus correspond to the beginning of the search). Bar: landmark position during test. Dashed line: landmark position during training. Stars: nest position. White circle: fictive nest position relative to the landmark.

In combining landmark and panorama, the panoramic model a particular homing strategy such as matching imagedifferencesgrowsasananttravelsfromthefee- gradient descent, but simply allowed us to quantify the der towards the landmark until a point of maximum modification of the panoramic scenery the ants were mismatch (around 7-8 m along the feeder nest axis), subjected to during the tests. Whatever the actual pro- beyond which the increasing size of the matching land- cess involved, any guidance strategy based on panoramic mark diminishes the global mismatch (Figure 2B). input should lead to disrupted behaviour if the global It is important to emphasise that the distributions of scenery is too much altered. Therefore, if ants are image differences presented here are not intended to guided by panoramic views, they should not be able to Wystrach et al. Frontiers in Zoology 2011, 8:21 Page 4 of 11 http://www.frontiersinzoology.com/content/8/1/21

reach the nest position in any of the tests conditions, as switch towards the landmark direction does not imply all of them present substantial panoramic image differ- that the ant is now attending to the landmark, but just ences around the nest location. With 16° displacements that the panoramic image difference is lower while facing of the landmark, however, the ants may end up search- in that new direction. Ants may also ‘hesitate’ between ing in front of the displaced landmark, but their the two directions when they provide equivalent match- approaching route should then be altered while crossing ing quality, leading potentially to wiggling paths for that the hill of high mismatch located at around 7-8 m. part of the route (see examples Figure 4G). Interestingly, a side difference arises in the 16° condi- Panoramic pictures: rotational image difference tions. When the landmark is displaced to the left, facing Panoramic images can be rotated until they produce the the landmark becomes the best matching rotation at best matching to the reference image. Here, rotational earlier locations than when the landmark is displaced to IDFs (i.e., Image Difference as a Function of the rota- the right (Figure 3B). Some ants might thus continue to tion) presented often two distinct best choices for walk in the training direction longer when the landmark matching (Figure 3A). One choice (distant panorama is displaced to the right. choice) was generally obtained while facing the same Following purely the strategy of walking in the best direction as during training, because the distant panora- matching direction should nonetheless lead the ant to masofbothimagesoverlapwell.Theotherchoice the displaced landmark in the 16° condition (Figure 3B) (landmark choice) was generally obtained while facing and to the nest or the landmark in the 32° condition roughly towards the displaced landmark, because the (Figure 3C). However, the actual image difference value landmarks of both images are superimposed. of the selected direction might be important too. Ants At the beginning of the route, the ‘distant panorama might stop following the best matching direction if choice’ provides a better matching value than the ‘land- the image difference is considered too high (Figure 2 mark choice’ (Figure 3B, C) because the landmark displays the distribution of the actual best matching appears very small and the distant panorama covers most values). of the view. However, as one travels towards the nest, the apparent size of the landmark increases and the part of Ant responses: control condition the field of view covered by the distant panorama Ants homing from the feeder were captured just before decreases. Therefore, the ‘landmark choice’ matching reaching their nest in front of the landmark, and quality grows and the ‘distant panorama choice’ released again at the feeder location. When the land- decreases in importance. Ants travelling towards their mark was left at its original position, the ants ran their nest in the training direction may thus suddenly switch route home again readily (Figure 4A) showing that they in orientation when the image difference along the ‘dis- were guided by the perceived scenery and were not tant panorama choice’ becomes too bad or when the affected by potentially conflicting information provided ‘landmark choice’ direction becomes better. Such a by their path integrator. However, changing either the

ABC 22 21 20 19 18 17 16

% panoramic mismatch 15

−180 −90 0 90 180 rotation of the test image (degrees) Figure 3 Rotational image differences. Images from the test conditions (i.e., current view) are compared with the ‘references images’ from the training condition (i.e., memorised view) for every possible rotation. The ‘reference images’ have been taken along the feeder-nest line and are all facing towards the nest. A. Example of the image differences distribution between a test image and the ‘reference image’ as a function of the rotation of the test image. The two lowest choices of image differences are indicated by the black (best choice) and grey (second best choice) arrows. ‘% of panoramic mismatch’ indicates the percentage of mismatching pixel across the image. B, C. Black and grey arrows of a given location represent respectively the best and second best matching rotation of a given location when the landmark is displaced by 16° (B) and 32° (C). The length of the arrows is proportional to the matching value. Wystrach et al. Frontiers in Zoology 2011, 8:21 Page 5 of 11 http://www.frontiersinzoology.com/content/8/1/21

A B C D

E FG

Figure 4 Test paths of individual ants. Ant were captured at the nest and released at the feeder (10 m away from the nest), with (A) the landmark in the same position as during training; (B) the landmark placed in a distant area (ant released either 2 m or 10 m in front of the fictive nest entrance); (C) the landmark removed; (D) the landmark rotated 32° away from the feeder-nest line (centred on the feeder) to the left or the right; (E, F, G) the landmark rotated 16° for 3 categories of ants: (E) ants that never display a dense ‘nest-search’ in front of the landmark; (F) ants that displayed a nest-search in front of the landmark but showed U-turns during their approach; (G) ants that displayed a nest-search in front of the landmark but showed no U-turn during their approach. Each path represents an individual ant, with one of them chosen at random highlighted in black. Bar: landmark position on the test (3 m wide). Dashed line: landmark position during training. Star: nest position. White circle: fictive nest position relative to the landmark. Diamond: release point on the distant test field. presence or the position of the landmark affected hom- larger spread than the searches displayed on the distant ing performance adversely, showing that the ants were area (Additional file 2). affected by such alteration of the scenery. Ant responses: 16° displacements of the landmark Ant responses: distant area, 32° displacements and With a smaller displacement of the landmark (16°), the removal of the landmark ants displayed different behaviours, which we cate- When the landmark was translated to a distant area pre- gorised into 3 groups (Figure 4E, F, G). Some ants (13 senting an unfamiliar panorama, the ants released at 10 out 49) never reached the nest or searched for it in m(i.e.,fictivefeeder)or2minfrontofthefictivenest front of the displaced landmark (Figure 4E). The others engaged immediately in a search pattern at the release (36 out of 49 individuals) eventually aimed at the land- pointandnoneofthem(0outof16and0outof15 mark and displayed a dense search for the nest in front respectively) went searching at the fictive nest relative to of it (Figure 4F, G). Determining whether (Figure 4F, G) the landmark (Figure 4B). or not (Figure 4E) an ant searched at the goal proved On the training field, with the landmark removed completely unambiguous, as two independent judges (Figure 4C) or displaced by 32° (Figure 4D), the ants could agree completely: the ‘nest-search’ pattern would tended to run a first relatively straight segment but then suddenly get much denser and the ants would not leave displayed a U-turn on average half way from the nest the area in front of the landmark for several minutes. and started searching. None of these ants (No-landmark: The 36 ants that searched for the nest (i.e., dense nest- 0 out of 23; Left and Right 32° displacements: 0 out of search) in front of the landmark were categorised in two 31) found the nest or reached the fictive nest in front of groups depending on whether or not they displayed the displaced landmark (within 3 min). Interestingly, the U-turns while approaching the landmark. U-turns con- approaches were on average centred along the feeder- sisted of more than a sharp turn, but also the stipulation nest axis in the no-landmark condition, but were a little that the ant walk back in a direction at least 113 degrees bit skewed towards the displaced landmark in the 32° away from the training direction (i.e., went at least 20 cm condition (Additional file 2). The searches appeared down along the Y axis within 50 cm of travel). As a centred on the first U-turn, but, interestingly, showed a result, an ant could display very sharp turns to the left Wystrach et al. Frontiers in Zoology 2011, 8:21 Page 6 of 11 http://www.frontiersinzoology.com/content/8/1/21

and to the right without being considered as displaying 0.8 U-turns. Around half of these ants (19 out of 36) displayed at 0.7 least one U-turn before reaching the displaced landmark rotation 16° (Figure 4F), a much higher proportion than in the con- 0.6 control trol group returning under training conditions (Fisher’s 0.5

exact test: 19/36 vs. 1/25, odds ratio 25.56, p < 0.0001). ty i Interestingly, their first U-turns were not located ran- 0.4

domly along the route (Chi-square against random dis- Tortuos tribution across categories of 1 m: c2 = 30, df = 9, p < 0.3 0.0001) but occurred mostly between 7 m and 8 m away 0.2 from the feeder (first U-turn average distance from the feeder ± sd: 7.43 ± 1.48 m) (Figure 2B yellow crosses, 0.1 Figure 4F). 0 The other half (17 out of 36) approached the land- 0 - 2 m 2 - 4 m 4 - 6 m 6 - 8 m mark without displaying any U-turns. The first U-turn Distance from the feeder of these 17 individuals occurred right in front of the Figure 5 Tortuosity along the path. Index of path tortuosity at landmark (Figure 2B blue crosses, Figure 4G) and, different distances away from the feeder (M ± sem) for ants from rather than showing uncertainty en route, corresponds the control group (in black) and from the Rotation 16° condition (in grey) that displayed no U-turn before searching in front of the to the beginning of the characteristic dense search for landmark. The tortuosity index corresponds to the averaged the nest entrance. However, a closer look at the absolute angle (in radians) between the directions of successive approach of those individuals revealed an increasing tor- chunks of 20-cm line segments connecting points on the path. A tuosity that reaches its maximum around 7 to 8 m away circle of 20 cm radius was placed at the starting point of the from the feeder, a pattern that was not observed in the digitised path, and where the circle intersected the path defined the first segment. The circle was then placed at the end of segment control group (ANOVA groups*distances: n = 17+25, F 1 to define segment 2, etc. = 9.008, p = 0.0001; between groups: F = 12.971, p = 0.0009) (Figure 5). Overall, even though most ants searched for the nest (average distance of the first U-turns from the feeder ± in front of the 16°-displaced landmark, its displacement sd: 7.9 ± 1.1 m). notably affected their approach. Their paths were more Other ants (8 out of 48) headed towards the 16°-dis- tortuous than in the control condition: wiggles and U- placed landmark from the start (see Additional file 3 for turns were strongest around 7 to 8 m away from the examples). Although the direction of travel was similarly feeder. oriented towards the landmark all along their approach (heading direction: paired sample t test: 0-4 meter vs. 4- Path tortuosity and compass direction 8 meter, t = -0.508, p = 0.627), the tortuosity of their To test whether or not this degradation was due to the paths increased significantly in the second half of the fact that the ants in the 16° condition were led in a journey (tortuosity: paired sample t test: 0-4 meter vs. 4- slightly different compass direction than during training, 8 meter, t = -4.635, p = 0.002) and half of them (4 out we focused on individuals that displayed long segments of 8) also displayed a first U-turn after 5 m of travel oriented towards the displaced landmark. Some ant towards the landmark (first U-turn average distance paths (17 out of 48) presented a neat transition in the from the feeder ± sd: 6.5 ± 1.3). direction of travel, with a first segment oriented towards Overall, ants were not equally perturbed everywhere the nest and a second segment oriented towards the along their way towards the landmark. Their paths were landmark (see Additional file 3 for examples). The disruptedmostlyaround7mawayfromthefeeder, switch in direction occurred on average around 5 m independently of the ant’s compass direction of travel. It away from the feeder (average distance from the feeder seems therefore unlikely that the observed degradation ± sd: 5.1 ± 1.3 m). Around half of those ants (8 out of of the path results from a discrepancy between the land- 17) displayed a first U-turn while approaching the landmark direction and a memorised celestial compass mark. Those first U-turns did not occur immediately information. after the switch towards the landmark as it would be expected if the path disruption was due to the new Side differences compass direction of travel, but several meters thereafter In both 16° and 32° conditions, displacing the landmark (average distance between switch and first Uturn ± sd: to the left or to the right had different effects on the 3.8 ± 0.9 m), that is, around 7-8 m away from the feeder ants’ first U-turn location. In 16° conditions, U-turn Wystrach et al. Frontiers in Zoology 2011, 8:21 Page 7 of 11 http://www.frontiersinzoology.com/content/8/1/21

location differences appeared along the x-axis. When the their homing. When released on the distant test field, landmark was displaced to the right, U-turns occurred the large landmark was not used: ants engaged immedi- closer to the feeder-nest axis and further away from the ately in a systematic search around the release point landmark side than when the landmark was displaced to (Figure 4B) as they typically do when released in an the left (t-test independent samples (values mirrored for unfamiliar environment [38]. When the landmark was one side): t = 4.254, p = 0.0002). Remarkably, such a dif- removed from the training field or displaced by 16° or ference was predicted by the panoramic image compari- 32° to the sides, most of the ants ran first a relatively sons (see “rotational matching of panoramic views”). straight segment, showing that they recognised the scen- Along the y-axis, the distribution of first U-turns were ery at the beginning of the route (Figure 4C, D, E, F, G). similar on average (t-test independent samples: t = Whether the ants recalled a local vector (i.e., segment of 1.587, p = 0.1234) but were more spread in the 16°Right travel based on compass information) or used a view condition (Levene’s test: F = 6.376, p = 0.0170). based matching strategy to achieve this first segment With 32° displacement of the landmark, no side differ- cannot be properly disentangled here. But previous ences in U-turns distribution appeared along the x-axis work on this species showed that panorama can be (t-test independent samples: t = -1.208, p = 0.2368). matched and used independently of the compass direc- Along the y-axis, however, U-turns occurred signifi- tion [26] stressing the use of a view based matching cantly earlier (t-test independent samples: t = 14.047, strategy rather than a local vector. Moreover, some p < 0.0001) and were significantly more scattered approaches were here skewed towards the landmark in (Levene’s test: F = 6.128, p = 0.0190) when the landmark both 16° and 32° conditions (see Additional file 2), con- was displaced to the right. testing the hypothesis of a pure local vector. The ants from the 32° displacements (Figure 4D) or Discussion no-landmark conditions (Figure 4C) engaged in winding Melophorus bagoti lives a habitat full of landmark infor- search loops on average half-way to the nest. None mation such as bush, trees or distant cliffs, and evolu- searched at the real nest or at the fictive nest position tion has tuned those ants to learn quickly [36] and rely in front of the landmark. Interestingly, these searches heavily on the so called ‘landmark information’ [7]. We were more spread than the systematic search displayed here investigate whether navigating ants functionally on unfamiliar terrain, revealing that other factors, possi- segregate the perceived scenery into landmarks for gui- bly view based matching or compass information, were danceandthepanoramaascontextualcue.Suchthe- influencing the search pattern (Additional file 2). ories infer that, for the animal, the given landmark is somewhat isolated from the rest of the panorama. For Guidance is not focused on the landmark this purpose, we gave ants every incentive to isolate a The hypothesis assuming that the distant panorama is landmarkfromthepanoramabychoosinganarea not used for guidance but as a contextual cue provides devoid of proximal trees, by clearing that area of any an explanation for the behaviours described above. The proximal clutter and providing them with a particularly panoramic context could be seen as delivering a nega- prominent artificial landmark at the nest entrance. tive verdict, rendering the landmark not worth approaching, and triggering the observed search beha- Initial segments and searches viours. However, two pieces of evidence show that gui- The ants accustomed to the landmark behind their nest dance was not purely based on the landmark, and that were captured as zero-vector ants (i.e., just before reach- ants were attending simultaneously to other cues from ing their nest entrance) and released again at the feeder the panorama. position. Because the ‘zero state’ of their path integrator Firstly, displacing the landmark to the left or to the cannot provide them with a homing direction, zero-vec- right had different effects on the ants. A 16° displace- tor ants have to rely on the visual surroundings to ment to the left led the ants to meander more towards home. In a landmark rich habitat, the recognition of the the landmark and less towards the feeder-nest middle surrounding overrides completely the information given line than a 16° displacement to the right. And a 32° dis- by the path integrator [7,9,37]. It is therefore not sur- placement to the left had an earlier impact on the ants’ prising that when the landmark was left at its original paths than a 32° displacement to the right. Since the position, the recognition of familiar surroundings led landmark presented highly contrasted edges against the zero-vector M.bagoti ants to run their home route again background, such side differences should not have readily and thoroughly (Figure 4A). arisen if guidance was purely based on the landmark. When the landmark was displaced from the training Secondly, in the 16° displacement condition, some position, however, the ant routes were notably altered, ants ended up searching for their nest in front of the revealing that such modification of the scenery affected landmark, but their approach did not resemble the Wystrach et al. Frontiers in Zoology 2011, 8:21 Page 8 of 11 http://www.frontiersinzoology.com/content/8/1/21

