EFFECTS OF INTERSPECIFIC INTERACTIONS ON INDIVIDUAL SPECIALISATION

Marine Raphaële Amélie Richarson

Department of Zoology

University of Otago

A thesis submitted for the degree of

Doctor of Philosophy

December 2019

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“‘Did you know there are certain deep-sea fish that create their own light and feed on lethal volcanic gasses? That's supposed to be impossible. And bats in Australia as large as dogs. And that American coyotes mate for life. And enormous octopi that can squeeze their bodies through holes no larger than a ha'penny coin. Can you imagine that? If only we would stop and look and wonder. And wonder. ‘Do you have a favourite? ‘Not meant to. But mostly the unloved ones. The unvisited ones. The cases that get dusty and ignored. All the broken and shunned creatures. Someone's got to care for them. Who shall it be if not us?”

John Logan – The Day Tennyson Died (2016) | ii

Abstract

In many species, some individuals are specialist users of a subset of the total resources available to their population, a phenomenon known as individual specialisation. This intraspecific variation has long been suspected of shaping population dynamics and has potentially large effects at the ecosystem level. Variation in the degree of individual specialisation can be driven by ecological interactions.

The common bully Gobiomorphus cotidianus, a New Zealand native fish, displays a generalised diet at the population level, with moderate levels of individual specialisation. Juvenile perch Perca fluviatilis compete for the same food resources, while larger perch are potential predators. These antagonistic biological interactions may have an impact on the trophic niche and the degree of individual specialisation in bullies.

I used surveys of fish communities and cross-sectional diet data to assess individual specialisation in wetland bully populations in relation to ecological factors. Then, in two series of controlled manipulative experiments, I asked how individual specialisation in bullies responded to intraspecific competition, interspecific competition and predation risk from perch. I hypothesised that ecological niches at the population and individual levels would vary according to the composition of the fish community. Finally, I led a collaborative comparative phylogenetic analysis of the literature which compiled indices of individual specialisation in diet across ecosystems and taxa and tested whether individual specialisation was predicted by cumulative measures of human impacts. I anticipated that due to global resource and habitat disruptions, human impacts on individual specialisation would generally be negative.

Consistent with the niche variation hypothesis, I found that intraspecific competition led to overall higher intraspecific diet diversity, while interspecific competition led to lower degrees of individual specialisation. Predation risk had | iii a positive effect on dietary, and a negative effect on habitat individual specialisation. However, the direction of these effects changed when interspecific competition occurred along predation risk. At the population level, I recorded niche shifts due to interspecific competition both in habitat use and in diet.

Finally, I found negative effects of human activities on individual specialisation in terrestrial and freshwater ecosystems. There was however no evidence of a similar negative relationship in marine ecosystems. This discrepancy in results might be linked to differences in the types of drivers that impact individual specialisation between each ecosystem type.

This work underscores the importance of integrating individual variation when evaluating a population’s response to antagonistic biological interactions.

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Preface & Acknowledgements

Individuals matter.

This had long been my personal philosophy towards human beings in general. Little did I know that, at the term of four years focusing on the dietary choices of a little fish I had never even heard of, my views on individuality and its importance would encompass, well, every living thing.

Individuals matter, because without individuals, there is no community.

The flip side of that coin is that there is nothing special about being exceptional. Individuals matter, yes – but the wealth of their differences is nothing when taken in isolation. The value of exceptionalism only appears in the group; it becomes apparent only once individuals start interacting with their congeners, with other species, with their environment. There is no value in exceptionality when it is not expressed within a system where all exceptionalities get a chance to thrive.

It is a terribly humbling realisation – or a very reassuring one, depending on where you stand on individualism.

And the community shapes individuals. It shackles possibilities beyond body shape and size and abilities and character. That is a terribly depressing realisation – or a very reassuring one, depending on your views on freedom.

What I got from it all, however, was a state of quasi-permanent awe about life itself. If all individuals are exceptional, well then, every living being is a miracle in and of itself. Who would have thought that getting more intimate with the ecology of individuals would lead me to gaze upon the world and see little wonders everywhere?

*** | v

It is difficult to wrap up what a PhD is in a few words. It is a challenge, but only a challenge to yourself. It is world-changing, but the world being changed is your own. It is hard, but only on yourself, and some precious times it is exhilarating – but only to yourself. I did not think I had it in me to keep myself focused on the same topic for four years. And, granted, there were times when I just hated my fish, my bugs, the endless cleaning of the tanks or getting stuck knee-deep – again – in soft, smelly mud. Sometimes I hated it because it was too boring, sometimes I could not believe my luck doing something so exciting.

There were highs and lows, a lot of good people, a few great people. There were some struggles and a few victories. There were lessons for the moment, and some lessons for life. During this PhD, I got to understand a little more about the world, and a whole lot more about myself – about what I could handle, about what I could not cope with, about what made me feel whole. It came together because many good people helped me along the way.

This project benefitted from the trust and generosity of the New Zealand Te Apārangi through the Marsden fund. Their financial support allowed me to contribute in my humble way to mankind’s ever-growing body of knowledge instead of benefitting from it, for once, and to travel and share my findings with a community of scientists I admire within and beyond New Zealand’s shores.

My thanks to the University of Otago and to the Department of Zoology for their logistic and financial support. I feel incredibly privileged to have been welcomed in this beautiful institution. Thank you to my advisory committee, Marc Schallenberg and Robert Poulin, for their support and feedback along the way. I’m also thankful for the many little hands that helped me get through field and lab work: the staff, Nicky McHugh, Kim Garrett, Ken Miller and Nat Lim; and the small army of interns and friends: Yvonne Kalhert, Alex Connolly, Pauline Guilloux, Stef Bennington, Rose-Marie Payet, Gregoire Saboret, Nicky Kerr, Lucian Funnell, Jules Travert, Ludovic Vincent, Raul Costa Pereira, Andrew Veale, and others I might forget… Many thanks to Glen Riley and Te | vi

Nohoaka o Tukiauau/Sinclair Wetlands Trust for opening their doors to my helpers and me.

Jared and Jane, thanks for your support in my side artistic endeavours; your encouragements meant a lot to me. To the Hot Yoga Dunedin teachers: thank you for keeping me (somewhat) sane in the last stretch (pun intended) of writing.

Katherine, Jaz, Matt, Scott, Sven, Lisa – thank you for the chats, the coffees, the support, the advice, the moments of fun.

Leida, Pasha, Jenny, Ash, Fasil, you were my little family here. I am missing those of you who aren’t around anymore, and I’m loving those of you who are.

To Travis, my patient, supportive, awesome supervisor; you welcomed me in your lab and in your home, and you were the best mentor any student could wish for.

Merluches, mes chéries, vous me manquez tous les jours. Ici j’essaie de me rendre heureuse, mais sans votre compagnie c’est un peu plus dur. Rose, je te promets que lors de ton prochain séjour, tu n'auras pas à photographier des poissons !

Et vous, ma famille, amours de ma vie - vous ne comprenez pas toujours pourquoi je fais ce que je fais, mais vous me donnez la liberté de le faire. Cela n’a pas de prix.

Now, let’s talk about fishes. | v i i

Table of Contents

Abstract ...... ii

Preface & Acknowledgements ...... iv

Table of Contents ...... vii

List of tables and figures ...... x

1 GENERAL INTRODUCTION ...... 1

Thesis background ...... 2

Biological models ...... 6

Thesis structure ...... 8

2 DIETARY NICHE VARIATION IN THE COMMON BULLY IN A NATURAL WETLAND

COMPLEX ...... 10

Chapter summary ...... 11

Introduction ...... 12

Methods ...... 17

Characterisation of fish communities ...... 17

Diet and isotope analysis ...... 20

Statistical analyses ...... 22

Results ...... 24

Fish communities ...... 24

Isotopic niches ...... 26 | v i i i

Population dietary niche and individual specialisation ...... 28

Discussion ...... 31

3 EFFECTS OF INTRA- AND INTER-SPECIFIC COMPETITION ON INDIVIDUAL

SPECIALISATION: A MESOCOSM STUDY ...... 36

Chapter summary ...... 37

Introduction ...... 38

Materials and methods ...... 44

Experimental setup ...... 44

Individual diet characterisation ...... 46

Statistical analyses ...... 48

Results ...... 50

Prey diversity at the population level and diet overlap between species ...... 50

Degree of individual specialisation ...... 52

Discussion ...... 55

4 EFFECTS OF INTRAGUILD PREDATION ON INDIVIDUAL HABITAT AND DIETARY NICHES

IN THE COMMON BULLY GOBIOMORPHUS COTIDIANUS ...... 59

Chapter summary ...... 60

Introduction ...... 61

Effects of competition on individual specialisation ...... 62

Effects of predation on individual specialisation ...... 63

Methods ...... 67 | ix

Experimental setup ...... 67

Diet monitoring ...... 71

Habitat use ...... 74

Behavioural profile of individual bullies ...... 76

Results ...... 78

Population prey choice and dietary overlap ...... 78

Habitat use ...... 82

Activity ...... 85

Discussion ...... 86

5 EFFECTS OF HUMAN ACTIVITY ON DIETARY INDIVIDUAL SPECIALISATION ACROSS

ANIMAL TAXA ...... 95

Chapter summary ...... 96

Introduction ...... 97

Methods ...... 101

Literature search and study selection ...... 101

Indices of individual specialisation ...... 104

Estimation of phylogenetic tree ...... 105

Human Footprint Index ...... 106

Statistical analysis ...... 108

Results ...... 109

Discussion ...... 112 | x

6 GENERAL DISCUSSION: EFFECTS OF ECOLOGICAL INTERACTIONS ON INTRASPECIFIC

NICHE VARIATION ...... 118

Conclusion ...... 126

References ...... 128

2 Appendix 1. Supplementary tables and figures for Chapter 2 ...... 156

3 Appendix 2. Supplementary tables and figures for Chapter 3 ...... 158

4 Appendix 3. Supplementary tables and figures for Chapter 4 ...... 161

5 Appendix 4. Supplementary material for Chapter 5 ...... 161

HFI data extraction: additional information ...... 161

List of tables and figures

Table 2-1. Fish abundances estimated from Multigear Mean Standardisation for ponds in the Waipori-Waihola wetlands for the years 2016 and 2017 ...... 25

Table 2-2. Model selection on generalised linear models of standard ellipse areas SEAc...... 27

Table 2-3. Results of Analysis of deviance on linear mixed-effect models of effects of size and sex on 15N and 13C values in individual bully muscles...... 28

Table 2-4. Model selection on generalised linear models on the individual specialisation index WIC/TNW...... 30

Table 2-5. Model selection on generalised linear models on the average pairwise dissimilarity E...... 31

Table 4-1. Experimental treatments applied in the mesocosms ...... 71 | xi

Table 4-2. Ethogram for categorising behavioural activity of bullies and perch during instantaneous surveys ...... 75

Table 4-3. Detail of recorded variables for individual fish (bullies and small perch) during instantaneous surveys...... 76

Table 4-4. Summary table of the effects on competition, predation risk and their combination on population- and individual-level resource use and activity ..... 90

Table 5-1. Taxonomic makeup of studies used in the meta-analysis ...... 102

Table 5-2. Response and predictor variables used in the fitted models...... 107

Table S2-1. Characteristics of the ponds sampled in the Waipori-Waihola wetlands over 2016 and 2017 ...... 156

Table S2-2.Characteristics of fyke nets used during fish sampling of the Waipori-Waihola wetlands ...... 156

Table S2-3. Area of the 95% standard ellipses (SEAc) of the isotopic signatures of common bullies captured in 2016 and 2017 in the Waipori-Waihola wetlands...... 157

Table S2-4. Count of identified prey items found in bully stomach contents from 7 ponds of the Waipori-Waihola wetlands in 2016 and 2017 ...... 157

Table S2-5. Indices of individual specialisation in diet of common bullies from the Waipori-Waihola wetlands ...... 157

Table S3-1. Prey categories, codes and regression parameters used in the estimation of dry biomass ...... 159

Table S4-1. Summary of prey items found in bully and perch stomachs ...... 161

Table S5-1. Estimates from phylogenetic mixed-effects meta-analysis for the relationship between HFI and indices of individual specialisation ...... 162 | x i i

Table S5-2. Summary table of individual specialisation dataset for the comparative analysis ...... 163

Figure 2-1. Satellite photo of the study area in the Waipori-Waihola wetlands. 19

Figure 2-2. Isotopic signatures of common bullies captured in 2016 and 2017 in the Waipori-Waihola wetlands...... 26

Figure 2-3. 95% standard ellipses areas (SEAc) for carbon and nitrogen isotopic signatures of common bully populations predicted by the linear distance of ponds to Lake Waihola...... 27

Figure 2-4. Non-metric multidimensional scaling of prey types found in common bullies of the Waipori-Waihola wetlands...... 29

Figure 2-5. Index of individual specialisation WIC/TNW (left) and average pairwise dissimilarity E (right) in common bully populations predicted by the density of Galaxias spp...... 30

Figure 3-1. Illustration of how competition might affect niche width and individual specialisation in a population of consumers...... 41

Figure 3-2. Visible Implant Elastomer tags on a common bully (left) and array of tag locations used for bully identification (right)...... 45

Figure 3-3. Frequency of prey taxa observed in fish stomach contents ...... 51

Figure 3-4. Proportion of estimated dry weights of prey taxa in fish stomach contents ...... 51

Figure 3-5. Difference between treatments in the captured prey community using non-metric multidimensional scaling, based on Bray-Curtis dissimilarity...... 52 | x i i i

Figure 3-6. WIC/TNW and E index values obtained for each competition treatment...... 53

Figure 3-7. Niche components BIC and WIC plotted against the total niche width TNW on prey count data (top row) and estimated biomass (bottom row)...... 54

Figure 4-1. Photographs of the experimental tank setup for the intraguild predation experiment (22/11/2017) (Right) and individually marked bullies (02/03/2018) (Left) ...... 68

Figure 4-2. Type and proportion of prey items consumed by bullies in each treatment and by small perch over all treatments...... 78

Figure 4-3. Difference between treatments in the captured prey community using non-metric multidimensional scaling, based on Bray-Curtis dissimilarity...... 79

Figure 4-4. Total niche width (a), index of individual specialisation WIC/TNW (b) and average pairwise dissimilarity E (c) in bully diets across interspecific competition and predation risk treatments...... 80

Figure 4-5. Effects of competition and predation risk on individual dietary niche components WIC (a) and BIC (b) relative to total niche width TNW...... 81

Figure 4-6. Type and proportion of habitat types used by bullies in each treatment...... 82

Figure 4-7. Difference between treatments in habitat choice using a non-metric multidimensional scaling analysis, based on Bray-Curtis dissimilarity...... 83

Figure 4-8. Effects of competition and predation risk on total habitat niche (a), index of individual habitat specialisation WIC/TNW (b) and average pairwise dissimilarity E (c)...... 84

Figure 4-9. Effects of competition and predation risk on individual niche components WIC (a) and BIC (b) relative to total niche width TNW...... 85 | x i v

Figure 4-10. Effects of competition and predation risk on the average activity profile within tanks ...... 86

Figure 5-1. Examples of potential impact pathways of human activities on the degree of individual specialisation in a single focal species...... 99

Figure 5-2. Site locations of individual populations in the meta-analysis dataset...... 103

Figure 5-3. Extracted values of indices of individual specialisation compared to terrestrial HFI. Left: PSi. Right: WIC/TNW ...... 109

Figure 5-4. Extracted values of indices of individual specialisation in marine ecosystems. Left: PSi. Right: WIC/TNW ...... 110

Figure 5-5. Forest plot of mean effect size with 95% credibility intervals of Human Footprint Index HFI, ecosystem type (terrestrial or aquatic), study design and data types estimated with linear mixed models...... 111

Figure 6-1. Index of Individual Specialisation WIC/TNW (top panel) and average pairwise dissimilarity E (bottom panel) in bully diets across all data chapters...... 123

Figure 6-2. Extracted values of indices of individual specialisation in freshwater ecosystems and comparison with the range of values obtained in bully populations of the Waipori-Waihola wetlands...... 126

Figure S2-1. 95% standard ellipse areas (SEAc) for carbon and nitrogen isotopic signatures of common bully populations plotted against distance to Lake Waihola (right) and pond surface (left)...... 158

Figure S2-2. Model fits of nitrogen isotopic signatures predicted by bully sex (left) and size (right)...... 158

Figure S3-1. Types of measurements made on zooplanktonic crustaceans ...... 160 | xv

Figure S5-1. Topology of the phylogenetic tree used for Bayesian phylogenetic mixed-effects meta-analysis for PSi, terrestrial HFI ...... 164

Figure S5-2. Topology of the phylogenetic tree used for Bayesian phylogenetic mixed-effects meta-analysis for WIC/TNW, terrestrial HFI ...... 165

Figure S5-3. Topology of the phylogenetic tree used for Bayesian phylogenetic mixed-effects meta-analysis for PSi, marine HFI ...... 166

Figure S5-4. Topology of the phylogenetic tree used for Bayesian phylogenetic mixed-effects meta-analysis for WIC/TNW, marine HFI ...... 167

Figure S5-5. Flow chart of study selection for the meta-analysis ...... 168

1. 1 General Introduction

General introduction | 2

Thesis background

Specialisation in resource use is a widespread phenomenon across taxonomic scales, from clades of specialist species to specialised individuals within populations (Bolnick et al., 2003; Futuyma and Moreno, 1988). Within species, naturalists have long acknowledged that individuals often differ in the way they occupy and interact with their environment. Despite this awareness, conspecifics were traditionally treated as ecologically equivalent, and the appreciation of intraspecific variation remained muted until Van Valen’s observation that greater phenotypic variation within bird populations was associated with wider ecological niches (Van Valen, 1965). This work was the inception of the ‘niche variation hypothesis’, which postulates that niche expansion, in the absence of interspecific competition, is achieved by an increased variation in resource use between individuals. Later theoretical work by Roughgarden led to the conceptualisation of the niche as the variance of total resource use of all individuals in a population (Roughgarden, 1974, 1972). With mixed empirical support, the importance of between-individual niche variation soon lost popularity, as Roughgarden, later supported by theoretical work on the coevolution of character displacement (Taper and Chase, 1985), concluded that it likely was much smaller than within-individual variation (Roughgarden, 1974). The ecological specialisation of individuals was considered weak, rare, or of trivial importance on ecological processes. In the early 21st century, the concept received renewed interest, notably through a seminal review cataloguing the existing evidence of individual specialisation, and affirming its potential importance for ecological and evolutionary processes (Bolnick et al., 2003). The use by individuals of a subset of the range of resources available to their population as a whole, for reasons not attributable to their age class, sex or phenotype, was found to be widespread. Its ecological drivers, such as resource availability and biological interactions were later identified (Araújo et General introduction | 3 al., 2011; Poisot et al., 2011). The present work furthers the exploration of the effects of biological interactions on individual specialisation.

Whether they are mutualistic or antagonistic, biological interactions may influence the density, the distribution or the behavioural or functional responses of individuals. As such, they have the potential to influence the incidence of individual specialisation (IS) in a population. In turn, IS can mediate the direction and magnitude of their effects within populations and communities (Araújo et al., 2011; Bolnick et al., 2003; Pruitt et al., 2012a; Pruitt and Ferrari, 2011). This reciprocal relationship between ecological dynamics and the amount of among-individual variation involves a variety of resource types and responses, and potentially has broad consequences for population and ecosystem processes (Bolnick et al., 2003; Dall et al., 2012; Dingemanse and Wolf, 2013; Phillips et al., 2017; Pruitt et al., 2012b). Extensive theoretical, empirical and experimental work has been conducted in recent years on the causes and consequences of individual niche variation, across a wide range of taxa and for a variety of ecological and behavioural traits (Abbey-Lee et al., 2016; Beleznai et al., 2015; Belgrad and Griffen, 2016; Phillips et al., 2017; Xu et al., 2012; Zhao et al., 2014). However, less attention has been given to the mechanisms by which interspecific interactions shape individual specialisation, even less so in complex environments where multiple interspecific interactions occur simultaneously (Poisot et al., 2011, but see Baines et al., 2014; Evangelista et al., 2014; Kernaléguen et al., 2015; Kortet et al., 2015, 2010). Moreover, little is known as to which ecological and evolutionary factors are the most important drivers of individual specialisation within populations (Maldonado et al., 2017).

Several approaches have been used over the years to characterise individual specialisation; common indices include measures of the degree to which a population’s niche is partitioned between and within individuals, quantification of the mean resource overlap between an individual and its population, and measures of the mean overlap between pairs of individuals (Bolnick et al., 2002). For example, the resource use at a population level, or total niche width General introduction | 4

(TNW), can be partitioned into two additive components: the average breadth of resources used by each individual (within-individual component WIC) and the variation among individuals in average resource use (between-individual component, BIC) (Bolnick et al., 2002; Roughgarden, 1972). These components are often used to determine the degree of individual specialisation in a population, defined in this case as the ratio between WIC and TNW (Bolnick et al., 2002). Following this definition, individual specialisation may increase through a contraction (narrow WIC) or an increased segregation (higher BIC) of individual niches. Therefore, mechanisms affecting one or both components also affect the degree of individual specialisation in the population. Such mechanisms can be linked to inherent characteristics of individuals themselves, such as morphology (Kern et al., 2016), physiology (Mazumder et al., 2016), cognition (van der Bijl et al., 2015; Warburton and Thomson, 2006), personality (Ólafsdóttir and Magellan, 2016) or a combination of these traits (Wey et al., 2015). On the other hand, resource diversity and availability as well as biological interactions, either mutualistic or antagonistic, constitute extrinsic drivers of individual specialisation that can influence niche sizes at the individual and population levels (Araújo et al., 2011; Britton and Andreou, 2016), as individuals respond to social and environmental cues and adjust their foraging strategy or use of space accordingly.

While there is some empirical evidence supporting these hypothesised effects of interspecific interactions, few manipulative studies have sought to disentangle the influence of complex, concurring abiotic and biotic environmental landscapes on individual specialisation (Poisot et al., 2011). Consequently, I focused my doctoral research on the effects of different types and combinations of antagonistic biological interactions on the degree of ecological individual specialisation. First, I used field surveys to ask whether there was measurable variation in the degree of individual specialisation among natural populations of the New Zealand-native common bully Gobiomorphus cotidianus (McDowall, 1975), and whether that variation could be attributed to ecological variables General introduction | 5 such as community structure and composition. Second, I conducted two controlled experiments to ask how individual specialisation in bullies responded to intraspecific competition and to interspecific competition and predation risk from Eurasian perch Perca fluviatilis (Linnaeus, 1758). I hypothesised that bully populations would display different ecological niches according to the presence of intra- and interspecific competitors or predators. I predicted that increased intraspecific competition would be associated with larger niches and increased inter-individual variation – i.e. greater individual specialisation. On the other hand, I predicted that competition from juvenile perch would lead to niche shifts and contraction. While the presence of a potential predator might induce habitat shifts that would reduce bully vulnerability to predation, its combined effects with competition might result in either a decrease or an increase in individual specialisation, depending on the niche overlap between species and the expression of their behavioural strategies. For instance, increasing competitive pressure might induce a contraction of the bully's niche (making the population’s niche narrower), or increase variation between individuals while triggering the emergence of narrower individual niches. Finally, I expanded this question of the effects of antagonistic interactions on individual specialisation by asking whether human activities could also affect intraspecific variation. Human activities can affect species distribution, abundances and behavioural responses, and overall are major disruptors of biodiversity and ecosystems dynamics and function (Di Marco et al., 2018; Doney, 2010; Jacobson et al., 2019; McKinney, 2006; Wagler, 2018; Watson et al., 2016). For a given species, they might alter the diversity, density and distribution of their competitors, predators and prey, which all can potentially affect individual and population niches. I led a collaborative comparative phylogenetic analysis of the literature to investigate this question. We compiled indices of individual specialisation in diet across ecosystems and taxa and tested whether individual specialisation was predicted by integrated measures of human impacts (Human Footprint Index, HFI). Strong human impacts are often associated with losses in biodiversity and habitat complexity (Jacobson et al., 2019; Watson et al., 2016), General introduction | 6 so we predicted an overall reduction in individual specialisation due to general niche contractions or increases in overlap between individuals.

Biological models

My empirical research focused on one primary study organism: the common bully G. cotidianus. I also used juvenile and subadult Eurasian perch P. fluviatilis as a heterospecific intraguild predator of the species.

The common bully is a small benthic fish, endemic to New Zealand. It is widely distributed throughout the main landmasses and the adjacent islands: North and South Islands, Stewart Island and near offshore islands (McDowall, 2010). It is found in a wide range of freshwater and brackish habitats, in wetland ponds, rivers and lakes. It matures at one year and lives up to 4 to 5 years. Lake populations spawn in spring and summer, while river populations spawn in late winter. The males display territorial behaviour during the breeding season. They establish nests under cobbles or rocks, onto which the females deposit several hundreds to a few thousand adhesive eggs. Males will guard and defend the nest site until egg hatching. The species is diadromous, although this trait appears facultative as some populations are landlocked (McDowall, 2010). Populations of the Waipori-Waihola wetlands have both migratory and non- migratory individuals (Closs et al., 2003; Saboret and Ingram, 2019). In turbid lakes, bullies feed on benthic invertebrates, in particular chironomid larvae (Rowe et al., 2001); zooplankton also make up a significant part of their diet. The species naturally cooccurs with galaxiids (McDowall, 1990). It is preyed upon by birds and larger fish, especially introduced species such as trout (Rowe et al., 2001) and perch (Ludgate and Closs, 2003). It is an intermediate host to a variety of helminth parasites, with trematodes such as Apatemon spp. which can infest individuals by the hundreds (Hammond-Tooke et al., 2012) and make for a significant part of the body mass of individual fish (Lagrue and Poulin, 2015). General introduction | 7

The Eurasian perch P. fluviatilis is a predatory species, native to Europe and northern Asia. It was introduced to New Zealand as a game fish in 1868 (Thomson, 1922). It is currently widely distributed throughout the country where it mostly dwells in slow-flowing rivers, deep lakes and ponds in both the North and the South islands. The species is not widely fished, despite its usual popularity for recreational fishing elsewhere, because of the small average size of fish due to the high-density of its populations in New Zealand (Jellyman, 1980). Perch undergo two well-documented ontogenetic dietary shifts: juvenile perch feed on zooplankton, while medium-sized individuals target macro- invertebrates and adults prey on fish (Svanbäck et al., 2015). There is some variation within cohorts in the timing of that dietary shift, which can in turn be associated with individual specialisation in foraging preferences. In lakes, some individuals feed on littoral while others target pelagic prey types (Svanbäck et al., 2015). Another consequence of this size-dependent diet shift lies in the impacts of the species outside its native range. In New Zealand, impacts on native fish communities include competition with small and medium-sized fishes, and predation from large adults (Ludgate and Closs, 2003). Behavioural changes have notably been recorded in common bullies exposed to perch chemical cues (Kristensen and Closs, 2004). Following a manipulative experiment of competitive and predatory interactions between perch of various sizes and bullies in pond enclosures, Goldsmith (2004) concluded that both species could coexist, provided the presence of suitable habitat in which the bullies can hide (Goldsmith, 2004). However, the influence and potential impacts of the species on niche variation in bullies, especially linked to ontogenetic shifts, have yet to be investigated.

General introduction | 8

Thesis structure

Four complementary data chapters explore distinct combinations of antagonistic interactions on individual ecological specialisation. The first three data chapters focus on common bully populations, while the fourth is a comparative review of the literature of the effects of human activities on individual specialisation (IS) across taxa.

The first data chapter presents a study of individual dietary and isotopic variation in a natural complex of ponds differing in their environmental and biological characteristics. In this chapter, I investigate the variation in dietary niche in common bullies in a wetland complex. I explore food resource use in individual common bullies by analysing their stomach contents and use these data to estimate the degree of individual dietary specialisation within pond populations. I use stable isotope analysis of carbon and nitrogen to characterise the size of the isotopic niche of pond populations. This study shows how these bully populations vary in dietary IS and isotopic niche. While a few pond characteristics such as pond size, proximity to a lake and abundance of galaxiids explain some of this variation, identifying the effects of inter- and intraspecific interactions within the communities on individual specialisation requires controlled experiments.

