Species Roles and Link Roles: a Richer Perspective on Network Ecology

Species roles and link roles: a richer perspective on network ecology

Alyssa R.Cirtwill

Submitted in partial fulﬁlment of the requirements for the degree of Doctor of Philosophy at the University of Canterbury

School of Biological Sciences University of Canterbury New Zealand

2016

Dedicated to my family and to my friends near and far. Your love, support, and encouragement made this possible.

The more accurate the map, the more it resembles the territory. The most accurate map possible would be the territory, and thus would be perfectly accurate and perfectly useless. The tale is the map that is the territory. –Neil Gaiman

Table of Contents

Contents v

Abstract ix

Preface xi

Co-authorship statements xiii

General introduction 1 Bibliography...... 8

Chapter 1: Species’ roles in food webs 13 Introduction ...... 14 Concepts of species’ roles in networks ...... 15 Grouping species with similar roles ...... 24 Limitations to role concepts and future directions ...... 30 Conclusions ...... 33 Acknowledgements...... 34 Box 1:Glossary ...... 35 Bibliography...... 36

Chapter 2: Knowledge of predator-prey interactions improves predictions of immigration and extinction in island biogeography 51 Abstract...... 52 Keywords ...... 52 Introduction ...... 53 Methods ...... 55 Results ...... 62 Discussion ...... 68 Acknowledgements...... 71 Supportinginformation ...... 71 Bibliography...... 72 vi

Chapter 3: Conservation of interaction partners between related plants varies widely across communities and between plant families. 77 Summary...... 78 Keywords ...... 78 Introduction ...... 79 Materialsandmethods ...... 81 Results ...... 86 Discussion ...... 89 Acknowledgements...... 93 Authorcontribution...... 93 Supportinginformation ...... 93 Bibliography...... 94

Chapter 4: Are high-arctic plant-pollinator networks unravelling in a warming climate? 99 Abstract...... 100 Keywords ...... 100 Introduction ...... 101 MaterialsandMethods...... 103 Results ...... 111 Discussion ...... 118 Acknowledgements...... 122 Supportinginformation ...... 122 Bibliography...... 123

Chapter 5: Concomitant predation on parasites is highly variable but constrains the ways in which parasites contribute to food-web structure. 129 Summary...... 130 Keywords ...... 130 Introduction ...... 131 Materialsandmethods ...... 137 Results ...... 142 Discussion ...... 144 Conclusions ...... 147 Acknowledgements...... 148 Dataaccessibility ...... 148 SupportingInformation ...... 148 Bibliography...... 150

Chapter 6: Taking the scenic route: trophic transmission of parasites and the properties of links along which they travel. 157 Abstract...... 158 Keywords ...... 158 vii

Introduction ...... 159 Methods ...... 161 Results ...... 167 Discussion ...... 170 Acknowledgements...... 174 Supportinginformation ...... 174 Bibliography...... 175

General discussion 179 Summaryofresults...... 179 Implications ...... 181 Nextsteps ...... 183 Conclusion...... 186 Bibliography...... 187

Appendix: Are parasite richness and abundance linked to prey species richness and individual feeding preferences in ﬁsh hosts? 191 Summary...... 192 Keywords ...... 192 Introduction ...... 193 MaterialandMethods ...... 196 Results ...... 201 Discussion ...... 206 Acknowledgements...... 211 FinancialSupport ...... 211 Bibliography...... 212

Supporting Information S2 219 S2.1:Fullmodels ...... 220 S2.2:Best-ﬁtmodels ...... 224 S2.3:Summarytablesforbest-ﬁtmodels ...... 226 S2.4: Details of models not described in the main text . . . . . 228 S2.5: Cumulative species richness plots for islands not showninthemaintext...... 235

Supporting Information S3 239 S3.1. Original sources for networks ...... 240 S3.2. Supplemental within-network results ...... 242 S3.3. Supplemental within-family results ...... 243 Bibliography...... 245

Supporting information S4 251 S4.1 - Simulated dates of ﬁrst interaction ...... 252 S4.2 - Tentatively-dated observations: methods ...... 258 viii

S4.3 - Tentatively-dated observations: results ...... 259 S4.4 -Supplementalﬁgures ...... 262

Supporting Information S5 263 S5.1. Additional References and Description of Food Webs . . 264 S5.2. Supplemental methods: quantifying species’ and links’ roles...... 266 S5.3. Supplemental methods: role dispersion & diversity . . . 267 S5.4. Supplemental results: median roles ...... 269 S5.5. Supplementalresults: speciesroles...... 271 S5.6.Linkroles ...... 273 Bibliography...... 276

Supporting Information S6 279 S6.1: Detailed methods for data collection ...... 280 S6.2: Supplemental methods and results for links’ structural properties ...... 294 S6.3:Resultsofmodelselection ...... 297 S6.4: Testing for correlations between link properties . . . . . 298 Bibliography...... 301

Supporting Information SA 305 S7.1. Supplemental results: biomass of trophically transmit- tedparasites ...... 306

Acknowledgements 309 Abstract

Food webs and other ecological networks can be seen as maps of species and their interactions (e.g., predation, pollination, and parasitism). Such mappings frame the complex intricacies of biological communities in a way that is analytically tractable, but also obscure species-level information. This can lead to a gap between studies of networks and the deep literature surrounding species’ idiosyncratic ecologies. Species roles— descriptions of the way each species is embedded into its community —offer one way to bridge this gap. As roles provide a species-level perspective on network structure, patterns in species roles can often be related to species traits in a way that the overall structure of a network usually cannot. Thus, role-based approaches give network ecologists a way to use species’ natural histories to understand patterns in network structure while also making network analyses more approachable for ecologists with different specialities.

This thesis uses a variety of definitions of species roles to explore a variety of ecological networks, demonstrating the broad range of questions to which species roles may be applied. The first chapter provides an overview of several different role concepts used in network ecology, and the second through fifth chapters each use one or more role concept to investigate specific ecological questions. Chapter two uses species roles to incorporate a predator-prey network into the Theory of Island Biogeography. Chapter three uses species roles to compare the overlap of plants’ interaction partners in plant-pollinator and plant-herbivore networks, while chapter four explores the changes to plants’ and insects’ roles in a single plant- pollinator network over 15 years of climate change. Chapters five and six are focused on aquatic food webs that include parasites. Chapter five compares the roles of parasites and free-living species, as well as different types of interactions between them (i.e., predation among free-living species, parasitism, antagonism among parasites, and x

concomitant predation on parasites inside their hosts). Chapter six uses the roles of feeding links between free-living species to better understand the trophic transmission of parasites. Finally, in an appendix we show how individual variation in ﬁshes diets affect their parasite loads.

The key ﬁndings of this thesis are i) that using species roles to incorporate information from food webs improves the predictions of the Theory of Island Biogeography, ii) that more closely related plants had more similar sets of interaction partners despite a great deal of variation across networks and between plant families, iii) that the roles of plants and pollinators have shown different changes after 15 years of warming, suggesting that phenological uncoupling may be a risk for this system, iv) that parasites and free-living species have different roles in food webs, but only when concomitant predation was considered, and v) that many properties of feeding links between free-living species affect the outcomes of these links for parasites. As well as providing answers to the driving questions behind each chapter, this thesis demonstrates the breadth of potential applications for species roles. We conclude species roles provide a framework that speaks to the heart of one of the fundamental unsolved questions in ecology— how species’ traits relate to the structure of ecological networks. Preface

This thesis has been written as a series of stand-alone scientific articles which nevertheless form a cohesive unit. The articles all share a common focus on using species roles to combine network theory with ecological questions. As of the date of submission of this thesis, the articles were in different stages of the publication process. The first, “Species roles in food webs” was in preparation for submission to Food Webs and represents a wider review of the relevant literature than is present in the introductions of the subsequent chapters. The second, “Knowledge of predator-prey interactions improves predictions of immigration and extinction in island biogeography”, was published in a special edition of Global Ecology and Biogeography June 2015: volume 25, issue 7, pages 900–911. The third, “Conservation of interaction partners between related plants varies widely across communities and between plant families” was under revision at New Phytologist, manuscript number NPH-MS-2016-21211. The fourth, “Are high- arctic plant-pollinator networks unravelling in a warming climate?” was under review at Ecography, manuscript number ECOG-02910. The fifth, “Concomitant predation on parasites is highly variable but constrains the ways in which parasites contribute to food-web structure” was published in the Journal of Animal Ecology May 2015: volume 84, issue 3, pages 734-744. The sixth, “Taking the scenic route: trophic transmission of parasites and the properties of links along which they travel” was under review at Ecology, manuscript number #ECY16-0885. The appendix, “Are parasite richness and abundance linked to prey species richness and individual feeding preferences in fish hosts?” was published in Parasitology January 2016: volume 143, issue 1, pages 75-86. In the “General introduction” and “General discussion” framing these articles, I discuss the relevance of the articles to each other and their application in ecology.

Co-authorship statements

The following pages contain co-authorship statements for each co- authored chapter in this thesis. xiv xv

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Please indicate the chapter/section/pages of this thesis that are extracted from co-authored work and provide details of the publication or submission from the extract comes: The entirety of chapter two was published in the journal Global Ecology and Biogeography (doi: 10.1111/geb.12332).

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Name: Daniel B. Stouffer Signature: Date: 29-09-2016 1 General introduction

2 Whether to our delight or our dismay, ecology is not like physics.

3 In physics, a reductionist approach to studying the world (and

4 beyond) has yielded centuries of phenomenal results such as the

5 discovery of the four fundamental forces, Newtonian mechanics,

6 and relativity (Meyer-Ortmanns, 2015). In ecology, meanwhile,

7 many senior researchers despair of uncovering fundamental laws

8 (Lawton, 1999; Simberloff, 2004; Poulin, 2007; but see Turchin,

9 2001). Exceptions abound to both observed patterns and theoretical

10 predictions (Lawton, 1999; Poulin, 2007). This is likely due to the

11 wide and wonderful variety of species, habitats, and communities

12 that characterises our world. The peculiarities of living things

13 are what draw many ecologists to the ﬁeld, but they also make

14 any pursuit of general truths very difﬁcult. To put it simply,

15 while a single species can be described relatively well in isolation,

16 knowledge of one species’ population dynamics, behaviour, or

17 habitat requirements may not be applicable when studying another

18 species.

19 One cannot, therefore, understand an ecological community

20 by scaling up from the properties of single species in the same

21 way that one can understand the behaviour of a gas by scaling

22 up from the properties of molecules. Unlike physical systems,

23 natural communities of species display emergent properties

24 that cannot be predicted based on the properties of the species

25 themselves (Emmerson and Yearsley, 2004; Beckerman et al., 2006;

26 Stouffer, 2010). For example, natural communities routinely support

27 higher numbers of species than are stable in naive models (May,

28 1972). This implies that there is some form of “organisation” in

29 ecological communities that stabilises them and allows them to

30 persist (Dunne et al., 2002, 2004; Fortuna et al., 2010; Stouffer and

31 Bascompte, 2011). To study these structures, it is thus necessary to

32 consider the structure of the community as a whole, and a leading 2

33 way to do so is within an ecological network framework (Heleno

34 et al., 2014).

35 Networks are essentially maps of interactions (‘links’) between

36 species. Networks have been used to map antagonistic (e.g.,

37 predation [Paire, 1966] and parasitism [Wells et al., 2013]) and

38 mutualistic (e.g., pollination [Olesen et al., 2007] and seed-dispersal

39 [Schleuning et al., 2011]) interactions in a wide variety of habitats

40 from around the globe. In each case, the network describes the

41 whole community’s structure and behaviour with respect to the

42 interaction of interest. By capturing the structure of interactions

43 in a community, networks allow us to address questions about

44 community stability (Dunne et al., 2002, 2004; Fortuna et al., 2010;

45 Stouffer and Bascompte, 2011) and ecosystem functioning (Mello

46 et al., 2011; Burkle et al., 2013; Poisot et al., 2013). Both the

47 density (Dunne et al., 2002, 2004) and arrangement (Fortuna et al.,

48 2010; Stouffer and Bascompte, 2011) of links within a community

49 have been linked to communities’ ability to remain stable with high

50 numbers of species. These structural characteristics, moreover, have

51 also been linked to environmental factors such as latitude (Cirtwill

52 et al., 2015), land use (Thompson and Townsend, 2004, 2010), and

53 spatial isolation of the community (Nogales et al., 2015).

54 In addition to facilitating analysis of the community as a

55 whole, ecological networks can also be used to study species

56 within the broader context of their community. Speciﬁcally, we

57 can use ecological networks to describe species’ roles within their

58 communities– that is, how they interact with other species. By

59 quantifying species’ places within ecological networks, roles provide

60 a bridge between species’ natural histories and the properties

61 and processes at play at the community level. Integrating the two

62 levels of knowledge allows us to test potential drivers of network

63 structure. For example, species’ body sizes have been shown to play

64 a fundamental role in structuring predator-prey interactions (Loeuille

65 and Loreau, 2005; Brose et al., 2006; Curtsdotter et al., 2011; Riede

66 et al., 2011; Brose et al., 2016), and the roles of plants and animals

67 in pollination and frugivory networks have been linked to their

68 phylogenies (Ehrlich and Raven, 1964; Jordano et al., 2003; Rezende

69 et al., 2007; Guimarães et al., 2011; Rohr et al., 2014; Nogales et al.,

70 2015). Moving in the other direction, knowledge of the relationships

71 between species’ traits and their roles in food webs are being used

72 to develop probabilistic interaction networks that better account for

73 incomplete sampling of communities (Guimerà and Sales-Pardo,

74 2009; Dalla Riva and Stouffer, 2015; Poisot et al., 2015). Along with 3

75 facilitating “pure science” research, species’ network roles can also be

76 used to inform conservation plans since species with different roles

77 have different responses to perturbations in their community (Eklöf

78 and Ebenman, 2006; Kaiser-Bunbury et al., 2010; Curtsdotter et al.,

79 2011; Wootton and Stouffer, 2016).

80 Throughout this thesis, I demonstrate several ways in which

81 species roles can provide a bridge between network ecology and

82 knowledge about species’ particular traits. In each case, I endeavour

83 to show that using species roles gives us a unique insight into

84 communities. Like the species they describe, roles come in a variety

85 of shapes and sizes and can be measured in different ways. For a

86 very simple summary, one can count the number of interactions in

87 which a species participates (its degree), or determine how ‘high’ in

88 the network a species feeds (its trophic level). At the other extreme,

89 concepts like betweenness centrality consider all paths through the

90 network to determine a species’ impact (Jordán et al., 2006; Newman,

91 2010; Lai et al., 2012). In the middle are role concepts that include

92 species’ direct interactions as well as indirect interactions that are

93 likely to affect the focal species, but do not include the structure

94 of the entire network in each species’ role. One such deﬁnition is

95 ‘motif roles’; an extension of the use of meso-scale ‘motifs’. These are

96 conﬁgurations of n species describing unique patterns of interactions

97 that can be used to measure the structure of a network (Milo et al.,

98 2002; Stouffer et al., 2007). Once a network has been described in

99 terms of its component set of motifs, a species’ motif role is the list

100 of frequencies with which a species appears in each unique position

101 in each motif (Stouffer et al., 2012; Baker et al., 2015; Cirtwill and

102 Stouffer, 2015). The motif role therefore provides a summary of the

103 species’ direct and indirect (up to n-1 steps removed) interactions—

104 a detailed description of the way the species is embedded in the

105 community. These and other role concepts are all valid ways of

106 describing species’ places within their ecological contexts, and the

107 choice of role for each study will depend on the precise question

108 being asked.

