Sensory Ecology of Bats Around Bodies of Water

Sensory Ecology of Bats around Bodies of Water

Dissertation submitted for the degree of Doctor of Natural Sciences

Presented by Stefan Greif

at the

Faculty of Sciences Department of Biology

Date of the oral examination: 24.04.2015 First supervisor: Dr. Barbara Helm Second supervisor: Dr. Lutz Wiegrebe

Konstanzer Online-Publikations-System (KOPS) URL: http://nbn-resolving.de/urn:nbn:de:bsz:352-0-291215

For my parents

and

PD Dr. Björn M. Siemers

“A simple biological question may arouse our interest, but as we gain more knowledge the questions ramify and appear to grow in complexity. This may take us to new and seemingly unrelated problems, but in retrospect they are all related to the desire to find out how things work. If we are fortunate we will gain some insight, and when we understand underlying principles, the greatest reward seems to be in the simplicity of the answers.”

Knut Schmidt-Nielsen How Animals Work Cambridge University Press, 1972

“This they do by skimming an inch or so above the surface, dipping the lower jaw ever so gently into the water, presumably scooping up a fraction of a drop, but skillfully avoiding too deep an immersion which might create a sudden drag sufficient to plunge the thirsty bat into the water.”

Donald R. Griffin on drinking behaviour of bats Listening in the Dark Yale University Press, 1958

TABLE OF CONTENTS

Summary…………………...... ……………………………….…………. 13

Zusammenfassung (German summary)…...... …………. 17

General introduction……………………………………….………………… 21 Bats……………………………………………………………………… 21 Echolocation……………………………………………………………. 22 Echolocation for habitat recognition……………………………………. 24 Water habitats…………………………………………………………… 24 Aims of this study………………………………………………………. 26

Chapter 1 – Innate recognition of water bodies in echolocating bats Abstract………………………………………………………………….. 30 Introduction……………………………………………………………… 31 Results…………………………………………………………………… 32 Echoacoustic water recognition…………………………………….. 32 Robustness to conflicting information……………………………… 35 Innate response of juvenile bats…………………………………….. 38 Discussion……………………………………………………………….. 40 Methods………………………………………………………………….. 42 Bats………………………………………………………………….. 42 Flight room and Experimental Setup……………………………….. 43 Experimental Procedure…………………………………………….. 44 Data Analysis……………………………………………………….. 45 Ensonification………………………………………………………. 45 Acknowledgements……………………………………………………… 46 Movie Legends…………………………………………………………... 46 Supplementary Figure……………………………………………………. 47

Chapter 2 – Acoustic mirages in the desert: testing spatial memory and echo cues in bats Abstract…………………………………………………………………….. 50 Introduction………………………………………………………………… 51 Methods…………………………………………………………………….. 52 (A) Flight room experiments in Bulgaria……………………………… 52 (B) Field experiments in Israel………………………………………… 53 Results……………………………………………………………………… 53 (A) Flight room experiments in Bulgaria……………………………… 53 (B) Field experiments in Israel………………………………………… 55 Discussion………………………………………………………………….. 56 Acknowledgments…………………………………………………………. 57 Movie Legends…….………………………………………………………. 57

Chapter 3 – Acoustic mirrors as sensory traps for bats Abstract…………………………………………………………………….. 60 Introduction………………………………………………………………… 61 Methods & Results…………………………………………………………. 62 Discussion………………………………………………………………….. 67 Acknowledgments………………………………………………………….. 69

Movie Legend…………………………..…………………………………... 69

Chapter 4 – An integrative approach to detect subtle trophic niche differentiation in the sympatric trawling bat species Myotis dasycneme and Myotis daubentonii Abstract…………………………………………………………………….. 72 Introduction………………………………………………………………… 73 Methods…………………………………………………………………….. 76 Study site and guano collection……………………………………….. 76 Functional morphology……………….……………………………….. 76 Wing morphology………………………………………………… 76 Weightlifting……………………………………………………… 76 Bite force…………………………………………………………. 77

Molecular diet analysis………………………………………………… 77 Morphological diet analysis..………………………………………….. 78 Data analysis…………………………………………………………... 79 Results..…………………………………………………………………….. 80 Functional morphology……………….……………………………….. 80 Molecular diet analysis………………………………………………… 84 Morphological diet analysis…………………………………………… 88 Discussion………………..………………………………………………… 90 Functional morphology………………………………………………… 91 Dietary analysis………………………………………………………… 93 Resource partitioning and mechanisms of species coexistence…….…. 95 Conclusions………………………………………………………………… 96 Acknowledgments………………………………………………………….. 96 Data accessibility………………………..…………………………………... 96

General discussion……...……………………………...…………………………. 97 Future directions………………………..………………………….………... 103

References……...... …………………………………………………………. 107

Publication of Results……...…………………………………..…………………. 126

Author contributions…………………………………………..…………………. 127

Acknowledgments………………………………………………………………… 128

Curriculum Vitae……………………………………………...………………….. 129

List of publications…………………………………………….………………….. 135

SUMMARY

In this thesis I investigated how bats interpret the echo scene of their environment and more precisely of smooth, acoustic mirrors like e.g. bodies of water. I found that the echoacoustic recognition of a distinct habitat can be encoded with very simple cues and that it is hardwired, robust and innate. I showed that these echoacoustic cues are both sufficient and necessary for a bat in order to instigate drinking behaviour. Other sensory cues or previous experience with a locality alone are not enough. And finally I demonstrated that the same echoacoustic cues lead to an erroneous decision if they are found in a different spatial position and therefore might pose a conservation issue. For all my experiments I managed to confirm my hypotheses both in laboratory and field settings. Whenever possible, I further tried to generalize my results in using a comparative approach with different species.

In chapter 1 I presented the results of laboratory experiments in our field station in Bulgaria and demonstrated how bats recognize bodies of water with echolocation. I showed that the cues for recognizing such an extended habitat structure can be relatively simple: any smooth, horizontal surface, acting like an acoustic mirror, is recognized as a water surface. This recognition pattern seems to be phylogenetically widespread as I was able to show it for 15 different European species. In an additional unpublished experiment we found the same behaviour for Neotropical bats as well. Very astonishing was the persistence of the bats’ drinking attempts, sometimes reaching over 100 attempts within ten minutes of flight time. I further explored and confirmed the robustness of this stereotypical behaviour in an additional experiment with a non-realistic physical situation for the smooth, horizontal surface. After repeating the main experiment under different conditions, I was able to suggest how bats value their different sensory inputs. Although bats apparently incorporate other sensory information, echolocation seems to be the dominating sense and even outweigh other contradicting information. To conclude this chapter, I showed that water recognition in bats is not learned but innate, which presents the first case of innate habitat recognition in a mammal.

In chapter 2 I conducted experiments to illustrate the role of spatial memory and echo cues around water recognition. I created an experimental setup to see if bats would drink from an area which they before have experienced to be water, even if the relevant echo cues are missing. I demonstrated both in a laboratory setting in Bulgaria and a field experiment in the Jehuda desert of Israel that spatial memory alone is not sufficient to elicit drinking behaviour. The precise echoacoustic cues of a smooth, horizontal surface are necessary and the recognition hypothesis from chapter 1 could be confirmed in the field. Furthermore, these cues can even evoke a drinking response in a novel location where bats never experienced water before. Finally, these experiments also allowed me to prove that olfactory cues alone are not sufficient to stimulate bats to drink, adding to the multisensory hypothesis of chapter 1.

In chapter 3 I demonstrated that acoustic mirrors can be sensory traps for bats. The same individual bats that would attempt to drink from a horizontal, smooth metal plate collided with a vertical, smooth metal plate, when passing at an acute angle. They likely perceived them as open flight paths. I grouped the behavioural response into three categories: bats that were on a collision course but managed to avoid the plate through evasive manoeuvres, bats that collided despite clear evasive manoeuvres and bats that collided without showing any reaction. Through 3D flight path analysis and echolocation recordings, I identified several factors that would contribute to such an erroneous decision. The amount of echolocation calls, the angle of approach and most importantly the time spent in a certain area close to the plate are the factors resulting in a collision when comparing the three reaction groups,. With increasing values all of them would increase the amount of information the bat receives and thereby reducing the risk of misinterpreting the echo scene. Supported by own field data and anecdotal reports of injured and dead bats around glass fronts of buildings, I argue that the detrimental effects of these sensory traps might be bigger than is known so far. To address this conservation issue I propose an increased monitoring effort to evaluate its real extent.

In chapter 4 we showed in a collaborative work that sympatrically occurring trawling species exhibit a fine-scaled use of foraging niche around bodies of water. Although Myotis daubentonii and Myotis dasycneme have a strong overlap in diet and habitat utilization, small differences seem to allow them to occupy microhabitats within their general foraging guild around water. The analyses of functional morphology traits revealed small variances in wing parameters like wing loading and wingtip shape although sex had a stronger influence than 14 species affiliation. Bite force, which could act as a proxy for prey choice and handling capabilities, showed more pronounced species differences, albeit expected as they were related to body size. A combination of classic, morphological and molecular diet analyses highlighted again the generally large overlap in diet but also pointed to small, consistent modifications in their prey choice. Overall the high similarity in morphological traits is as expected for trawling bats, confirms their niche affiliation but has only limited potential to explain the species’ sympatry. The dietary data, however, identified small differences like an apparent emphasis of Myotis dasycneme on Chironomids, increased preying on Chironomid pupae or the generally greater variety of consumed prey in Myotis daubentonii. In conclusion we highlight the importance of combining various methods to achieve a comprehensive understanding of a species’s foraging ecology.

ZUSAMMENFASSUNG

In der vorliegenden Arbeit habe ich untersucht, wie Fledermäuse die ‚Echolandschaft‘ ihrer Umgebung deuten und hier im Speziellen akustische Spiegel wie Wasserflächen wahrnehmen. Meine Ergebnisse zeigen, dass die Erkennung eines bestimmten Habitats mittels Echoortung sehr einfach kodiert sein kann und dass diese Verhaltensantwort auf einen Stimulus fest verdrahtet, robust und angeboren ist. Ich konnte darlegen, dass die echoakustischen Merkmale eines Wasserhabitats für eine Fledermaus sowohl ausreichend aber auch notwendig sind um ein Trinkverhalten auszulösen. Andere Sinnesinformationen oder vorherige Erfahrung an einer bestimmten Lokalität genügen dafür nicht. Abschließend zeige ich, dass die selben echoakustischen Merkmale falsch interpretiert werden können wenn sich ihre Lage im Raum ändert und dies ein mögliches Naturschutz-Problem darstellen könnte. Alle Ergebnisse konnten sowohl unter kontrollierten Laborbedingungen als auch im Freiland bestätigt werden. Wo möglich habe ich desweiteren versucht die generelle Aussagekraft meiner Ergebnisse durch einen vergleichenden Ansatz mit mehreren Fledermausarten zu unterstreichen.

In Kapitel 1 zeigte ich, wie Fledermäuse Wasserhabitate mittels ihrer Echoortung erkennen. Die Ergebnisse meiner Flugraum-Versuche in unserer Feldstation in Bulgarien belegen, dass die echoakustischen Schlüsselmerkmale einer ausgedehnten Habitatstruktur sehr einfach sein können: jede glatte, horizontale Oberfläche funktioniert wie ein akustischer Spiegel und wird von den Fledermäusen als Wasserfläche wahrgenommen. Dieses Erkennungsmuster scheint phylogenetisch weitverbreitet zu sein, da ich es für 15 europäische Fledermausarten nachweisen konnte. In weiteren, unpublizierten Versuchen zeigten auch neotropische Arten das gleiche Verhalten. Erstaunlich war auch die Beharrlichkeit mit welcher die Fledermäuse versuchten von den glatten Platten zu trinken, wobei manche es über hundert mal in zehn Minuten Flugzeit versuchten. Die Robustheit dieses Verhaltensmusters habe ich weiter auf die Probe gestellt und bestätigt, indem ich die Fledermäuse einer physikalisch unrealistischen Situation für die glatte, horizontale Oberfläche aussetzte. Nachdem ich den Hauptversuch noch einmal unter abgeänderten Bedingungen wiederholte, konnte ich Aussagen über die Verarbeitung von verschiedenen Sinneseindrücken bei Fledermäusen treffen. Obwohl diese offenbar andere sensorische

Informationen in ihre Entscheidungsfindung mit einbeziehen, scheint Echoortung das klar dominierende Sinnessystem zu sein und sogar andere widersprüchliche Informationen zu überwiegen. Zum Abschluss dieses Kapitels konnte ich nachweisen, dass Wassererkennung bei Fledermäusen nicht gelernt sondern angeboren ist. Dies stellt den ersten Beleg für eine angeborene Habitaterkennung bei einem Säugetier dar.

In Kapitel 2 beleuchtete ich die Rolle und das Zusammenspiel von Ortsgedächtnis und Echomerkmalen bei der Wassererkennung. Durch das experimentelle Design konnte ich untersuchen, ob Fledermäuse von einer Stelle, die sie zuvor als Wasserfläche erfahren hatten, trinken würden obwohl die relevanten Echomerkmale einer Wasserfläche fehlten. In einem Flugraum-Versuch in Bulgarien und einer Feldstudie in der Judäischen Wüste Israels konnte ich zeigen, dass das Ortsgedächtnis und vorherige Erfahrung nicht ausreichen um Trinkverhalten auszulösen. Dafür sind die echoakustischen Merkmale einer glatten, horizontalen Fläche notwendig, was gleichzeitig auch meine Hypothese der Wassererkennnug aus Kapitel 1 im Freiland unterstützte. Weiterhin zeigten meine Versuche, dass diese echoakustischen Merkmale ein Trinkverhalten auch einem komplett neuen Ort auslösen können, an dem die Fledermäuse zuvor noch nie Wasser angetroffen hatten.

In Kapitel 3 wies ich nach, dass akustische Spiegel als sensorische Fallen für Fledermäuse fungieren können. Die selben Fledermäuse die von einer horizontalen, glatten Metallplatte zu trinken versuchten, kollidierten mit einer vertikalen, glatten Metallplatte wenn sie an dieser in relativ spitzem Winkel vorbeiflogen. Offenbar interpretierten sie diese als offenen Flugweg. Ich gruppierte ihr Verhalten in die folgenden drei Kategorien: Fledermäuse die auf Kollisionskurs waren, aber dies durch Ausweichmanöver vermeiden konnten, Fledermäuse die trotz klarer Ausweichmanöver mit der Platte kollidierten und Fledermäuse die kollidierten ohne zuvor eine Reaktion zu zeigen. Mittels 3D Analyse der Flugbahn und Echoortungsaufnahmen, konnte ich mehrere Faktoren identifizieren die zu solch einer fehlerhaften Entscheidung beitrugen. Bei einem Vergleich der drei Gruppen waren diese Faktoren die Anzahl der Echoortungsrufe, der Anflugwinkel und die Zeit die in unmittelbarer Nähe zu der Platte verbracht wurde. Mit ansteigenden Werten ermöglichen diese Faktoren einen Anstieg der Informationen die eine Fledermaus erhält und verringern dadurch das Risiko die Echoszene falsch zu interpretieren. Unterstützt durch eigene Felddaten aber auch durch weitere anekdotische Berichte über verletzte und tote Fledermäuse nahe von Gebäude- Glassfassaden, stelle ich zur Diskussion, dass die Auswirkungen dieser sensorischen Fallen 18 größer sein könnten als soweit bekannt ist. Um diese Naturschutzfrage besser beurteilen zu können, schlage ich verstärkte Anstrengungen im Monitoring vor um das reale Ausmaß zu bewerten.

In Kapitel 4 zeigten wir in einer Zusammenarbeit, dass sympatrisch auftretende, Gewässer bejagende Fledermausarten eine fein justierte Nutzung von Nahrungsnischen rund um Wasserflächen aufweisen können. Obwohl Nahrungs- und Habitatsnutzung bei der Wasserfledermaus, Myotis daubentonii, und der Teichfledermaus, Myotis dasycneme, stark überlappen, scheinen kleine Unterschiede es ihnen zu erlauben Mikrohabitate innerhalb ihrere Nahrungsgilde um Gewässer zu nutzen. Die Untersuchung bestimmter Eigenschaften der funktionellen Morphologie dieser Arten erbrachte kleine Abweichungen in Flügelparametern wie Flächenbelastung und Flügelspitzenform, die jedoch mehr durch Geschlechtzugehörigkeit erklärt werden. Stärker ausgeprägt waren die Unterschiede in der Beißkraft welche als Erklärungsvariable für Beutewahl und Hantierungsfähigkeit in Frage kommen könnte. Jedoch waren die höheren Werte in diesem Rahmen hier allein durch die größere Körpergröße der Teichfledermaus zu erwarten. Eine Kombination von klassisch morphologischer und molekularer Nahrungsanalyse betonte wieder die starke Überlappung im Nahrungsspektrum, zeigte jedoch auch kleine, beständige Unterschiede in der Beutewahl. Gesamt gesehen ist die hohe Ähnlichkeit der morphologischen Merkmale wie erwartet für Gewässer bejagende Arten und bestätigt ihre Nischenzugehörigkeit, hat aber nur eingeschränktes Erklärungspotential für das sympatrische Auftreten der Arten. Die Daten der Nahrungsanalyse zeigen jedoch kleine Differenzen auf, wie eine anscheinend stärkere Spezialisierung der Teichfledermaus auf Zuckmücken und deren Puppen oder die generell größere Variabilität im Beutespektrum der Wasserfledermaus. Abschließend betonen wir die Bedeutung des kombinierten Einsatzes verschiedener Methoden um ein umfassendes Verständnis für die Nahrugsökologie einer Art zu erlangen.

GENERAL INTRODUCTION

Bats

With over 1200 species (Simmons 2005a), bats are the second largest group of mammals after rodents and species numbers are still increasing constantly. They lead a predominantly nocturnal lifestyle and are the only mammals that mastered active flight. Together with their ability to echolocate, this laid the foundation for their enormous evolutionary success. The huge diversity of bat species promotes a multitude of sensory adaptations which allowed them to occupy a great variety of ecological niches (Fenton & Ratcliffe 2010). Except for Antarctica, bats are found on all continents and in a wide array of habitats (Neuweiler 2000). An additional important factor leading to great species diversity was their ability to exploit a wide range of food resources, stretching from insects to nectar, pollen, fruits, small vertebrates, and in some specialists even blood (Arita & Fenton 1997; Kunz & Fenton 2003; Altringham 2011). Their ability to use torpor, both daily and over seasons, as a means to conserve energy, opened their ways into temperate regions when food is scarce. This is aided in some species by extensive, long-distance migrations (Kunz & Fenton 2003, Altringham 2011).

The phylogenetic origins of bats are not yet clear, as already the oldest fossil bats from the Eocene, like Icaronycteris index (Jepsen, 1966) or Onychonycteris finneyi (Simmons et al. 2008), resemble closely our extant bat species (Simmons 2005a). The order of bats (Chiroptera) is considered monophyletic (Simmons & Geisler 1998), and was classically divided into Micro- and Megachiroptera, based on morphology, sensory specializations and lifestyle (Neuweiler 2000). Although a new publication confirmed this grouping based on morphological analysis (O’Leary et al. 2013), most of the recent evidence paints a convincing alternative picture that has changed due to molecular phylogenetic studies (Teeling et al. 2000; Springer et al. 2001; Teeling et al. 2005; Jones & Teeling 2006, Tsagkogeorga et al. 2013). Chiroptera are now split into two new suborders: The Yinpterochiroptera include the former Megachiroptera (Old World fruit bats, Pteropodidae) and as a sister group the superfamily Rhinolophoidea, which used to belong to the Microchiroptera. The rest of the former Microchiroptera now constitute the Yangochiroptera.

Echolocation

In a case of convergent evolution, echolocation – the localization of objects with echoes – can also be found in certain bird species and especially toothed whales (Odontoceti) (Madsen & Surlykke 2013). But the signal variability, dynamic modulation and sophistication of bat echolocation is unrivalled. Its evolution supported a wide adaptive radiation allowing bats to occupy an abundance of very diverse niches. It has been discussed at length whether echolocation or flight developed first or maybe both simultaneously (e.g. Simmons & Geisler 1998; Fenton et al. 1995; Speakman 1993). However, the finding of the fossil bat Onychonycteris finneyi now supports the hypothesis that flight developed first, as it was clearly able to fly but not to echolocate, being deduced from the ear morphology (e.g. size of the cochlea). All other known fossil bats are younger and already possess the ability to echolocate (Simmons et al. 2008, Simmons et al. 2010).

Today, echolocation can be found in all Yangochiroptera. In the Yinpterochiroptera it is present in all of the Rhinolophoidea but generally not in the Old World fruit bats, Pteropodidae (with the exception of the genus Rousettus). The evolution of echolocation is still not fully resolved (Teeling 2009, Teeling et al. 2012, Jones et al. 2013). As Icaronycteris index is at the base of all extant bats and was already capable of echolocation, this would either mean that the ancestors of the Yinpterochiroptera lost this ability and consequently bats within the Rhinolophoidea would have had to evolve echolocation convergently. Alternatively echolocation was only lost in the pteropodid lineage, potentially due to a highly sophisticated night vision. Also molecular work involving the “speech gene” FoxP2 and the “hearing gene” Prestin couldn’t yet fully resolve the evolution of echolocation (Li et al 2007; Li et al 2008, Teeling et al. 2012, Jones et al. 2013). The potentially contradicting side of the Prestin data which suggested that all echolocating bats are monophyletic, has been debated by the authors as convergent, molecular evolution and since been supported by a study showing convergence in Prestin of bats and dolphins (Liu et al. 2010). Further support in this direction is the general flexibility of habitat- and resource-driven adaptations in call designs, of which many evolved independently (Jones & Teeling 2006).

As mentioned above, another convergent evolution of an echolocating system is found within the Pteropodidae, where some species in the genus Rousettus developed a click- echolocating system using their tongue. This has long been regarded as a primitive system, but recent work demonstrated that it is far more sophisticated than previously expected (Yovel et al. 2010; Yovel et al. 2011a; Yovel et al 2011b). Recent evidence even suggested

22 an additional form of echolocation in Old World fruit bats: some species seem to use click- like sounds produced intentionally with their wings to detect and discriminate objects (Boonman et al. 2014).

In contrast to the tongue-clicking of Rousettus, all other bats produce their calls laryngeally and emit them either through their mouth or their nose. They listen for the returning echoes, being reflected e.g. by prey or objects, and evaluate them. Their echolocation system enables bats not only to detect, but also to localize and classify objects. With astounding accuracy they determine distance, direction, size and shape of an object (Schnitzler & Kalko 2001).

Most of the echolocation calls are in the ultrasonic range and some can reach up to more than 200 kHz (Altringham 2011). Extremely loud maximum amplitude levels of up to 140 dB SPL in open space hunting bats represent the one end of the amplitude scale (Surlykke & Kalko 2008) and so-called whispering bats the other with search call levels of 94 dB SPL (Goerlitz et al. 2010). The call design shows two general types, either constant- frequency (CF) or frequency-modulated (FM) calls, or a combination of these (Schnitzler et al. 2003). Both types have various advantages and come with specializations, enabling bat species to diversify and occupy their countless niches (Schnitzler & Kalko 2001, Denzinger & Schnitzler 2013). The call design can vary for example in frequency, call duration, bandwidth of the call, duty cycle or sonar beam shape. Bats will adjust their calls depending on the flight habitat or hunting situation (Schnitzler et al. 2003, Wund 2005). Further modification and sophistication of their echolocation is based on morphological features like varying nose and ear structures or an acoustic fovea in the cochlea of CF bats. In addition the morphological adaption of the acoustic fovea is supported by a behavioural adaption as these bats are able to compensate for the Doppler shift they experience during flight (Schnitzler 1968, Smotherman & Metzner 2003).

However, all echolocation designs face similar acoustic problems and are thus restricted in their plasticity. A longer duration of calls for example would increase detection probability, but is limited in order to avoid overlap with returning echoes. Higher frequencies of calls allow for a better resolution of structures, but would also suffer decreased detection range due to (geometric and atmospheric) attenuation. Masking effects also play a role in echolocation: in forward masking, loud echoes temporarily increase the auditory threshold and in backward masking, a faint echo would be masked if it is directly followed by a loud echo (Schnitzler & Kalko 2001, Schnitzler et al. 2003, Dietz et al. 2009, Denzinger &

Schnitzler 2013). On the one hand, bats use echolocation for orientation in space and navigation on varying scales. On the other, they mastered its use for foraging, applying several strategies depending on the respective situation (Schnitzler et al. 2003).

Echolocation for habitat recognition

Bats do not only use their echolocation to detect, localize and classify prey targets, but also to avoid objects and find their way in a three-dimensional air space. So far we have a very good understanding of how bats manage to evaluate the echoes of point-targets, but only in recent years more evidence accumulated how they could distinguish extended objects like plants or even whole habitats (Müller & Kuc 2000, Grunwald et al. 2004, Firzlaff et al. 2006, Firzlaff et al. 2007, Yovel et al. 2008, Yovel et al. 2009, Genzel et al. 2012, Heinrich & Wiegrebe 2013). This would be a very beneficial task for a bat to solve as it aids in recognizing landmarks or even building a cognitive map for orientation (Verboom et al. 1999, Serra-Cobo et al. 2000). Furthermore, certain plant species might be better foraging grounds than others at certain times of the year. Insects could accumulate on certain plants or fruits and might be limited in spatial and temporal patterns. Bats could use temporal as well as spectral cues to classify extended objects and statistical acoustic features are evaluated e.g. in neural models in order to understand complex echo classification (see Yovel et al. 2011c for a review). But also spatial cues (i.e. the spatial spread of echoes) seems to be important (Goerlitz et al. 2012). In this study I focused on a very distinct and simply shaped habitat – bodies of water.