The second and third data chapters focus on experimental pond mesocosm studies of bully and perch populations. The former investigates the effects of intra- and interspecific competition on the dietary niche of the common bully. The latter considers the combined effects of interspecific competition and predation risk from perch on individual variation in bully habitat use, diet and activity level. In both experiments, I individually monitor marked bullies subjected to various degrees of competition and predation risk from conspecifics and from perch to estimate the level of individual specialisation in each population. Both studies indicate that interspecific interactions with perch General introduction | 9 induce changes in niche partitioning of bully populations. The direction of these effects depends on the type of antagonistic interaction facing the population.

The last data chapter uses a comparative phylogenetic analysis of peer-reviewed literature to assess the effects of anthropogenic impacts on dietary IS across taxa. In this collaborative chapter, my co-authors and I use the Human Footprint Index (HFI) as a measure of human impact on terrestrial and marine ecosystems. We show that terrestrial anthropogenic footprint, estimated as the mean HFI centred around single populations with IS data, has a significant negative effect on IS, in both terrestrial and freshwater food webs. However, we find no similar evidence in marine ecosystems.

Each chapter was written as a stand-alone manuscript, leading to some conceptual overlap between chapters.

Author contribution statement Chapter 5 - Effects of human activity on dietary individual specialisation across animal taxa (in prep.) is a collaborative chapter that benefitted from the contribution of multiple co-authors. Main contributors: Marine Richarson, Travis Ingram. Collaborators: Raul Costa-Pereira, Christina N. Service, Tyler D. Jessen, Hilary Thomson, Chris T. Darimont, Márcio S. Araújo and Priscilla M. Wehi. MR wrote and edited the manuscript. MR, RCP and CNS developed the initial idea for the study. MR, RCP, HT and TI compiled and reviewed the databases. TDJ performed the GIS data extraction and provided the technical details in Appendix 4. MR and TI analysed the data. All authors provided critical feedback at various stages of the project.

2. 2 Dietary Niche Variation in the Common Bully in a Natural Wetland Complex

Aerial view of the Waipori-Waihola wetlands, August 2018 Chapter 2. Natural variation in Common Bully diet | 11

Chapter summary

Ecological niche variation within populations is ubiquitous; in many species, some individuals are specialist users of a subset of the total resources available to their population. Among other factors, this interindividual variation can be influenced by interactions with other species. The common bully Gobiomorphus cotidianus is a small, widespread New Zealand native fish which displays moderate levels of individual dietary specialisation on its pool of benthic and pelagic invertebrate prey. In the present chapter, I investigate the variation in dietary niche in common bullies in a natural wetland complex, using stomach content and stable isotope analysis. I sought to identify predictors of the population niche size and the degree of individual specialisation across populations. I conducted fish surveys in 22 ponds over two years, to characterise their aquatic communities. I used stable isotope analysis of carbon and nitrogen to characterise the isotopic niche of common bully populations in each pond. I also explored food resource use and estimated the degree of individual dietary specialisation through stomach content analysis. I used linear mixed models to test which ecological factors, such as habitat and community characteristics, best explained the extent and shape of the observed niche variation. Results indicated a high variability between ponds in both the size and position of the isotopic niche, as well as in the degree of individual diet specialisation of bully populations. The density in potential interspecific competitors explained some of the variation in individual specialisation, while other predictors had no significant effects. While the studied populations were geographically close, small sample sizes associated with high variability in ecological factors between ponds made the identification of patterns of niche variation difficult. This study highlights the benefit of using controlled experiments to explore the effects of inter- and intraspecific interactions on individual specialisation.

Chapter 2. Natural variation in Common Bully diet | 12

Introduction

Traditional models of the functioning and dynamics of ecological communities often assume that resource use and environmental interactions within a species are performed by ecologically - or behaviourally - interchangeable individuals. This approach, though convenient, overlooks the importance of intraspecific variation at the population and community levels (Bolnick et al., 2011, 2003; Pruitt and Ferrari, 2011; Violle et al., 2012). Beyond the loss of information that can arise from averaging species data (Ings et al., 2009), naturalists have long been aware of the potential consequences of phenotypic variation on the ecological attributes of individuals: the range of ways in which conspecifics occupy and interact with their environment fundamentally stems from the variation in their morphological, physiological, phenological, cognitive or behavioural features. In their synthesis on individual behavioural differences, Dall and colleagues identify three angles to examining individuality in animals: the division of labour and castes in social insect colonies, behavioural traits which are at the centre of research on behavioural syndromes and animal personality, and the study of individual niche specialisation which focuses on variation in ecological resource use (Dall et al., 2012). Over the past twenty years and for a large range of taxa, these approaches have uncovered evidence of consistent and varied strategies deployed by conspecific individuals in response to spatial and temporal heterogeneity in their environment (Bolnick et al., 2003; Futuyma and Moreno, 1988; Lichtenstein et al., 2017; Toscano et al., 2016a).

The potentially large consequences of intraspecific variation on ecological and evolutionary processes underscore the importance of understanding its drivers (Bolnick et al., 2011; Dall et al., 2012; Ings et al., 2009; Phillips et al., 2017). For instance, the exploitation of different resources by different individuals might drive divergent selection on ecologically relevant traits, which can in some cases be a precursor to ecological speciation (Schluter, 2001; Turelli et al., 2001; Via, 2001). Further, there is also a growing body of theoretical and Chapter 2. Natural variation in Common Bully diet | 13 empirical literature that shows how variation among conspecifics can affect biological interactions and species coexistence (Allgeier et al., 2017; Bolnick et al., 2011; Costa-Pereira et al., 2018; Hausch et al., 2018; Start and Gilbert, 2017; Violle et al., 2012). Individuals, after all, do not exist in a vacuum: the combination of individual decisions has population- and community-level outcomes. This emphasises the potential for individual-level trait variation to have effects beyond the scale of individuals. For example, the effects of personality on an aquatic food web were highlighted in a recent mesocosm experiment, in which intraspecific variation in dragonfly nymphs Epitheca canis impacted absolute and relative abundances in zooplanktonic prey by reducing overall prey densities and selecting for predator-resistant species, while altering the strength of trophic cascades (Start and Gilbert, 2017). In fish assemblages, interindividual variation within a few species can be a key driver of the overall niche space occupied by the entire community (Allgeier et al., 2017). At the same time, this resource selection process at the individual level itself depends on community-level characteristics – such as the degree of competition and predation (Railsback and Harvey, 2013).

Individual variation can be altered by the abiotic and biotic environment, with biological interactions potentially playing a major role in shaping individual ecological niches. In a recent review, Araújo and colleagues (2011) explored the empirical evidence for four likely ecological drivers of interindividual differences in resource use: intraspecific competition, interspecific competition, ecological opportunity and predation, while the potential role of parasitism on this variation was more recently reviewed (Britton and Andreou, 2016). For example, intraspecific competition is mostly thought to promote diversifying effects within populations (Araújo et al., 2011; Svanbäck et al., 2011; Svanbäck and Bolnick, 2007; Svanbäck and Persson, 2009), although there are hints that such effects might actually be nonlinear (Mateus et al., 2016). Similarly, consumptive and non-consumptive effects of predation have the potential to either increase or decrease niche variation by inducing changes in prey Chapter 2. Natural variation in Common Bully diet | 14 densities and behaviours (Beleznai et al., 2015). While a large part of the literature focuses on the effects of antagonistic ecological interactions on individual niche variation, mutualisms and other positive interactions also have the potential to affect a population’s niche and the way it is partitioned (Bulleri et al., 2016). In ecosystems where biological interactions occur simultaneously, the degree of interindividual variation within a population likely results from a push and pull between constricting and diversifying effects (Poisot et al., 2011 but see Baines et al., 2014; Evangelista et al., 2014; Kernaléguen et al., 2015; Kortet et al., 2015, 2010). To add to that complexity, the direction and intensity of cumulative effects might differ from the sum of each effect taken separately (Baines et al., 2014; Belarde and Railsback, 2016).

Methodological approaches to explore the dietary niche

As underscored earlier, there are many ways in which interindividual variation may occur. While food web studies typically focus on species average values, individual-level responses, on the other hand, can further our understanding of ecological processes and consumer-resource relationships. For example, the existence of flexible foraging traits in a species potentially affects the strength of its ecological interactions (Abrams, 2010; Calcagno et al., 2011; Schmidt et al., 2012). Individuals may vary in the size, trophic position or habitat distribution of their prey, in effect partitioning their population’s trophic niche along one or several axes (Ingram et al., 2018; Nagelkerke and Rossberg, 2014). Individual specialists, as opposed to generalists, occupy a substantially narrower niche than their population as a whole (Bolnick et al., 2003, 2002). Within a population, this degree of individual specialisation (IS) varies along a gradient between obligate specialisation and full generalisation (Waser, 2006).

Repeated observations of feeding activities by individuals or gut content analysis, used in conjunction with analysis of population isotope variances, allow estimating the degree of IS in a population (Araújo et al., 2007; Bolnick et al., 2002). The measure of the isotopic niche typically provides an averaged proxy of the dietary niche of an individual, spanning a few days to several Chapter 2. Natural variation in Common Bully diet | 15 months depending on the turnover rate of the analysed tissue; as such it mostly ignores within-individual variation (Fry, 2006) and is less sensitive to variability in the data which might arise from shorter-term fluctuations in resource use captured by the direct examination of prey items – unless performed on multiple tissues or over repeated measures. The analysis of stable isotopes (typically of carbon and nitrogen) can provide considerable insight into many aspects of the life history, resource use or biotic interactions of organisms (Boecklen et al., 2011; Cucherousset et al., 2012, 2007; Fry, 2006; Jackson et al., 2012). Stable isotope analysis can be used as an integrative tool to explore trophic diversity and analyse trophic interactions, for instance between native and non-native species (Cucherousset et al., 2012, 2007; Jackson et al., 2012) or sympatric congeneric species (Guo et al., 2014). Stable isotope analysis can also help identify and monitor foraging patterns and movements of individuals (Carneiro et al., 2017; Estes et al., 2003), provide evidence of individual specialisation and in some cases measure it (Bond et al., 2016). There is a predictable relationship between the isotopic composition of a consumer and that of its prey; in other words, the diet and feeding behaviour of a consumer affect its isotopic signature (Busst and Britton, 2016). Stable isotopes therefore constitute an integrated record of a consumer’s diet, as such providing one representation of the trophic niche space occupied by individuals and populations (Layman et al., 2007a). Isotopic ratios of nitrogen are commonly used to estimate a consumer’s trophic position, as heavy nitrogen isotopes are enriched between consumers and diet (Post, 2002). On the other hand, δ13C values are fairly consistent through trophic transfer (France and Peters, 1997), but help distinguish carbon sources along resource gradients, for instance along the littoral-pelagic axis in aquatic food webs (Caut et al., 2009; France, 1995; Post, 2002).

Rationale for the study and hypotheses

Taking individual differences into account and assessing their importance at the population level can help understand the underlying mechanisms that control Chapter 2. Natural variation in Common Bully diet | 16 the structure of ecological networks (Ings et al., 2009) and be used to explain emergent properties of a population. It might also help assessing the possible responses of communities to biotic and abiotic changes in their environment, whether those changes occur over short – e.g. diel, seasonal – or long timeframes. Despite the growing appreciation of the importance of individual specialisation, however, studies comparing the degree of diet variation of populations of a species across natural ecological gradients remain relatively rare. In the present chapter, I set out to characterise the dietary niche and the extent of niche variation in the New Zealand-native common bully Gobiomorphus cotidianus. The purpose of this study was to evaluate the potential effects of abiotic factors and the presence of competing and predating species on bully dietary resource use. To do so, I characterised the diet of individual bullies from a natural wetland complex using non-lethal gastric lavage and stomach content analysis, in association with stable isotope analysis of bully muscle tissue. Using fish surveys, I investigated how fish communities might influence this niche variation through competition and predation. I tested three predictions motivated by the niche variation hypothesis. First, I predicted that the population niche size would be affected by pond abiotic and biotic characteristics: for instance, a higher density of interspecific competitors would have a constraining effect. Second, I predicted that the degree of individual specialisation would vary between pond populations, with higher degrees of specialisation in ponds with higher densities of bully competitors (i.e. high levels of intraspecific competition) and lower IS under high interspecific competition conditions. Finally, I predicted that predator densities would also affect IS, and that the direction of that effect would depend on the size of the effect of both types of competition.

Chapter 2. Natural variation in Common Bully diet | 17

Methods

Characterisation of fish communities

Study site

Fish sampling was conducted in the Waipori-Waihola wetlands, a low-altitude complex made of ponds of various sizes, river channels, man-made drains, swamps and islands located in the lower reaches of the Taieri River catchment, 40 km south-west of Dunedin, New Zealand (Figure 2-1, p.19). The wetlands spread over 2000 ha between two shallow, tidal lakes: Lake Waipori (220 ha) to the north-east, and Lake Waihola (640 ha) to the south-west. The area supports regionally significant whitebait (postlarval Galaxias spp.) and commercial eel (Anguilla dieffenbachii and A. australis) fisheries, as well as recreational fishing for introduced brown trout Salmo trutta and Eurasian perch Perca fluviatilis (Department of Conservation, 2015). The ponds themselves display various levels of connectivity to other water bodies through the network of natural and man-made channels. While some small ponds are completely isolated, much of the wetland is subjected to tidal influences and occasional flooding (Goldsmith, 2004). Although the biological communities from Lake Waihola have been quite extensively studied (e.g. Kattel and Closs, 2007; Lagrue et al., 2011; Schallenberg and Burns, 2003), there is little recent substantial data on the fish or the macro-invertebrate communities present in the ponds themselves (David, 2001).

Twenty-two ponds of varying sizes and degrees of connectivity were selected for the present study (Figure 2-1 p.19). While the bully populations across the wetlands are not genetically distinct (Ingram et al, unpub. data), the ponds were treated as independent replicates in the subsequent analyses, as they were sufficiently apart from each other that the assumption could be made of little to no movement of bullies between them over the time scale of the study. Variables representative of pond abiotic characteristics included surface area, presence/absence of open channels and direct distance to Lake Waihola. Ponds Chapter 2. Natural variation in Common Bully diet | 18 were categorised as connected (if they featured open channels connecting them to other water bodies in the wetland) or isolated. Pond surface area and linear distance to Lake Waihola were estimated from Google Maps® satellite photos (Table S2-1, p.156).

Fish capture

Depending on the pond size and accessibility, I used a combination of large- meshed and fine-meshed nets and traps, following NZ standardised protocols (Joy et al., 2013). Small fishes were trapped using 6.4-mm mesh minnow traps with 30-mm circular entrances. In large ponds accessible by boat, larger taxa such as eels, perch and brown trout were sampled using a combination of coarse- and fine-mesh fyke nets (Table S2-2, p.156).

Traps were randomly set within each pond, their number adjusted according to the size of the pond, from a couple of Gee minnow traps in very small ponds (a few square metres water surface), up to 6 fyke nets and 10 Gee minnow traps in the largest ponds. Depending on pond accessibility, traps were either set in the afternoon and left overnight or set early morning and retrieved late afternoon. Soak duration ranged from 4 to 26 hours and was recorded for each trap, as well as environmental variables including water depth, distance from shore, weather conditions, and substrate and vegetation type if visible. Sampling was initially supposed to occur seasonally for each pond. However, due to difficult weather conditions and no access to the more remote ponds in 2016, sampling was limited to summer and winter that year and completed over the first two weeks of November 2017. In total, the 22 ponds were sampled over 31 sampling sessions between January 2016 and November 2017, with 7 ponds sampled twice and one sampled 3 times (Table S2-1, p.156).

Chapter 2. Natural variation in Common Bully diet | 19

Figure 2-1. Satellite photo of the study area in the Waipori-Waihola wetlands. Fish trapping occurred in the ponds highlighted in blue. (Credits: Google Earth Pro ® 2018, ©Mapbox, ©OpenStreetMap) Chapter 2. Natural variation in Common Bully diet | 20

Captures were identified to the genus level for whitebait and eels, and to the species level for the other fishes. Captures were counted, while up to 30 individual bullies were measured to the nearest millimetre. Subsets of bullies were sampled for diet using gastric lavage or euthanised for dissection and stable isotope analysis (see below), and all other fish were released near the site of capture. Fish abundance was calculated for each pond as standardised catch per unit of effort (CPUE) using Multigear Mean Standardisation, a method which allows accounting for the use of different gears, as well as handling variation in sampling effort (Gibson-Reinemer et al., 2017). Briefly, in each pond, for each type of gear, the mean total CPUE was adjusted for soak time

(TCmean), which provided the average number of fishes captured per hour and per gear type. Then, for each taxon in each gear, the mean standardised catch (MSC) was calculated for each species as the number of captures divided by

TCmean. The standardised CPUE was the sum of the MSC values calculated for each gear in each pond. Correlations between the observed abundance and the pond surface area and connectivity were tested using 2-way analysis of variance.

Diet and isotope analysis

In Lake Waihola, bullies have been found to prey on a variety of pelagic and benthic invertebrates, including amphipods, copepods, snails, chironomids, Daphnia, mysids and isopods (Wilhelm et al., 2007). During the 2016 trapping season, stomach contents were retrieved in up to 30 common bullies per pond by using non-lethal gastric lavage (Kamler and Pope, 2001). This procedure consists of securely holding the fish with its head angled downward and inserting a thin, soft polyethylene tube through its mouth to the back of its stomach; 1 to 3 mL of filtered source water are pushed into the stomach with a syringe and wash back through the mouth into a receptacle along with the stomach contents. In the field, this whole process required less than a minute. The fish were housed in buckets filled with fresh pond water and allowed to recover for a few minutes after flushing. Stomach contents were stored on ice in Chapter 2. Natural variation in Common Bully diet | 21 the field and eventually preserved at -18°C until identification. In 2017, bullies captured for stomach content analysis and stable isotope analysis were euthanised by overdose in a 0.32 mL/L AQUI-S (iso-eugenol) solution; death was confirmed by an absence of any movement or reflexive response to sharp taps on the container. These fish were placed in an ice box until storage at - 18°C. They were later dissected, and the prey taxa present in their stomachs were counted and used for analysis. All fish trapping and handling performed in the course of this study complied with the Animal Welfare Act, under authorisation D85/15 form the Animal Ethics Committee of the University of Otago.

I identified prey items under a dissecting microscope to the lowest taxonomic level possible. The bully dietary niche within each pond was estimated using the absolute and relative abundance of each prey item in each individual stomach. Prey categories were delineated using a combination of taxonomic and functional considerations: molluscs comprised Potamopyrgus sp., Physidae and other unidentified bivalves and gastropods, each of which was individually rare. Conversely, some groups such as chironomids were further split into functional groups, i.e. benthic larvae, pelagic pupae and pelagic or terrestrial adults.

I used carbon and nitrogen stable isotope analysis to determine the diet variability among individuals and pond populations. Caudal muscle tissue of the fish was oven-dried at 60°C for 48 hrs; ca. 0.70 mg of dry matter was packaged in sealed tin cans for stable isotope analysis of carbon and nitrogen (C, N) (Fry, 2006; Layman et al., 2007b; Zhao et al., 2014). The analysis was conducted at the Iso-trace laboratory of the Department of Chemistry (University of Otago), using a Europa Scientific Hydra isotope ratio mass spectrometer interfaced to a Carlo Erba NC2500 elemental analyser. Isotopic values are reported in the standard delta notation relative to the PDB for carbon and to atmospheric air for nitrogen. C:N ratios for muscle tissues were overall higher than 3.5, therefore the δ13C values were mathematically corrected for lipid content using

ퟏퟑ the following normalisation equation for aquatic organisms: 휹 푪풏풐풓풎풂풍풊풔풆풅 = Chapter 2. Natural variation in Common Bully diet | 22

ퟏퟑ 휹 푪풖풏풕풓풆풂풕풆풅 − ퟑ. ퟑퟐ + ퟎ. ퟗퟗ × 푪: 푵 (Post et al., 2007). Internal replicates were performed on 23 individual samples to evaluate the precision of sample runs; the average standard deviation for paired samples was 0.5‰ for δ15N and 0.4‰ for δ13C.

Statistical analyses

All analyses were conducted using R 3.4.4. (R Core Team, 2018) in R Studio 1.1.383.

Predictor variables

Due to unbalanced data and the small sample sizes obtained regarding metrics of individual specialisation and isotopic data for standard ellipse areas corrected for small sample sizes (SEAc, 95% standard ellipses), characteristics of ponds and their fish communities were limited to a few representative predictor variables. Fish communities were characterised by the relative densities of eels (main potential predators of bullies by density), interspecific competitors (whitebait species or perch) and intraspecific competitors (common bullies), which were estimated using their respective standardised CPUE values. Characteristics of the bully population included sex and size of individual fish on isotope values, and average total length for isotopic niches and IS metrics.

Isotopic niche analysis

Nitrogen isotopes across ponds were normally distributed (Shapiro-Wilk test, W=0.99, P=0.08), but δ13C was not (W=0.95, P<0.001). Carbon signatures however were normally distributed within ponds, except for ponds “TQ” and “BP”, so no transformation was used for the analyses. δ13C and δ15N were tested separately (Guo et al., 2014). I used linear mixed models to evaluate the effects of the characteristics of sex and size (total length) of individual bullies on individual isotopic values, with pond within sampling season as a nested random effect in order to account for pond ID and seasonality. Chapter 2. Natural variation in Common Bully diet | 23

The size of the isotopic niche within each pond and sampling date was

15 13 quantified using SEAc in δ N-δ C space (Jackson et al., 2011, 2012). As sample size per pond varied between 1 and 36, only the ponds with at least 5 samples of individual bully muscle tissue were considered for this part of the analysis. Since pond “BP” was sampled twice, the samples were not all independent, so I used generalised linear models, with rank selection using AICc, using one predictor variable at a time representing either pond or fish populations characteristics. Due to the small sample size and to avoid overfitting data, I did not test for possible interactions between predictors. For all models, however, predictive variables were checked for collinearity. Model residuals for the best- fitted models (95% cumulative sum of weights) were visually checked for normality and dispersion using scatterplots and histograms.

Population dietary niche and individual specialisation

To visualise variation in diet among pond populations, I performed non-metric multidimensional scaling (NMDS) on the population average prey proportions, using the Bray-Curtis dissimilarity index. I used a stress plot to visually check the effectiveness of dimension reduction, as well as the stress value which I considered acceptable when close to 0.1. The effects of pond-level predictors were tested one after another using a permutational multivariate analysis of variance (PERMANOVA) with a Bray-Curtis dissimilarity using functions of the ‘vegan’ package, version 2.5-1 (Oksanen et al., 2018).

Prey data of individual bullies were used to calculate metrics of individual specialisation within each pond. I used the ‘RInSp’ package (Zaccarelli et al., 2013) to calculate the dietary niche components WIC, BIC and TNW within each pond and to estimate the WIC/TNW index of individual specialisation, which tends towards 0 when individuals are fully specialised and towards 1 when they all are full generalists on their population’s resources (Bolnick et al., 2002; Roughgarden, 1972). In addition, I estimated the average pairwise dissimilarity index E and used it to compare resource use between individuals (Bolnick et al., 2002). This index ranges from 0, when all individuals from a given population Chapter 2. Natural variation in Common Bully diet | 24 use the same resources in the same proportions, to 1, when single individuals rely on entirely different resources compared to other individuals. To avoid potential bias resulting from large number of prey items of a kind found in some stomach samples, I used the proportions for each prey type in individual stomach samples (Bolnick et al., 2002; Zaccarelli et al., 2013). A minimum of 17 bullies with stomach contents per pond and per capture session were used for these calculations.

I checked for collinearity between metrics of individual specialisation WIC/TNW and E. I also asked whether there was a linear relationship between the size of the dietary niche TNW and the isotopic niche SEAc. I used generalised linear models to identify which predictor variables among pond characteristics, density of predators and inter- and intraspecific competitors, best explained the observed variation in WIC/TNW and E, using the 95% cumulative sum of weights. Those effects were tested separately and ranked using AICc. Model residuals were visually checked for normality.

Results

Fish communities

Overall, captures included 3 native and 2 exotic fish taxa. Common bullies and galaxiids (inanga Galaxias maculatus and possibly kōaro G. brevipinnis juveniles and small adults) were the most abundant taxa. Large eels Anguilla spp. were also commonly captured in the larger ponds (while both species were present, shortfin and longfin eels A. australis and A. dieffenbachii, respectively, were not differentiated). Perch and brown trout were found in large ponds as well. Contrary to expectations, perch captures were scarce in the ponds, with only 4 juveniles captured in total in November 2017. It should be noted that sampling later in the month (late November) in neighbouring Lake Waihola showed that perch were abundant at both juvenile and subadult stages along the lake shores, with seining sessions allowing the capture of hundreds of individuals within a Chapter 2. Natural variation in Common Bully diet | 25 few passes. There was substantial variability in the capture rates between ponds, as well as some temporal variability within ponds. In total, 4540 individuals belonging to 5 taxa were caught, with 6 ponds yielding no capture at all. The number of taxa ranged from 0 to 5 in each sampling session (mean 2 ± 1.6 taxa), with captures ranging from 0 to 1526 fish in total (average of 151 individuals captured). Mean fish abundance was 10 ± 9.5 standardised CPUE (see Table 2-1 below). The total abundance was strongly correlated with the pond surface area, but not to the number of channels (2-way ANOVA: F=25.49, p<0.001, adjusted R2=0.56, ηp²=0.49).

Table 2-1. Fish abundances estimated from Multigear Mean Standardisation for ponds in the Waipori-Waihola wetlands for the years 2016 and 2017 Pond Date N Abundance (Standardised CPUE) BUL TRO PER ANG GAL WHI BA Nov 2017 15 0 0 0 0.67 1.33 0 BP Feb 2016 18 8 0 0 0 0 0 BP Nov 2017 37 8.26 0 0 0.16 0.05 0.53 DM Nov 2017 0 0 0 0 0 0 0 FB Nov 2017 10 8 0 0 0.43 0.57 0 GP Jan 2016 58 23.46 0 0.48 4.52 1.54 0 GP Nov 2017 61 10.22 0 0 1.29 0 1.49 HS Nov 2017 8 5 0 0 0 0 0 LP Nov 2017 66 5.45 0 0 0 2.55 0 MP Jun 2016 1 0 0 0 0 25 0 MP Nov 2017 1513 10.6 0.01 0 2.09 1.54 1.75 PU Feb 2016 26 0 0 7 0 0 0 PU Nov 2017 17 0 0 0 0.35 0.06 0.59 RI Jun 2016 0 0 0 0 0 0 0 RI Nov 2017 0 0 0 0 0 0 0 SO Feb 2016 0 0 0 0 0 0 0 SO Nov 2017 17 0.63 0 1.31 9.06 0 0 SP Mar 2016 120 3.3 0 0 0 14.7 0 SQ Feb 2016 105 5.54 0 12.31 4 0.15 0 SQ Jan 2016 52 0.64 0 16.55 7.82 0 0 SQ Nov 2017 1526 0.1 0 0.01 0.42 0.03 5.45 ST Nov 2017 487 10.02 0 0 0 0.03 1.96 SX Nov 2017 0 0 0 0 0 0 0 SW Nov 2017 0 0 0 0 0 0 0 TD Nov 2017 2 2.5 0 0 0 2.5 0 TI Feb 2016 289 22.81 0 0.56 5.48 0.15 0 TI May 2016 56 23 0 0 0 0 0 TQ Nov 2017 52 11.35 0 0 0 0.65 0 TU Nov 2017 0 0 0 0 0 0 0 WR Nov 2017 4 3 0 0 0 1 0 BUL: Common bully G. cotidianus PER: Eurasian perch P. fluviatilis ANG: Eels Anguilla spp. GAL: Unidentified adult galaxiids TRO: Brown trout S. trutta WHI: Galaxiids, juveniles

Chapter 2. Natural variation in Common Bully diet | 26

Isotopic niches

There was substantial overlap in the isotopic signatures of bullies from the Waipori-Waihola ponds (Figure 2-2). Standard ellipse areas ranged from 0.83 to 3.33‰2 (mean 1.66±0.82‰2). The linear distance to Lake Waihola was the best predictor for fitted models with a significant positive effect on the size of

2 standard ellipses for most ponds ( =4.12, p=0.04) (Figure 2-3, p.27). SEAc was also negatively correlated with surface area (2=5.45, p=0.02) and presence/absence of open channels (2=6.17, p=0.01)(see supplementary material, Figure S2-1, p.158). No significant trend was detected with the other predictors (Table 2-2, p.27).