straight approach found in control ants (compare Figure A 4A with Figures 4F, G). Instead, they exhibited beha- 25 rotation 16° viours indicative of ‘uncertainty’:theyU-turnedor rotation 32° showed more tortuous paths during their approach (Fig- 20 no beacon ure 4F, G). The presence of this uncertainty in the ant paths was independent of their direction of travel, 15 first Uturn rejecting the hypothesis that uncertainty was resulting first Uturn % panoramic mismatch purely from a conflict with a stored local vector (i.e., a 10 0.15 memorised compass direction) pointing along the train- B ing direction. Consistent with this, previous work 0.10 showed that M.bagoti can readily match and use familiar panorama presented in a wrong compass direction [26]. 0.05 As ants reached – and therefore used – the displaced landmark, the path uncertainty observed cannot be proportion of trajectory 0 0 2 4 6 8 12 attributed to a negative verdict of a hypothetical contex- meters along the Y axis FN NFN tual cue either. Such path uncertainty must therefore Figure 6 Panoramic mismatches and search distribution along result from an alteration of the terrestrial cues the ants the Y-axis. A. Panoramic mismatch along the feeder-landmark line were using for guidance. As the highly contrasted land- for the Rotation 16° and 32° tests and along the feeder-nest line for mark was not altered in itself, we can conclude that gui- the no-landmark test. The vertical arrows represent the average dance was simultaneously based on other terrestrial cues. position of the first U-turn displayed by each ant (counting only ants that displayed U-turns before reaching the landmark) (ANOVA between groups: F = 11.096; p < 0.0001; all pairs Tukey’s post hoc: Functional segregation landmarks/panorama or Rotation16° a, Rotation32° b, no-landmark b). The horizontal arrows panoramic views? represent the average value of mismatch where the first U-turns As guidance was not focused on the landmark only, the were displayed (inferred from the 2D distribution of image class of theories assuming a functional segregation difference, Figure 2) (ANOVA between groups: F = 0.3823; p = between panorama (as context) and landmarks (as gui- 0.6835). The open circles on the side of each arrow indicate the inter-individual standard deviation. B. Search distribution of the ants dance cues) needs to invoke other processes like the along the Y-axis. Paths were limited to the first 30 m travelled. The simultaneous use of other landmarks extracted from the relative distribution was first calculated for each individual and then distant landscape for guidance and not for context. But pooled for each condition so that each individual ant contributed then the process of deciding which landmarks are to be equally to the distribution. The arrows indicate the position of the used for guidance and which ones are used as contex- nest (for the no-landmark condition) or the fictive nest in front of the landmark (for Rotation 16° and 32° conditions). tual cues appears complex and cannot be based on a simplistic distinction between proximal landmarks and distal panorama. We find it most parsimonious to How to match panoramic views? account for the results by proposing that the ants in our Despite a great amount of work [31,32,35,39-42] how experiment were using guidance strategies based on insects match memorised and current views to produce large panoramic views, without summoning the need such efficient navigational behaviours is far from fully to segregate such panoramic views into context and understood. Recent evidence shows that ants are able to landmarks. align their body in order to match the retinal position of By comparing panoramic images in simple ways, we the features memorized along a familiar route [40]. Such could explain here the sharp transitions in the direction asimplemechanismbasedonpanoramicimagesalso of travel observed, the presence of wiggling paths at par- explains spontaneous biases in ant routes observed in an ticular locations, some of the differences observed when artificial arena [31]. displacing the landmark to the left or to right (see ‘Rota- The present work also supports this hypothesis for tional matching of panoramic views’ in Results), as well route following (see ‘Rotational matching of panoramic as why the ant routes were disrupted at different loca- views’ in Results), but suggests that ants may not always tions across groups (U-turns and searching) (see follow that strategy. Indeed, in the 32° condition, the ‘Panoramic image difference distribution’ in Results). ants U-turned and started searching on average half-way The correspondence between the regions where the to the nest although our analysis of rotational image dif- ants’ travel was disrupted and the regions of high ferences shows that walking in the best matching direc- panoramic image differences (on average ~15% in this tions should lead the ants all the way towards the nest case, but with individual variation) (Figure 6) suggests or the landmark. We suggest that individual ants may that guidance cues must be widespread on the ants’ possess a mismatch tolerance threshold that allows panoramic visual field. them to switch between navigational strategies. If the Wystrach et al. Frontiers in Zoology 2011, 8:21 Page 9 of 11 http://www.frontiersinzoology.com/content/8/1/21

mismatch is considered too bad, ants might stop such a on individual objects. Added to that is an entire specia- route strategy (i.e., walking along the best matching lised stream, the so-called ventral stream that is dedi- direction) and start another strategy. Due to the artificial cated to object perception [47,48]. To those humans alteration of the scenery in this situation, this second who have seen this landmark, it seemed the obvious one strategy led the ants to meander in loops, but in a dif- to use. Yet the evidence suggests that the ants did not ferent fashion than the systematic search displayed in a focus only on the landmark but relied simultaneously totally unknown area (see Additional file 2). In more on the distant panorama for guidance. Is this pattern naturalistic situations, such a strategy might be adapted peculiar to our experimental situation? We have reasons for navigation in less familiar environments that do not to think that the use of panoramas as a whole would be show excessive mismatch, such as situations in which more widespread in insect navigation. Using cues that the ant has been led astray or blown away from her are encoded and processed simultaneously across a large familiar route corridor by a small distance (work in part of the retina can well explain present and past preparation). results obtained in ants and seems an appropriate strat- Although analysing panoramic images explains a lot, egy to cope with the complexity of natural scenes, the other puzzles remained. We did not manage to explain poor resolution of insects’ eyes, and the lack of dedi- the early U-turns observed in the 32° left condition. A cated object-perception visual streams. It is still unclear better knowledge of the nature of insects’ perceptions what the nature of the parameters is that comprise and memories would be precious for further illuminat- insects’ perceptions and memories, but future studies ing guidance mechanisms. should not assume that insects functionally segregate landmarks and distal panorama without evidence for Nature of the insect views? such a dichotomy. Do insects use landmarks for guidance? Yes. Much evidence in bees, wasps and ants shows that insects are Methods highly influenced by landmarks (for a review [18]; in Nest area and Landmark Melophorus bagoti [9,13]). Do insects focus on indivi- We chose a nest located in an area devoid of any proxi- dual landmarks only, filtering out the distant panorama mal trees and provided the ants with a feeder 10 m from guidance mechanisms? We think not. The present away from their nest. The area between the nest and work shows that, even when the dichotomy between feeder, where the ants navigated, was open, flat, and proximal landmark and distal panorama is artificially cleared of any natural debris. The artificial landmark emphasised, guidance is not focused solely on the land- consisted of a huge black sheet (3 m wide and 2 m mark. But then, are insects’ panoramic views constituted high) stretched between two poles 90 cm behind the of an ensemble of individual landmarks? Probably not. nest entrance (Figure 1). The landmark width subtended Evidence shows that insects store a pallet of features/ an angular size of 118° at the nest location and 15° at parameters like strong boundaries [43], spots of light, the most distant location (i.e., feeder). Melophorus centre of gravity, and colour of areas [44] and appear to bagoti acuity being about 4° [49], the landmark could be do so without reconstructing the actual pattern [45]. perceived all along their homeward route. To the ants, Insects also have access to landmark distance informa- the landmark presented a strong dynamic change in size tion based on motion parallax [20-25]. The motion par- (increasing in retinal angle of 64° (from 54° to 118°) allax creates a pattern of optic flow that can be used to along the azimuth in the last 2 m of the route). In con- pinpoint a target location [21]. As with static cues, such trast, the rest of the panorama presented very little dynamic cues can potentially be matched across the apparent displacement, the closest tree being located whole panoramic view [46] and the insect may not be roughly 14 m away behind the nest. All in all, our artifi- using them exclusively on isolated landmarks. Rather cial landmark stood as an obvious beacon for the nest than isolated landmarks, encoding such a pallet of static entrance. and dynamic parameters simultaneously across a large part of the retina seems an appropriate strategy to cope Protocol with the complexity of natural scenes and the poor reso- M. bagoti lives in the semi-arid terrain of central Aus- lution of insects’ eyes. tralia, which is typically filled with bushes, grass tussocks and trees. The ants were given food ad libitum in Conclusion a fixed feeder 10 m from the nest entrance, and painted We have created conditions in which a landmark at their first visit to the feeder with a colour that seemed prominent, easy to extract, and very useful, at marked the day of arrival. After 2 days of spontaneous least to our primate visual system. But we (primates) shuttling between the nest and the feeder, with the arti- have high acuity frontal foveal vision that can be focused ficial landmark immediately behind the nest, the marked Wystrach et al. Frontiers in Zoology 2011, 8:21 Page 10 of 11 http://www.frontiersinzoology.com/content/8/1/21

foragers were tested. An ant returning from the feeder differences values using the 17 pictures provided an esti- was captured near the nest and released again at the fee- mate of mismatch across the whole terrain of travel. der location with the artificial landmark either left at its original position, removed or displaced by 0°, ± 16°, or ± Additional material 32° relative to the feeder-nest direction. Another test consisted of releasing the ants 10 m or 2 m in front of Additional file 1: Panoramic picture comparison. Illustration of an identical landmark located in a distant test field panoramic pictures. The recording locations and procedure used to roughly 100 m away in the same absolute orientation as transform and compare them are explained. Additional file 2: Approaches and searches. Comparison of the initial in the training condition. Ants were tested singly, and approach directions and subsequent search distribution/density in each ant was only tested once. conditions where ants did not reach the landmark. Distinguishes between searches on training and test field. Path analysis Additional file 3: Directional switch and first U-turn. Examples of paths showing segments oriented towards the landmark. Sudden The training and test fields were covered by a grid of 1- switches in direction and first U-turns are pointed out. Illustrates the m squares made out of strings stretched between tent independence between direction of travel and ‘path uncertainty’. pegs that allowed the recording of paths by hand. The recorded paths were digitised into (x, y) coordinates ™ with the software Graphclick http://www.arizona-soft- Acknowledgements ware.ch/ and processed using Matlab™ (Math Works, We thank Sebastian Schwarz, Patrick Schultheiss and Madeleine Tempé for Natick, MA, USA) programs. Paths were analysed for U- their help on the field, and Paul Graham for helping, lending his panoramic camera, and providing comments on the manuscript. We also wanted to turns and tortuosity. U-turns were defined as walking thank two reviewers for many thoughtful remarks and suggestions. This back at least 20 cm along the Y axis within 50 cm of research was supported by a Macquarie University research excellence displacement. The maximum angle away from the train- scholarship (iMQRES) to AW and an Australian Research Council Discovery Project grant (DP110100608). We thank the CSIRO Centre for Arid Zone ing direction that can be travelled for more than 50 cm Research for letting us use their grounds for research. without being considered as U-turn was thus 113 degrees. The tortuosity index of a path corresponded to Author details 1Department of Biological Sciences, Macquarie University, Sydney, NSW 2109 theaveragedabsoluteturnangle(inradians)between Australia. 2Centre de Recherches sur la Cognition Animale, CNRS, UMR 5169, successive chunks of 20-cm line segments connecting Université Paul-Sabatier, Toulouse F-31062, France. points on the path. A circle of 20 cm radius was placed Authors’ contributions at the starting point of the digitised path, and where the AW, GB and KC designed the experiment. AW carried out the experiment, circle intersected the path defined the first segment. performed the analysis and wrote the manuscript. All authors read, revised, The circle was then placed at the end of segment 1 to and approved the final manuscript. define segment 2, etc. Competing interests The authors declare that they have no competing interests. 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Zeil J, Hofmann MI, Chahl JS: Catchment areas of panoramic snapshots in and take full advantage of: outdoor scenes. J Opt Soc Am A 2003, 20:450-469. 33. Pastergue-Ruiz I, Beugnon G, Lachaud JP: Can the ant Cataglyphis cursor (Hymenoptera, Formicidae) encode global landmark-landmark • Convenient online submission relationships in addition to isolated landmark-goal relationships. J Insect • Thorough peer review Behav 1995, 8:115-132. • No space constraints or color ﬁgure charges 34. Graham P, Durier V, Collett TS: The binding and recall of snapshot memories in wood ants (Formica rufa L.). J Exp Biol 2004, 207:393-398. • Immediate publication on acceptance 35. Stürzl W, Cheung A, Cheng K, Zeil J: The information content of • Inclusion in PubMed, CAS, Scopus and Google Scholar panoramic images I: The rotational errors and the similarity of views in • Research which is freely available for redistribution rectangular experimental arenas. J Exp Psychol Anim Behav Process 2008, 34:1-14. Submit your manuscript at www.biomedcentral.com/submit

Appendix II

APPENDIX II

Appendix II contains publications related to insect navigation to which I contributed during my PhD.