Water habitats

Bodies of water are used by bats for a variety of reasons. Most bats need to drink regularly and do so readily on the wing (for a description of this behaviour see chapter 1) (Grindal et al. 1999, Seidman & Zabel 2001, Taylor & Tuttle 2007, Salsamendi et al. 2012, Seibold et al. 2013, Lison & Calvo 2014). Although bats can use their preys’ body liquids to some extent to regulate their water balance, evaporative water loss is extremely high during flight (Carpenter 1969, Neuweiler 2000). In the tropics some species use water for nutritional supplementation (Voigt et al. 2008, Ghanem et al. 2013). Likely more species than is shown so far, might use e.g. rivers for orientation or navigation (Verboom et al. 1999, Serra-Cobo et al. 2000). And probably most important is the fact that water habitats often are very profitable

24 hunting habitats (Thomas 1988, Rautenbach et al. 1996, Zahn & Maier 1997, Grindal et al. 1999, Warren et al. 2000, Whitaker et al. 2000, Fukui et al. 2006, Akasaka et al. 2006, Vindigni et al. 2009) with several bat species worldwide that specialised in these habitats. In Europe we find three of those specialists hunting predominantly around water areas, the pond bat Myotis dasycneme, the Daubenton’s bat Myotis daubentonii and the Long-fingered bat Myotis capaccinii. These trawling bats hunt for insects on or above the water surface and show a fine-scaled niche differentiation and can therefore occur in sympatry (Jones & Rayner 1988a, Kalko & Schnitzler 1989, Biscardi et al. 2007, Almenar et al. 2008, Krüger et al. 2012, Krüger et al. 2014). But also other species are closely associated to water and use the often abundant prey in the air above the surface or the riverine vegetation (Rachwald et al. 2001, Ciechanowski 2002, Bartonicka & Rehák 2004, Lundy & Montgomery 2010, Akasaka et al. 2012). At least some of these trawling bats not only forage on insects but also have been shown to catch fish (Siemers et al. 2001, Levin et al. 2006, Aihartza et al. 2008), especially when looking into species outside of Europe (e.g. Noctilio leporinus, Myotis vivesi, Myotis macrotarsus, Nycteris grandis) (Schnitzler et al. 1994, Übernickel et al. 2013). Their foraging grounds are freshwater bodies but also the open sea, especially in the case of Myotis vivesi which has specialised exclusively on marine fish and crustaceans. Bats foraging on or above water, try to avoid turbulent or cluttered areas as this would interfere with prey detection due to background masking (Frenckell & Barclay 1987, Mackey & Barclay 1989, Boonman et al. 1998, Rydell et al. 1999, Biscardi et al. 2007, Lundy & Montgomery 2010, Almenar et al. 2013). If necessary (due to higher clutter or shifting prey availability) Daubenton’s bats switch between aerial hawking close to and gaffing from the water surface (Todd & Waters 2007). Bats capitalize on specific acoustic qualities of the water surface to catch insects: it acts like a mirror, reflecting most of the call energy away (see chapter 1). Compared to cluttered environments like vegetation or ground the signal-to-noise ratio is optimal on water due to a single returning echo from the prey, which consequently results in a better target detection performance (Zsebok et al. 2013). The sound pressure level of this echo is even higher than for prey in midair, probably due to additional sound wave fronts from the water surface (Siemers et al. 2001). This subsequently leads to an increased detection distance and therefore higher search efficiency for trawling bats (Siemers et al. 2005).

Aims of this study

In this thesis I investigated questions on the sensory ecology of bats around bodies of water. My aim was to further our understanding of how bats evaluate their echolocation information when they do not deal with localized point targets like e.g. prey, but with extended surfaces and habitats. Water is a very distinct habitat regarding its echolocation properties. It is two dimensional, relatively uncluttered compared to vegetation and often smooth. Apart from water there is no other natural, smooth surface in a bat’s environment. As described above it offers several advantages as hunting grounds and only the open air space would be a comparable foraging habitat with even less background clutter. Finally, bodies of water are essential for bats as a regular drinking resource. For all these reasons, I considered water to be an ideal model system to answer questions of how bats perceive their extended surroundings.

In chapter 1 I investigate how bats recognize bodies of water with echolocation. With laboratory experiments in our Bulgarian field station I ask which cues are necessary and how they relate to the unique echoacoustic properties of water surfaces in nature. I discuss other sensory modalities and give ideas on the multisensorial decision process. Extending the experiment to a variety of species I assess the general applicability of my theoretical concept of water recognition. I further elaborate on the robustness of this concept and discuss whether it needs to be learned or not.

In chapter 2 I test a conflicting situation between spatial memory and echo cues in a laboratory setting in Bulgaria and in a field experiment in the Jehuda desert in Israel. As bats can demonstrate a precise spatial memory, I ask whether prior knowledge of the location of a body of water would be sufficient for drinking attempts. Would they drink from an area which they before have experienced to be water, despite missing relevant echo cues? After the initial laboratory experiments I took my hypothesis to the field to demonstrate its validity with bats that had long-term experience with a water location. Finally, I investigate how bats would react to water coding cues in a novel and previously unrewarded location.

In chapter 3 I explore whether acoustic mirrors can be sensory traps for bats if they are presented in a different orientation than horizontal. In our anthropogenically-altered environment water is no longer the only smooth surface. Most prominent are many smooth vertical areas like windows. In flight room experiments different flight and echolocation parameters are analysed and related to bats interpreting their environment erroneously. With

26 evidence from the field I discuss potential detrimental effects and urge for more research to investigate this conservational issue.

In chapter 4 I joined Dr. Frauke Krüger in her effort to elucidate the finer mechanisms of niche evolution in two trawling bat species. The two water specialists Myotis daubentonii and Myotis dasycneme can occur sympatrically and seemingly occupy the same foraging niche. But would a combination of classical morphological as well as molecular diet analysis reveal a fine-scaled specialisation? We further investigated if functional morphology could explain the co-occurrence of these species.

CHAPTER 1

Innate recognition of water bodies in echolocating bats

Miniopterus schreibersii drinking from real water

Abstract In the course of their lives, most animals must find different specific habitat and microhabitat types for survival and reproduction. Yet, in vertebrates, little is known about the sensory cues that mediate habitat recognition. Here, we investigated how echolocating bats recognize ponds or other water bodies that are crucial for foraging, drinking and orientation. While the echolocation of insect-sized point targets is well understood, our study now addressed how free flying bats recognize and classify spatially-extended echo-targets. With wild bats of 15 different species (7 genera from 3 phylogenetically distant, large bat families), we found that bats perceived any extended, echo-acoustically smooth surface to be water, even in the presence of conflicting information from other sensory modalities. In addition, naïve juvenile bats that had never before encountered a water body showed spontaneous drinking responses from smooth plates. This provides the first evidence for innate recognition of a habitat cue in a mammal.

Figure 1 - Drinking bat. A greater mouse-eared bat, Myotis myotis, closing in on a water surface, opening its mouth and lowering the head to take a mouthful of water. (Photo by Dietmar Nill)

Introduction It is crucial for animals to find their often species-specific, suitable habitat or microhabitat for fitness relevant behaviours such as mating, breeding, foraging or drinking (Adamik & Bures 2007, Tonnis et al. 2005). While both empirical and theoretical work have investigated whether and under which conditions habitat preference is innate or learned (Jaenike & Holt 1991, Davis & Stamps 2004, Beltman & Metz 2005, Slagsvold & Wiebke 2007, Stamps et al. 2009), very little is known about the sensory cues that actually mediate habitat recognition in vertebrates. The only studies known to us show that fish innately find riverine habitats by olfaction (Vrieze et al. 2010) and that migrating birds may use song of bird species with similar habitat requirements to find suitable stopover sites (Mukhin et al. 2008). Bats are an especially interesting group in which to study the sensory basis of habitat recognition, because they are highly mobile, can cover 200 km in one night’s flight and yet predominantly rely on a short-range sensory system, echolocation (Schnitzler et al. 2003). While it is well understood how bats echolocate insect-sized point targets (Schnitzler et al. 2003, Schnitzler & Kalko 2001), here we present the first study that investigates how free flying bats recognize and classify spatially-extended echo-targets – such as forest edges or lakes - in an ecologically and evolutionarily relevant context. We took a behavioural approach to find out how bats recognize a key habitat element in their environment: bodies of water. Ponds, lakes and rivers are important for bats in various ways. They offer an abundance of prey, often soft bodied and easily digestible (Fukui et al. 2006), and several bat species are specialized to forage in aquatic habitats (Fenton & Bogdanowicz 2002). Due to acoustic mirror effects, bats can detect insects sitting on the smooth water surface easier (Boonman et al. 1998, Siemers et al. 2001) and from further away (Siemers et al. 2005) than on vegetation or when air-borne. With respect to flight costs bats benefit from the ground effect when flying close to the water surface (Aldridge 1988). Many bat species likely use bodies of water as landmarks for orientation and navigation (Serra-Cobo et al. 2000). Also, most of the about 1000 extant species of echolocating bats must visit ponds or rivers for drinking (Fig. 1). But how do bats find and recognize the most prominent element of such an aquatic habitat, the water body? Water surfaces are special in that they represent the only extended, acoustically smooth surfaces in the natural environment. We therefore hypothesized that bats would rely on the mirror-like echo reflection properties of smooth water surfaces to detect and recognize bodies of water. When a bat flies over a water surface

31 and the axis of its echolocation beam intersects with the surface at an acute angle, the main energy of the echolocation calls is reflected away from rather than back towards the bat, so it does not receive an echo from ahead (Fig. 2). However, some off-axis energy of the sound beam hits the surface perpendicularly and does generate an echo returning from straight below the bat. Based on our above hypothesis, we predicted that bats confronted with any sufficiently large smooth, horizontal surface having these acoustic mirror properties will perceive it to be water. We found that bats take horizontal, acoustical mirrors to be water. This behaviour is extremely stereotypical, phylogenetically widespread among echolocating bats and innate. Echolocation is the key sensory modality triggering water recognition and takes dominance over conflicting information.

Figure 2 - Schematic and simplified representation of sound propagation and echo generation at a smooth surface. Most of the call energy is reflected away from the bat, with the exception of the small off-axis fraction that hits the surface perpendicularly.

Results Echoacoustic water recognition In a large flight room with weak red illumination we presented experimentally-naïve, wild-caught bats with two plates (1.2 x 2 m) positioned on a sandy floor (Supplementary Figure S1). The two plates presented simultaneously in each trial were always of the same material - either metal, plastic or wood - but one had a smooth and the other a textured surface (Fig. 3). Ensonification and qualitative assessment of the reflected echo scenes

32 showed that the smooth plates were good echoacoustic mimics of a water surface, while the echoes of the textured plates resembled those of grained sand (Fig. 4). It is important to note that the smooth experimental plates only mimicked water in the echoacoustic domain, but did not in other modalities including olfaction, vision, taste and touch. We scored a bat’s attempt to drink from an experimental plate as our behavioural measure for the bats’ perception of the experimental plate as a water body. To evaluate whether the bats were generally motivated to drink, we presented the bats a real water pool at the end of each experimental session (Supplementary Figure S1).

a bc

d e f

Figure 3 – Experimental plates. In the first row all smooth surfaces are shown: metal (a), wood (b) and plastic (c); and below the respective textured plates (d-f). Each photo shows a 0.13 x 0.13m detail from the 1.2 x 2m plates.

We tested four different species of bat (each n=6 individuals), from distinct ecological (Schnitzler et al. 2003) and phylogenetic groups (Jones & Teeling 2006) with all three plate materials. Schreiber’s bat (Miniopterus schreibersii) is an example of a species hunting in open space; Daubenton’s bat (Myotis daubentonii) is specialized at hunting over bodies of water; the greater mouse-eared bat (Myotis myotis) forages predominantly for ground-running arthropods; and the greater horseshoe bat (Rhinolophus ferrumequinum) uses a distinct and

33 highly specialized echolocation system to detect fluttering insects.. All 24 bats of all four species spontaneously tried to drink from the smooth plates of all three materials, but never from the textured plates (Fig. 5, Fisher's combined probability test, all p < 0.0001). When they were offered a real water pool at the end of each experimental session (control of drinking motivation), they drank 4-19 times in 10 min (species means). To further explore the generality and taxonomic spread of echoacoustic water recognition, we additionally tested one individual from eleven more species with the metal plate setup. Our total data set thus comprises 15 species (7 genera) from three large bat families, Vespertilionidae, Miniopteridae and the phylogenetically distant Rhinolophidae (Jones & Teeling 2006). All of the eleven additional species likewise tried to drink from the smooth but never from the textured metal plate (Table 1). The bats’ behaviour during drinking attempts on the smooth plates and when drinking from the real water was identical (compare Fig. 5a with 5b and Supplementary Movies 1 with 2), which shows that the bats indeed tried to drink from the plates. Miniopterus schreibersii, the most persistent species, performed an average of 104 ± 15 (mean ± s.e.m.) drinking attempts on the smooth metal plate in two 5-min trials (Fig. 5c), while the other three species reached values of 95 ± 20 (Myotis daubentonii), 47 ± 15 (Myotis myotis) and 43

± 11 (Rhinolophus ferrumequinum) attempts (ANOVA, F1,3=4.23, p=0.0182). The material of the plates had no effect on the number of drinking attempts in M. schreibersii (repeated measures ANOVA, F2,10=0.01, p=0.9886, Fig. 5c) and M. daubentonii (F2,10=1.06, p=0.3838, Fig. 5d). By contrast, material type did affect the number of drinking attempts in M. myotis

(F2,10=4.57, p=0.0389, Fig. 5e) and Rhinolophus ferrumequinum (F2,10=4.52, p=0.0399, Fig. 5f). This was driven by a lower response to the wooden plate as compared to metal and plastic.

Table 1 - Drinking attempts of additional bat species. All attempts to drink from the metal plates and average drinking events on real water are listed.

Smooth Textured Species (1 individual each) Water plate plate Myotis emarginatus 66 0 5 Myotis nattereri 144 0 9 Myotis capaccinii 13 0 0 Myotis blythii oxygnathus 26 0 2 Myotis bechsteinii 94 0 0 Myotis aurascens 163 0 9 Hypsugo savii 8 0 0 Plecotus austriacus 125 0 9 Nyctalus noctula 1 0 0 Pipistrellus pipistrellus 64 0 10 Rhinolophus mehelyi 56 0 47

Robustness to conflicting information On rare occasions, bats even resumed their drinking attempts after having accidentally landed on the smooth plate shortly before, whereby they should have perceived that it is not a water body. To further explore the behavioural response of M. schreibersii to an acoustically simulated water surface in a physically unrealistic situation, we placed the metal plate on a table (Supplementary Figure S1). We were interested to see whether water recognition triggered by the acoustic mirror was imperative enough to override the generated conflict, namely being able to fly underneath a perceived water surface. Some even flew underneath the tabletop. Nevertheless, they repeatedly tried to drink from the metal plate (43 ± 9 attempts in 10 min, n=6 bats), suggesting that water-like echoacoustic cues take dominance over any other conflicting information. In a next step we evaluated the role of conflicting sensory stimuli with another set of Schreiber’s bats. We assume conflicting sensory information in the domains of vision, chemoreception and touch, as a metal plate does not look, smell, taste or feel like real water. We repeated the initially described experiment with two metal plates on the ground, but this time eliminating potentially conflicting visual input by conducting it in complete darkness.

water sand a 125 b S G E S G U

smooth textured c 125 d

metal

e 125 f

wood

g 125 h frequency plastic (kHz)

-60 dB 0 dB 36

Figure 4 - Echo signatures of natural and experimental surfaces. In the first row a comparison of a natural smooth (water) (a) and a natural textured surface (sand) (b) is given. Below, the echo signatures of our three experimental materials (metal, wood and plastic) are compared for smooth and textured plates. Smooth plates are depicted on the left (c, e, g) and textured surfaces on the right (d, f, h). The white scale bar in (g) corresponds to 10 ms. The colour bar codes for the amplitude of the signal in a relative dB scale. Smooth (left side): After the outgoing signal (S) there is a time delay until the first echo returns; this is the echo front reflected perpendicularly from the ground (G). All other parts of the signal are reflected away and thus do not reach the microphone (see Fig. 2 for a schematic representation). In the water sonogram (a), an additional echo from the back edge (E) of the water pool shows up. Textured (right side): After the perpendicular ground echo (G), a series of many overlapping echoes from the uneven surface structures follows (U). Overall, the echo reflections of the smooth experimental plates strongly resemble those of a water surface, while the reflections of the textured plates mimic those of uneven ground.

Indeed, the number of drinking attempts rose from the previously recorded 104 attempts under red light conditions to 166 in darkness (t-test, t10=2.48, p=0.0325), whereas the number of drinking events with real water did not differ between the two illumination treatments

(t10=1.25, p=0.2408) (Fig 6a).

Innate response of juvenile bats Bats are able to efficiently learn from conspecifics (Page & Ryan 2006), but they typically roam and forage alone (Kerth et al. 2001, Rossiter et al. 2002). We thus hypothesised that echoacoustic recognition of water surfaces would most likely be innate. To test this hypothesis, we raised six juvenile Geoffroy’s bats (Myotis emarginatus) at our field station together with their mothers. They were captured in a cave before they became volant and hence had never encountered a pond or river in their life. As soon as they flew well, these naïve bats were tested with the metal plate setup. Five of the six juveniles, on this first contact in their life with an extended, horizontal smooth surface, spontaneously tried to drink from the smooth metal plate (18 ± 8 times; Fig 6b), but never from the textured plate (Fisher's combined probability test, p < 0.0001, n = 6 bats). The juvenile drinking attempts very much resembled those observed in the adults. The one juvenile bat that did not attempt to drink from the metal plate also did not drink from the subsequently presented real water and thus probably lacked sufficient motivation.

Figure 5 - Drinking attempts on textured versus smooth surfaces for the four tested bat species. The top panels show a Miniopterus schreibersii drinking from real water (a) and attempting to drink from a metal plate (b). All bats were tested (each species n=6) on all three plate materials: metal (black bars), wood (light grey bars) and plastic (dark grey bars). The textured plates are portrayed on the left and marked with a ‘0’, as no drinking attempts occurred. All smooth plates are grouped on the right side. For each species the average drinking events on real water are depicted on the far right. Drinking attempts of (c) Miniopterus schreibersii, (d) Myotis daubentonii, (e) Myotis myotis and (f) Rhinolophus ferrumequinum. Error bars show one standard error; for statistics see text.

Figure 6 – Sensory conflict and innate water recognition. In further experiments we examined the role of conflicting information and an innate basis of water recognition. Error bars show one standard error; for statistics see text. (a) Drinking attempts of Schreiber’s bat, Miniopterus schreibersii (n=6) on a smooth metal plate in different light conditions. No attempts occurred on the simultaneously present textured plate (data not shown). The bats tried to drink significantly more often in complete darkness (black bars) compared to the dim light condition (light grey bars). The drinking numbers on real water did not differ between the two treatments. (b) Drinking attempts of naïve, juvenile Geoffroy’s bats, Myotis emarginatus, (n=6) from metal plates (black bars). Not a single attempt occurred on the textured plate, thus marked with a ‘0’. On the right the number of drinking events on real water is shown.

Discussion The behavioural data corroborate our hypothesis that bats rely on the mirror-like echo reflection properties of smooth water surfaces to detect and recognize water bodies. It is astonishing that all individuals attempted to drink repeatedly, some even 100 times and more, from the plates with the water-like echo signature, despite conflicting information from other sensory modalities like touch, taste, olfaction and vision. This suggests that bats rely heavily on echolocation for assessment of their environment at close range and for the recognition of habitat elements. The observation that all 15 species, representative for three large and phylogenetically distant bat families, very reproducibly showed drinking attempts on large smooth plates furthermore suggests that echoacoustic water recognition is taxonomically wide spread, if not universal, among echolocating bats.

The high number of consecutive drinking attempts that the bats showed within a short time, despite being unsuccessful, indicates a hardwired neural processing of echoacoustic water recognition. However, the fact that two species showed fewer attempts on the wooden than on the metal and plastic plates indicates that other modalities also played some inferior role. Possibly the light wooden plates were visually most dissimilar from water or had the most distinct non-water smell and the conflicting information of these modalities lowered the bats’ behavioural response. By conducting the experiment with Miniopterus schreibersii again in complete darkness, we removed the conflicting visual information and thereby altered the sensory scenery. We observed an increase of drinking attempts by almost 60% in complete darkness. Since the drinking events on real water after the experiment stayed on the same level as before, this is not the result of a potential side effect due to increased drinking motivation. Our experiments suggest that the bats integrate information from several modalities to form a percept of their environment (Ernst & Bülthoff 2004) and to inform their behavioural decisions. However, cue importance in this weighted sensory integration process seems to be heavily biased towards echoacoustic information, given that the echoacoustic illusion was sufficient to make bats perceive a water surface. Merely the robustness of this percept could be slightly modulated by other sensory modalities. With respect to small-scale navigation (Schnitzler et al. 2003) and habitat recognition, bats thus appear to be an extreme example of predominant reliance on one main sensory modality. For large-scale navigation – where echolocation plays a much smaller role (Schnitzler et al. 2003) - bats use and integrate information across modalities, such as visual and magnetoreceptive information (Holland et al. 2010). The present extreme case of one sensory input’s prevalence might be an interesting model to further increase the current understanding of multisensory integration in the vertebrate brain (Ernst & Banks 2002, Ghazanfar & Schroeder 2006). To date, many other multimodal studies - often focused on communication – found a more balanced integration of multisensory stimuli. Communicating dart-poison frogs, for example, require concurrent visual and auditory cues for cross-modal integration to elicit a behavioural response (Narins et al. 2003). With the bats’ response being so extremely stereotypical and repetitive, questions about learning arise. Do bats have to learn water recognition – and thereby the places to drink - by following conspecifics, e.g. their mother? The answer is no. By contrast, the spontaneous and repeated drinking attempts of the juvenile, naïve bats strongly argue for an innate basis of the echoacoustic recognition of water bodies. Given that bats mistake large horizontal mirrors innately and persistently for water, one might hypothesize that they occasionally try to drink 41 from man-made smooth surfaces such as car roofs, winter gardens and the like. Future studies will be necessary to assess the occurrence, extent and potential conservation relevance of such a scenario. Certainly bats also need to recognize other specific foraging habitats to which the respective species are adapted, in e.g. wing morphology, echolocation system and food requirements (Schnitzler et al. 2003, Aldridge 1999, Arlettaz 1999, Siemers & Schnitzler 2004). Computers can classify tree species based on echo statistics (Yovel et al. 2008) – so bats may as well. Bats can distinguish the roughness of computer generated echoes (Grunwald et al. 2004); an ability that might help them classifying complex vegetation echoes. From a technical perspective, a detailed understanding of how bats echolocate and recognize spatially-extended objects and habitat types will further the development of sonar- based autonomous robots. In summary, our experiments revealed that the recognition of water bodies in bats is mediated by echoacoustic cues (mirror-like reflection). This recognition mechanism is taxonomically wide-spread among bats, and is innate. To our knowledge, this is the first example of innate recognition of a habitat cue in mammals. The innateness and the physically well-defined cues make water recognition in bats an ideal model to study the neural basis and potentially even the genetic correlates of habitat recognition.

Methods Bats This study was conducted at the Tabachka Bat Research Station of the Sensory Ecology Group (Max Planck Institute for Ornithology, Seewiesen) that is run in cooperation with the Directorate of the Rusenski Lom Nature Park in the district of Ruse, northern Bulgaria. Capture, husbandry and behavioural studies were carried out under license of the responsible Bulgarian authorities (Bulgarian Ministry of Environment and Water and Regional Inspectorate (RIOSV) Ruse, permits # 57/18.04.2006 and 100/04.07.2007). Bats were captured in the area of the Rusenski Lom Nature Park at or close to their roost caves by a handnet, mistnets or harp trap. For the duration of the experiment, bats were kept in a separate keeping room (temperature 18-24°C, humidity around 75%; close to natural conditions in the caves, own data). Depending on the species, they were accommodated in either a 2.2 x 0.9 x 1.1m mesh tent or 50 x 35 x 35cm cages. On the capture night, bats were

42 handfed with mealworms and watered until satiated. The experiment was usually started on the following night. All bats were released again at their respective capture site after completion of the experiment. Four species of bat were used for the full set of the experiments with all three plate materials (metal, plastic and wood): Miniopterus schreibersii, Myotis daubentonii, Myotis myotis, and Rhinolophus ferrumequinum. Six adult individuals per species were tested in a balanced sex ratio. To test for the generality and the extent of the taxonomic spread of our findings, one individual each from eleven additional bat species was tested with the metal plate setting (see below). This group consisted of Myotis emarginatus, M. nattereri, M. capaccinii, M. blythii oxygnathus, M. bechsteinii, M. aurascens, Hypsugo savii, Plecotus austriacus, Nyctalus noctula, Pipistrellus pipistrellus and Rhinolophus mehelyi. Six females Geoffroy’s bat (Myotis emarginatus) were captured inside a cave with their young shortly before those became volant. Mothers were kept with the juveniles and nursed them until natural weaning. When released together into their home cave after completion of experiments, the juveniles were able to fly and forage independently. Six additional adult M. schreibersii were used to test their drinking response in complete darkness (dark condition).

Flight room and Experimental Setup All experiments were conducted in a large flight room (4 x 8 x 2.4m). The floor was covered with sand, and the walls and ceiling with a felt-like, sound-dampening material (‘Velter’, thickness 5mm, Arbanasy EOOD, Veliko Ternovo, Bulgaria). The room was lit with two red bulbs (25 W, Osram, Germany), except for the dark condition (see below), where custom made infrared strobe lights (Animal Physiology Department, University of Tübingen, Germany; 875 nm wavelength) were used. In the centre of the room, a water pool was inserted into the sandy floor (1.8 x 1 m, 4 cm water depths) (Supplementary Figure S1). The pool could be covered by a plate and sand, or uncovered to give the bats access to real water. To test our hypothesis that the bats would take any extended, acoustically smooth horizontal surface for water, we presented experimental plates (1.2 x 2m) on the flight room floor. Always two plates of the same material but with different surface structure (one smooth, one textured) were presented side by side in the centre of the flight room (25cm distance between plates; pool covered) (Supplementary Figure S1). We used three different materials for the experimental plates; metal (aluminium), plastic (PVC) and wood (MDF). For the textured surfaces, we chose a 43 metal diamond plate with 35 x 5 mm (~2mm height) bumps at 4 cm spacing, while we carved depressions of the same size and spacing into the plastic and wooden plates (see Fig. 3). The bats’ behaviour was filmed with 4 synchronized video cameras (Watec, WAT- 902H2 Ultimate, Yamagata-Ken, Japan; 2 for overview, 2 for close-up at the two experimental plates on the ground) for online observation from an adjacent room and for later off-line analysis (ABUS Security Center; Digi-Protect Video Surveillance PCI Card, 4 channel / 100fps). In addition, a high-speed camera (Mikrotron MotionBLITZ EoSens mini) was used for detailed comparison of drinking behaviour from real water with attempts to drink from the smooth experimental plates.