109 To clarify the variety of concepts and methodologies used

110 to describe species’ roles, I begin this thesis with a review of the

111 literature surrounding species’ roles. This review takes the place

112 of the literature review that would normally occur in a thesis

113 introduction. In it, my co-authors and I ﬁrst summarise several

114 deﬁnitions of species roles (including those mentioned above). We

115 then highlight similarities among deﬁnitions of role that address

116 similar questions about the ways in which species interact, and ﬁnish 4

117 by noting extensions to role concepts currently being developed.

118 One such extension, the association of species’ roles with their

119 phylogenetic history, is also a major focus of Chapter 4.

120 Having established deﬁnitions for a variety of role concepts and

121 their broader context within the literature in Chapter 1, Chapters

122 2-6 of my thesis each explore one or more role concepts in detail.

123 In the second and third chapters, we deﬁne species’ roles as their

124 sets of interaction partners. In Chapter 2, these are the predators

125 and prey of arthropod species in a classic island biogeography

126 dataset (Simberloff, 1969). Here, my co-author and I attempt to use

127 knowledge of species’ roles in their local community, drawn from a

128 mainland food web (Piechnik et al., 2008), to improve the accuracy of

129 predictions based on the Theory of Island Biogeography (MacArthur

130 and Wilson, 1963). We expect that, as species’ roles change, their

131 probabilities of immigrating to or going extinct from a given

132 mangrove island will also change. Speciﬁcally, we ﬁt models which

133 include terms for the presence of species’ arthropod predators

134 and/or prey and/or their ability to consume basal resources as

135 well as similar “classic” models which include only island size and

136 isolation. We compare models based on both their ﬁt to the empirical

137 data and on the similarity of their predictions. This allows us to

138 determine whether incorporating species’ roles into the Theory of

139 Island Biogeography results in a meaningful improvement.

140 In Chapter 3, we consider plant’s roles in terms of their

141 pollinators or herbivores in a wide array of plant-pollinator and

142 plant-herbivore networks. Unlike the food web used to determine

143 arthropods’ roles in Chapter 2, these networks are all bipartite.

144 That is, they are composed of two groups of species (e.g., plants

145 and pollinators) that interact only with species from the opposite

146 group (i.e., plants are pollinated by animals, not by other plants). We

147 use these networks to investigate the relationship between plants’

148 phylogenies and their roles. We expect that, since related plants tend

149 to have similar traits and since herbivory and pollination interactions

150 are both strongly affected by plants’ traits, more closely-related

151 plants will have more similar roles. As herbivory is detrimental to

152 plants while pollination is beneﬁcial, we also expect that the strength

153 of this relationship might differ between network types. Finally,

154 we compare the strength of the relationship between phylogenetic

155 relatedness and similarity of interaction partners across plant

156 families. 5

157 In Chapters four and ﬁve, my co-authors and I deﬁne species’

158 roles more explicitly using the motif roles deﬁned in Stouffer et al.

159 (2012). That is, we decompose networks into their component motifs

160 and track species’ participation in each unique position across the

161 set of motifs. In Chapter four, we are interested in the motif roles of

162 plants and their insect pollinators. Our particular focus in this case

163 is the response of each group to climate change and the associated

164 changes to plants’ ﬂowering phenologies. As pollinators depend on

165 ﬂoral resources for their food, we expect that pollinators’ phenologies

166 may also have advanced over time. However, since plants and insects

167 active at different points in the season require different abiotic

168 conditions, we also expect that changes in roles may be linked to the

169 date on which a species becomes active each year. We test all of these

170 hypotheses in a plant-pollinator community in Northern Greenland

171 which has experienced substantial warming over the past 14 years

172 and in which plant phenologies are known to have changed (Høye

173 et al., 2013).

174 In Chapters ﬁve and six, we return to unipartite food webs to

175 explore several aspects of parasites’ participation in aquatic food

176 webs. Chapter ﬁve compares the roles of parasites and free-living

177 species across seven estuarine food webs to test i) whether parasites’

178 roles are similar to those of free-living species at particular trophic

179 levels and ii) whether parasites’ roles change as different types of

180 interactions unique to parasites are included in their roles. We divide

181 free-living species into basal resources (those with predators but no

182 prey), intermediate consumers (those with predators and prey), and

183 top predators (those with prey but no predators), and calculated the

184 median motif roles of each group. We next compare these median

185 roles to those of parasites. To test whether concomitant predation

186 (the consumption of parasites along with their hosts) has a different

187 effect on parasites’ roles than interactions in which the parasite is

188 more directly involved (i.e., parasitism, predation on free-living

189 life stages of the parasite, and predation among parasites sharing

190 a host), we calculate parasites roles’ both including and excluding

191 concomitant predation and compare each to the roles of free-living

192 species.

193 Chapter ﬁve also extends the motif role concept to links between

194 species. Just as species’ motif roles can be described by calculating

195 the frequencies with which a species occupies each unique position

196 in a set of motifs, an interaction’s role can be described by calculating

197 the frequency with which it occupies each unique link position in

198 the same set of motifs. This description captures the different ways 6

199 in which each link contributes to the ﬂow of energy and biomass

200 through a web just as a species’ motif role describes the species’

201 participation in the web. To compliment our investigation of the

202 changes to parasites’ roles when different types of interactions are

203 included, we also examine the roles of several types of link directly.

204 Because species’ motif roles are determined by the interactions in

205 which they participate and vice versa, taking both a species- and link-

206 focused view of network structure provides a unique window into

207 how each type of species and interaction is embedded in the network

208 as a whole.

209 In Chapter six we build on this concept of links’ roles to

210 investigate the consequences of links between free-living species

211 for parasites. Many parasites have complex life cycles which involve

212 multiple hosts. In some cases, parasites move from one host to the

213 next via trophic transmission when the parasite’s next host consumes

214 its current host. As parasites generally must complete their life cycles

215 in order to sexually reproduce, we expect that they will tend to use

216 transmission routes that are very likely to occur (giving the parasite

217 the best chance of reaching its ﬁnal host). We used several deﬁnitions

218 of links’ roles within networks to determine whether links resulting

219 in trophic transmission have different properties from links in which

220 the parasite is killed or links which do not affect the parasites.

221 The latter occurs when the prey in an interaction is not a host for

222 any parasites in the study system. We tested this hypothesis in a

223 spatially and temporally-replicated dataset from four New Zealand

224 lakes (Cirtwill et al., 2016).

225 In an appendix following the main body of the thesis, I present

226 additional work done during my PhD candidature at the University

227 of Canterbury. As a companion study to the work in Chapter six,

228 my co-authors and I test whether ﬁsh with broader diets are more

229 likely to host large numbers of parasites or highly-diverse parasite

230 assemblages. In particular, we are interested in the associations

231 between diet and parasite load across individuals of different ﬁsh

232 species. Although the six main chapters of this thesis address

233 questions at the level of species and the interactions between them,

234 it is worth remembering that individuals within species do not

235 necessarily all participate in the same interactions or have the same

236 roles. Indeed, several studies have shown that generalist species can

237 be composed of much more specialised individuals (Pires et al., 2011).

238 Investigating spatial, temporal, and intra-speciﬁc variation in species’

239 roles is likely to be an important area of study in the future; this 7

240 appendix provides only one example of the questions that may be

241 asked.

242 Readers will note that this body of work does not explore a

243 narrow area in great depth but instead applies species roles to a

244 variety of questions in network ecology. This is by design. As species

245 roles, particularly motif roles, are a relatively recent development

246 even within network ecology (itself a young subdiscipline within

247 ecology), it is not yet clear which questions require the most in-

248 depth study. Instead, I have opted to demonstrate the breadth of

249 potential applications for species roles. In addition to contributing to

250 the ecological literature surrounding each chapter, this broad-based

251 approach has also revealed strengths and weaknesses of different

252 role concepts and, more importantly, ways in which I and others can

253 improve them in the future. 8

254 Bibliography

255 Baker, N. J., Kaartinen, R., Roslin, T., and Stouffer, D. B. (2015).

256 Species’ roles in food webs show ﬁdelity across a highly variable

257 oak forest. Ecography, 38:130–139.

258 Beckerman, A. P., Petchey, O. L., and Warren, P. H. (2006). Foraging

259 biology predicts food web complexity. Proceedings of the National

260 Academy of Sciences of the United States of America, 103:13745–13749.

261 Brose, U., Blanchard, J. L., Eklöf, A., Galiana, N., Hartvig, M., Hirt,

262 M. R., Kalinkat, G., Nordström, M. C., O’Gorman, E. J., Rall, B. C.,

263 Schneider, F. D., Thébault, E., and Jacob, U. (2016). Predicting the

264 consequences of species loss using size-structured biodiversity

265 approaches. Biological Reviews, 49:0–0.

266 Brose, U., Jonsson, T., Berlow, E. L., Warren, P., Banasek-Richter,

267 C., Bersier, L. F., Blanchard, J. L., Brey, T., Carpenter, S. R.,

268 Cattin Blandenier, M.-F., Cushing, L., Dawah, H. A., Dell, T.,

269 Edwards, F., Harper-Smith, S., Jacob, U., Ledger, M. E., Martinez,

270 N. D., Memmott, J., Mintenbeck, K., Pinnegar, J. K., Rall, B. C.,

271 Rayner, T. S., Reuman, D. C., Ruess, L., Ulrich, W., Williams, R. J.,

272 Woodward, G., and Cohen, J. E. (2006). Consumer-resource body-

273 size relationships in natural food webs. Ecology, 87:2411–2417.

274 Burkle, L. A., Marlin, J. C., and Knight, T. M. (2013). Plant-pollinator

275 interactions over 120 years: loss of species, co-occurrence, and

276 function. Science, 339:1611–1615.

277 Cirtwill, A. R. and Stouffer, D. B. (2015). Concomitant predation

278 on parasites is highly variable but constrains the ways in which

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433 Theoretical Ecology, 9:185–195. 434 Chapter 1: Species’ roles in food webs

1 2 435 Alyssa R. Cirtwill , Giulio Valentino Dalla Riva , 1 3 1 436 Matthias Dehling ,Marilia Gaiarsa ,Malyon Bimler , 1 4,5 437 Fernando Cagua , & Camille Coux

1 438 Centre for Integrative Ecology

439 School of Biological Sciences

440 University of Canterbury

441 Private Bag 4800

442 Christchurch 8140, New Zealand

2 443 Beaty Biodiversity Research Centre

444 Zoology Department

445 University of British Columbia

446 2329 West Mall

447 Vancouver, Canada V6T 1Z4

3 448 Departamento de Ecologia

449 Instituto de Biociências

450 Universidade de São Paulo

451 São Paulo, SP, Brazil 05508-900

4 452 UMR 1065 Santé et Agroécologie du Vignoble

453 INRA Bordeaux-Aquitaine

454 71 Avenue Edouard Bourlaux

455 33140 Villenave-d’Ornon, France

5 456 Centre d’Etudes Biologiques de Chizé

457 UMR 7372, CNRS & Université de La Rochelle

458 23 Avenue Albert Einstein

459 17000 La Rochelle, France

460 Prepared in the style of Food Webs; not yet submitted. 14

461 Introduction

462 Ecologists often wish to understand a species’ “place in the biotic

463 environment, its relations to food and enemies” (Elton, 1927 in

464 Johnson and Steiner, 2000) or, in short, its Eltonian niche. To do this,

465 one must ﬁrst map the biotic environment (community) to which

466 the focal species belongs. Food webs provide just such a mapping

467 by connecting species based on their trophic interactions. These

468 interactions include antagonistic interactions such as predation and

469 parasitism, but can also include mutualisms, such as pollination

470 and seed-dispersal, where one species obtains food while aiding

471 the reproduction of the other. Once a food web describing the focal

472 species’ community has been assembled, there are several methods

473 that can be used to describe the species’ role within the web (i.e., how

474 the focal species participates in its community). Because food webs

475 describe energy and biomass ﬂows through a community (Lindeman,

476 1942; Wootton, 1997), represent ecosystem functions (Memmott et al.,

477 2007; Reiss et al., 2009; Thompson et al., 2012), and even offer insights

478 into the community’s overall stability (Neutel et al., 2002; Thébault

479 and Fontaine, 2010), describing species’ roles in food webs allows

480 us to assess their niches both in terms of species’ requirements for

481 survival and their impacts on their communities (Chase and Leibold,

482 2003).

483 Roles and Eltonian niches are related, in that both address the

484 ways in which species affect and are affected by each other, but they

485 are not equivalent. This is true even when we completely ignore

486 species’ abiotic requirements (Peterson, 2011). Food webs generally

487 only include one type of interaction (e.g., predation or pollination

488 but not both [Fontaine et al., 2011]). A species’ role in a food web

489 therefore describes only the portion of its niche that relates to the

490 kind of interaction being described in the food web. For example, the

491 roles of a species of Lepidoptera will be quite different in networks

492 describing pollination, herbivory, and predation. Moreover, the

493 Eltonian niche aims to identify those biotic conditions that are able

494 to support a species on moderate timescales (i.e., from individual

495 lifespans up to thousands of years) (Peterson, 2011), while food webs

496 describe communities at a particular point in time with no guarantee

497 that the species present during sampling will persist. Finally, the

498 portion of a species’ niche that is described by its role in a network

499 will be affected by the exact deﬁnition of role that is used. Given the

500 variety of definitions used across different fields, it can be difficult

501 to make comparisons across studies. To tackle this problem, here

502 we review several commonly-used concepts of species’ roles in 15

503 food webs. In each case, we summarise the methodology used to

504 obtain the role and highlight its connection to the species’ Eltonian

505 niche. We are particularly interested in areas of overlap between

506 role concepts, and take care to point out connections between roles

507 wherever possible. We then outline ways in which researchers

508 identify and group species with similar roles, and conclude with a

509 very brief survey of current limitations to the idea of species’ roles,

510 and how researchers are working to overcome these limitations.

511 Terms in italics are deﬁned in Box 1..

512 Concepts of species’ roles in networks

513 Degree

514 One of the mathematically simplest deﬁnitions of a species’ role

515 is it’s degree: the number of interactions in which the species

516 participates (Fig. 1). Degree depends only on the focal species’ local

517 neighbourhood within the network. That is, degree only considers

518 other species which directly interact with the focal species. Thus,

519 degree provides a measure of species’ participation in a food web

520 without requiring any knowledge of the global structure of the web

521 (i.e., the species that indirectly affect the focal species). Degree can

522 also be used to investigate particular subsets of a species’ local

523 neighbourhood. If the focal species’ role as a predator (or prey)

524 speciﬁcally is of greater interest than its overall role, degree can be

525 divided into in-degree— the number of incoming links (interactions)

526 — and out-degree— the number of outgoing links (Fig. 1B). Note that

527 this is only applicable in unipartite networks as in bipartite networks

528 each group of species has only in-links or only out-links and such a

529 division is not meaningful. Whether or not degree is subdivided, in

530 niche terms degree tells us how important the focal species is likely

531 to be, in terms of the interaction described in the food web.