Figure 2-2. Isotopic signatures of common bullies captured in 2016 and 2017 in the Waipori-Waihola wetlands. The area of the 95% standard ellipses (SEAc) is represented in solid lines. Symbols represent individual bullies.

Chapter 2. Natural variation in Common Bully diet | 27

Figure 2-3. 95% standard ellipses areas (SEAc) for carbon and nitrogen isotopic signatures of common bully populations predicted by the linear distance of ponds to Lake Waihola. Each circle represents a pond population. Colours correspond to sampling season. Green: spring. Yellow: summer. Brown: Autumn. Solid line: fitted model. Dotted lines: 95% confidence intervals.

Sex was the strongest predictor of individual isotopic values, with males showing significantly higher 15N than females (Type II ANOVA, 2=14.60, p<0.001). While sex also had a significant effect on 13C (Type II ANOVA, 2=6.61, p=0.01), that direction varied from one pond to another. No significant effect of fish size nor interactive effect between sex and size was detected (Table 2-3, p.28).

Table 2-2. Model selection on generalised linear models of standard ellipse areas SEAc.

Model df logLik AICc delta weight Distance to Lake Waihola (*) 3 -10.87 31.18 0 0.31 Null model 2 -12.95 31.4 0.22 0.28 Perch density 3 -12.28 33.98 2.8 0.08 Total fish density 3 -12.56 34.55 3.37 0.06 Connectivity 3 -12.61 34.64 3.46 0.06 Mean bully size 3 -12.73 34.9 3.72 0.05 Common bully density 3 -12.74 34.91 3.73 0.05 Galaxiid density 3 -12.88 35.19 4.01 0.04 Pond surface area 3 -12.89 35.21 4.04 0.04 Eel density 3 -12.92 35.26 4.09 0.04 Level of significance of calculated P-values on type II analysis of variance: 0.01 (*).

Chapter 2. Natural variation in Common Bully diet | 28

Population dietary niche and individual specialisation

Seven ponds provided a large enough sample size of individual stomach contents for the estimation of specialisation indices (see Table S2-4, p.157), representing 122 individuals with at least one item present in their stomachs, and 58 individuals which had empty stomachs. Prey richness in the bully populations of each pond ranged from 6 to 11 taxa, with a total of 20 different taxonomic groups identified. The most common prey items were chironomid larvae, followed by amphipods, ostracods and cladocerans. Other taxa included other insect larvae, small gastropods and cyclopoid copepods. The number of prey items in individual stomachs ranged from 0 to 82, with an average of 5.3±11.

No notable pattern was identified from the NMDS on prey data within ponds (Figure 2-4 p.29). There was no apparent clustering related to underlying characteristics of prey types, such as trophic position or functional taxonomy, characteristics of the ponds themselves, such as their proximity to one another, nor seasonal variation, therefore it was not possible to explain the position of individual ponds along the NMDS axes. The PERMANOVA did not detect any significant effect of pond-level predictors on the taxonomic composition of prey found in individual ponds.

Table 2-3. Results of Analysis of deviance on linear mixed-effect models of effects of size and sex on 15N and 13C values in individual bully muscles. Model Individual 15N values Individual 13C values 2 P 2 P Total Length 2.77 0.096 5e-4 0.98 Sex 14.60 <0.001 6.61 0.01 Total Length : Sex 0.31 0.579 0.10 0.75

Chapter 2. Natural variation in Common Bully diet | 29

Figure 2-4. Non-metric multidimensional scaling of prey types found in common bullies of the Waipori- Waihola wetlands. Each circle represents a pond population. Colours correspond to sampling season. Green: spring. Yellow: summer. Brown: Autumn. Names indicate the centroid position of the taxa’s ordination scores.

The WIC/TNW index of individual specialisation ranged between 0.30 (pond “MP”) to 0.62 (“TI”), although the value for “MP” was substantially lower than the other six observed values, all higher than 0.5. Overall, this range reflects moderate levels of individual specialisation (mean WIC/TNW 0.53±0.11). The average pairwise dissimilarity E ranged from 0.56 to 0.75 (mean 0.68±0.08), indicating little to moderate overlap in resource use between individuals (see Table S2-5, p.157). Model selection indicated that no pond-level predictor explained the distribution of WIC/TNW and E better than the null model (Table 2-4 below and Table 2-5, p.31). The density of interspecific competitors had a significant negative effect on WIC/TNW (Type II ANOVA, 2=3.97, p=0.046) and represented 13% of the weight of selected models (Figure 2-5 below), although it is worth noting that the strong negative slope is due to the low WIC/TNW observed in pond “MP”. No significant effect from any selected predictor on the E index was detected. Finally, I found no significant correlation between TNW and SEAc (1-way ANOVA, F=0.15, P=0.71). Chapter 2. Natural variation in Common Bully diet | 30

Figure 2-5. Index of individual specialisation WIC/TNW (left) and average pairwise dissimilarity E (right) in common bully populations predicted by the density of Galaxias spp. Circles represent single pond bully populations. Solid line: fitted model. Dotted lines: 95% confidence intervals.

Table 2-4. Model selection on generalised linear models on the individual specialisation index WIC/TNW. Model df logLik AICc delta weight Null model 2 6.29 -5.58 0 0.60 Galaxiid density (*) 3 8.34 -2.67 2.91 0.14 Distance to lake 3 7.83 -1.67 3.92 0.08 Mean bully size 3 7.17 -0.33 5.25 0.04 Perch density 3 6.89 0.22 5.8 0.03 Common bully density 3 6.56 0.88 6.46 0.02 Connectivity 3 6.49 1.01 6.6 0.02 Total fish density 3 6.35 1.31 6.89 0.02 Pond surface area 3 6.3 1.4 6.98 0.02 Eel density 3 6.3 1.41 6.99 0.02 Level of significance of calculated P-values on type II analysis of variance: 0.01 (*).

Chapter 2. Natural variation in Common Bully diet | 31

Table 2-5. Model selection on generalised linear models on the average pairwise dissimilarity E.

Model df logLik AICc delta weight Null model 2 8.48 -9.97 0 0.75 Distance to lake 3 9.3 -4.6 5.37 0.05 Galaxiid density 3 8.62 -3.25 6.72 0.03 Mean bully size 3 8.6 -3.19 6.77 0.03 Perch density 3 8.57 -3.15 6.82 0.02 Connectivity 3 8.54 -3.09 6.88 0.02 Pond surface area 3 8.52 -3.04 6.93 0.02 Common bully density 3 8.51 -3.03 6.94 0.02 Eel density 3 8.5 -2.99 6.97 0.02 Total fish density 3 8.49 -2.98 6.99 0.02 Models with weights<1% are not presented.

Discussion

The present study sought to characterise with two complementary methods the dietary niche of bully populations in wetland ponds across a range of biotic and abiotic conditions. There was limited support for my main hypotheses. Among pond-level predictors, only pond surface area explained some of the observed variation in the size of bullies’ isotopic niche. Contrary to expectations, biotic characteristics of ponds, such as estimated fish densities and derived estimates of the degree of competition and predation within ponds, had little to no observable effect on the measured population-level variables SEAc and WIC/TNW. While both the observed bully isotopic niche and the degree of individual diet specialisation substantially varied between ponds, the causes of this variation were likely multifactorial and difficult to identify with this dataset. While there was a weak, negative correlation between individual specialisation and levels of interspecific competition, no link between levels of intraspecific competition or density of predators and individual specialisation was found. Finally, at the individual level, nitrogen and carbon isotopic ratios significantly varied between sexes, but were not linked to bully size.

Ecological niches, sample size and model performance

Measuring individual dietary specialisation based on the examination of stomach contents is highly dependent on resource availability and foraging Chapter 2. Natural variation in Common Bully diet | 32 conditions, – e.g. prey local distribution and abundances, opportunity for encounter – fish activity at the time of capture and, in this study, the opportunity of captured bullies to forage within a trap in relation to soak time. In cases of low or heterogeneous samples, using prey proportions might also lead to overestimation of IS indices (Bolnick et al., 2002; Coblentz et al., 2017; Zaccarelli et al., 2013). Here, several captured bullies had empty stomachs or had ingested single prey types. However, the measures of individual specialisation obtained in this study were consistent with other studies on freshwater, generalist fishes (e.g. Hopper and Tobler, 2016; Vejřík et al., 2017). Overall, the obtained values were also significantly lower than expected under a null model of no individual specialisation. The differences in values observed between ponds could not be explained by the selected pond abiotic and biotic characteristics, although a small, negative effect of potential interspecific competition on the degree of individual specialisation was measured, mostly driven by pond “MP”. While the direction of the effect is contrary to my hypotheses, it still however reflects the fact that antagonistic interaction with other species may affect IS both directly and indirectly (Araújo et al., 2011).

The use of stable isotope analysis of muscle tissue allowed quantifying the niche size of pond populations. While baseline isotopic data for common prey items were not obtainable due to some samples getting accidentally destroyed, it was possible to estimate the size of the isotopic niche of bully populations using standard ellipse areas. Stable isotope analysis offers several advantages in food web studies, including cost-effectiveness, sensitivity and temporal integration throughout the ponds resulted in limitations to confidently estimate the effects of biotic predictors on the size of the isotopic niche of each bully population. The collection of consistent baseline data from the invertebrate prey pool might have allowed further comparison of diets between population. At the individual level, isotopic values for both carbon and nitrogen were sex-dependent. Diets may vary between sexes due to sex-specific nutritional requirements or foraging behaviours, and those differences are often enhanced in sexually Chapter 2. Natural variation in Common Bully diet | 33 dimorphic species (Magurran and Garcia, 2000). Although some behavioural differences between sexes exist, particularly during reproduction period (Stephens, 1982), common bullies display limited morphological sexual dimorphism. This hints at the possibility that sexual differences in the isotopic composition of bully muscle tissue may be linked, in part, to physiological differences between sexes beyond differences in their foraging strategies or diet preferences.

Geographically close communities can vary extensively in terms of their composition, abundance and ecological traits. Here, estimated abundances, but not richness, were strongly predicted by pond surface area. While this might reflect general diversity in available resources within ponds, that possibility could not be formally tested: habitat described during trapping essentially consisted of shallow, soft-bottomed, silty substrate, with the occasional presence of submerged macrophytes and willow stumps under highly turbid water conditions. In the case of small isolated ponds, decaying riparian vegetation represented a more substantial proportion of available habitat; however, these ponds generally yielded no capture. While it is generally assumed that structural habitat complexity provides a wider array of niches and is therefore associated with an increase in species diversity and abundance, it sometimes fails to explain patterns of abundance and distribution of species in some fish assemblages (Grenouillet et al., 2002). It is unclear whether the observed variability between the ponds in the Waipori-Waihola wetland originates from sampling error due to different gear, effort, weather and other trapping conditions, or from inherent characteristics of the pond themselves – in which case the observed differences might be consistent over time.

Sampling effort and use of multiple gear types

Quantifying the effects of biotic interactions on the dietary niche and degree of individual specialisation within bully populations hinged on robust estimates of the competition and predation levels, which in this study might be affected by Chapter 2. Natural variation in Common Bully diet | 34 the inherent variability in sampling effort and methodology. This variability was in part due to differences in pond accessibility: for instance, ponds “WR” and “LP” were only accessible by foot and it wasn’t possible to use a boat, therefore minnow traps were set from the shore and no fyke net was used. In shallow ponds such as “SP”, it was not possible to use fyke nets. A possible consequence of the impossibility of using the whole array of fishing gear throughout the ponds is that large fish, particularly eels, were likely present in remote ponds but were not caught due to the gear type. Conversely, while the use of a single type of fishing gear would have standardised the catch data between ponds, the use of different types of fishing gear was justified by the presence of species with varying catchability (Hanchin et al., 2002; Layman and Smith, 2001) – e.g. large eels vs. whitebait species.

In the present study, soak time was accounted for in the estimation of fish abundances. Different soak times can lead to additional variability in capture rates. Although the variation in capturability of fish over time in each gear type was not evaluated here, longer soak times might mean that some traps were close to or had reached saturation. In fyke nets, and despite the presence of exclusion chambers for small fish, it might also have increased the probability of large fish preying on smaller fishes, as was evidenced in some cases by the presence of fish remains (those individuals were not included in the abundance estimation). Overall, the spatial and temporal heterogeneity in trapping effort, associated with the use of different gear types, might have affected catch efficiency and ultimately abundance estimates (Budria et al., 2016; Jackson and Harvey, 1997). The use of a standardisation method for CPUE accounting for the use of different gear types and adjusted for soak time, was aimed at mitigating this variability (Gibson-Reinemer et al., 2017). Compared to other methods – including those that require to estimate gear-specific constants (Campbell, 2004; Maunder and Punt, 2004) – Multigear Mean Standardisation presents the advantage of providing comparable values between ponds using a fairly straightforward approach. In ponds with little diversity and low capture rates Chapter 2. Natural variation in Common Bully diet | 35 for a given type of gear, this method can overvalue densities. For instance, in the pond “MP” in 2016, the density value obtained for galaxiids in 2016 stemmed from the capture of only one individual over the entire sampling effort; a similar effect was observed in pond “SQ” which yielded low capture rates in January 2016, high capture rates in 2017, but with higher abundance values obtained in the former (Table 2-1, p.25).

Conclusion

Niche partitioning, e.g. through temporal and spatial segregation, can reduce or mitigate competition between and within species for resources that are limiting (Bergmüller and Taborsky, 2010; Carniatto et al., 2017): in heterospecific assemblages, dissimilar resource preferences might help promote coexistence of sympatric species sharing similar requirements (Barrett et al., 2016; Barros et al., 2017; Di Bitetti et al., 2010). The survey of the fish communities and the characterisation of the dietary niche of common bullies of the Waipori-Waihola wetlands by means of stable isotopes or direct measures of stomach content diversity yielded interesting results. Despite the intensive sampling effort, however, relatively few ponds in this study had sufficient bully populations for analysis, limiting tests for predictors of variation in individual specialisation. General habitat characteristics such as pond size explained some of the variation in the size of the isotopic niche of bully populations, although individual isotopic signatures themselves were harder to characterise. It was not possible to find a strong link between IS and potential antagonistic interactions within a pond as estimated via abundances in inter- and intraspecific competitors and predators. These limitations, as well as other caveats associated with the use of single stomach content samples or isotope data to infer IS, speak to the challenges of robustly identifying drivers of IS in natural populations and highlight the appeal of using mesocosm pond experiments for the purpose of untangling complex interspecific trophic relationships (Tran et al., 2015).

3. 3 Effects Of Intra- And Inter-Specific Competition On Individual Specialisation: A Mesocosm Study

Stomach contents of a bully under a dissecting microscope. Chapter 3. Effects of inter- and intraspecific competition on IS | 37

Chapter summary

Intraspecific ecological niche variation has long been suspected of shaping population dynamics, with potentially large effects at the ecosystem level. Within species, there is some evidence of variation in the degree of individual specialisation across populations. However, there has been relatively little work on the effects of ecological context on the degree of individual specialisation, and few experimental tests of the role of antagonistic biological interactions. In this chapter, I used a mesocosm pond experiment to explore the response of the long-term dietary niche of common bullies Gobiomorphus cotidianus to low to moderate levels of intraspecific or interspecific competition. As hypothesised, responses occurred both at the population and the individual levels, and different modes of competition affected different aspects of niche partitioning and diet similarity between individuals. Contrary to expectations, interspecific competition with juvenile perch Perca fluviatilis (TL<150 mm) did not lead to niche contraction at the population level; however, bully populations subjected to interspecific competition displayed a lower degree of individual specialisation and a higher degree of between-individual overlap. Conversely, intraspecific competition had a weak positive effect on individual specialisation, increasing within-population diversity in resource use by increasing niche partitioning between individuals. These results highlight that different types of competition can result into different responses in individual and population diets, confirming the importance of integrating individual variation when evaluating a population’s response to a competing species. Chapter 3. Effects of inter- and intraspecific competition on IS | 38

Introduction

Individuals within a population can deploy a variety of ecological and behavioural strategies to exploit a wide array of resources. This intraspecific variation is ubiquitous, and potentially has large consequences on communities and ecosystem processes (Bolnick et al., 2003). Individual specialisation (IS) refers to the use by individuals of a subset of their population’s resources, for reasons not attributable to their age, sex or phenotype (Bolnick et al., 2003; Roughgarden, 1972). In this framework, generalists exploit the same resources in similar proportions, while specialists differ from each other in their resource use. The ecological causes of this phenomenon have spurred a growing body of theoretical and empirical research in recent years. Individuals respond to social and environmental cues and adjust their foraging strategy or habitat use accordingly. Resource availability and diversity, as well as biological interactions, can influence ecological niches at both the population and the individual levels (Araújo et al., 2011; Britton and Andreou, 2016). In particular, strategies to reduce the effects of competition can involve changes in food choice, prey specialisation, foraging patterns and tactics (Kondoh, 2003; Stein et al., 2015). Indeed, multiple studies have demonstrated that IS can be substantially shaped by inter- and intraspecific competition (Bolnick et al., 2010; Costa-Pereira et al., 2018; Evangelista et al., 2014; Hopper and Tobler, 2016; Svanbäck et al., 2011; Svanbäck and Bolnick, 2007, 2007; Svanbäck and Persson, 2009). It is generally suggested that intraspecific competition promotes niche diversification and that interspecific competition has constraining effects on the ecological niche (Araújo et al., 2011). While resource use by individuals is widely believed to be shaped by the balance between intraspecific and interspecific competition, there has been little research directly comparing their effect on IS within a single species and environment.

Just as sympatric species with similar resource requirements may undergo niche shifts to reduce competition, conspecific individuals may adjust their resource use to limit intraspecific competition. The presence of a superior Chapter 3. Effects of inter- and intraspecific competition on IS | 39 competitor generally comes with negative consequences for individual fitness and survival. Under low competition, individuals might have access to resources that are limiting or more profitable, better shelter from predation, or better opportunities for offspring rearing and survival. As competition increases and favoured resources are depleted or less accessible, individuals might shift to novel, less profitable resources. Different modes of competition can induce different mechanisms leading to changes in the degree of individual specialisation in a population. The amount, or the diversity, of resources available to each individual may be altered through exploitation competition, or access to certain resources may be reduced through interference competition. Under high intraspecific competition, niche expansion can help maintain individual energy requirements (Svanbäck and Bolnick, 2007), a process which under some conditions promotes among-individual variation (Costa et al., 2008; Kobler et al., 2009; Svanbäck et al., 2011; Svanbäck and Persson, 2009; Tinker et al., 2008). In contrast, populations under interspecific competition tend to undergo niche shifts (Závorka et al., 2017) or occupy smaller niches, which can facilitate species coexistence (Tran et al., 2015).

The ‘niche variation hypothesis’ (Van Valen, 1965) postulates that individual specialisation increases when population niche expands. This proposition, and its corollary that IS decreases when population niche width contracts, find mixed support in the literature (Bolnick et al., 2010, 2007; Costa et al., 2008). Niche expansion does not always result from the combination of individual niche expansions: resource accessibility depends on the physical and cognitive abilities of individuals, so individual niche widths tend to be constrained. Among conspecifics, increased competition might lead to niche diversification by opening access to alternative resources while the overlap between individuals tends to decrease (Costa et al., 2008; Tinker et al., 2008). However, such effects are not necessarily linear, nor are they consistent among individuals or over time. Density-mediated effects were recorded for example in the pike cichlid Crenicichla lepidota: increasing between-individual variability at Chapter 3. Effects of inter- and intraspecific competition on IS | 40 low to intermediate densities did result in greater niche widths, but this effect was reversed at high densities (Mateus et al., 2016). In threespine stickleback Gasterosteus aculeatus, some individuals under high competitive pressure switched from their most-preferred to secondary prey, in effect maintaining their individual niche breadth instead of expanding it (Araújo et al., 2008). In addition, prey scarcity can lead to a lower degree of IS, not because of a reduction in the frequency of specialist individuals within the population, but because their preferred prey is less often available (Svanbäck and Bolnick, 2007). The diversifying effects of intraspecific competition thus not only depend on the capacity of foragers to access and use novel resources (Bolnick et al., 2010; Robertson et al., 2014), but also on the spatial and temporal variation in ecological opportunities (Jones and Post, 2016; Kernaléguen et al., 2015; Kobler et al., 2009) and environmental conditions (Folks et al., 2014).

Assemblages of competing species provide additional support for the niche variation hypothesis, as niche displacement and contraction at the population level appear to occur under increased interspecific competition (Araújo et al., 2011; Costa et al., 2008; Costa-Pereira et al., 2018; Eloranta et al., 2013; Tran et al., 2015). Reduction in niche overlap between competitors can be achieved through temporal and spatial segregation in addition to changes in preferences (Barrett et al., 2016; Campera et al., 2019; Di Bitetti et al., 2010). Restriction of a population’s niche can stem from a contraction of individual niches, a decrease in among-individual variation, or both mechanisms occurring simultaneously (Figure 3-1, p.41). The potential effects of interspecific competition on individual niche widths can therefore be either positive or negative. For one, an increase in specialisation within a subset of the population can help mitigate the intensity of competition with other foragers targeting the same resources (Lewis et al., 2006), for example by increasing the performance of individuals on certain resource types. However, there can be a discrepancy between the response patterns of individuals and the changes observed in the population niche: in the freshwater isopod Asellus aquaticus, interspecific Chapter 3. Effects of inter- and intraspecific competition on IS | 41

Figure 3-1. Illustration of how competition might affect niche width and individual specialisation in a population of consumers. Arrows indicate resource consumption, with their relative thickness indicating proportional contribution. (a) Consider a population in which two consumers types – c1 and c2 – each have two preferred resource types out of three available to the population. (b) An increase in intraspecific competition might lead to an increase in the degree of individual specialisation, as consumer types c3 mostly consume resource types r1, which are consequently dropped by individual types c1. This increase in IS occurs while the population’s niche remains unchanged. (c) IS might also increase if consumers have access to alternative resource types. Competition between consumers c1 and c2 is alleviated through c2 partially shifting their diet to resource r4. (d) In the presence of a generalist consumer which outcompetes species c on some of their preferred resource types, the population niche might contract in the absence of alternative resources. The competition between c1 and c2 might be alleviated as segregation in the niches of c1 and c2 increases, leading to an increase in IS. (e) If alternative resources are available, the presence of a heterospecific competitor might lead to a shift in the population’s niche. If consumers retain the width of their individual niches by shifting their preferred resource types, the degree of IS might remain unchanged. Chapter 3. Effects of inter- and intraspecific competition on IS | 42 competition led to population niche expansion and to lower variability among individuals (Costantini et al., 2005). The opposite effect was observed in G. aculeatus, as niche expansion was observed after ecological release from competing juvenile cutthroat trout Oncorhynchus clarki; this effect was linked to an increase in the variation among individuals, while individual niches remained constant (Bolnick et al., 2010).

While a number of studies have refined our understanding of the mechanisms by which intra- and interspecific competition affect individual specialisation, there has been little experimental study of the strength of their respective effects within communities (but see Bailey et al., 2013; Eloranta et al., 2013). The present chapter asks how dietary IS in the common bully Gobiomorphus cotidianus (McDowall, 1975) varies under different modes of competition. This small, New Zealand-native fish is widely distributed in the country’s lakes, ponds and streams. The species is an opportunistic predator that feeds on benthic and pelagic invertebrates and displays moderate levels of dietary IS (see Chapter 2). It often occurs in sympatry with the introduced Eurasian perch Perca fluviatilis (Linnaeus, 1758). Small size classes of perch display similar prey preferences to bullies, while larger, piscivorous individuals are predators of the species. Competition with young-of-year and juvenile perch might affect the degree of IS in bully populations. Here, I use a mesocosm pond experiment to explore how the dietary niche of bullies responds to low to moderate levels of intraspecific and interspecific competition.

As underscored earlier, the degree of IS in a population results from multiple mechanisms either favouring or restricting resource availability and accessibility, both at the individual and the population levels. At the population level, three types of effects might be observed under higher competition: (1) providing both preferred and alternative resources are available, the population niche width might expand; (2) if alternative resources are available but original resources are not, for instance due to scarcity or to the presence of a more efficient competitor, the population niche might shift; (3) if original resources Chapter 3. Effects of inter- and intraspecific competition on IS | 43 are limiting and alternative resources are unavailable, I might observe a contraction of the niche (Figure 3-1, page 41). Consistent with the niche variation hypothesis, I expect to observe larger dietary niches in bully populations under higher intraspecific competition and smaller niches in populations exposed to competing perch. Under a population niche expansion scenario, a higher degree of individual specialisation might result from a decrease in niche overlap between individuals, if bullies maintain the size of their individual niches while being able to access alternative prey types. Alternatively, if interspecific competition results in a shift and/or contraction of the population niche, IS might decrease if individual niche widths remain the same and overlap between individuals increases; individual niche widths might however decrease under high competition, as individuals have less access to their favoured resources. The overlap in resource use between individuals might also decrease if individuals lower their competitive interactions. The degree of IS will therefore depend on the strength of the responses to competition within and among individuals (Figure 3-1, p.41). Chapter 3. Effects of inter- and intraspecific competition on IS | 44

Materials and methods

Experimental setup

I used a mesocosm pond experiment to monitor the diet of individual common bullies in response to moderate levels of competition from young-of-year Eurasian perch and low or moderate levels of intraspecific competition. I used repeated, non-lethal gastric lavage to obtain information on individual diets over time. The study was conducted at the Sawyers Bay mesocosm facility on University of Otago grounds in Dunedin, New Zealand from November 2016 to February 2017.

Fish marking and biometry

120 common bullies were captured on the shores of a shallow coastal lake (Lake Waihola, 46°01'29.5"S, 170°05'26.9"E) in October 2016, using Gee minnow traps left overnight. The fish were transported in large chilly bins of lake water to the Parker building (Department of Zoology, University of Otago) where they were housed for about three weeks in 20L tanks, with a density of 10 individuals per tank. The tanks were left at room temperature and subjected to a 12hr/12hr light/dark cycle. The fish were fed ad libitum with commercial fish pellets once a day, six days a week throughout their time in the tanks.

After a week of acclimatisation, fish were lightly anaesthetized with a 0.025 mL/L solution of isoeugenol (AQUI-S®, Aqui-S New Zealand Ltd, Lower Hutt) and tagged following Goldsmith et al. (2003) with unique, two-colour combinations of Visible Implant Elastomer (VIE) tags (Northwest Marine Technology, Inc., Shaw Island), allowing individual identifications (Figure 3-2, p.45). Fish were measured and weighed after tagging.

In addition, 12 small perch (TL < 150 mm) were captured from the wetlands and housed under similar conditions until their transfer to the mesocosm tanks. Chapter 3. Effects of inter- and intraspecific competition on IS | 45

Tagged bullies and perch were transported in large buckets to the Sawyers Bay aquaculture facility (Dunedin, New Zealand) where they were randomly assigned and released into the experimental tanks.

Figure 3-2. Visible Implant Elastomer tags on a common bully (left) and array of tag locations used for bully identification (right).