Experimental Procedure Experiments were conducted at night during the natural activity phase of the bats. The night before the experiment, bats were fed and watered until satisfied. They had access to food and water ad libitum for the rest of the pre-experimental night. Water was taken away in the morning to prevent drinking during the day and early evening. We thereby mimicked natural conditions and thirst levels of bats emerging from their day roosts at dusk. Prior to each experiment, the bat was fed three to five mealworms. It was then released into the flight room where a smooth and a textured experimental plate were presented. If a bat did not fly and explore the flight room within 1 h, it was excluded from the experiment (total of 19 bats). All other bats attempted to drink from the plates within 1 h. With the first attempt a five minutes time window (time in flight) opened, during which the drinking attempts were counted in later off-line video analysis. We defined a drinking attempt as the bat touching the plate in a head-down position which corresponds to drinking behaviour from a real water surface (compare Supplementary Movies 1 and 2). After these five minutes, the plate positions were exchanged. Again, the bat was given one hour and when it resumed drinking attempts, a second five minutes time window began during which attempts were counted. With completion of this time slot, the plates were removed from the room and the water pool was uncovered. This was done to assess whether the bat was indeed motivated to drink. It was given one final hour, with a ten minutes time window starting as soon as the bat began to drink. All drinking events were counted. During all these trials the bat was free to fly around and explore the room. However, when a bat hung without moving longer than about three minutes, the experimenter went inside to gently stimulate flight by e.g. tapping on the wall. This was done in order to prevent the bat from falling asleep.

After the experiment, the bat was fed and watered until satisfied, and then returned to its keeping cage. In the two consecutive nights, the experiment was repeated with the remaining two substrate types. In order to factor out effects of presentation sequence, the three plate materials (metal, plastic and wood) were assigned to nights and bats following a Latin square design. To test for the persistence of the bats’ drinking response in a physically unrealistic situation for a pond or river, the metal plate was placed on a standard plastic garden table (1.5 x 1m, 1m height) in a way that the bats could assess the open airspace below the tabletop by echolocation and fly underneath (Supplementary Figure S1). The M. emarginatus juveniles received regular flight training in the empty flight room to ensure natural development of flight abilities. Once fully volant, they were tested individually in the metal plate setup as described above for the adult bats. Also the test of the six additional adult M. schreibersii in the dark condition (metal plates only) followed all experimental details as previously described, with the exception that infrared light was used instead of dim red light (see above).

Data Analysis Statistical analyses were run in SPSS 15.0 and Excel 2003. Because all tested bats showed zero drinking attempts for all of the textured plates, we refrained from using parametric tests for an assessment of surface structure on the bats’ behaviour. Instead, we computed separate Chi-Square tests to compare the number of drinking attempts for the smooth versus the textured plates for each bat and plate material. From these, we calculated combined p-values using Fisher's combined probability test.

Ensonification For qualitative evaluation of the echo scenes reflected back by the experimental plates, by real water and a sand surface, we ensonified these surfaces with an artificial echolocation call created in Adobe Audition, sweeping from 120 down to 20 kHz with 3 ms duration (results given in Fig. 4). This artificial call encompasses the main frequency range used by all tested bat species. The call was played via a Polaroid loudspeaker and amplifier (custom-made, University of Tübingen, Germany), which was connected via a PCMCIA card (DAQ Card 6062E, National Instruments, Munich, Germany) with a computer running Avisoft (Avisoft Bioacoustics, Berlin, Germany) software. Returning echoes were recorded by an Avisoft microphone (Type CM16/CMPA, Avisoft Bioacoustics, Berlin, Germany) via 45 an ultrasound recording interface (UltraSoundGate 416H, Avisoft Bioacoustics, Berlin, Germany) and using Avisoft recording software (Avisoft Recorder USGH) with 500 kHz sampling rate. Speaker and microphone on top were mounted in parallel 62 cm above the ensonified surface and tiled downwards in a way that the speaker acoustic axis intersected with the surface at an angle of 50°.

Acknowledgements We thank Ivailo Borissov, Christiana Popova, Maike Schuchmann and Markus Schuller for help during fieldwork, Henrik Brumm, Richard Holland, Nachum Ulanovsky, Sue-Anne Zollinger and Niels Dingemannse for comments and discussion on the manuscript, as well as the Directorate of the Rusenski Lom Nature Park (director Milko Belberov) and the Bulgarian Bat Research and Conservation Group for cooperation and support. The study was funded by a Human Frontier Science Program grant to B.M.S. and by the Max Planck Society.

Movie Legends

Legend to Supplementary Movie 1 A Miniopterus schreibersii drinking from a real water surface. Note the careful approach with a final head down movement. With the mouth open the bat takes up a loading of water and flies upwards again.

Legend to Supplementary Movie 2 A Miniopterus schreibersii trying to drink from a metal surface. Note the exact same sequence of movements compared to drinking from a real water surface (Suppl. Movie 1). After a careful approach with the head extending towards the surface, the bat tries to take up water with the open mouth.

A CD containing the supplementary movies is enclosed in the back of this thesis. They are also available at: http://www.nature.com/ncomms/journal/v1/n8/full/ncomms1110.html#supplementary- information

Supplementary Figure 1 – Flight room set up. In (a) the plate set up (here with metal) is depicted, on the left the smooth, on the right the textured plate. In (b) the water pool is shown which was hidden underneath the plates in (a). In (c) the smooth metal plate is placed on a table.

CHAPTER 2

Acoustic mirages in the desert: testing spatial memory and echo cues in bats

Asellia tridens flying above a metal plate swimming on the pond

Abstract Spatial memory and cue recognition interact when animals revisit important sites in their environment, such as foraging areas, mating places, hides or water sources. Many bats have a very precise spatial memory and rely on echolocation as their key modality for close- range sensing. Bats recognize water surfaces – ponds or rivers - by their echoacoustic mirror properties, as shown in a previous lab study. But what happens if a bat comes to a well- known pond and does not encounter the water-coding echo cues? We first tested in a flight room experiment whether bats would recognize a pool with experimentally manipulated echo reflection properties. The bats never attempted to drink from the pool while the surface was echoacoustically textured, irrespective of their previously acquired and consolidated spatial memory of the pool’s location. We then showed that also desert-dwelling bats in Israel do not try to drink from a pond when the relevant echo cues were removed, despite them likely having a spatial memory of the pond for years. However, the wild bats readily tried to drink from a smooth metal plate that mimicked water in the echoacoustic domain, even in a nearby new location where water never pools naturally. Our data show that echoacoustic cues are both necessary and sufficient to trigger water-recognition in bats and that spatial memory alone is not.

Introduction Spatial memory and cue recognition interact when animals revisit important sites in their environment, such as foraging areas, mating places, hides or water sources (Hurly & Healy 1996), Shettleworth 2009). For example, desert ants return straight to the area of their nest using vector integration and – once in the vicinity – locate the entrance by visual landmarks. In case of a mismatch, they start local searching movements (Merkle et al. 2006). In the present study we asked how bats use spatial memory and echo cues to find and relocate bodies of water. Bats have a very precise spatial memory, which they use both on a small spatial scale, e.g. to locate profitable food sources like bat-pollinated flowers, and on large spatial scales to navigate towards distant foraging patches (Thiele & Winter 2005, Toelch et al 2008, Tsoar et al. 2011, Schnitzler et al. 2003, Ulanovsky & Moss 2008). It is as yet unclear which sensory modalities support large scale navigation in bats, but magnetic orientation and visual cues are likely candidates (Holland et al. 2010, Tsoar et al. 2011). Many bats visit bodies of water like ponds or rivers every night for drinking and some for foraging (Taylor & Tuttle 2007, Razgour et al. 2010). We showed in an earlier lab study that bats recognize water surfaces by very simple means: looking for extended, smooth surfaces (Greif & Siemers 2010). In the world of an echolocating bat a smooth water surface acts like an acoustic mirror. Most of the call energy is reflected away from the bat, only the part hitting the surface perpendicularly will be reflected back. Bats recognize this characteristic echo pattern innately and react astonishingly persistent, indicated by drinking attempts from smooth metal plates up to over 100 times in 10 minutes in the lab (Greif & Siemers 2010). The echoacoustic mirror characteristics will disappear if the smooth surface is broken, e.g., by strong wind, floating leaves or raindrops modulating the surface. In these cases, the surface will be textured and no longer echo-acoustically smooth. So what happens if a bat comes to a well-known pond and does not encounter the water coding echo cues? An often stated hypothesis argues that “bats performing routine tasks in familiar environments rely on memory in favour of echo information to orient and navigate” (Moss & Surlykke 2010) and have been shown to do so (Griffin 1958, Neuweiler & Mohres 1966). So if bats link water locations strongly to their spatial memory, we would expect them to drink even from a non-smooth water surface. First, we tested in a flight room experiment in Bulgaria whether bats would recognize and drink from a pool with a textured surface without, after short (4 drinking events) and after repeated (3 nights) previous experience of a smooth water surface at the same location.

If the echo cues are indeed necessary for bats to recognize water bodies (Greif & Siemers 2010), we predicted them not to drink. However, if the above “routine-task-hypothesis” applies, we expected drinking from the known pool despite conflicting echo cues. Next, we moved on to test wild bats in the Jehuda desert, Israel. We first asked whether the acoustic-mirror-paradigm (‘smooth equals water’) also works in nature, where bats could fly to another water source if a smooth metal plate does not fit their search image as adequately as real water. We then tested – analogously to the flight room experiments - whether texturing the water surface of a pond that bats likely have known for years, keeps them from trying to drink. Finally, we dissociated spatial memory and echo cues by presenting a smooth metal plate in close vicinity to the pond, where water never could pool naturally for topographic reasons. If the hypothesis holds that echo-acoustic mirror cues are necessary and sufficient for bats to recognize a water body (Greif & Siemers 2010), we expected the bats to try to drink from the metal plate.

Methods (A) Flight room experiments in Bulgaria We caught six Schreiber’s bats (Miniopterus schreibersii), fed and watered them ad libitum and kept them in a cage till the next evening. Then we let them fly separately for 30 minutes in a large flight room (4x8x2.4m) with a pool in the middle (1x1.8m, 4cm water depth). A net in a holding frame (mesh size 50mm, 2mm thick) floated on the water surface (Fig. 1A), thus creating a structured surface with 1-2mm high ripples. It was still possible to smell and touch the water through the net. With infrared cameras and lighting we recorded the bats’ behaviour and analysed afterwards whether they showed interest in the water pool. This was quantified as passes within 0.5m above the water area. After these 30 minutes we removed the net and waited until the bats started to drink. This species drinks on average 10 times before it is satiated (Greif & Siemers 2010). We let them drink around four times and textured the surface again with the net. We continued analyses of the bats’ behaviour to test whether they would resume drinking although the echoacoustic mirror cues were now absent. To increase the time for establishing a spatial memory, we repeated the experiment with five new Schreiber’s bats. The new bats flew and drank from the pool with a smooth surface 30 minutes for three consecutive nights. On the fourth night we repeated the experiment as described above.

(B) Field experiments in Israel We worked at a natural pond (3x4m) near Ein Tamar (southern area of the Dead Sea region, Israel) where many bats come to drink each evening. It is likely that they have established the location of this pond in their spatial memory over many months or even years. We placed a floating metal plate (1x2m) in the centre of the pond surrounded with a net (mesh size 50mm, 2mm thick) floating on the surface (aided by several wooden pickets) (Fig. 1B). In a final experiment we textured the surface of the natural pond with the net (Fig. 1C). In addition we placed a smooth metal surface (2x3m) approximately two meters away from the pond, where reed and a sloping terrain would impede real water from pooling (Fig. 1D). For all field experiments, we documented the bats’ behaviour with HD infrared cameras and lighting for one hour (corresponding to peak activity). We scored drinking attempts (see Greif & Siemers 2010) and passes within one meter above the pond or metal plate. A continuous circling was counted as one pass until an individual left the observed area.

Results (A) Flight room experiments in Bulgaria Before encountering the smooth water surface, bats flew extensively through the room at usually 1m height or more. They passed above the textured pool surface within half a meter on average 0.5 times (±0.1 s.e.) min-1. After the net was removed the bats were allowed to drink on average four (4.0 ±0.4 s.e.) times which they did in quick succession once they had discovered the pool (after on average 14.7 (±5.9 s.e.) min). When the surface was textured again with the net, the bats’ behaviour changed noticeably. They now exhibited a distinct searching behaviour over the pool area and within the next 30 minutes passed it 3.6 times (±1.1 s.e.) min-1 within half a meter, mostly however even within 20-30cm. This constituted an almost sevenfold, significant rise in passes compared to the flight behaviour before they first experienced the smooth pool (paired-t-test, t5=-2.85, p=0.036) (Fig. 2). None of the bats ever tried to drink from the water surface while textured by the net. The second set of bats, after learning the pool position for three nights, showed a markedly different behaviour. In the fourth night while the pool was still covered by the net, they crossed the pool area within half a meter 2.7 times (±0.8 s.e.) min-1, thereby differing from the animals of

53 the first experiment significantly (Mann-Whitney-test, p=0.018). When the net was removed they started to drink from the pool quicker (within on average 1.4 (±0.7 s.e.) min compared to 14.7 min in the first group of bats; Mann-Whitney-test, p=0.039), but again did not resume drinking once the pool surface was retextured by the net. Two of the bats even landed on the sand next to the covered pool and walked in a searching movement into the water. The number of passes before and after drinking did not differ significantly (2.7±0.8 versus

4.0±2.0 passes per minute; t4=-0.76, p=0.503; Fig. 2).

A B

C D

Figure 1 - (A) The flight room pool in Bulgaria with the net floating on the surface (suspended in a frame). (B) The pond in Israel covered by a net with a floating metal plate in the centre. A bat attempting to drink can be seen in the inset. (C) The same pond covered by the net (swimming aid with pickets). (D) The metal plate next to the pond.

(B) Field experiments in Israel Mostly Bodenheimer’s pipistrelles (Hypsugo bodenheimeri, ~68% of recorded calls) and Trident Leaf-nosed bats (Asellia tridens, ~12%) came to visit the pond (species ID assessed with Anabat recordings). In the first experiment, the bats readily attempted to drink from the floating metal plate (304 times in one hour) (Movie 1), with 427 additional passes counted within one meter above the pond. In the second experiment, when the pond surface was echo-cluttered by the net, bats were still passing over the covered pond (175 passes), but did not attempt to drink (Movie 2). However, we counted 86 drinking attempts (plus 230 additional passes) by bats on the metal plate presented next to the pond (Movie 3).

Figure 2 - Flight room experiment with a net-covered pool. The graph shows the mean number of bat passes per minute (± s.e.) within 0.5m above the net-covered pool area before the net was taken away and after it was put back on. ‘naive’ displays bats from experiment 1 and ‘experienced’ bats from experiment 2 where they were given three nights of establishing a spatial memory. For statistics details, see text.

Discussion With our experimental manipulation of only the surface texture (and thus the echo image) we were able to let a water surface appear or disappear in the bats’ perception - like a mirage in the desert. In the flight room experiments, the bats never attempted to drink from the pool while the surface was echoacoustically textured by the floating net. This was the case both after short and after repeated, extensive (3 nights) experience with the pool in this very location. The data clearly suggest that echo cues are necessary for a bat to take a surface to be water and that spatial memory alone or olfactory cues are not sufficient. This notwithstanding, the bats quickly built up and retained a precise memory of the spatial location of the pool, as evidenced by their many passes - likely in search of the water - after the net was put on again. The bats showed this spatial fidelity already within a few minutes and only four drinking events, as well as 24 h after the last time experiencing water in this location. Their flight behaviour can be described as search flight and not careful approach flight due to an obstruction on the water surface which might have discouraged them to attempt drinking. The field data from Israel showed that also wild bats perceive extended, horizontal acoustic mirrors as water, as many bats tried to drink from the metal plate. When the water surface was textured by the net, bats did persistently investigate exactly the area of the pond, but never tried to drink from it. The former verifies their use of spatial memory; the latter is evidence that, also in the wild, echo-acoustic mirror cues are necessary for bats to perceive a surface as water. Finally, the wild bats showed spontaneous drinking attempts when coming across a previously unknown smooth surface at a new nearby location. Thus, the echo cues are not only necessary, but also sufficient to trigger water recognition, despite missing olfactory cues and conflicting spatial memory.

Acknowledgments We would like to thank Ivailo Borissov, Sara Troxell, Michael G. Anderson, Nachum Ulanovsky and Holger Goerlitz for help and discussion. All Bulgaria experiments were conducted under license of the Bulgarian Ministry of Environment and Water and Regional Inspectorate (RIOSV) Ruse (permits #57/18.04.2006 and 100/04.07.2007), all Israel experiments under permission by the Israel Nature and Parks Protection Authority (permit #2011/38192). This study was funded by a Human Frontier Science Program grant to B.M.S., a Minerva Short-Term Research grant to S.G. and by the Max Planck Society.

Movie Legends

Legend to Movie 1 Continuous drinking attempts of a bat (likely Bodenheimer’s pipistrelle, Hypsugo bodenheimeri) on a metal plate in the wild.

Legend to Movie 2 A group of bats flying above the pond covered with a net. They are not showing any drinking attempts or closer inspection behaviour.

Legend to Movie 3 Drinking attempts of a bat from a metal plate in a completely novel location.

A CD containing the supplementary movies is enclosed in the back of this thesis.

CHAPTER 3

Acoustic mirrors as evolutionary traps for bats

Vertical plate on the wall

Abstract Evolutionary traps pose a considerable and often fatal risk for animals, leading them to misinterpret their environment (Fletcher et al. 2012, Robertson & Hutto 2006, Longcore & Rich 2004, Horváth et al. 2009, Kunz et al. 2007, Madliger 2012). Bats predominantly rely on their echolocation system to forage, orientate and navigate (Greif & Siemers 2010, Schnitzler & Kalko 2001, Schnitzler et al. 2003, Moss & Surlykke 2010). Here we show that bats mistake smooth, vertical surfaces (such as glass windows) as clear flight paths, and repeatedly collide with them due to their acoustic mirror properties. We demonstrate evidence for this problematic behaviour in a laboratory and field setting, and analyse the factors contributing to this erroneous decision. These factors include the number of echolocation calls, angle of approach and time spent in close vicinity to the surface. Reporting on anecdotal casualties from the wild, we argue that it is necessary to more closely monitor smooth, vertical surfaces in the future, as humans introduce an increasing number of these evolutionary traps in nature and the true frequency of fatalities and impact on bat populations is still unclear.

Introduction Anthropogenic changes to the environment are often detrimental for wild animals. In addition to e.g. habitat loss or the destruction of food resources, animals’ sensory systems can be tricked into misinterpreting environmental cues and hence fall for these evolutionary traps (Schläaepfer et al. 2002, Robertson & Hutto 2006, Fletcher et al. 2012, Robertson et al. 2013). Well known examples are artificial light sources that attract insects and birds at night Longcore & Rich 2004) or smooth surfaces that are mistaken by aquatic insects for water bodies due to their similar light polarization patterns (Horváth et al. 2009). The dramatic increases in the number of wind farms led to birds and bats dying in considerable numbers at these installations (Rydell et al. 2010, Voigt et al. 2012). However, why they cannot avoid them or are potentially even attracted remains unclear so far (Kunz et al. 2007).

Figure 1a - Schematic of the echolocation propagation at a smooth, vertical surface (view from above). Most of the call energy is reflected away from the bat. Only within the red-dashed ‘plate zone’ (compare also Fig. 1b) the perpendicular echo is reflected back from the grey plate to the bat.

To find, evaluate and mitigate potential evolutionary traps it is crucial to consider the sensory ecology of the particular animals (Madliger 2012). The primary sensory modality for most bats is their echolocation system (Greif & Siemers 2010). They detect, classify and localize their environment using the returning echoes of emitted calls (Schnitzler & Kalko 2001, Schnitzler et al. 2003, Moss & Surlykke 2010). In a previous study, we showed that bats perceive any extended, smooth, horizontal surface as a water body. This is due to the acoustic mirror properties of smooth surfaces which reflect most of the echolocation calls away from the calling bat (Greif & Siemers 2010) (compare Fig. 1a for the same principle). This finding raises concern about the millions of artificial vertical smooth surfaces that are introduced in bat habitats by human buildings. Several observations of bats colliding with glass windows hint at the possibility that bats have problems recognizing them as hazards (Davis & Barbour 1965, McGuire & Fenton 2010, Howard 1995). Here, we investigated the exact sensory mechanism of these collisions and the possible occurrence in natural settings. We conducted flight room experiments to analyse echolocation and flight path, as well as field experiments in which we presented bats smooth, vertical surfaces in a familiar environment.

Methods & Results For our flight room experiments 25 greater mouse-eared bats (Myotis myotis) were caught at Orlova Chuka cave (Rusenski Lom Nature Park, Bulgaria). In experimental nights they were fed and watered ad libitum after the experiments. Twenty-one bats were flown in a continuous, rectangular flight tunnel (2.3 m height, 1.2 m width, 8 m length of the long side, 4 m length of the short side) in the dark. A 1.2 x 2 m smooth metal plate was placed about 1.2 m away from one corner of the tunnel either horizontally on the ground or vertically on the wall. The bats’ flight behaviour was recorded with two high-speed infrared cameras (100fps) and their echolocation calls with an ultrasound microphone (Fig. 1b). Half of the bats were presented with the horizontal plate in the first night and the vertical plate in the second. The order was reversed for the other half. A trial started when the bat entered the flight room and ended when it passed the plate 20 times or after 15 minutes. We counted drinking attempts and collision events (for description of drinking behaviour see Greif & Siemers 2010). After the experiments all bats were carefully examined and no injuries were found. Of the 21 individuals 19 collided with the vertical plate at least once (on average 22.8% of passes with collisions) but never with the horizontal plate. (Wilcoxon matched-pair test, N=21, p<0.001).

Thirteen individuals made at least one drinking attempt from the horizontal plate (on average 13.0% of passes were drinking attempts), but none from the vertical plate (Wilcoxon matched-pair test, N=21, p=0.0015) (Fig. 2).

Figure 1b - Flight tunnel setup depicting the vertical situation. Depicted is experimental corner of the continuous, rectangular flight tunnel (only the lower part of the 2.3m high tunnel is visible). The smooth metal plate is shown in grey on the wall and the dotted lines represent the ‘plate zone’ (the space where perpendicular echoes are reflected back to the bat). In the horizontal situation the smooth plate was lying on the floor of the ’plate zone’.

Figure 2 - Comparison of drinking attempts and collision events. Shown for the same individuals in the different set-ups (vertical plate versus horizontal plate). (Median, interquartile range and range, N = 21 bats)

We conducted a detailed analysis of the flight path and echolocation behaviour of all 25 bats while flying towards the vertical plate. Based on the high-speed recordings we categorized the events close to the plate into three groups: 1) “near collision”, where bats approached to within 25 cm of the plate but did not collide. 2) “collision with manoeuvre”, where bats collided with the plate despite clear evasive manoeuvres at the last moment. 3) “collision without manoeuvre”, where bats collided without any noticeable evasive action. For the analysis we considered the space immediately in front of the plate (‘plate zone’, Fig.1b). As soon as a bat enters the ‘plate zone’, any part of the call reaching the plate perpendicularly will be reflected back to the bat (Fig.1a). We calculated the three dimensional angle between the bat’s flight trajectory and the plate, its flight speed, and its distance to the plate when it entered the ‘plate zone’. We also counted echolocation calls and measured the time from entering the ‘plate zone’ until reaching the closest point to the plate (either collision or turning point). We analysed 78 passes from 25 individuals (3.1 ± 1.8 events per individual, mean ± st.dev.) which consisted of 25 “near collision”, 13 “collision with manoeuvre” and 40 “collision without manoeuvre” events (see Supplementary Movie). Comparing the three groups we found significant differences (Table 1): the approach angle was smaller when bats collided (31°) than when they nearly collided (47°) (ANOVA, F2,75 = 4.17, p = 0.019) (Fig. 3a). Bats that collided, entered the ‘plate zone’ at a median distance of 39.8 cm, compared to

81.4 cm when not colliding (Kruskal-Wallis test, H2,78 = 8.14, p = 0.017) (Fig. 3b). The average flight speed was significantly higher in collisions (3.5 m/s) than near collisions (2.8 m/s) (ANOVA, F2,75 = 6.54 , p = 0.002) (Fig. 3c). The time from entering the ‘plate zone’ to reaching the closest point was significantly higher in collisions (0.29 s) than in near collisions

(0.55 s) (ANOVA, F2,75 = 13.90, p < 0.001) (Fig. 3d). On average bats made more echolocation calls when they avoided collision (11) than when colliding (7) (ANOVA, F2,75 =6.87, p = 0.002) (Fig. 3e). Values of the group “collision with manoeuvre” were generally closer to “collision without manoeuvre” but tended to be in-between the latter and “near collision”.

Table 1 – Parameters during approach. Median values of the three groups when the bat entered the ‘plate zone’: the approach angle (°), the distance to the plate (cm), the flight speed (m/s), the time spent from entering the ‘plate zone’ until reaching the closest point (s) and the number of echolocation calls produced within this time.

flight approach distance number of speed time [s] angle [°] [cm] calls [m/s] near collision 46.8 81.4 2.7 0.55 11 collision with manoeuvre 33.0 43.9 3.1 0.31 9 collision without 31.4 39.8 3.5 0.29 7 manoeuvre

Next, we investigated the impact of vertical, smooth surfaces on the flight behaviour of different bat species in the field. To this end, we conducted experiments at three different bat colonies in Hungary for one night each. The first colony was in a cave and consisted of greater mouse-eared bats (Myotis myotis) and Schreiber’s bats (Miniopterus schreibersii). The second (roost at a building) and third colonies (bat box) consisted of Soprano Pipistrelles (Pipistrellus pygmaeus). After all individuals had left the colony we placed one or two smooth, black, flexible plastic plates (2 x 1 m or 2 x 2 m when combined) vertically 1-3 m from the colony entrance. We observed returning bats for four hours with an infrared camera while presenting the plate uncovered (i.e. smooth) or covered with a rough, ribbed plastic mat or branches (alternated in 15 minute intervals). We counted several collisions at the three colonies (12, 10, and 1 respectively) when presenting the smooth plate, but none with the covered plate.