532 The notion that species with high degrees are particularly

533 important to their communities is based on the fact that if the

534 abundance of such a species changes, this will directly affect many

535 other species (Lai et al., 2012). Perturbations to high-degree species

536 may therefore have larger effects on the food web than perturbations

537 to low-degree species. Moreover, it is more likely that high-degree

538 species will have interaction partners that depend very strongly upon

539 them. As such, the removal of a high-degree species is more likely

540 to cause secondary extinctions than the removal of a low-degree

541 species (Dunne et al., 2002; Memmott et al., 2004; Eklöf and Ebenman,

542 2006; Kaiser-Bunbury et al., 2010; Curtsdotter et al., 2011). Degree

543 can also have implications for the management of introduced species. 16

Figure 1: These two food webs each A TL contain species with different degrees and trophic levels. A) In this bipartite food web, pale blue squares represent 2 pollinators and dark green circles represent plants. Note that species do not interact with other species 1 of the same type (i.e., plants do not pollinate other plants). B) In a unipartite food web, any species could potentially interact with any other. Here, degrees can also be subdivided into in- and out-degrees based on a TL focal species’ numbers of prey and B predators, respectively. For example, the species highlighted in the red, 3 dashed box has an in- degree of 2 and an out-degree of 1, giving an overall degree of 3. In both networks, the size of a shape increases with its degree while the fill represents trophic level (TL; height in food chains). In A), the two groups of species are at different trophic levels. In B), trophic levels increase from primary producers (TL=1; dark green) to predators (TL=3, very pale green). Most of the species in this 2 food web have integer trophic levels. The species highlighted in the dotted red box, however, is an omnivore with both plant and animal resources. Its trophic level therefore depends on the exact definition of trophic level used. Short-weighted trophic level considers only the most direct path from the focal species to a primary producer; under this definition, the focal species has a trophic level of 2. Prey-averaged trophic 1 level, in contrast, considers the trophic levels of all the focal species’ prey. If interaction strengths (indicated by line weights) are not considered, the focal 544 In particular, specialist pollinators are more likely than generalists species has a trophic level of 2.5. If interaction strengths are accounted for, 545 to interact with exotic plants, suggesting that it may be important to however, the focal species’ PATL will be 546 consider the degrees of native species when developing management closer to 2. 547 plans for introduced species (Stouffer et al., 2014).

548 As well as predicting species’ effects on their communities,

549 degrees can also be used to predict which species are most likely to

550 go extinct after the loss of an interaction partner. Specialist predators

551 (those with low in-degrees) are particularly vulnerable to the loss of

552 prey (Allesina, 2012). This difference in vulnerability to secondary

553 extinction in turn has implications for biogeography. As specialists

554 are more likely to go extinct following the loss of a prey species, they

555 are likely to have smaller geographic ranges than generalists (Gaston,

556 1991). At a landscape level, these trends mean that specialists should 17

557 appear in fewer patches than generalists (Holt, 2010; Gravel et al.,

558 2011), leading to increased beta diversity (Ødegaard, 2006). This has

559 the potential to create a feedback loop, with geographically-restricted

560 species having access to fewer partners than species with broader

561 ranges and therefore becoming more specialised.

562 Despite its utility, some have argued that the qualitative degree

563 described above, which is calculated based only on the presence or

564 absence of links between species, does not accurately reﬂect species’

565 specialisation or importance to the community (e.g., Blüthgen et al.,

566 2006). To address this, several quantitative extensions of degree

567 have been formulated. These extensions all weight interactions to

568 reﬂect the importance of the focal species to each of its partners

569 rather than assuming all interactions are equal (Blüthgen et al., 2007;

570 Dormann, 2011; Nilsson and McCann, 2016). Weighted measures may

571 provide a more realistic measure of a species’ effect on its interaction

572 partners than qualitative degree (Wootton, 2005; Vázquez et al., 2005).

573 However, calculating weighted degrees requires detailed data that

574 include interaction weights. As these data are more costly and time-

575 intensive to collect, datasets including weights are much rarer than

576 food webs that include only the presence or absence of interactions.

577 Trophic level

578 As well as describing the importance of a species’ niche, degree can

579 also be used to give an idea of a species’ vertical position in a food

580 web— i.e., its trophic level. This role concept refers to a species’ place

581 in the food chains that make up a food web, relative to the primary

582 producers that support the community. Species that do not consume

583 any other species in the web (i.e., those with an in-degree of zero) are

584 primary producers. At the other extreme, species with no predators

585 (i.e., those with an out-degree of zero) are top predators (Fig. 1B).

586 Those with both predators and prey (i.e., non-zero in- and out-

587 degrees) are intermediate consumers. In niche terms, trophic levels

588 tell us whether a focal species relates to its biotic environment as

589 a predator, prey, or both. These categorical descriptions, however,

590 are relatively imprecise. By deﬁning the trophic level of primary

591 producers to be one and those of consumers’ to be one greater than

592 that of their prey (Lindeman, 1942), numerical trophic levels can be

593 calculated for each species in a food web.

594 For species other than primary producers and top predators,

595 degree alone is not enough to calculate trophic levels. Instead, it

596 is necessary to consider the network structure beyond the focal 18

597 species’ local neighbourhood. Speciﬁcally, trophic levels can be

598 calculated by following food chains from the focal species to primary

599 producers. Each step up the food chain is a new trophic level, with

600 strict herbivores (that consume only basal resources) assigned a

601 trophic level of two and consumers occupying ever higher values

602 based on their sets of prey species (Lindeman, 1942; Darnell, 1961;

603 Baird and Ulanowicz, 1989; Christian and Luczkovich, 1999). This

604 simple deﬁnition was developed under the assumption that species

605 feed on sets of prey with the same trophic level (Lindeman, 1942).

606 As the prevalence and importance of omnivory in food webs has

607 become clear (Holt, 1997; Emmerson and Yearsley, 2004; Thompson

608 et al., 2007), however, non-integer trophic levels have become the

609 norm (Cousins, 1987; Vander Zanden and Rasmussen, 1996; Williams

610 and Martinez, 2004; Thompson et al., 2007). To emphasise this shift,

611 some researchers prefer the term “trophic position” (e.g., Levine,

612 1980; Cohen et al., 2003). As the two terms refer to the same quantity,

613 we will continue to use trophic level to refer to a species’ vertical

614 position in a food web.

615 A variety of methods have been developed to account for species

616 which feed on prey at different trophic levels (Fig. 1B). Each approach

617 emphasises different interactions. “Shortest trophic level”, for

618 example, assumes that because losses occur during the transfer of

619 energy between trophic levels, species obtain most of their energy

620 along the shortest food chain in which they participate (Hairston,

621 Jr. and Hairson, Sr., 1993; Williams and Martinez, 2004). Under this

622 concept, therefore, a species’ trophic level is one greater than the

623 lowest trophic level among its prey (Hairston, Jr. and Hairson, Sr.,

624 1993; Williams and Martinez, 2004). Other methods such as prey-

625 averaged trophic level take all food chains in which the focal species

626 participates into account (Williams and Martinez, 2004). Regardless

627 of the precise methodology, however, trophic levels always rank

628 species based on their vertical position in food webs, with primary

629 producers setting the baseline.

630 Trophic levels can also be calculated independent of food-web

631 topology by using stable isotopes (Peterson and Fry, 1987; Vander

632 Zanden and Rasmussen, 1996; Post, 2002). This approach uses the

633 different rates of bioaccumulation of carbon and nitrogen isotopes to

634 measure species’ average trophic levels without requiring knowledge

635 of speciﬁc interactions between species. While the stable isotopes

636 approach is therefore useful in cases where the structure of the

637 food web is not known, it is also difﬁcult to use when comparing

638 across food webs. Stable isotope ratios vary between taxa and tissue 19

639 types depending on their particular biochemistries (Vander Zanden

640 et al., 2015) and between study cites, requiring the use of baseline

641 species in each food web under study (Kling et al., 1992; Cabana

642 and Rasmussen, 1994; O’Reilly et al., 2002; Boecklen et al., 2011).

643 Despite the differences in how trophic levels are calculated from

644 stable isotopes and network topology, they have been shown to be

645 strongly correlated (Williams and Martinez, 2004; Carscallen et al.,

646 2012). This supports the idea that topological deﬁnitions of trophic

647 levels are grounded in sound ecological characteristics, and suggests

648 that trophic levels may be comparable across studies even if different

649 methodologies are used.

650 As well as different carbon and nitrogen isotopes, environmental

651 contaminants such as DDT and mercury tend to accumulate moving

652 up food chains (Rowan and Rasmussen, 1992; Gray, 2002; Wang and

653 Wang, 2005; Tavares et al., 2009; Coelho et al., 2013). Trophic levels

654 can therefore be used to predict the level of contamination in ﬁsh

655 species that are targeted for human consumption (Beltran-Pedreros

656 et al., 2011), and assess the risk of contamination for species of

657 conservation concern (Bossart, 2011). The bioaccumulation of DDT in

658 predatory birds is perhaps the most famous example of this process,

659 and identiﬁcation of this trend and its effects on bird populations led

660 to the banning of DDT in North America (Grier, 1982). Apart from

661 tracking the accumulation of contaminants, a species’ trophic level

662 can be used to predict its potential to cause a trophic cascade (Spiller

663 and Schoener, 1994; Dyer and Letourneau, 2003; Borrvall and

664 Ebenman, 2006; Eklöf and Ebenman, 2006; Boersma et al., 2014; Estes

665 et al., 2015; Rodríguez-Lozano et al., 2015), with top predators and

666 primary producers tending to have particularly large effects on the

667 rest of their communities. Like degree, therefore, trophic level offers

668 information about how important a species is to its biotic community.

669 Motif roles

670 A major limitation to both trophic level and degree, is that they give

671 little information on a species’ indirect interactions— interactions

672 which can have major impacts on the focal species despite not

673 involving the focal species directly (Wootton, 1994; Jordán et al.,

674 2006). This limits the ability of these role concepts to describe

675 species’ niches because indirect effects can modulate the relationships

676 between the focal species and their predators or prey. For example,

677 if the focal species’ predator has other prey and the focal species

678 becomes rare, the predator might consume more of the alternative

679 prey. The interaction between the predator and its alternate prey 20

680 might thereby provide the focal species with relief from predation

681 pressure (Hammill et al., 2015). Similarly, the removal of a predator

682 might allow its prey to increase in abundance, having knock-on

683 effects on other predators (Sanders et al., 2013). These patterns of

684 interactions describe network structure at an intermediate scale

685 between the local interactions accounted for in degree and the full,

686 global structure of the network. Some of these meso-scale have been

687 shown to affect the focal species’ population size and dynamics (Polis

688 et al., 1989; Holt, 1997; Zabalo, 2012), suggesting that meso-scale

689 structures can affect species’ Eltonian niches. One way to take these

690 structures into account is by deﬁning species’ motif roles. These

691 roles extend the concept of network structural motifs— unique

692 patterns of n interacting species (Milo et al., 2002) —to the species

693 level and aim to provide a more holistic picture of species’ niches by

694 explicitly including direct and indirect interactions (Stouffer et al.,

695 2012; Cirtwill and Stouffer, 2015; Fig. 2).

696 To determine a species’ motif role, the network is ﬁrst

697 decomposed into a set of motifs (Milo et al., 2002; Stouffer

698 et al., 2007). In unipartite food webs, there are 13 three-species

699 motifs (Stouffer et al., 2007). Some of these motifs, such as “three-

700 species food chains” (Hastings and Powell, 1991; Bascompte and

701 Melián, 2005; Laws and Joern, 2013; Fig. 2), “apparent competition”

702 (two prey sharing a predator [Holt and Kotler, 1987; Bascompte

703 and Melián, 2005; Lefèvre et al., 2009; McKinnon et al., 2013]), and

704 “intraguild predation” (two predators sharing a prey, where one

705 predator also consumes the other [Polis et al., 1989; Holt, 1997;

706 Kondoh, 2008; Zabalo, 2012]) have clear biological meanings and

707 have been studied in isolation. Others, including many of the motifs

708 involving two-way interactions (i.e., A eats B and B eats A), have not

709 yet been interpreted. In bipartite food webs, there are only two three-

710 species motifs. To fully describe species’ roles in these networks it

711 is therefore necessary to use larger, less well-studied motifs (Baker

712 et al., 2015). Where possible, however, it is best to use relatively small

713 motifs. This is partly because of computational limitations and the

714 difﬁculty in interpreting large motifs but also because the impact of

715 indirect effects is expected to decrease moving farther from the focal

716 species (Jordán and Scheuring, 2002; Jordán et al., 2006).

717 Whatever the size of motifs being used, each motif contains one

718 or more unique positions. In a three-species food chain motif, each

719 species occupies a unique position as the top, bottom, and middle

720 species all have different biological meanings (Stouffer et al., 2012;

721 Cirtwill and Stouffer, 2015). In an apparent competition motif, in 21

Figure 2: Motif roles describe the way a species is embedded in a food web by decomposing the web into its component motifs (unique conﬁgurations of n interacting species) and tracking the participation of the species in each motif. There are 13 different three-species motifs; this simple food web contains only the ﬁve motifs that contain only one-way interactions. The motif roles of two species are shown below the food web.

6 1 2 0 0

11 7 4 1 0

722 contrast, there are only two unique positions as the two predators

723 are indistinguishable in the context of that motif. Once a network

724 has been broken down into its component motifs, species’ motif

725 roles can be calculated by counting the number of times the focal

726 species occurs in each position within each motif (Stouffer et al., 2012;

727 Baker et al., 2015; Cirtwill and Stouffer, 2015). This yields a vector 22

728 of frequencies which describes the focal species’ role in terms of its

729 direct and indirect interactions, providing a detailed picture of the

730 way in which the species is embedded in its community (Stouffer

731 et al., 2012; Baker et al., 2015; Cirtwill and Stouffer, 2015). Because a

732 motif role provides a detailed picture of a focal species’ relationships

733 to other species in the community (as predator, prey, competitor, etc.),

734 the motif role can be seen as a description of the species’ niche from

735 the perspective of the interaction described in the food web.

736 Motif roles are a relatively new development, but have already

737 been used to compare the ways in which free-living species

738 and parasites ﬁt into food webs (Cirtwill and Stouffer, 2015), to

739 measure variation in species’ roles over space and time (Baker

740 et al., 2015), and to test whether species’ roles are phylogenetically

741 conserved (Stouffer et al., 2012). Motifs more broadly have also been

742 linked to community stability, with some motifs appearing much

743 more commonly in stable networks (Stouffer, 2010; Borrelli et al.,

744 2015). This approach has been extended to predict which species

745 contribute most to the stability of their communities (Stouffer et al.,

746 2012). Motifs have also been used to track the extent of regime shifts

747 in the Baltic Sea (Yletyinen et al., 2016), demonstrating the promise of

748 the approach for detailed analysis of particular study systems.