 Wystrach, A. (2009). Ants in rectangular arenas: A support for the global matching theory. Commun Integr Biol. 2, 388 - 390.  Schultheiss, P., Schwarz, S. and Wystrach, A. (2010). Nest Relocation and Colony Founding in the Australian Desert Ant, Melophorus bagoti Lubbock (Hymenoptera: Formicidae). Psyche 2010, 4pp.  Schwarz, S., Albert, L., Wystrach, A. and Cheng, K. (2011). Ocelli contribute to the encoding of celestial compass information in the Australian desert ant Melophorus bagoti. J. Exp. Biol. 214, 901-906.  Schwarz, S., Wystrach, A. and Cheng, K. (2011). A new navigational mechanism mediated by ant ocelli. Biology Letters. (doi:10.1098/rsbl.2011.0489)  Schwarz, S. and Wystrach, A. (2011). Visual input and path stabilisation in walking ants. Commun Integr Biol. 4.

271 Appendix II

Article Addendum Ants in rectangular arenas A support for the global matching theory

Antoine Wystrach

CRCA; CNRS-UPS UMR 5169; Toulouse, France Abbreviation: MTT, mismatch tolerance threshold Key words: spatial cognition, ant navigation, global matching, geometry, mismatch tolerance threshold

Although spatial cognition is studied by neuroscientists, matching is simple and requires no feature extraction: the agent psychologists, biologists and computer scientists, it suffers relies on a panoramic view previously stored at the goal location, from a lack of integrative studies. The topic of geometry of and moves in order to minimize the mismatch between its current space for instance, has been studied since twenty years only view and the memorized view. When the two views match, the in vertebrates and only in artificial and visually poor environ- goal is reached. Global matching can not only explain the results ments. But recently, similar results have been obtained with ants, obtained in rectangular arenas6 but can also be applied in natural supporting the recent idea of global matching. Contrary to the conditions7 (Wystrach et al., in preparation). other theories about geometry, global matching is parsimonious, In nature, once a forager ant has caught a prey, it immediately testable in natural conditions and makes sense in an ecological runs back to its nest. In our laboratory experiments,5 the loaded context. Here, further investigations into the data obtained in ant was released at the centre of a rectangular arena and had to ants describe and support a new concept for the global matching reach one of the four corners to get back to its nest. All four corners theory: the Mismatch Tolerance Threshold (MTT). This new led to the nest. idea can be tested in other species and we stress the importance When the rectangular arena was simply put on a table, all the of considering the whole paths displayed by the animals in future tested ants spontaneously chose a particular corner and systemati- experiments. cally returned to it, trial after trial (Fig. 1A). They could do so by using the extra-arena cues from the experimental room (above How animals encode the visual shape of their environment has the walls of the arena). These results support a global matching drawn an increasing interest since the seminal work of Cheng.1 hypothesis. The homing ant matches its current view with the This topic is now studied across many vertebrate species2 and one memorized during the previous trials (when it succeeded in several theories on how the geometry of space is extracted and reaching its nest), and thus always returns home via the same encoded are debated.3 However, most of those studies were corner. conducted in artificial rectangular arenas and little attention has When the plain white walls arena was covered by an opaque been paid to the relevance of such spatial information in nature.4 plastic dome, however, the ants could not see the extra-arena cues Recently, Wystrach and Beugnon5 used Cheng’s paradigm1 and had to rely on the shape of the arena only. Because it is a rect- to test a visual ant species (Gigantiops destructor) in the typical angle, each corner is indistinguishable from its diagonally opposite rectangular arena, a première among invertebrates. The results one. In this condition, only 53.8% of the ants managed to solve obtained with ants are similar to those of vertebrates but, without the task (i.e., displayed a preference for two diagonally opposite summoning theories about geometry, can be parsimoniously corners) (Fig. 1C). The others ants displayed random choices. This explained by a global matching hypothesis.5 The concept of global significant decrease of the number of ‘fixed ants’ is not surprising: the presence of the dome, in hiding the extra-arena cues, removes a lot of visual information. Here, most of the panoramic scene is Correspondence to: Antoine Wystrach; CRCA, CNRS-UPS UMR 5169; Toulouse just white, and the mismatch in the global view between pairs of cedex 9 F-31062 France; Email: [email protected] corners from different diagonals becomes low. It is likely that all Submitted: 04/09/09; Accepted: 04/10/09 the ants matched their view from one trial to another. The 53.8% Previously published online as a Communicative & Integrative Biology ‘fixed ants’ were subtle enough to detect that mismatch and thus E-publication: returned systematically to two diagonally opposite corners; the http://www.landesbioscience.com/journals/cib/article/8717 other ants tolerated it and therefore displayed random choices. Addendum to: Wystrach A, Beugnon G. Ants learn geometry and features. Curr A third group of ants was also tested within the dome, but in the Biol 2009; 19:61–6; PMID: 19119010; DOI: 10.1016/j.cub.2008.11.054. presence of featural cues: one distinct black shape in each corner

388 Communicative & Integrative Biology 2009; Vol. 2 Issue 5 Ants support the global matching theory

Figure 1. This figure is divided horizontally in three different parts. The top part illustrates the different experimental conditions: (A) presence of conspicuous extra-arena cues; the ants systematically return to a preferred corner. (B) No extra-arena cues but black features (10 x 10 cm) emphasize the corners and the ants display a preference for two diagonally opposite corners. (C) Only the cue given by the shape of the rectangular arena is present and the ants display a preference for two diagonally opposite corners. (D and E) Same visual condition than in (B) but the ants use the subtle difference between the features to select a unique preferred corner. In (A–D) the four corners lead to the nest (spontaneous) whereas in (E), only one corner leads to the nest (conditioning). The middle part illustrates the concept of Mismatch Tolerance Threshold (MTT). The graph shows the assumed amount of visual information given by the global view in order to display the corner preferences illustrated in the top part. Four hypothetical MTTs are represented: depending on their MTT, the figured ants are black (fixed ants, display corner preferences) or white (random ants, display random corner choices). The bottom part presents the actual data. In black, the proportion of fixed ants (display the corner preference). In grey, the time spent by the fixed ants in the arena before reaching a corner. Both “time” and “% of fixed ants” are correlated to the amount of visual information (p < 0.0001).

(Fig. 1B and D). Although they could discriminate the different views than required, and returned systematically to the same features, no ant used them to systematically return in the same corners. Interestingly, the relative frequencies of those ‘fixed ants’ corner (Fig. 1D). This makes sense: as all the features together across the different conditions reflect the quantity of information cover only 7% of the arena, therefore the mismatch in the global given by the global view (correlation r= 0.65411, p < 0.0001). It view between facing one corner and its diagonally opposite one is seems like each ant possesses a spontaneous “mismatch tolerance minor, and the ants paid no heed to it. However, the percentage of threshold”, which vary between individuals. Individuals with a ants displaying a preference for one diagonal increased to 72.7% high threshold tolerate high mismatch and thus, if the visual infor- (Fig. 1B). This makes sense insofar as the presence of the black mation is not obvious enough, display random choice. Individuals features in the corners outlined the rectangular shape of the arena with a lower threshold, however, tolerate lesser mismatch and better, and thus increased the mismatch between two corners from consequently, use more details of the visual information and different diagonals. systematically return to the same corners. Overall, in those three experiments, because all the corners led The question then arises as to whether that MTT is fixed by to the nest, the ants only needed to store and use views that were individual physiological factors, or is flexible and can be adapted precise enough to lead them towards any corner. Yet individual to the situation. The next experiment gives the answer. The visual differences appeared. Some ants spontaneously used more precise conditions were the same as in the experiment with the black www.landesbioscience.com Communicative & Integrative Biology 389 Ants support the global matching theory

Figure 2. The red lines represent 16 successive paths of a same ant released at the centre of a rectangular arena. The black arrows and grey areas illustrate a global matching model based on panoramic pictures (after Stürzl et al. model6 Fig. 2E). The model’s reference picture is taken at one corner (black square) and a picture of each position in the arena is compared to the reference picture. Each arrow points to the neighbour that is the most similar to the reference picture. Catchment areas appear (grey and white areas). The ant’s paths are matching the model’s prediction remarkably well. features in the corner, but in this case, only one particular corner References led to the nest (Fig. 1D). The ants were thus forced to use the 1. Cheng K. A purely geometric module in the rats spatial representation. Cognition 1986; 23:149-78. subtle information given by the differences between the features 2. Cheng K, Newcombe NS. Is there a geometric module for spatial orientation? Squaring in order to succeed. In the previous experiment, no ant chose a theory and evidence. Psychon Bull Rev 2005; 12:1-23. single corner spontaneously (Fig. 1D). But in the present case, 3. Cheng K. Whither geometry? Troubles of the geometric module. Trends Cogn Sci 2008; 12:355-61. they all did (Fig. 1E). Ants can therefore lower this “matching 4. Sutton JE. What is geometric information and how do animals use it? Behav Process tolerance threshold” if required for reaching the nest. Why would 2009; 80:339-43. the ants not always apply a low threshold? Probably because 5. Wystrach A, Beugnon G. Ants Learn Geometry and Features. Curr Biol 2009; 19:61-6. 6. Stürzl W, Cheung A, Cheng K, Zeil J. The information content of panoramic images I: a low threshold costs in storing visual details, and the process The rotational errors and the similarity of views in rectangular experimental arenas. J Exp of matching detailed views may be computationally intensive. Psychol Anim Behav Proc 2008; 34:1-14. Indeed, the subtler the visual information was (the harder the 7. Zeil J, Hofmann MI, Chahl JS. Catchment areas of panoramic snapshots in outdoor scenes. J Opt Soc Am Opt Im Sci Vis 2003; 20:450-69. process of matching), the slower the ‘fixed ants’ were in choosing 8. Beugnon G, Pastergue-Ruiz I, Schatz B, Lachaud JP. Cognitive approach of spatial and a corner (correlation inf/time = -0.7323, p < 0.0001). temporal information processing in insects. Behav Process 1996; 35:55-62. The global matching hypothesis fits the present results remark- 9. Pastergue-Ruiz I, Beugnon G, Lachaud JP. Can the ant Cataglyphis cursor (Hymenoptera, 8-10 Formicidae) encode global landmark-landmark relationships in Addition to Isolated ably well, and can explain most of the data in insect navigation. Landmark Goal Relationships. J Insect Behav 1995; 8:115-32. It can also parsimoniously explain results obtained with vertebrates 10. Graham P, Fauria K, Collett TS. The influence of beacon-aiming on the routes of wood in rectangular arena11 that other models cannot.3 Our concept of ants. J Exp Biol 2003; 206:535-41. 11. Cheung A, Stürzl W, Zeil J, Cheng K. The information content of panoramic images an adaptable “mismatch tolerance threshold” explains the use of II: View-based navigation in nonrectangular experimental arenas. J Exp Psychol—Anim featural details in a conditioning procedure, consistent with asso- Behav Process 2008; 34:15-30. 12 12. Miller NY, Shettleworth SJ. Learning about environmental geometry: An associative ciative principles applied to spatial learning. Furthermore, global model. J Exp Psychol—Anim Behav Process 2007; 33:191-212. matching can be applied as well in outdoor scenes7 and describes paths in addition to corner choices. In reality, an animal integrates information and makes decisions before even starting; it continues to do so while moving, until eventually reaching its goal. The “corner choice” is just the tip of the iceberg. Future models and experiments should take into account the paths, so much rich in information (Fig. 2).

390 Communicative & Integrative Biology 2009; Vol. 2 Issue 5

Hindawi Publishing Corporation Psyche Volume 2010, Article ID 435838, 4 pages doi:10.1155/2010/435838

Research Article Nest Relocation and Colony Founding in the Australian Desert Ant, Melophorus bagoti Lubbock (Hymenoptera: Formicidae)

Patrick Schultheiss, Sebastian Schwarz, and Antoine Wystrach

Department of Brain, Behaviour & Evolution, Macquarie University, 209 Culloden Road, Sydney, NSW 2109, Australia

Correspondence should be addressed to Patrick Schultheiss, [email protected]

Received 14 January 2010; Accepted 27 February 2010

Academic Editor: Lars Chittka

Copyright © 2010 Patrick Schultheiss et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Even after years of research on navigation in the Red Honey Ant, Melophorus bagoti, much of its life history remains elusive. Here, we present observations on nest relocation and the reproductive and founding stages of colonies. Nest relocation is possibly aided by trail laying behaviour, which is highly unusual for solitary foraging desert ants. Reproduction occurs in synchronised mating flights, which are probably triggered by rain. Queens may engage in multiple matings, and there is circumstantial evidence that males are chemically attracted to queens. After the mating flight, the queens found new colonies independently and singly. Excavation of these founding colonies reveals first insights into their structure.