Figure 3 - Comparison of several behavioural parameters within the ‘plate zone’. Different reactions to the plate are compared: near collision (in black, N = 25), collision with manoeuvre (in grey, N = 13) and collision without manoeuvre (in white, N = 40). (Median, interquartile range and range, N = number of events)

Discussion Our results demonstrate that bats repeatedly collide with smooth, vertical but not horizontal surfaces, both in the laboratory and in a natural habitat. The echoacoustic pattern of the vertical and horizontal surfaces is the same, but the orientation of the surface seems to change the animal’s interpretation of the echoacoustic scene. Due to the lack of returning echoes from these acoustic mirrors, bats seem to interpret vertical smooth surfaces as a clear flight path (e.g. a hole in the wall). However, horizontal smooth surfaces are perceived by the same individuals as water surfaces. We also found that the number of echolocation calls and the time spent in the ‘plate zone’ affected the probability of collision with the wall. Time spent in the ‘plate zone’ was further influenced by flight speed, the angle of approach and the distance to the plate when entering. In particular, slow flight speed, a greater distance to the plate and a more acute (more parallel) angle to the plate increased time in the ’plate zone’ and thus the chance to detect the plate and avoid a collision. Angle of approach also influences echo strength: at more acute angles the perpendicular echo strength decreases due to the directionality of the echolocation beam where most energy is aimed to the front (Surlykke et al. 2009, Jakobsen et al. 2012), thus increasing the chance of collision. Most importantly a higher number of echolocation calls provided the bat with more information about its environment and thus likely is the main reason for a reduced collision probability. Our data show that when bats evaluate on average 8 echolocation calls they exhibit obstacle recognition. This supports a previous study which found a steep detection threshold increase when bats had less than 7 echoes available for processing (Surlykke 2004). Our anthropogenically altered environment is full of vertical acoustic mirrors. Anecdotal reports from bat caretakers indicate that bats in a room with windows are likely to collide with them repeatedly. Bats have been found to fly against smooth surfaces in the lab and the field (Davis & Barbour 1965, McGuire & Fenton 2010, Howard 1995), but these observations were interpreted with a focus on visual influence and failed to explain the underlying sensory mechanism (see however Howard 1995). Furthermore, bats have been reported to collide with man-made structures such as the glass-front of a convention centre (minimum of 107 found dead during morning searches between 1972 and 2003 and anecdotal reports of at least 12 collided but alive bats from 1982 till 1983; all from the same building and now in the collection of the Field Museum, Chicago) (Timm 1989). From Toronto’s FLAP organization (Fatal Light Awareness Program) we received note that they occasionally

67 found dead bats during morning surveys. However, these reports were often only a side result of investigations on avian mortality and a more targeted search for bats may likely reveal even higher numbers. Our experiments show that bats collide with smooth, vertical surfaces. Though none of our bats was hurt, the often higher flight speed of bats in natural settings might lead to serious injuries like broken wings or jaws. Injured bats on the ground might not only crawl away and hide, but also often fall prey to predators such as cats or foxes (Huso 2010), which may further obscure the actual numbers of fatalities. Bats are an invaluable part of our ecological systems and also have substantial economic value (Kunz et al 2011, Boyles et al. 2011). As our anthropogenically-altered landscapes provide an abundance of these acoustic evolutionary traps for bats, we should closely monitor them to evaluate and mitigate potential detrimental effects on bat populations (Robertson et al. 2013).

Acknowledgements We thank all members of the Sensory Ecology Group and especially Ivailo Borissov, Christiana Popova, Tamás Görföl and Imre Dombi for help during fieldwork and discussion. Henrik Brumm, Holger Goerlitz and Sue Anne Zollinger contributed valuable comments on the manuscript. We are grateful to the Directorate of the Rusenski Lom Nature Park (director Milko Belberov) and the Bulgarian Bat Research and Conservation Group for cooperation and support. We further thank William Stanley, Dave Willard and Lawrence Heaney from the Field Museum, Chicago, for data from the museum collection, and Paloma Plant from FLAP, Toronto, for comments on casualties during their surveys. All experiments were carried out under license of the responsible Bulgarian authorities (Bulgarian Ministry of Environment and Water, and Regional Inspectorate (RIOSV) Ruse, permits # 193/01.04.2009 and 205/29.05.2009). The study was funded by a Human Frontier Science Program grant to B.M.S. and by the Max Planck Society.

Supplementary Movie Legend Two simultaneous camera views for the same situations are shown: First a bat attempts to drink from a horizontal, smooth metal plate. In the second part, a bat is on collision course to the vertical, smooth metal plate, but manages to avoid it. In the third part, a bat is colliding with the vertical plate despite evasive manoeuvres. In the last part, a bat is colliding with the vertical plate without any apparent perception of the plate beforehand.

A CD containing the supplementary movie is enclosed in the back of this thesis.

CHAPTER 4

An integrative approach to detect subtle trophic niche differentiation in the sympatric trawling bat species Myotis

dasycneme and Myotis daubentonii

Setting up mist nets to capture Myotis dasycneme

Abstract Bats are well known for species richness and ecological diversity thus they provide a good opportunity to study relationships and interaction between species. To assess putative interactions we consider distinct traits which are likely to be triggered by niche shape and evolutionary processes. In this study we present data on the trophic niche differentiation between the two sympatric European trawling bat species, Myotis dasycneme and M. daubentonii, incorporating a wide spectrum of methodological approaches. As prey is one potential limiting resource in bats, we measure morphological traits involved in foraging and prey handling performance like bite force, weight lifting capacity and wing morphology. We then estimated trophic differentiation via morphological as well as molecular diet analysis. The species closely resemble each other in morphological traits. Subtle but significant difference were apparent in bite force and lift capacity which can be related to differences in the basic body or head size. Both morphological and molecular diet analyses show strong trophic niche overlap. We found subtle differences in less frequent prey items, as well as differences in the exploitation of terrestrial and aquatic-based prey groups. M. dasycneme feeds more on aquatic prey, like Chironomidae and their pupal stages, or the aquatic moth Acentria ephemerella. M. daubentonii feeds more on terrestrial prey, like Brachycera, or Coleoptera. This indicates that these bats use different micro-habitats within the habitat where they co-occur and coexist.

Introduction Understanding species’ interactions is a fundamental research area in ecology (Ricklefs & Schluter 1993), and these interactions (e.g. predation, competition) are frequently cited as causal factors in adaptive speciation (Dieckman et al. 2004). This theory implies that each species is adapted to a specific ecological niche, separated from other species by reproductive isolation and eco-morphological traits (Hutchinson 1957; Schluter 2001; Holt 2009) that permit coexistence. Ecological interactions and selective processes contribute to the evolution of new phenotypes and the maintenance of morphological diversity (Dieckmann & Doebeli 1999; Ryan et al. 2007). In many vertebrates, species radiation and diversification of ecological niches are accompanied by corresponding diversification of morphological characters and adaptations. Case studies, for example, Darwin’s finches, have shown that morphological traits can be formed through natural selection interacting with trophic resources (Grant 1985; Schluter et al. 1985; Grant & Grant 2006). Often these morphological traits are directly related to the performance of a species during resource exploitation and the ability to sustain that performance in a changing environment (Lack 1974; Schluter et al. 1985; Herrel et al. 2005). High diversity in such adaptations and variable resource exploitation in time and space may facilitate the coexistence of even highly similar species (Coyne & Orr 2004). Bats (Chiroptera) exploit a great diversity of trophic niches with a variety of morphological and behavioural adaptations, but up to 70% are primarily insectivorous (Simmons 2005b). Due to high species richness and diversity in trophic adaptations, bat communities and guilds have been the focus of numerous studies dealing with species interaction and community structure (Findley & Black 1983; Dumont 1997; Schnitzler & Kalko 2001; Aguirre et al. 2002; Kalko et al. 2007; Clare et al. 2009; Bohmann et al. 2011; Razgour et al. 2011, Emrich et al. 2013). To understand the interactions between species and to measure their ecological niche, one can observe competitive interactions directly, but for cryptic and elusive species like bats, indirect measures are necessary including analysis of both morphological traits and behavioural mechanisms of resource exploitation. Echolocation call structure has been shown to separate ecological niches of bat species (Schnitzler & Kalko 2001; Schnitzler et al. 2003; Siemers & Schnitzler 2004). Similarly, wing morphology and corresponding flight habits and foraging behaviour are highly diverse and contribute to niche segregation (Norberg & Rayner 1987). Bite force also influences resource partitioning on the base of food hardness and prey

73 handling (Freeman 1981; Dumont 1999; Aguirre et al. 2003; Santana et al. 2010). Often a complex of interacting parameters, including subtle traits such as temporal partitioning of resources (e.g. Emrich et al. 2013), must be considered to accurately measure the mechanisms of partitioning. These parameters partly define ecological niches (habitat, foraging, etc.) and shape bat communities. One method of assessing whether these characters effectively result in niche partitioning and specialization is to measure the effect on food resource exploitation and examine postforaging resource divisions. For example, through analysis of ecomorphological characters in conjunction with dietary analysis and modelling of niche differentiation between potentially competing species (Emrich et al. 2013). Traditionally, dietary studies have employed morphological identification of prey remains in faecal samples or stomach content (Whitaker 1972; Kunz & Whitaker 1983; Whitaker et al. 2009), or culled prey remains (Bell 1982; Jones 1990; Lacki & Ladeur 2001) to assess prey occurrences and dietary biomass. While effective, these methods are normally limited to ordinal or family- level identification of prey and may overlook more subtle niche differentiation. More recently, molecular-based approaches were introduced and have become sophisticated both in efficiency and productivity (Symondson 2002; King et al. 2008; Pompanon et al. 2012). Molecular methods provide the possibility of species-level taxonomic assignment of unknowns (Hebert et al. 2003a, b) and rapid analyses particularly using high-throughput sequencing platforms, such as electronic-current-based Ion Torrent (Pourmand et al. 2006; Rothenburg et al. 2011; Pompanon et al. 2012). These methods have been used to great effect in insectivorous bats with both traditional Sanger sequencing (Clare et al. 2009, 2011; Zeale et al. 2011) and high-throughput next-generation sequencing (Bohmann et al. 2011; Razgour et al. 2011; Clare et al. 2014a, 2014b; Emrich et al. 2013 Krüger et al. 2014). Two species of trawling Myotis, Myotis dasycneme and Myotis daubentonii, share behavioural and morphological traits such as large feet, foraging close to the water surface and scooping prey from the surface with their feet or tail membrane. During foraging, both species use short, downward-frequency-modulated echolocation signals, of 1.7–3.0 ms length and a sweep range of 38.9–54.5 kHz (Jones & Rayner 1988b; Kalko & Schnitzler 1989; Britton et al. 1997; Siemers et al. 2001). A previous study based only on morphological diet analysis showed high overlap in prey groups, with little difference among the less frequent prey items (Krüger et al. 2012). In contrast, these species show dissimilarities in roosting behaviour and migration behaviour. Myotis dasycneme prefers synanthropic roosting, using attics and cavity walls as maternity roosts while M. daubentonii is frequently found in hollow 74 trees and artificial roosts in forests. In addition, it is believed that they do not share a recent phylogenetic history, and thus, resource competition may not have been a primary factor in their radiation. Myotis dasycneme probably diverged more than 10 MYA from a group of Myotis, which includes M. daubentonii. This leaves M. dasycneme more closely related to Myotis mystacinus. Yet, the phylogenetic position of M. dasycneme is debated (Stadelmann et al. 2004, 2007). Razgour et al. (2011) assessed resource use between cryptic, closely related long- eared bats (Plecotus auritus and Plecotus austriacus) occurring in sympatry while Bohmann et al. (2011) considered resource use between two morphologically different species that share roosts and foraging grounds (Chaerephon pumilus and Mops condylurus). Here, we consider the intermediate case, two sympatric species of the same guild that do not share a sister-species relationship. While resource partitioning in general may be important in diversification and coexistence, morphological convergence and the limits of bats’ perceptual abilities may limit prey partitioning. In this scenario, morphology and echolocation may lead to habitat selection, and thus, dietary convergence, particularly in insects, is not limiting and thus competition unlikely. Here, we test the hypothesis that morphological and behavioural convergence corresponds with resource overlap when there is no reasonable expectation of past radiation via competitive interactions (e.g. allopatric origin, reproductive isolation). We use morphological traits, including wing morphology, bite force and a novel aspect, the physiomorphological ability to lift objects from the surface of the water (and thus important for trawling bats), along with molecular and morphological dietary analysis, to assess mechanisms of coexistence between these two predators. We hypothesize that the dietary niche of the two bat species will overlap to a large extent, as both species should perceive similar sized prey and use similar hunting modes in the same habitats. Their physiomorphological abilities, though similar (same guild), may vary reflecting more recent competitive interactions in secondary sympatry. We expect that both species share major prey types and do not show significant eco-morphological differences.

Methods The study uses a combined approach including data collected from molecular and morphological analyses of diet from faecal pellets, measurements of wing morphology, bite force and laboratory experiments on hunting performance.

Study site and guano collection We collected faecal samples between May and August 2009 from bats mist-netted along their commuting routes between roosts and foraging habitat over the Schwentine River in Schleswig-Holstein, Germany (54,195°N; 10,308°E). The distances between the sampling sites varied from 2.94 to 14.61 km. Thus given the proximity and similarity of the landscape at each site, we consider them to be ‘sympatric’ (able to commute freely between sites) and that any observed difference in diet between the species is unlikely to be explained through access to different species of insects via habitat selection. We kept bats in clean soft cotton bags for approximately half an hour after capture for collection of faecal samples. Permission and ethical approval were provided by the State Agency for Agriculture, Environment and Rural Areas, Schleswig-Holstein, Germany (LANU 314/5327.74.1.6).

Functional morphology Wing morphology. We photographed wings and measured wing parameters of 30 bats with the program AXIOVISION 4.7.10 (Carl Zeiss Microscopy GmbH, Jena, Germany) along with collection of traditional morphological data, like body mass and forearm length (FA) (Norberg & Rayner 1987).

Weightlifting. To estimate weightlifting in foraging performance, we took six male Myotis daubentonii and three male Myotis dasycneme into captivity for laboratory experiments. We collected data in the laboratory facilities at the Max-Planck Institute for Ornithology, Seewiesen, Germany. We housed these animals in air-conditioned rooms (20 °C / 80% humidity) with ad libitum water and food supply (mealworm larvae, Tenebrio molitor, vitamins and minerals in addition). Animals were habituated to a 12 h shift in their photoperiod. Permission and ethical approval was provided by the State Agency for Agriculture, Environment and Rural Areas (LLUR), Schleswig-Holstein, Germany (LLUR 515/5327.74.1.6). We trained bats to take mealworms from the water surface of an artificial pond (3 x 4 m), built in a 4 x 9 metre flight room. For measurements of maximal

76 weightlifting performance, we connected a dummy mealworm with a piezo electric force transducer (type 5015A; KISTLER, Inc.) via a nylon thread and a custom-made deviating mechanism. The dummy was connected permanently with the nylon thread so that maximum lift force could be obtained. After each catching attempt (successful or unsuccessful), a real mealworm was provided on the water surface.

Bite force. All bats caught in the field were identified, sexed, weighed and measured. We only choose adult bats for bite force assessment. Measurements included FA (the standard proxy for bat body size) and upper tooth row length (distance from the canine to the third molar, CM3), used as a proxy for head size. We measured maximum bite force in 20 bats each of M. dasycneme and M. daubentonii, by letting the bats bite onto a custom-made lever that connected to a piezo electric force transducer (KISTLER, Type 9217A) (Aguirre et al. 2002). The distance of the bite plates was adjusted to accommodate a standardized gape angle of approximately 25° (Dumont & Herrel 2003). A series of six bite sessions were conducted, some sessions consisting of multiple bites. The maximum bite force obtained across all bite sessions was used for further analysis. Bite forces were corrected for the effect of the lever and transducer system. We released bats directly after measurements at the site of capture.

Molecular diet analysis We extracted DNA from each pellet (n M. dasycneme = 34; n M. daubentonii = 36) using the QIAamp DNA Stool Mini Kit (Qiagen, UK) following standard protocol with adjustments suggested by Zeale et al. (2011). We stored extracted DNA at -80 °C prior to PCR analyses. We amplified insect DNA from faecal pellets using insect general COI primers ZBJ-ArtF1c and ZBJ-ArtR2c modified as described by Clare et al. 2014 (a). The original primers were described by Zeale et al. (2011) and have been tested by many recent studies (e.g. Bohmann et al. 2011; Razgour et al. 2011; Clare et al. 2014 [a, b] Emrich et al. 2013). The target region is a 157-bp amplicon located at the 5′ end of the 658-bp COI barcode region (Hebert et al. 2004). Prior to experimental use, we confirmed the efficiency of the primers on additional common local arthropod genera (i.e. Diptera, Aranea, Lepidoptera, Coleoptera, data not shown) by amplification following Zeale et al. (2011). We did not use unique multiplex identifier recognition methods (e.g. Clare et al. 2014 [a]), rather, all independently amplified samples were pooled within predator species (following Emrich et al. 2013) for DNA sequencing via the Ion Torrent sequencing platform (Life Technology) at 77 the University of Bristol Genomics facility (School of Biological Sciences, Bristol, UK). To remove primers and adaptors postsequencing, collapse to unique haplotypes and for further sequencing processing, we used the GALAXY V platform (https://usegalaxyp.org/; Giardine et al. 2005; Blankenberg et al. 2007, 2010; Goecks et al. 2010). We removed haplotypes represented by <2 haplotypes and clustered the sequences into molecular operational taxonomic units (MOTU) using the program JMOTU (Jones et al. 2011). We tested grouping thresholds from 1 to 10 bp and selected a 4-bp threshold for this data set (see Razgour et al. 2011). We extracted representative sequences for each MOTU for comparison with a known reference library. We compared sequences against known reference sequences within the Barcode of Life Data Systems (Ratnasingham & Hebert 2007; Clare et al. 2009). If sequences matched completely to a reference sequence without matching any other arthropod, we regarded the sequence as belonging to the same species. However, the short amplicon length also constrains some species identifications. Following Clare et al. 2014 a, b, we used a modified version of the criteria in Razgour et al. (2011) as follows:

1a True species match (>99% similarity) 1b True species match (>98% similarity) 2 Match (>98%) to more than one species, only one of which belongs to local assemblage 3 Match (>98%) to several species or genera—genus or family-level assignment made and considered provisional.

Morphological diet analysis For morphological faecal analysis, we dried guano samples (n = 206) at room temperature and stored them at -20 °C to avoid coprophagous insects. Before analysis, pellets were soaked for 48 h in 70% ethanol and dissected under a binocular microscope (x40 - 60). Characteristic fragments were separated and mounted in Euparal for further examination. We identified prey groups by fragments to class, order, family or genus level (where feasible), by comparison of fragments with whole-collected insects and arthropod identification keys (Shiel et al. 1997, Krüger et al. 2012). For each individual bat, we calculated the occurrence of each prey group as the relative proportion of all sampled individual bats (N) (‘percentage occurrence’, total > 100%). We further determined the relative proportion for each prey group of the total of consumed prey groups (Nc) (‘percentage frequency’, total = 100) (Vaughaun 1997; Krüger et al. 2012). 78

Data analysis We assessed differences in functional–morphological traits (e.g. wing morphology, bite force and lifting performance) using R (R Development Core Team 2009, Version 2.15.1). To estimate niche differences between M. dasycneme and M. daubentonii based on the molecular dietary data, we calculated Hamming distance and Bray–Curtis index for similarity. The Hamming distance gives the number of positions at which the corresponding symbols of two strings of the same length are different (Hamming 1950). It is calculated on the entire pool of available prey. A smaller value for Hamming distances indicates more similar dietary choices and includes shared prey and shared avoidance of prey in the similarity score. The Bray–Curtis index (eqn 1) (Bray & Curtis 1957) is used to quantify the dissimilarity in the dietary composition of the study species, where Cij is the sum of the lesser value for only those species in common between both samples. Si and Sj are the total number of species counted in both samples. The Bray–Curtis dissimilarity is 0, if the two samples share all species and 1, if the two samples do not share any species (Bloom 1981).

(Equation 1)

(Equation 2)

(Equation 3)

To assess dietary niche breadth based on the morphological diet data, we used the

Simpson’s index for diversity and heterogeneity (eqn 2), where ni is the relative proportion of a prey item i (with i = 1…n) of a total of n prey items. Thus, D is 0, if all eaten prey belongs to one prey group. The higher the diversity, the closer D gets to 1 (Simpson 1949). To estimate the degree of similarity in prey exploitation based on the presence–absence data, we calculated Pianka’s index of niche overlap (eqn 3), where pi is the frequency of occurrence of prey item i in the diet of species j and k (Pianka 1973). The Pianka’s index reaches 1, if diets of j and k overlap to a 100%. To test the effect of species or sex on the variance in the dietary data, we conducted a permutation analysis of variance (ADONIS, Anderson 2001). Additionally, we performed nonmetric multidimensional scale ordination (NMDS) with

Jaccard distance to visualize differences between the two species (Clarke & Warwick 2001). We tested differences in single prey groups, also including the prey habitat, between species with generalized linear models (GLM) and Tukey post hoc test. We estimated species richness and diversity using morphological dietary data with the VEGAN library (Oksanen et al. 2010). We conducted multivariate methods, NMDS, Adonis and GLM, using the VEGAN R library (Oksanen et al. 2010) and the MASS R library (Venables & Ripley 2002).

Results Functional morphology We measured wing parameters from 30 bats using digital photos of live animals (Table 1). The two species differed significantly in their basic body measures: body mass (χ² = 21.08, d.f. = 1, P < 0.001) and FA (χ² = 18.73, d.f. = 1, P < 0.001). Within species, we found differences, with females being larger in Myotis daubentonii and males being larger in Myotis dasycneme. The species differed in wingtip shape index (I) (t = 2.0739, d.f. = 27, P < 0.05), but not in wing loading (t = 1.3785, d.f. = 27, P = 0.179). Yet, these parameters show high variability within and between species when taking the sex into account: Male M. dasycneme showed higher I than male M. daubentonii, vice versa for female bats (Table 1). We measured weightlifting performance in seven male M. daubentonii and three male M. dasycneme, each represented by 10 individual measurements, under the same settings and conditions. The two species differed significantly in maximal weightlifting performance (t = 7.08, d.f. = 8, P < 0.001). We found M. dasycneme individuals to perform less well than M. daubentonii. The Pearson correlation shows that wing-loading and weightlifting performance are negatively correlated (cor = -0.83, P < 0.01, Fig. 1), though this is not significant in M. daubentonii, when tested separately. The values for maximal bite force differed significantly between species (t = 8.68, d.f. = 37, P < 0.001). We found M. dasycneme to have higher maximal bite force congruent with a longer upper tooth row length (CM3) (bite force = 31 N; CM3 = 6.12 mm, SD = 0.21) than M. daubentonii (bite force = 19 N; CM3 = 5.2 mm, SD = 0.23). In addition, we correlated the maximal bite force with mean FA, which is a proxy for body size, and mean upper tooth row length (CM3), which indicates head size. Both size parameters correlated positively with maximal bite force when tested in all species (Fig. 2) though if tested

80 separately, only M. dasycneme shows positive correlation between tooth row length (CM3) and maximal bite force (ρ = 0.51, P < 0.05).

Table 1 - Values of body mass and forearm length taken from live bats and wing morphology measurements, taken from pictures, for M. dasycneme and M. daubentonii (mean ± SD).

Myotis dasycneme Myotis daubentonii

Variable Male Female Male Female

n = 10 n = 4 n = 11 n = 5

Body mass 17.51 ± 0.6 16.95 ± 0.3 10.125 ± 0.6 12.12 ± 1.3

Forearm length 4.687 ± 0.036 4.6725 ± 0.008 3.754 ± 0.03 4.095 ± 0.23

Wing loading 13.793 ± 0.739 11.86 ± 0.35 11.839 ± 0.68 13.08 ± 1.28

Wingtip shape index 1.746 ± 0.152 1.273 ± 0.141 1.135 ± 0.073 1.923 ± 0.412

Figure 1 - Plot of maximal weightlifting performance (N) against wing loading [Nm-²] in M. dasycneme and M. daubentonii. Each point represents the max value of ten measurements under the same conditions and settings. Additionally the Pearson correlation of maximal measured weightlifting performance and wing loading shows a strong negative correlation.

Figure 2 - Plot of the maximal measured bite force against the upper tooth row length (CM³) from M. dasycneme (circles) and M. daubentonii (triangles). Additionally the result of Spearman rank correlation of these two parameters is given, indicating a significant positive correlation.

Molecular diet analysis We identified a total of 176 MOTUs, of which 125 could be assigned to insect taxa (Table 2). For 51 MOTUs, we found no matches in the BOLD Systems. We rejected three MOTUs, either because they were too short or because they matched unrelated taxa (e.g. Fungus). We found 135 MOTUs in samples from M. daubentonii, whereas 77 MOTUs were assigned to samples from Myotis dasycneme. We found high values for Bray–Curtis index (BC) between M. dasycneme and M. daubentonii (Table 3). However, there are gender-specific differences. Females show lowest similarity between species. Similarly, there is a high Hamming distance between M. daubentonii females and M. dasycneme females (Table 3). We found lower distances within M. dasycneme, between males of both species and between M. daubentonii males and M. dasycneme females. Overall, dietary divergence as measured by Hamming distance between M. dasycneme and M. daubentonii was higher than similar comparisons within species (Table 3). Within the identified prey species (n = 51), some specific prey habitat interactions are apparent. The Lepidoptera we found in the samples from M. dasycneme encompasses three species, which either have aquatic life stages (Acentria ephemerella) or develop in close proximity to aquatic ecosystems (Nonagria typhae, Leucania obsoleta). Other species like Epinotia demarniana or Mompha epilobiella are known from riverine habitats with larvae feeding on riverine plant species (e.g. Alnus glutinosa, Epilobium sp.). The prey species in the order of Hemiptera clearly indicate aquatic habitats, as all found species show subaquatic life cycles, with occasional flight events (e.g. Sigara striata). Beetles, assigned to truly terrestrial species (Copris sp. and Carabidae), were only consumed by M. daubentonii.

Table 2 - Taxa, identified in the diet of Myotis dasycneme (Mdas) and M. daubentonii (Mdau), which were assigned to molecular operational taxonomic units utilising the BOLD search system (V.3). The confidence levels (Conf) signify (1a) perfect match to one genus or species (>99%), (1b) match to one genus or species (>98%), (2) match to more than one species, of which only one was a local species, (3) match >98% to several species of different genera or to reference sequences only identified to family level. In the species columns (Mdas/Mdau) 1 stand for presence and 0 for absence of prey.