749 Centrality

750 Structural roles incorporate meso-scale structures as well a focal

751 species’ local neighbourhood. Some measures of centrality also take

752 this approach to describe a species’ ability to inﬂuence the rest of the

753 food web (Estrada, 2007; Lai et al., 2012). These measures extend the

754 thinking behind degree (which considers only the focal species’ local

755 neighbourhood) and also consider the focal species’ impact through

756 indirect interactions (Jordán et al., 2006; Lai et al., 2012).

757 Measures of centrality that incorporate meso-scale network

758 structures are usually based on identifying the food chains in which

759 the focal species participates, just as with trophic level. Unlike

760 trophic levels, however, measures of centrality also consider the food

761 chains which do not involve the focal species. Two such measures,

762 “betweenness centrality” (Fig. 3) and “information centrality”, both

763 quantify the frequency with which the focal species appears on paths

764 between pairs of other species (White and Borgatti, 1994; Jordán

765 et al., 2006; Estrada, 2007). The main difference between the two is

766 that betweenness centrality includes only the shortest paths between 23

767 species, while information centrality includes all paths (Jordán et al.,

768 2006; Estrada, 2007).

769 While betweenness and information centrality are based on

770 food chains (meso-scale structures), other deﬁnitions of centrality are

771 based on the global structure of the food web. One such measure,

772 “eigenvector centrality”, is based on the deﬁning eigenvector—the

773 eigenvector associated with the largest eigenvalue—of the matrix of

774 interactions for a food web (Bonacich, 1972; Allesina and Pascual,

775 2009). In this formulation, the centrality of species i is the ith entry

776 in the deﬁning eigenvector (Bonacich, 1972; Allesina and Pascual,

777 2009; Lai et al., 2012). Eigenvector centrality can be understood as a

778 weighted version of degree, where each neighbour j contributes to

779 the degree of species i in proportion to j’s centrality (Lai et al., 2012).

780 At least nine other measures of centrality have been proposed (Jordán

Figure 3: Betweenness centrality deﬁnes a species’ role as its ability to affect the rest of the food web as determined by the number of times the species appears on the shortest path between pairs of other species. Species A appears on 2 such paths while species B appears on 11. Species B is therefore more likely to have a large effect on its community than is species A. Note that because C only the shortest path between a pair of species is considered, the path D-B-C (traced by the dotted arrow) does not contribute to the betweenness centrality of species B. A B

D 24

781 et al., 2007). Comparative studies have generally found strong

782 correlations between different centrality measures (Jordán et al.,

783 2006; Estrada, 2007). This suggests that the various centrality

784 measures may capture equivalent information about species’

785 niches. We therefore will not describe the other measures in detail

786 here (see Jordán et al. (2006, 2007); Estrada (2007) for detailed

787 descriptions).

788 The logic behind all of these measures of centrality draws

789 heavily on the keystone species concept— the notion that certain

790 species will have a much larger effect on their community than

791 would be expected based on the species’ biomass alone (Paire,

792 1966; Jordán et al., 2006). Indeed, because highly-central species

793 are expected to affect many other species, centrality has been used

794 to identify potential keystone species in several studies (Jordán

795 et al., 2006; Estrada, 2007; Lai et al., 2012; Mello et al., 2015). Like

796 the keystone species concept, centrality does not tell us so much what

797 a species’ niche is, but rather suggests which species might have

798 particularly important niches.

799 As well as highlighting species that are potential keystones

800 within free-living food webs, centrality has also been used to

801 understand the transmission of parasites through food webs. Many

802 parasites are trophically transmitted between hosts when the host for

803 one life stage is consumed by the host for the next, and highly-central

804 free-living species tend to host more parasites than other free-living

805 species (Chen et al., 2008; Thompson et al., 2013). This suggests that

806 species which have strong effects on the free-living components of

807 food webs can also be important to the parasite components of the

808 same communities.

809 Grouping species with similar roles

810 Structural and regular equivalence

811 Having completed a brief survey of methods for calculating species’

812 roles within networks, we will now introduce equivalence methods

813 for identifying species with similar roles. These approaches differ

814 from the previous deﬁnitions of role by focusing explicitly on the

815 identities of species’ interaction partners (Yodzis and Winemiller,

816 1999). For instance, two species with the same degree may or may not

817 interact with the same partners, but two species are only structurally

818 equivalent if they share identical sets of interaction partners (Borgatti,

819 2002; Fig. 4). In fact, two structurally-equivalent species will have the

820 same roles under any of the deﬁnitions above, but not necessarily 25

821 vice versa. This strict deﬁnition can be relaxed slightly to quantify

822 the degree of structural equivalence on a continuous scale by using a

823 distance metric such as Jaccard dissimilarity to compare the overlap

824 in species’ interaction partners (Yodzis and Winemiller, 1999). While

825 such quantitative measures provide more information by placing

826 species on a continuous scale from fully equivalent to completely

827 distinct, they are still restricted because species which interact with

828 ecologically similar, but not taxonomically identical, partners will

829 not be considered equivalent. For example, consider two species of

830 herbivorous insects, each of which is specialised on a different plant

831 from the same genus and which is preyed upon by similar spider

832 species. Intuitively, we understand that these two insects have similar

833 roles in their community (and niches) despite having low structural

834 equivalence. To capture this intuitive similarity, another technique is

835 evidently necessary.

836 As one solution to this problem, some researchers (e.g., Johnson

837 et al., 2001; Luczkovich et al., 2003) have proposed adopting

838 the concept of regular equivalence from the study of social

839 networks (White and Reitz, 1983). In this framework, nodes within

840 a network are equivalent if they interact with the same “types” of

841 partners (Fig. 4). In a network of several corporations, company

842 presidents are equivalent because they each interact with boards

843 of directors, venture capitalists, etc. (Johnson et al., 2001). Even

844 though the board of directors is made up of different individuals

845 in each company, the boards form a recognisable “type” or “group”

846 of people that interact with company presidents. In ecological

847 networks, researchers often wish to avoid deﬁning such groups

848 a priori in order to avoid biasing analyses towards collections of

849 species that are appealing to humans but may not be ecologically

850 relevant. Several algorithms have therefore been developed to do

851 this by iteratively assigning species to groups until the best-ﬁtting

852 arrangement of groups has been reached (Borgatti and Everett, 1993;

853 Johnson et al., 2001; Luczkovich et al., 2003). Happily, the groups

854 determined by such algorithms (e.g., predatory insects, scavengers,

855 and aquatic larvae) usually do tend to be intuitive and biologically

856 meaningful (Johnson et al., 2001; Luczkovich et al., 2003). Thus, by

857 identifying species with similar roles, regular equivalence groups can

858 point to elements of niches that are shared by the species in a group.

859 Structural and regular equivalence groups are being used

860 increasingly often in food web research, with structural equivalence

861 having the longer pedigree. Structurally equivalent species are often

862 collapsed into trophospecies in order to reduce bias in the resolution 26

Figure 4: Sets of structurally equivalent species (nodes with the same grey ﬁll) interact with exactly the same sets of partners. Sets of regularly equivalent species (enclosed in red, dashed boxes) interact with partners from the same sets of groups. In this web, regular equivalence groups correspond to trophic levels such that primary producers (bottom group) only interact with herbivores (second group from bottom), herbivores interact with primary producers and consumers (second group from top), and so on. Note that structurally-equivalent species are also regularly-equivalent, but the reverse is not necessarily true.

863 of unipartite food webs (e.g., Martinez, 1991; Vermaat et al., 2009).

864 Larger, higher-trophic level species are often easier to identify than

865 smaller, lower-trophic level, or cryptic species, leading to better

866 resolution at the top of the food web than among basal species. This

867 greater detail at the top of the food web can then bias estimates of

868 food-web structural properties, hindering efforts to understand the

869 true structure and function of communities. Collapsing structurally-

870 equivalent species into a single node can reduce this bias and

871 facilitate comparisons between communities by ensuring that each

872 node represents a unique niche (Martinez, 1991).

873 Regular equivalence, on the other hand, has much in common

874 with the concept of functional redundancy, in which species with

875 similar “functions” in a community are grouped together. This

876 redundancy is believed to be important because species with similar

877 niches may be able to compensate if one species becomes rare or goes 27

878 extinct (Naeem, 1998; Rosenfeld, 2002; Aizen et al., 2012). The loss of

879 a species with a redundant role in a community will therefore have

880 little effect on the rest of the community (Naeem, 1998; Rosenfeld,

881 2002; Aizen et al., 2012). As well as identifying groups of species

882 with redundant roles, food web models based on regular equivalence

883 groups perform remarkably well (Allesina and Pascual, 2009). This

884 has lead to the suggestion that groups might be the appropriate level

885 of analysis in future studies of food webs, particularly as larger and

886 more detailed data become available (Allesina and Pascual, 2009).

887 This approach holds great promise, especially as more approaches

888 are developed to incorporate more ecological information into

889 regular equivalence groups (Gauzens et al., 2015).

890 Module-based roles

891 Another way of grouping species according to their types of

892 interaction partners is through module roles, which measure the

893 extent to which species interact with different modules (tightly-knit

894 groups) within a network. Such modules are deﬁned mathematically

895 by interacting more tightly among themselves than with any species

896 that is not a part of the module (Guimerà and Amaral, 2005a,b).

897 They are usually detected algorithmically using techniques such

898 as simulated annealing that aim to ﬁnd the set of modules that

899 minimises the number of links between different modules (Guimerà

900 and Amaral, 2005a). Once modules have been deﬁned, species can

901 be classiﬁed based on A) the focal species’ importance to its own

902 module and B) the extent to which the focal species’ interactions

903 are distributed across modules (Guimerà and Amaral, 2005a). The

904 focal species’ importance within its module is determined by on

905 its “within-module degree”, a Z-score of whether the focal species

906 has signiﬁcantly more interactions with other species in the same

907 module than the average (Guimerà and Amaral, 2005a). Species with

908 a within-module degree of at least 2.5 are designated “hubs” and

909 have signiﬁcantly more interactions within their module than the

910 average (p«0.005; Guimerà and Amaral, 2005a). Both hub and non-

911 hub species can then be further divided based on the participation

912 coefﬁcient, which measures the evenness of the distribution of the

913 focal species’ interactions. Values near 0 indicate species which

914 interact almost entirely within their own modules, whereas values

915 near 1 indicate species who interact with species in all modules

916 equally (Fig. 5).

917 Using these two parameters, species can be divided into varying

918 numbers of roles. In general, however, hubs with low participation 28

Figure 5: This unipartite food web contains three modules (circled in red, dashed lines). It is possible to group species with similar roles based on how often they interact within their module and with species in other modules. The network hub (black square) interacts with signiﬁcantly more partners than other species within its module and has many interactions with other modules. Module hubs (black triangles) interact with many partners within their modules, but rarely with species from other modules. The connector (black star) has interactions spread evenly among modules. Finally, peripheral species (white circles) have few interaction partners within their modules and few links to other modules.

919 coefﬁcients are module hubs, which are important to the cohesion

920 of their modules but have few interactions with other modules,

921 while hubs with high participation coefﬁcients are also important

922 to the coherence of the network as a whole (Guimerà and Amaral,

923 2005a; Olesen et al., 2007; Poulin et al., 2013). In non-hub species,

924 low participation coefﬁcients indicate peripheral species while high

925 participation coefﬁcients indicate connector species which “glue”

926 different modules together (Guimerà and Amaral, 2005a; Olesen

927 et al., 2007; Poulin et al., 2013).

928 As with structural roles, module-based roles are relatively new

929 and their potential is only beginning to be explored. So far it has

930 been shown that plants’ and pollinators’ module-based roles are

931 conserved between the species’ native and exotic ranges (Olesen

932 et al., 2007), and that the module-based roles of parasites and free-

933 living species are phylogenetically conserved (Poulin et al., 2013).

934 In seed-dispersal networks, modules tend to include species from

935 different taxa (mammals, birds, ﬁsh, etc. [Donatti et al., 2011; Mello

936 et al., 2011]). At a ﬁner scale, however, closely-related species may

937 not belong to the same modules (Donatti et al., 2011) and within-

938 module degree tends not to be phylogenetically conserved (although

939 participation coefﬁcients were [Schleuning et al., 2014]). These 29

940 results emphasise the importance of both ecological and evolutionary

941 processes in shaping food webs and species’ roles within them.

942 Functional roles

943 Rather than identifying species with potentially redundant functional

944 roles using regular equivalence, it is also possible to group species

945 according to their functional roles directly. This method is based

946 on the premise that species with similar traits (e.g., gape sizes

947 or ﬂower morphologies) should fulﬁl similar functions in their

948 community (Tilman, 2001; Petchey and Gaston, 2002; Dehling et al.,

949 2016). Extending this notion to interactions, we expect that traits

950 that represent species’ functional roles will also inﬂuence which

951 interactions they participate in (Thompson and Townsend, 2005;

952 Dehling et al., 2016). One trait that has been found to explain a

953 great deal of variation in predator-prey interactions is body mass,

954 as many taxa feed on smaller prey (e.g., Williams and Martinez, 2000;

955 Stouffer et al., 2006; Petchey et al., 2008; Williams, 2008; Stouffer, 2010;

956 Williams et al., 2010; Gravel et al., 2011; Stouffer et al., 2011; Zook

957 et al., 2011. In most cases, however, more than one trait is necessary

958 to describe all of the interactions in a community (Cattin Blandenier,

959 2004; Allesina et al., 2008; Allesina, 2011; Eklöf et al., 2013). Moreover,

960 while using empirical traits to create model food webs can reproduce

961 general structural properties, such approaches often fail to predict

962 speciﬁc interactions (Petchey et al., 2008; Bartomeus et al., 2016). In

963 an attempt to address both of these shortcomings, some researchers

964 have used artiﬁcial traits based on the properties of the observed

965 network (Rohr et al., 2010; Dalla Riva and Stouffer, 2015; Rohr

966 et al., 2016). These abstract traits cannot be directly mapped onto

967 morphological traits, but they can reveal similarities between species

968 that are not evident based on morphology or behaviour. Such

969 hidden similarities, despite the absence of an obvious ecological

970 interpretation, nevertheless identify species that may fulﬁl redundant

971 functions in the community or strongly compete with each other; i.e.,

972 species with similar niches.

973 An alternative way to identify species with similar functional

974 roles is to analyse the traits of the focal species’ interaction partners

975 rather than the traits of the focal species itself (Fig. 6). This approach

976 is common in studies of plant-pollinator communities, where

977 pollination syndromes are often used to predict which species will

978 interact (Waser et al., 1996; Fenster et al., 2004; Ollerton et al., 2009).