1. Introduction 2. Materials and Methods The Australian desert ant, Melophorus bagoti Lubbock, is The study site is located 10 km south of Alice Springs, NT, a widespread species of arid Central Australia. It inhabits Australia, on the grounds of CSIRO Alice Springs. The low-shrub and grassland deserts, where it builds fairly large area is characterised by an arid climate, with an average underground nests [1]. The outdoor activity is mainly annual rainfall of 279.4 mm [15]. The soil consists of sandy restricted to the hotter summer months, when the ants are flood plain alluvium [16], and the vegetation is a mosaic of active during the heat of the day. Foragers usually begin Acacia low open woodland and Triodia low open hummock their activity at soil surface temperatures of about 50◦C and grassland [17], although much of the latter has been replaced continue to forage at temperatures above 70◦C[2]. They by the invasive Buffel Grass Cenchrus ciliaris. M. bagoti is forage solitarily for food such as dead insects, seeds, and common in the area, and their nests occur at a density of sugary plant exudates ([3], personal observations) and are 3/ha, which is much lower than previously reported by well known for their ability to store liquids in the abdomens Muser∼ et al. [3] from a different location. of specialised workers, the so-called repletes or “honey pots” The observation of a nest move was made in December (hence their common name “Red Honey Ant” and indeed 2008, and colony founding was observed between December the genus name Melophorus, meaning “honey carrier”). This 2008 and March 2009. As these incidents were unpredictable, method of food storage is also adopted by several other observations could not be made systematically. Due to seasonally active ants, for example, Cataglyphis [4] of North unusually high rainfall in November 2008 (wettest November Africa, Camponotus [5] of Australia, and Myrmecocystus [6] on record with 156 mm rain), much of the area was covered and Prenolepis [7] of North America (the latter store fat, not by fresh vegetation for most of the summer. sugar). In the recent years, M. bagoti has attracted increasing 3. Results and Discussion attention for its navigational abilities (e.g., [8–13]; for a review see [14]), thus making a broader understanding of its 3.1. Nest Move. After a full week of rainy weather, some nests behaviour and life history desirable. of M. bagoti reopened their entrance holes on 21 November 2 Psyche

2008. In the following three weeks, 12 of 16 observed nests relocated the position of their entrances several times by 5– 191 cm (average: 73 cm). This behaviour is usually displayed much rarer. Occasionally several entrances were in use at the same time. In preparation for other experiments, the area around one of these nests was cleared of vegetation on 25 November whereby a nest chamber very close to the surface was accidentally opened. In the following days, the nest relocated its entrance to this new opening (distance: 47 cm, bearing: 190◦), closing the old entrance. On 3 December (partly cloudy, max. temp. 40.9◦C) at 17.00 hour we noticed that this nest was in the middle of relocating to a new nest site (distance: 17.75 m, 205◦). A continuous but sparse moving column of ants, including repletes, was observed between the two nest sites. The column was directed to the new nest in almost a straight line. Although most workers went Figure 1: A worker of M. bagoti dragging her abdomen across the from the old to the new nest, some were observed going sandy surface during a nest relocation. Arrows indicate the track left the other way. The width of the column varied from a few behind in the sand. Still photo taken from a ﬁlm sequence, credit A. cm to about 1 m but always seemed to consist of distinct Wystrach. trails. Most, but not all of the repletes, were pulled or pushed out of the old nest opening by workers and proceeded to move to the new nest on their own (see Supplementary 50 50 Material), where some were dragged into the entrance by 40 40 C)

workers. Because foragers are usually the only ants that ◦ leave a nest, repletes are necessarily unfamiliar with the 30 30 environment around the nest. They must therefore rely on other cues to ﬁnd the direction and location of the new nest. 20 20 Rainfall (mm)

There are three possible explanations. Other workers within Temperature ( the nest could convey the information, they might simply 10 10 follow other ants on the trail, or they might use a system of 0 0 chemical (olfactory) marking. Indeed, on several occasions Jan5 Jan9 Dec4 Dec8 workers were seen dragging the tip of their abdomen across Jan13 Jan17 Jan21 Jan25 Jan29 Dec12 Dec16 Dec20 the sandy soil (see Figure 1 and the Supplementary Material), Nov18 Nov22 Nov26 Nov30 Data a behaviour which has not been observed in M. bagoti or any Rainfall (mm) other solitary foraging desert ant so far. These ants may be Minimum temperature (◦C) laying intermittent odour trails. If this conclusion holds true, Maximum temperature (◦C) it will have important implications for future studies on the navigational strategies of this ant species. Figure 2: Timing of mating flights in M. bagoti during the summer 2008/09. Daily rainfall and temperature (min./max.) are shown We could distinguish two types of repletes, as previously for the time period from 18.11.08 to 31.01.09, excluding the described by Conway [1]: ones with clear, amber-coloured period from 23.12.08 to 02.01.09 when no observations were made abdomens and ones with milky white abdomens. The sizes (indicated by grey bar). Arrows indicate observed mating flights. of their inflated abdomens were variable. One dealate queen Climate data from [15]. was also observed, and one winged male, but no eggs, larvae or pupae. The queen was dragged all the way from the old to the new nest (see the Supplementary Material). All activity ceased at 17.30 hour. Over the next few days we checked observation). In the described case the move was probably for activity sporadically. The old nest was now presumably triggered by our disturbance. abandoned. On one occasion some workers and one replete from another nearby nest (distance: 19.98 m) entered the old 3.2. Colony Founding. The founding stage of an ant colony abandoned nest. However, no further activity was observed is usually characterised by the same sequence of events. at the old nest after this incident. At the new nest excavating The virgin queen leaves the nest in a mating flight and is activity was at first very high, but during the following days inseminated by one or several males. She then looks for a the activity slowed down considerably and eventually came new nest site and starts excavating a small nest, where she to a stop. The nest reopened on 8 January and remained lays eggs and rears a small brood [19]. active until the end of the season. Several nuptial flights were observed during the summer Although nest emigration behaviour seems to be com- of 2008/09, always after rainy days (see Figure 2) and always mon in forest-dwelling ant species [18], this does not seem in the mornings. Heavy rain is a common trigger for the to be the case for M. bagoti. Once a nest is established, its timing of mating flights in desert ants [19]. Sometimes location usually does not change over many years (personal queens and males left the nest together to fly off, at other Psyche 3

5 cm

(b)

5 cm

(a)

Figure 3: (a) Overview of an excavated founding colony of a Melophorus bagoti queen. Arrow indicates the location where the dead queen was found. (b) Close-up of the chamber encountered during excavation, the part of the channel leading to the chamber has been removed. Arrow indicates the channel leaving the chamber on the other side; see text for details. Photo credit P. Schultheiss.

times only queens did so. At about 10.30 hour on 21 January Queens founded new colonies independently and with- 2009, mating flights occurred at four nests simultaneously. out the help of other queens or workers (haplometrosis, As it had rained for the two previous days, it was humid, see [22]); this mode of colony founding is common in overcast, and warm (61% RH, 29◦C at 9.00 hour). From this formicine ants [19, 23]. However, nothing is known about synchronised behaviour, we can surmise that mating occurs the number of queens in later colony stages or other in swarms, although no such mating site could be located. populations of M. bagoti. For example, in North American One mating was actually observed: an already dealate queen ants of the genus Myrmecocystus, which can be regarded was found on the ground, surrounded by several males, of as the ecological equivalent to Melophorus [24], founding which one copulated with the queen once for a few seconds. queens are often joined by other queens after they have The following day, a dealate queen was observed leaving a excavated the first nest chamber alone [25]. Also, some desert nest at 10.15 hour and was followed as she wandered around ants in North America, including Myrmecocystus, display the area up to a maximum distance of 50 m from the nest considerable geographic variation in their mode of colony entrance, regularly seeking thermal refuge on small plants founding [26]. We observed a total of 21 dealate queens at and twigs. During this time, she copulated once with one their attempts to establish new colonies (all on 21 January). male and three times with another male. On both occasions Of these, only five were in a completely open place, while the the queen had climbed onto a small plant and remained remaining queens chose a spot in the shade of a little plant motionless while the male flew around her. This behaviour or twig. Here the queens started to dig at a shallow angle, is somewhat reminiscent of the sexual calling behaviour of using their mandibles (see the Supplementary Material). some ponerine ants [20]. The copulations lasted from a They continued digging for sometimes several hours. In few seconds to about half a minute. As all the observed one case, the queen had chosen a site that was close to an copulations involved dealate queens, they were obviously not already existing nest (distance: 7.70 m), and workers from regular matings; it seems though that queens readily mate this colony apparently attacked and killed the queen. While even after they have broken off their wings and possibly even several workers dragged the dead queen away, one worker attract males chemically. After 1 hour 50 minutes we stopped closed the hole of the queen rapidly. After two days, 12 of following the queen; it is not known if she returned to the the 21 holes were closed, rising to 15 after another four days; nest. by 10 March, only one remained open (although obstructed Another dealate queen was seen being followed by a by a branch). All colonies can thus be regarded as failed, flying insect (probably Diptera, Syrphidae, of which the for reasons unknown. Four of the closed founding colonies subfamily Microdontinae has larvae that prey on ants in their were then excavated. Three of these continued as a narrow nests; the adults are usually found in the vicinity of ant nests channel underground for 2–10 cm, ending in a dead end [21]). It followed the exact path the ant took at a constant with no remains of the queen, being wholly or partially distance of about 10 cm (see the Supplementary Material) filled with debris. The fourth hole started as a narrow until it eventually lost the ant and flew away after searching channel, slowly sloping downward before opening into a for a little while. small chamber (length: 7.5 cm). This was oriented at a right 4 Psyche angle to the channel but diagonally to the surface, at a depth of Sciences of the United States of America, vol. 105, no. 1, pp. of 4–9 cm below ground (see Figure 3(b)). The channel 317–322, 2008. then continued downwards at roughly 45◦ for another 8 cm, [12] P. Graham and K. Cheng, “Ants use the panoramic skyline as a turned abruptly downward, and ended without a chamber visual cue during navigation,” Current Biology, vol. 19, no. 20, at a total depth of 16 cm below ground (see Figure 3(a)). pp. R935–R937, 2009. Remains of a dead queen were found at the end of the [13] P. Graham and K. Cheng, “Which portion of the natural panorama is used for view-based navigation in the Australian channel, and parts of the channel were filled with debris. desert ant?” Journal of Comparative Physiology A, vol. 195, pp. The fact that there was no nest chamber at the end of 681–689, 2009. the channel indicates that the queen died before she had fully [14] K. Cheng, A. Narendra, S. Sommer, and R. Wehner, “Traveling excavated the founding nest. Although the observations pre- in clutter: navigation in the central Australian desert ant sented here are necessarily incomplete and many important Melophorus bagoti,” Behavioural Processes, vol. 80, no. 3, pp. questions remain unanswered, they do offer a fascinating 261–268, 2009. insight into the early stages of an ant colony. [15] Commonwealth of Australia, “Climate Data Online,” Verified 13.01.10, 2009, http://www.bom.gov.au/climate/averages. [16] K. H. Northcote, R. F. Isbell, A. A. Webb, G. C. Murtha, H. Acknowledgments M. Churchward, and E. Bettenay, “Atlas of Australian soils, sheet 10: central Australia. With explanatory data,” in Atlas The authors would like to thank CSIRO Alice Springs of Australian Soils, Melbourne University Press, Melbourne, for permission to use their property, and Ken Cheng for Australia, 1968. contributing observations. The funding was provided by [17] Northern Territory Government, “National Resources, Envi- Macquarie University, Sydney, and the Australian Research ronment, The Arts and Sport—NRETAS Maps,” Verified Council. 13.01.10, 2004, http://www.ntlis.nt.gov.au/imfPublic/imf.jsp? site=nreta. [18] J. Smallwood, “Nest relocations in ants,” Insectes Sociaux, vol. References 29, no. 2, pp. 138–147, 1982. [19] B. Holldobler¨ and E. O. Wilson, The Ants, Springer, Berlin, [1] J. R. Conway, “Notes on the excavation of a nest of Germany, 1990. Melophorus bagoti Lubbock in the Northern Territory, Aus- [20] B. Holldobler¨ and C. P. Haskins, “Sexual calling behavior in tralia (Hymenoptera, Formicidae),” Journal of the Australian primitive ants,” Science, vol. 195, pp. 793–794, 1977. Entomological Society, vol. 31, pp. 247–248, 1992. [21] X. Y. Cheng and F. C. Thompson, “A generic conspectus of [2] K. A. Christian and S. R. Morton, “Extreme thermophilia the Microdontinae (Diptera: Syrphidae) with the description in a central Australian ant, Melophorus bagoti,” Physiological of two new genera from Africa and China,” Zootaxa, no. 1879, Zoology, vol. 65, no. 5, pp. 885–905, 1992. pp. 21–48, 2008. [3] B. Muser, S. Sommer, H. Wolf, and R. Wehner, “Foraging [22] B. Holldobler¨ and E. O. Wilson, “The number of queens: an ecology of the thermophilic Australian desert ant, Melophorus important trait in ant evolution,” Naturwissenschaften, vol. 64, bagoti,” Australian Journal of Zoology, vol. 53, no. 5, pp. 301– no. 1, pp. 8–15, 1977. 311, 2005. [23] L. Keller, “Queen lifespan and colony characteristics in ants [4] P. Schmid-Hempel and R. Schmid-Hempel, “Life duration and termites,” Insectes Sociaux, vol. 45, no. 3, pp. 235–246, and turnover of foragers in the ant Cataglyphis bicolor 1998. (Hymenoptera, Formicidae),” Insectes Sociaux, vol. 31, no. 4, [24] A. N. Andersen, “Functional groups and patterns of organiza- pp. 345–360, 1984. tion in North American ant communities: a comparison with [5] W. W. Froggatt, “Honey ants,” in Report on the Work of the Australia,” Journal of Biogeography, vol. 24, no. 4, pp. 433–460, Horn Scientific Expedition to Central Australia II, B. Spencer, 1997. Ed., pp. 385–392, Dulau, London, UK, 1896. [25] S. H. Bartz and B. Holldobler,¨ “Colony founding in Myrmeco- [6] R. R. Snelling, “A revision of the honey ants, genus Myrmeco- cystus mimicus Wheeler (Hymenoptera: Formicidae) and the cystus (Hymenoptera: Formicidae),” Natural History Museum evolution of foundress associations,” Behavioral Ecology and of Los Angeles County, vol. 24, pp. 1–163, 1976. Sociobiology, vol. 10, pp. 137–147, 1982. [7] W. R. Tschinkel, “Seasonal life history and nest architecture of [26] S. W. Rissing, R. A. Johnson, and J. W. Martin, “Colony a winter-active ant, Prenolepis imparis,” Insectes Sociaux, vol. founding behavior of some desert ants: geographic variation 34, no. 3, pp. 143–164, 1987. in metrosis,” Psyche, vol. 103, pp. 95–101, 2000. [8] M. Kohler and R. Wehner, “Idiosyncratic route-based memories in desert ants, Melophorus bagoti: how do they interact with path-integration vectors?” Neurobiology of Learning and Memory, vol. 83, pp. 1–12, 2005. [9] A. Narendra, A. Si, D. Sulikowski, and K. Cheng, “Learning, retention and coding of nest-associated visual cues by the Australian desert ant, Melophorus bagoti,” Behavioral Ecology and Sociobiology, vol. 61, no. 10, pp. 1543–1553, 2007. [10] A. Narendra, K. Cheng, D. Sulikowski, and R. Wehner, “Search strategies of ants in landmark-rich habitats,” Journal of Comparative Physiology A, vol. 194, no. 11, pp. 929–938, 2008. [11] S. Sommer, C. von Beeren, and R. Wehner, “Multiroute memories in desert ants,” Proceedings of the National Academy

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RESEARCH ARTICLE Ocelli contribute to the encoding of celestial compass information in the Australian desert ant Melophorus bagoti

Sebastian Schwarz1,*, Laurence Albert1, Antoine Wystrach1,2 and Ken Cheng1 1Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia and 2Centre de Recherches sur la Cognition Animale, CNRS, Université Paul Sabatier, Toulouse F-31062, France *Author for correspondence ([email protected])

Accepted 22 November 2010

SUMMARY Many animal species, including some social hymenoptera, use the visual system for navigation. Although the insect compound eyes have been well studied, less is known about the second visual system in some insects, the ocelli. Here we demonstrate navigational functions of the ocelli in the visually guided Australian desert ant Melophorus bagoti. These ants are known to rely on both visual landmark learning and path integration. We conducted experiments to reveal the role of ocelli in the perception and use of celestial compass information and landmark guidance. Ants with directional information from their path integration system were tested with covered compound eyes and open ocelli on an unfamiliar test field where only celestial compass cues were available for homing. These full-vector ants, using only their ocelli for visual information, oriented significantly towards the fictive nest on the test field, indicating the use of celestial compass information that is presumably based on polarised skylight, the sun’s position or the colour gradient of the sky. Ants without any directional information from their path-integration system (zero- vector) were tested, also with covered compound eyes and open ocelli, on a familiar training field where they have to use the surrounding panorama to home. These ants failed to orient significantly in the homeward direction. Together, our results demonstrated that M. bagoti could perceive and process celestial compass information for directional orientation with their ocelli. In contrast, the ocelli do not seem to contribute to terrestrial landmark-based navigation in M. bagoti. Key words: ocelli, compound eye, ant, navigation, celestial compass, compass orientation.