Order Family Species Conf Mdas Mdau

Diptera Anthomyiidae Delia florilega 1b 0 0 Chaoboridae unknown 3 1 1 Chironomidae Chironomus sp. 3 1 1 Conchapelopia melanops 1b 0 1 Cryptochironomus supplicans 1b 1 1 Cryptochironomus sp. 1b 0 1 Dicrotendipes tritomus 1b 1 1 Microtendipes brevitarsis 1a 1 1 Paracladopelma winnelli 1q 0 1 Paratanytarsus tenuis 1b 0 1 Procladius nigriventris 1a 0 1 Procladius signatus 1b 0 1 Procladius sp. 3 1 1 Tanytarsus brundini 1a 0 1 Tanytarsus mendax 1a 0 1 Xenochironomus xenolabis 1a 0 1 unknown 1b 0 1 Chloropidae Aedes sp. 3 1 0 Culicidae Anopheles sp. 1b 1 1 Hilara quadrifasciata 1b 0 0 Empididae Atophpthalmus inustus 3 1 0 Limoniidae Euphylidorea sp. 1a 1 0 Helius flavus 1b 0 1 Limnophila pictipennis 1a 1 1 Limonia nubeculosa 1b 1 1 Molophilus sp. 1a 0 1 Pseudolimnophila lucorum 3 0 1 unknown 3 1 1 Pedicidae unknown 3 1 1

Psychodidae Simulium sp. 1b 0 1 Simuliidae Rachispoda lutosa 3 0 1 Sphaeroceridae unknown 3 0 1 Stratiomyidae unknown 3 1 0 Syrphidae Nephrotoma scalaris 1b 0 1 Tachinidae Tipula scripta 1a 0 1 Tipulidae Tipula sp. 3 0 1

Lepidoptera Coleophoridae Coelophora kuehnella 1a 0 0 Crambidae Acentria ephemerella 1a 1 1 Herpetogramma sp. 2 1 0 Scoparia sp. 3 1 1 Elachistidae Agonopterix sp. 3 1 1 Semioscopis sp. 2 0 1 Erebidae Laspeyria flexula 1a 1 0 Geometridae Hydriomena impluviata 1a 0 1 Idaea biselata 1b 0 0 Momphidae Mompha epilobiella 1a 0 1 Noctuidae Apamea monoglypha 1a 1 0 Elaphria sp. 2 1 0 Hoplondrina blanda 1a 0 1 Leucania sp. 3 1 0 Noctua sp. 3 1 1 Nonagria typhae 1a 1 0 Pterophoridae Geina sp. 2 1 1 Tortricidae Acleris forsskaleana 1a 1 0 Epinotia demarniana 1a 1 1 Epinotia sp. 3 0 1

Ephemeroptera Baetidae Baetis fuscatus 1a 1 0 Caenidae Caenis horaris 1b 1 1 Caenis sp. 3 1 1 Ephemerellidae unknown 3 1 1 Heptageniidae Eurylophella sp. 2 1 0 Heptagenia dalecarlica 2 1 1

Trichoptera Goeridae Goera pilosa 1a 0 0 Leptoceridae Athripsodes albifrons 1a 0 1 Athripsodes cinereus 3 1 1

Limnephilidae Ceraclea sp. 1b 1 1 Limnephilus stigma 1b 0 1 Molannidae Molanna albicans 1a 1 1 Molanna angustata 1b 0 1 Phryganeidae Agrypnia varia 1a 1 1

Neuroptera Chrysopidae Nineta sp. 3 0 1 Hemerobiidae Hemerobius pini 1a 0 1 Hemerobius sp. 3 0 1

Hemiptera Corixidae Callicorixa praeusta 1a 0 1 Paracorixa concinna 1a 0 1 Sigara falleni 1a 1 0 Sigara striata 1a 1 0

Coleoptera Carabidae unknown 3 0 1 Scarabaeidae Copris sp. 2 0 1

Plecoptera Perlodidae Clioperla sp. 1a 1 1

Table 1 - Bray-Curtis Index and Hamming Distance values calculated on the molecular presence- absence diet data of Myotis dasycneme (Mdas) and M. daubentonii (Mdau) and the associated sexes (F = female; M = male).

Hamming Distance

Mdas_F Mdas_M Mdau_F Mdau_M Mdas_total

Mdas_F 65 118 61

Mdas_M 0.80 121 46

Mdau_F 0.78 0.85 115

Mdau_M 0.81 0.82 0.85

117 Mdau_total 0.703 Index Bray-Curtis

Morphological diet analysis Overall, we analysed 206 samples of M. dasycneme (n = 84) and M. daubentonii (n = 122). In the diet of M. dasycneme, we identified 12 prey groups and for M. daubentonii 17 prey groups. Within identified Diptera, we could identify the suborder Nematocera with the families of Tipulidae and Chironomidae and the genus Glyptotendipes, and the suborder Brachycera. Within the Hemiptera, we were able to identify the families Corixidae, Gerridae and Aphidoidea. The two predators showed high dietary overlap and similar niche breadth. The ADONIS analysis indicated significant differences in the diet of the two species (ADONIS: F = 2.53, P < 0.05). The NMDS ordination resulted in a two-dimensional solution with a final stress of 0.132. Samples of M. dasycneme and M. daubentonii are evenly spread out in the diagram and overlap strongly (Fig. 3). The Simpson’s index showed no statistically significant differences between species in diet breadth or the diversity of prey taxa (M. dasycneme: 0.75; M. daubentonii: 0.82; χ² = 90.3281, d.f. = 1, P < 0.001). Additionally, Pianka’s index for niche overlap indicated an overlap of nearly 100% (Table 4). Comparing the single prey groups between the species’ diets, only chironomids differed significantly between the two bat species (Table 5). Unknown Diptera and Brachycera also occurred, but not significantly more often in the diet of M. daubentonii. Similar observations concern chironomid pupae in the diet of M. dasycneme (Table 5). Both species displayed differences in prey occurrence regarding the major habitat where prey groups are found (GLM, aquatic: z = -0.009, P < 0.05; terrestrial: z = 0.902, P = 0.367).

Table 4 - Simpsons diversity and species richness (=number of prey) calculated from the morphological dietary data.

Myotis daubentonii Myotis dasycneme

Female Male Female Male

0.81 0.79 0.77 0.69 Simpson’s Index 0.82 0.75

12 14 10 90 Species richness 16 12

Pianka’s Index 0.97

Table 5 - Prey occurrence in the morphologically analysed diet of M. dasycneme and M. daubentonii. We tested data with generalized liner model and Tukey post-hoc test. Bold P-values indicate significant differences and values in italics almost significant cases (P < 0.1).

Prey occurrence

M. dasycneme M. daubentonii z P Prey (n = 84) (%) (n = 122) (%)

Diptera 1.2 8.2 1.647 0.099

Nematocera 17.9 26.2 0.264 0.792

Chironomidae 95.2 82.0 -2.628 0.008

Chironomid Pupae 17.9 11.5 -1.709 0.088

Tipulidae 9.5 10.7 0.264 0.792

Brachycera 4.8 11.5 1.772 0.076

Corixidae 6.0 5.7 1.647 0.948

Gerridae 0.0 0.8 0.003 0.997

Trichoptera 46.4 50.8 0.619 0.536

Lepidoptera 14.3 12.3 -0.416 0.678

Ephemeroptera 0.0 1.6 0.005 0.996

Neuroptera 1.2 4.1 1.146 0.252

Coleoptera 1.2 4.9 1.337 0.181

Hymenoptera 0.0 3.3 0.009 0.993

Aphidoidea 2.4 4.1 0.661 0.509

Aranea 0.0 0.8 0.003 0.997

Figure 3 - Plot of a nonmetric two-dimensional ordination scale based on the presence- absence prey data derived from the morphological diet analysis on Myotis dasycneme (circle) and Myotis daubentonii (cross) (n= 206, stress = 0.132).

Discussion We test whether morphological and habitat convergence correlates with dietary overlap, and we assess the potential for microniche differentiation in morphological and behavioural characteristics. Our analysis suggests that these two bat species overlap largely in both in morphological features and diet, but may demonstrate minor differentiation based on behaviour and microhabitat selection. We provide a multifactor analysis of the trophic interactions between two morphologically similar species that lack a recent phylogenetic divergence.

Functional morphology Flight modes and behaviour vary among flying animals. Bats show great diversity in wing morphology and flight patterns (Findley 1972; Norberg & Rayner 1987), triggered by adaptive processes in response to resource availability, for example, prey exploitation and habitat utilization. In bats, wing morphology has been used to identify and characterize structures of communities, guilds and assemblages (Findley 1972; Norberg & Rayner 1987; Britton et al. 1997). Our results support the classification of Myotis dasycneme and Myotis daubentonii as trawling Myotis, of the Leuconoe guild (Findley 1972; Baagøe 1987; Norberg & Rayner 1987). Myotis dasycneme and M. daubentonii both show adaptations like lower wing loading, compared with fast-flying species like Nyctalus noctula, which allow relatively slow flight above water surfaces. Both bat species show high similarity in wing morphology, which, together with high similarity in echolocation (Siemers et al. 2001), implies that both bat species perceive and exploit the same prey when they are in the same habitat. We found wingtip shape (I) to be highly variable within the species (female–male difference). Still the higher wingtip shape index (I) in M. daubentonii might indicate better manoeuvrability. M. daubentonii is known to utilize heterogeneous foraging habitats, like riverine forests, river banks and lake shores, but also occurs and hunts within forests and cluttered backgrounds (Taake 1992; Dietz et al. 2009, Nissen et al. 2013). For M. dasycneme, less is known about habitat preferences though they are thought to hunt primarily over and along large water bodies (e.g. lakes, canals, rivers) (Limpens 2001), but other, more structured habitats like reeds and forest edges are also used (personal observation). The variance in wing parameters found within species may be explained by adaptive radiation following competition. Many insectivorous bat species exhibit sexual segregation regarding habitat differences. Different morphological adaptations would facilitate different habitat utilization. For example, male and female parti-coloured bats (Vespertilio murinus) use different foraging habitats (Safi et al. 2007), as do barbastelle bats (Barbastella barbastellus) (Hillen et al. 2011). Within M. daubentonii, females and males may utilize different habitats and even regions (Dietz et al. 2009). In M. dasycneme, it has been observed that female and male individuals inhabit different regions with different habitat interior in the Netherlands (A.-J. Haarsma, personal communication). The ability to carry higher load is correlated with behaviour. The ghost bat, Macroderma gigas (0.12 kg), can carry up to 60 g (=50% of its own weight), which allows it to sustain a diet of small mammals (Kulzer et al. 1984). The vampire bat Desmodus rotundus can take up 100% of its own weight in blood, also a necessary adaptation, which allows this 91 species to maintain a nutritionally low blood diet (Wimsatt 1969). Fruit bats regularly carry heavy fruits and seeds, like avocado or mangoes (Marshall 1983; Richards 1990). Myotis capaccinii, also a trawling Myotis and facultative piscivore, is able to carry 0.5 g fish (Aihartza et al. 2008). In all, lift capacity may be a fundamental character in niche specialization in bats thus the subtle differences measured here are intriguing. However, these measurements should be treated cautiously. Although these same flight room parameters have been successfully used previously with these species (Siemers et al. 2001), the difference we found in weightlifting performance might be partially explained by the aerodynamic constraints pond bats had to face in the flight room. Due to kinetic laws, M. dasycneme would probably reach a higher weightlifting capacity with higher flight speed (F = m*a). Indeed, higher speeds have been observed in the wild (Baagøe 1987) and are apparent in the square root of their wing loading, which is proportional to flight speed (Norberg & Rayner 1987). Despite these potentially subtle differences, Myotis dasycneme and M. daubentonii can be regarded as similar in morphological terms, hence the same guild and subgenus. The results for bite force show some differences between the species. Although both are insectivorous and feed mainly on soft-bodied prey (e.g. Diptera, Lepidoptera), M. dasycneme had a higher bite force than M. daubentonii. These differences result from the overall size differences between the species, particularly head and jaw length, head width and resulting jaw muscle size (Herrel et al. 2001; Aguirre et al. 2002; Herrel et al. 2005) that are larger in M. dasycneme. Both species lie well within the variation range in bite force and size measurements for their family Vespertilionidae (S. Greif unpublished data). This morphological distinction cannot be fully explained by the prey. On the one hand, the bats show subtle differences in consumed prey size. Moths of larger wingspan (>20 mm), like Apamea monoglypha, Nonagria typhae or Laspeyra flexula, appear only in guano samples of M. dasycneme. A bigger mouth may lead to a more efficient handling of bigger prey items (Herrel et al. 2005). On the other hand, both bats prey on beetles, as well as other hard-bodied prey like water boatman (Corixidae). Although the molecular diet data only show beetles (Carabidae) to occur in the diet of M. daubentonii, the morphological results show no difference in beetle consumption between the two species. Hence, bite force needs to be discussed cautiously as meaningful trait within niche differentiation of M. dasycneme and M. daubentonii. The major prey items (Diptera, Lepidoptera) are all soft-bodied prey. One limitation of our morphological and behavioural data was a limited sample size. The conservation situation for both species limited the number of individuals that we may take into captivity. To compensate we have performed a repeated measures design and 92 analysis, but the conclusions drawn must be considered preliminary in light of the small sample.

Dietary analysis As predicted, the dietary niches overlap to a high degree between species, which mirrors the morphological and behavioural similarities. In particular, both M. dasycneme and M. daubentonii feed to a large extent on Diptera and Trichoptera. Although the niche breadth differed between the species, the morphological dietary data overlapped nearly 100%. M. daubentonii seems to exploit a larger variety of prey compared with M. dasycneme, which seems to rely on chironomids to a larger extent. The comparison of prey regarding their major habitats shows that M. dasycneme overall depends more on the aquatic prey fauna and less on the terrestrial, contrasting slightly with M. daubentonii. The molecular data indicates that females may be particularly different between species. Females have higher energy demands and nutrition requirements during pregnancy and lactation. This is due to a reduction in time spent torpid and to promote growth and development of the foetus (Swift et al. 1985; Wilde et al. 1999). To compensate for this increase in total energy demand, female bats need to increase food consumption (Anthony & Kunz 1977; Kurta et al. 1989; Kunz et al. 1995; Racey & Entwistle 2000; Encarnacao & Dietz 2006). Often they are found to forage in areas with higher insect abundance compared with males (Dietz et al. 2006). In our data, the higher energy demand of females may translate into the broader niche breadth compared with males, because generalistic feeding behaviour may provide their optimal foraging strategy (Stephen & Krebs 1986). In this context, the higher dietary distance between females of the different species appears reasonable. If females choose to forage in patches with high food supply within aquatic habitats, they are more likely to meet and compete for food resources. Consequently, the greater distance between females may be a result of a mechanism to avoid such competition. The molecular results show high resolution in prey identification and exceed the number of identified prey found through morphological analysis. Molecular analysis is particularly powerful for the identification of small morphologically cryptic prey such as chironomid species. With the morphological tools, we could only identify one genus (Glyptotendipes, Chironomidae) within this prey group. But the molecular approach revealed and estimated 11 species, though this is still small compared with the actual number of chironomid species that can be expected in the central Europe (e.g. ~700 species are found in Germany). The highly diverse group of Chironomidae harbour many cryptic species and are 93 morphologically hard to distinguish (Cranston 1995) leading to a significant taxonomic ambiguity in both morphological and molecular reference collections. A lack of species sequences in the barcode archives certainly constrains output in molecular data. While molecular analysis is becoming common within dietary studies because of its significant taxonomic resolution, there are key advantages of traditional morphological analysis. For example, we were able to distinguish different life stages of prey groups, like the pupal form of Chironomidae. This can provide very valuable information on the hunting mode of the focal species, in this case true trawling behaviour, when the bat scoops the not yet fully emerged Chironomid together with the pupal case directly from the water surface. It can also indicate foraging areas, like the pelagic areas of lakes, where Chironomidae undergo mass emergences. There are clearly advantages of pairing molecular and morphological data for measuring niche differentiation. The abundance of prey species in the foraging habitats is high. For example, many of the Lepidoptera species are highly numerous and abundant during their adult stage (Idea biselata, Acentria ephemerella, Mompha epilobiella). Also Diptera (Nematocera, like Chironomidae), Trichoptera and especially Ephemeroptera are known to be numerous and abundant in water habitats (Ward 1992; Racey et al. 1998; Warren et al. 2000). Hence, our results reflect the diet of generalist predators in this particular habitat. Both, the morphological and the molecular data, suggest the bats share major prey groups like Diptera, Trichoptera and Lepidoptera. The phylogenetic position of M. dasycneme within the old world Myotis bats is still disputed (Ruedi & Mayer 2001; Stadelmann et al. 2007; Jiang et al. 2010). But regardless of this ambiguity, all agree that Myotis dasycneme and M. daubentonii do not to share a recent phylogenetic history and likely evolved in allopatry and thus without competition. It is thought that M. dasycneme is genetically situated more close to Myotis mystacinus, whereas M. daubentonii belongs to a group of Myotis nathalinae and Myotis bechsteinii. The geographical origins are unknown (Stadelmann et al. 2007). Additionally, early studies have shown that morphological similarities rarely reflect close phylogenetic relationships, which is illustrated by the close phylogenetic relation of the ecologically and morphologically different M. daubentonii and M. bechsteinii (Ruedi & Mayer 2001).

Resource partitioning and mechanisms of species coexistence Our data confirm that these species show high morphological and behavioural convergence that leads directly to high trophic overlap. But we also distinguish subtle but significant differences in bite force and lift force that corresponds to small differences in predator body size and explains subtle differences in prey exploitation. Partitioning of resources and microresource differentiation is leading hypothesis to explain the coexistence of species and radiations. Emrich et al. (2013) explored the resource use by an ensemble of Jamaican bats and found that a variety of behavioural and morphological characters contribute to patterns of resource use including things as subtle as temporal partitioning of hunting grounds. The hypothesis that resources must be partitioned rests on the assumption that some aspect of the resources is limited and thus leading to competition. We have found little evidence of partitioning of insect resources here, and in fact, there is very little evidence to suggest that insects are a limited resource in general. Thus, competition for this resource may be minimal among sympatric bats. The alternative hypothesis is that habitat selection is based on morphological and perceptual abilities, and thus, similar habitat selection by bats with similar echolocation should result in a high degree of dietary overlap. This is largely what we have observed here. In our analysis, we noted subtle differences in the dietary profile of these bats. While these are real, it is particularly interesting to consider whether these differences are biologically meaningful. First, it is important to note that while morphological data are limited in its ability to recognize subtle differences, molecular data, which identified prey at the species level, is probably biased towards the detection of resource partitioning. This method will tend to over-represent rare items and underestimate the importance of common items (Clare et al. 2014 a, b). As such, it is almost certain that two dietary analyses will contain species that are different (as we have seen here). To differentiate these random differences from biologically meaningful partitioning, we must consider whether the bats can differentiate at this level. While low duty-cycle bats very likely perceive insects by size, shape, speed and acoustic reflectivity, it is unlikely that they differentiate subtle morphological differences between species. As such, we must treat minor species-level differences conservatively. Of particular interest in our analysis are aspects that suggest a significant behavioural difference, for example, we observed that M. dasycneme was almost twice as likely to consume Chironomid pupae and more likely to consume prey with aquatic habitats. This suggests a difference in hunting style that may be a far more significant form of microresource partitioning than any particular species-level difference in diet. As such, strict 95 differences should be considered in light of their relevance to behaviour. The power of these analyses will be seen when these high-resolution dietary analyses are used to test specific behavioural hypotheses and to guide perceptual tests of bats’ echolocation ability.

Conclusions By selective adaptation of morphological and sensory features, evolution permits a species to improve its capacity to use certain food resources in distinct ways and thus shapes communities of foraging bats. Both bat species show high overlap in their functional morphology and also in their diets. Yet, we cannot overlook that the dietary differences found between the two species suggest behavioural differences in hunting style. Our study strongly advocates that the integration of different methodologies is crucial to address characteristics of ecological niches and species interactions.

Acknowledgements Thanks go to Florian Gloza-Rausch, Matthias Göttsche, Inka Harms, Henning Nissen and Antje Seebens for help in the field. We thank Victoria San Andrés Aura and David Brown for advice on molecular methods. We thank Renate Heckel and Leonie Baier for their great help with animal maintenance. We are grateful to Erich Koch for his creativity and great help with experiment set-ups. Many thanks to Klemen Koselj, Sara Troxell and Sandor Zsebök for help in the flight room. We thank Brock Fenton and two anonymous reviewers for valuable comments on an earlier draft of this manuscript.

Data accessibility Molecular (sequences) and morphological (binary presence–absence) dietary data as well as data for morphological measurements (wing data, bite force and weightlifting performance data) are provided on a DRYAD account (doi:10.5061/dryad.6n94m).

GENERAL DISCUSSION

Already Griffin (1958) reported anecdotes of bats apparently showing the same behaviour in rooms with a smooth floor as when drinking from a water surface. He also observed that when bats were given water in hand, they were less likely to show this behaviour afterwards, concluding that thirst was indeed the motivation for their conspicuous flight manoeuvres. I put these stories on scientific solid ground and found that indeed bats perceive any smooth surface as a potential water source which they could drink from. In a comparative study I showed that bats would attempt to drink from smooth, horizontal surfaces, no matter whether I presented a metal, wood or plastic plate. Simultaneously offered, slightly textured plates of the same material were ignored by the bats. This behaviour seems to be phylogenetically widespread as all 15 species that I tested reacted in the same way. In their long evolutionary history bats never encountered anything else than water which presented a smooth surface. This is probably the reason why this recognition pattern is so hardwired that they would even attempt to drink from physically unnatural places like a table and that it seems to be innate, as young bats of one species immediately attempted drinking from a metal plate when encountering a smooth surface for the first time in their life. These results give a first indication that for bats the echoacoustic recognition of a landscape element or habitat might depend on relatively simple acoustic cues. Another example for such a simple but robust habitat cue from the animal kingdom is the reliance of many aquatic insects on polarization to recognize a water surface (Horváth 2014). This might also be true for other extended objects like distinct tree species, the differentiation between a meadow and bare ground or roosting opportunities like caves or crevices. If experienced bats are able to build up a specific acoustic pattern for such objects they might be able to classify them with relatively few echoes for quick categorization. For example tropical nectarivorous bats recognize some specialized flowers with distinct echoacoustic cues delivered by their flowers or leaves (von Helversen & von Helversen 1999, Simon et al. 2011). Bats will likely use such “search patterns” derived from statistical echoacoustic cues using temporal, spectral and spatial parameters (Yovel et al. 2011c, Goerlitz et al. 2012). When flying through an environment they likely perceive the constant change of acoustic information as an acoustic flow which has been shown to have a differentiated cortical representation (Bartenstein et al. 2014). Potentially these search patterns can stick out of or characterize certain temporal parts of this acoustic flow, enabling the bat to distinguish their environment on the fly. However, it

97 should also be kept in mind that additional sensory cues might help in recognizing habitats and locations as has been shown in other vertebrates (Rossier et al. 2000, Huijbers et al. 2012). For bats these additional sensory cues might come from the visual or olfactory system. This idea is supported by the evidence that I found in the multisensory part of chapter 1. Vision seems to play a role when bats evaluate their environment. When I repeated the basic experiment but now in complete darkness, drinking attempts significantly increased. The amount of drinking attempts was almost at the maximum for the restricted time, considering that a bat had to fly a bit through the room and manoeuver after each drinking attempt. Nevertheless, even in the basic experiment with the red light on, the bat continued to drink although the visual system probably gave the bat information which didn’t correspond to a water surface (especially when considering the bright wood surface). In conclusion it shows that vision is taken into account when examining the environment, but is dominated by the information received through echolocation. This underlines the importance of this sensory modality for nocturnal bats. For humans visual information is more reliable in regards to perception of space, which is why for us vision seems to dominate other sensory systems (Witten & Knudsen 2005). However, this dominance seems to be weaker when a third modality (e.g. haptics) is integrated as well (Hecht & Reiner 2009), which leaves the question what role haptic sensory information play for bats when they touch down on the metal plate repeatedly.

The result that was most surprising to me was the extent of the bats’ drinking attempts despite never receiving any reward. The average numbers varied for each species but the most extreme species – Schreiber’s bat, Miniopterus schreibersii – showed around 100 drinking attempts in ten minutes flight time. This extent is likely caused by the extreme cue reliance and hardwired recognition described earlier. On the other hand, the reasons why we get these differences in species are not yet clear. In an as yet unpublished follow-up study with Theresa Clarin we investigated whether the reason might be due to species-specific cognitive constraints. For some species foraging ecology can be used as a predictor for behavioural flexibility (Clarin et al. 2013). Our hypothesis was that bats that usually forage in more complex, cluttered environments, like e.g. Myotis myotis or Rhinolophus ferrumequinum, might be more flexible in their behaviour and able to learn from failed foraging or drinking attempts. In contrast bats belonging to the niches of open-space or trawling bats are more accustomed to acoustically simpler, less cluttered habitats and therefore don’t need to show high levels of flexibility. We tested eight different European

98 species of different size and ecological background and compared their response to a metal plate. Species like Myotis daubentonii, Myotis capaccinii, Pipistrellus pipistrellus and Miniopterus schreibersii were classified as bats from less complex foraging habitats (i.e. open or edge space and trawling bats). Species like Myotis myotis, Myotis nattereri, Rhinolophus ferrumequinum and Rhinolophus mehelyi forage in more complex foraging situations like gleaning or hunting in highly cluttered space. The results did not confirm our hypothesis and are inconclusive. This was mainly due to the effect that the “clutter bat” Myotis nattereri showed relatively high numbers of drinking attempts (similar to the open space bat Miniopterus schreibersii) and that Pipistrellus pipistrellus, a presumed less flexible species, showed the lowest numbers of drinking attempts. An alternative hypothesis for the lower numbers of Myotis myotis and Rhinolophus ferrumequinum (seen in the original study and confirmed in the before-mentioned follow-up) might be found when considering the multisensory side again. Both species have comparatively large eyes and thus vision might play a bigger role, thereby having a greater counterweight in the balance of senses described in chapter 1.

I confirmed the results from the laboratory studies in the field and showed that bats would perceive a smooth, artificial surface as water and try to drink from it under natural conditions. This was done at the location of a natural pond in order to set up the experiments in a promising location with natural bat occurrence, excluding confounding factors like bats not searching for water. Here we knew that bats would appear with the intention to drink. They extensively attempted drinking on the metal plate; however, it is hard to compare the numbers with the laboratory study as individuals could not be tracked continuously when they left the camera range. Few individuals showed maximum numbers of up to 40 consecutive drinking attempts. Bats seem to rely on spatial memory in familiar environments for small and presumably large-scale orientation (Griffin 1958, Neuweiler & Mohres 1966, Adams & Simmons 2002, Moss & Surlykke 2010). I was able to show, both in laboratory and field studies, that spatial memory is not sufficient for a bat to start drinking in a previously real water location. The respective echoacoustic mirror properties need to be present for a bat to illicit drinking behaviour. This question is all the more important when considering that water surfaces might not always be smooth, for example when wind creates ripples on the surface. The results got confirmed in an independent field study, where they also showed that bats use spatial memory to visit alternative, close-by water locations if the experimental locations offers no reward (Russo et al 2012). However, in my experiments in chapter 2 I

99 could show that bats even drink from previously unknown areas: I placed a metal plate in a location where there could have never been a water surface due to topographical properties. Bats quickly reacted to the plate and started drinking attempts. It would be interesting to test whether this flexibility is especially pronounced in desert-dwelling species like in my field study. But I would hypothesize that also bats in temperate areas with ample access to water would react to a new location that acoustically promises water.