979 Pollinators vary in their adherence to classical syndromes (Fenster

980 et al., 2004; Ollerton et al., 2009), but in general species do tend to 30

981 interact with partners whose traits are relatively similar and match

982 some limiting trait of the focal species (Stiles, 1975; Wolf et al., 1976;

983 Dalsgaard et al., 2009; Stang et al., 2009; Junker et al., 2013; Dehling

984 et al., 2014). By grouping species that interact with partners that

985 have similar traits, we can infer species that have similar functional

986 roles in their community. Grouping species this way is somewhat

987 analogous to grouping regularly-equivalent species based on the

988 types of species with which they interact. The major distinction

989 is that regular-equivalence groups are emergent propertiesofa

990 network’s topology whereas functional roles are linked at least

991 implicitly to a functional mechanism (e.g., fruit size [Dehling et al.,

992 2014, 2016] or ﬂower characteristics [Fenster et al., 2004; Ollerton

993 et al., 2009]). This focus on biologically-explicit groups means that

994 functional roles provide a convenient summary of species’ niches in

995 the type of network being studied.

996 Functional roles have been used to demonstrate co-adaptation

997 between interaction partners, as mutualists are expected to converge

998 on compatible traits (Blüthgen et al., 2007). Species with unique

999 functional roles interact with partners that have extreme or unusual

1000 values of the traits that affect the interaction being studied. Because

1001 of this, they tend to interact with fewer partners (Junker et al., 2013;

1002 Maglianesi et al., 2014; Coux et al., 2016; Dehling et al., 2016) and,

1003 as specialists, may then be more vulnerable to extinction (Allesina,

1004 2012).

1005 Limitations to role concepts and future directions

1006 As described above, one of the main limitations of species roles is

1007 that while they do offer insight into a species’ niche— its “place in

1008 the biotic environment, its relations to food and enemies” (Elton,

1009 1927 in Johnson and Steiner, 2000), a role will only capture one

1010 aspect of the niche. For some role concepts this might be a speciﬁc

1011 property such as the niche’s position in food chains (trophic level)

1012 or the niche’s importance (degree and other measures of centrality).

1013 Other concepts such as motif roles and functional roles attempt to

1014 summarise all of a species’ interactions. These roles give a better

1015 picture of species’ niches from the perspective of food webs, but the

1016 fact remains that roles deﬁned in a food web describing only one

1017 type of interaction will overlook components of species’ niches that

1018 do not involve that interaction (Fontaine et al., 2011; Kéﬁ et al., 2016).

1019 Combining different network types has the potential to improve this

1020 by integrating different aspects of a species’ niche (e.g., as pollinators

1021 and as prey [Fontaine et al., 2011]). Kéﬁ et al. (2016) offer one way

1022 forward by identifying species’ module roles in a network which 31

Figure 6: The functional roles A framework uses the traits of interaction partners to group species with similar roles. A) In this plant-pollinator network, we are interested in comparing the roles of the three pollinators. B) The functional role of each pollinator is the area of trait space that includes all plants that the pollinator visits. In this community, the red and green pollinators’ roles (lower left) overlap while the blue pollinator has a unique role (upper right). Note that the axes used to describe the trait space may be concrete traits, as

Plants Pollinators shown here, or abstract axes describing variation in many traits. B Plant trait space Deep Nectary depth Shallow Red Blue Flower color

1023 includes trophic interactions and positive and negative non-trophic

1024 interactions (including provision of refuges, increased recruitment,

1025 competition for space, predator importance, etc.). The roles in

1026 this study therefore provide a much more comprehensive picture 32

1027 of species’ niches than do roles in webs which describe a single

1028 interaction.

1029 Another important limitation in studies of species’ roles is the

1030 point-sample nature of most ecological networks. Species’ niches

1031 encompass their relationships to the biotic environment as a whole,

1032 but networks provide a spatially and temporally limited snapshot

1033 of communities. As more networks are published that include

1034 replication over time and/or space (e.g., Olesen et al., 2008, 2011;

1035 Leong et al., 2015), we will obtain more thorough descriptions

1036 of species’ roles. As information about the spatial and temporal

1037 variability of species’ roles becomes available, we may be able to

1038 better understand the differences between species’ fundamental

1039 Eltonian niches (all of the interactions in which a focal species

1040 could reasonably participate) and those that they actually realise

1041 in a particular community. This is especially intriguing with respect

1042 to species which have moved outside of their historical ranges (i.e.,

1043 introduced species). It is possible that a species’ role in its native

1044 community could be used to predict the way in which it will interact

1045 with a novel set of potential partners (Aizen et al., 2008; Emer et al.,

1046 2016). If this is true, then species’ roles will be a powerful tool for

1047 conservation biologists.

1048 As well as exploring the spatial and temporal variation of

1049 species’ roles, researchers are increasingly connecting species’ roles

1050 to their phylogenies. Related species tend to have similar roles for

1051 several of the role concepts we describe above (Stouffer et al., 2012;

1052 Poulin et al., 2013; Rohr and Bascompte, 2014). Species’ phylogenies

1053 are believed to shape their roles because phylogenetically-conserved

1054 traits affect interactions between species (Gómez et al., 2010; Dalla

1055 Riva and Stouffer, 2015). Thus, conserved traits lead to conserved

1056 interactions which lead to conserved roles. As well as explaining

1057 similarities between the roles of related species, incorporating

1058 evolutionary processes into studies of ecological networks can

1059 suggest historical drivers of the structure of current communities.

1060 Most current studies attempt to explain trends in network structure

1061 based on species’ traits (Woodward et al., 2005; Brose, 2010) or

1062 neutral processes (Siepielski et al., 2010; Canard et al., 2014; Poisot

1063 et al., 2015). These approaches have been valuable, but evolutionary

1064 explanations may be more parsimonious. Explanations based on

1065 species’ evolutionary histories may also explain species which seem

1066 to lack appropriate interaction partners in modern networks. This

1067 is most obvious in the case of “evolutionary anachronisms” such

1068 as the large-seeded plants of South America that are believed to 33

1069 have been dispersed by large mammals that are now extinct (Janzen

1070 and Martin, 1982). Adaptations to extinct interaction partners can

1071 also explain species’ interactions with introduced species, as when

1072 the plants described above are dispersed by introduced cattle and

1073 horses (Barlow, 2000).

1074 Perhaps the most important factor limiting the applicability of

1075 species’ roles is that role concepts are often abstract and difﬁcult

1076 to connect to species’ natural histories. This abstraction can be

1077 beneﬁcial, as it allows us to identify groups of species when we

1078 are not conﬁdent that any particular taxonomic level or species

1079 trait is the appropriate basis for categories (Luczkovich et al., 2003).

1080 However, network researchers must admit that such abstractions

1081 can make our work less accessible to non-specialist readers. Mello

1082 et al. (2015) suggest that ecological concepts should be used to guide

1083 the choice of network measures. We agree, with the proviso that

1084 ecological prior knowledge should not be allowed to restrict species’

1085 roles so as to ignore unexpected interactions such as frugivory and

1086 seed dispersal by crocodilians (Platt et al., 2013) or predation on

1087 nestlings by herbivores such as deer and sheep (Furness, 1988; Pietz

1088 and Granfors, 2000). Such interactions may be more common than

1089 previously suspected. Even if they are indeed rare, rare or weak

1090 interactions may still be important for community stability because of

1091 their potential for dissipating perturbations (Emmerson and Yearsley,

1092 2004; Allesina and Tang, 2012; Wootton and Stouffer, 2016). After

1093 selecting network measures that speciﬁcally address the aspects

1094 of a species’ niche that are of most interest, we also suggest that

1095 researchers bear in mind the part of a species’ niche that they are

1096 analysing (e.g., niche size or vertical position in food chains, or a

1097 more holistic summary such as structural roles) and use this to place

1098 their results in the context of the focal species’ ecology.

1099 Conclusions

1100 Throughout this review, we have sketched some of the questions that

1101 have been asked using each role concept. To conclude, we return to

1102 the question of why species roles, in general, are useful. Networks

1103 allow us to place the focal species in its community context, but the

1104 network as a whole is difﬁcult to interpret. By reducing the network

1105 to a single value or vector, species’ roles compress the network into

1106 a tractable form. If we consider food webs as maps of ecological

1107 communities, roles provide the topographic lines, borders, and

1108 roadways that simplify a map and provide meaning. Just as different

1109 types of maps have different themes (e.g., political maps, terrain

1110 maps, geological maps, etc.) different role concepts provide different 34

1111 perspectives on a food web. Our task as researchers working with

1112 species’ roles is to make our choice of role concept, and the aspect of

1113 species’ niches that it is meant to capture, as clear as cartographers

1114 make their maps.

1115 Acknowledgements

1116 We would like to acknowledge the support of the National Science

1117 and Engineering Research Council of Canada (to ARC), a Marsden

1118 Fast-start Grant (to MD; administered by the Royal Society of New

1119 Zealand), the São Paulo Research Foundation (to MPG; FAPESP

1120 grants 2013/13319-5 and 2014/20572-1), a University of Canterbury

1121 Masters Scholarship and the Miss E. L. Hellaby Indigenous

1122 Grasslands Research Trust (to MB), and a University of Canterbury

1123 Doctoral Scholarship, Meadow Mushrooms Postgraduate Scholarship,

1124 and New Zealand International Doctoral Research Scholarship (to

1125 FC). We would like to thank Daniel B. Stouffer for conversation and

1126 comments that greatly strengthened this review. 35

1127 Box 1: Glossary

Eltonian niche A species’ interactions with food sources and natural enemies.

Role A species’ relationship to others in its food web.

Stability The ability of a food web to withstand perturbations.

Degree The number of interactions in which a species participates.

Centrality A species’ ability to affect the rest of the network.

Local The portion of the food web that directly affects the focal species.

Global The entire food web.

Link A connection between two nodes, indicating an interaction between them.

Unipartite web A web containing one group of species that interact amongst themselves. A web containing two groups of species where all interactions occur between Bipartite web groups. A web in which links are present or absent (i.e., not weighted). Also called a binary Qualitative or topological web. A web where links are weighted by frequency, biomass transfer, or some other Quantitative property. Also called a weighted web.

Trophic level A species’ vertical position in a food web or height in a food chain. A path from a primary producer to a top predator, where each step up the chain Food chain corresponds to an increase in trophic level. The structure of the network including the focal species’ local neighbourhood and Meso-scale some indirect interactions, but not the entire network.

Motifs Unique patterns of n interacting species; building blocks of networks. Structural When a set of species all interact with exactly the same set of partners. equivalence When a set of species interacts with partners from the same groups, but not Regular equivalence necessarily with the same sets of partners.

Node A component of a network. In food webs, usually a species.

Trophospecies A set of structurally equivalent species, collapsed into a single node. A group of species that interact more often amongst themselves than with other Module species.

Functional roles Roles deﬁned by the traits of the focal species’ interaction partners. Phylogenetic The tendency for related species to have more similar traits because of their shared conservation common ancestry. 36

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1628 Chapter 2: Knowledge of predator-prey interactions

1629 improves predictions of immigration and extinction in

1630 island biogeography

1 1 1631 Alyssa R. Cirtwill & Daniel B. Stouffer

1 1632 Centre for Integrative Ecology

1633 School of Biological Sciences

1634 University of Canterbury

1635 Private Bag 4800

1636 Christchurch 8140, New Zealand

1637 Published in Global Ecology and Biogeography (2015).

1638 DOI: 10.1111/geb.12332.

1639 This chapter has been modiﬁed from its original version. It has been

1640 formatted to match this thesis. 52

1641 Abstract

1642 Aim: MacArthur and Wilson’s original formulation of the Theory

1643 of Island Biogeography (TIB) included the corollary hypothesis

1644 that species richness might affect immigration and extinction rates.

1645 Building on this, other researchers have suggested additional top-

1646 down and bottom-up effects. We compare these hypotheses to

1647 identify the strongest candidates for inclusion in a “trophic TIB".

1648 Location: Six mangrove islands in the Florida Keys, USA

1649 Methods: We studied a classic island-biogeography time series

1650 featuring lists of species observed on six mangrove islands during

1651 roughly 16 censuses each across 700 days. We ﬁrst used this time

1652 series to determine the number of opportunities for species to

1653 immigrate to an island for the ﬁrst time (N=18,420), to go locally

1654 extinct (N=1,943), or to re-immigrate to an island after having

1655 previously gone extinct (N=1,813). We then leveraged information

1656 on those species’ predators and prey to estimate the potential for

1657 top-down and bottom-up interactions during each census period.

1658 Finally, we constructed statistical models to test for species richness,

1659 top-down, and bottom-up effects on per-species immigration and

1660 extinction probabilities and validated them by comparing each model

1661 with a similar model based on the classic TIB.

1662 Results: We found that models including bottom-up effects gave

1663 the greatest improvement over the classic TIB models. Extinction

1664 probability in particular decreased sharply for species with both

1665 basal resources and animal prey available. Species-richness and top-

1666 down effects had far weaker impacts on per-species probabilities of

1667 immigration and extinction.

1668 Main conclusions: Our ﬁndings suggest that incorporating

1669 information on the trophic structure of island communities–

1670 particularly the species-speciﬁc availability of resources –

1671 can substantially alter predictions of extinction probabilities.

1672 Immigration probability, on the contrary, appeared largely stochastic.

1673 Incorporating trophic information into predictions of extinction rates

1674 therefore represents the most promising and best-supported way to

1675 extend the TIB.

1676 Keywords

1677 Theory of Island Biogeography, top-down effects, bottom-up effects, community assembly,

1678 predator-prey interactions, species richness, food web 53

1679 Introduction

1680 The Theory of Island Biogeography combines elegant simplicity

1681 of formulation (Hubbell, 2009) with the ability to reliably predict

1682 properties such as equilibrium species richness across both

1683 islands and a range of island-like habitat patches (Simberloff and

1684 Abele, 1982; Eadie et al., 1986). As such, it has become one of the

1685 cornerstones of ecological theory (MacArthur and Wilson, 1963; Holt,

1686 2010; Hanski, 2010; Harte, 2011). In essence, the TIB supposes that

1687 immigration rates should be higher on islands that are closertoa

1688 source of immigrants and that extinction rates should be higher as

1689 islands get smaller (MacArthur and Wilson, 1963; Schoener, 2010).

1690 These two predictions were tested empirically immediately after the

1691 publication of the TIB and have generally matched observations

1692 well (Diamond, 1969; Case, 1975; Gilpin and Diamond, 1976),

1693 although some authors note important differences in immigration

1694 and extinction rates across species (Gilpin and Diamond, 1976;

1695 Whittaker et al., 2000; Piechnik et al., 2008).

1696 The original TIB partially anticipates these differences by

1697 predicting variation in immigration and extinction rates as species

1698 richness changes on an island. Speciﬁcally, the authors of the

1699 TIB predicted that, as species richness on an island increases,

1700 immigration rates should decrease while extinction rates increase

1701 (MacArthur and Wilson, 1963). The effect of species richness on

1702 immigration is expected because species vary in their dispersal

1703 abilities (Simberloff, 1969), which could bias island faunas towards

1704 the best dispersers. Once these species are already present, the pool

1705 of remaining colonists will therefore tend to contain poorer and

1706 poorer dispersers, decreasing immigration rates (Schoener, 2010).