INTRODUCTION independently (Christian and Morton, 1992; Muser et al., 2005). To Almost all animals, from humans to insects, encounter the problem find food and their way back to the nest, M. bagoti rely mainly on of navigating through spatial environments. Visual orientation is two navigational strategies: landmark learning and path integration often an important mode of animal navigation (Cheng, 2006; (reviewed by Wehner et al., 1996; Collett and Collett, 2002; Cheng Gibson, 1998; Srinivasan, 1998). The visual system of many et al., 2009). Landmark guidance is based on learning and insects, for instance, is particularly sophisticated: in addition to their memorising the positions of terrestrial landmarks, such as bushes large multifaceted compound eyes they possess one, two or three and trees, as well as the panorama and the skyline along their route less-conspicuous ocelli (reviewed by Taylor and Krapp, 2007). For and enables the ants to relocate a precise earth-based absolute over a hundred years, scientists have studied the function and location (Graham and Cheng, 2009a; Graham and Cheng, 2009b; evolution of compound eyes (Land and Fernald, 1992; Nilsson and Wystrach et al., 2011). In contrast, path integration is based on Kelber, 2007). For instance, it has been shown that insect compound egocentric information and enables foraging ants to return to the eyes are fundamental for orientation and colour vision, and that they nest on the shortest direct track at any time and from any position are sensitive to UV-light and polarised skylight (von Frisch, 1914; without help of terrestrial cues such as landmarks or panoramic Wehner, 1984; Wehner et al., 1996; Briscoe and Chittka, 2001). In views (Graham and Cheng, 2009a; Kohler and Wehner, 2005; contrast, little is known about the function of the ocelli. In locusts, Narendra, 2007b; Narendra et al., 2008). To use path integration, it has been demonstrated that ocelli are light sensitive (Wilson, 1978) the ants derive the directional (compass) information from the and can serve as a visual flight and gaze stabiliser and aid in detecting polarised skylight and the sun’s position (reviewed by Wehner, the horizon (Taylor, 1981) (for reviews, see Goodman, 1981; Taylor 2003), while a special odometer, a step-counter (Wittlinger et al., and Krapp, 2007). Ocelli are usually bigger in crepuscular and 2006), provides them with information on the distance (Narendra nocturnal flying insects and probably play a role in flight control et al., 2007a). Compass and odometric information are integrated (Warrant, 2008). to compute a vector home, which is, in turn, continuously updated In central place foragers such as social hymenoptera, visual according to the distance and direction of the nest relative to the navigation is particularly important for finding a food source as well insect. as returning to the safety of the colony. The Australian desert ant Both landmark learning and path integration are dependent on Melophorus bagoti is one of these central place foragers. The the compound eyes of M. bagoti, but the role and function of the extreme heat of the ground prevents the ants from using chemical ocelli have yet to be revealed. It is assumed that the ocelli are trails and every single forager has to learn her own routes connected to the celestial compass, which registers the pattern of

THE JOURNAL OF EXPERIMENTAL BIOLOGY 902 S. Schwarz and others the polarised skylight and the position of the sun, as has been shown Experimental procedure and treatment conditions in the North African desert ant Cataglyphis bicolor, where foragers Forging ants that reached the feeder on the training field for the can perceive celestial compass information with their ocelli (Fent first time and picked up a food item were marked on the abdomen and Wehner, 1985). In the present study we analysed the role of with a daily colour of enamel paint. During training, the ants could the ocelli in spatial navigation in the desert ant M. bagoti. More gain familiarity with the vicinity. All marked ants were able to shuttle precisely, we determined whether the ocelli could be used to back and forth between the feeder and the nest for at least 2days determine direction according to celestial cues and/or terrestrial before they were subjected to one of the four treatment conditions landmark information. and tested. Just before a test (and hence not during training), we covered either the eyes (Oc), the ocelli (Ey) or both compound eyes MATERIALS AND METHODS and ocelli (Bl) with acrylic paint (Fig.1). In addition, a sham control Study area group was included in which a small dot of paint was placed dorsal From November 2009 to March 2010, ~10 km south of Alice to the ocelli and between the compound eyes (Sh) (Fig.1). We Springs, Northern Territory, experiments on Melophorus bagoti painted ants using household pins with the help of a magnifying Lubbock 1883 were carried out in the semidesert of central Australia. glass. We placed the manipulated, painted ants back in the feeder All tested ants were foragers of the same colony located in a cluttered and, after they grabbed a food item, we transferred them in the dark habitat that consisted mainly of Acacia woodland, Triodia grassland in small plastic tubes to the release point on the test or training field. and buffel grass (Cenchrus ciliaris) (Muser et al., 2005; Schultheiss To ensure a high homing motivation, only ants that held on to a et al., 2010). Throughout experimentation, the sky was either clear food item were tested. The tested ants were always released at the or only slightly cloudy. Therefore, the ants could in principle rely centre of the goniometer. However, the exit direction from the plastic on celestial compass cues – primarily the polarised skylight but also tube was chosen randomly to prevent any potential directional biases. the position of the sun and the colour gradient – for the determination The sector crossed on the goniometer at 0.6m and the subsequent of heading directions (Wehner and Müller, 2006). Experiments were paths taken by the ant were recorded. Recording of the paths was usually carried out from 09:00 to 17:00h with a break during conducted by following the route of the homing ant and drawing noontime, a period during which foraging activity decreases. the route of each ant on a piece of paper that was printed with a similar grid as those found on the test and training fields. The Experimental set-up recording of the path ended when the ant had left the grid or lost A feeder with cookie crumbs and mealworm pieces was embedded its food item. All paths were digitised and analysed (see Data in the ground 10m away from the nest. The distance between nest analyses). and feeder lies within the usual foraging area of M. bagoti and the Two experiments were conducted. First, to investigate whether ants could be trained to scamper repeatedly between their colony the ocelli are used to encode celestial compass information, we tested and the feeder. The training field was surrounded with landmarks the four treatment groups as full-vector (FV) ants on the distant test such as trees, rocks, bushes and tussocks. The test field was situated field. These ants were removed from the feeder, painted as one of 60m away from the training field in order to avoid any familiar the four treatments (i.e. Oc, Ey, Bl or Sh) and then taken to the test landmarks, including the shape of the skyline. Thus, the foragers field after they grabbed a piece of food. FV ants are, in principle, could rely only on the celestial compass information on the test able to gather both terrestrial (landmarks and skyline contour) and field during their homebound runs. celestial cues (vector direction) to find the way back to their nest. To assess the directional choice of the ants in tests, a goniometer The unfamiliar test field, however, excluded the use of familiar was used. It consisted of a wooden board (1.2m diameter) divided landmarks and panoramic information. Therefore, the FV ants were into 24sectors of 15deg each. All sectors were numbered to reduced to using information from the sky, the sun’s position and simplify the recording of the ants’ initial direction (0.6m) from the the polarised skylight pattern for homing. Second, to assess whether centre of the goniometer. For an assessment of the path direction the ocelli are used to encode familiar terrestrial landmarks or later in the journey, the position at which the foragers crossed a panoramic information, we tested the same four treatment groups 3.0m circle around the release point was noted (see Data analyses). as zero-vector (ZV) ants on the training field. Homing ants were The distance was sufficiently long, but a good deal shorter than the captured just before they entered the nest entrance, manipulated and ~44% of outbound distance that M. bagoti runs off on average on released on the training field goniometer. By capturing foraging ants an unfamiliar test field before initiating looping search movements just before entering the nest, the directional (vector) information (Narendra, 2007a; Narendra, 2007b). To trace the homebound path was set to zero, thus excluding sky compass information based on of the foragers, a grid was set up in both the test and training fields. path integration. Past research has shown that ZV M. bagoti do not The grids were divided into 100 (10ϫ10) 1m squares using pegs use sky compass information and rely instead on the shape and and string and were oriented in the same direction as the nest-feeder contour of the panorama (Graham and Cheng, 2009a). We thus direction in the training field. A goniometer placed at the position constrained the foragers to rely only on their visual memory of of the feeder was used for tests on the training field. terrestrial landmarks and the panorama to find the way back to the

Fig.1. Melophorus bagoti in different test conditions: Ey, open compound eyes and covered ocelli; Oc, open ocelli and covered compound eyes; Bl, covered compound eyes and ocelli; Sh, sham condition with small dot of paint between the compound eyes and dorsal of the ocelli.

Ey Oc Bl Sh

THE JOURNAL OF EXPERIMENTAL BIOLOGY Ocelli function in desert ants 903 nest. Otherwise, tests on the training field proceeded in the same route and the circle defined the end of segment 2, etc. To calculate manner as tests on the test field. straightness, the direction of each segment (from start to end) was plotted on a circular plot, and the r-parameter in circular statistics Data analyses of all the segments pooled was defined as straightness, with values We used circular statistics to analyse the directions chosen by the ranging from 0 (no dominant orientation) to 1 (straight orientation). tested ants at 0.6 and 3m from the release point (Batschelet, 1981). The meander index measures how much the path changes direction The V-test for circular uniformity with a given direction was used from segment to segment, or how much the path ‘wiggles’ along to determine whether the distribution of the orientations of the ants the way. The absolute angular deviation (rad) from one segment to from each group was significantly different from a random the next was averaged over all segments. Thus, a deviation of 0rad distribution and with the nest direction (or relative nest direction indicates that the two segments are collinear whereas a deviation on the test field) within the 95% confidence intervals. The circular of prad means that the ant turned straight back. K-test was used to investigate whether two samples had significantly different concentrations. Because of multiple comparisons between RESULTS the test groups, we lowered the alpha level in the K-test from 0.05 Full-vector ants on the test field to 0.017. Furthermore, the individual paths of the tested ants were Except for the totally blinded foragers (FV–Bl), all treatment groups digitised and analysed at a fine scale in terms of their sinuosity. showed an unambiguous orientation towards the fictive nest at 0.6m Two sinuosity measures were computed for each path: meander and (V-test, P<0.001) and at 3.0m distance (V-test, P<0.001) (Fig.2). straightness. To calculate both of these measures, we divided the Foragers with covered eyes (FV–Oc), covered ocelli (FV–Ey) or a path into 0.3m line segments. A circle of 0.3m radius was placed dot on the head (FV–Sh) were able to use the celestial compass at the start of a route and a straight line segment was drawn to where information to run in the direction of their fictive nest. The absence the route crossed this circle; this defined segment 1. The circle was of any significant orientation towards the fictive nest in the blind then centred at the end of segment 1, and the crossing between the group (FV–Bl), both at 0.6m (V-test, P0.729) and at 3.0m (V-test,

Full-vector ants, test field Zero-vector ants, training field Fig.2. Ocelli functions were tested in M. bagoti under four 0.6 m 3.0 m 0.6 m 3.0 m different conditions: Ey, Oc, Bl and Sh. Each row presents one treatment out of the four tested groups. Circular histograms show the headings of the tested ants after travelling 0.6 m (with each Ey N=41 20 N=11 5  sector of 15deg). The number of ants per sector is relative to the number on the right side of the histogram circle. A plot of the paths of the different groups within a 3.0m circle around the release point (the paths were not recorded during the initial 0.6m on the goniometer) is also shown. Direction and length of the black Oc N=40 10 N=33 10 arrows represent the direction and length of the mean vector for each distribution. Grey circles mark the positions on the homebound trips of each tested ant when she crossed a circle of 3.0m radius centred at the release point. The correct nest direction is indicated by the small arrowhead on the top of each histogram. The central point in Bl N=21 2 N=20 5 the histograms symbolises the release point.