When I conducted my initial experiments I realized that bats would not only react to horizontal, smooth surfaces. I observed bats that would approach and seemingly collide with a vertical, smooth metal plate. Considering the echoacoustic mirror properties of smooth surfaces, I hypothesized that bats would not receive any echo from the plate on the wall which consequently would present itself as an open flyway (and potential escape route) to the bat. In experiments using 3D tracking of the bats’ flight path I confirmed this hypothesis. The same individual bats that would try to drink from a horizontal, smooth plate would collide with a vertical, smooth surface. In a more detailed analysis of this behaviour I found that I could group it in three categories: bats that would collide without any reaction, bats that would collide but showing last-minute evasive manoeuvres and bats that were on a collision course but just managed to avoid a collision. The reason for bats to recognize their faulty initial decision is the second component of the acoustic properties for smooth surfaces. As described in chapter 1, the smooth surface reflects all call energy away from the bat and no echo back. Only when the bat is in a space that I coined the ‘plate zone’ in chapter 3 it will receive an echo from the call part that hits the surface perpendicular. In combination with the spatial position of the plate, which is impossible for a water surface, the fast approach of this strong echo when the bat is flying towards the vertical, smooth surface should alarm the animal regarding a potential obstacle in its flight path. This obviously depends on the information update that the bat is achieving and the currency for this is the number of echolocation calls. Indeed we found a threshold of around 8 calls which changes the bat’s behaviour. With 7 calls the bats were colliding without any visible reaction. With 9 calls the bats seemed to have gathered and processed enough information to recognize the obstacle (as is obvious from their evasive manoeuvres) but where a little too late to manage avoiding a collision. This coincides nicely with a study that showed that when bats have less than 7 echoes available for processing, their detection thresholds increase steeply (Surlykke 2004). Whether a bat receives the necessary amount of echoes depends on two main variables: the echolocation behaviour and the time spent inside the ‘plate zone’. No clear, systematic

100 behavioural change was noticeable in the echolocation behaviour, meaning no sudden buzz sequences were visible as when approaching an obstacle. However, there was considerable variation visible in the flight behaviour and thereby time spent inside the ‘plate zone’. The time depended on and could be further broken down in three parameters: the flight speed, the approach angle and distance to the plate when entering the ‘plate zone’. All these factors work together to increase time in the ‘plate zone’, so no single, most important aspect could be determined. Nevertheless, all of them showed some degree of gradation which was mostly visible when comparing ‘near collision’ with ‘collision without evasive manoeuvre’. A bat was less likely to collide when it flew slower, approached at a bigger angle (thus increasing the amount of energy in the perpendicular echo due to the forward-directed sonar beam characteristics) and entered the ‘plate zone’ at a greater distance. Careful (and very conservative) experiments from the field showed that these collisions with vertical, smooth surfaces can also happen in the wild. Additionally, I observed in one of our main study caves in Bulgaria that bats occasionally collided with a metal information plate just outside the cave entrance. This is all the more noticeable as bats would fly in and out of this entrance on a daily basis presumably for years. It would be interesting to follow up this observation as the observer (and thus disturbance) in front of the cave might have played a role and led to a spontaneous evasive reaction, resulting in neglect of previously gathered information due to a speed-accuracy trade-off (Chittka et al 2009). Further anecdotal and increasing reports support the evidence that this might play a more important role in the wild than previously imagined. It is hard to get systematic data, but the most compelling came from the Chicago Fields Museum with 120+ dead individuals from just one glass front building in about 30 years (collected as by-catch during bird collision studies in spring and autumn migration). It should be noted that this is the absolute minimum number as there might still be more unprocessed bats, sometimes people did not want to pick up dead bats, it does not take carcass removal by predators into account and it does not include the injured and live bats that were found (pers. comm. William Stanley, David Willard & Lawrence Heaney). Similar but more unsystematic anecdotal evidence comes from Bulgaria and Germany. It would be interesting to further investigate reports of bats colliding with free-standing TV towers (Van Gelder 1956, Crawford & Baker 1981). Did these bats fall victim to a fatal, erroneous decision regarding a smooth tower surface or did they potentially reduce their echolocation due to spatial memory? Recent studies also highlight the potential role of vision in certain cases of bat collisions, especially at lighted structures (McGuire & Fenton 2010, Orbach & Fenton 2010). 101

Regarding the misinterpretation of perceived sensory information, it resembles another story of perceptual mismatch. As mentioned above, aquatic insects rely on a water surface’s polarization pattern to recognize it as a potential reproductive habitat. However, they also do mistake anthropogenic, polarizing surfaces for water habitats, like is the case in asphalt, car bodies or vertical glass surfaces (Horváth et al 2009, Horváth 2014). I also observed swallows in the Negev desert of Israel which seemingly tried to drink several times from my metal plate that I laid out on the sand. It might be interesting to look at other sensory recognition systems which are based on relatively simple decision rules. Are these simple systems more error-prone and under what conditions? Questions like this have been put forward as some of the ‘fundamental questions in biology’ by Levin (2006) regarding future challenges: “What features convey robustness to systems?... How does robustness trade off against adaptability? How does natural selection deal with environmental noise and the consequent uncertainty at diverse scales?”

As described in the introduction, hunting in a water habitat offers a number of advantages to bats which led to a specialisation of several species in this habitat. For trawling bats that hence forage in a similar habitat the requirements for morphology and prey detection are relatively comparable (Fish et al. 1991, Jones & Rayner 1991, Dietz et al. 2009). Indeed we found considerable overlap in our analyses of the functional morphology in Daubenton’s bat, Myotis daubentonii, and pond bat, Myotis dasycneme. The differences in wing loading and wingtip index are largely sex-specific and the greater bite force in the pond bat is owed to the larger body size and as expected. The greater bite force is likely not responsible to a noticeable degree for any niche separation as the diet was very similar. Only the bigger mouth of the pond bat might allow for slightly larger prey due to handling advantages, which was confirmed by some larger moths in its diet. The difference in lift capacity is to be considered with caution as the flight room restrictions might have limited the full flight potential of Myotis dasycneme. But considering the general lack of large, challenging prey in both species’ diet, an increased potential lift capacity of the larger pond bat likely would have also not exerted any meaningful and niche separating advantage. So why do these two bat species coexist in the same habitat? It is known that different bat species can show temporal resource partitioning around bodies of water (Adams & Thibault 2006), however this has never been observed in trawling bats and both species can be caught at the same spot and time. Another reason might also be a relaxation of selection pressure due to the overall high abundance of insect prey around bodies of water. Although the general niche overlap is

102 almost 100%, the combination of molecular and morphological diet analyses offers a more detailed insight. First of all, most of the differences we see are driven by the animals’ sex. Especially Myotis daubentonii is known for spatial separation of sexes at times, whereupon females are more linked with aquatic habitats (Grindal et al. 1999, Russo 2002, Senior et al. 2005, Dietz et al. 2009). In general, we also find a more strict association of Myotis dasycneme with aquatic prey fauna, contrasted by a larger variety of prey (including terrestrial) in Myotis daubentonii. Also previous research confirmed that this species exhibits more variability and ecological flexibility within its niche (Nissen et al. 2013). An advantage of morphological analyses is that also behavioural differences can be noted which are not visible in the molecular data. Myotis dasycneme not only seems to be more specialised on Chironomids, but also shows more pupae of these insects. This adds direct evidence to the idea of microresource partitioning which is also supported by the more aquatic prey spectrum. In a study with Christian Voigt (under review) we found a very similar pattern showing that Myotis daubentonii and Myotis capaccinii overlap to a high degree in their dietary niche. However, nitrogen and hydrogen isotope data suggest that the diet of Daubenton’s bats seems to be more diverse than that of Myotis capaccinii which seems to be more specialised. This supports the finding of our comparison between Daubenton’s and pond bat. As Myotis capaccinii and Myotis dasycneme do not overlap in their occurrence, it might be that Myotis capaccinii occupies the niche that the pond bat inhabits in the northern areas of Europe, thus enabling coexistence with Myotis daubentonii in both cases.

Future directions

This research opened up many more questions related to bats and bodies of water. For example, I was wondering how big a water surface would have to be for a bat to be still recognizable as such. In unpublished data, I found that the overall size can be fairly small and likely depends on two factors: the manoeuvrability and the size of the bat’s sonar beam. Less manoeuvrable species like Miniopterus schreibersii were still able to drink from a plate which was 100x100 cm big. With 50x50 cm some individuals still showed drinking attempts but struggled hard to ‘hit the spot’ and for 25x25 cm they did not show any attempts anymore. In contrast, a slow and highly controlled flyer like the horseshoe bat Rhinolophus mehelyi was still attempting to drink from a 25x25 cm metal plate. As a consequence of the morphology of their nasal structures, the sonar beam of horseshoe bats is also very bunched and focused to the front (Dietz et al. 2009). This might enable them to detect smaller smooth

103 surfaces as even a small plate will reflect most of the call energy away. For a broader sonar beam that overlaps the smooth surface, the area around it will start reflecting the echoes back considerably and mask the typical echoacoustic cues of water.

However, the most common question asked after presentations was what bats would do under natural conditions when water is not smooth, e.g. in windy conditions. I showed in chapter 2 that spatial memory of a familiar water location is not enough for a bat to start drinking. Additionally I showed that bats are fairly flexible in their behaviour. I propose a shift in the precise drinking locations when wind creates ripples and small waves on the water. Imagining a lake I would predict that on a windless night bats would drink rather in the centre or at least open space of the lake. In windy conditions I would expect them to shift to the vegetated edges of the lake as trees and bushes will shield the wind and create smooth areas. As I mentioned in the previous paragraph a potential water source can have a relatively small smooth surface in order for bats to be able to recognize and drink from it. This might be one possible behavioural strategy how bats deal with adverse effects like wind. When considering rivers we know from anecdotal observations that bats prefer the quiet, smooth parts, for example after a river bend.

Regarding conservation I already stressed in chapter 3 the importance of increased monitoring efforts around smooth surfaces. Most species that were reported from the Field Museum in Chicago being involved in collisions with glass fronts were migratory species like the Eastern red bat, Lasiurus borealis and Silver-haired bat, Lasionycteris noctivagans. Therefore, known migratory routes should be a main concern in the beginning. But also anecdotal reports from bat caretakers describing bats with broken wings are sometimes already linked to smooth surfaces like windows or again glass buildings. It is necessary to raise awareness with bat caretakers and conservationists to record every occurrence precisely and ask for details of the findings. In locations with frequent collisions an ultrasonic bat deterrent might be a mitigation option. Even if it does not deter bats, it might make them pay more attention in such a potentially dangerous situation.

Monitoring should not only include vertical areas but also consider tilted smooth surfaces. With increasing numbers of solar panels spreading all over the world, questions on how bats are coping with those in the wild are pressing. Preliminary results from a trial study indicate that they would continue to drink from tilted smooth surfaces (tested up to a 30° slope with Myotis myotis). What are the prerequisites for this maladaptive behaviour, where are the limits and how could we consequently modify these surfaces to prevent it? The extent

104 of this potential problem should be investigated in the wild to see whether bats would continuously try to drink from artificial smooth surfaces like solar panels. This would of course be especially detrimental for bats in arid regions with little natural water sources if they waste their energy without getting any water.

It would also be very interesting to further investigate the bats’ perception under controlled laboratory conditions. For example, by switching the experimental conditions of the smooth surface gradually from horizontal to vertical, a situation would be created that is very interesting from a conceptual cognitive/perceptual viewpoint. When would the same sensory stimulus – but in a different position – elicit a different interpretation? More specifically when would the laryngeally echolocating bats stop drinking and start interpreting the smooth surface as a flythrough possibility? I would hypothesize that bats will react to the increasing steepness of the plate by a categorical and not a gradual switch in behaviour.

Further questions arose when considering the echolocation calls of bats while they are drinking. Would their echolocating behaviour differ when approaching a water surface compared to when they are catching insects or landing on a surface. Unpublished own data for the three species Myotis capaccinii (drinking versus catching prey from a water surface), Miniopterus schreibersii (drinking versus landing on the wall) and Rhinolophus ferrumequinum (drinking versus catching prey mid-air) showed that indeed their echolocation strategy changes. When catching or landing they would usually show a typical Buzz sequence, separated into Buzz I and II. However, when drinking they dismissed Buzz II. This is interesting as it underlines the flexibility that bats can exhibit in their echolocation calls. A small field study recently confirmed that bats would emit buzz calls when drinking, but unfortunately did not distinguish between the two buzz components or species (Griffiths 2013).

I was also wondering about the role of the perpendicular echo coming back to a bat when flying above a smooth surface. It can be expected that bats use this very strong, singular echo to measure precisely how far they are above ground through temporal target range discrimination (Simmons 1971). This could then be true for any ground, not just water. As can be observed in Fig. 4 of chapter 1 the perpendicular echo stands out even above a cluttered surface like sand and would make a good flight height measurement. It might also play a role when bats fly through a multi-layered 3D environment like a cave for the first time. It will inform the bat if the lack of echo from a certain region ahead turns out to be a water surface or an opening to a deeper level of the cave. Furthermore, can they interpret this

105 echo situation when coming from a smooth surface above? I was told anecdotes where bats would repeatedly collide with smooth ceiling lamps as if they would try to fly through an opening.

And finally the multisensory side in this behavioural paradigm promises to provide more interesting results. One approach could be to investigate old world fruit bats which rely predominantly on vision to orientate and forage. Bats from the genus Rousettus are the only old world fruit bats that also echolocate, using a very different echolocation system than other bats (tongue clicking as opposed to laryngeal sound production). It would be very interesting to see whether Egyptian fruit bats, Rousettus aegyptiacus, also recognize a smooth, horizontal plate as a water surface using only echolocation? And how would these highly visual animals modify their drinking attempts when parameters like brightness, colour, or the visual pattern of the plate are varied to make the plate more or less similar to water. Also, would they change their echolocation strategy, which usually seems to be dominated by pointing their sonar beam off-axis when approaching a point target (Yovel et al. 2010)? I would hypothesize that also Egyptian fruit bats will recognize a smooth surface as water using their very different echolocation system. However, I expect a strong influence and weighting of the visual domain and thus a much more drastic and early reduction in drinking attempts with increasing brightness. Regarding the tongue-clicking echolocation behaviour itself, I expect a switch in beam focusing strategy from their normal off-axis pointing for point targets to an on-axis pointing for extended targets as this might increase detection performance in Rousettus aegyptiacus in such a situation.

In general the drinking behaviour of bats proved to be an extremely exciting and instructive topic to work on and opened up more questions than it answered. And yet, the simplicity of some answers is indeed very rewarding and pleasing (see pre-face quotation from Knut Schmidt-Nielsen).

106

REFERENCES

Adamík P & Bures S 2007 Experimental evidence for species-specific habitat preferences in two flycatcher species in their hybrid zone. Naturwissenschaften 94, 859-863

Adams RA & Simmons JA 2002 Directionality of drinking passes by bats at water holes: is there cooperation? Acta Chiropterol 4, 195-199

Adams RA & Thibault KM 2006 Temporal resource partitioning by bats at water holes. J Zool 270, 466-472

Aguirre LF, Herrel A, van Damme R & Matthysen E 2002 Ecomorphological analysis of trophic niche partitioning in a tropical savannah bat community. Proc R Soc B 269, 1271– 1278

Aguirre LF, Herrel A, van Damme R & Matthysen E 2003 The implications of food hardness for diet in bats. Funct Ecol 17, 201–212

Aihartza J, Almenar D, Salsamendi E, Goiti U & Garin I 2008 Fishing behaviour in the long-fingered bat Myotis capaccinii (Bonaparte, 1837): an experimental approach. Acta Chiropterol 10, 287-301

Akasaka T, Nakano D & Nakamura F 2009 Influence of prey variables, food supply, and river restoration on the foraging activity of Daubenton’s bat (Myotis daubentonii) in the Shibetsu River, a large lowland river in Japan. Biol Conserv 142, 1302-1310

Akasaka T, Akasaka M & Nakamura F 2012 Scale-dependent significance of river and riparian zones on three sympatric Myotis species in an agricultural landscape. Biol Conserv 145, 15-23

Aldridge H & Rautenbach IL 1987 Morphology, echolocation and resource partitioning in insectivorous bats. J Anim Ecol 56, 763–778

Aldridge H 1988 Flight kinematics and energetics in the little brown bat, Myotis lucifugus (Chiroptera: Vespertilionidae), with reference to the influence of ground effect. J Zool 216, 507-517

Almenar D, Aihartza J, Goiti U, Salsamendi E & Garin I 2008 Diet and prey selection in the trawling long-fingered bat. J Zool 274, 340-348

Almenar D, Aihartza J, Goiti U, Salsamendi E & Garin I 2013 Hierarchical patch choice by an insectivorous bat through prey availability components. Behav Ecol Sociobiol 67, 311- 320

107

Altringham JD 2011 Bats – From evolution to conservation. Oxford University Press, Oxford

Anderson MJ 2001 A new method for non-parametric multivariate analysis of variance. Austral Ecol 26, 32–46

Anthony ELP & Kunz TH 1977 Feeding strategies of the Little Brown Bat, Myotis lucifugus, in Southern New Hampshire. Ecology 58, 775–786

Arita HT & Fenton MB 1997 Flight and echolocation in the ecology and evolution of bats. Trends Ecol Evol 12, 53-58

Arlettaz R 1999 Habitat selection as a major resource partitioning mechanism between the two sympatric sibling bat species Myotis myotis and Myotis blythii. J Anim Ecol 68, 460-471

Baagøe HJ 1987 The Scandinavian bat fauna: adaptive wing morphology and free flight in the field. In: The Recent Advances in the Study of Bats (eds Fenton MB, Racey P, Rayner JMV), pp. 57–74. Cambridge University Press, Cambridge

Bartenstein SK, Gerstenberg N, Vanderelst D, Peremans H & Firzlaff U 2014 echoacoustic flow dynamically modifies the cortical map of target range in bats. Nat Commun 5:4668, doi 10.1038/ncomms5668

Bartonicka T & Rehák Z 2004 Flight activity and habitat use of Pipistrellus pygmaeus in a floodplain forest. Mammalia 68, 365-375

Bell GP 1982 Behavioral and ecological aspects of gleaning by a desert insectivorous bat, Antrozous pallidus (Chiroptera: Vespertilionidae). Behav Ecol Sociobiol 10, 217–223

Beltman JB & Metz JA 2005 Speciation: more likely through a genetic or through a learned habitat preference? Proc R Soc B 272, 1455-1463

Biscardi S, Russo D, Casciani V, Cesarini D, Mei M & Boitani L 2007 Foraging requirements of the endangered long-fingered bat: the influence of micro-habitat structure, water quality and prey type. J Zool 273, 372-381

Blankenberg D, Taylor J, Schenck I, He J, Zhang Y, Ghent M, Veeraraghavan N, Albert I, Miller W, Makova KD, Hardison RC & Nekrutenko A 2007 A framework for collaborative analysis of ENCODE data: making large-scale analyses biologist-friendly. Genome Res 17, 960–964

Blankenberg D, Gordon A, Von Kuster G, Coraor N, Nekrutenko A & Galaxy Team 2010 Manipulation of FASTQ data with Galaxy. Bioinformatics 26, 1783–1785

Bloom SA 1981 Similarity indices in community studies: potential pitfalls. Mar Ecol Prog Ser 5, 125–128

108

Bohmann K, Monadjem A, Lehmkuhl Noer C, Rasmussen M, Zeale MRK, Clare E, Jones G, Willerslev E & Gilbert TP 2011 Molecular diet analysis of two African free-tailed bats (Molossidae) using high throughput sequencing. PLoS ONE 6, 1–11, doi 10.1371/journal.pone.0021441

Boonman AM, Boonman M, Bretschneider F & van De Grind WA 1998 Prey detection in trawling insectivorous bats: duckweed affects hunting behaviour in Daubenton's bat, Myotis daubentonii, Behav Ecol Sociobiol 44, 99-107

Boonman A, Bumrungsri S & Yovel Y 2014 Nonecholocating Fruit Bats Produce Biosonar Clicks with Their Wings. Curr Biol 24, 2962-2967

Boyles GJ, Cryan PM, McCracken GF & Kunz TH 2011 Economic importance of bats in Agriculture. Science 332, 41-42

Bray JR & Curtis JT 1957 An ordination of the upland forest community of southern Wisconsin. Ecol Monographs 27, 325–349

Britton ARC, Jones G & Rayner JMV 1997 Flight performance, echolocation and foraging behaviour in pond bats, Myotis dasycneme. J Zool 241, 503–522

Carpenter RE 1969 Structure and function of the kidney and the water balance of desert bats. Phys Zool 42, 288-302

Chittka L, Skorupski P & Raine NE 2009 Speed-accuracy tradeoffs in animal decision making. Trends Ecol Evol 24, 400-407

Ciechanowski M 2002 Community structure and activity of bats (Chiroptera) over different water bodies. Mamm Biol 67, 276-285

Clare EL, Fraser EE, Braid HE, Fenton MB & Hebert PD 2009 Species on the menu of a generalist predator, the eastern red bat (Lasiurus borealis): using a molecular approach to detect arthropod prey. Mol Ecol 18, 2532–2542

Clare EL, Barber BR, Sweeney BW, Hebert PDN & Fenton MB 2011 Eating local: influences of habitat on the diet of little brown bats (Myotis lucifugus). Mol Ecol 20, 1772– 1780

Clare EL, Symondson WOC, Broders H, Fabianek F, Fraser EE, MacKenzie A, Boughen A, Hamilton R, Willis CK, Martinez-Nuñez F, Menzies AK, Norquay KJ, Brigham M, Poissant J, Rintoul J, Barclay RM & Reimer JP 2014a The diet of Myotis lucifugus across Canada: assessing foragin quality and diet variability. Mol Ecol 23, 3618- 3632

109

Clare EL, Symondson WOC & Fenton MB 2014b An inordinate fondness for beetles? Variation in seasonal dietary preferences of night roosting big brown bats (Eptesicus fuscus). Mol Ecol 23, 3633–3647

Clarin TMA, Ruczynski I, Page RA & Siemers BM 2013 Foraging ecology predicts learning performance in insectivorous bats. PLoS ONE 8(6), e64823, doi 10.1371/journal.pone.00641823

Clarke KR & Warwick RM 2001 Change in Marine Communities: An Approach to Statistical Analysis and Interpretation, 2nd edn. PRIMER-E, Plymouth.

Coyne JA & Orr HA 2004 Speciation. Sinauer Associates, Sunderland, Massachusetts.

Cranston PS 1995 Introduction. In: The Chironomidae: The Ecology and Biology of Non- Biting Midges (eds Armitage PD, Cranston PS, Pinder LCV), pp. 1–5. Chapman and Hall, London.