Experimental setup

36 mesocosm tanks (1200 L cattle troughs, Wilson Plastics, Palmerston North) were filled with city water and a bottom layer consisting of small pebbles (particle size 1-2 cm) and six opaque plastic hides (diameter 10 cm, height 6 cm) large enough to host a couple of fish, that had a bottom layer of pebbles. After being sprayed through a hose and left for 48hrs to allow volatilisation of chlorine, the tanks were seeded with 4 L of benthos-rich mud and 50 mL of concentrated plankton collected using a 250-µm plankton net from Lake Waihola. Additional 1200 L tanks were filled with city water and a 3-cm layer of fine sand to house spare fish.

The fish were introduced to these tanks after a fortnight, on the 2nd and 3rd of November 2016. Individuals were randomly allocated between three different experimental treatments. There was no significant difference in the initial fish size and weight distributions among treatments (ANOVA, P=0.12, F2,114=2.14). Treatments were set up as follows: low interspecific competition (“low inter”, 12 replicates), low intraspecific competition (“low intra”, 12 replicates) and high intraspecific competition (“High intra”, 8 replicates due to limited number of fish), which represented respective communities of 3 bullies and one perch, 3 Chapter 3. Effects of inter- and intraspecific competition on IS | 46 bullies and 6 bullies. Previous trials established that the mesocosm tanks were able to support up to 6 fish without having to use food supplementation (Ingram, unpub. data).

Blooms of filamentous algae occurred during the course of the experiment, prompting the need for periodical mechanical removal with a scoop net. These temporary disturbances were applied to all tanks in order to maintain consistency throughout the treatments.

Individual diet characterisation

Sampling and identification of stomach contents

Every two weeks over 14 weeks, fish diets were sampled using gastric lavage (stomach flushing). Fish were captured using overnight Gee minnow trapping or, if this method failed, captured by hand using small hand nets. Fish were housed for <30 min in a bucket filled with cool water from the tank they were collected from. Stomach flushing was performed on each fish by securely holding it with the head angled slightly downward, gently inserting a thin polyethylene tube down its oesophagus to the back of its stomach and injecting 1-3 mL of filtered source water with a syringe. The retrieved stomach contents were stored on ice on site then kept frozen in water at -18°C until identification. Each fish was left to recover for a few minutes in cool source water before being returned to its tank. All fish were handled in compliance with the Animal Welfare Act, under animal use protocol 58/16 approved by the University of Otago Animal Ethics Committee.

Prey items were identified to the lowest taxonomic level possible and enumerated under a dissecting microscope. Main prey categories included chironomid larvae and pupae, cladocerans belonging to the families Chydoridae, Daphniidae and Bosminidae, copepods, several insect taxa as well as bully larvae and eggs resulting from reproduction within the mesocosms. Body parts allowing identification of a taxon such as heads of chironomid larvae were included in the content counts, while stomach contents too digested to Chapter 3. Effects of inter- and intraspecific competition on IS | 47 recognise any prey category were not included; stomachs containing this type of digested material only were considered empty. Average prey dimensions were obtained from the measurements of up to 10 prey items of each category per individual stomach (cf. Figure S3-1, p.160).

Estimation of consumed prey weights

To assess prey size specialisation, I estimated the dry weights of prey items in individual stomachs using length-weight regressions found in the literature (Table S3-1, p.159). As specific length-weight equations are not available for most New Zealand taxa, I used data for closely related and morphologically similar taxonomic groups (resolved to genus whenever possible). For bully eggs (see further), I assumed a linear relationship between egg volume (estimated as 4휋 the volume of an ellipsoid: 푉 = 퐿푙2, with L the largest diameter and l the 3 width of the individual egg) and dry weight (Robertson and Collin, 2015). Estimates of larval weight or length-weight regression were not available for G. cotidianus; I used instead the estimated median weight of bully eggs obtained with my dataset. While the individual prey weight was calculated for the subset of items that were directly measured, I estimated the dry weights of the remaining prey items based on their mean length values. As individual weight estimates were highly skewed towards small values, weight data were cube- root then natural log-transformed prior to analysis.

Calculation of indices of individual specialisation

Prey counts and estimated dry weights were used to calculate two metrics of specialisation, Roughgarden’s WIC/TNW and the E index (Bolnick et al., 2002; Roughgarden, 1972) using the ‘RInSp’ package version 1.2.3 in R (Zaccarelli et al., 2013). The E index, or average pairwise dissimilarity, quantifies the degree of dietary overlap that exists between two individuals. It ranges from 0, when all individuals from a given population use the same resources in the same proportions, to 1, when single individuals rely on entirely different resources compared to other individuals. The ratio WIC/TNW represents the degree of Chapter 3. Effects of inter- and intraspecific competition on IS | 48 individual specialisation in the population: this index tends towards 0 the more specialised the population, and towards 1 in populations fully composed of individual generalists. Prey categories were used to calculate taxonomic specialisation (discrete resources), while cube root and log-transformed weights were used to measure size specialisation (continuous resource dimension). The estimation of WIC/TNW required different calculation procedures for each approach to resource characterisation (discrete or continuous), detailed in Zaccarelli et al. (2013). In the discrete case, the total niche width (TNW) of the population was computed as the Shannon-Weaver index of diversity. For the weight data, TNW equalled the variance of the prey weights consumed by every individual within a tank. The procedure also estimated the two components of TNW: the average of the individual variances in resources use (WIC) and the variance in mean resource use among individuals (BIC).

Statistical analyses

Single sampling sessions yielded too many empty stomachs and uncaptured individual fish to account for variation between sessions or to calculate metrics for single sessions. Therefore, all diet analyses were conducted on individual diet data aggregated across up to 7 sessions of gastric lavage, representing the diet composition of 118 individual bullies. Each bully was captured at least twice, up to 7 times. The number of prey items in aggregated individual data ranged from 1 to 748 (mean=84±113 items).

To assess whether there was a qualitative difference between treatments in the average size of consumed prey items by individual fish, I used a one-way ANOVA on the estimated prey weights of single items, averaged for each fish and then within tanks.

The dietary niche of bully mesocosm populations was visualised using non- metric dimensional scaling (NMDS). Diet proportions were calculated for individual bullies and then averaged per tank. As prey categories vastly differed Chapter 3. Effects of inter- and intraspecific competition on IS | 49 in size, diet proportion data were weighted in two different ways, by prey counts and by prey biomass. Dissimilarities between tank populations were then calculated using the Bray-Curtis dissimilarity index. The model fit was visually checked using a stress plot and considered good for stress values <0.2. I used a permutational multivariate analysis of variance (PERMANOVA) with a Bray-Curtis dissimilarity to test for differences in diet proportion data between treatments. This part of the analysis was conducted using the ‘vegan’ package, version 2.5-4 in R (Oksanen et al., 2018).

I conducted one-way analyses of variance to quantify the effect of each treatment on the WIC/TNW and E indices of specialisation. I also used one-way ANOVA to evaluate the effect of inter- and intraspecific competition on each niche component BIC, WIC and TNW. Spawning events occurred in 18 tanks, and although bully eggs and larvae represented a small dry biomass compared to other consumed prey items, they accounted for a substantial numeric proportion of some diets. For this reason, I considered using the occurrence of spawning as a binary covariate and tested models with and without this factor. There was no difference in the model outputs between methods, and spawning did not have a significant marginal effect on the response variables, so it was excluded from analyses. Model residuals were visually checked for normality. Post-hoc Tukey’s HSD pairwise comparisons were used to test for significant differences between treatment levels where overall significant effects were present.

All analyses were conducted in R Studio, version 1.1.463 using R 3.4.4 (R Core Team, 2018). Chapter 3. Effects of inter- and intraspecific competition on IS | 50

Results

Prey diversity at the population level and diet overlap between species

Twenty-eight main taxonomic prey groups were identified over the course of 10 weeks of monitoring in both bullies and perch (see Table S3-1, p.159), which were divided into 12 broad categories (Figure 3-3, next page). The prey items most frequently found in stomach contents were chironomid larvae (24.4% of the prey total count), cladocerans belonging to the Chydoridae (27.3%) and Daphniidae (8.7%) families, bully eggs and larvae (26.1%) and calanoid and cyclopoid copepods (9.2%). Perch overall consumed similar prey types as bullies, with a notably higher proportion of cyclopoid copepods consumed (37% median in perch vs. 3 to 6% median in bullies across all treatments). A PERMANOVA on the diets of perch and bullies in the “Low inter” treatment indicated a significant difference between species’ diets (F1,23=6.78, p=0.002). A substantial proportion of bully diet came from conspecific larvae and eggs, while perch did not appear to predate on either prey type.

In terms of estimated dry weight consumed, prey composition indicated a strong prevalence of chironomid larvae and pupae, both for bullies and perch, with the other prey categories combined to represent less than half of the total weight of consumed prey (Figure 3-4, next page). No significant difference in the average prey size consumed by bullies within each tank was detected between treatments (ANOVA F2,29=0.3, p=0.74).

The PERMANOVA indicated no significant difference in the bully prey pool between treatments, whether diet proportions were weighted with prey counts

(F2,29=1.39, p=0.21) or prey biomass (F2,29=1.36, p=0.23) (Figure 3-5, p.52).

Chapter 3. Effects of inter- and intraspecific competition on IS | 51

Figure 3-3. Frequency of prey taxa observed in fish stomach contents

Figure 3-4. Proportion of estimated dry weights of prey taxa in fish stomach contents The boxplots depict the median, first and third quartiles with whiskers extending up to 1.5 times the interquartile range. . Chapter 3. Effects of inter- and intraspecific competition on IS | 52

Figure 3-5. Difference between treatments in the captured prey community using non-metric multidimensional scaling, based on Bray-Curtis dissimilarity. Left: Prey count. Right: Estimated dry biomass. Individual points represent tanks and are grouped using convex hulls. Names indicate the centroid position of the taxa’s ordination scores.

Degree of individual specialisation

The WIC/TNW index of specialisation based on prey categories ranged from 0.39 to 0.95 across treatments (mean=0.75±0.15), indicating moderate to low levels of specialisation in bully populations. The lower index values – indicative of higher individual specialisation – were observed in tanks with high bully densities, while the higher values were obtained in populations subjected to interspecific competition (Figure 3-6a, next page). The average pairwise dissimilarity E ranged from 0.17 to 0.89 (mean=0.55±0.19), with lower values in the interspecific competition treatment and generally higher values in the monospecific tanks (Figure 3-6b, p.53). There was a weak but significant effect of competition treatment for both WIC/TNW (ANOVA F2,29=4.18, p=0.03,

η²=0.22) and E (ANOVA F2,29=4.66, p=0.02, η²=0.24).

There were positive, albeit weak correlations between WIC/TNW values obtained with continuous data and with categorical data (p=0.009, adjusted R2=0.18). Similar general trends were observed for the WIC/TNW index calculated using prey weights, with significantly higher values (ANOVA

F2,29=3.46, p=0.04, η²=0.19) in the interspecific treatment (Figure 3-6ac, p.53). Chapter 3. Effects of inter- and intraspecific competition on IS | 53

Post-hoc comparisons indicated significant differences in mean WIC/TNW between high intraspecific and interspecific competition treatments on prey counts (Tukey HSD p-adj=0.02) and weights (Tukey HSD p-adj=0.04). The E index was also significantly lower in the interspecific treatment for prey counts (Tukey HSD p-adj=0.03). No significant difference was detected between the low and high intraspecific treatments (Tukey HSD p-adj=0.999), while a marginal difference was noted between the low interspecific and high intraspecific treatments (p-adj=0.06).

(a) (b) (c)

Figure 3-6. WIC/TNW and E index values obtained for each competition treatment. Index calculation done on prey counts for figures (a) and (b); on estimated prey weights for figure (c). The boxplots depict the median, first and third quartiles with whiskers extending up to 1.5 times the interquartile range.

There was a marginal difference in the total niche width calculated on prey categories (F2,29=3.01, p=0.06), with the lower values observed on average in the high intraspecific treatment and the higher values in the low intraspecific treatment. The lower mean WIC/TNW value in the high intraspecific treatment was due to a significant reduction in WIC (ANOVA F2,29=5.32, p=0.01), associated with little variation in BIC (ANOVA F2,29=2.45, p=0.1) (Figure 3-7, p.54). On the other hand, the higher WIC/TNW observed in the perch treatment Chapter 3. Effects of inter- and intraspecific competition on IS | 54 was linked to a smaller BIC, while neither WIC nor TNW substantially varied on average.

Figure 3-7. Niche components BIC and WIC plotted against the total niche width TNW on prey count data (top row) and estimated biomass (bottom row). Individual points represent component values for each tank (closed circles) and their mean (crosses). Arrows illustrate the direction of the effect of competition treatments compared with the low intraspecific treatment.

Chapter 3. Effects of inter- and intraspecific competition on IS | 55

Discussion

This experiment provided evidence of variation in the degree of individual specialisation in the diet of common bullies, resulting from the degree of intraspecific competition and the presence of heterospecific competitors. Contrary to expectations, the average size of the population dietary niche did not significantly vary under different modes of competition, although it substantially varied within treatments. This might indicate that, within this relatively simple experimental set-up, prey resources in the mesocosms were non limiting in either treatment; in other words, the fish densities in the tanks and the associated degree of competition in the experiment were generally low. There are inherent limitations stemming from the use of small population sizes and a relatively low degree of replication of each experimental treatment, as was the case in this mesocosm study. This uncertainty was offset, however, by relying on repeated diet measures for every individual over a long period of time, which allowed a robust estimation of individual diets and indices of specialisation. As a result, I was able to detect significant effects on niche partitioning and overlap between individuals. Overall, increased intraspecific competition led to an increase in IS, coupled with an increased dissimilarity between individuals. Interspecific competition was associated with opposite effects: individuals displayed increased similarity with one another and had more generalist diets. This study further supports the idea of diversifying effects of intraspecific competition and homogenising effects of interspecific competition on the dietary niche of individuals. Moreover, these results are consistent with the niche variation hypothesis, in that increased intraspecific variation in resource use was observed in the absence of interspecific competition (Van Valen, 1965).

The prey categories in this study blended taxonomy, functional level and development stage. This categorisation reflected some aspects of foraging preferences and strategies specific to this aquatic system, and would not be applicable to other contexts or focal species. In that regard, using continuous Chapter 3. Effects of inter- and intraspecific competition on IS | 56 data such as prey weights allowed a more objective estimation of individual specialisation. Within tanks, there was a weak correlation between estimates of niche partitioning estimated with continuous and with discrete resource types. Strikingly, similar effects of different competition modes were reported for both data types, indicating that the response of individual specialisation to competition is consistent regardless of the data type used to estimate it.

Despite similar degrees of diversity of consumed prey between the mesocosm and natural pond communities, the values obtained for the WIC/TNW index in the present study were generally higher than the values observed in the environment these bullies originate from, while the E index values were similar in the monospecific treatments and lower in the heterospecific treatment (WIC/TNW=0.53±0.11 and E=0.68±0.08 for bullies from the Waipori-Waihola wetlands, see Chapter 2). Because they reflect prey capture over a limited amount of time, cross-sectional data of individual diets using individual stomach contents, tend to overestimate the degree of specialisation in a population (Coblentz et al., 2017). Indeed, in a habitat complex, rather than favoured prey resources, feeding episodes by an individual might reflect opportunity, prey patchiness or availability of profitable foraging grounds. This potential issue was circumvented in the present experiment through the use of longitudinal data, with multiple dietary samples per individual over time constituting the basis for the calculation of indices, which might have resulted in overall higher WIC/TNW values. Longitudinal data might also lead to an overestimation of the variation in resource use within individuals (WIC) and therefore a higher WIC/TNW ratio, if the degree of individual specialisation is high in a population at any given time, but changes in the prey community composition occur over time. Variations in the latter reflected differences in niche partitioning rather than differences in population niche width, as interspecific and intraspecific competition affected either one of the niche components, WIC and BIC, rather than TNW. On the other hand, the degree of diet overlap among individuals was comparable to observations in the field. Chapter 3. Effects of inter- and intraspecific competition on IS | 57

This might imply that, over longer timescales, measuring trophic segregation among pairs of individuals is more conservative than measuring niche partitioning within the whole population.

The effects of intraspecific competition can be mitigated through the combination of increased niche partitioning and decreased overlap between individuals (Navarro et al., 2017; Svanbäck and Persson, 2004). On the other hand, the coexistence of sympatric species sharing similar resource requirements can be facilitated by a diversity of mechanisms, including temporal and spatial segregation (Barrett et al., 2016; Di Bitetti et al., 2010; Guo et al., 2014; Munday et al., 2001; Schenck and Vrijenhoek, 1986), as well as differences in trophic ecology, prey performances and foraging behaviours (Carniatto et al., 2017; Eloranta et al., 2013; Knudsen et al., 2007). In the present experiment, increased intraspecific competition was associated with both a lower individual variation in resource use (WIC) and a higher dissimilarity, although some of these effects were subtle. In the heterospecific assemblages, perch presence was associated with lower variance (BIC) and a greater similarity between individuals. The slight discrepancy in effect sizes might indicate that niche variation and partitioning are subtler between conspecifics than heterospecifics.

As the perch and bullies used in my experiment had different size classes, additional mechanisms might also have occurred in the heterospecific assemblages, due to different responses from one or more prey species. When two competing predators differing in size share the same pool of prey, niche partitioning might result from the consumption of prey species of different sizes or different size classes of the same species of prey, which in turn might have strong effects on one or more prey species, leading to shifts in the prey community (Brink et al., 2015). Interference competition could also impact plastic traits, such as feeding rates, foraging behaviours or activity patterns, causing bullies to be restricted to certain prey types or habitats (Di Bitetti et al., 2010; Hentley et al., 2016). In my experiment, prey categories recorded within Chapter 3. Effects of inter- and intraspecific competition on IS | 58 each treatment did not reflect potential habitat segregation, and despite slightly higher prey diversity values at the lower fish densities, there was no significant difference in the niche size of bully populations between treatments. Furthermore, I found no evidence of a difference in the average size of prey consumed by individuals between treatments. Perch diets were pooled across tanks to compare them to bully populations within the interspecific competition treatment. While this comparison is imperfect, both species appeared to target similar prey categories, whether in terms of taxonomic groups or size classes. However, significant differences in their respective dietary patterns appeared to reflect differences in consumption of a subset of prey types, particularly cyclopoid copepods and bully larvae and eggs. Coexistence between the two species might therefore be promoted via multiple mechanisms, including changes in niche partitioning among bullies and partial niche segregation between competing species.

In conclusion, this study was the first to explore the effects of different degrees and forms of competitive pressure on the dietary niche using longitudinal data. The experimental design allowed detecting subtle effects of relatively low levels of competition within small populations, where resources appeared non limiting. It provided further demonstration that generalist use of prey resources tended to increase in populations subjected to interspecific competition, whereas individual specialisation and among-individual overlap increased with the intensity of intraspecific competition. While other ecological factors, including resource diversity and availability, environmental variation and compensatory responses such as behavioural changes from both individuals and their competitors might influence the direction and intensity of these effects, this study provides support for considering individual- and population- level responses to antagonistic interactions, to better understand their emergent consequences on food webs and ecosystems.

4. 4 Effects Of Intraguild Predation on Individual Habitat and Dietary Niches in the Common Bully Gobiomorphus cotidianus

Tagged common bullies in a holding tank Chapter 4. Effects of intraguild predation on IS | 60

Chapter summary

Intraguild predation from alien species potentially affects the incidence and degree of individual specialisation in native species. In the previous chapter, I evaluated the effects of competition from juvenile perch on the dietary preferences of common bullies. The results confirmed that interspecific competition reduced individual specialisation levels in bully populations, an effect which was detectable at a low perch/bully density ratio. Here I expand this approach and ask how the combination of competition and perceived predation risk from perch impacts the resource use diversity and degree of individual specialisation in bully populations. I used a mesocosm pond setting to obtain a time series of habitat use and dietary preferences of individual bullies exposed to different levels of competition and predation from perch. This is the first study, to my knowledge, to test for the separate and combined effects of competition and predation from a single species on the degree of individual specialisation in a population. I used a 2x2 factorial design to monitor the response of individual fish to moderate levels of competition from young-of- year perch and predation risk from sub-adult perch over a 12-week period. I performed instant focal surveys to monitor habitat use every couple of days, and I sampled stomach contents of individually tagged fish every fortnight for diet estimates. In addition, I used behavioural assays in an open field test to assess whether individual activity levels were impacted by interspecific interactions. The results indicate that (1) interspecific interactions with perch induce dietary, habitat and behavioural shifts in individual bullies and (2) that the direction of these effects differs, depending on the type of stressor the population is subject to. Overall, this study confirms the importance of integrating individual variation when evaluating a population’s response to an intraguild predator species.

Chapter 4. Effects of intraguild predation on IS | 61

Introduction

Among conspecifics, variations in morphology, phenotype, physiology, cognition and behaviour might affect the range of ways in which individuals occupy and interact with their environment, resulting in variation in their responses to biotic and abiotic conditions. This heterogeneity in resource use allows the display of different ecological strategies within a population. Individual specialisation is defined as the use by individuals of only a subset of the total resources available to their population (Bolnick et al., 2003, 2002), resulting in the partitioning of their population’s niche along one or several axes (Ingram et al., 2018; Nagelkerke and Rossberg, 2014). Following Roughgarden (1972), a population’s niche (total niche width or TNW), representing the cumulative resource use of all individuals in the population, can be partitioned into two components: the variance in mean resource use among individuals (between- individual component or BIC) and the average variance of resource use found within each individual (within-individual component or WIC). In Roughgarden’s conceptual approach, the TNW is the variance of total resource use of all individuals within a population. Individual specialisation can then be quantified as the ratio between the WIC and the total variance in resource use of the population. In a population of generalists, there will be little variation of resource use between individuals, and the TNW will mostly originate from the WIC; on the contrary, the WIC/TNW ratio will be small in a population made of individual specialists. The WIC/TNW ratio therefore will tend towards 0 in populations of fully specialised individuals, and towards 1 in populations of fully generalised individuals.

Within populations, individuals respond to spatial and temporal heterogeneity in their environment by deploying a variety of consistent strategies. Over the past twenty years, theoretical and empirical studies have outlined several ecological drivers which might influence the expression of these individual strategies (Bolnick et al., 2003; Futuyma and Moreno, 1988; Lichtenstein et al.,

Chapter 4. Effects of intraguild predation on IS | 62

2017; Toscano et al., 2016a). Many of these hypotheses correspond to antagonistic ecological interactions such as competition, predation and parasitism (reviewed in Araújo et al., 2011; Britton and Andreou, 2016). For example, shifts and contraction of population niches associated with decreased niche partitioning have been observed in heterospecific assemblages in which individuals compete for the same given pool of resources (e.g. Costa-Pereira et al., 2018; Layman et al., 2007), while intraspecific competition is often mitigated through niche expansion and a reduction in resource overlap among individuals (Araújo et al., 2011).

Effects of competition on individual specialisation

Strategies to avoid competition can affect ecological traits both at the species and the population levels, such as food choice and prey specialisation (Kondoh, 2003) and foraging patterns and tactics (Stein et al., 2015). Within populations, the degree of inter-individual variation in resource use is widely thought to depend on the contrasting effects of interspecific and intraspecific competition. Empirical and theoretical studies on numerous taxa show mixed support for the proposition that individual specialisation increases when population niches expand, coined “niche variation hypothesis” (Van Valen, 1965), and its corollaries (e.g. Bolnick et al., 2010; Costa et al., 2008; Jones and Post, 2016; Tran et al., 2015). However, it is generally suggested that intraspecific competition promotes niche diversification, while interspecific competition has constraining effects (Araújo et al., 2011). Intraspecific competition for resources can be alleviated through niche expansion, a process which can promote inter- individual variation as individuals might access novel resources and the overlap in resource use between individuals tends to decrease (e.g. Huss et al., 2008; Kobler et al., 2009; Svanbäck et al., 2011; Svanbäck and Persson, 2009; Tinker et al., 2008). Conversely, populations under high interspecific competition tend to occupy smaller niches, which facilitates species coexistence (Tran et al., 2015). There can be, however, a discrepancy between individual- and population-level responses to these interactions. In the freshwater isopod

Chapter 4. Effects of intraguild predation on IS | 63

Asellus aquaticus, interspecific competition led to population niche expansion but lower individual specialisation (Costantini et al., 2005). To this point, Jones and Post (2016) argue that resource depletion limits the diversifying effects of competition for consumers with larger ecological effects.

Effects of predation on individual specialisation

Predator-prey interactions are not limited to the effects of direct consumption: predators can also affect prey in their individual resource use through density- mediated (changes in prey abundance) and behaviour-related effects (Preisser et al., 2005; Werner and Peacor, 2003). The impacts of non-consumptive effects of predation on prey demographics and performance are potentially at least as strong as direct consumption (Peacor et al., 2007; Preisser et al., 2005) and have been observed in a variety of taxa and ecosystems. Strategies to avoid predators often involve a trade-off between food and safety, and include delayed and less frequent feeding in the presence of predator or predator cues (Beleznai et al., 2015), rejection of potentially advantageous foraging patches (Cronin et al., 2004), and changes in movement patterns (Beleznai et al., 2015) and other escape responses (Kristensen and Closs, 2004). Predation risk can thus generate a wide range of life history, morphological, physiological and behavioural responses in organisms (Harris et al 2010) with potential evolutionary implications (Peacor et al., 2007, Jiang 2018). Responses to predatory cues often involve increased risk-aversion strategies, with a variety of ecological consequences on individual behaviour and personality (Abbey-Lee et al., 2016; Bell and Sih, 2007; Moses and Sih, 1998; Oliveira et al., 2016; Vanderpham et al., 2012), phenotypical traits (Abbey-Lee et al., 2016; Bestion et al., 2014), reproductive strategy (Abbey-Lee et al., 2018; Teyssier et al., 2014), population dynamics including dispersal (Clobert et al., 2012; Cote et al., 2013; Cronin et al., 2004; McCauley and Rowe, 2010), recruitment (Bucher et al., 2015; McCauley et al., 2011) and survival (MacLeod et al., 2018). The direction of these effects is influenced by individual personalities and the ecological

Chapter 4. Effects of intraguild predation on IS | 64 context, from predator-induced to predator-suppressed behaviours (Cote et al., 2013).

Whether they elicit density-mediated or behaviour-related responses, predation and predation risk can potentially increase or decrease the degree of individual specialisation (Araújo et al., 2011). Prey removal via consumption directly decreases intraspecific competition, which, as was pointed earlier, generally has a diversifying effect on populations. In addition, habitat accessibility has been linked to niche variation and individual specialisation (Marklund et al., 2018). By inducing behavioural changes in terms of habitat use, predator avoidance strategies might therefore result in changes in foraging patterns, which may in turn translate into changes in individual specialisation. Suppression of diet variation has indeed been observed in populations of Eurasian perch Perca fluviatilis, where individuals retreated to safe areas to avoid predators (Eklöv and Svanbäck, 2006). On the other hand, evidence of increases in dietary niche breadth and in individual specialisation was found in the cyprinid Rastrineobola argentea, following the introduction of predatory Nile perch Lates niloticus in Lake Victoria (Sharpe and Chapman, 2014).