Sh N=27 5

THE JOURNAL OF EXPERIMENTAL BIOLOGY 904 S. Schwarz and others P0.892), showed, not surprisingly, that either the eyes or the ocelli neither after 0.6m (V-test, P0.999) nor after 3.0m (V-test, P0.978) were necessary for using celestial information and that no non-visual (Fig.2). Ants with covered ocelli but eyes open (ZV–Ey), however, source of direction was available to the ants (Fig.2). showed a clear nestward orientation at both 0.6m (V-test, P<0.001) We next compared the inter-individual scatter of the homing and 3.0m (V-test, P<0.001). Not surprisingly, sham ants with both direction between pairs of treatment groups at the goniometer (0.6m) ocelli and eyes functioning (ZV–Sh) were also significantly oriented and after travelling 3.0m, excluding the disoriented blind ants. Ants towards the nest at both 0.6m (V-test, P<0.001) and 3.0m (V-test, with compound eyes only (FV–Ey) were more accurate than ants P<0.001) (Fig.2). with ocelli only (FV–Oc) at 3.0m (K-test, P<0.001). The initial Because of the fact that only some tested ants from the ZV–Oc headings on the goniometer showed no significant difference and ZV–Bl groups passed the 3.0m circle around the release point between groups (Fig.2). on the training field, a comparison between the scatter of the homing Both measures of sinuosity differed clearly across treatment directions became unnecessary. groups, with the blind ants showing the highest meander and lowest The sinuosity of the tested ant paths on the training field showed straightness (Fig.3). Both meander and straightness differed significant differences between the test groups (one-way ANOVA, significantly between groups (one-way ANOVA, meander: meander: F6,18671.83 P<0.001; straightness: F6,18624.33, F6,18671.83, P<0.001; straightness: F6,18624.33, P<0.001). P<0.001). In terms of meander (Tukey’s post hoc test, P<0.001) Tukey’s post hoc tests were then used to compare all pairs of groups and straightness (Tukey’s post hoc test, P<0.001), ZV–Ey foragers (Fig.3). The FV–Oc foragers displayed a similar sinuosity to the had less sinuous homing paths than ZV–Oc or ZV–Bl foragers FV–Sh and FV–Ey foragers. However, FV–Oc foragers showed a (Fig.3). Accordingly, all ZV–Ey foragers passed the 3.0m circle significantly higher meander than FV–Sh (Tukey’s post hoc test, and ran back to the nest on the training field. In contrast, only 27% P<0.001) or FV–Ey (Tukey’s post hoc test, P<0.001) foragers of the ZV–Oc foragers and 1% of the ZV–Bl foragers reached 3.0m (Fig.3). No differences in straightness between FV–Sh, FV–Ey and in any direction (Fig.2). FV–Oc foragers were found (Tukey’s post hoc test, P>0.73). DISCUSSION Zero-vector ants on the training field The purpose of the present study was to investigate the navigational After being released on the training field, the blinded ants (ZV–Bl) functions of the ocelli in the Australian desert ant M. bagoti. This appeared lost and displayed no significant orientation in the nest species uses vision in two interacting systems of navigation: direction at either 0.6m (V-test, P0.426) or 3.0m (V-test, P0.960). landmark learning, which is based on terrestrial cues, and path Foragers with covered eyes and uncovered ocelli (FV–Oc) also integration, which relies on celestial cues and a step counter displayed no significant orientation towards the nest direction, (reviewed by Cheng et al., 2009). The first part of this study tested whether M. bagoti foragers use e their ocelli to encode celestial compass cues in an unfamiliar test 1.6 d field. Because the totally blind ants (FV–Bl) were not able to find 1.4 d the nest direction on the test field, we can conclude that visual c compass information, from either the compound eyes or the ocelli, 1.2 is essential for determining directional headings in path integration. In contrast, ants with untouched eyes (FV–Sh) as well as ants with 1.0 a,b covered ocelli (FV–Ey) could readily orient towards the fictive nest 0.8 b Meander on the test field. The crucial tested group with covered eyes but 0.6 a uncovered ocelli (FV–Oc) also oriented their homebound trips in 0.4 the direction of the nest, revealing that ocelli contribute to the encoding of celestial compass information (Fig.2). 0.2 The second part of the study focused on the ability of M. bagoti ocelli to detect terrestrial cues. In order to determine whether ocelli 1.0 b a,b b a are sufficient for processing terrestrial visual cues, we tested ZV ants with no directional vector information from the path integrator c 0.8 in the familiar training field. ZV–Bl foragers ran disoriented over the training field with no peak in directional heading. ZV–Ey and 0.6 ZV–Sh foragers showed good orientation towards the nest and were c therefore able to use terrestrial cues for homing (Fig.2). This c replicates earlier findings in which ZV ants were shown to home 0.4 Straightness successfully using terrestrial landmarks, and confirms that this ability is based on the compound eyes (Narendra, 2007b; Graham and 0.2 Cheng, 2009a; Graham and Cheng, 2009b; Wystrach et al., 2011). Crucially, ants with only ocelli for acquiring visual information (ZV–Oc) failed to orient towards the nest (Fig.2). These ants proved FV–Sh FV–Ey FV–Oc FV–Bl ZV–Ey ZV–Oc ZV–Bl unable to rely on their ocelli for using any kind of terrestrial Test field Training field information. We can conclude, as the main outcome of this study, that the ocelli in M. bagoti can read the celestial compass, whose Fig. 3. Sinuosity of the paths. Meander and straightness in all tested M. nature requires and deserves more investigation, but probably does bagoti groups from full-vector (FV) ants on the test field and zero-vector not encode terrestrial landmark cues. (ZV) ants on the training field. Whiskers correspond to the extreme values of each testing group. Test groups with identical letters are not significantly Surprisingly, the ZV–Oc foragers tended to head more in the different by Tukey’s post hoc test. direction opposite to the nest (Fig.2). We plan to study this

THE JOURNAL OF EXPERIMENTAL BIOLOGY Ocelli function in desert ants 905 phenomenon in greater detail as it might illuminate the nature of home direction according to the polarised light. Indeed, in desert the compass processing that is based on the ocelli. At this point we ants with normal vision, the role of the polarised light is far more can only rule out certain explanations. The bias in the path directions important than that of the position of the sun (Wehner and Müller, was not due to the form or direction of shades and shadows on the 2006). In M. bagoti, the relative importance of spectral cues, the training field or the sun’s position because the tests were performed sun’s position and the polarised skylight in the celestial compasses throughout the day. Possible odours from conspecifics or food were – both that based on the compound eyes and that derived from the also unlikely as explanations. The same trend was found even when ocelli – has yet to be determined. All these cues are used in insect the release point was much farther away from the feeder (data not celestial compass systems studied to date (Wehner, 1984; Wehner shown). and Müller, 2006). The ocelli of desert ants are UV sensitive and Another interesting result is that ZV–Bl foragers walked less in this is a prerequisite for the detection of polarised light in bees and any direction than FV–Bl foragers (Fig.2, Bl). The ZV–Bl foragers ants (Wehner, 1984). We strongly suspect that the polarised skylight just made several loops around the release point, a pattern provides essential navigational information for M. bagoti as well, characteristic of the behaviour of ants in searching for the nest both for their ocelli and for their compound eyes. The possible roles (Narendra et al., 2007b). Only 10% of the ZV–Bl foragers (2/20) of spectral gradients and the position of the sun remain unknown. reached the edge of the 3.0m radius circle whereas 76% of their Although M. bagoti ocelli can process celestial compass FV counterparts (15/21) passed this threshold (Fisher’s exact test, information for orientation, they were incapable of encoding P<0.001). Although the travel was not oriented, it seems that the sufficient terrestrial visual cues to lead the ants back to their colony. odometric information supplied by the path integrator induced the It is already known that ocelli can support the compound eyes in FV–Bl foragers to walk both farther and with less winding as phototaxis because of their high light sensitivity (Cornwell, 1955). compared to the ZV–Bl ants (Tukey’s post hoc test, meander: The large and thick neurons in ocelli (L-neurons) enable a rapid P<0.001). transmission of information to the next processing stage and The analysis of the sinuosity of paths provides further information contribute to the functions of the ocelli as flight and gaze stabilisers on the ocelli-based compass. Fent and Wehner (Fent and Wehner, (Taylor, 1981) (reviewed by Taylor and Krapp, 2007). The role of 1985) found that homing FV–Oc C. bicolor ants performed a more ocelli in navigating in dim light has also been reviewed (Warrant, winding path than ants with uncovered compound eyes, a finding 2008). None of these studies, however, mention any form of that matches our quantitative results. In FV ants on the test field in landmark perception mediated by the ocelli. Ocelli in flying insects the present study, the straightness measure did not differ significantly do not provide detailed images on the retina, which implies that between groups of ants with visual input (FV–Oc, FV–Ey and little or no spatial information from the visual scene is extracted FV–Sh). However, the meander of FV–Oc foragers was significantly (reviewed by Taylor and Krapp, 2007). Our negative results on the higher than that of FV–Ey and FV–Sh foragers (Figs3, 4). One ocelli of zero-vector M. bagoti corroborate the implication raised hypothesis is that the FV–Oc foragers needed to perform a more by these studies that the ocelli are not used to perceive terrestrial winding homebound trip to obtain the required celestial compass information (Fig.4). A second hypothesis is that ocelli may be less accurate than compound eyes for estimating the direction from the Nest Nest celestial compass, thus inducing more waggling (Fig.4). Both hypotheses would explain why the group using only ocelli for navigation was more scattered in their homing directions at 3.0m. A third hypothesis concerns the capacity to perceive terrestrial landmarks. In contrast to the ocelli system with its characteristically poor spatial resolution (reviewed by Taylor and Krapp, 2007), compound eyes are able to perceive terrestrial landmarks, a process that might help to reduce the sinuosity of the paths. Surrounding landmarks probably help the ant to steer in her heading direction. This would explain why ants with covered compound eyes display FV–Ey FV–Oc more winding paths. Perception of familiar landmarks (as opposed to unfamiliar landmarks) might especially help the ants to steer, perhaps explaining why the ZV–Ey foragers in the presence of the familiar terrestrial landmarks had a lower meander than FV–Ey foragers on the unfamiliar test field (Tukey’s post hoc test, meander: P0.024; Fig.3). Our results from FV ants replicate in general what was found in the North African desert ant C. bicolor (Fent and Wehner, 1985). Tested as FV ants on an unfamiliar test field, C. bicolor foragers 0.5 m with only ocelli (and their compound eyes covered) also travelled 0.5 m back in the general direction of the fictive nest. They showed larger directional scatter than ants with compound eyes open. Moreover, Fig.4. Typical homing path of a full-vector (FV) ant on an unfamiliar test further manipulations implicated the pattern of polarised skylight field where only celestial cues could be used to home to the fictive nest. as the source for the ocelli-based compass in C. bicolor. By moving The left path was performed by a M. bagati forager with covered ocelli and a small trolley over the ant as she travelled, the view of the sun uncovered compound eyes (FV–Ey); the right path shows the more winding homing path from a forager with covered compound eyes and uncovered could be blocked, the spectral pattern of light gradients neutralised ocelli (FV–Oc). The small goniometer represents the release point and the and the direction of the polarised sky pattern changed. When the dashed line the direct connection between the release point and the fictive pattern of polarised light was rotated, the ants followed the rotated nest.