Crawford RL & Baker WW 1981 Bats killed at a North Florida television tower: a 25-year record. J Mammal 62, 651-652

Davis WH & Barbour RW 1965 The use of vision in flight by the bat Myotis sodalist. Am Midl Nat 74, 497-499

Davis JM & Stamps JA 2004 The effect of natal experience on habitat preferences. Trends Ecol Evol 19, 411-416

Denzinger A & Schnitzler H-U 2013 Bat guilds, a concept to classify the highly diverse foraging and echolocation behaviors of microchiropteran bats. Front. Physiol 4:164, doi 10.3389/fphys.2013.00164

Dieckman U, Metz JAJ, Doebeli M & Tautz D 2004 Introduction. In: Adaptive Speciation (eds Dieckman U, Metz JAJ, Doebeli M, Tautz D), pp. 1–16. University Press, Cambridge

Dieckmann U & Doebeli M 1999 The origin of species by sympatric speciation. Nature 400, 354–357

Dietz M, Encarnacao JA & Kalko EKV 2006 Small scale distribution patterns of female and male Daubenton’s bats (Myotis daubentonii). Acta Chiropterol 8, 403–415

Dietz C, Helversen Ov & Nill D 2009 Handbook of the Bats of Europe and Northwest Africa. A & C Black, London

Dumont ER 1997 Cranial shape in fruit, nectar, and exudat feeders: implication for interpreting the fossil record. Am J Phys Anthropol 102, 187–202

Dumont ER 1999 The effect of food hardness on feeding behaviour in frugivorous bats (Phyllostomidae): an experimental study. J Zool 248, 219–229

110

Dumont ER & Herrel A 2003 The effect of gape angle and bite point on bite force in bats. J Exp Biol 206, 2117–2123

Emrich MA, Clare EL, Symondson WO, Koenig SE & Fenton MB 2014 Resource partitioning by insectivorous bats in Jamaica. Mol Ecol 23, 3648–3656

Encarnacao JA & Dietz M 2006 Estimation of food intake and ingested energy in Daubenton’s bats (Myotis daubentonii) during pregnancy and spermatogenesis. Eur J Wildl Res 52, 221–227

Ernst MO & Banks MS 2002 Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429-433

Ernst MO & Bülthoff HH 2004 Merging the senses into a robust percept. Trends Cogn Sci 8, 162-169

Fenton MB, Audet D, Obrist MK & Rydell J 1995 Signal strength, timing, and self- deafening: the evolution of echolocation in bats. Paleobiol 21, 229-242

Fenton MB & Bogdanowicz W 2002 Relationships between external morphology and foraging behaviour: bats in the genus Myotis. Can J Zool 80, 1004-1013

Fenton MB & Ratcliffe JM 2010 Bats. Curr Biol 20, R1060-R1062

Findley JS 1972 Phenetic relationships among bats of the genus Myotis. Syst Zool 21, 31–52

Findley JS, Black H 1983 Morphological and dietary structuring of a Zambian insectivorous bat community. Ecology 64, 625–630

Firzlaff U, Schörnich S, Hoffmann S, Schuller G & Wiegrebe L 2006 A neural correlate of stochastic echo imaging. J Neurosci 26, 785-791

Firzlaff U, Schuchmann M, Grunwald JE, Schuller G & Wiegrebe L 2007 Object- oriented echo perception and cortical representation in echolocating bats. PLoS Biol 5(5), e100, doi 10.1371/journal.pbio.0050100

Fish FE, Blood BR & Clark BD 1991 Hydrodynamics of the feet of fish-catching bats: influence of the water surface on drag and morphological design. J Exp Zool 258, 164-173

Fletcher RJ, Orrock JL & Robertson BA 2012 How the type of anthropogenic change alters the consequences of ecological traps. Proc R Soc B doi:10.1098/rspb.2012.0139

Freeman PW 1981 Correspondence of food habits and morphology in insectivorous bats. J Mammal 62, 166–173

Frenckell Bv & Barclay RMR 1987 Bat activity over calm and turbulent water. Can J Zool 65, 219-222

111

Fukui D, Murakami M, Nakano S & Aoi T 2006 Effect of emergent aquatic insects on bat foraging in a riparian forest. J Anim Ecol 75, 1252-1258

Genzel D, Geberl C, Dera T & Wiegrebe L 2012 Coordination of bat sonar activity and flight for the exploration of three-dimensional objects. J Exp Biol 215, 2226-2235

Ghanem SJ, Ruppert H, Kunz TH & Voigt CC 2013 Frugivorous bats drink nutrient- and clay-enriched water in the Amazon rain forest: support for dual function of mineral-lick visits. J Trop Ecol 29, 1-10

Ghazanfar AA & Schroeder CE 2006 Is neocortex essentially multisensory? Trends Cogn Sci 10, 278-285

Giardine B1, Riemer C, Hardison RC, Burhans R, Elnitski L, Shah P, Zhang Y, Blankenberg D, Albert I, Taylor J, Miller W, Kent WJ & Nekrutenko A 2005 Galaxy: a platform for interactive large-scale genome analysis. Genome Res 15, 1451–1455

Goecks J, Nekrutenko A, Taylor J & The Galaxy Team 2010 Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol 11, R86

Goerlitz HR, ter Hofstede HM, Zeale MRK, Jones G & Holderied MW 2010 An Aerial- Hawking Bat Uses Stealth Echolocation to Counter Moth Hearing. Curr Biol 20, 1568-1572

Goerlitz HR, Genzel D & Wiegrebe L 2012 Bats’ avoidance of real and virtual objects: Implications for the sonar coding of object size. Behav Proc 89, 61-67

Grant BR 1985 Selection on bill characters in a population of Darwin’s finches: Geospiza conirostris on Isla Genovesa, Galapagos. Evolution 39, 523–532

Grant PR & Grant BR 2006 Evolution of character displacement in Darwin’s finches. Science 313, 224

Greif S & Siemers BM 2010 Innate recognition of water bodies in echolocating bats. Nat Commun 1:107, doi 10.1038/ncomms1110

Griffin DR 1958 Listening in the Dark. Yale University Press, New Haven

Griffiths SR 2013 Echolocating bats emit terminal phase buzz calls while drinking on the wing. Behav Proc 98, 58-60

Grindal SD, Morissette JL & Brigham RM 1999 Concentration of bat activity in riparian habitats over an elevational gradient. Can J Zool 77, 972-977

Grunwald J, Schörnich S & Wiegrebe L 2004 Classification of natural textures in echolocation Proc Natl Acad Sci USA 101, 5670-5674

112

Hamming RW 1950 Error detecting and error correcting codes. Bell Syst Tech J 29, 147–160

Hebert PDN, Cywinska A, Ball SL & de Waard J 2003a Biological identification through DNA barcodes. Proc R Soc B 270, 313–321

Hebert PDN, Ratnasingham S & de Waard J 2003b Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proc R Soc B 270, S596–S599

Hebert PDN, Stoeckle MY, Zemlak TS & Francis CM 2004 Identification of birds through DNA barcodes. PLoS Biology 2, e312, doi:10.1371/journal.pbio.0020312

Hecht D & Reiner M 2009 Sensory dominance in combinations of audio, visual and haptic stimuli. Exp Brain Res 193, 307-314

Heinrich M & Wiegrebe L 2013 Size constancy in bat biosonar? Perceptual interaction of object aperture and distance. PLoS ONE 8(4), e61577, doi 10.1371/journal.pone.0061577

Helversen Dv & Helversen Ov 1999 Acoustic guide in bat-pollinated flower. Nature 398, 759-760

Herrel A, de Grauw E & Lemosa-Espinal JA 2001 Head shape and bite performance in xenosaurid lizards. J Exp Zool 290, 101–107

Herrel A, Podos J, Huber SK & Hendry AP 2005 Bite performance and morphology in a population of Darwin’s finches: implications for the evolution of beak shape. Funct Ecol 19, 43–48

Hillen J, Kaster T, Pahle J, Kiefer A, Elle O, Griebeler EM & Veith M 2011 Sex-specific habitat selection in an edge habitat specialist, the western barbastelle bat. Ann Zool Fenn 48, 180–190

Holland RA, Borissov I & Siemers BM 2010 A nocturnal mammal, the greater mouse- eared bat, calibrates a magnetic compass by the sun. Proc Natl Acad Sci USA 107, 6941-6945

Holt RD 2009 Bringing the Hutchinsonian niche into the 21st century. Proc Natl Acad Sci USA 106, 19659–19665

Horváth G, Kriska G, Malik P & Robertson R 2009 Polarized light pollution: a new kind of ecological photopollution. Front Ecol Environ 7, 317-325

Horváth G 2014 Polarized Light and Polarization Vision in Animal Sciences. Springer, Berlin

Howard RW 1995 Auritus: A Natural History of the Brown Long-Eared Bat. Ebor Press, York

113

Huijbers CM, Nagelkerken I, Lössbroek PAC, Schulten IE, Siegenthaler A, Holderied MW & Simpson SD 2012 A test of the senses: Fish select novel habitats by responding to multiple cues. Ecology 93, 46-55

Hurly TA & Healy SD 1996 Memory for flowers in rufous hummingbirds: location or local visual cues? Anim Behav 51, 1149-1157

Huso MMP 2010 An estimator of wildlife fatality from observed carcasses. Environmetrics 22, 318–329

Hutchinson GE 1957 Concluding remarks. Cold Spring Harbor Symp Quant Biol 22, 415– 442

Jaenike J & Holt R 1991 Genetic variation for habitat preference: evidence and explanations. Am Nat 137, 67–90

Jakobsen L, Ratcliffe JM & Surlykke A 2012 Convergent acoustic field of view in echolocating bats. Nature 493, 93-96

Jepsen GL 1966 Early Eocene bat from Wyoming. Science 154, 1333-1339

Jiang T, Sun K, Chou C, Zhang Z & Feng J 2010 First record of Myotis flavus (Chiroptera: Vespertilionidae) from mainland China and a reassessment of its taxonomic status. Zootaxa 2414, 41–51

Jones G & Rayner JMV 1988a Flight performance, foraging tactics and echolocation in free-living Daubenton’s bats Myotis daubentonii (Chiroptera: Vespertilionidae). J Zool 215, 113-132

Jones G & Rayner JMV 1988b Foraging behavior and echolocation of wild horseshoe bats Rhinolophus ferrumequinum and R. hipposideros (Chiroptera, Rhinolophidae). Behav Ecol Sociobiol 25, 183–191

Jones G 1990 Prey selection by the greater horseshoe bat (Rhinolophus ferrumequinum): optimal foraging by echolocation? Anim Ecol 59, 587–602

Jones G & Rayner JMV 1991 Flight performance, foraging tactics and echolocation in the trawling insectivorous bat Myotis adversus (Chiroptera: Vespertilionidae). J Zool 225, 393- 412

Jones G & Teeling EC 2006 The evolution of echolocation in bats. Trends Ecol Evol 21, 149-156

Jones M, Ghoorah A & Blaxter M 2011 jMOTU and Taxonerator: turning DNA Barcode sequences into annotated operational taxonomic units. PLoS ONE 6, e19259

114

Jones J, Teeling EC & Rossiter SJ 2013 From the ultrasonic to the infrared: molecular evolution and the sensory biology of bats. Front Physiol 4:117, doi: 10.3389/fphys.2013.00117

Kalko EKV & Schnitzler H-U 1989 The echolocation and hunting behaviour of Daubenton’s bat, Myotis daubentoni. Behav Ecol Sociobiol 24, 225-238

Kalko EKV, Herre EA & Handley CO Jr 2007 Relation of fig fruit characteristics to fruit- eating bats in the New and Old World tropics. J Biogeogr 23, 565–576

Kerth G, Wagner M & König B 2001 Roosting together, foraging apart: information transfer about food is unlikely to explain sociality in female Bechstein's bats (Myotis bechsteinii). Behav Ecol Sociobiol 50, 283-291

King RA, Read DS, Traugott M & Symondson WOC 2008 Molecular analysis of predation: a review of best practice for DNA-based approaches. Mol Ecol 17, 947–963

Krüger F, Harms I, Fichtner A, Wolz I & Sommer RS 2012 High trophic similarity in the sympatric North European trawling bat species Myotis daubentonii and Myotis dasycneme. Acta Chiropterol 14, 347-356

Krüger F, Clare EL, Greif S, Siemers BM, Symondson WOC & Sommer R 2014 An integrative approach to detect subtle trophic niche differentiation in the sympatric trawling bat species Myotis dasycneme and Myotis daubentonii. Mol Ecol 23, 3657-3671

Kulzer E, Nelson JE, McKean JL & Moehres FP 1984 Prey-catching behaviour and echolocation in the Australian ghost bat, Macroderma gigas (Microchiroptera: Megadermatidae). Austral Mammal 7, 37–50

Kunz TH, Whitaker JO Jr 1983 An evaluation of fecal analysis for determining food habits of insectivorous bats. Can J Zool 61, 1317–1321

Kunz TH, Whitaker JO Jr & Wadanoli MD 1995 Dietary energetic of the insectivorous Mexican free-tailed bat (Tadarida brasiliensis) during pregnancy and lactation. Oecologia 101, 407–415

Kunz TH & Fenton MB 2003 Bat ecology. The University of Chicago Press, Chicago

Kunz TH, Arnett BA, Erickson WP, Hoar AR, Johnson GD, Larkin RP, Strickland MD, Thresher RW & Tuttle MD 2007 Ecological impacts of wind energy development on bats: questions, research needs, and hypotheses. Front Ecol Environ 5, 315–324

Kunz TH, de Torrez EB, Bauer D, Lobova T & Fleming TH 2011 Ecosystem services provided by bats. Ann NY Acad Sci 1, 1-38

115

Kurta A, Bell GP, Nagy KA & Kunz TH 1989 Energetics of pregnancy and lactation in freeranging little brown bats (Myotis lucifugus). Physiol Zool 62, 804–818

Lack D 1974 Darwin’s Finches. Cambridge University Press, Cambridge

Lacki MJ & Ladeur KM 2001 Seasonal use of lepidopteran prey by Rafinesque’s big-eared bats (Corynorhinus rafinesquii). Am Midl Nat 145, 213–217

Levin E, Barnea A, Yovel Y & Yom-Tov Y 2006 Have introduced fish initiated piscivory among the long-fingered bat? Mamm Biol 71, 139-143

Li G, Wang JH, Rossiter SJ, Jones G & Zhang S 2007 Accelerated FoxP2 evolution in echolocating bats. PLoS ONE 2(9), e900. doi:10.1371/journal.pone.0000900

Li G, Wang JH, Rossiter SJ, Jones G, Cotton JA & Zhang S 2008 The hearing gene Prestin reunites echolocating bats. Proc Natl Acad Sci USA 105, 13959-13964

Limpens HJGA 2001 Assessing the European distribution of the pond bat (Myotis dasycneme) using bat detectors and other survey methods. Nietoperze 2, 169–178

Lisón F & Calvo JF 2014 Bat activity over small ponds in dry Mediterranean forests: implications for conservation. Acta Chiropterol 16, 95-101

Liu Y, Cotton JA, Shen B, Han X, Rossiter SJ & Zhang S 2010 Convergent sequence evolution between echolocating bats and dolphins. Curr Biol 20, R53-R54

Longcore T & Rich C 2004 Ecological light pollution. Front Ecol Environ 2, 191-198

Lundy M & Montgomery I 2010 Summer habitat associations of bats between riparian landscapes and within riparian areas. Eur J Wildl Res 56, 385-394

Mackey RL & Barclay MR 1989 The influence of physical clutter and noise on the activity of bats over water. Can J Zool 67, 1167-1170

Madliger CL 2012 Toward improved conservation management: a consideration of sensory ecology. Biodivers Conserv 21, 3277-3286

Madsen PT & Surlykke A 2013 Functional Convergence in Bat and Toothed Whale Biosonars. Physiology 28, 276-283

Marshall AG 1983 Bats, flowers and fruit: evolutionary relationship in the Old World. Biol J Linnaean Soc 83, 351–369

McGuire LP & Fenton MB 2010 Hitting the wall: light affects the obstacle avoidance ability of free-flying little brown bats (Myotis lucifugus). Acta Chiropterol 12, 247-250

116

Merkle T, Knaden M & Wehner R 2006 Uncertainty about nest position influences systematic search strategies in desert ants. J Exp Biol 209, 3545-3549

Moss CF & Surlykke A 2010 Probing the natural scene by echolocation in bats. Front Behav Neurosci 4:33, doi:10.3389/fnbeh.2010.00033

Mukhin A, Chernetsov N & Kishkinev D 2008 Acoustic information as a distant cue for habitat recognition by nocturnally migrating passerines during landfall. Behav Ecol 19, 716- 723

Müller R & Kuc R 2000 Foliage echoes: a probe into the ecological acoustics of bat echolocation. J Acoust Soc Am 108, 836–845

Narins PM, Hödl W & Grabul DS 2003 Bimodal signal requisite for agonistic behavior in a dart-poison frog, Epipedobates femoralis. Proc Natl Acad Sci USA 100, 577-580

Neuweiler G & Mohres FP 1966 The role of special memory in the orientation. In Animal Sonar Systems Vol 1, ed Busnel, R.-G., pp 129-140, Laboratoire de Physiologie Acoustique, Jouy-en-Josas

Neuweiler G 2000 The biology of bats. Oxford University Press, Oxford

Nissen H, Krueger F, Fichtner A & Sommer RS 2013 Local variability in the diet of Daubenton’s bat (Myotis daubentonii) in a lake landscape of northern Germany. Folia Zool 62, 36–41

Norberg UM & Rayner JMV 1987 Ecological morphology and flight in bats (Mammalia; Chiroptera): wing adaptations, flight performance, foraging strategy and echolocation. Phil Trans R Soc B 316, 335–427

Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O'Hara RB, Simpson GL, Solymos P, Stevens MHH & Wagner H 2010 Vegan: community ecology package. Available from http://cran.r-project.org/ and http://vegan.r-forge.r-project.org

O’Leary MA, Bloch JI, Flynn JJ, Gaudin TJ, Giallombardo A, Giannini NP, Goldberg SL, Kraatz BP, Luo Z, Meng J, Ni X, Novacek MJ, Perini FA, Randall ZS, Rougier GW, Sargis EJ, Silcox MT, Simmons NB, Spaulding M, Velazco PM, Weksler M, Wible JR & Cirranello AL 2013 The Placental Mammal Ancestor and the Post-K-Pg Radiation of Placentals. Science 339, 662-667

Orbach DN & Fenton B 2010 Vision impairs the ability of bats to avoid colliding with stationary obstacles. PLoS ONE 5(11), e13912, doi 10.1371/journal.pone.0013912

Page RA & Ryan MJ 2006 Social transmission of novel foraging behavior in bats: frog calls and their referents. Curr Biol 16, 1201-1205

117

Pianka ER 1973 Niche overlap and diffuse competition. Proc Natl Acad Sci USA 71, 2141– 2145

Pompanon F, Deagle BE, Symondson WOC, Brown DS, Jarman SD & Taberlet P 2012 Who is eating what: diet assessment using next generation sequencing. Mol Ecol 21, 1931– 1950

Pourmand N1, Karhanek M, Persson HH, Webb CD, Lee TH, Zahradníková A & Davis RW 2006 Direct electrical detection of DNA synthesis. Proc Natl Acad Sci USA 103, 6466– 6470

R Development Core Team 2009 R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org

Racey PA & Entwistle AC 2000 Life-history and reproductive strategies in bats. In: Reproductive Biology of Bats (eds Crichton EG & Krutzsch PH), pp. 363–401. Academic Press, New York

Racey PR, Swift SM, Rydell J & Brodie L 1998 Bats and insects over two Scottish rivers with contrasting nitrate status. Anim Conserv 1, 195–202

Rachwald A, Boratynski P & Nowakowski WK 2001 Species composition and activity of bats flying over rivers in the Bialowieza Primeval Forest. Acta Theriol 46, 235-242

Ratnasingham S & Hebert PDN 2007 BOLD: the Barcode of Life Data system. Mol Ecol Notes 7, 355–364

Rautenbach IL, Fenton MB & Whiting MJ 1996 Bats in riverine forests and woodlands: a latitudinal transect in southern Africa. Can J Zool 74, 312-322

Razgour O, Korine C & Saltz D 2010 Pond characteristics as determinants of species diversity and community composition in desert bats. Anim Conserv 13, 505-513

Razgour O, Clare EL, Zeale MRK, Hanmer J, Schnell IB, Rasmussen M, Gilbert TP & Jones G 2011 High-throughput sequencing offers insight into mechanisms of resources partitioning in cryptic bat species. Ecol Evol 1, 556–570

Richards GC 1990 The spectacled flying-fox, Pteropus conspicillatus (Chiroptera: Pteropodidae), in North Queensland—2. Diet, seed dispersal and feeding ecology. Austral Mammal 13, 25–31

Ricklefs RE & Schluter D 1993 Species Diversity in Ecological Communities: Historical and Geographical Perspectives. University of Chicago Press, Chicago, Illinois

118

Robertson BA & Hutto RL 2006 A framework for understanding ecological traps and an evaluation of existing evidence. Ecology 87, 1075-1085

Robertson BA, Rehage JS & Sih A 2013 Ecological novelty and the emergence of evolutionary traps. Trends Ecol Evol 28, 552-560

Rossier J, Haeberli C & Schenk F 2000 Auditory cues support place navigation in rats when associated with a visual cue. Behav Br Res 117, 209-214

Rossiter S, Jones G, Ransome R & Barratt E 2002 Relatedness structure and kin-biased foraging in the greater horseshoe bat (Rhinolophus ferrumequinum). Behav Ecol Sociobiol 51, 510-518

Rothberg JM, Hinz W, Rearick TM, Schultz J, Mileski W, Davey M, Leamon JH, Johnson K, Milgrew MJ, Edwards M, Hoon J, Simons JF, Marran D, Myers JW, Davidson JF, Branting A, Nobile JR, Puc BP, Light D, Clark TA, Huber M, Branciforte JT, Stoner IB, Cawley SE, Lyons M, Fu Y, Homer N, Sedova M, Miao X, Reed B, Sabina J, Feierstein E, Schorn M, Alanjary M, Dimalanta E, Dressman D, Kasinskas R, Sokolsky T, Fidanza JA, Namsaraev E, McKernan KJ, Williams A, Roth GT & Bustillo J 2011 An integrated semiconductor device enabling non-optical genome sequencing. Nature 475, 348–352

Ruedi M & Mayer F 2001 Molecular systematics of bats of the genus Myotis (Vespertilionidae) suggests deterministic ecomorphological convergences. Mol Phylogenet Evol 21, 436–448

Russo D 2002 Elevation affects the distribution of the two sexes in Daubenton’s bats Myotis daubentonii (Chiroptera: Vespertilionidae) from Italy. Mammalia 66, 543-551

Russo D, Cistrone L & Jones G 2012 Sensory ecology of water detection by bats: A field experiment. PLoS ONE 7(19, e48144, doi 10.1371/journal.pone.0048144

Ryan PG, Bloomer P, Moloney CL, Grant TJ & Delport W 2007 Ecological speciation in South Atlantic island finches. Science 315, 1420–1423

Rydell J, Miller LA & Jensen ME 1999 Echolocation constraints of Daubenton’s bat foraging over water. Funct Ecol 13, 247-255

Rydell J, Bach L, Dubourg-Savage M-J, Green M, Rodrigues L & Hedenström A 2010 Bat mortality at wind turbines in Northwestern Europe. Acta Chiropterol 12, 261-273

Safi K, König B & Kerth G 2007 Sex differences in population genetics, home range size and habitat use of the parti-colored bat (Vespertilio murinus, Linnaeus 1758) in Switzerland and their consequences for conservation. Biol Conserv 137, 28–36

119

Salsamendi E, Arostegui I, Aihartza J, Almenar D, Goiti U & Garin I 2012 Foraging ecology in Mehely’s horseshoe bats: influence of habitat structure and water availability. Acta Chiropterol 14, 121-132

Santana SE, Dumont ER & Davi JL 2010 Mechanics of bite force production and its relationship to diet in bats. Funct Ecol 24, 776–784

Schlaepfer MA, Runge MC & Sherman PW 2002 Ecological and evolutionary traps. Trends Ecol Evol 17, 474-480

Schluter D 2001 Ecology and the origin of species. Trends Ecol Evol 16, 372–380

Schluter D, Price TD & Grant PR 1985 Ecological character displacement in Darwin’s finches. Science 227, 1056–1059

Schnitzler H-U 1968 Die Ultraschallortungslaute der Hufeisennasen-Fledermäuse (Chiroptera, Rhinolophidae) in verschiedenen Orientierungssituationen. Z Vergl Physiol 57, 376-408

Schnitzler H-U, Kalko EKV, Kaipf I & Grinnell AD 1994 Fishing and echolocation behaviour of the Greater bulldog bat, Noctilio leporinus in the field. Behav Ecol Sociobiol 35, 327-345

Schnitzler H-U & Kalko EK 2001 Echolocation by Insect-Eating Bats. Bioscience 51, 557- 569

Schnitzler H-U, Moss CF & Denzinger A 2003 From spatial orientation to food acquisition in echolocating bats. Trends Ecol Evol 18, 386-394

Seibold S, Buchner J, Bässler C & Müller J 2013 Ponds in acidic mountains are more important for bats in providing drinking water than insect prey. J Zool 290, 302-308

Seidman VM & Zabel CJ 2001 Bat activity along intermittent streams in Northwestern California. J Mammal 82, 738-747

Senior P, Butlin RK & Altringham JD 2005 Sex and segregation in temperate bats. Proc R Soc B 272, 2467-2473

Serra-Cobo J, Lopez-Roig M, Marqucs-Bonet T & Lahuerta E 2000 Rivers as possible landmarks in the orientation flight of Miniopterus schreibersii. Acta Theriol 45, 347-352

Shettleworth SJ 2009 Getting around: Spatial Cognition. In Cognition, Evolution and Behavior, pp 261-312, 2nd edn. Oxford University Press, Oxford

Shiel CB, McAney CM, Sullivan C & Fairley JS 1997 Identification of Arthropod Fragments in Bat Droppings. Occasional Publication 17, The Mammal Society, London

120

Siemers BM, Stilz P & Schnitzler H-U 2001 The acoustic advantage of hunting at low heights above water: behavioural experiments on the European 'trawling' bats Myotis capaccinii, M. dasycneme and M. daubentonii, J Exp Biol 204, 3843-3854

Siemers BM, Dietz C, Nill D & Schnitzler H-U 2001 Myotis daubentonii is able to catch small fish. Acta Chiropterol 3, 71-75

Siemers BM & Schnitzler H-U 2004 Echolocation signals reflect niche differentiation in five sympatric congeneric bat species. Nature 429, 657-661

Siemers BM, Baur E & Schnitzler H-U 2005 Acoustic mirror effect increases prey detection distance in trawling bats. Naturwissenschaften, 92, 272-276

Simmons JA 1971 Echolocation in bats: signal processing of echoes for target range. Science 171, 925-928

Simmons NB & Geisler JH 1998 Phylogenetic relationships of Icaronycteris, Archaeonycteris, Hassianycteris, and Palaeochiropteryx to extant bat lineages, with comments on the evolution of echolocation and foraging strategies in Microchiroptera. Bull Am Mus Nat His 235, 1-182

Simmons NB 2005a Evolution - An Eocene big bang for bats. Science 307, 527-528

Simmons NB 2005b Order Chiroptera. In: Mammal Species of the World: A Taxonomic and Geographic Reference, 3rd edn (eds Wilson DE, Reeder DM), pp. 312–529. Smithsonian Institution Press, Washington

Simmons NB, Seymour KL, Habersetzer J & Gunnell GF 2008 Primitive early Eocene bat from Wyoming and the evolution of flight and echolocation. Nature 451, 818-822

Simmons NB, Seymour KL, Habersetzer J & Gunnell GF 2010 Inferring echolocation in ancient bats. Nature 466, E8-E9

Simon R, Holderied MW, Koch CU & Helversen Ov 2011 Floral acoustics: conspicuous echoes of a dish-shaped leaf attract bat pollinators. Science 333, 631 – 633

Simpson EH 1949 Measurement of diversity. Nature 163, 688

Slagsvold T & Wiebe KL 2007 Learning the ecological niche. Proc R Soc B 274, 19-23

Smotherman M & Metzner W 2003 Effects of echo intensity on Doppler-shift compensation behavior in horseshoe bats. J Neurophysiol 89, 814-821

Speakman JR 1993 The evolution of echolocation for predation. Symp Zool Soc Lond 65, 39-63

121

Springer MS, Teeling EC, Madsen O, Stanhope MJ & de Jong WW 2001 Integrated fossil and molecular data reconstruct bat echolocation. Proc Natl Acad Sci USA 98, 6241- 6246

Stadelmann B, Jacobs DS, Schoemann C & Ruedi M 2004 Phylogeny of African Myotis bats inferred from cytochrome b sequences. Acta Chiropterologica, 6, 177–192

Stadelmann B, Lin LK, Kunz TH & Ruedi M 2007 Molecular phylogeny of New World Myotis Chiroptera, Vespertilionidae) inferred from mitochondrial and nuclear DNA genes. Mol Phylogenet Evol 43, 32–48

Stamps JA, Krishnan VV & Willits NH 2009 How different types of natal experience affect habitat preference. Am Nat 174, 623-630

Stephens DW & Krebs JR 1986 Foraging Theory. Princeton University Press, Princeton

Surlykke A 2004 The relationship of detection thresholds to the nu,ber of echoes in the big brown bat, Eptesicus fuscus. In Echolocation in Bats and Dolphins, eds Thomas, J.A., Moss, C.F. & Vater, M., pp 268-272, University of Chicago Press, Chicago

Surlykke A, Kalko EKV 2008 Echolocating bats cry out loud to detect their prey. PLoS ONE, 3(4), e2036. doi:10.1371/journal.pone.0002036

Surlykke A, Pedersen SB & Jakobsen L 2009 Echolocating bats emit a highly directional sonar sound beam in the field. Proc R Soc B 276, 853-860

Symondson WOC 2002 Molecular identification of prey in predator diets. Mol Ecol 11, 627–641