When both competitors and predators are present, the degree of individual specialisation results from the diversifying effects of intraspecific competition, the constricting effects of interspecific competition, and the context-dependent effects of predation. Disentangling their interactive effects is a long running issue in ecology (Schoener, 1974), and can be particularly arduous since these effects can be influenced or caused by trait plasticity, either directly or indirectly (Werner and Peacor, 2003) and might depend on the local ecological factors (Baines et al., 2014). Intraguild predation (IGP), the predation upon competitors, is widespread in nature (Arim and Marquet, 2004), and is a particularly interesting case of interactive competition and predation. IGP can occur in relation to a species’ life history, with individuals undergoing dietary shifts over the course of their ontogenetic development (Polis et al., 1989). These shifts may imply that a species competing with a second species as a

Chapter 4. Effects of intraguild predation on IS | 65 juvenile will later prey upon it as an adult. The consequences of IGP on individual fitness, and on the evolution and expression of behavioural, morphological and life history traits have extensively been described (Hin et al., 2011; Polis et al., 1989; Rudolf, 2007; van de Wolfshaar et al., 2006). For example, IGP has been shown to lead to changes in distribution and oviposition of predatory mites (Choh et al., 2010), to induce strong behavioural responses resulting in cascading effects on habitat use and aggressiveness in predatory bird assemblages (Mueller et al., 2016), and to lead to evolutionary diet and character shifts in threespine stickleback Gasterosteus aculeatus (Ingram et al., 2012; Miller et al., 2015). However, the specific effects of IGP on the degree of individual specialisation in a population have been poorly, if at all studied. In the presence of an intraguild predator, it is likely that the degree of individual specialisation in the prey population stems from a trade-off between access to resources, intensity of competition from both conspecifics and heterospecifics (Toscano et al., 2016b), anti-predator strategies, and the physiological cost associated with a shift in resource use (Hooker et al., 2017). For risk-averse individuals for instance, occupying more sheltered habitat might hinge on their competitive ability to stay there. On the other hand, divergent behavioural responses from competing prey species might counterbalance the effects of competition. If a prey species occupies a constrained niche as an outcome of interference competition, for example, differences in anti-predator tactics from each competitor might increase spatial or temporal segregation between species, inducing a reduction in competition intensity, which, in turn, might lead to niche expansion. Conversely, predation might enhance the effects of competition by further reducing a population’s niche through reduced habitat choice. Overall, potential outcomes of interacting predation and competition effects likely depend on the strength of the response of either prey species to predator presence.

The Eurasian Perch P. fluviatilis was introduced in New Zealand in 1868, and currently occurs in a wide range of lowland freshwater ecosystems across the

Chapter 4. Effects of intraguild predation on IS | 66 country (McDowall, 1990). Perch undergo long-documented ontogenetic diet and habitat shifts (Persson, 1988). Juveniles (up to 80-120 mm long) inhabit pelagic habitats and have a zooplanktonic diet, while intermediate sizes primarily feed on macroinvertebrates and larger sizes switch to piscivory when reaching around 150-240 mm long (Hjelm et al., 2000). Perch has demonstrated significant impacts on the abundance, population dynamics and ecology of the New Zealand-native G. cotidianus, both as a competitor and a predator of the species (Goldsmith, 2004; Ludgate and Closs, 2003). G. cotidianus is a habitat generalist and an opportunistic, omnivorous species which preys on a variety of benthic and pelagic prey, within the same pool of resources as juvenile perch; it also is a common prey for piscivorous perch (Duncan, 1967; Griffiths, 1976) and exhibits anti-predator strategies in response to perch chemical cues (Kristensen and Closs, 2004). The species also displays moderate levels of individual dietary specialisation (see Chapter 2), as well as habitat-related phenotypic variability (Vanderpham et al., 2013). In a previous experiment, I demonstrated that competition from Eurasian perch negatively affected individual specialisation levels in common bully Gobiomorphus cotidianus populations. Here, I expand this approach and ask how life history intraguild predation from perch in the form of competition, perceived predation risk and their interaction, impacts the resource use diversity and degree of individual specialisation of common bullies. I used a mesocosm pond experiment to measure individual specialisation in diet and habitat, and explored the question of whether variation in individual diet reflects variation in microhabitat use. Finally, by assaying activity levels in individual bullies, I asked whether this impact of antagonistic interactions is also measurable in one behavioural trait. First, I predicted that interspecific competition would induce a niche contraction at the population level (lower TNW) and a decrease in individual specialisation. Second, I predicted that predation risk would result in an increase in individual specialisation and to behavioural shifts. Predator avoidance might lead to a reduction in the segregation in resource use between individuals (decreased BIC), corresponding to an increase in overlap between individuals while the

Chapter 4. Effects of intraguild predation on IS | 67 population’s niche width remains similar; it could also lead to a reduction in the population’s total niche width. Both mechanisms could result in a decrease in IS. The opposite effect might, however, be observed: the deployment of different predator avoidance strategies by individuals might result in a diversification in habitat use or foraging patterns towards new prey, resulting in an increase in total niche width. If individuals retain the average breadth in their resource use, this might result in an increase in individual specialisation. Finally, I expected to find an interactive effect between predation risk and competition. The direction and strength of that effect would depend on the effect of predation risk on both bullies and small perch, as both competing species might adjust their behaviour and resource use according to predator presence.

Methods

Experimental setup

I used a 2x2 factorial mesocosm pond experiment to monitor the response of individual bullies to moderate levels of competition from young-of-year (‘small’) perch and predation risk from sub-adult (‘large’) perch in terms of habitat use, activity and diet. I used instant focal surveys to gather habitat and activity data and performed repeated, non-lethal gastric lavage to obtain dietary data. The study was conducted at the Sawyers Bay mesocosm facility on University of Otago grounds in Dunedin, New Zealand.

Mesocosm setup and fish housing

3 weeks prior to fish introduction, 36 mesocosm tanks (1200 L cattle troughs, Wilson Plastics, Palmerston North) were filled with tap water and 32 L of sand, which represented a layer 1 to 2 cm deep and a total surface of approximately 2.8 m2. Two patches of gravel (particle size 0.5-3 cm, average gravel patch size 978 ± 302 cm2) and two patches of macrophytes were randomly dispatched around the tank. The macrophyte patches consisted of weighed down 25 cm * 25

Chapter 4. Effects of intraguild predation on IS | 68 cm pieces of plastic trellis mesh maintaining aquatic plants retrieved at Sullivans dam (pondweeds, mostly water milfoil Myriophyllum triphyllum and Horse’s mane weed Ruppia polycarpa) at the bottom (vegetation patch size 625 cm2). In each tank we also positioned a “cage” consisting of a ~40 cm diameter cylinder of plastic trellis mesh (mesh size 1.7 cm) placed upright in the tank and extended above the water level to prevent possible escape from large perch. The mesh size did not hinder free swimming of the bullies nor the small perch. The cage was secured against the bottom of the tank with 5 to 10 pebbles (diameter 5-15 cm, average surface 43 ± 23 cm2). Overall, while sand covered between 85 and 90% of the bottom of the tanks, vegetation and pebbles respectively represented 5% and less than 2% of the available habitat; gravel patches, which had a more variable spread, made for 4 to 9% of the available substrate. Finally, the tanks were seeded with zooplankton and benthic communities collected from various ponds located around the Sinclair Wetlands, and left to settle until fish introduction (Figure 4-1 below).

Figure 4-1. Photographs of the experimental tank setup for the intraguild predation experiment (22/11/2017) (Right) and individually marked bullies (02/03/2018) (Left)

Additional 1200 L tanks were filled with tap water and set up to house the spare fish and the large perch. Spare bullies and small perch were kept in tanks equipped in the same fashion as experimental tanks, while the large perch were housed in three tanks partly covered with wooden structures for shade and equipped with PVC pipes to provide shelter. The large perch were initially fed every second day with commercial fish pellets, however they never appeared to respond to this type of food. As various aerial insects, as well as garden snails

Chapter 4. Effects of intraguild predation on IS | 69 were regularly found floating in all the tanks, it is likely that the large perch fed on them. The two smallest individuals had to be euthanised, after sustaining significant injuries due to aggression from the largest perch when they were housed together. The largest perch was subsequently housed in a separate tank. As the condition of the remaining perch did not deteriorate over time, I stopped the food supplementation and concluded that the tanks were able to sustain up to 2 large perch. Housing tanks were rotated to limit potential fouling.

Variable success in pondweed growth, as well as algal outbreaks differing in importance and coverage across the tanks in the early weeks of the experiment, led to inherent variability in the habitat quality within and across tanks. Tanks indiscriminately received anti-sludge (Pondzyme® Anti-sludge) and anti-algae treatments (Pondfix®) over the course of the experiment, at the height of algae outbreaks and when insufficient water clarity in some tanks prevented habitat observations. The products used were harmless to fish, and no adverse effect on their behaviour or fitness was noticed.

Fish collection and tagging

Small perch and bullies were captured on December 18, 2017 by seining along the shore of Lake Waihola. 168 bullies in the size range 37-54 mm TL (144 for experiment + 24 spare), and 22 young-of-year perch (TL 52-68 mm, 18 for experiment + spares) were transported in well-oxygenated lake water in chilly bins to the Sawyers Bay mesocosm facility on University of Otago grounds in Dunedin, New Zealand. Fish were placed in spare mesocosm tanks (see following section) until tagging. 6 sub-adult perch (TL 170-250 mm) were captured on November 24, 2017 by seine netting in the same location in Lake Waihola and housed in separate tanks.

On December 22, 2017, each bully was lightly anaesthetised (0.025 mL/L solution of AQUI-S), measured for total length and weighed to the nearest 0.1 g, before being individually marked with a Visible Implant Elastomer (VIE) tag (Goldsmith et al., 2003). Each individual received a single VIE tag in one of four

Chapter 4. Effects of intraguild predation on IS | 70 colours (yellow, orange, pink or blue) in one of two locations, either anterior to the first dorsal fin or between the two dorsal fins (Figure 4-1, p.68). These tag locations had previously shown very small rates of tag loss while allowing sufficient visibility from above to identify individuals at a glance during tank monitoring. One fish per colour was introduced to each tank, making for a population of four individuals per tank. Bullies were left to acclimatise for two days, after which half the tanks received an additional small perch. Large perch treatment and habitat observations started early January 2018. All fish were collected under authorisation D73/17 from the University of Otago Animal Ethics Committee and handled in compliance with the Animal Welfare Act, under authorisations D58/16 and D20/17 from the AEC.

Experimental treatments

4 individually marked bullies were placed in each of the 36 experimental tanks on December 22, 2017 and left to acclimatise for two days. One small perch was then added to half of the tanks. Further, each tank was randomly assigned one of two “predation risk” treatments (Table 4-1, p.71). Fish subjected to predation risk were exposed to a large perch three times a week. The treatment consisted of gently releasing a large perch into the cage (Figure 4-1, p.68) and leaving it for 30 minutes before removal with a hand net. No actual predation was expected to occur during the experiment as bullies and small perch were able to swim out of the cage and likely had previously been exposed to large perch as potential predators. In the predator-free treatments, a clean hand net was dipped into the cage and shaken around to account for any disturbance associated with perch addition in predation risk treatment. This treatment was applied over 16 weeks, 3 times per week. Large perch were first introduced on January 3rd, 2018.

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Table 4-1. Experimental treatments applied in the mesocosms Small perch Small perch absent present Exposure to large perch PP (7 replicates) BP (7 replicates) No exposure to large perch PA (6 replicates) BA (6 replicates) The numbers in parentheses indicate the number of replicates per treatment used in the analyses out of 9 replicates per treatment initially.

Due to unusually hot weather conditions over the month of January, several events of fish mortality led to varying numbers of bullies between and within tanks over time. Dead fish were replaced whenever possible, until all available replacement fish (n=24) were used. Consequently, some tanks were removed from the analyses: I used the sum of the number of days spent by each fish present in a given tank to select the replicates, choosing a threshold of 350 fish×days. This operation reduced the number of replicates in each treatment (Table 4-1 above). The number of fish per tank was also unbalanced as a result, and the number of bully competitors in those tanks at any given time varied between 3 and 4. The statistical analyses were performed on individual fish which had been captured the most during diet sampling for the dietary monitoring, and on individuals that gathered the most observations for habitat and activity monitoring (see detail below). As a result, different sets of individuals were used between the analyses of individual diet and habitat specialisation.

Diet monitoring

Once a fortnight during the experiment, bullies were captured by Gee minnow trapping (set during the day and checked every hour at least) and hand nets and subjected to gastric lavage to retrieve their stomach contents (see details of methods in previous chapters). One to 5 stomach content samples per fish were obtained over the course of the experiment and used for prey identification and analysis.

Fish were housed for <30 min in a bucket filled with cool water from the tank they were collected from. Fish were held securely and slightly tilted

Chapter 4. Effects of intraguild predation on IS | 72 downwards. Using a syringe, 1-3 mL of filtered source water was injected at the back of their stomach, via a thin polyethylene tube gently inserted through the mouth and down the oesophagus. The stomach contents and the water were regurgitated into an Eppendorf tube and immediately placed in an icebox before storage at -19°C. The fish were then returned to cool source water and allowed to recover before being returned to their tank.

Prey identification

Stomach items were identified to the lowest taxonomic level possible. Main prey categories included chironomid larvae, cladocerans, cyclopoid copepods, ostracods and gastropods. Cuticle parts allowing identification of a taxon (e.g. heads of chironomid larvae) and plant parts such as seeds or stem pieces were counted, but not used in the data analysis. Conversely, sand grains which might have been absorbed alongside benthic prey and material too digested to recognise any prey category were not included; stomachs containing these types of items only were considered empty. While some individual prey items could be identified to the specific level, those belonging to rarer taxa such as Odonata were pooled together when running the analyses. Pooling of taxa depended not only on taxonomic group, but also tried to reflect the functional position of the taxon in the tank ecosystem (benthic or pelagic). Prey counts were used to calculate Roughgarden’s metrics of specialisation.

Statistical analyses on diet data

Diet analyses were conducted on the aggregated prey data obtained on individual bullies in each lavage session, representing 109 individual sets of prey data. All analyses were conducted using R 3.4.4. (R Core Team, 2018) in R Studio 1.1.383.

Non-metric multidimensional scaling (NMDS) and permutational multivariate analysis of variance (PERMANOVA) were performed to examine the effects of predation risk and interspecific competition on population dietary data, using functions of the ‘vegan’ package, version 2.5-1 (Oksanen et al., 2018).

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To visualise the dietary niche of each mesocosm’s bully population, prey proportions for individual bullies were calculated, then averaged per tank. These means were used to run NMDS on the proportion data, with a Bray-Curtis dissimilarity index. The model fit was visually checked using a stress plot, and the stress value was calculated and considered acceptable when close to 0.1. The main and marginal effects of predation risk and interspecific competition were tested in a PERMANOVA, using a Bray-Curtis dissimilarity matrix. The main effects of both predictors were tested sequentially. While the results presented in this chapter use predation risk as the first term, the consistency of the statistical output was checked by reversing the order of the predictors.

I calculated individual specialisation metrics using the ‘RInSp’ package (Zaccarelli et al., 2013). Niche components WIC, BIC and TNW were calculated within each tank to obtain the individual specialisation index WIC/TNW. The population diet was calculated by converting individual diets into proportions, then averaging these proportions for each resource. This approach circumvents the potential bias resulting from individuals eating large numbers of prey items (Bolnick et al., 2002; Zaccarelli et al., 2013). The average pairwise dissimilarity E was used to compare resource use between individuals. This index ranges from 0, when all individuals from a given population use the same resources in the same proportions, to 1, when single individuals rely on entirely different resources compared to other individuals.

As the design was unbalanced due to fish mortality, I applied linear models (LM) to compare the four treatments using predation risk, interspecific competition and their interaction as fixed factors, with a 2-way ANOVA using type III sums of squares (SS) in which contrasts were set to sum to zero. If no interaction between competition and predation was found, the analysis was run again using a type II SS, checking for the order of the predictors. Model residuals were visually checked for normality.

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Habitat use

Habitat use was recorded three times a week using a variant of instant focal surveys (Fulton C. et al., 2001). Observations were performed for up to 5 min, starting from the moment the first fish was spotted. Fish that were not spotted were noted as ‘sheltering’. Tanks were approached cautiously to limit the possibility of getting noticed by the fish. Individual fish colour, position in the water column and behaviour were recorded, as well as the habitat type directly below the fish. Direct responses to my presence, such as escape manoeuvres, were noted separately. Table 4-2 (p.75) details the different categories of behavioural activity, and Table 4-3 (p.76) presents the individual variables recorded.

Behaviours were classified as either normal or responsive. Normal behaviours included swimming, foraging and being stationary, either on the bottom or in the water column. Response behaviours consisted of escape manoeuvres (dashes and zigzags) and accessing cover directly after my presence had been noticed. Some activities were regarded as either normal or responsive, depending whether they notably differed from the one performed before the fish appeared to spot me: a fish in a cover, e.g. within a patch of vegetation, could be considered either resting or actively hiding. Activities were further detailed depending on the fish position relative to covers or intensity of behaviour. For instance, fish could move in small “hops” over the substrate with pauses in between, longer swoops at the bottom, or actively swim in the water column. Foraging modes included sudden strikes towards the substratum (benthic target) or the water column (pelagic target).

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Table 4-2. Ethogram for categorising behavioural activity of bullies and perch during instantaneous surveys Behaviour Description Normal Idlesubst Stasis on substrate (with possible fin movement to maintain position) IdleWatCol Stasis in the water column (with possible fin movement to maintain position) Foraging / One or multiple strikes performed towards the substratum or the Feeding water column, or obvious jaw motion (“spitting” or chewing) Drifting Swimming motion with body position parallel or subparallel to the substratum Sheltering Body located within benthic microhabitat (buried in sand, hiding in vegetation, under pebble or using cage structure) Responsive Escaping Sudden escape manoeuvre using a fast swimming motion (dash or zigzag)

For each observation, microhabitat type directly below the fish and fish position within the environment were recorded (Table 4-3, next page). Substratum type included sand, gravel, vegetation, pebble, as well as tank and cage wall and floor since both bullies and small perch were found to use these as actual habitat structures.

A similar statistical approach to the one used in the dietary analysis was applied to habitat data within each tank. The 108 bullies included in the dataset had the largest number of individual observations (6 individuals in this dataset were not in the diet dataset). Each bully was spotted on average 10.5 ± 3.3 times over a total of 1272 individual observations. Habitat proportions data on individual bullies were averaged per tank, to estimate the treatment effect at the population level using a PERMANOVA (Bray-Curtis dissimilarity) and visualising it with NMDS. Individual specialisation on habitat was estimated by calculating Roughgarden’s indices WIC/TNW. The average pairwise dissimilarity E was used to compare habitat use between individuals. A linear model was run to estimate the effects of interspecific competition and predation risk on the degree of specialisation, with an ANOVA test using type III SS, assuming an interaction between predictors. In the absence of an interaction, a type II SS was used instead. Model checking was done by plotting residuals and verifying they were normally distributed.

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Table 4-3. Detail of recorded variables for individual fish (bullies and small perch) during instantaneous surveys. Variable Description Substratum Microhabitat directly below the fish. 6 different types of substratum were recorded based on the observed usage of the fish within the tanks: sand, pebble, gravel, vegetation, cage (including wall and floor), tank. Position in water column Vertical height above substratum Distance to shelter Distance to the nearest substratum/habitat structure different from sand Closest shelter Nearest substratum / habitat structure different from sand All distances up to 70 cm (maximum tank depth) were estimated to the nearest 10 cm.

Behavioural profile of individual bullies

Estimation of activity levels

Activity levels in non-foraging situations have been linked to foraging activity levels in several species (e.g. Belgrad and Griffen, 2016). To established whether the presence of perch might induce such behavioural changes in bullies, I assessed individual bully activity at the end of the previous experiment using novel environment assays (Butler et al., 2012) conducted between March 28th and April 5th, 2018 in a laboratory on site. Fish were captured within 2 hours of their assay by hand net or Gee minnow trap and kept in pottles containing water from their tank of origin. Two minutes before being introduced to the assay arena, each fish was individually kept in a covered, opaque container containing fresh water from a tank that never contained any fish. From that point onwards, the operator took care not to be seen by the fish until the end of the manipulation. The same “fishless” water was used in the arena and was systematically replaced after each assay.

The assay arena was a semi-opaque white 34 cm * 24 cm plastic tank filled fresh water up to a 5-cm depth, split into a small holding zone and an exploration zone. The two zones were separated by an opaque, removable wall which could be operated from the side. A camera (GoPro Hero5) was positioned directly above the arena. Recording started before the assay. The fish was introduced from its individual container to the holding zone where it was left to acclimatise for 2 minutes; the operator then rapidly removed the wall. The fish

Chapter 4. Effects of intraguild predation on IS | 77 was then allowed to explore for 5 minutes, by which point it was returned to its housing tank. The arena was then reset with fresh water.

I performed these tests three times per fish to assess repeatability of individual activity, defined as the average amount of time spent actively moving by the fish in seconds. One individual (‘F1-A’) was assessed four times, as over the course of the first assay it dashed briefly when the wall was removed and settled afterwards, remaining immobile for the rest of the assay. This behaviour was not observed in any other fish, nor did F1-A display it in the following assays. The final analysis was thus conducted after removing this value for activity, and the results compared with the full dataset to ensure their validity. The videos were processed using EthoVision XT® (version 11.5). Fish subjected to 3 video assays were finally euthanised using an overdose of AQUI-S.

Analysis of behavioural data

After checking for normality of the distribution of the activity values, I tested for the potential effect of habituation in each fish by fitting a linear mixed effect model (Bates et al., 2015) with the assay number as a fixed factor, and the fish ID nested in the tank number as a random factor. Once verified that the assay number had no significant effect on the activity values, I checked for the repeatability R of the response variable, defined as the individual-level variance over the sum of individual-level and residual variance (Dingemanse and Dochtermann, 2013; Nakagawa and Schielzeth, 2010). I used a linear mixed effect model, with the assay number as a fixed effect and the fish ID as a random effect, nested within tank ID. Repeatability scores were estimated for 10000 new datasets simulated from that model fits, based on the original data. The 95% parametric bootstrap confidence intervals were estimated as the 2.5 and 97.5 quantiles of the resulting distribution of repeatability scores. Finally, I used the average values per fish and per tank to test for potential effects of the perch treatments, using a linear model with a type III SS to account for possible interactions between predictors, and switching to a type II SS if no interaction was detected.

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Results

Population prey choice and dietary overlap

Number of prey items in individual bully stomachs ranged from 0 to 334, with an average of 10.8 ± 33.3 prey items. For perch, individual stomachs contained 71.8 ± 120.9 prey items, with a minimum of 0 and a maximum of 716 prey counted. In a single sampling session, fish were found to prey on a maximum of 8 taxonomic groups, with an average of 1.2 ± 1.5 in bullies and 3.0 ± 2.3 in perch overall. Over all sessions, these values rose to 3.03 ± 2.1 and 5.9 ± 2.1 for bullies and perch, respectively (see Table S4-1, p.161).

Over all stomach flushing sessions, 17 bullies out of 109 (15%) were removed from the dietary analysis as they had empty stomachs every time they were flushed. This number includes fish which were captured only once as well as fish that were flushed multiple times.

Figure 4-2. Type and proportion of prey items consumed by bullies in each treatment and by small perch over all treatments. The boxplots depict the median, first and third quartiles with whiskers extending up to 1.5 times the interquartile range. BA: Bully only. BP: Predation risk. PA: Interspecific competition. PP: Interspecific competition + Predation risk. Median values are indicated above the boxes.

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Bullies and small perch consumed similar prey types (Figure 4-2 above). Consumed prey consisted of 15 taxonomic groups, cladocerans (Chydoridae from the Chydorinae and Aloninae subfamilies, Ceriodaphnia dubia), chironomid larvae, cyclopoid copepods and ostracods being most commonly found in the stomachs.

Non-metric multidimensional scaling (NMDS) of the captured prey community matrix indicated a separation of treatments along a benthic-pelagic axis (Figure 4-3 below) with cyclopoid copepods and daphniids on one side, chydorids and gastropods on the other, mostly driven by the presence or absence of small perch. Stress value was 0.102 when using Bray-Curtis dissimilarity matrix on the prey community.

Figure 4-3. Difference between treatments in the captured prey community using non-metric multidimensional scaling, based on Bray-Curtis dissimilarity. Individual tanks are represented by points and grouped by convex hulls. Names indicate the centroid position of the taxa’s ordination scores.

Competition from juvenile perch induced a significant shift along the first axis, which followed a generally zooplanktonic (cyclopoid copepods, Ceriodaphnia dubia) to a generally benthic (gastropods, chironomid larvae) type of diet

2 (F1,22=12.01, p=0.001, R =0.33). There was no significant dietary shift linked to

2 predation risk (treatments BP and PP) (F1,22=1.72, p=0.16, R =0.05) and no

Chapter 4. Effects of intraguild predation on IS | 80

2 interaction between effects (F1,22=0.76, p=0.52, R =0.02), although bullies under both perch competition and predation risk appear to shift further along

NMDS1. The marginal effect of each factor was not significant (F1,22=0.76, p=0.55, R2=0.02).

Individual specialisation in dietary preferences

Over all treatments, the WIC/TNW index ranged from 0.39 to 0.98 (mean WIC/TNW=0.77±0.17) which denotes moderate to low levels of specialisation in the considered population (Figure 4-4 below).

(a) (b) (c)

Figure 4-4. Total niche width (a), index of individual specialisation WIC/TNW (b) and average pairwise dissimilarity E (c) in bully diets across interspecific competition and predation risk treatments. The boxplots depict the median, first and third quartiles with whiskers extending up to 1.5 times the interquartile range. Circles represent outliers.

The main effect of predation risk on the degree of individual specialisation was significant but trivial (F1,22=6.01, p=0. 02, ηp²=0.07), and the main effect from competition was not significant (F1,22=0.22, p=0.65). The interaction between predation risk and competition was significant (type III SS, F1,22=5.08, p=0.03) and overrode the main predation risk effect (ηp²=0.19), with a positive effect on WIC/TNW (Figure 4-4b). Regarding niche components (Figure 4-5, p.81), competition had a small, significant effect on the WIC (Type II SS, F1,22=4.69, p=0.04, ηp²=0.18), with WIC slightly higher where small perch were present

Chapter 4. Effects of intraguild predation on IS | 81 and lower in the BP treatment. Predation risk had a significant effect on BIC

(Type III SS, F1,22=5.53, p=0.03), although this effect was small (ηp²=0.20). The interactive effect of predation risk and competition on BIC was small and marginally significant (F1,22=3.19, p=0.09, ηp²=0.13). BIC was lower in both treatments where small perch were present but was significantly higher in tanks in which bullies were exposed to large perch only. The total niche width, however, did not change significantly between treatments.

The average pairwise dissimilarity E ranged from 0.16 to 0.97 (mean E=0.54±0.22) with the lower values observed in the treatment featuring both small and large perch and the higher values, on average, in the predation risk treatment (Figure 4-4, p.80). The observed values in each treatment denote a high degree of variability among replicate tanks. E was weakly but significantly correlated to WIC/TNW (adjusted R2=0.125). The linear model indicates a significant competition effect (Type II SS, F1,22=8.10, p=0.009), with a moderate size (ηp²=0.27). Neither predation (F1,22=2.16, p=0.16) nor interaction of the two factors (F1,22=2.83, p=0.11) were significant.

(a) (b) Figure 4-5. Effects of competition and predation risk on individual dietary niche components WIC (a) and BIC (b) relative to total niche width TNW. Tanks appear as circles, and tank averages are represented by diamonds. The segments illustrate the direction and intensity of the shift in niche components values vis-à-vis the monospecific treatment (BA). At WIC/TNW = 1 (grey line), all individuals use the same resources as their population as a whole (total generalism).

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Habitat use

Habitat use at the population level

Bullies across tanks used the entire set of habitats, in proportions consistent with habitat availability (Figure 4-6 below). Sand and water column were the habitats where the largest amounts of individual observations were made. Individuals were on average spotted on sandy bottoms between 37% and 57% of the time, with lower values observed overall in tanks without small perch present. On average, bullies used the water column more in small perch-free tanks (27% in BP treatments, 22% in BA treatments) than in the two other treatments (18% in PA tanks and 13% in PP tanks). Where small perch were present, the bullies also appeared to use the tank walls more (12% and 18% in PA and PP tanks, respectively). Pebbles and vegetation were marginally used more often in tanks where bullies were exposed to large perch, when comparing tanks under the same competition treatment.