THE JOURNAL OF EXPERIMENTAL BIOLOGY 906 S. Schwarz and others landmark cues. In general, it seems that the function of ocelli differs Cheng, K., Shettleworth, S. J., Huttenlocher, J. and Rieser, J. J. (2007). Bayesian integration of spatial information. Psychol. Bull. 133, 625-637. between walking insects such as ants and flying insects such as flies. Cheng, K., Narendra, A., Sommer, S. and Wehner, R. (2009). Traveling in clutter: So far it has been shown that ants use ocelli for navigational purposes navigation in the central Australian desert ant Melophorus bagoti. Behav. Processes 80, 261-268. whereas flying insects use them mainly for flight and gaze Christian, K. A. and Morton, S. R. (1992). Extreme thermophilia in a central stabilisation. Australian desert ant, Melophorus bagoti. Physiol. Zool. 65, 885-905. Collett, T. S. and Collett, M. (2002). Memory use in insect visual navigation. Nat. How might the sky compass perception derived from the Rev. Neurosci. 3, 542-552. compound eyes and from the ocelli interact with each other? As Cornwell, P. B. (1955). The functions of the ocelli of Calliphora (Diptera) and Locusta mentioned before, the function of the ocelli is often characterised (Orthoptera). J. Exp. Biol. 32, 217-236. Fent, K. and Wehner, R. (1985). Ocelli: a celestial compass in the desert ant as a support for the compound eyes. Perhaps the ocelli supply a Cataglyphis. Science 228, 192-194. compass reading for the vector based on path integration that is Gibson, J. J. (1998). Visually controlled locomotion and visual orientation in animals. Ecol. Psychol. 10, 161-176. combined with the compass reading derived from the compound Goodman, L. J. (1981). Organisation and physiology of the insect dorsal ocellar eyes. The use of multiple sources of spatial information would system. In Handbook of Sensory Physiology, Vol. 7 (ed. H. Autrum), pp. 201-286. Berlin: Springer. increase accuracy (Cheng et al., 2007). If the ocelli add accuracy, Graham, P. and Cheng, K. (2009a). Ants use the panoramic skyline as a visual cue homing ants with both compound eyes and ocelli (FV–Sh) should during navigation. Curr. Biol. 19, 935-937. Graham, P. and Cheng, K. (2009b). Which portion of the natural panorama is used have performed better than ants with only compound eyes (FV–Ey) for view-based navigation in the Australian desert ant? J. Comp. Physiol. A 195, in the test field. But we found no significant differences in the 681-689. Kohler, M. and Wehner, R. (2005). Idiosyncratic route-based memories in desert ants, homing performance of FV–Sh and FV–Ey foragers, neither in the Melophorus bagoti: how do they interact with path-integration vectors? Neurobiol. scatter of directional headings nor in their straightness or meander Learn. Mem. 83, 1-12. Land, M. F. and Fernald, R. D. (1992). The evolution of eyes. Annu. Rev. Neurosci. (Figs2, 3). It is possible, however, that the additional accuracy 15, 1-29. contributed by the ocelli is too little to be measurable by our methods. Muser, B., Sommer, S., Wolf, H. and Wehner, R. (2005). Foraging ecology of the thermophilic Australian desert ant, Melophorus bagoti. Aust. J. Zool. 53, 301-311. In blowflies, it has been shown that the neuronal pathways of visual Narendra, A. (2007a). Homing strategies of the Australian desert ant Melophorus input from compound eyes and ocelli are combined by common bagoti. I. Proportional path-integration takes the ant half-way home. J. Exp. Biol. interneurons (Parsons et al., 2010). Thus, the speed of the ocelli 210, 1798-1803. Narendra, A. (2007b). Homing strategies of the Australian desert ant Melophorus and the accuracy of the compound eyes are both utilised to good bagoti. II. Interaction of the path integrator with visual cue information. J. Exp. Biol. advantage. In ants, however, the questions of whether one sensory 210, 1804-1812. Narendra, A., Cheng, K. and Wehner, R. (2007a). Acquiring, retaining and integrating system (ocelli) has access to the other (compound eyes) and whether memories of the outbound distance in the Australian desert ant Melophorus bagoti. the information from the path integrator is processed differently in J. Exp. Biol. 210, 570-577. Narendra, A., Si, A., Sulikowski, D. and Cheng, K. (2007b). Learning, retention and each system remain unanswered. We would not rule out the coding of nest-associated visual cues by the Australian desert ant, Melophorus hypothesis that the ocelli supply a different kind of compass bagoti. Behav. Ecol. Sociobiol. 61, 1543-1553. Narendra, A., Cheng, K., Sulikowski, D. and Wehner, R. (2008). Search strategies information, and we are currently investigating this possibility. of ants in landmark-rich habitats. J. Comp. Physiol. A 194, 929-938. In summary, we have demonstrated that in M. bagoti, as in C. Nilsson, D. E. and Kelber, A. (2007). A functional analysis of compound eye evolution. Arthropod Struct. Dev. 33, 373-385. bicolor (Fent and Wehner, 1985), the ocelli supply the ants with Parsons, M. M., Krapp, H. G. and Laughlin, S. B. (2010). Sensor fusion in identified celestial compass information. In addition, we have demonstrated visual interneurons. Curr. Biol. 20, 624-628. Schultheiss, P., Schwarz, S. and Wystrach, A. (2010). Nest relocation and colony that the ocelli of M. bagoti could not utilise terrestrial landmark founding in the Australian desert ant, Melophorus bagoti Lubbock (Hymenoptera: information for homing, at least under our conditions of testing. Formicidae). Psyche 2010, doi:10.1155/2010/435838. Srinivasan, M. V. (1998). Ants match as they march. Nature 392, 660-661. The function of the ocelli-based compass in M. bagoti may extend Taylor, C. P. (1981). Contribution of compound eyes and ocelli to steering of locusts in beyond or differ from supplying additional compass information flight: I. Behavioural analysis. J. Exp. Biol. 93, 1-18. based on celestial cues, a topic that we are currently investigating. Taylor, G. K. and Krapp, H. G. (2007). Sensory systems and flight stability: what do insects measure and why? Adv. Insect Physiol. 34, 231-316. von Frisch, K. (1914). Der Farbensinn und Formensinn der Biene. Jena: Fischer ACKNOWLEDGEMENTS Verlag. Warrant, E. J. (2008). Seeing in the dark: vision and visual behaviour in nocturnal We are grateful for the facilities provided by The Centre for Appropriate bees and wasps. J. Exp. Biol. 211, 1737-1746. Technology (CAT), Alice Springs, Australia. The study, S.S. and A.W. were Wehner, R. (1984). Astronavigation in insects. Annu. Rev. Entomol. 29, 277-298. supported by a graduate research fund and graduate scholarships from Wehner, R. (2003). Desert ant navigation: how miniature brains solve complex tasks. Macquarie University. K.C. was supported by a grant from the Australian J. Comp. Physiol. A 189, 579-588. Research Council (DP0770300). Wehner, R. and Müller, M. (2006). The significance of direct sunlight and polarized skylight in the ant’s celestial system of navigation. Proc. Natl. Acad. Sci. USA 103, 1275-1279. REFERENCES Wehner, R., Michel, B. and Antonsen, P. (1996). Visual navigation in insects: Batschelet, E. (1981). Circular Statistics in Biology. London and New York: Academic coupling of egocentric and geocentric information. J. Exp. Biol. 199, 129-140. Press. Wilson, M. (1978). The functional organisation on locust ocelli. J. Comp. Physiol. A Briscoe, A. D. and Chittka, L. (2001). The evolution of color vision in insects. Annu. 124, 297-316. Rev. Entomol. 46, 471-510. Wittlinger, M., Wehner, R. and Wolf, H. (2006). The ant odometer: stepping on stilts Cheng, K. (2006). Arthropod navigation: ants, bees, crabs, spiders finding their way. In and stumps. Science 312, 1965-1966. Comparative Cognition: Experimental Explorations of Animal Intelligence (ed. E. A. 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Biol. Lett. obtained from ocelli contribute to a single global doi:10.1098/rsbl.2011.0489 path integrator together with directional information Published online from the compound eyes and distance information Animal behaviour from the step-counter [1,9]. By manipulating the visual input of either the compound eyes or the ocelli of the ant Melophorus bagoti, we found that ocelli med- A new navigational iate a second navigational mechanism separate from mechanism mediated the one mediated by the compound eyes. by ant ocelli 2. MATERIAL AND METHODS Data collection took place in Alice Springs, Northern Territory, Sebastian Schwarz1,*,†, Antoine Wystrach1,2,† Australia. To ensure that the ants had access to celestial compass and Ken Cheng1 cues [10], all experiments were conducted under clear or slightly cloudy sky. 1Department of Biological Sciences, Macquarie University, Ants were free to collect food items at the end of a straight or two- Sydney, Australia leg training route. The two segments of the two-leg route were 5.8 m 2CRCA, Universite´ Paul Sabatier, Toulouse, France long and approximately 1.0 m wide and formed an angle of approxi- *Author for correspondence ([email protected]). mately 1408. White wooden planks, which were sunk into the ground †These authors contributed equally to the study. and stuck out approximately 0.10 m, enclosed the nest and the outbound route, thus preventing the ants from foraging elsewhere. The Many animals rely on path integration for navi- ‘walls’ were low enough to allow a view of the sky and the surround- gation and desert ants are the champions. On ing landscape. Foraging ants that reached the feeder on the training leaving the nest, ants continuously integrate field for the first time, and picked up a food item, were marked on the their distance and direction of travel so that abdomen with a daily colour of enamel paint. All marked ants were they always know their current distance and able to dash between feeder and nest for at least one full day before being tested. A test consisted of releasing the ant on an unfamiliar direction from the nest and can take a direct test-field after one out of three painting treatments: either the eyes path to home. Distance information originates (Oc), the ocelli (Ey) or the back of the head (Ct) were covered from a step-counter and directional information with acrylic paint (figure 1a). The treatment itself had no noticeable is based on a celestial compass. So far, it has effect on the homing behaviour of the tested ants (electronic sup- been assumed that the directional information plementary material, figure S2). Treated ants that picked up a obtained from ocelli contribute to a single cookie crumb were transferred in the dark to the unfamiliar test- field approximately 60 m away from the training area. The unfamiliar global path integrator, together with directional surrounding of the test-field ensured that the ants relied only on information from the dorsal rim area (DRA) of celestial compass information for homing. A goniometer (diameter the compound eyes and distance information 1.2 m) with 24 sectors of 158 each was used to record the initial from the step-counter. Here, we show that ocelli headings of the ants at 0.6 m from the release point. After travelling mediate a distinct compass from that mediated 0.6 m, the tested ants tended to stay with their initial headings and by the compound eyes. After travelling a two-leg no switch in their homing direction appeared. The ants’ directional outbound route, untreated foragers headed choices were analysed with circular statistics [11]. towards the nest direction, showing that both legs of the route had been integrated. In contrast, 3. RESULTS foragers with covered compound eyes but uncovered ocelli steered in the direction opposite to the In previously published results [6], we caught M. last leg of the outbound route. Our findings bagoti foragers at a feeder after they had travelled a suggest that, unlike the DRA, ocelli cannot by straight outbound route. These so-called full vector themselves mediate path integration. Instead, (FV) ants have information about distance and direc- ocelli mediate a distinct directional system, tion in order to integrate the shortest way home. We which buffers the most recent leg of a journey. released the FV ants with untreated compound eyes but covered ocelli (figure 1a; Ey) onto an unfamiliar Keywords: ocelli; ants; navigation; path integration; compound eyes; Melophorus bagoti test-field that ruled out the possible use of panoramic cues. On the test-field, FV_Ey foragers headed straight towards the (fictive) nest direction [6]. In this study, we caught foragers after travelling a straight out- and 1. INTRODUCTION inbound route just before they entered the nest. Such To navigate in the world, insects are guided visually by zero-vector (ZV) ants lack any distance and directional both celestial and terrestrial cues [1–3]. Both com- information from the global path integrator. Surpris- pound eyes and the less conspicuous ocelli encode ingly, when released on the test-field with covered visual information. Unlike compound eyes, ocelli do compound eyes and uncovered ocelli, these ants not encode detailed image information [4–6]. In (ZV_Oc) did not orient randomly, as the ZV of the flying insects, it has been shown that ocelli stabilize global path integration input would predict, but flight by quickly detecting changes of light intensities headed significantly in the direction opposite to the in the dorsal visual hemisphere owing to sudden feeder-nest direction (figure 1b; V test: ZV_Oc11.53, deviations from a given flight attitude [4,5,7]. In n ¼ 20, p , 0.001). ground-based ant species, it is only known that ocelli To examine the compass information obtained from extract directional information from celestial compass ocelli, we tested ants after they had travelled a two-leg cues (e.g., polarized skylight, sun’s position), whereas foraging route (figure 2). Foragers were caught at the terrestrial compass information from surrounding feeder, treated and released on the unfamiliar test-field. landmarks are not computed [6,8]. So far, it has Ants with covered ocelli (Ct; Ey) were significantly been assumed that such directional information oriented towards the (fictive) nest direction on the test- Electronic supplementary material is available at http://dx.doi.org/ field (figure 2a; V test: Ct31.9, n ¼ 36, p , 0.001; 10.1098/rsbl.2011.0489 or via http://rsbl.royalsocietypublishing.org. Ey21.3, n ¼ 30, p , 0.001; t-test: Ct21.6, p ¼ 0.12;

Received 9 May 2011 Accepted 15 June 2011 This journal is q 2011 The Royal Society Downloaded from rsbl.royalsocietypublishing.org on July 6, 2011

2 S. Schwarz et al. Ocelli navigation in ants

nest (b) (a)

10 m

Ct Ey Oc n = 20 control covered ocelli covered eyes ZV_Oc

feeder Figure 1. (a) Different test conditions with sham painted control ants (Ct), ants with covered ocelli (Ey) and ants with covered compound eyes (Oc). Control ants were painted on a region of the head that covered neither ocelli nor compound eyes. (b) Directional choices of zero-vector (ZV) ants released on unfamiliar terrain with ocelli input only (Oc) after a straight out- and inbound trip (route is not to scale). Circular histogram shows ants’ headings after travelling 0.6 m in sectors of 158. Black arrow, mean vector of the distribution. Black arc, 95% conﬁdence intervals. Black arrowhead, nest compass direction.

(a) nest 4.5 m feeder 1st leg 5.8 m 2nd leg n = 36 n = 31 n = 41 5.8 m Ct Ey Oc (b) nest

10 m covered ocelli

n = 20 n = 47 Ey Oc feeder Figure 2. Directional choices of treated ants released on unfamiliar terrain after a straight or two-leg outbound trip (routes are not to scale). Circular histograms show the ants’ heading after travelling 0.6 m in sectors of 158. Black arrow, mean vector of the distribution. Black arc, 95% conﬁdence intervals. Black arrowhead, nest compass direction. Star, signiﬁcant orientation (V test, ps , 0.001). (a) Headings of ants released on unfamiliar terrain with both compound eyes and ocelli inputs (Ct), compound eye input only (Ey) or ocelli input only (Oc). Grey arrowhead, compass direction opposite to the second leg of the outbound route. (b) Headings of ants with covered ocelli during the straight outbound trip and released on unfamiliar terrain with compound eyes input only (Ey) or ocelli input only (Oc).

Ey0.91, p ¼ 0.37). Ants with covered compound eyes but conditions (figure 2b). Ey ants showed no difficulties functional ocelli (Oc), however, did not run towards the in heading home (V test: Ey14.20, n ¼ 20, p , 0.001), nest direction (t-test: Oc21.57, p , 0.001). They chose but Oc foragers displayed random directional choices the direction opposite to the second, last leg of (figure 2b; V test: Oc7.68, n ¼ 47, p ¼ 0.06). The direc- the outbound route (figure 2a; V test: Oc17.8, n ¼ 41, tional information encoded by the DRA is not p ¼ 0.001; t-test against second leg direction: Oc20.66, transferred to the compass to which the ocelli contri- p ¼ 0.52; Ct8.37, p , 0.001; Ey6.25, p , 0.001). The bute, supporting the hypothesis of two distinct headings of Oc foragers differed significantly from those compasses. The random directional choices of Oc of Ey (Watson-Williams test, F ¼ 21.4 p , 0.001) and ants could be also due to the unlikely possibility that Ct ants (Watson-Williams test, F ¼ 17.0 p , 0.001). ocelli might not be functional for several minutes To show that ocelli mediate a distinct compass after the removal of the paint. mechanism, it is necessary to confirm that the directional information derived from the dorsal rim area (DRA) is not accessible via the ocelli. To test this, we 4. DISCUSSION captured ants at the feeder that had just run a straight After travelling a two-leg foraging route, ants on an outbound trip with covered ocelli (Ey) and tested them unfamiliar test-field with covered compound eyes but either with unchanged (Ey) or reversed (Oc) open ocelli (figure 2a; Oc) did not compute direction

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Ocelli navigation in ants S. Schwarz et al. 3 and distance towards the nest; instead these ants alates [13] and other flying insects [7]. Therefore, headed in a direction opposite to the last leg of the ocelli-driven compass might be just an exaptation travel. These results are consistent with the behaviour derived from flying ancestors or flying reproductive of the ZV ants with covered eyes but functional ocelli alates. Conversely, the question of whether the ocelli (figure 1b; ZV_Oc), heading opposite to the home of alates are used for some directional purpose, as direction that was the direction of the last leg of they are by the foragers in this study, or merely for travel. The directional information mediated by the flight and gaze stabilization as in other flying insects DRA of the compound eyes appears to be inaccessible [4,7], remains to be investigated. to ocelli (figure 2b). This suggests the presence of two In summary, our results demonstrated that ocelli distinct mechanisms. The DRA provides directional could not by themselves mediate path integration in information to the global path integrator—which ground-based insects. Such a discovery, with its keeps track of the nest position—whereas ocelli associated mechanistic, functional and evolutionary supply directional information to a distinct mechan- questions, reminds us how complex, flexible and well- ism—which buffers the most recent leg of travel and adapted the structure underlying insect navigation is. overrides previous information (electronic supplementary material, figure S1b). Then what could be the We thank CSIRO and CAT, Alice Springs for providing function of the additional directional compass driven facilities. We appreciate the assistance of Laurence Albert in data collection and thank Paul Graham for comments on by the ocelli? Ant ocelli might act as a supporting the manuscript. The work was funded by graduate system for the global path integrator mediated by the scholarships of Macquarie University and a Discovery compound eyes. However, foragers with continuously Grant from the Australian Research Council (DP0770300). covered ocelli during the two-leg outbound route, headed solidly in the direction of the fictive nest when released on the test-field (electronic supplementary material, figure S1a). Therefore, ocelli are not 1 Wehner, R., Michel, B. & Antonsen, P. 1996 Visual navigation in insects: coupling of egocentric and geocentric necessary for global path integration. information. J. Exp. Biol. 199, 129–140. It could be assumed that the function of ant ocelli 2 Cheng, K. 2006 Arthropod navigation: ants, bees, crabs, resembles that of flying insects in supplying a means of spiders finding their way. In Comparative cognition: exper- maintaining and controlling direction and body orien- imental explorations of animal intelligence (eds E. A. tation through a variety of cues (e.g., horizon, image Wasserman & T. R. Zentall), pp. 189–209. Oxford, motion, sun). However, we know that the homing NY: Oxford University Press. paths of ants with covered compound eyes and uncov- 3 Collett, T. S. & Collett, M. 2000 Path integration in ered ocelli (Oc) are fairly tortuous and not as insects. Curr. Opin. Neurobiol. 10, 757–762. (doi:10. accurately oriented as those of ants with compound 1016/S0959-4388(00)00150-1) eyes only (Ey). In fact, Oc ants’ homing paths were 4 Goodman, L. J. 1970 The structure and function of the almost as tortuous as paths of totally blinded ants [6]. insect dorsal ocellus. Adv. Insect Physiol. 7, 97–195. (doi:10.1016/S0065-2806(08)60241-6) Therefore, it seems that the maintenance and stabiliz- 5 Taylor, G. K. & Krapp, H. G. 2007 Sensory systems and ation of the homing paths in walking ants are mediated flight stability: what do insects measure and why? Adv. by the compound eyes and not the ocelli. Another Insect Physiol. 34, 231–316. (doi:10.1016/S0065- explanation is thus required. 2806(07)34005-8) Melophorus bagoti foragers follow visually guided 6 Schwarz, S., Albert, L., Wystrach, A. & Cheng, K. 2011 idiosyncratic routes through a cluttered environment Ocelli contribute to the encoding of celestial compass [12]. Sometimes, a newly appeared obstacle or the information in the Australian desert ant Melophorus presence of aggressive conspecifics from other colonies bagoti. J. Exp. Biol. 214, 901–906. (doi:10.1242/jeb. may force the forager to leave her familiar route, 049262) ending up in unfamiliar surroundings. In such cases, 7 Krapp, H. G. 2009 Ocelli. Curr. Biol. 19, 435–437. (doi:10.1016/j.cub.2009.03.034) the ocelli-driven compass could possibly allow the 8 Fent, K. & Wehner, R. 1985 Ocelli: a celestial compass ant to return to her well-known route rather than in the desert ant Cataglyphis. Science 228, 192–194. homing towards the nest through unknown terrain. (doi:10.1126/science.228.4696.192) The discovery of this distinct navigational mechanism 9 Wittlinger, M., Wehner, R. & Wolf, H. 2006 The ant mediated by the ocelli also raises mechanistic and evol- odometer: stepping on stilts and stumps. Science 312, utionary questions. Ocelli are known for their fast 1965–1966. (doi:10.1126/science.1126912) neurological response [5] and it may be advantageous 10 Wehner, R. & Mu¨ller, M. 2006 The significance of direct to process directional information independently. sunlight and polarized skylight in the ant’s celestial The directional information encoded by the ocelli system of navigation. Proc. Natl Acad. Sci. USA 103, appears indeed to be processed separately from that 1275–1279. (doi:10.1073/pnas.0604430103) derived from the compound eyes, but what about the 11 Batschelet, E. 1981 Circular statistics in biology. London, NY: Academic Press. odometric information? Is the ocelli-mediated compass 12 Cheng, K., Narendra, A., Sommer, S. & Wehner, R. processed together with the step-counter that is also 2009 Traveling in clutter: navigation in the Central used for the global path integrator, or is it processed Australian desert ant Melophorus bagoti. Behav. Process. with another odometric cue such as optic flow or 80, 261–268. (doi:10.1016/j.beproc.2008.10.015) encoded only as a direction without odometric infor- 13 Ho¨lldobler, B. & Wilson, E. O. 1990 The ants. mation? Interestingly, in many ant species ocelli are Cambridge, MA: The Belknap Press of Harvard seldom found in workers but often present in winged University Press.