Swift SM, Racey PA & Avery MI 1985 Feeding ecology of Pipistrellus pipilstrellus (Chiroptera, Vespertilionidae) during pregnancy and lactation. II Diet. J Anim Ecol 54, 217– 225

Taake KH 1992 Strategien der Ressourcennutzung an Waldgewässern jagender Fledermäuse. Myotis 30, 7–73

Taylor DAR & Tuttle MD 2007 Water for Wildlife: A Handbook for Rangers and Range Managers. Bat Conservation International

Teeling EC, Scally M, Kao JD, Romagnoli ML, Springer MS & Stanhope MJ 2000 Molecular evidence regarding the origin of echolocation and flight in bats. Nature 403, 188- 192

Teeling EC, Springer MS, Madsen O, Bates P, O’Brien SJ & Murphy WJ 2005 A Molecular Phylogeny for Bats Illuminates Biogeography and the Fossil Record. Science 307, 580-584

122

Teeling EC 2009 Hear, hear: the convergent evolution of echolocation in bats? Trends Ecol Evol 24, 351-354

Teeling EC, Dool S & Springer MS 2012 Phylogenies, fossils and functional genes: the evolution of echolocation in bats. In Evolutionary History of Bats: Fossils, Molecules and Morphology, eds Gunnell, G.F. & Simmons, N.B., pp 1–21, Cambridge University Press, Cambridge

Thiele J & Winter Y 2005 Hierarchical strategy for relocating food targets in flower bats: spatial memory versus cue-directed search. Anim Behav 69, 315-327

Thomas DW 1988 The distribution of bats in different ages of Douglas-fir forests. J Wildl Manag 52, 619-628

Timm RM 1989 Migration and molt patterns of red bats, Lasiurus borealis (Chiroptera: Vespertilionidae), in Illinois. Bulletin of the Chicago Academy of Sciences 14, 1-7

Todd VLG & Waters DA 2007 Strategy-switching in the gaffing bat. J Zool 273, 106-113

Toelch U, Stich KP, Gass CL & Winter Y 2008 Effect of local spatial cues in small-scale orientation of flower bats. Anim Behav 75, 913-920

Tonnis B, Grant PR, Grant BR & Petren K 2005 Habitat selection and ecological speciation in Galápagos warbler finches (Certhidea olivacea and Certhidea fusca). Proc R Soc B 272, 819-826

Tsagkogeorga G, Parker J, Stupka E, Cotton JA & Rossiter SJ 2013 Phylogenomic Analyses Elucidate the Evolutionary Relationships of Bats. Curr Biol 23, 2262-2267

Tsoar A, Nathan R, Bartan Y, Vyssotski A, Dell’Omo G & Ulanovsky N 2011 Large- scale navigational map in a mammal. Proc Natl Acad Sci USA 108, E718-E724

Übernickel K, Tschapka M & Kalko EKV 2013 Flexible echolocation behaviour of trawling bats during approach of continuous or transient prey cues. Front Physiol 4:96, doi 10.3389/fphys.2013.00096

Ulanovsky N & Moss CF 2008 What the bat’s voice tells the bat’s brain. Proc Natl Acad Sci USA 105, 8491-8498

Van Gelder RG 1956 Echo-location failure in migratory bats. Trans Kans Acad Sci 59, 220- 222

Vaughaun N 1997 The diets of British bats (Chiroptera). Mammal Rev 27, 77–94

Venables WN & Ripley BD 2002 Modern Applied Statistics with S, 4th edn. Springer, New York

123

Verboom B, Boonman AM & Limpens HJGA 1999 Acoustic perception of landscape elements by the pond bat (Myotis dasycneme). J Zool 248, 59-66

Vindigni MA, Morris AD, Miller DA & Kalcounis-Rueppell MC 2009 Use of modified water sources by bats in a managed pine landscape. For Ecol Manag 258, 2056-2061

Voigt CC, Capps KA, Dechmann DKN, Michener RH & Kunz TH 2008 Nutrition or detoxification: why bats visit mineral licks of the Amazonian rainforest. PLoS ONE 3(4) e2011, doi 10.1371/journal.pone.0002011

Voigt CC, Popa-Lisseanu, Niermann I & Kramer-Schadt S 2012 The catchment area of wind farms for European bats: A plea for international regulations. Biol Conserv 153, 80-86

Vrieze LA, Bjerselius R & Sorensen PW 2010 Importance of the olfactory sense to migratory sea lampreys Petromyzon marinus seeking riverine spawning habitat. J Fish Biol 76, 949-964

Ward JV 1992 Aquatic Insect Ecology, Vol. 1. Biology and Habitat. Wiley, New York

Warren RD, Waters DA, Altringham JD & Bullock DJ 2000 The distribution of Daubenton’s bats (Myotis daubentonii) and pipistrelle bats (Pipistrellus pipistrellus) (Vespertilionidae) in relation to small-scale variation in riverine habitat. Biol Conserv 92, 85- 91

Whitaker JO 1972 Food habits of bats from Indiana. Can J Zool 50, 877–883

Whitaker DM, Carroll AL & Montevecchi WA 2000 Elevated numbers of flying insects and insectivorous birds in riparian buffer strips. Can J Zool 78, 740-747

Whitaker JO, McCracken GF & Siemers BM 2009 Food habitat analysis of insectivorous bats. In: Ecological and Behavioural Methods for the Study of Bats, 2nd edn (eds Kunz TH, Parsons S), pp. 567–592. The John Hopkins University Press, Baltimore

Wilde CJ, Knight CR & Racey PA 1999 Influence of torpor on milk protein composition and secretion in lactating bats. J Exp Zool 284, 35–41

Wimsatt WA 1969 Transient behavior, nocturnal activity patterns, and feeding efficiency of vampire bats (Desmodus rotundus) under natural conditions. J Mammal 50, 233–244

Witten IB & Knudsen EI 2005 Why seeing is believing: merging auditory and visual worlds. Neuron 48, 489-496

Wund MA 2005 Learning and the development of habitat-specific bat echolocation. Anim Behav 70, 441-450

Yovel Y, Franz MO, Stilz P & Schnitzler H-U 2008 Plant classification from bat-like echolocation signals. PLoS Comput Biol, 4(3):e100032

124

Yovel Y, Stilz P, Franz MO, Boonman A & Schnitzler H-U 2009 What a plant sounds like: The statistics of vegetation echoes as received by echolocating bats. PLoS Comput Biol, 5(7):e1000429

Yovel Y, Falk B, Moss CF & Ulanovsky N 2010 Optimal localization by pointing off axis. Science 327, 701-704

Yovel Y, Falk B, Moss CF & Ulanovsky N 2011a Active control of acoustic field-of-view in a biosonar system. PLoS Biol 9(9), e1001150. doi:10.1371/journal.pbio.1001150

Yovel Y, Geva-Sagiv M, Ulanovsky N 2011b Click-based echolocation in bats: not so primitive after all. J Comp Physiol A 197, 515-530

Yovel Y, Franz MO, Stilz P & Schnitzler H-U 2011c Complex echo classification by echolocating bats: a review. J Comp Physiol A 197, 475-490

Zahn A & Maier S 1997 Jagdaktivität von Fledermäusen an Bächen und Teichen. Z Säugetierkunde 62, 1-11

Zeale MRK, Butlin RK, Barker GLA, Lees DC & Jones G 2011 Taxon-speficic PCR for DNA barcoding arthropod prey in bat faeces. Mol Ecol Res 11, 236–244

Zsebok S, Kroll F, Heinrich M, Genzel D, Siemers BM & Wiegrebe L 2013 Trawling bats exploit an echo-acoustic ground effect. Front Physiol 4:65, doi 10.3389/fphys.2013.00065

125

PUBLICATION OF RESULTS

The results of chapter 1 are published in the journal Nature Communications. The reference is: Greif S. & Siemers B.M. 2010 Innate recognition of water bodies in echolocating bats. Nat. Commun. 1:107, doi:10.1038/ncomms1110

Chapter 2 is submitted as “Greif S., Korine C., Siemers B.M..: Acoustic mirages in the desert: testing spatial memory and echo cues in bats”.

Chapter 3 is submitted as “Greif S., Zsebok S., Schmieder D., Siemers B.M.: Acoustic mirrors as evolutionary traps for bats”.

Chapter 4 is published in the journal Molecular Ecology. The reference is: Krüger F., Clare E.L., Greif S., Siemers B.M., Symondson W.O.C., Sommer R.: An integrative approach to detect subtle trophic niche differentiation in the sympatric trawling bat species Myotis dasycneme and Myotis daubentonii. Mol. Ecol. 23: 3657-3671

The structure and length of the single chapters corresponds to the respective journal requirements.

126

AUTHOR CONTRIBUTIONS

For chapter 1 S.G. and B.M.S. designed the study, analysed data and wrote the paper. S.G. performed the experiments.

For chapter 2 S.G. and B.M.S. designed the study, analysed the data and wrote the paper. S.G. performed the experiments and C.K. provided logistics and help in Israel.

For chapter 3 S.G, S.Z. and B.M.S. designed the study. S.G. and S.Z. performed the experiments but only data from S.Z. was included in this manuscript, S.G. and S.Z. analysed the data and wrote the manuscript. D.S. helped in the field. S.G. and S.Z. are equally contributing authors.

For chapter 4 the scientific question and the scientific approach were developed by R.S.S., B.M.S., W.O.C.S., E.L.C. and F.K.. Samples in Germany were collected by F.K.. Bite force measurements were done by S.G. Morphological experiments (weight lifting) and measurements (wings) were done by F.K. Molecular lab work was done by F.K. and E.C. Morphological analysis was done by F.K. F.K. did the statistical analysis and wrote the manuscript which all authors commented on.

S.G. and B.M.S. are members of the Sensory Ecology Group at the Max Planck Institute for Ornithology, 82319 Seewiesen, Germany. C.K. is member of the Mitrani Department of Desert Ecology at the Jacob Blaustein Institutes for Desert Research, 84990 Midreshet Ben-Gurion, Israel. F.K. and R.S.S. are members of the Landscape Ecology Department of the University of Kiel, 24418 Kiel, Germany. E.L.C. is part of the School of Biological and Chemical Sciences at Queen Mary University of London, London, UK. W.O.C.S. is member of the Cardiff School of Biosciences at Cardiff University, Cardiff, UK.

This thesis is my (S.G.) own work. B.M.S. supervised my work for this thesis and provided facilities and equipment.

127

ACKNOWLEDGMENTS

First and foremost I would like to thank Björn M. Siemers for being an amazing supervisor, always believing in me and giving me the freedom to follow all my ideas. You shaped me as a scientist, stood by as a friend and I will be forever grateful to you. You left too early and will be sorely missed.

My greatest gratitude also goes to PD Dr. Barbara Helm and Prof. Dr. Lutz Wiegrebe for stepping in without hesitation in times of need.

Barbara, your encouragement and trust was more than appreciated. You have a heart of gold and are an inspiring person!

Lutz, your continuous efforts to help our group are valued above all! Thanks for your patience, being there, helping out with everything and examining my thesis.

The whole Sensory Ecology Group supported me beyond and above in all these years. Thank you guys and girls for cakes, discussion, music and laughter. You know that you will always have a place in my heart. This goes especially to the ones enduring and living the Bulgarian field seasons with me.

All you folks in Seewiesen, thanks for making these last years so wonderful. No matter if soccer, kicker, dancing, birdwatching, partying,… you were great!

All my old friends from Tübingen, my new ones from other parts of the world, thanks for sticking with me, supporting me, feeling with me!

Mine, you unbelievingly never stopped believing in me! I will never forget.

Tina, you’re laughter is still in my ears and heart. Thank you!

Henrik, Wolferl, Stefan, thanks for your friendship and support! Ich hoffe, dass wir noch oft klopfen und die Sau suchen können.

Uli, thanks for pushing me and showing that it can be done.

There are so many that I have to thank and cannot name all. This is for you, thanks so much!

The most important thanks go to my family. Without you I would not be where I am today, would not have had such great possibilities and an amazing life! Thank you for your continuous support and belief!

128

CURRICULUM VITAE

PERSONAL DETAILS

Surname: Greif Given name Stefan Date of birth: 23rd February 1979 Place of birth: Augsburg, Germany

EDUCATION

06/2013 – present Max Planck Institute for Ornithology, Seewiesen Guest Researcher

06/2013 – 01/2014 Queen’s University, Belfast, UK Research Fellow with Dr. R. Holland

05/2007 – 05/2013 Max Planck Institute for Ornithology, Seewiesen, Germany Sensory Ecology Group PhD studies: Sensory ecology of bats around bodies of water PhD supervisors: Dr. B. M. Siemers (deceased), PD Dr. Barbara Helm, Prof. Dr. Lutz Wiegrebe

11/2003 – 10/2004 University of Auckland, New Zealand Honorary Research Assistant with Assoc. Prof. Dr. S. Parsons and Prof. Dr. D. Brunton

10/1999 – 12/2006 Eberhard-Karls-University Tuebingen, Germany Studies: biology, diploma Major: Animal Physiology Minors: Zoology, Palaeontology Diploma thesis: Strategies for the detection of cryptic prey in Natterer’s bat, Myotis nattereri. Diploma supervisor: Dr. B. M. Siemers

PRIZES & AWARDS

2014 Young Investigator Award of the International Society for Neuroethology: 1200 US$ 2013 2nd Best Talk Prize at the Berlin Bat Meeting 2013: book prize 2011 Best Poster Prize (DAB) at the SICB Meeting 2011: 150 US$, one year subscription of Ethology

129

GRANTS (total sum 12.160 €)

2015 Short-Term Research Grant of the Minerva Foundation for fieldwork in Israel: 3400 € 2012 Travel grant for ISBE 2012 (International Society for Behavioral Ecology): 1500 US$ (≈ 1207€) 2011 Short-Term Research Grant of the Minerva Foundation for fieldwork in Israel: 1886 € 2011 Travel grant for the SICB Annual Meeting 2011 (Reinhold-and- Maria-Teufel Foundation): 1050 € 2010 Travel grant for ISBE 2010 (International Society for Behavioral Ecology): 2200 US$ (≈ 1770€) 2009 Travel grant for the Summer School on Animal Navigation (University of Queensland – Queensland Brain Institute): 2000 AU$ (≈ 1560€) 2008 Travel grant for the XIth European Bat Research Symposium (Munich Graduate School for Evolution, Ecology and Systematics): 332 € 2008 Conference grant for the ASAB Easter Meeting 2008 (Association for the Study of Animal Behaviour): 385 € 2006 Travel grant for the 11th International Behavioral Ecology Congress (Reinhold-and-Maria-Teufel Foundation): 570 €

INVITED PRESENTATIONS

2014-07-30 11th International Congress of Neuroethology; 28.07.-01.08.2014 in Sapporo (JP) (Young Investigator symposium talk) 2013-11-05 Queen’s University Belfast (UK): School of Biological Sciences Seminar 2013-08-15 16th International Bat Research Conference; 11.-15.08.2013 in San José (CR) (symposium talk) 2012-07-20 VIth European Conference on Behavioural Biology; 19.-22.07.2012 in Essen (D) (symposium talk) 2011-01-14 Scribbs Institution of Oceanography, University of California San Diego (USA): Scripps Whale Acoustic Lab seminar 2011-01-11 University of California Los Angeles (USA): Center for the Advanced Study of Behavior (Prof. P. Narins) 2010-07-17 Vth European Conference on Behavioural Biology; 16.-18.07.2010 in Ferrara (I) (symposium talk)

130

CONFERENCE PRESENTATIONS

2014 107th Annual Meeting of the German Zoological Society (DZG); 11.-14.09.2014 in Goettingen (D): A functional role of the sky’s polarization pattern in orientation in a mammal (talk) 2014 7th Max Planck Institute for Ornithology Retreat; 13.-15.02.2014 in Blaubeuren (D): A functional role of the sky’s polarization pattern in orientation in a mammal (talk) 2014 Topical Meeting of the Ethological Society; 06.-08.02.2014 in Tutzing (D): A functional role of the sky’s polarization pattern in orientation in a mammal (talk) 2013 106th Annual Meeting of the German Zoological Society (DZG); 13.-16.09.2013 in Munich (D): Acoustic Mirrors as Sensory Traps for Bats? (talk) 2013 16th International Bat Research Conference; 11.-15.08.2013 in San Jose (CR): Björn Siemers’ Sensory Ecology of Bats – From Caves to Cowsheds (talk); Acoustic Mirrors as Sensory Traps for Bats? (talk) 2013 3rd Berlin Bat Meeting: Bats in the Anthropocene; 01.-03.03.2013 in Berlin (D): Acoustic mirrors as ecological traps for bats? (talk) 2013 Biannual Meeting of German Bat Researchers; 11.-13.01.2013 in Rottenburg-Ergenzingen (D): 1) Acoustic mirages in the desert: echo cues versus spatial memory (talk). 2) The Tabachka Bat Research Station (talk) 2012 14th International Behavioral Ecology Congress; 12.-18.08.2012 in Lund (S): Acoustic mirages in the desert: testing spatial memory and echo cues in bats (talk) 2012 5th Max Planck Institute for Ornithology Retreat; 26.-28.01.2012 in Biberach (D): Acoustic mirrors in the desert: echo cues versus spatial memory (talk) 2011 4th Max Planck Institute for Ornithology Retreat; 03.-05.02.2011 in Lindau (D): Boldly biting birds: correlations between bite force, testo and aggression (talk) 2011 Biannual Meeting of German bat researchers; 20.-22.01.2011 in Loccum (D): To buzz or not to buzz? Intentional control of echolocation in drinking bats (talk) 2011 SICB Annual Meeting 2011; 03.-07.01.2011 in Salt Lake City (USA): Sensory basis of habitat recognition in echolocation (poster) 2010 13th International Behavioral Ecology Congress; 26.09.-01.10.2010 in Perth (AUS): Sensory basis of habitat recognition in bats (talk); To buzz or not to buzz? Intentional control of echolocation in drinking bats (poster) 2010 15th International Bat Research Conference; 23.-27.08.2010 in Prague (CZ): 1) Sensory basis of habitat recognition in bats (talk). 2) To buzz or not to buzz? Intentional control of echolocation in drinking bats (poster). 3) Sensory specializations compromise bite force in bats (poster). 4) The effect of mirror orientation on bats’ perception of

131

echoacoustic mirror images (poster). 5) Perch-hunting in Rhinolophus bats is related to the high energy costs of manoeuvring in flight (poster) 2010 3rd Max Planck Institute for Ornithology Retreat; 21.-23.01.2010 in Schwangau (D): Echoacoustic detection of water – Drink and fly (talk) 2009 DZG Graduate Meeting of Behavioural Biology; 11.-13.11.2009 in Seewiesen (D): Water recognition in bats (talk) 2009 102nd Annual Meeting of the German Zoological Society (DZG); 25.-28.09.2009 in Regensburg (D): Echoacoustic detection of water (poster) 2009 5th Animal Sonar Symposium; 14.-18.09.2009 in Kyoto (J): To buzz or not to buzz – Echolocation behaviour of drinking bats suggests an intentional trigger of Buzz II emission (poster); Echoacoustic detection of water (poster) 2009 Annual Meeting of Bat Conservationists in Southern Bavaria: 28.03.2009 in Munich D): Echoacoustic detection of water – Mirror, mirror on the ground, is it water that I found? (talk) 2009 Biannual Meeting of German bat researchers; 22.-24.01.2009 in Frauenchiemsee (D): Echoacoustic detection of water – Mirror, mirror on the ground, is it water that I found? (talk) 2008 XIth European Bat Research Symposium; 17.-22.08.2008 in Cluj Napoca, (RO): Echoacoustic detection of water – Mirror, mirror on the ground, is it water that I found? (talk) 2008 12th International Behavioral Ecology Congress; 09.-15.08.2008 in Ithaca, NY (USA): Echoacoustic detection of water (poster) 2008 ASAB Easter Meeting 2008; 02.-04.04.2008 in Edinburgh (UK): Echoacoustic detection of water – Mirror, mirror on the ground, is it water that I found? (talk) 2007 1st Max Planck Institute for Ornithology Retreat; 07.-09.12.2007 in Frauenchiemsee (D): Echoacoustic detection of water – Mirror, mirror on the ground, is it water that I found? (talk) 2007 100th Annual Meeting of the German Zoological Society (DZG); 21.-24.09.2007 in Cologne (D): When bats catch flies – Sex kills (talk) 2007 Biannual Meeting of German bat researchers; 19.-21.01.2007 in Berlin: When bats catch flies – Sex kills (talk) 2006 Max Planck Institute for Ornithology Alpine Workshop; 13.- 14.10.2006 in Garmisch-Partenkirchen D): When bats catch flies – Sex kills (talk) 2006 11th International Behavioral Ecology Congress; 23.-29.07.2006 in Tours (F): Strategies for the detection of cryptic prey in the bat Myotis nattereri (poster)

132

AD HOC REFEREE FOR SCIENTIFIC JOURNALS

• Behavioral Ecology and Sociobiology • Current Biology • European Journal of Wildlife Research • Frontiers in Integrative Physiology • Journal of Zoological Systematics and Evolutionary Research • PloS One

MEMBERSHIPS

• Association for the Study of Animal Behaviour (ASAB) • International Society for Neuroethology (ISN) • International Society for Behavioral Ecology (ISBE) • Society for Integrative and Comparative Biology (SICB) • Royal Institute for Navigation (RIN) • German Society for Mammalian Biology (DGS) • German Zoological Society (DZG) • Ethological Society (Ethologische Gesellschaft) • German Ornithological Society (DO-G)

ORGANISATION OF SYMPOSIA AND MEETINGS

2014 Topical Meeting of the Ethological Society “Function and Mechanisms of Animal Behaviour”, Tutzing (D), Organizing Committee 2013 “Two curious explorers: the work of Elisabeth Kalko and Björn Siemers” Symposium at the International Bat Research Conference, San Jose (CR) with Mirjam Knörnschild (University of Ulm), Kirsten Jung (University of Ulm) & Holger Görlitz (MPI Seewiesen) 2012 “Björn Siemers Memorial Symposium: Sensory Ecology of Bats” at the European Conference on Behavioural Biology, Essen (D) with Dr. Christian Voigt (IZW, Berlin) 2009 “Grant Possibilities” presentation of EU-Office, organisation 2008 “Project Management” seminar for PhD students at the MPI for Ornithology, organisation 2007 1st Max Planck Institute for Ornithology Retreat, organisation

133

ADDITIONAL WORK EXPERIENCE

08/2010 – present one out of six eTOC monitors for the Animal Navigation Group (ANG) of the Royal Institute for Navigation

11/2008 – 11/2009 chairman of the Seminar Group for the Max Planck PhDnet

11/2007 – 11/2010 member of the Seminar Group for the Max Planck PhDnet

09/2007 – 09/2009 PhD student representative of the Max Planck Institute for Ornithology

06/2005 – 12/2006 lab assistant of Prof. H.-U. Schnitzler and Dr. B. Siemers (Eberhard- Karls-University Tuebingen, Germany)

11/2003 – 10/2004 honorary research assistant with Assoc. Prof. Dr. S. Parson and Prof. Dr. D. Brunton (University of Auckland, New Zealand)

08/2001 – 07/2002 lab assistant of Prof. Dr. M. Kiebler (Max-Planck-Institute for Developmental Biology, Tuebingen, Germany)

03/2000 – 04/2000 trainee at the Bavarian Environmental Protection Agency

PUBLIC OUTREACH & MEDIA

• reports on my work in NY Times, LA Times, USA Today, Telegraph India, Zeit, Welt, Süddeutsche Zeitung, Spiegel Online, GEO, Highlights for Children, Science Illustrated, National Geographic, Scientific American, local newspapers and several internet websites • radio interviews for America In The Morning, BR, NDR, Antenne Bayern, Deutschlandfunk • material for TV science productions: Galileo; Quarks & Co • podcast interviews for Science Update, Deutsche Welle • specialist opinion for New Scientist • 2014 Nat Commun paper featured in Nature Highlights and ScienceNow • 2012 Curr Biol paper featured in Nature Highlights and ScienceNow • 2010 Nat Commun paper featured in Nature and Nature Video Channel (viewed ~500.000 times so far), and chosen as Faculty 1000 paper • 2008 JEB paper featured in ‘Inside JEB’ • evening talk for the Deutsche Gesellschaft für Ultraschall in der Medizin (DEGUM) • pictures used for several print and online articles, exhibitions and book “Les Chauves-souris de France, Belgique, Luxembourg et Suisse”

134

LIST OF PUBLICATIONS

2015 • Greif S, Zsebok S, Schmieder D & Siemers BM: Acoustic mirrors as evolutionary traps for bats (under review)

• Greif S, Korine C & Siemers BM: Acoustic mirages in the desert: testing spatial memory and echo cues in bats (under review)

• Dominoni DM, Greif S, Nemeth E & Brumm H: Airport noise affects

the timing of dawn song of European birds? (under review)

• Voigt CC, Lehmann D & Greif S: Stable isotope ratios of hydrogen separate mammals of aquatic and terrestrial food webs (under review)

2014 • Greif S, Borissov I, Yovel Y & Holland R: A functional role of the sky’s polarization pattern in orientation in the greater mouse-eared bat. Nature Communications, 5:4488, doi 10.1038/ncomms5488

• Krüger F, Clare EL, Greif S, Siemers BM, Symondson WOC & Sommer R: An integrative approach to detect subtle trophic niche differentiation in the sympatric trawling bat species Myotis dasycneme and Myotis daubentonii. Molecular Ecology, 23: 3657-3671

2012 • Siemers BM, Kriner E, Kaipf I, Simon M & Greif S: Caught in the act: Bats eavesdrop on the sound of copulating flies. Current Biology, Vol 22, No 14: R563-R564

2011 • Siemers BM, Greif S, Borissov I, Voigt-Heucke S & Voigt CC: Divergent trophic levels in two cryptic sibling bat species. Oecologia, 166: 69-78

2010 • Greif S & Siemers BM: Innate recognition of water bodies in echolocating bats. Nature Communications, 1:107 doi:10.1038/ncomms1110

• Voigt CC, Schuller M, Greif S & Siemers BM: Perch-hunting in insectivorous Rhinolophus bats is related to the high energy costs of manoeuvring in flight. Journal of Comparative Physiology B, 180: 1079-1088

2008 • Goerlitz HR, Greif S & Siemers BM: Cues for acoustic detection of prey: insect rustling sounds and the influence of walking substrate. Journal of Experimental Biology, 211: 2799-2806

2007 • Guilbert JM, Walker MM, Greif S & Parsons S: Evidence of homing following translocation of long-tailed bats (Chalinolobus tuberculatus) at Grand Canyon Cave, New Zealand. New Zealand Journal of Zoology, 34: 239-246

135