Figure 4-6. Type and proportion of habitat types used by bullies in each treatment. The boxplots depict the median, first and third quartiles with whiskers extending up to 1.5 times the interquartile range. BA: Bully only. BP: Predation risk. PA: Interspecific competition. PP: Interspecific competition + Predation risk. Median values are indicated above the boxes. The median values are indicated above the boxes.

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Competition had a significant effect on habitat choice at the population level

2 (F1,23=3.62, p=0.006, R =0.13) but no significant effect of predation (F1,23=0.86, p=0.53) nor interactive effect with competition (F1,23=0.95, p=0.46) were detected. NMDS indicates that populations spread along a benthic habitat- pelagic axis (NMDS1), with the cage, water column and tank wall on one side and sand, gravel and pebbles on the other (Figure 4-7 below). While the cage itself was not a prominent habitat for bullies overall (see Figure 4-6 above), the second axis appears to indicate the degree of proximity to it, along a cage-tank wall gradient. In heterospecific treatments, bully populations shift mostly along NMDS2, away from the cage structure. The largest variability among populations within treatments was observed in the monospecific treatment.

Figure 4-7. Difference between treatments in habitat choice using a non-metric multidimensional scaling analysis, based on Bray-Curtis dissimilarity. Individual tanks are represented by points and grouped by convex hulls. Names indicate the centroid position of the ordination scores of each habitat type.

Individual specialisation in habitat use

The degree of habitat specialisation among individuals was generally low, with an average WIC/TNW of 0.83±0.06 over all treatments (Figure 4-8, p.84). Some p-values obtained on this index were significant, rejecting the null model of a population of generalists randomly using the available habitats in some tanks.

Chapter 4. Effects of intraguild predation on IS | 84

No significant treatment effect on WIC/TNW was detected (Type II SS, predation F1,23=1.89, p=0.18, competition F1,23=2.72, p=0.11) although there were treatment effects on niche components. All perch treatments led to a decrease on average of the total variation TNW (Figure 4-8a below), with the largest shift observed in the treatment with both competing perch and predation risk (PP) (Type III SS, predation F1,23=2.38, p=0.14, competition

F1,23=7.28, p=0.01, predation*competition F1,23=4.20, p=0.05). Variation in the

WIC component was driven by competition (F1,23=5.06, p=0.03), while BIC was significantly affected by predation (Type III SS, F1,23=6.42, p =0.02) and the interaction between predation and competition (F1,23=5.00, p=0.04). The average BIC decreased in the BP treatment, resulting in the increase in WIC/TNW observed in BP tanks (Figure 4-8b and Figure 4-9, p.85).

(a) (b) (c) Figure 4-8. Effects of competition and predation risk on total habitat niche (a), index of individual habitat specialisation WIC/TNW (b) and average pairwise dissimilarity E (c). The boxplots depict the median, first and third quartiles with whiskers extending up to 1.5 times the interquartile range. Circles represent outliers.

There was a moderate correlation (adjusted R2=0.46) between the average pairwise dissimilarity E and WIC/TNW. On average, individuals in the BP treatment tended to use resources in more similar proportions compared to the three other treatments (Figure 4-8c above). There were weak, but significant effects of predation risk (Type III SS, F1,23=9.70, p=0.005, ηp²=0.15) and the

Chapter 4. Effects of intraguild predation on IS | 85

interaction between predation risk and competition (F1,23= 5.75, p=0.03,

ηp²=0.2), while the effect of competition alone was not significant (F1,23=1.43, p=0.24).

(a) (b)

Figure 4-9. Effects of competition and predation risk on individual niche components WIC (a) and BIC (b) relative to total niche width TNW. Tanks appear as circles, and tank averages are represented by diamonds. The segments illustrate the direction and intensity of the shift in niche components values vis-à-vis the monospecific treatment (BA). At WIC/TNW = 1 (grey line), all individuals utilise the whole population niche width (total generalism).

Activity

Activity was moderately repeatable across assays (median R=0.37, 95% CI 0.12- 0.57), indicating that bullies displayed a behavioural type in activity. Perch presence appeared to generally lower the duration of movement (Figure 4-10, next page), with competition having a small, close to significant effect (2-way

ANOVA with type II SS, F1,21=4.1, p=0.06, ηp²=0.16). Predation risk did not have any significant effect (F1,21= 0.52, p=0.48, ηp²=0.02), and there was no evidence of an interactive effect was between the two predictors (F1,21=0.29, p=0.60).

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Figure 4-10. Effects of competition and predation risk on the average activity profile within tanks

Discussion

With this mesocosm experiment, I demonstrated how competition, predation risk and their interaction could affect the habitat and dietary niche of bullies at the population and individual levels. This study presents a first assessment of the interactive effects of competition and predation risk on individual specialisation. Bully populations responded to interspecific competition by shifting towards more benthic prey types and habitats. Competition negatively impacted individual activity level and habitat WIC, whereas it had a positive effect on dietary WIC. On the other hand, while predation risk did not affect diet and habitat use on average, it led to significant changes in the degree of individual specialisation and interindividual variation. A lower degree of IS in diet (i.e. higher WIC/TNW values) was observed in the two interspecific competition treatments, while the highest mean degree of IS (lowest mean WIC/TNW) was observed in tanks with predation risk alone. Some interactive effects between competition and predation risk were detected as well: combined, the two interactions led on average to higher WIC/TNW values than in any other treatment. Predation risk also interacted with competition on habitat use, with predation moderating the negative effects of competition on

Chapter 4. Effects of intraguild predation on IS | 87 the average pairwise dissimilarity (E index). Finally, there was no response variable for which both competition and predation risk had significant main effects, which suggests that each interaction type affects different niche components and, beyond that, different aspects of individual resource use.

Individual- and population-level effects of competition

As expected, interspecific competition led to niche shifts at the population level both in habitat use and diet. Overall, more benthic prey items were found in bully stomach contents in tanks where small perch were present. This result was consistent with habitat use observations, as bullies overall tended to use tank walls and sandy bottoms more often when small perch were present. Benthic organisms, including snails (Planorbidae and Potamopyrgus sp.) and chironomid larvae tubes, were visibly abundant on these types of microhabitats. This reduction in niche overlap between interspecific competitors via a population niche shift was consistent with observations in other terrestrial (Alatalo, 1981; González-Solís et al., 1997; Steinmetz et al., 2011), freshwater (Young, 2004) and marine systems (Bonin et al., 2009). Furthermore, my results indicate a contraction in the dietary niche due to competition, although no such effect was observed in habitat use. It is a common observation that niche shifts in response to interspecific competition contribute to reducing niche overlap between competitors (Araújo et al., 2011; Brink et al., 2015; Tran et al., 2015), including in systems where ecological niches of these competitors are segregated (Závorka et al., 2017). This is of particular interest in studies of biological invasions, as trophic and habitat niche divergence might help promote or maintain coexistence between species. A study of habitat use in voles Microtus spp. indicated a contraction of the habitat niche due to competition, with an increase in use of exposed habitats in the presence of competitors (Koivisto et al., 2018). While diverse habitat patches were available to bullies in my experiment, my results show a similar trend with competition leading to a decrease in the habitat niche. Contrary to my expectations, small perch presence alone had no effect on WIC/TNW in the bully populations,

Chapter 4. Effects of intraguild predation on IS | 88 although it did increase the degree of similarity in diet between individuals and significantly impacted the within-individual variance in resource use. Exposure to small perch led to a reduction of the average individual variation in habitat use (WIC), but an increase in the WIC in diet.

The magnitude of the effect of competition from young-of-year perch might be contingent on their individual traits. Foraging behaviour and ultimately diet might be affected by individual variation in phenotypes including personality traits (Winandy and Denoël, 2015). Indeed, bolder or more aggressive individuals are potentially stronger competitors, particularly if they display better foraging efficiency themselves (Juette et al., 2014). Perch exhibit both stable personalities (Magnhagen and Staffan, 2005) and moderate levels of individual specialisation (Eklöv and Svanbäck, 2006). On the other hand, predation risk has been shown to influence the competitive ability and habitat use of YOY perch (Westerberg et al., 2004). The behavioural variation and individual specialisation of small perch might therefore have mediated the response of bully individuals and populations to interspecific competition. In wild communities, this mediation might be size-dependent, as perch undergo both ontogenetic dietary shifts and IS changes between size classes (Svanbäck et al., 2015).

Individual-level effects of predation

While no actual predation from large perch was observed during the experiment, some fish were unaccounted for over the weeks beyond the dead individuals found in the tanks due to hot weather conditions or to possible interindividual aggression, or both. Predation while perch were present in the cages, cannibalism, as well as bird predation on site were all possibilities. Both bullies and YOY perch were observed to sometimes engage in predator inspection behaviours, which might have increased the risk of being preyed upon. While individual traits of YOY perch might explain the variation in bully responses to competition, it is not the case for the ones used as potential predators, as all tank populations were randomly exposed to each one of them.

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Predation risk affected the degree of individual specialisation through the variation in mean resource use (BIC), although the direction of this effect depended on the presence or absence of small perch. In mesocosms without perch, the higher variation between individual bullies led to a higher proportion of individual specialists. The opposite trend was observed when small perch were present, as the BIC decreased. I anticipated that niche shifts in response to perceived predation risk would be related to habitat use or activity, with possible consequences for dietary preferences due to a change in habitat partitioning (Eklöv and Svanbäck, 2006). At the population level, however, such effects were not detected.

Active predators can generate substantial shifts in prey activity levels (Miller et al., 2014), whereas competition is generally expected to favour increased activity. This was not observed in the bully-perch system, as predation risk did not appear to impact movement rate, contrary to competition level. As aggressive behaviour was sometimes observed in small perch towards bullies, it is possible that bullies exposed to competing perch reduced their activity level to limit agonistic encounters, indicating a stronger response to a dominant competitor than to a less acute predation threat (Koivisto et al., 2018), with the latter possibly linked to acclimation due to regular exposure to large perch.

Interactive effects of predation risk and interspecific competition

Taken in isolation, both competition and predation risk to some degree affected bullies at the individual and population levels. As predicted, they also interacted in a significant way, with a negative effect on the segregation of individual habitat and diet niches, a negative effect on the total habitat niche and a positive effect on WIC/TNW. These interactive effects were of two kinds: synergistic effects were observed for the dietary niche, while antagonistic effects affected the habitat niche (Table 4-4 below). In the former, the direction of the effects of predation risk on BIC changed depending on the competition level, affecting the degree of individual specialisation accordingly as the population’s niche did not significantly change. In the latter, although both

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TNW and BIC in habitat use decreased due to the interaction between competition and predation risk, a stronger negative effect of competition on WIC meant that there was no response detected on WIC/TNW.

Table 4-4. Summary table of the effects on competition, predation risk and their combination on population- and individual-level resource use and activity Resource type Variable C PR C+PR Diet Population Mean Shift 0 0 E - 0 0 WIC/TNW 0 - + TNW 0 0 0 WIC + 0 0 BIC 0 + - Habitat Population Mean Shift 0 0 E 0 - - WIC/TNW 0 0 0 TNW - 0 - WIC - 0 0 BIC 0 - - / Activity - 0 0

The observed responses to the two types of interactions might stem from traits accounting for flexible foraging and anti-predator defences. Movement speed, feeding rate and choice of foraging location are some of the traits that determine the absolute time foraging time or effort (Abrams, 2010). In the present study, both habitat use (at the population level) and movement rate were found to be impacted by competition. On the other hand, traits such as habitat choice and predator switching behaviour determine the relative intake of different foods (Abrams, 2010). This was the case to some extent here, as predation risk indeed influenced individual diet specialisation. Finally, ecological traits that determine or modulate the total amount of predator exposure – what Abrams (2010) refers to as “general defence” – have been shown to modify the outcomes of competitive interactions between prey species that share predators. In the context of intraguild predation, the presence and identity of the interspecific competitor might affect trait plasticity, mitigating the response to IGP. Conversely, predation risk might modify the outcomes of interspecific competition (Koivisto et al., 2018) due to responses from bullies, small perch or both species, depending on the level of asymmetry in the competitive interactions between them. In reef fishes Pomacentrus spp., for

Chapter 4. Effects of intraguild predation on IS | 91 instance, predation threat exacerbated agonistic behaviours while also mediating swimming and sheltering behaviours in competing species (Hall and Kingsford, 2016). My results show interactive effects of predation and competition on BIC, dietary specialisation and habitat TNW, indicating how ecological traits of bullies might be impacted in systems where they are exposed to intraguild predation from perch.

Links between habitat, diet and behaviour

Environmental variation can moderate interspecific interactions as well as intraspecific variation in resource use. Summer 2018 was unusually hot and dry for the region. The heat waves of January and early February 2018 resulted in water temperatures in the tanks potentially higher than the temperature range encountered in the Waipori-Waihola wetlands (Hall and Burns, 2002) where the fish were collected. While elevated temperatures caused episodes of fish mortality, other compounding effects might have influenced habitat use and foraging patterns by both bully and perch. Most empty stomachs in bullies were found during the month of January, while the highest individual prey counts were observed in February and March. Temperature is a critical environmental factor, which can affect food web structure and dynamics (O’Connor et al., 2009). In fish, increases in temperature have been linked to physiological and behavioural changes (e.g. Clarke and Johnston, 1999; Neuman et al., 1996) as well as increased between- and within individual- variation in activity (Nakayama et al., 2016) as individuals respond heterogeneously to temperature change. Furthermore, individual state can alter the strength of the response to indirect interactions (Gravem and Morgan, 2016). As a result, temperature might have influenced the degree of individual specialisation within each tank by mediating individual behaviours on the one hand, and the strength of the responses to predation risk and competition on the other hand. However, the values of WIC/TNW obtained on the dietary data in this experiment are comparable to the ones obtained in the previous competition experiment and are consistent across treatments, which might indicate that the aggregation of

Chapter 4. Effects of intraguild predation on IS | 92 prey and habitat data over all sampling sessions can circumvent these additional sources of variation. In recent years, efforts to show a link between personality traits and ecological niches have grown (Reale et al., 2010; Toscano et al., 2016a; Wey et al., 2015), with mixed results (Schröder et al., 2016). In burbot Lota lota, for example, the between-individual variance in mean movement rates was driven significantly by interactions between trophic niche and habitat-related prey reliance, demonstrating in this species a correlation between behavioural inter- individual variation and dietary specialisation. Consumer foraging behaviour, which potentially shapes individual specialisation, can be influenced by behavioural traits across several contexts, including foraging activity, foraging under predation risk and foraging within a social context (Toscano et al., 2016a). Foraging activity of individual predators as well as the activity level of their prey, can be predicted by activity level measured in non-foraging situations (e.g. Belgrad and Griffen, 2016; Pruitt et al., 2012). Intermediate predators often compromise between their foraging demands and predation threats by adjusting their activity level (Toscano and Griffen, 2014). While this potential link between individual niches and activity level was not tested in this study, it is worth noting that movement duration of individual bullies was negatively impacted by interspecific competition. Furthermore, I found moderate levels of repeatability in this measure of activity. Some authors caution against rapid assays, arguing that such tests don’t necessarily predict short-term repeatability of labile traits and can lead to misclassifying behavioural types, particularly in contexts to which the individuals have not been acclimated (Biro, 2012); however, the degree of repeatability I estimated was consistent with previous values found for bullies from the same area (Hammond-Tooke et al., 2012). The measured change in activity could therefore be indicative of the existence of a behavioural shift, due to the presence of an heterospecific competitor. Behavioural types might mitigate or enhance the individual functional response to antagonistic interactions (Toscano et al., 2016; but see Schröder et al., 2016).

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Both bullies and perch exhibit behavioural syndromes (Conrad et al., 2011; Hammond-Tooke et al., 2012). Activity level is correlated to aggression in common bullies, and both traits appear to be mediated by the presence of predator cues (Hammond-Tooke et al., 2012). The existence of personalities might mitigate or enhance the degree of competition between the two species. For instance, in a study of interacting groups of katydids nymphs Scudderia sp. and froghoppers Philaenus spumarius, interspecific competition was higher in a context in which both species occupied a large behavioural space (Lichtenstein et al., 2017). Habitat use can significantly affect food resource use and trophic relationships between co-occurring, competing species (Guo et al., 2014). However, I found that, contrary to my expectations, the degree of individual diet variation did not reflect IS in habitat use. In a long-term study of the Brünnich’s guillemot Uria lomvia, Woo and colleagues (2008) established a link between dietary specialisation and foraging behaviour of individual birds (Woo et al., 2008). This apparent contrast with my results might be due to the way the habitat observations themselves were conducted, as they included, but were not focused on foraging habitats per se. Moreover, the maximum likelihood approach used to calculate Roughgarden’s indices of specialisation WIC/TNW and E tends to lead to overestimation of the degree of individual specialisation when sample sizes are low or heterogeneous (Coblentz et al., 2017), which was the case in the present study. This limitation was particularly apparent in the estimation of Roughgarden’s indices of individual specialisation in habitat use, as the level of IS obtained by the Monte Carlo resampling procedure did not always exceed that expected by chance, indicating that individuals may have been using habitat types non-selectively. The significance values obtained during the resampling procedure are, however, sensitive to sample size (Fodrie et al., 2015). It is worth noting that the degree of habitat specialisation estimated in this study is similar to values obtained for habitat specialisation in Red drum Sciaenops oscellatus (Fodrie et al., 2015).

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Conclusion

Under different levels of competitive pressure and predation risk, the level of individual specialisation would depend on the strength of antagonistic interactions balanced with the degree of environmental heterogeneity and the accessibility to resources. The strength of competitive and predatory interactions can be density-dependent (Lichtenstein et al., 2017) or, more generally, be linked to population structure; it may also change over ontogenetic stages, implying that the effects of competition can prevail between certain size or age classes, while predation effects might be stronger between others (Persson, 1988). The outcomes of antagonistic interactions often occur along gradients themselves, whether due to shifting environmental conditions or, for instance, variation in the abundance of a third-party species (i.e. one that modifies a focal interaction between two other species) (Chamberlain et al., 2014). Conversely, behavioural changes can be associated with habitat complexity (Church and Grant, 2018; Enefalk and Bergman, 2016) and food abundance (Borcherding and Magnhagen, 2008); greater environmental variation might allow greater behavioural variation among individuals (Bell et al., 2009), which itself can lead to inter-individual differences in resource use. The present experimental design allowed detecting both main and interactive effects of predation risk and competition within small bully populations in each mesocosm tank. This study provides further demonstration that the outcomes of multiple antagonistic interactions are highly context-dependent, both at the population and the individual levels. The direction and intensity of intraguild predation might be mitigated by environmental variation and by compensatory mechanisms and behavioural responses from both predator and intraguild prey. Considering individual-level responses to both competition and predation risk can help better understanding the emergent consequences of interspecific interactions for food webs and ecosystems.

5. 5 Effects of Human Activity on Dietary Individual Specialisation Across Animal Taxa

Pond in the Waipori-Waihola wetlands, Nov. 2017

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Chapter summary

Human impacts on species and ecosystems take multiple forms: they might involve habitat modifications such as urbanisation and land conversion, but also may modify species composition, richness and abundance via mechanisms including exploitation and species introduction. While these impacts are well documented at the ecosystem, community and population levels, their potential effects on ecological diversity within species have rarely been examined. Individual specialisation describes the fact that individuals have narrower ecological niches than their population considered as a whole. Nearly ubiquitous across taxa, it has a range of potential consequences from the population to the ecosystem levels. Here, we ask whether human influence on ecosystems impacts the level of individual dietary specialisation across animal taxa. To answer this question, we undertook a comparative phylogenetic analysis of peer-reviewed literature. We first compiled published estimates of individual dietary specialisation, then used the Human Footprint Index (HFI) as a measure of human impact on terrestrial, freshwater and marine ecosystems. Although we found no significant relationship between human activities and individual specialisation in marine ecosystems, we demonstrate that terrestrial anthropogenic footprint has a significant negative effect on IS, in both terrestrial and freshwater food webs. This result reveal the widespread but unappreciated effects of anthropogenic impacts on intraspecific diversity and reinforce the importance of considering individual responses to environmental stressors in anthropogenic landscapes.

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Introduction

In May 2019, the United Nations’ Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) published a dire warning regarding the unprecedented human-induced biodiversity loss on a global scale. The report paints a grim picture of the effects of human activities on ecosystems, with their impacts being found on approximately 75% of the planet’s land surface (Venter et al., 2016a) and 87% of its oceans (Jones et al., 2018). It has been widely acknowledged that human activities globally affect ecosystems, and are the primary driver of the current mass extinction, dubbed the Anthropocene extinction (Wagler, 2018; Watson et al., 2016). Human- induced ecosystem disruptions are varied and far-ranging. They affect ecosystem structure, processes and dynamics by altering habitat quality (Jacobson et al., 2019), biodiversity, composition and abundances of species assemblages (Correll et al., 2017; Sebastián‐González et al., 2019; Vega et al., 2000), as well as global dispersal and biological interactions (Rodewald et al., 2011). For instance, the conversion of wildlands to developed habitat has been linked to the creation of biotically homogeneous communities, a loss in native species diversity and an overall net loss in global biodiversity (McKinney, 2006). Similarly, the intensification of agricultural practices comes with a host of issues such as an increased dissimilarity between transformed land cover and native cover, the introduction of exotic species, the disruption of nutrient cycles, and the addition of pollutants (Jørgensen et al., 2016; Marzluff and Hamel, 2001; Norris, 2008). Associated with reductions in resource availability, these disruptions can also affect ecological interactions within communities.

While these impacts are well-documented at the ecosystem and population level, their potential effects on the ecological diversity within species had, until recently, received less attention. Nevertheless, human activities have been linked to a variety of behavioural (Beckmann and Berger, 2003; Kühl et al., 2019; Lapiedra et al., 2017; Wilmers et al., 2013; Wong and Candolin, 2015) and Chapter 5. Effects of human activities on IS | 98 ecological changes (Bestion et al., 2019; Crooks, 2002; Moss et al., 2016; Navarro et al., 2017; Newsome et al., 2014; Smith et al., 2018; Tucker et al., 2018; Zeug et al., 2017) across a wide range of taxa. Intraspecific variability can be a substantial force shaping ecosystems and their function on par with interspecific variability (Bolnick et al., 2011, 2003; Zhao et al., 2019). Within species, individual ecological niches are often narrower than population niches, a phenomenon referred to across the literature as intraspecific niche variation or individual specialisation. Individual specialisation (hereafter IS) is nearly ubiquitous across taxa. A growing number of empirical and observational studies have described the implications of individual variability in ecological and behavioural traits on ecosystem function and dynamics. For example, IS can mediate the direction and magnitude of ecological interactions within populations and, more broadly, within communities (Beleznai et al., 2015; Belgrad and Griffen, 2016; Bolnick et al., 2011; Hart et al., 2016; Phillips et al., 2017; Pruitt et al., 2012a; Pruitt and Ferrari, 2011). Conversely, several studies have examined the possible mechanisms which might affect the degree of individual specialisation within a population. Among those mechanisms, ecological interactions, which include inter- and intraspecific competition, predation and ecological opportunity, constitute a fundamental driver of IS (Araújo et al., 2011; Britton and Andreou, 2016; Evangelista et al., 2014; Kernaléguen et al., 2015; Poisot et al., 2011). Human activities can affect individual specialisation in a given population by potentially affecting the diversity and availability of prey and the presence, density and dynamics of competing organisms and potential predators.

Anthropogenic impacts on a population can be direct, disrupting the survival, behaviour, and population dynamics of particular species. They can also be mediated through effects on other interacting species that modify resource availability and biological interactions (Figure 5-1, next page).

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Figure 5-1. Examples of potential impact pathways of human activities on the degree of individual specialisation in a single focal species. Solid lines: positive effect. Dashed lines: negative effects. Habitat quality and structure affect species’ distribution and abundances, with higher habitat quality generally being associated with greater diversity, greater resource availability and greater ecological opportunity. Two-way interactions occur between ecological interactions – which include antagonistic interactions such as predation and competition, – species distribution, abundance and behaviour, and resource availability. Consumptive effects of predation are linked to decrease in intraspecific competition. Increased interspecific competition has been linked to a decrease in ecological opportunity (Araújo et al., 2011).  Scenario 1: Top-down effects of species exploitation on that species’ individual specialisation. The reduction in abundance leads to a direct reduction of intraspecific competition, which is generally linked to a decrease in IS (Araújo et al., 2011).  Scenario 2: Potential effects of the introduction of an interspecific competitor on a focal species’ individual specialisation (e.g. free roaming domestic animals). In addition to direct negative effects, indirect negative effects of increased competition on IS occur through a decrease in ecological opportunity.  Scenario 3: Potential effects of predator reduction on a focal species’ IS. Predation can affect the degree of IS either positively or negatively, depending on the type of response of the prey population. In this example, predation promotes IS in prey organisms by reducing intraspecific competition. Release from predation leads to increased intraspecific competition and results in increased IS (Araújo et al., 2014). However, predation might also result in a decrease in IS, for instance if convergence in resource use by the prey species occurs as a result from seeking refuge in protected habitats.  Scenario 4: Effects of human-resource subsidies on IS (e.g. invasive prey species, crop fields). An increase in available resources can result in increased ecological opportunity and subsequent increased degrees of IS, for example via population niche expansion (Darimont et al., 2009; Moss et al., 2016). However, behaviour-based increases in dietary specialisation have also been observed in species exposed to food-poor environments (Tinker et al., 2008).  Scenario 5: Negative effect of habitat degradation on IS (e.g. habitat fragmentation (Layman et al., 2007b), habitat homogenisation). In this example, habitat degradation triggers a general decrease in resource availability (or accessibility), which in turns negatively affects ecological opportunity.

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Within anthropogenically modified ecosystems, multiple mechanisms are likely to occur that can lead to changes in multiple drivers of individual specialisation and therefore the overall degree of individual specialisation in a given population. For example, ecological opportunity is related to the diversity of resources including food and habitats (Parent and Crespi, 2009), along with lower levels of interspecific competition (Araújo et al., 2011). Human activities leading to modifications in resource availability and accessibility might therefore alter not only ecological opportunity, but also population niche widths and predator-prey relationships (Newsome et al., 2014). Scenario 4 (Figure 5-1, p.99) illustrates, as an example, how anthropogenic resource subsidies might lead to an increase in individual specialisation. Similarly, increased intraspecific competition has often been linked to niche expansion at the population level and to an overall increase in individual specialisation (e.g. Costa et al., 2008; Kobler et al., 2009; Svanbäck and Bolnick, 2007; Svanbäck and Persson, 2009). Species exploitation might induce a release from intraspecific competition, and thus potentially have a direct, negative effect on IS (scenario 1 in Figure 5-1). Predator-prey dynamics might also be altered in anthropogenic landscapes and result in changes in individual specialisation, whether mediated by effects on density or behaviour (scenario 3). For example, changes in predator activity and foraging patterns in human-dominated landscapes can lead to modifications in habitat use and foraging patterns in their prey (Kuijper et al., 2016; Smith et al., 2018). Finally, two or more of these effects might occur simultaneously, and might even counteract each other: while invasive species have well-documented predatory and competitive impacts, they also can facilitate native species through such mechanisms as trophic subsidy, competitive release, and predatory release (Rodriguez, 2006). These cascading effects across trophic levels and this restructuring of communities might in turn lead to variation in the degree of IS among populations.

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Due to their variety and breadth, human activities have the potential to affect individual specialisation either positively or negatively. In this study, we assessed for the first time the direction and magnitude of human impacts on individual specialisation at a global scale. We compiled a database of published estimates of individual specialisation in diet across a wide range of taxa and habitats, and we used standardised ‘human footprint’ indices to assess whether they predicted variation in IS. As global human impacts generally are linked to a net loss in biodiversity and habitat degradation, we hypothesised that IS in diet would generally decrease in more impacted organisms, due to a global disruption of natural food web processes and an associated decrease in ecological opportunity. However, as a multitude of drivers can affect IS positively and negatively, and given the complexity of cumulative human impacts, we also expected that in some circumstances the degree of IS within populations might remain unchanged, or even increase.