Biol. Lett.

2 Visual input and path stabilisation in walking ants 3 4 Sebastian Schwarz & Antoine Wystrach 5 Department of Biological Sciences, Macquarie University, Sydney, Australia 6 7 Addendum to: 8 Schwarz S, Albert L, Wystrach A, Cheng K. Ocelli contribute to the encoding of 9 celestial compass information in the Australian desert ant Melophorus bagoti. J Exp 10 Biol 2011; 214: 901-6. 11 12 13 Submitted: 10 August 2011 14 Accepted: 15 16 17 Key words: navigation, ants, path stabilisation, ocelli, compound eye, Melophorus 18 bagoti 19 20 21 Correspondence to: 22 Sebastian Schwarz, Macquarie University - Sydney, Department of Biological 23 Sciences, Brain, Behaviour and Evolution, Phone: +61298504189,Fax: +61298509231 24 Email: [email protected] 25 26 27 28 29 30 31 32 33 34 Abstract 35 Most animals use vision to navigate the outside world. Eyes are the sensory organs for 36 visual perception and can vary in their form, structure and function to suit the visual 37 requirement of the individual species. In insects, mainly the two compound eyes but 38 also the less-conspicuous ocelli are in charge for visual input. Much knowledge has 39 been obtained about compound eyes but little is known about the role of ocelli in 40 walking insects. Recently it has been shown that ant ocelli contribute to encoding 41 celestial compass information for homing. However, ocelli could not compute 42 terrestrial cues for navigating back to the nest. Here we focus on further investigations 43 on the ants’ paths stabilisation under different visual input conditions. The pitch and 44 roll stabilisation of walking paths seems to be independent of visual input and 45 controlled by idiothetic cues. The yaw (meander) stabilisation in walking paths is 46 adjusted for navigational rather than for stabilising purposes and depends on at least 47 three factors: the odometric component of the path integrator (via idiothetic cues), the 48 perception of the celestial compass information (via ocelli and compound eyes), and 49 the visual matching of the familiar route scenery (via the compound eyes). 50 51 TEXT 52 For central place foragers, such as ants, it is necessary and important to find the way 53 back to the nest after every foraging trip. To achieve that, some ant species use mainly 54 chemical trails1,2 but most end up learning their foraging routes independently by relying 55 on visual cues.3,4 The Australian desert ant Melophorus bagoti is one of these solitary 56 foraging ants.5 While navigating through its cluttered terrain it is mainly guided by 57 idiothetic information and vision based on celestial (e.g., polarised skylight, sun’s 58 position) and terrestrial cues (e.g., landmarks, skyline contour).5 Compound eyes and 59 the three ocelli represent the sensory organs for the visual input. It is known that 60 compound eyes can read celestial compass cues for path integration in ants. They also 61 provide sufficient navigational details of the surrounding landmark panorama for 62 foraging and homing.6 Far less is known about the function of the ocelli in ants. In 63 flying insects, ocelli detect quickly differences in light gradients and stabilise the gaze 64 and flight via specialised neurons (L-neurons).7,8 In walking insects however, it has 65 been demonstrated in desert ants that ocelli can encode celestial compass information 66 for navigating back to the nest.9 Recently, Schwarz et al10 tested homing performances 67 of ants with different visual input conditions. The results showed that M. bagoti ocelli 68 obtain compass information from celestial cues but cannot encode terrestrial landmark 69 information for homing. 70 71 Here we address the role of the visual input in walk stabilisation. First, neither 72 compound eyes nor ocelli are necessary for steady walks in ants. Even totally blinded 73 ants could walk readily without any noticeable forward, backward (roll) or sideway 74 (pitch) stumbling and tripping. Thus, it seems that the pitch and roll stabilisation in 75 ant paths is independent of the visual input but might be based on sensory input from 76 the legs. In contrast to flying insects, pitch and roll stabilisation is directly associated 77 with the ground surface and therefore enables walking insects to receive stabilisation 78 information from the moving legs. Most natural surfaces are uneven or rugged. In 79 order to keep a proper roll and pitch position it seems plausible to rely on the direct 80 information of the legs rather than on an absolute pitch and roll stabilisation relative 81 to the visual scenery, which would lead to complications in walks on uneven grounds. 82 83 What about the yaw stabilisation or the level of meander in walking ants? On the one 84 hand we analysed the yaw angle of ant walks with no information from the path integrator 85 (zero-vector, ZV) when released on unfamiliar grounds. All tested ants started moving 86 activity but totally blinded ants (Figure 1A, Bl) displayed paths with the highest 87 meander,10 which leads to the conclusion that visual input is heavily involved in yaw 88 stabilisation. Ants with covered compound eyes but open ocelli (Figure 1A, Oc) 89 showed lower meander levels in their walks.10 Thus, ocelli play a little role in 90 controlling the yaw angle. The gain in yaw stability might result from the ability of 91 ocelli to read celestial compass cues. Additional visual input from the compound eyes 92 (Figure 1A, Ct) decreased the meander and therefore increased the yaw stabilisation 93 of the ant walks. This could be due to a combination of celestial compass information 94 perceived by the dorsal rim area of the compound eyes or the perception of terrestrial 95 landmarks although neither information from the path integrator nor the familiarity of 96 the surrounding were available under this experimental condition. 97 98 On the other hand we analysed the yaw angle of ant paths with the full information 99 from the path integrator (full-vector, FV) when released on unfamiliar grounds. 100 Totally blind ants, ants with ocelli only and ants with compound eyes and ocelli all 101 showed a lower meander as FV ants than as ZV ants.10,11 Information about the 102 distance and direction from the path integrator induces straighter walks. Remarkably, 103 the difference between ZV and FV blind ants (Figures 1A & B, Bl) even reveals that 104 visual cues are not necessary for lowering the meander, implying that the yaw 105 stabilisation in walks is also controlled by idiothetic information. It suggests that the 106 odometric component of the path integrator influences the ants’ walking behaviour – 107 even without visual input. 108 109 We also investigated the yaw angle of ZV ant paths in familiar visual scenery. The 110 focus lies on ants with open compound eyes and covered ocelli (Ey) since blind ants 111 and ants with ocelli only cannot compute terrestrial landmark information.10 ZV_Ey ants 112 lacked any information from the path integrator but displayed walks with the lowest 113 meander amongst all test conditions (Figure 1B, Ey).10 It appears that the familiar visual 114 surrounding provides the best information for yaw angle stabilisation but only if the 115 match between the current and memorised view is sufficiently correct (Figure 1B, Ey). 116 Indeed, when ants are displaced several metres from their foraging route, they performed 117 fairly meanderous walking paths although the scenery is familiar enough to lead them 118 towards their well-known route corridor.12 Paths become only very straight when ants 119 hit their familiar route corridor. This might be due to a particular view-based strategy 120 which consist in aligning the body as to match the features on memorised and current 121 views.13,14 This phenomenon can be called using a visual compass and provides a 122 non-ambiguous direction for travel on a familiar route.15-17 The yaw stabilisation on 123 familiar routes seems to be further improved when the direction of travel is in synergy 124 with the direction dictated by the path integrator.14 This suggests that odometric 125 information, perception of celestial compass cues and matching of the familiar scenery 126 need to be in congruence to output the straightest path. 127 128 In conclusion, we want to point out that contrary to flying insects7, pitch and roll 129 stabilisation in ground-based insects seems to be controlled by idiothetic rather than 130 visual cues. In walking insects ocelli are not crucial for movement stability but help to 131 maintain lower variation in yaw angles by providing celestial compass information. 132 The straightness of the path in pedestrian ants depends on at least three factors: the 133 odometric component of the path integrator (via idiothetic cues), the perception of the 134 celestial compass (via ocelli and likely the compound eyes), and most importantly, the 135 visual matching of the familiar route scenery (via the compound eyes). These factors 136 act in synergy to produce straighter paths in certain navigational contexts and more 137 meander in uncertain or conflicting navigational contexts. Thus, in walking ants, the 138 maintenance of a stable yaw angle is mostly adjusted for navigational purposes, and 139 not for stability purposes. 140 141 142 143 References 144 1. Wilson EO. The insect societies. Cambridge, Massachusetts: Harvard 145 University Press, 1971:458. 146 2. Moser JC, Blum MS. Trail marking substance of the Texas leaf-cutting ant: 147 source and potency. Science 1963; 140:1228. 148 3. Wehner R, Michel B, Antonsen P. Visual navigation in insects: coupling of 149 egocentric and geocentric information. J Exp Biol 1996; 199:129-40. 150 4. Collett TS, and Zeil J. Places and landmarks: an arthropod perspective in 151 Spatial representation In: Healy S, ed. Spatial representation in animals. 152 Oxford, New York: Oxford University Press 1998:18-53. 153 5. Cheng K, Narendra A, Sommer S, and Wehner R. Traveling in clutter: 154 navigation in the Central Australian desert ant Melophorus bagoti. Behav 155 Process 2009; 80: 261-8. 156 6. Schwarz S, Narendra A, Zeil J. The properties of the visual system in the 157 Australian desert ant Melophorus bagoti. Arth Struct Dev 2011; 40:128-34. 158 7. Taylor GK, Krapp HG. Sensory systems and flight stability: what do insects 159 measure and why? Adv Insect Physiol 2007; 34:231-316. 160 8. Goodman LJ. The structure and function of the insect dorsal ocellus. Adv 161 Insect Physiol 1970; 7:97-195. 162 9. Fent K, Wehner R. Ocelli: a celestial compass in the desert ant Cataglyphis. 163 Science 1985; 228:192-4. 164 10. Schwarz S, Albert L, Wystrach A, Cheng K. Ocelli contribute to the encoding 165 of celestial compass information in the Australian desert ant Melophorus bagoti. 166 J Exp Biol 2011; 214:901-6. 167 11. Wystrach A, Schwarz S, Schultheiss P, Beugnon G, Cheng K. Views, landmarks, 168 and routes: How do desert ants negotiate an obstacle course? J Comp Physiol 169 A 2011; 197:167-79. 170 12. Kohler M, Wehner R. Idiosyncratic route-based memories in desert ants, 171 Melophorus bagoti: how do they interact with path-integration vectors? 172 Neurobiol Learn Mem 2005; 83:1-12. 173 13. Lent DD, Graham P, and Collett TS. Image-matching during ant navigation 174 occurs through saccade-like body turns controlled by learned visual features. 175 Proc Natl Acad Sci USA 2010; 107:16348-53. 176 14. Narendra A. Homing strategies of the Australian desert ant Melophorus bagoti 177 II. Interaction of the path integrator with visual cue information. J Exp Biol 178 2007; 210:1804-12. 179 15. Zeil J. Catchment areas of panoramic snapshots in outdoor scenes. J Opt Soc 180 Am A 2003; 20:450-69. 181 16. Graham P, Philippides A, Baddeley B. Animal cognition: multi-modal 182 interactions in ant learning. Curr Biol 2010; 20:639-40. 183 17. Wystrach A, Cheng K, Sosa S, and Beugnon G. Geometry, features, and 184 panoramic views: Ants in rectangular arenas. J Exp Psychol: Anim Behav 185 Process 2011; In press (DOI: 10.1037/a0023886). 186 187 188 189 190 Figure 1 191 Homing path examples of full-vector (FV) and zero-vector (ZV) ants under different 192 visual input conditions (black dot represents the starting point of the path). 193 (A) Released on unfamiliar grounds, blinded ants (Bl) display paths with the highest 194 meander (yaw angle variation) followed by ants with covered compound eyes but 195 open ocelli (Oc). Ants with untreated eyes (Ct) show stable walks with low meander. 196 (B) FV_Bl ants walk straighter paths than ZV_Bl ants from panel (A). The odometric 197 component of the path integrator, via idiothetic cues, leads to a less meanderous path. 198 Released on familiar grounds, ZV ants with uncovered compound eyes (Ey) stabilise 199 their yaw angle by using the familiar scenery and perform paths with the lowest 200 meander among all experimental groups.