1707 At the same time, a species-rich island may include more extinction-

1708 prone species (e.g., species with low population sizes or specialised

1709 diets) and will therefore tend to lose more species than one which is

1710 species-poor (Schoener, 2010). Increasing species richness could also

1711 directly cause increasing extinction rates if increasing species richness

1712 leads to stronger inter-speciﬁc competition (Gilpin and Diamond,

1713 1976). However, the effect of competition on island faunas is very

1714 difﬁcult to observe experimentally (Simberloff, 1978).

1715 Apart from competition, the presence of other species on an

1716 island could affect immigration and extinction rates through top-

1717 down and/or bottom-up effects (Knops et al., 1999; Piechnik et al.,

1718 2008; Holt, 2010; Gravel et al., 2011). Top-down effects of predators

1719 on their prey may increase extinction rates either directly (Savidge, 54

1720 1987; Hanna and Cardillo, 2014), by causing trophic cascades (Spiller

1721 and Schoener, 1994; Ryberg and Chase, 2007; Spiller and Schoener,

1722 2007), or by reducing population sizes such that stochastic extinctions

1723 are more common (Ryberg et al., 2012). Alternatively, the presence

1724 of predators can mediate competition between species and decrease

1725 the probability of any of them going extinct (Snyder and Cheson,

1726 2000; Bull and Bonsall, 2010). It is intuitively less likely that there

1727 will be top-down effects on immigration rates, as this would seem

1728 to require species to adaptively immigrate depending on conditions

1729 on islands they have not yet reached. However, given the fact that

1730 any new immigrant must persist on an island for some time before

1731 being recorded, it becomes easy to envisage effects of predators on

1732 observed immigration rates following the mechanisms described

1733 above. In such a situation, the presence of predators could either

1734 reduce observed immigration rates as new arrivals are consumed

1735 before being recorded or, alternatively, could reduce competition and

1736 thereby increase the survival of new immigrants.

1737 Bottom-up effects of resource availability on the TIB have

1738 also been postulated. Species with no resources available should

1739 quickly go extinct while species with abundant or varied prey may

1740 be more likely to persist (Holt et al., 1999; Holt, 2002; Piechnik

1741 et al., 2008; Holt, 2010). It is also possible that the presence of basal

1742 resources (e.g., plants, detritus, or bacteria) can affect immigration

1743 rates. In order for an island to support resident animal life, it must

1744 already have some basal resource present while the converse is not

1745 necessarily true (Holt et al., 1999; Holt, 2002, 2010). Basal resources

1746 should therefore be present on all islands that support animals as

1747 well as some that do not. This might result in a greater inclination

1748 of herbivores to immigrate to new islands since doing so entails less

1749 risk of starvation. Indeed, while most islands support herbivores,

1750 species at higher trophic levels are much rarer (Terborgh, 2009). This

1751 suggests that species which cannot consume basal resources may

1752 be less likely to immigrate or establish viable populations, perhaps

1753 because islands often support fewer prey species (and smaller prey

1754 populations) than mainland habitats (Terborgh, 2009).

1755 Finally, top-down and bottom-up effects are known to interact

1756 in structuring communities, with the strengths and directions of

1757 each type of effect varying over time and across species (Power, 1992;

1758 Denno et al., 2002; Gratton and Denno, 2003; Gripenberg and Roslin,

1759 2007). This wide variety of potential effects of interactions between

1760 species has prompted the development of “trophic TIB” models

1761 that incorporate community structure into island biogeography 55

1762 theory (Holt et al., 1999; Holt, 2002; Ryberg and Chase, 2007; Gravel

1763 et al., 2011). Although these models often preserve the TIB’s spirit

1764 of simplicity and clarity, it is not clear whether they signiﬁcantly

1765 improve on the classic version when confronted with empirical data.

1766 Further, most of these models tend to be structured in a way that

1767 complicates rigorous comparisons between them.

1768 Rather than investigate a single mathematical model in great

1769 depth, here we use empirical data to compare and contrast multiple

1770 potential effects of community structure on island biogeography.

1771 We are especially interested in measuring the potential effects of

1772 predator-prey interactions and examining how they differ when

1773 considering immigration and extinction. To this end, we construct

1774 a statistical framework with which to test the following non-

1775 exclusive hypotheses: 1) immigration probability will decrease

1776 with increasing species richness while extinction probability will

1777 increase; 2) immigration probability will decrease with the presence

1778 of predators while extinction probability will increase; 3) immigration

1779 probability will be higher for species that can consume basal

1780 resources and extinction probability will decrease; and 4) there

1781 will be no effect of the presence of animal prey on immigration

1782 probability but extinction probability will decrease for species with

1783 prey available. By comparing similarly-structured models built

1784 around each hypothesis, our approach allows us to isolate models

1785 with little support as well as demonstrating which hypotheses

1786 explain similar variation in empirical data. Together, we argue that

1787 these two endeavours reveal the strongest candidates for future

1788 efforts to extend the TIB.

1789 Methods

1790 Dataset

1791 We studied a classic island-biogeography time series for arthropod

1792 immigration and extinction on six mangrove islands (Simberloff,

1793 1969) of known diameter (11-25m) and distance from the mainland (2-

1794 533m). In these experiments, each island was artiﬁcially defaunated

1795 and then censused 16-18 times during the following two years for

1796 a total of 96 post-defaunation censuses. Over the course of the

1797 experiment, 5 basal resources (mangrove trees, fungus, lichens,

1798 detritus, algae) and 231 arthropod species were observed, with most

1799 resolved to the species level.

1800 Using this dataset, we were able to directly estimate when the

1801 different species immigrated to islands after defaunation. Speciﬁcally, 56

Initial Repeat Extinction Table 1: Number of opportunities Model immigration immigration for initial immigrations, repeat immigrations, and extinctions (i.e., Opportunities 18,420 1,813 1,943 sample size), and the number of Successes 476 127 461 successes and proportion of successes in each case. Proportion of 0.026 0.070 0.237 successes

1802 for a given island during a given census k, we considered all species

1803 that were not observed to be potential immigrants. Note that we

1804 did not consider species which were present before defaunation

1805 but never returned during the experiment as part of this mainland

1806 species pool. All potential immigrants were counted as successful

1807 if they were observed during the next census k + 1 or as failed

1808 otherwise. As it is possible that different mechanisms affect species

1809 which are frequent immigrants than those that more rarely leave the

1810 mainland, we considered initial immigration (i.e., for a given species

1811 s and island i, all censuses up to and including the ﬁrst successful

1812 immigration to island i by species s) and repeat immigration (i.e.,

1813 all immigration opportunities after species s had previously gone

1814 extinct from island i) separately. Note that this distinction allowed

1815 us to examine factors affecting species which immigrate relatively

1816 frequently without deﬁning this set of species a priori.

1817 We estimated extinctions on each island in the dataset using a

1818 similar procedure. For a given island i during a given census k, any

1819 species present could potentially go locally extinct and those not

1820 observed during the following census (k + 1) were considered to have

1821 done so. Species observed again in census k + 1 were considered to

1822 have persisted. See Table 1 for the numbers of potential and observed

1823 immigrations and extinctions across the complete time series.

1824 In order to relate these species-occupancy lists to the potential

1825 interactions between species on a given island at a given time, we

1826 combined them with a published list of potential prey for each

1827 species based on interactions observed or inferred on the mainland

1828 (see Piechnik et al., 2008 for details on the construction of this list).

1829 Potential prey were restricted to other arthropods (hereafter ‘animal

1830 prey’) which had been observed on at least one of the islands during

1831 the time series, plus the basal resources which were assumed to

1832 be present on all islands throughout the experiment (Piechnik

1833 et al., 2008). As basal resources were assumed to be omnipresent

1834 throughout the experiment (Piechnik et al., 2008), the ability of a

1835 species to consume basal resources (or not) was recorded as one

1836 measure of resource availability. The presence of animal prey, on 57

1837 the contrary, varied between censuses. To determine the potential

1838 for bottom-up interactions involving animal prey, we compared the

1839 list of potential prey for the focal species with the occupancy list for

1840 that island and census. If any of the species’ mainland prey items

1841 were present, that species was assumed to be able to prey on the

1842 same species on the island. Similarly, if the focal species featured

1843 in the prey lists of any other species on the island at the same

1844 time, there was potential for top-down interactions (i.e., predation

1845 on the focal species) to occur. Determining the potential for top-

1846 down and bottom-up effects on each species on each island at

1847 each census allowed us to directly examine the effects of predator-

1848 prey interactions on initial immigration, repeat immigration, and

1849 extinction probabilities. See Table 2 for further details of the typical

1850 values and ranges of these predictors.

1851 Statistical Models

1852 Based on the aforementioned data, we created parallel sets of

1853 candidate models for the probability of a given species immigrating

1854 to, re-immigrating to, or going extinct from a given island at a given

1855 census. For each model, we estimated parameters using the function

1856 glmer from the lme4 library (Bates et al., 2014) in R (R Core Team,

1857 2014) with binomial distributions and logit link functions. We then

1858 used these models to test our hypotheses relating to the effects

1859 of species richness, top-down effects, bottom-up effects, and their

1860 interactions using a null model and a model based on the TIB for

1861 comparison.

1862 Null Models

1863 The simplest models for initial immigration, repeat immigration,

1864 and extinction (henceforth referred to as our initial immigration null

1865 model, repeat immigration null model, and extinction null model,

1866 respectively) included an intercept and two random effects (S2.1,

1867 Supporting Information S2). The ﬁrst random effect was for focal

1868 census (that is, the census from which predictor data were drawn,

1869 speciﬁc to a particular island). It accounted for variation in time

1870 between censuses as well as other hidden variables such that the

1871 predicted immigration or extinction probability for each census is

1872 expected to match that observed empirically.

1873 The second random effect was intended to account for

1874 pseudoreplication within the data created by repeated observations 58

(A) Initial immigration Table 2: Number of opportunities for initial immigrations, repeat Predictor Min Max Mean immigrations, and extinctions (i.e., Distance 2 533 213 sample size), number of successes and Diameter 11 25 14.9 proportion of successes in each case, and minima, maxima, and means for Time between censuses 10 400 36.5 model predictors. As each set of models Species richness 2 47 18.8 was based on slightly different data, we present the means and ranges for each Predators 0 1 0.782 separately. Ability to eat plants 0 1 0.578 Animal prey available 0 1 0.440

(B) Repeat immigration Predictor Min Max Mean Distance 2 533 154 Diameter 11 25 15.1 Time between censuses 10 400 68.9 Species richness 11 47 32.3 Predators 0 1 0.933 Ability to eat plants 0 1 0.536 Animal prey available 0 1 0.523

(C) Extinction Predictor Min Max Mean Distance 2 533 164 Diameter 11 25 14.8 Time between censuses 10 400 41.5 Species richness 2 47 30.7 Predators 0 1 0.956 Ability to eat plants 0 1 0.600 Animal prey available 0 1 0.514

1875 of population-level behaviour of the same species across the

1876 experiments. For initial immigration, this was a species-by-island

1877 random effect as all potential immigrations of a given species to a

1878 given island were drawn from the same mainland population. On

1879 average, there were 8.2 pseudoreplicates per level of this random

1880 effect.

1881 For repeat immigration and extinction, we further distinguished

1882 between different “event windows” to produce a species-by-

1883 island-by-window random effect. That is, we considered repeat

1884 immigration opportunities for species s to island i after the species’

1885 ﬁrst extinction on island i up to and including the ﬁrst successful

1886 repeat immigration—the ﬁrst event window—to be independent 59

1887 from opportunities for species s to re-immigrate to island i after

1888 it had gone extinct a second time up to and including the second

1889 successful repeat immigration—second event window. For extinction,

1890 we distinguished between opportunities for extinction associated

1891 with different event windows for species s on island i (e.g., potential

1892 extinctions after an initial immigration, potential extinctions after

1893 the ﬁrst repeat immigration, and so on). These two models included

1894 fewer pseudoreplicates per random effect (mean 4.7 and mean 3.6,

1895 respectively) than did the initial immigration model.

1896 Theory of Island Biogeography Models

1897 We next tested initial immigration, repeat immigration,

1898 and extinction TIB models based on the original formulation

1899 of island biogeography. The two immigration TIB models each

1900 included terms for distance, diameter, and their interaction. The

1901 extinction TIB model included only the diameter term as isolation

1902 was not hypothesised to affect the extinction of established

1903 populations (MacArthur and Wilson, 1963). In addition, each model

1904 included a term for the time between the focal census and the next

1905 census (i.e., the amount of time a species would have to immigrate

1906 or become extinct) since this interval varied across censuses (Table 2).

1907 To account for potential differences in the strength of the time effect

1908 on different islands, we also included all interaction terms between

1909 diameter, distance (immigration models only), and time between

1910 censuses (Table S2.1, Supporting Information S2). As in the null

1911 models, random effects of census and source population were also

1912 included.

1913 Species-richness Models

1914 We then extended the TIB models to test the hypotheses that

1915 initial and repeat immigration probability will decline and that

1916 extinction probability will increase with increasing species richness.

1917 To do this, we studied statistical models including all terms in the

1918 corresponding TIB models, species richness during the focal census,

1919 and interactions between species richness and all other terms in the

1920 TIB models (Table S2.1, Supporting Information S2).

1921 Top-down Models

1922 Next, we tested the hypotheses that top-down effects decrease

1923 the probability that a new immigrant survives long enough to be 60

1924 observed and increase extinction probabilities for species that have

1925 already been observed. This was done by adding a term quantifying

1926 the presence of any of the focal species’ predators during the

1927 focal census to the corresponding TIB models. We also included

1928 interaction terms between the presence of predators and all terms in

1929 the TIB models. In order to ensure that any observed effect of top-

1930 down interactions was distinct from the effect of species richness,

1931 we further compared each top-down model to a similar top-down

1932 & species-richness model which included all terms in the top-down

1933 model, as well as terms for species richness and interactions between

1934 species richness and all other terms in the top-down model (Table

1935 S2.1, Supporting Information S2).

1936 Bottom-up Models

1937 To test the bottom-up hypothesis that the ability to eat basal

1938 resources, having access to animal prey, or both, will increase a

1939 species’ initial or repeat immigration probability, we created a

1940 statistical model that combined all of the terms in the corresponding

1941 TIB model with new terms that quantify whether or not the focal

1942 species consumes basal resources, whether or not any of the focal

1943 species’ animal prey were available during the focal census, and their

1944 interaction. The bottom-up model also included interactions between

1945 terms in the TIB model and the terms describing bottom-up effects.

1946 As with the top-down model, we ensured that species-richness and

1947 bottom-up effects were distinct by comparing each bottom-up model

1948 to a bottom-up & species-richness model including all terms in

1949 the bottom-up model, terms for species richness, and interactions

1950 between species richness and all other terms in the bottom-up model

1951 (Table S2.1, Supporting Information S2).