Methods

Literature search and study selection

We developed a dataset of published studies on individual specialisation in diets by conducting a Web of Science database search. We used a combination of keywords articulated around the general topics of individual specialisation and trophic ecology, such as “diet”, “food”, “foraging” and “individual specialisation”, “individual variation”, “niche variation”. We limited our search to ecological, zoological and anthropological studies. We restricted the search to journal articles published between 2003 and 2017, which had the dual effect of ensuring the relevance of contemporary measures of HFI and including only studies published after the most influential modern treatment of individual specialisation (Bolnick et al. 2003 Am. Nat.). This search term combination yielded 3214 results. We performed a first screening by reviewing titles and abstracts, and excluded review articles and studies that were obviously irrelevant. We retained observational or experimental studies that contained

Chapter 5. Effects of human activities on IS | 102 some measure of dietary individual specialisation. We assessed 152 full-text articles, extracting index values when provided in text, tables or figures. In the latter case, we used the Figure Calibration plugin in ImageJ (version 1.51J8) to extract index values. Some records were excluded during this process due to lack of data, methodological incompatibility for our purpose, or in some cases unidentified locations of study sites. We also excluded records in which indices had been calculated for groups of multiple species (e.g. "Genus spp."), although we did use data from single species that were unidentified or unknown ("Genus sp.”).

We obtained a final dataset of 98 studies which covered 162 unique taxa across 8 taxonomic groups (Table 5-1 below and Figure S5-5, p.168). While many studies were conducted on the American and European continents, the dataset covered a range of biomes spanning the entire globe (Figure 5-2, next page).

Table 5-1. Taxonomic makeup of studies used in the meta-analysis Taxonomic group Number of unique taxa Number of studies Actinopterygii 60 38 Amphibia 17 8 Arthropoda 31 13 Aves 8 9 Elasmobranchii 6 3 Mammalia 15 18 Mollusca 3 3 Reptilia 22 7

In addition to indices of individual specialisation (see below), for each study we recorded the type of ecosystem, site coordinates, and methodology as explanatory variables (see detail in Table 5-2, p.107). Ecosystem type – terrestrial, freshwater or marine – was determined following the focal taxon’s main foraging habitat, rather than the type of ecosystem where the taxon was sampled.

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Figure 5-2. Site locations of individual populations in the meta-analysis dataset. Green triangles: Terrestrial ecosystems (including freshwater). Blue circles: Marine ecosystems Chapter 5. Effects of human activities on IS | 104

Indices of individual specialisation

Our analysis focused on two commonly-used indices of individual specialisation.

The mean proportional similarity index 푃푆̅̅̅̅푖 measures the average diet overlap between individuals and their population (this is often referred to as “IS” in the literature but we use 푃푆̅̅̅̅푖 to avoid confusion with the general concept). The 푊퐼퐶/푇푁푊 index measures the proportion of the total niche width of a population (푇푁푊) explained by variation in resource use within individuals (푊퐼퐶), and can be calculated from variances in continuous measures or diversity indices for discrete diet types (Bolnick et al., 2002). Both indices range from 0 to 1; for historical reasons, lower values indicate higher individual specialisation and higher values indicate lower individual specialisation. Other measures of specialisation cited in the literature, such as average pairwise overlap, measures of nestedness or Levins’ index were not included in the analysis, because they were either too rare or do not measure IS per se. We used averaged index values in the case of studies in which indices were provided for multiple groups, replicates or populations of the same species at the same location, and weighted those values based on the number of individuals used in the index calculations.

Multiple approaches were used to estimate indices across publications. We classified them into two categories: estimates based on diet composition data, which included methods such as stomach content analysis and scat analysis, and estimates that used stable isotope analysis, including serial isotopic signatures.

We tested the strength of the relationship between 푃푆̅̅̅̅푖 and 푊퐼퐶/푇푁푊 using studies where both indices were reported. A simple linear regression indicated a significant, but not especially strong relationship (p<0.001, adjusted R2=0.411); we therefore analysed the indices separately. Both indices were normally distributed, so we ran our statistical analyses without transformation. Chapter 5. Effects of human activities on IS | 105

Estimation of phylogenetic tree

We built a phylogenetic tree to account for non-independence of species due to shared evolutionary history (Felsenstein, 1985; Lajeunesse, 2009). We extracted the taxa names as listed in the original publications; we then matched our list of taxa to the Open Tree of Life (OTL) database using the 'tnrs_match_names' function in the 'rotl' R package v.3.0.7 (Michonneau et al., 2016). After checking for appropriate taxonomic references, we replaced taxa that did not have a match in the OTL with close relatives present in the OTL phylogeny. We extracted an OTL tree for a total of 162 taxa, using the function 'tol_induced_subtree', and added starting branch lengths using the Grafen method with 'compute.brlen' in the 'ape' package (Paradis, 2019).

We used the online tool TimeTree (http://www.timetree.org/home) for the time calibration of the phylogenetic tree. The TimeTree database featured 116 of the 162 taxa in our list. While 47 taxa did not have a match in that database, we found 31 phylogenetically close taxa that we used as temporary substitutes for the calibration. We identified calibration points for the full tree using the function 'congruify.phylo' in the 'geiger' package v.2.0.6.1 (Harmon et al., 2008), based on the estimated ages for nodes in the TimeTree. We set each calibration with minimum and maximum bounds 5% below and above the point estimate in TimeTree. For one spider genus (Philodromus sp. for which there were three species in our dataset) we added a calibration to ensure that the genus age would not exceed the age of divergence of related families of jumping spiders (ca. 116 MYA). Finally, we made the full tree ultrametric using the ‘chronos’ function in ‘ape’, with a smoothing parameter of 1. Three calibrations caused problems with optimisation and were removed; all three were in fish clades that had numerous other calibrations (supplementary material, Figure S5-1 to Figure S5-4, p.164-167).

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Human Footprint Index

The Human Footprint Index (퐻퐹퐼) quantifies human influence on a global scale (Sanderson et al., 2002). It integrates multiple structural and functional proxies of human influence, encompassing land use – e.g. urban spread and extent of built environments, transportation networks, agricultural landscape – level of species exploitation, density of human population, light pollution, and traffic intensity (Venter et al., 2016b). We used two raster datasets which featured the most comprehensive cumulative human impacts: the 2009 human footprint layer from Venter et al. (2016) for terrestrial ecosystems and the 2013 footprint layer from Halpern et al. (2015) for marine systems (hereafter terrestrial 퐻퐹퐼 and marine 퐻퐹퐼, respectively). While both indices provide a scale of intensity of human impacts, there are conceptual and methodological specificities to each index: the terrestrial 퐻퐹퐼 ranges from 0 for pristine environments to 50 for high-density built environments (global average 5.86 ± 6.84) (Di Marco et al., 2018), while the marine 퐻퐹퐼 ranges from 0 to 90.1 (global average 3.33 ± 1.01) with impact values considered low below 4.95 and high above 12 (Halpern et al., 2008). For this reason, we analysed data from terrestrial and marine ecosystems separately (see p.108).

All the spatial analyses were performed in ArcGIS 10.6.1. For the GPS locations of each study site, we computed species-specific 퐻퐹퐼 values at a 1-km2 resolution for terrestrial and marine areas (as well as 80 km offshore). Taxa were assigned to one of the two 퐻퐹퐼 datasets based on their primary foraging habitat. Seabirds and pinnipeds, for example, were assigned marine 퐻퐹퐼 values even when they were sampled on land. Further, we ascribed terrestrial 퐻퐹퐼 values to freshwater fish species, including eight estuarine species. We did this both because comparable global 퐻퐹퐼 datasets are not available for freshwater habitats, and because we expected the majority of impacts on most freshwater systems to come from activities on the surrounding landscape. We differentiated terrestrial and aquatic biomes in the terrestrial 퐻퐹퐼 dataset in the statistical analysis (cf. Table 5-2, next page). We computed the minimum value,

Chapter 5. Effects of human activities on IS | 107 maximum value, mean and standard deviation within buffer regions with 50, 100, or 500-km radii, selected using our best judgment as to the mobility of the focal species. For instance, for Anolis lizards which display low mobility, we used a 50-km radius; for sea otters Enhydra lutris we used a 100-km buffer, and for loggerhead sea turtles Caretta caretta we used a 500-km radius. We used geodesic buffers with no dissolve for all studies. Terrestrial and marine footprints were projected in World Mollweide (WGS 1984). When buffer zones covered both terrestrial and marine habitats, only values in the 퐻퐹퐼 dataset appropriate for the taxon of interest were extracted, and the other dataset was ignored. To overcome issues stemming from feature classes and from extracting 퐻퐹퐼 values for multiple studies and overlapping zones in ArcGIS, we used the Python script from the “ZonalStatsOverlappingPolys” geoprocessing tool to break up and re-join these zones, which divides the feature class into 1 to 10 feature layers, prior to removing overlaps and running zonal statistics. We used 5 feature class divides (min 1, max 10) for this analysis.

Finally, we standardised each 퐻퐹퐼 index before statistical analyses by subtracting the mean and dividing by two standard deviations to facilitate comparison of effect sizes with effects of categorical predictors (Gelman, 2008).

Table 5-2. Response and predictor variables used in the fitted models. Role1 / Type2 / Range or Variable name Description number of levels Mean PSi Mean of Czekanowski’s proportional similarity index 푃푆̅̅̅̅푖 Rs / Cont / [0-1] WIC/TNW Relative degree of individual specialisation Rs / Cont / [0-1] Ecosystem type Terrestrial, freshwater or marine Pr / Cat / 3 Latitude Absolute value of a site’s latitude on a 0 to 1 scale (0 = Pr / Cont / [0-1] Equator, 1 = Pole). Data type Basis for calculation of IS indices: diet composition data or Pr / Cat / 2 (terrestrial HFI) stable isotopes Study design Cross-sectional studies (short timescale) Pr / Cat / 2 (terrestrial HFI) Longitudinal and time-series studies (long timescale) Study design Cross-sectional diet composition Pr / Cat / 3 (marine HFI) Longitudinal diet composition Cross-sectional stable isotopes (푃푆̅̅̅̅푖 only) Time series stable isotopes (푊퐼퐶/푇푁푊 only) Phylogeny Species ID in calibrated phylogenetic tree Rd / Dis HFI Standardised mean HFI values per species per site. Pr / Cont 1Rs – Response, Pr – Predictor, Rd – Random effect 2Cont-continuous, Dis-discrete, Cat-categorical

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Statistical analysis

We used linear mixed models with the ‘MCMCglmm’ v.2.29 package (Hadfield,

2010) with indices of individual specialisation 푊퐼퐶/푇푁푊 and 푃푆̅̅̅̅푖 as our response variables and the standardised 퐻퐹퐼 as our predictor of interest. We ran a total of four models on subsets of the data: both indices of specialisation in terrestrial (+ freshwater) and in marine ecosystems. We used different combinations of the following predictors for our models: standardised 퐻퐹퐼, ecosystem type, study design and data type were the main predictors, and we accounted for non-independence due to shared phylogenetic history by including the phylogeny as a random effect (Table 5-2 above). Terrestrial models included interactions between 퐻퐹퐼 and habitat type (terrestrial or aquatic) and between study design and data type. Marine data sets did not contain all combinations of study design and data type, so we used a 3-level study design predictor. Since most studies focused on one taxon at a time, and the chosen fixed effects accounted for major methodological differences between studies, we did not include study as a random effect. We also fitted the models with absolute latitude as an additional predictor to ensure that it was not confounded with 퐻퐹퐼, but we removed it as it did not have any noteworthy effects. We used non-informative inverse Wishart priors for both analyses (V=1, nu=0.02). To assess the sensitivity of the analyses to alternative prior specifications, we also ran the models with different priors; results were consistent. We allowed the Markov chains to run 130000 iterations to ensure that the effective samples sizes were large enough (>1000), with 30000 iterations of burn-in and 100 iterations of thinning interval. A fixed effect was deemed statistically significant if 95% credible intervals did not overlap zero. We checked our models by visually checking traces and by running convergence diagnostics based on Gelman and Rubin statistics, using the ‘coda’ package v.0.19-2 (Plummer et al., 2006). We extracted the phylogenetic signal by dividing the variance estimate for phylogeny by the total residual variance.

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Results

We obtained a dataset of 269 푃푆̅̅̅̅푖 values, including n=76 values for terrestrial ecosystems, n=138 for freshwater ecosystems, and n=55 values for marine ecosystems. Our dataset for 푊퐼퐶/푇푁푊 contained n=78 values in total for terrestrial ecosystems (39 values for terrestrial populations and 39 values for freshwater populations) and n=47 values for marine ecosystems (Table S5-2, p.163). The 푊퐼퐶/푇푁푊 index of specialisation ranged from 0 to 0.98 (mean

0.47±0.23) while 푃푆̅̅̅̅푖 ranged from 0.03 to 0.93 (mean 0.47±0.18), i.e. from populations entirely composed of individual specialists to populations fully composed of generalist individuals.

Before standardisation, 퐻퐹퐼 in our dataset ranged from 0.16 to 26.71 (mean 9.71±6.18) and from 1.16 to 5.15 (mean 3.53±1.01) in terrestrial and marine ecosystems, respectively, indicating study systems with low to moderate human impacts (Di Marco et al., 2018; Halpern et al., 2008)(Figure 5-3).

Figure 5-3. Extracted values of indices of individual specialisation compared to terrestrial HFI. Left: 푃푆̅̅̅̅푖. Right: 푊퐼퐶/푇푁푊 Blue: Freshwater species. Brown: Terrestrial species. Each symbol represents a single population. Shapes illustrate the methodology used to estimate indices of individual specialisation. Open circles: Indices calculated using diet proxies. Triangles: Indices calculated using stable isotope analysis.

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We detected a significant positive effect of terrestrial 퐻퐹퐼 on 푃푆̅̅̅̅푖 across studies (b =0.128, 95% credible interval, CI: 0.021, 0.237, pMCMC=0.022, Figure 5-5, p.111). Study design (cross-sectional vs. longitudinal), data type (diet composition vs. isotopic data) and the interaction between 퐻퐹퐼 and habitat type

(aquatic or terrestrial) also had significant effects on 푃푆̅̅̅̅푖, with higher index values generally observed in studies using cross-sectional designs and studies using dietary data, and higher slopes in studies of aquatic populations compared to terrestrial populations (b=0.202, 95% CI: -0.006, 0.370, pMCMC=0.037; b=0.126, 95% CI:0.051, 0.209, pMCMC=0.002 and b=-0.178, 95% CI: -0.336, - 0.016, pMCMC=0.04 respectively). Similar general trends in effect sizes were observed for terrestrial 푊퐼퐶/푇푁푊, although no significant effect was detected (b =0.093, 95% CI: -0.187, 0.350, pMCMC=0.47, Figure 5-5). Study design and the interaction between study design and data type had marginally significant effects, with higher 푊퐼퐶/푇푁푊 values generally observed in longitudinal studies rather than cross-sectional studies (b=0.302, 95% CI: -0.033, 0.542, pMCMC=0.06), and generally lower values recorded in longitudinal studies using isotopic data (b=-0.43, 95% CI: -0.914, 0.038, pMCMC=0.09).

Figure 5-4. Extracted values of indices of individual specialisation in marine ecosystems. Left: 푃푆̅̅̅̅푖. Right: 푊퐼퐶/푇푁푊 Each symbol represents a single population. Shapes illustrate the methodology used to estimate indices of individual specialisation. Open squares: Cross-sectional diet composition. Triangles: Longitudinal diet composition. Circles: Cross-sectional isotopes. Diamonds: Time series isotopes.

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In marine ecosystems, there was a negative trend between 퐻퐹퐼 and 푊퐼퐶/푇푁푊, although the credible interval included zero (Figure 5-4, previous page). No significant effect of 퐻퐹퐼 was detected on either index of specialisation (b=0.025, 95% CI: -0.106, 0.144, pMCMC=0.676 and b=-0.136, 95% CI: -0.342,

0.068, pMCMC=0.186 for 푃푆̅̅̅̅푖 and 푊퐼퐶/푇푁푊, respectively) (Figure 5-5 below). Cross-sectional diet studies showed significantly lower 푊퐼퐶/푇푁푊 values compared to IS studies based on isotopic time series data (b=-0.348, 95% CI: - 0.645, -0.102, pMCMC=0.006).

The phylogenetic signal was strong in all models, explaining 55% of the residual variance in marine 푃푆̅̅̅̅푖, 83% of the residual variance in terrestrial 푃푆̅̅̅̅푖, and 87% and 77% of the residual variance in marine and terrestrial 푊퐼퐶/푇푁푊, respectively (Table S5-1, p.162).

Figure 5-5. Forest plot of mean effect size with 95% credibility intervals of Human Footprint Index HFI, ecosystem type (terrestrial or aquatic), study design and data types estimated with linear mixed models. Left: terrestrial HFI. Right: marine HFI. Red: 푃푆̅̅̅̅푖. Grey: 푊퐼퐶/푇푁푊. *pMCMC<0.05. **pMCMC<0.01

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Discussion

Changes in foraging strategies and prey selection are among the first quantifiable responses to anthropogenic impacts. With this study, we showed that in terrestrial biomes, such responses include an overall decrease in intraspecific variation in diet. We found an adverse effect of human activities on the degree of individual specialisation across taxa, by linking human footprint measures and the average diet overlap between individuals and their population. We also found that individuals tended to have narrower dietary niches relative to their population’s in more heavily impacted areas, although this trend was more subtle. In marine ecosystems, we found no indication of similar responses. On the contrary, the strongest (although non-significant) relationship observed was in the opposite direction. The overall weak effect of human footprint on terrestrial IS might be explained by differences in magnitude and directionality of responses between species, as well as by the fact that most studies of individual specialisation were conducted in areas with low to moderate impacts. For many species, it is also possible that the chosen buffers for human footprint estimates were too large. In addition, the design and timescale of studies explained some of the observed variation in IS indices, while phylogeny explained most of the residual variance in our models.

In our compilation of published measures of IS, fishes were the most frequently studied organisms, followed by arthropods and reptiles. Higher taxa were unevenly represented in the different analyses: for example, mammals dominated the 푊퐼퐶/푇푁푊 dataset while fishes were most common in the other three datasets. Similarly, cross-sectional diet studies were the most frequent type of study, except in the marine 푊퐼퐶/푇푁푊 dataset in which study designs were slightly skewed towards isotopic time-series. This unevenness in both methodologies and focal organisms are, to some extent, related: cross-sectional stomach content data are comparatively easier to obtain than longitudinal data, while stable isotope analysis remains less widely used than diet composition data for the specific purpose of investigating individual niche variation

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(Boecklen et al., 2011; Fry, 2006). As a result, the values of IS indices used in our analysis rely on the type of data used in their estimation. Indeed, specialisation can be overestimated in methods using diet proportion data when the number of prey per individual varies across individuals (Coblentz et al., 2017). Similarly, the presence of monophagous individuals in a population also tends to bias 푊퐼퐶/푇푁푊 towards small values, which might lead to overestimation of the degree of specialisation in the population (Zaccarelli et al., 2013). The estimation of individual specialisation through cross-sectional data, such as data obtained from single stomach content analysis or scat samples, is also prone to overestimation, as such data might reflect opportunity, prey patchiness or availability of profitable foraging grounds rather than differences in rank preferences for prey types. In our dataset, studies of individual specialisation using diet composition data indeed tended to provide higher IS estimates than studies using stable isotope analysis, as well as cross- sectional studies compared to longitudinal and time-series studies. A notable exception to this was in the marine 푊퐼퐶/푇푁푊 dataset, as individual specialisation was significantly higher in studies using isotope time-series. Isotopic time series data integrate both spatial and temporal elements of trophic niches (e.g. Cherel et al., 2005), which means that, for instance, low 푊퐼퐶/푇푁푊 might reflect foraging site fidelity while individuals actually eat the same food type.

The absence of a relationship between 퐻퐹퐼 and individual specialisation in our marine datasets might be due, in part, to the way the marine human footprint itself is estimated. While both terrestrial and marine 퐻퐹퐼 are based on similar general principles and reflect cumulative impacts, there are qualitative differences in the types of impacts measured by each index. It is possible for instance that elements of the terrestrial 퐻퐹퐼 reflect impacts on ecological processes that are more closely linked to predictors of feeding ecology, while the set of anthropogenic activities considered in the marine 퐻퐹퐼 integrate different stressors with fewer links to IS drivers in the taxa considered in our

Chapter 5. Effects of human activities on IS | 114 datasets. Land-use change generally is the dominant anthropogenic impact in terrestrial systems, while in marine systems the human footprint heavily hinges on biogeochemical processes (Doney, 2010) and overexploitation (Pereira et al., 2010). A number of impacts accounted for in marine ecosystems might also be less reflected in dietary IS due to their spatial or temporal characteristics. For example, some marine activities might induce slower changes (e.g. ocean acidification), be less spatially consistent over time (e.g. fisheries) or have more concentrated impact points that only constitute parts of a population’ s range (e.g. point source pollution) than the types of impacts accounted for in the terrestrial 퐻퐹퐼. In the latter case, there is evidence that transient and highly localised human impacts can have both substantial and lasting consequences on the behaviour and ecology of aquatic organisms (Weilgart, 2007). Finally, marine organisms in our dataset had on average larger home ranges than terrestrial and freshwater taxa, which affected the 퐻퐹퐼 values used in the analysis that were integrated over a larger geographic buffer. In this context, the set of cumulative anthropogenic stressors accounted for in those species might be larger and more diverse than within shorter geographic ranges, which makes it more difficult to distinguish a direction of ecological responses in marine ecosystems than in terrestrial ones.

While there was no clear link between human activities and IS levels in marine ecosystems, we found a significant reduction in the degree of IS with human impacts in terrestrial (and freshwater) ecosystems. Beyond the available resources utilised (Robertson et al., 2014), the foraging niche of a species or individual also encompasses the foraging strategies or behaviours exhibited to obtain them. The foraging niche might result from sexual dimorphism (Elliott Smith et al., 2015), ontogenetic stage (Hjelm et al., 2000; Svanbäck et al., 2015; Zhao et al., 2014), social status as well as learned foraging behaviours (Estes et al., 2003; Tinker et al., 2007). Individuals might differ in their selection of foraging grounds and prey, their foraging strategy and methods, or in their spatial and temporal foraging windows (Araújo et al., 2011). Further, as among-

Chapter 5. Effects of human activities on IS | 115 individual niche variation generally increases in populations with access to greater resource diversity, differential ecological opportunity and competition intensity in heterogeneous landscapes can generate intrapopulation niche diversity (Darimont et al., 2009). Human activities which lead to a reduction or a constraint on one or several of these components, might thus reduce the potential foraging niche of individuals within a population.

Species in which individuals display more flexibility in their foraging ecology or display larger individual niches, might more easily shift or expand their niche to exploit resources in modified environments. Adaptation to anthropogenic landscapes depends on species-specific behavioural and dietary plasticity. Some species, including apex predators, successfully adapt to anthropogenically modified landscapes, in some cases through population niche expansion (Moss et al., 2016) or increased niche overlap with interspecific competitors (Smith et al., 2018). In many species, dietary preferences between urban and non-urban populations can vary starkly (Auman et al., 2011; McKinney, 2011), sometimes but not necessarily always due to anthropogenic subsidies (Newsome et al., 2015). The implications of restricted or anthropogenic diets are not always clear (Kavčič et al., 2015; Santos et al., 2011; Schurr et al., 2012), however, and the impacts of anthropogenic food consumption are generally difficult to thoroughly assess. At the individual level for instance, the consumption of anthropogenic resource subsidies might have fitness consequences. In gannets Morus bassanus, scavenging on discards from fishing vessels is associated with lower foraging effort and adjusted foraging behaviour in relation to fishing vessel activity, but also with poorer body conditions (Votier et al., 2010). There is also evidence that human-induced reductions in diet diversity can alter population structure (Riley, 2007). At a wider scale, diet alteration might also alter ecological relationships (Moss et al., 2016) and trophic cascades (Araújo et al., 2014; Newsome et al., 2014). Cougars (Puma concolor) in urban interfaces shift their diet from near full specialisation on native herbivores to exotic and invasive species, which might modify the dynamics in native prey populations,

Chapter 5. Effects of human activities on IS | 116 restructure community assemblages and result in modified ecosystem function (Moss et al., 2016).

Although changes in population niche width are not systematically associated with changes in the degree of individual specialisation (Svanbäck et al., 2011; Yurkowski et al., 2016), our results indicate that across taxa, IS decreases with human impacts. Dietary plasticity in response to changing resources and landscapes varies from one species to another, however specialist users might be more negatively affected by anthropogenic impacts than generalist users. Such a case is observed at the community level: specialist species are more sensitive to global change and are being replaced by generalist species, causing functional homogenisation at the community level (Clavel et al., 2011; Ozinga et al., 2013). Our results suggest that similar patterns might occur within species, as the proportion of individual specialists in a population decreases in areas with higher human footprint. While both specialist and generalist user types might experience the same effects of human activities, they might display differential responses. In particular, population collapse linked to the accumulation of multiple anthropogenic stressors at different spatial and temporal scales are stronger among specialist species (Cane et al., 2006; Correll et al., 2017; Devictor et al., 2008; Smith et al., 2018). Within species, the overall simplification of food webs and energy-flow pathways stemming from anthropogenic change might result in global decreased diet diversity from impoverished resource bases (Araújo et al., 2014).

In conclusion, this comparative analysis highlights an unappreciated impact of human activities on intraspecific diversity: a decrease in the degree of individual specialisation in terrestrial and freshwater systems. The consequences of dietary shifts might trickle down on individual fitness and survival and, as such, might be at the root of observed changes in life history, altering population dynamics within communities. Management policies designed to safeguard “average” population niches might fail to protect intraspecific diversity. Understanding the underlying mechanisms leading to

Chapter 5. Effects of human activities on IS | 117 ecological shifts is essential to design and implement effective conservation plans in anthropogenic landscapes.

6. 6 General Discussion: Effects of Ecological Interactions on Intraspecific Niche Variation

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Within a population, the use of available resources by individuals is contingent on the biotic and abiotic conditions in which they dwell. Individual specialists utilise a subset of their population’s total available resources, but their degree of specialisation might be altered by ecological interactions. My doctoral research focused primarily on the trophic niche and the degree of individual specialisation in the common bully Gobiomorphus cotidianus, in a dyadic relationship with the Eurasian perch Perca fluviatilis. My objectives were to disentangle the effects of three types of antagonistic ecological interactions on the diet and habitat use of bullies, both at the population and individual levels. This case study essentially describes the effects of an introduced species on the ecological niche of a native one, which constitutes an example of anthropogenic impacts on a non-domestic species. There are many other ways in which anthropogenic changes could impact population and individual diets. To explore those impacts, I expanded my scrutiny to the global effects of human activities measured by integrative indices (Human Footprint Index) on individual specialisation in diet across animal taxa.

First, I investigated wild bully populations of shallow coastal wetlands using stable isotope analysis of muscle tissue and cross-sectional diet data. Bullies in the surveyed pond complex are exposed to a wide range of antagonistic interactions with both native and exotic species. In particular, competition and predation from perch are strong structural drivers of the local bully populations (Goldsmith, 2004). Diet characterisation through stomach content analysis indicated generally moderate levels of individual specialisation, with the caveat that cross-sectional, highly heterogeneous data might lead to overestimates of IS (Coblentz et al., 2017; Zaccarelli et al., 2013). Despite efforts to standardise measures and maximise comparability between ponds, patterns of dietary resource use in individual bullies were hard to discern. The high heterogeneity of ecological conditions existing in the ponds, associated with a limited dataset, made it difficult to identify the ecological mechanisms shaping IS in those communities. While I recorded substantial variation in both bully diets and

Chapter 6. General Discussion | 120 levels of IS, only one descriptor of the fish community – the abundance of galaxiid fish – appeared to negatively affect dietary IS. While galaxiids in the captured size range feed on zooplanktonic and benthic prey (Cervellini et al., 1993), I did not seek to identify from this dataset whether galaxiids and bullies competed for the same resources; however, this observation hinted at a possible effect of interspecific competition on IS in those pond communities.