1952 Top-down & Bottom-up Models

1953 Finally, we tested the possibility that top-down and bottom-

1954 up effects act synergistically. To do this, we examined a top-down

1955 & bottom-up model including all of the terms in the bottom-up

1956 model as well as terms for the presence of predators and interactions

1957 between the presence of predators and all terms in the bottom-up

1958 model. In keeping with the spirit of elegant simplicity of the original

1959 TIB, we did not include terms for species richness in this model

1960 (Table S2.1, Supporting Information S2). This decision was supported

1961 by our ﬁnding that the trophic & species-richness models described 61

1962 in S2.1, Supporting Information S2 were all very similar to the trophic-

1963 only models (see S2.4 & S2.5, Supporting Information S2).

1964 Model simpliﬁcation

1965 For each of the aforementioned statistical models, we started by

1966 ﬁtting the most complex models including all interactions. Where

1967 a full model was non-convergent (i.e., parameter estimates could

1968 not be robustly determined, indicative of over-ﬁtting), we removed

1969 all interactions of the highest order (e.g., 6-way interactions) and

1970 attempted to re-ﬁt the model; we repeated this procedure (i.e.,

1971 removing 5-way interactions, etc.) until we obtained a convergent

1972 model from which we could proceed with simpliﬁcation. We then

1973 measured the AIC of these “full” models as well as each of the suite

1974 of potential simpliﬁed models. Simpliﬁed models were obtained by

1975 systematically removing all possible combinations of terms from the

1976 full model. When an interaction term was included in a simpliﬁed

1977 model, all main effects involved in that interaction term were also

1978 retained.

1979 Once the AIC of each model was calculated, we selected the

1980 model with the lowest AIC as the best-ﬁtting model. We performed

1981 this simpliﬁcation automatically using the R (R Core Team, 2014)

1982 function dredge from package MuMIn (Bartón, 2014). We then

1983 used the R (R Core Team, 2014) function glmer from the package

1984 lme4 (Bates et al., 2014) to estimate the standardised effects (βs)

1985 for each ﬁxed effect in the best-ﬁtting models as well as their

1986 corresponding p-values. Note that all standardised effects presented

1987 in the results reﬂect the per-unit (e.g., per 1m increase in diameter)

1988 impact of each predictor on logit-transformed initial immigration,

1989 repeat immigration, or extinction probability.

1990 Hypothesis Comparison

1991 We also wished to quantify the degree to which different hypotheses

1992 give similar predictions across the dataset. If the speciﬁc predictions

1993 of the species-richness and top-down models for extinction agree, for

1994 example, this would indicate that the effect of species richness on

1995 extinction rates is capturing the same variability in the data as does

1996 the effect of predators. To compare the models and hypotheses in

1997 this way, we ﬁrst generated 10,000 simulated datasets for each model

1998 using the R (R Core Team, 2014) function rbinom and the models’

1999 predicted probabilities of immigration or extinction. If, for example, 62

2000 a given model predicted that species s on island i at census k had an

2001 immigration probability of 0.005, approximately 50 of the simulated

2002 immigration events would be successful. Next, we used the best-ﬁt

2003 parameters of the various models (when ﬁt to the empirical data)

2004 to calculate the likelihood of observing each simulated dataset. We

2005 repeated this procedure for each pair of initial immigration, repeat

2006 immigration, and extinction models, including comparisons of

2007 every model to itself, producing 10,000 likelihoods for each pairwise

2008 comparison.

2009 To quantify the degree of similarity between the set of

2010 likelihoods obtained when data generated using model A were

2011 ﬁt by model A to those obtained when the same data were ﬁt by

2012 a different model B, we calculated the area under the receiver

2013 operating characteristic (ROC) curve. The area under the curve

2014 (AUC) represents the probability that a randomly chosen likelihood

2015 for model A is greater than a randomly-chosen likelihood from

2016 model B. When models A and B explain exactly the same variation

2017 in the data, and therefore ﬁt data generated by A or B equally

2018 well, AUC=0.5; as model B’s ability to ﬁt data generated by model

2019 A decreases, the AUC increases towards 1. An AUC close to 0.5

2020 therefore indicates that the two models explain very similar variation

2021 while an AUC close to 1 indicates that the models account for very

2022 different variation.

2023 Results

2024 Initial Immigration

2025 The best-ﬁt versions of all alternate models for initial immigration

2026 had signiﬁcantly lower AIC’s than the null model and explained

2027 greater variance (Table 3A). The best-ﬁt species-richness, top-

2028 down, bottom-up, and top-down & bottom-up models all provided 2 2029 signiﬁcantly better ﬁts to the data than the TIB model (χ =8.97, 2 2 2030 df=2, p=0.011; χ =8.68, df=3, p=0.034; χ =11.7, df=4, p=0.020; and 2 2031 χ =16.425, df=5, p=0.006, respectively). The top-down & bottom-up

2032 model provided the best ﬁt to the data, and signiﬁcantly improved 2 2033 upon both the top-down and bottom-up models (χ =7.74, df=2, 2 2034 p=0.021 and χ =4.74, df=1, p=0.029).

2035 In the top-down & bottom-up model, and similar to the other

2036 models, a species’ probability of immigration decreased with

2037 increasing distance from the mainland (βDistance=-56.3) and increased 2038 with increasing intervals between censuses (βTime=18.1, Fig. 7; Table 63

Model Table 3: Terms included in the best-fit (A) Initial immigration models for A) initial immigration, B) TD& TD& BU& repeat immigration, and C) extinction Effect TIB SR TD BU BU SR SR when comparing a null model (not Dist. ------shown), a model based on the Theory Diam. + + - - - + + of Island Biogeography (TIB), and Time + + + + + + + models based on the TIB that also Species richness + + + include effects of species-richness (SR), Predators + + 0 top-down interactions (TD), bottom-up Animal prey + + + interactions (BU), top-down & bottom- Dist.:Diam. + + + + + + + up interactions (TD & BU), top-down Dist.:Animal + 0 0 interactions & species-richness (TD Diam.:Species + + + & SR), or bottom-up interactions & Diam.:Predators + + 0 species-richness (BU & SR). In all cases, Diam.:Animal + + + ‘Dist.’ is short for distance and ‘Diam.’ Time:Predators - - 0 is short for diameter. Each ‘+’ indicates Dist.:Diam.:Animals + 0 0 0 a positive effect, ‘-’ indicates a negative AIC 4271 4266 4268 4267 4264 4266 4264 effect, and a ‘0’ indicates that the effect Marginal R2 0.061 0.068 0.070 0.070 0.075 0.068 0.072 was not included in the best-fit model. Conditional R2 0.213 0.214 0.214 0.228 0.223 0.214 0.222 An empty cell indicates that the term NB: The best-fit TD & SR model was identical to the SR model. The marginal R2 of the Null model was 0 and the conditional R2 was not part of the model and hence of the Null model was 0.169. could not appear in the best-fit version. (B) Repeat immigration For the full list of terms included TD& TD& BU& in each model, see S2.1, Supporting Effect TIB SR TD BU BU SR SR Information S2. Below the individual Diameter - - - + + - + effects, we give the Akaike Information Time ------Criterion (AIC) and marginal and 2 Basal resources - - - conditional R values for each model, 2 Diameter:Time ------where marginal R is the amount of Diameter:Basal - - - variance explained by a model’s fixed 2 Time:Basal + + + effects and conditional R is the amount AIC 922 922 922 912 912 922 912 of variance explained by both fixed Marginal R2 0.026 0.026 0.026 0.060 0.060 0.026 0.060 and random effects (Nakagawa and Conditional R2 0.141 0.141 0.141 0.222 0.222 0.141 0.222 Schielzeth, 2013). Sample size for all NB: The best-fit SR, TD, and TD & SR models were identical to the TIB model, while the best-fit TD & BU and BU & SR models initial immigration models was 18,420 were identical to the best-fit BU model. The marginal R2 of the Null model was 0 and the conditional R2 of the Null model was opportunities for species to immigrate, 0 148 . . for all repeat immigration models (C) Extinction was 1,813 opportunities for species to TD& TD& BU& Effect TIB SR TD BU re-immigrate following an extinction, BU SR SR and for all extinction models was 1,943 Diameter + - + 0 0 - + opportunities for species to go extinct. Time ++++ + + + Species richness + + + Basal resources - - - Animal prey + + - Diameter:Time + - + 0 0 - 0 Diameter:Species - - - Time:Species + + + Time:Basal - - - Time:Animal 0 0 - Species:Basal - Basal:Animal - - 0 Diameter:Time:Species + + 0 AIC 1912 1904 1912 1874 1874 1912 1864 Marginal R2 0.114 0.153 0.114 0.231 0.231 0.114 0.251 Conditional R2 0.296 0.373 0.296 0.497 0.497 0.296 0.524 NB: The best-fit TD and TD & SR models were identical to the best-fit TIB model, while the best-fit TD & BU model was identical to the best-fit BU model. The marginal R2 of the Null model was 0 and the conditional R2 of the Null model was 0.325.

2039 S2.7). Unlike in the TIB model, a species’ probability of immigration

2040 decreased with increasing island size 64

no animal prey animal prey 0.10 Figure 7: Per-species probabilities of A B initial immigration in the top-down & 0.08

no predators bottom-up model were affected by the 0.06 presence of animal prey, the presence Distance (m) of predators, island diameter, distance 0.04 from the island, and time between censuses (based on N=18,420 potential 0.02 initial immigrations). In each panel, 0.00 we show the model predictions for 0.10 Time (days) different scenarios with line colour C D 10 25 indicating island distance and line type 0.08 56 indicating interval between census. predators 0.06 Light lines are for islands close to the mainland (2m), medium lines for Probability of initial immigration 0.04 moderately isolated islands (163m), 0.02 and dark lines for very isolated islands (533m). Similarly, dashed lines are for 0.00 0 5 10 15 20 25 0 5 10 15 20 25 the lowest observed interval between Island diameter (m) censuses (10 days), solid lines for the mean interval between censuses (25 days), and dotted lines for the mean interval between censuses plus one 2041 (βDiameter=-0.711), but this effect was overwhelmed by a positive standard deviation (56 days). (A) When 2042 interaction between distance and diameter (βDistance:Diameter=333). neither predators nor animal prey were present, predicted immigration 2043 Probability of immigration also increased for species with either probability decreased with increasing 2044 predators or animal prey present. Both of these trends were stronger island diameter except for islands that were farthest from the mainland. (B) 2045 on larger islands (βDiameter:Predators=1.29, βDiameter:Animal=1.32). & (C) The presence of either animal prey or predators weakened this trend 2046 Despite the statistical improvement of the other alternate models such that immigration probability increased with island diameter for 2047 over the TIB, each model described data generated by any of the all islands except those closest to the 2048 others well (Fig. 8; Fig. S2.2). In addition, each alternative model mainland. (D) When both animal prey and predators were present, 2049 provided a good fit to data generated by the null model, and vice immigration probability increased 2050 versa. This means that all models captured similar variation in the with increasing island diameter for 2051 empirical data; the extra terms in the alternative models therefore all islands. In all cases, increasing the time between censuses increased 2052 may represent over-fitting. the probability of immigration. As no large islands were observed at moderate to high degrees of isolation, the corresponding predictions are 2053 Repeat Immigration truncated to reflect the observed range only. 2054 The best-fit versions of all alternate models for repeat immigration

2055 had lower AIC’s and explained greater variance than the null model

2056 (Table 3B), although the TIB model did not signiﬁcantly improve on 2 2057 the null model (χ =6.09, df=3, p=0.107). The best-ﬁt species-richness

2058 and top-down models were identical to the best-ﬁt TIB model, while

2059 the best-ﬁt top-down & bottom-up model was identical to the best-

2060 ﬁt bottom-up model (S2.2, Supporting Information S2). Contrary to

2061 our expectations, none of the best-ﬁt alternate models included any

2062 effects of distance from the mainland on repeat immigration. The

2063 bottom-up model provided the best fit to the data, significantly 2 2064 improving upon the fits of the null and TIB models (χ =22.4, df=6, 2 2065 p=0.001, and χ =16.0, df=3, p=0.001, respectively). 65

A Figure 8: Hypothesis comparison of Null best-fit statistical models based on the AUC statistic. (A & B) All best- TIB fit models for initial immigration generated very similar predictions, as SR did all models for repeat immigration. TD (C) Among best-fit models for extinction probability, there were TD & SR two clusters of models which generated predictions that were similar to each BU other but distinct from those in the BU & SR other cluster. In all panels, comparisons are made between a Null model, a TD & BU model based on the Theory of Island Biogeography (TIB), and models B based on the TIB that also include 1.0 effects of species richness (SR), top- Null down interactions (TD), top-down TIB interactions and species richness (TD 0.9 & SR) , bottom-up interactions (BU), SR bottom-up interactions and species AUC richness (BU & SR), or top-down & TD 0.8 bottom-up interactions (TD & BU). Each TD & SR cell containing an asterisk indicates that 0.7 two best-fit models were identical. BU BU & SR 0.6 TD & BU 0.5 C Null TIB Model used to generate simulated data SR TD TD & SR BU BU & SR TD & BU TD BU SR TIB Null TD & BU TD & SR BU & SR Model used to fit simulated data

2066 Again contrary to our expectations, a species’ probability of

2067 repeat immigration in the bottom-up model decreased as the interval

2068 between censuses increased (βTime=-76.8, Fig. 9, Table S2.8). This 2069 effect was stronger on larger islands, but weaker for species able

2070 to consume basal resources (βDiameter:Time=-431; βTime:Basal=-2.52). 66

0.25 Figure 9: Per-species probabilities of cannot eat basal resources A repeat immigration in the bottom-up 0.20 model were affected by the ability to consume basal resources, island diameter, and interval between censuses 0.15 (based on N=1,813 opportunities for species to re-immigrate). In both panels, Time (days) we show model predictions for different 0.10 10 scenarios with line type indicating 69 interval between census; dashed lines 0.05 172 are for the lowest observed interval between censuses (10 days), solid lines for the mean interval between 0.00 censuses (69 days), and dotted lines for 0.25 the mean interval between censuses B plus one standard deviation (172 days).

eats basal resources (A) For species unable to consume 0.20 basal resources, repeat immigration probability increased with increasing island diameter except when the 0.15 interval between censuses was very large. (B) For species able to consume Probability of repeat immigration 0.10 basal resources, repeat immigration probability increased with increasing diameter when the interval between 0.05 censuses was short and decreased with increasing island diameter when 0.00 the interval between censuses was 0 5 10 15 20 25 moderate to large. Island diameter (m)

2071 Species able to consume basal resources were, however, less likely to

2072 immigrate to larger islands (βDiameter:Basal=-2.52).

2073 Despite the statistical improvement of the bottom-up model

2074 over the null and TIB models, all models captured very similar

2075 variation in the empirical data (Fig. 8). Similarly, while the bottom-up

2076 model explained signiﬁcantly greater variance than the null model

2077 (Table 3B), this increase was relatively small. This suggests that the

2078 additional terms in the bottom-up model may indicate over-ﬁtting,

2079 and that its counterintuitive predictions may be spurious.