To further understand how dyadic antagonistic interactions, taken in isolation or combined, shaped individual specialisation, I set up two manipulative experimental studies based on the monitoring of individual bullies in 3- to 6- individual mesocosm pond populations, in the presence or absence of perch. The first experiment explored the effects of inter- and intraspecific competition on the bully dietary niche. In a second study, I sought to understand and quantify the effects of intraguild predation, modelled as a combination of predation risk and interspecific competition, on the diet, habitat use and activity levels of individual bullies. As expected, I found that both perch presence and increased densities of bully conspecifics led to population niche shifts and to changes in the degree of individual specialisation, and that the direction and strength of those effects varied depending on the composition of the community. Higher conspecific densities led to a slight increase in IS, while the presence of small perch had a significant negative effect on the degree of IS in bully populations. Predation risk alone increased bully dietary IS and decreased habitat IS. I detected interactive effects of small perch presence with large perch presence, with a larger negative effect on dietary IS than competition alone.

Despite an arguably greater diversity of habitat patches in the second manipulative study, as opposed to a single habitat type in the first one, the ranges of WIC/TNW and E index estimates obtained in small monospecific (“Bully (Low)”) and heterospecific populations (“Bully + Perch (S)”) were similar (see Figure 6-1, p.123). While the comparison is imperfect due to the caveats highlighted earlier about the Waipori-Waihola wetlands individual specialisation data, the values observed in those wild populations indicated a

Chapter 6. General Discussion | 121 higher level of IS, associated with a higher dissimilarity between individuals. Greater behavioural variation among individuals, potentially leading to increased between-individual differences in resource use, can be linked to greater environmental variation (Bell et al., 2009). The degree of IS in wetland bullies results from a wide variety of ecological interactions, including antagonisms, mutualisms and ecological opportunity, i.e. resource availability and accessibility. Additional interaction types might explain some of the variation observed in the ponds. For example, parasitism has been demonstrated as not only a strong driver of body condition and fitness, but also personality and behaviour with potential consequences on trophic niches (Britton and Andreou, 2016; Kortet et al., 2010). Common bullies of the Waipori-Waihola wetlands host a variety of parasites, with a particularly high prevalence of helminths (Lagrue et al., 2011), which have been associated with altered behavioural syndromes in the species (Hammond-Tooke et al., 2012). Intra- and inter-population differences in parasite biomass might therefore result in variation in resource use both at the population and the individual levels. Similarly, positive interspecific interactions might affect foraging niches. For instance, follower-feeding association, in which individuals of a species follow and benefit from the foraging activities of individuals of another species are a common feeding tactic in fish communities in oceanic islands (Inagaki et al., 2019) in which the degree of individual specialisation in the follower might result from the degree of IS in the other forager.

In monospecific populations, I found a subtle trend of increased individual specialisation (lower WIC/TNW) and increased between-individual dissimilarity (higher E index) with higher bully densities. Contrary to my expectations, there was no evidence of niche expansion at the population level. However, an apparent trend towards increased dissimilarity between individuals suggested a potential reduction in resource overlap, which would be consistent with the niche variation hypothesis (Van Valen, 1965). In bully populations exposed to small-sized perch, as predicted, I recorded a shift in resource use, although that

Chapter 6. General Discussion | 122 effect was not consistently significant, as the prey base was not consistent between studies. Contrary to expectations, I observed no significant contraction of the population’s dietary or habitat niches, which might be an indication that in my experimental setup, resources were not limiting. Finally, the effects of exposure to potential predators were most significant when combined with interspecific competition. Taken in isolation, predation risk did not significantly affect the size and position of the bully population’s dietary niches. However, niche shifts appeared more pronounced when bullies were exposed to both small and large perch.

When it comes to examining the ecological drivers of individual specialisation, most studies tend to consider them in isolation (but see Costantini et al., 2005; Costa-Pereira et al., 2018; Evangelista et al., 2014; Hopper and Tobler, 2016; Kernaléguen et al., 2015; Svanbäck et al., 2011; Svanbäck and Persson, 2004; Tran et al., 2015; Yurkowski et al., 2016). More rarely are the effects of multiple, co-occurring drivers of IS tested, let alone their interactive effects, although the aggregate effect of multiple drivers might differ from the independent combination of a single modifier (Golubski and Abrams, 2011). The present work attempted to address this gap by considering the interactive effects of two of those ecological drivers on the dietary IS. The compounded effects of predation risk and interspecific competition did appear to significantly reduce the degree of individual specialisation in bully populations and increase the similarity between individuals. Indeed, the lowest degree of IS was observed in the heterospecific populations in which bullies were exposed to both small and large perch (Figure 6-1, p.123). In this setting, the changes in resource use did not appear to be linked to resource availability, but rather by an ecological response from individual bullies themselves. While I did not differentiate foraging activities from other types of behaviours during habitat monitoring, my results indicate significant responses to exposure to both small and large perch, with an increased overlap in habitat use between individuals.

Chapter 6. General Discussion | 123

This homogenisation of the population in habitat use might explain, in part, the decrease in individual specialisation in diet.

Figure 6-1. Index of Individual Specialisation WIC/TNW (top panel) and average pairwise dissimilarity E (bottom panel) in bully diets across all data chapters. Colours correspond to individual chapters. Grey: Chapter 2, pink: Chapter 3, purple: Chapter 4. The boxplots depict the median, first and third quartiles with whiskers extending up to 1.5 times the interquartile range. Circles represent outliers. (S): Small perch, (L): Large perch. (Low): 3-4 individuals per mesocosm pond. (High): 6 individuals per mesocosm pond.

These two experiments demonstrated that, even in small-sized populations, intraguild competition and predation led to substantial variation in dietary IS, by altering niche partitioning (evidenced by an increased WIC/TNW ratio), and

Chapter 6. General Discussion | 124 by increasing the diet similarity between individuals. These results are consistent with the niche variation hypothesis, in that increased intraspecific variation in resource use was observed in the absence of interspecific competition. My results also further support the idea of diversifying effects of intraspecific competition and homogenising effects of interspecific competition on the dietary niche of individuals (Araújo et al., 2011). They expand on that idea by showing that the direction and magnitude of both population-level and individual responses depend on the interactive effects between intraguild competition and predation. In terms of dietary resource use, the exposure to those combined antagonistic interactions enhances the overall negative effects of interspecific competition on individual specialisation.

This result puts into perspective the effects of human activities in general on the degree of IS in animal populations. Indeed, humans, as global ecosystem modifiers, have a disproportionate influence on community niches. The Waipori-Waihola wetlands are nowadays subjected to low to moderate human activities related to landscape modification and human settlement. Once part of a large forested swamp, the area was a major mahinga kai (a place where food and natural resources can be obtained) site for Māori people. In the middle of the 19th century, European settlers began the drainage of the larger Taieri Plains for agriculture, converting an estimated 70% of the original wetland to farmland. A century later, Horace (Horrie) Sinclair bought a 315-hectare property in the remaining wetlands with the intent to restore it to its natural state. In 1998, the Sinclair Wetlands were returned to Te Rünanga o Ngāi Tahu by the Crown, as part of the Ngāi Tahu Claim Settlement Act. While ongoing restoration and protection efforts are undertaken by Te Nohoaka o Tukiauau/Sinclair Wetlands Trust, the wetlands remain vulnerable to anthropogenic impacts including drainage and farming activities across the greater catchment (Department of Conservation, 2015), which vary from low to moderate. The common bully populations in this area display levels of IS in the lower range of freshwater taxa (Figure 6-2, p.126), with even higher WIC/TNW

Chapter 6. General Discussion | 125 values estimated in the experimental mesocosm ponds. This might simply be due to general characteristics of their trophic ecology: common bullies might have more generalised individual trophic niches than model organisms in other studies. Another possibility could be the presence and impact of exotic species. The Waipori-Waihola wetlands are home to a variety of now established communities of invasive alien species – perch included –which can have, as demonstrated in this research, a significant effect on individual specialisation. This highlights the idea that individual specialisation should be understood within the community context, as the observed degree of IS in a population is contingent on multiple ecological factors.

Indices such as the Human Footprint Index (HFI) are highly integrated measures of complex, cooccurring environmental impacts on wild ecosystems. As they can simultaneously affect multiple ecological requirements of wild species, human activities might be considered as a compilation of multiple potential antagonistic interactions towards a foraging population. Consistent with my hypotheses, the HFI compiled for terrestrial and freshwater biomes – although not for marine ecosystems – was related to the degree of individual specialisation across a wide range of animal taxa. Responses to human impacts showed an overall decrease in dietary variation within species. This pattern might be linked to disproportionate impacts on the foraging niche of individuals in anthropogenic landscapes, through direct or indirect disruptions of species’ survival, behaviour, and population dynamics. In the Waipori-Waihola wetlands, the alteration and reduction of available habitat for the aquatic fauna, for example, might have altered ecological opportunity, increased inter- and intraspecific competition and, as a result, modified predator-prey dynamic via density-mediated effects, which eventually resulted in the observed degree of individual specialisation in common bully populations.

Chapter 6. General Discussion | 126

Figure 6-2. Extracted values of indices of individual specialisation in freshwater ecosystems and comparison with the range of values obtained in bully populations of the Waipori-Waihola wetlands. Grey band: Range of WIC/TNW values obtained in bully populations of the Waipori-Waihola wetlands. The dashed line represents the median value. A simple linear regression line is included to illustrate general trends. Each symbol represents a single population. All index data were obtained using single diet data.

Conclusion

This research provided a deeper understanding of the mechanisms shaping individual specialisation in a small generalist species exposed to antagonistic ecological interactions. It underscored the importance of integrating individual variation to fully comprehend a population’s resource use within its community, by showing that the outcomes of multiple biological interactions are context- dependent. While changes in intraspecific diet variation were recorded within the span of a few months for common bully populations, little is known about the long-term effects of niche shifts both at the population and at the community levels. It is also unclear whether these shifts are reversible or not. In addition, because of the difficulty of systematically accounting for the direction and magnitude of the effects of co-occurring interactions on individual specialisation, integrated habitat quality indices such as measures of human footprint are potentially useful explanatory variables of observed IS levels in animal populations.

Chapter 6. General Discussion | 127

Various factors, such as environmental variation and species-specific compensatory mechanisms, might mitigate the effects of ecological drivers on individual and population niches. It remains, however, that intraspecific niche variation contributes to shape ecosystem dynamics and function. The degree of intraspecific variation in a predator, for instance, can determine prey abundance and community composition. There is still much to explore on how individual specialisation at each node of an interaction network shapes the outcomes of interactions, taken in isolation or combined. Future research might explore the effects of IS at the wider community scale, for example by investigating the top-down of trophic IS on prey communities or its role in the facilitation or impediment of species coexistence. Further examination of the direction and intensity of the combined effects of multiple ecological drivers on individual specialisation is also required, with an expansion of the scrutiny beyond antagonistic biological interactions.

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2 Appendix 1. Supplementary tables and figures for Chapter 2

Table S2-1. Characteristics of the ponds sampled in the Waipori-Waihola wetlands over 2016 and 2017 Pond Sampling Dates X* (m Y* (m S) Surface Distance Presence E) area (ha) to Lake of open Waihola channels (m) DM Nov 2017 428630 4906890 0.01 1224 N BP Feb 2016, Nov 2017 428690 4906930 0.1 1205 N FB Nov 2017 428700 4907010 0.03 1259 N SW Nov 2017 428750 4907200 0.14 1357 N SS Nov 2017 428775 4907070 0.005 1273 N SQ Jan 2016, Feb 2016, Nov 2017 428950 4907800 10.3 1735 Y PU Feb 2016, Nov 2017 429000 4907450 2.7 1298 Y SO Feb 2016, Nov 2017 429120 4907640 0.3 1638 Y GP Jan 2016, Nov 2017 429200 4907500 5.7 1272 Y SX Nov 2017 429233 4907163 0 1354 N RI Jun 2016, Nov 2017 429350 4907630 0.1 1613 N LP Nov 2017 429700 4908400 4.3 2158 Y MP Jun 2016, Nov 2017 429700 4907200 5.7 1067 Y SP Mar 2016 429800 4906350 7.1 170 Y HS Nov 2017 430450 4908150 0.7 2260 N TQ Nov 2017 430950 4905250 0.4 158 N ST Nov 2017 431000 4905420 0.4 324 N WR Nov 2017 431000 4907650 2.4 1882 N TI Feb 2016, May 2016 431240 4905690 12.4 437 Y BA Nov 2017 431250 4907600 1.2 1918 N TD Nov 2017 431500 4905490 0.7 480 N TU Nov 2017 431590 4905380 0.1 463 N *Coordinate system UTM 59

Table S2-2.Characteristics of fyke nets used during fish sampling of the Waipori-Waihola wetlands Fine-mesh nets Large-mesh nets Material 4 mm balsam green woven mesh 10 mm black woven mesh Leader Dimensions 5 m x 0.6 m 4.1 m x 0.5 m Trap Length 3.5 m 3.5 m Number of hoops 6 3 Number of 3 2 compartments Compartment First & second: 15 cm throat NA separations Second & third: 2.5 cm plastic grill D-ring (width x 0.6 m x 0.7 m 0.6 m x 0.5 m height)

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Table S2-3. Area of the 95% standard ellipses (SEAc) of the isotopic signatures of common bullies captured in 2016 and 2017 in the Waipori-Waihola wetlands. 2 Pond Year SEAc (‰ ) Pond Year SEAc (‰2) BP 2016 0.85 SP 2016 1.06 BP 2017 1.97 SQ 2017 1.32 GP 2017 1.77 ST 2017 1.02 HS 2017 2.21 TI 2016 0.83 LP 2017 2.72 TQ 2017 1.21 MP 2017 3.33

Table S2-4. Count of identified prey items found in bully stomach contents from 7 ponds of the Waipori-Waihola wetlands in 2016 and 2017 Pond BP GP LP MP ST TQ TI Date Nov-17 May-16 N fish 18 22 29 31 17 33 30 Incl. fish with stomach contents 14 15 21 22 17 17 16 Cladocera (*) Chydoridae 0 21 55 11 0 0 0 (*) Ceriodaphnia spp. 60 2 0 2 0 0 0 (*) Paracalliopidae (Amphipoda) 0 1 0 3 0 0 143 (*) Cyclopoida 19 0 2 0 0 0 0 (*) Tenagomysis chiltoni (Mysida) 0 0 0 19 0 0 19 (*) Ostracoda 2 7 11 13 6 44 8 (*) Ceratopogonidae larvae 0 0 0 0 0 12 0 Chironomidae (*) Chironomid larvae 68 59 111 39 30 57 11 (*) Chironomid pupae 2 0 12 1 21 9 0 (*) Tanypodinae larvae 0 4 0 0 4 2 0 (*) Trichoptera larvae Unid. 1 0 0 0 0 0 6 Hydroptilidae 0 0 0 0 0 0 6 Paroxyethira spp. 0 0 0 0 0 0 2 (*) Zygoptera larvae 12 3 1 0 3 1 0 (*) Dytiscidae larvae 0 0 0 0 1 0 0 (*) Molluscs Potamopyrgus sp. 0 1 0 0 0 2 4 Physa spp. 7 1 1 0 0 0 0 Gastropoda 0 2 0 0 0 1 0 Bivalves 0 1 0 0 0 5 0 (*) Hygrobatidae 0 0 0 0 0 0 1 Total number of taxa 8 11 7 7 6 9 9 Total prey count 171 102 193 88 65 133 200 (*) Taxonomic groups used in the calculation of indices of individual specialisation.

Table S2-5. Indices of individual specialisation in diet of common bullies from the Waipori-Waihola wetlands Pond WIC BIC TNW WIC/TNW E

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BP 0.73 0.70 1.43 0.51** 0.73 GP 0.70 0.63 1.33 0.52** 0.73 LP 0.65 0.47 1.11 0.58** 0.56 MP 0.45 1.04 1.49 0.30** 0.75 ST 0.75 0.57 1.32 0.57¤ 0.60 TI 0.65 0.39 1.04 0.62** 0.65 TQ 0.82 0.58 1.40 0.58** 0.75 mean 0.68 0.62 1.30 0.53 0.68 sd 0.12 0.21 0.16 0.11 0.08

Figure S2-1. 95% standard ellipse areas (SEAc) for carbon and nitrogen isotopic signatures of common bully populations plotted against distance to Lake Waihola (right) and pond surface (left). Closed symbols represent isolated ponds, open symbols represent connected ponds. Colours represent seasons. Green: spring. Yellow: summer. Brown: autumn.

Figure S2-2. Model fits of nitrogen isotopic signatures predicted by bully sex (left) and size (right). The boxplots depict the median, first and third quartiles with whiskers extending up to 1.5 times the interquartile range. Circles represent outliers. Model estimates appear in pale blue.

3 Appendix 2. Supplementary tables and figures for Chapter 3

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Table S3-1. Prey categories, codes and regression parameters used in the estimation of dry biomass Prey category Code a b Model Total organism Freq. Annelida Other - - 0.0% Arachnida Acari Other 0.13265547 1.66 1 Hydracarina 0.0% Other Other - - 0.0% Cladocera Bosmina spp. Bosm. 17.7369 2.2291 3 Bosmina 1.7% longirostris Ceriodaphnia spp. Daph. 4.0216 1.9763 3 Ceriodaphnia 2.6% lacustris Chydorus spp. Chyd. 14.0793 1.9796 3 Chydorus 27.3% sphaericus Daphnia spp. Daph. 7.4997 1.5644 3 Daphnia galeata 0.0% mendotae Other Daphniidae 4.0216 1.9763 3 Ceriodaphnia 6.1% lacustris Other Cladocera 4.0216 1.9763 3 Ceriodaphnia 0.2% lacustris Coleoptera Dytiscidae Other 0.0077 2.91 2 Coleoptera 0.0% insects Other 0.0077 2.91 2 Coleoptera 0.0% Collembola Poduromorpha Other 0.0056 2.809 4 Collembola 0.0% Copepoda Calanoid Cala. 7.7416 2.3482 3 Diaptomus 3.9% oregonensis Cyclopoida Cycl. 7.0729 2.5563 3 Cyclops vernais 5.3% Other Other 7.0729 2.5563 3 Cyclops vernais 0.0% Diptera Chironomidae larvae Chiron. 1.55270722 2.23 1 Chironominae 24.4% Chironomidae pupae Di.Pup 1.55270722 2.23 1 Chironominae 0.8% Culicidae larvae Other - - 0.1% insects Culicidae pupae - - 0.2% Limoniinae 0.0029 2.681 2 Tipulidae 0.0% Tanypodinae 0.407 1.01 5 Tanypodinae 0.2% G. cotidianus Eggs Gcotid. 0.21 -0.001 7 Multiple species 15.5% Larvae - - 10.6% Gastropoda Potamopyrgus sp. Other 0.00856561 3.23 1 Bithynia 0.0% tentaculate Unidentified insect parts, caseless Trichoptera larvae Other - 0.1% instects Ostracoda Ostracoda Ostr. 0.00495242 2.035161 6 Conchoecia sp. 0.7%

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Length-weight equations General form: 푾 = 풂 ∗ 푳풃, with W = dry mass (g), L = total body length (except for Chironomidae, including Tanypodinae, for which L = head width). Units of length units adjusted to obtain W in grams. For bully eggs: 푾 = 풂 ∗ 푽 + 풃, with V = egg volume For bully larvae: W = median egg weight

References: 1Baumgärtner, D., & Rothhaupt, K. O. (2003). Predictive length–dry mass regressions for freshwater invertebrates in a pre‐alpine lake littoral. International Review of Hydrobiology, 88(5), 453-463. 2Benke, A. C., Huryn, A. D., Smock, L. A., & Wallace, J. B. (1999). Length-mass relationships for freshwater macroinvertebrates in North America with particular reference to the southeastern United States. Journal of the North American Benthological Society, 18(3), 308-343. 3Culver, D. A., Boucherle, M. M., Bean, D. J., & Fletcher, J. W. (1985). Biomass of freshwater crustacean zooplankton from length–weight regressions. Canadian Journal of Fisheries and Aquatic Sciences, 42(8), 1380- 1390. 4Gruner, D. S. (2003). Regressions of length and width to predict arthropod biomass in the Hawaiian Islands. Pacific Science, 57(3), 325-336. 5Johnston, T. A., & Cunjak, R. A. (1999). Dry mass–length relationships for benthic insects: a review with new data from Catamaran Brook, New Brunswick, Canada. Freshwater Biology, 41(4), 653-674. 6Mizdalski, E. (1988). Weight and length data of zooplankton in the Weddell Sea in austral spring 1986 (ANT V/3). Berichte zur Polarforschung (Reports on Polar Research), 55. 7Robertson, D. R., & Collin, R. (2015). Inter-and intra-specific variation in egg size among reef fishes across the Isthmus of Panama. Frontiers in Ecology and Evolution, 2, 84.

Figure S3-1. Types of measurements made on zooplanktonic crustaceans Top row, left to right: Chydoridae, Daphniidae, Ceriodaphnia spp., Bosminidae. Bottom row, right: Copepoda, left: Ostracoda.

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4 Appendix 3. Supplementary tables and figures for Chapter 4

Table S4-1. Summary of prey items found in bully and perch stomachs n Count of prey items Taxon richness Mean sd Min Max Mean Sd Min Max Individual Bullies 431 10.8 33.3 0 333 1.2 1.5 0 6 stomach samples Perch 57 71.8 120.9 0 716 3.0 2.3 0 8 (total) Aggregated BA (bullies) 28 50.8 79.4 0 331 3.1 2.0 0 6 samples BP (bullies) 28 72.4 90.3 1 334 3.9 1.7 1 7 PA (bullies) 24 22.3 37.1 0 166 2.3 2 0 7 PA (perch) 6 457.3 307 11 867 6.2 2.1 3 8 PP (bullies) 29 22.6 21.9 0 77 2.8 2.1 0 7 PP (perch) 7 193 162.6 23 407 5.7 2.1 3 8 Total (bullies) 109 42.6 66.9 0 334 3.0 2 0 7 Total (perch 13 315 267 11 867 5.9 2.1 3 8 Stomach contents were aggregated over all sampling sessions. Data analysis was conducted on aggregated values. Values are rounded to the nearest decimal.

5 Appendix 4. Supplementary material for Chapter 5

HFI data extraction: additional information

Tyler D. Jessen, Marine Richarson

Terrestrial human footprint layer drawn from Venter et al. (2016) Nature Scientific Data (available at: https://www.nature.com/articles/sdata201667). 1km2 resolution for terrestrial areas and 80km offshore. Data available from Dryad: https://datadryad.org//resource/doi:10.5061/dryad.052q5 File information: 897.4Mb 7-zip file archive with individual GeoTIFF datasets.

Marine human footprint layer drawn from Halpern et al. (2015) Nature Communications (available at: http://science.sciencemag.org/content/319/5865/948). 1km2 resolution available. Pressure data were calculated for each stressor by: (1) multiplying the rescaled stressor (rescaled using only the 2013 data) by each habitat layer and the corresponding stressor/habitat vulnerability score (for each stressor this generates 20 rasters); (2) summing the resulting stressor/habitat/vulnerability rasters (generates 1 raster for each stressor); (3) dividing by the number of habitats found in each raster cell layer. The cumulative impacts were calculated by summing all the pressure rasters. Data at Ecoinformatics.org: https://knb.ecoinformatics.org/#view/doi:10.5063/F19Z92TW

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File information: 2Gb ZIP file with .tif files. Dataset 6 of 7, “Cumulative human impacts: pressure and cumulative impacts data (2013, all pressures)”, file name: cumulative_impact_one_2013_global_cumul_impact_2013_mol_2015071405314 6

Table S5-1. Estimates from phylogenetic mixed-effects meta-analysis for the relationship between HFI and indices of individual specialisation Dataset Parameter Coeff. Lower Upper pMCMC estimate 95% 95% (b) CI CI Terrestrial PSi Intercept 0.47 0.08 0.92 0.04 * DIC: -256.59 HFI 0.13 0.02 0.24 0.02 * Phylogenetic Ecosystem type | Freshwater v. -0.08 -0.44 0.36 0.60 signal: 0.82 Terrestrial Study design | Short v. Long 0.20 -0.01 0.37 0.04 * Data type | Diet composition v. Isotopes 0.13 0.05 0.21 0.00 ** HFI : Ecosystem type -0.18 -0.34 -0.02 0.04 * Study design v. Data type 0.12 -0.24 0.54 0.55

Terrestrial Intercept 0.44 0.00 0.93 0.06 WIC/TNW HFI 0.09 -0.19 0.35 0.47 DIC: -50.57 Ecosystem type | Freshwater v. -0.10 -0.56 0.35 0.62 Phylogenetic Terrestrial signal: 0.77 Study design | Short v. Long 0.30 -0.03 0.54 0.06 Data type | Diet composition v. Isotopes 0.07 -0.12 0.26 0.48 HFI : Ecosystem type 0.09 -0.22 0.44 0.58 Study design v. Data type -0.43 -0.91 0.04 0.09 Marine PSi Intercept 0.54 0.37 0.75 <0.001 *** DIC: -34.3 HFI 0.03 -0.11 0.14 0.68 Phylogenetic Cross-sect. diet comp. v. Cross-sect. 0.13 -0.10 0.38 0.28 signal: 0.55 isotopes Cross-sect. diet comp. v. Longit. diet 0.03 -0.13 0.19 0.73 comp. Marine Intercept 0.63 0.17 1.16 0.02 * WIC/TNW HFI -0.14 -0.34 0.07 0.19 DIC: -26.76 Cross-sect. diet comp. v. Longit. diet -0.18 -0.45 0.08 0.18 Phylogenetic comp. signal: 0.87 Cross-sect. diet comp. v. Time series -0.35 -0.65 -0.10 0.01 ** isotopes SD: standard deviation of estimate. CI: credible interval

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Table S5-2. Summary table of individual specialisation dataset for the comparative analysis ̅̅̅̅̅ 푾푰푪/푻푵푾 푷푺 풊 Marine Terr Total Mar Terr Total HFI Design Method Taxonomic.group Mar FW Terr Mar FW Terr Cross- Diet Actinopterygii 6 35 41 25 120 145 sectional composition Amphibia 7 7 17 17 Arthropoda 2 19 21 2 2 4 Aves 5 5 4 4 Elasmobranchii 4 4

Mammalia 3 3 Mollusca 10 10

Reptilia 37 37 Isotopes Actinopterygii 2 18 20 Amphibia 4 4 8 8 Aves 1 1 Longitudinal Diet Arthropoda 1 4 5 1 6 7 composition Aves 2 1 3 1 1 Mammalia 8 8 3 2 5 Mollusca 3 3 Time series Isotopes Actinopterygii 4 4 Aves 3 3 Mammalia 14 3 17 1 1 Reptilia 6 6 Grand Total 47 39 39 125 55 138 76 269

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Figure S5-1. Topology of the phylogenetic tree used for Bayesian phylogenetic mixed-effects meta-analysis for 푃푆̅̅̅̅푖, terrestrial HFI

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Figure S5-2. Topology of the phylogenetic tree used for Bayesian phylogenetic mixed-effects meta-analysis for 푊퐼퐶/푇푁푊, terrestrial HFI

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Figure S5-3. Topology of the phylogenetic tree used for Bayesian phylogenetic mixed-effects meta-analysis for 푃푆̅̅̅̅푖, marine HFI

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Figure S5-4. Topology of the phylogenetic tree used for Bayesian phylogenetic mixed-effects meta-analysis for 푊퐼퐶/푇푁푊, marine HFI

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Figure S5-5. Flow chart of study selection for the meta-analysis