2080 Extinction

2081 Compared to the initial and repeat immigration models, the best-ﬁt

2082 alternate models for extinction showed much greater improvements

2083 over the extinction null model (Table 3C). The best-ﬁt top-down

2084 model was identical to the best-ﬁt TIB model and the best-ﬁt top-

2085 down & bottom-up model was identical to the best-ﬁt bottom-up

2086 model (S2.2, Supporting Information S2). In addition, the best-ﬁt 67

1.0 Figure 10: Per-species probabilities of extinction in the bottom-up model were affected by the presence of animal prey, the ability to eat basal resources, 0.8 and time between censuses (based on N=1,943 opportunities for species to go extinct). (A) For species unable to eat basal resources, extinction 0.6 probability increased rapidly with interval between censuses. Extinction probability saturated near 1 after roughly 300 days. Species with animal 0.4 prey available were slightly more likely to go extinct. (B) Species able to eat

Probability of extinction No resources basal resources had lower probabilities 0.2 Animal prey only of extinction overall, and probability of Basal resources only extinction increased more slowly with Animal prey interval between censuses. Species with + basal resources both basal resources and animal prey 0.0 available were least likely to go extinct. 0 100 200 300 400 Interval between censuses (days)

2087 species-richness and bottom-up models both improved signiﬁcantly 2 2 2088 on the best-ﬁt TIB model (χ =16.6, df=4, p=0.002 and χ =41.9, df=2,

2089 p<0.001, respectively).

2090 The effects included in the alternate extinction models varied

2091 a great deal. Notably, the bottom-up model did not include any

2092 effects of island diameter. The TIB and species-richness models

2093 both did, although the TIB model predicted that species where

2094 more likely to go extinct on larger islands while the species-richness

2095 model predicted the opposite trend (Table S2.8). The bottom-up

2096 model predicted that probability of extinction would be lower for

2097 species able to eat basal resources, especially those which also had

2098 access to animal prey, but that species with access to animal prey

2099 only would be more likely to go extinct (βBasal=-0.470, βAnimal=-1.64, 2100 βBasal:Animal=0.201; Fig. 10).

2101 As a consequence of the signiﬁcant trophic effects included in

2102 the bottom-up model, it described data generated by the null, TIB,

2103 and species-richness models poorly, and vice versa (Fig. 8). This

2104 suggests that adding bottom-up effects and removing the effect of

2105 diameter allowed this model to capture different variation in the

2106 data than that accounted for by the other models. While the model

2107 containing both bottom-up and species-richness effects provided a 2 2108 signiﬁcantly better ﬁt to the data than the bottom-up model (χ =19.5,

2109 df=5, p=0.002), it nevertheless captured very similar variation in the 68

20 Figure 11: Initial immigrations, repeat A B 80 immigrations, extinctions, and species 15 richness over time for a representative 60 island (island E9, 18m in diameter, 10 379m from the mainland). (A)-(D) 40 We show the cumulative values for the observed experiment (white 20 5 circles) along with the equivalent Initial immigrations

Repeat immigrations values as predicted by the the best- 0 0 ﬁtting models for initial immigration, repeat immigration, and extinction 70 C 40 D 60 (i.e., species-richness, bottom-up, and bottom-up models, respectively). We 50 30 obtained the model predictions for 40 20 total species richness at each census 30 by adding predicted immigrants and Extinctions 20 10 subtracting predicted extinctions. In

10 Species richness all panels, the solid line indicates the 0 0 mean prediction while the shaded 0 200 400 0 200 400 area corresponds to one standard Days since defaunation deviation. Comparable ﬁgures for all other islands can be found in S2.6, Supporting Information S2.

2110 data (average pairwise AUC=0.618; Fig. S2.5) As such, we expect

2111 that the extra terms in the bottom-up & species-richness model may

2112 constitute over-ﬁtting.

2113 Discussion

2114 We compared statistical models based on several factors predicted

2115 to affect per-species probabilities of initial immigration, repeat

2116 immigration, or extinction in the context of island biogeography

2117 theory. In our dataset, species richness generally had little impact

2118 on immigration or extinction. Top-down and/or bottom-up effects,

2119 however, were included in each best-ﬁt model. When directly

2120 compared to the empirical data, it is apparent that each of our

2121 best-ﬁt models provides an excellent ﬁt to the observed sequence

2122 of initial immigrations, repeat immigrations, and extinctions on all

2123 islands (Fig. 11 and S2.6, Supporting Information S2). This success

2124 of our trophic TIB models therefore stands in contrast to previous

2125 examinations of these same data where, when focusing on changes

2126 in species richness over time, it has been suggested that stochastic

2127 models of immigration and extinction may accurately describe the

2128 system (Simberloff, 1969; Simberloff and Wilson, 1969) and that

2129 colonisation as a whole does not depend on trophic interactions

2130 (Simberloff and Simberloff, 1976). These differences also suggest that

2131 considering immigration and extinction separately provides an extra

2132 level of detail which allows us to better disentangle the underlying

2133 ecology of island biogeography. 69

2134 Although the best-ﬁtting initial and repeat immigration models

2135 showed varying structures (for example, there was evidence that

2136 initial immigration varied with the availability of animal prey and

2137 repeat immigration with the ability to consume basal resources),

2138 they generated very similar predictions for patterns of immigration.

2139 This indicates that our expectations that island characteristics

2140 and interactions between species would affect immigration

2141 probabilities were incorrect. In particular, the prediction– based on

2142 the TIB (MacArthur and Wilson, 1963) –that immigration probability

2143 would decline with increasing distance from the mainland was

2144 ultimately not supported in this system. One possible explanation

2145 is that many of the arthropods in this system are highly mobile and

2146 can easily reach all of the mangrove islands in this study (Simberloff,

2147 1969). This scenario would appear even more likely because potential

2148 colonists were restricted to arthropods that were observed on the

2149 islands prior to defaunation (Wilson and Simberloff, 1969), meaning

2150 that they were all previously successful immigrants.

2151 Alternatively, it is possible that immigrants in this system are

2152 not arriving from the mainland but rather from other mangrove

2153 islands. There are many small mangrove islands in the area of the

2154 study islands that could serve as sources of colonists in addition

2155 to the mainland (see maps in Wilson and Simberloff, 1969). As

2156 the source of arthropod immigrants was not determined, the

2157 distance from each island to the mainland may not always be the

2158 best reﬂection of the distance immigrants actually travelled. In

2159 this regard, the mangrove islands in this study are quite different

2160 from isolated oceanic islands but similar to habitat patches which

2161 interact both amongst each other and with a larger source habitat.

2162 Limitations of the TIB when dealing with complex geographies are

2163 well known (Hanski, 2010), and the inability of the TIB to account for

2164 multiple sources of colonists (Hanski, 2010), the existence of predator-

2165 free refuges (Ryberg et al., 2012), or varying island-mainland

2166 geographies (Taylor, 1987) may all contribute to the relatively poor ﬁt

2167 of TIB-based immigration models to this dataset. They may also help

2168 to explain the apparently stochastic immigration patterns observed

2169 here.

2170 Just as the expected distance effects were not observed in the

2171 immigration models, the best-ﬁtting extinction model did not include

2172 the expected effect of island diameter. It is possible that the islands

2173 in this study were similar enough in size that arthropod population

2174 sizes did not vary greatly between islands, or that other factors had

2175 stronger effects. For example, populations on small islands might 70

2176 be maintained by occasional arrivals from the mainland (i.e., the

2177 ‘rescue effect’), preventing extinctions. While the bottom-up model

2178 for extinction did not include any effect of island diameter, it did

2179 include effects for the ability to consume basal resources and the

2180 presence of animal prey which suggest that, all else being equal,

2181 having access to both plant and animal prey makes extinction less

2182 likely than having access to only one type of resource.

2183 The synergistic effects of basal and animal resources are

2184 surprising in light of the fact that many arthropod species form part

2185 of the aerial plankton in the region (Simberloff, 1969), and others

2186 such as Diptera that were seen on the islands were not recorded

2187 during the experiment (Simberloff, 1969). As such, recorded animal

2188 prey may have been only a small part of the diet of even obligate

2189 insectivores. The strength of the observed effects therefore strongly

2190 suggests that bottom-up effects provide a promising avenue for

2191 extending the TIB, in agreement with previous work (Gravel et al.,

2192 2011). The reduction in extinction probability where both types of

2193 resources were available also suggests that prey switching between

2194 basal resources and animal prey may be particularly important

2195 in determining extinction probabilities (Murdoch, 1969; Coll and

2196 Guershon, 2002) as well as potentially inﬂuencing immigration

2197 order (Piechnik et al., 2008). It is also possible that the availability

2198 of many prey species might encourage further migration from the

2199 mainland and provide stronger rescue effects for these species.

2200 Overall, our results suggest that incorporating bottom-up

2201 interactions provides the greatest improvement over the classic

2202 TIB. However, we note that our relatively weak results for top-

2203 down effects contrast with the strong effects of predators observed

2204 in other island systems (Spiller and Schoener, 1994; Kotiaho and

2205 Sulkava, 2007; Spiller and Schoener, 2007). The apparent weakness

2206 of top-down effects in this system could be due to the presence of

2207 transient predators which were observed visiting the islands during

2208 the experiment but not recorded in the censuses because they do

2209 not breed on mangroves (Simberloff, 1969). The effects of these

2210 predators cannot be measured from the available data, but could

2211 potentially be large. Further complicating matters, the effects of

2212 resident arthropod predators are difﬁcult to detect in this system

2213 because they were almost always present (Table 2), making the effects

2214 of predators a “black box” in this system. Given these caveats, and

2215 because a rich record exists of top-down and bottom-up effects acting

2216 simultaneously to structure mainland communities (Power, 1992;

2217 Amarasekare, 2008), we advocate that the potential for top-down 71

2218 effects still be considered along with bottom-up effects in any further

2219 attempts to combine food-web ecology and island biogeography:

2220 “two of the most important conceptual frameworks in community

2221 ecology” (Holt, 2010).

2222 Acknowledgements

2223 We thank Kate Wootton, Camille Coux, Jason Tylianakis, and Nick

2224 Baker for comments on the manuscript. This research was supported

2225 by an NSERC-PGS-M graduate scholarship and a University of

2226 Canterbury Doctoral Scholarship (to ARC) and a Marsden Fund

2227 Fast-Start grant (UOC-1101), administered by the Royal Society of

2228 New Zealand (to DBS). We are thankful to the BlueFern University of

2229 Canterbury Super Computer for computing facilities.

2230 Supporting information

2231 S2.1: Full models

2232 S2.2: Best-ﬁt models

2233 S2.3: Summary tables for best-ﬁt models

2234 S2.4: Details of models not described in the main text

2235 S2.5: Cumulative species richness plots for islands not shown in the

2236 main text 72

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2365 Chapter 3: Conservation of interaction partners between

2366 related plants varies widely across communities and

2367 between plant families.

1 2 1 2368 Alyssa R. Cirtwill , Giulio V. Dalla Riva ,Nick J. Baker , 1,3 1 1 2369 Joshua A. Thia , Christie J. Webber , Daniel B. Stouffer

1 2370 Centre for Integrative Ecology

2371 School of Biological Sciences

2372 University of Canterbury

2373 Private Bag 4800

2374 Christchurch 8140, New Zealand

2 2375 Biomathematics Research Centre

2376 School of Mathematics and Statistics

2377 University of Canterbury

2378 Private Bag 4800

2379 Christchurch 8140, New Zealand

3 2380 Present Address: School of Biological Sciences

2381 University of Queensland

2382 St Lucia

2383 Brisbane, QLD 4072, Australia

2384 Manuscript prepared in the style of New Phytologist.

2385 This chapter has been modiﬁed from its original version. It has been

2386 formatted to match this thesis. 78

2387 Summary

2388 • Related plants are often hypothesised to interact with similar sets

2389 of pollinators and herbivores, but empirical support for this idea

2390 is mixed. Here we argue that this may be because some plant

2391 families vary in their tendency to share interaction partners.

2392 • We introduce a novel approach with which to quantify overlap of

2393 interaction partners for each pair of plants in 59 pollination and 11

2394 herbivory networks. We then tested for relationships between

2395 phylogenetic distance and overlap within each network, and

2396 whether these relationships varied with the composition of the

2397 plant community. Finally, we tested for different relationships

2398 within well-represented plant families.

2399 • Across all networks, more closely-related plants tended to have

2400 greater overlap, and this tendency was stronger in herbivory

2401 networks than pollination networks. These relationships were

2402 also signiﬁcantly related to the composition of the network’s plant

2403 component. Within plant families, relationships varied greatly in

2404 both network types.

2405 • The variety of relationships between phylogenetic distance and

2406 interaction partners in different plant families likely reﬂects a

2407 variety of ecological and evolutionary processes. To understand

2408 the distribution of interactions within a community, it is therefore

2409 important to consider factors affecting particular plant families.

2410 Keywords

2411 defensive syndrome, ecological networks, herbivory, niche

2412 overlap, phylogenetic signal, pollination, pollination syndrome,

2413 specialisation 79

2414 Introduction

2415 Interactions with animals affect plants’ life cycles in several critical

2416 ways (Mayr, 2001; Sauve et al., 2015). On one hand, pollination and

2417 other mutualistic interactions contribute to the reproductive success

2418 of many angiosperms (Ollerton et al., 2011). On the other, herbivores

2419 consume plant tissues (McCall and Irwin, 2006) which costs plants

2420 energy and likely lowers their ﬁtness. In both cases, these interactions

2421 do not occur randomly but are strongly inﬂuenced by plants’

2422 phenotypes. For example, plants that produce abundant or high-

2423 quality nectar may receive more visits from pollinators (Robertson

2424 et al., 1999) whereas plants that produce noxious secondary

2425 metabolites may suffer fewer herbivores (Johnson et al., 2014).A

2426 plant’s traits are also likely to determine which speciﬁc pollinators

2427 and herbivores interact with that plant. Plants with different defences

2428 (e.g., thorns vs. chemical defences) may deter different groups of

2429 herbivores (Ehrlich and Raven, 1964; Johnson et al., 2014), and the

2430 concept of pollination syndromes has often been used to group

2431 plants into phenotypic classes believed to attract certain groups of

2432 pollinators (Waser et al., 1996; Fenster et al., 2004; Ollerton et al.,

2433 2009).

2434 If attractive and/or defensive traits are heritable, then we can

2435 reasonably expect that related plants will have similar patterns of

2436 interactions with animals (Schemske and Bradshaw, 1999). Recent

2437 studies that have investigated this question at the level of whole

2438 communities, however, have yielded mixed results (Rezende et al.,

2439 2007b; Gómez et al., 2010; Rohr and Bascompte, 2014; Fontaine

2440 and Thébault, 2015; Lind et al., 2015). In particular, signiﬁcant

2441 phylogenetic signal in plants’ interaction partners —the tendency

2442 for more closely-related plants to have more similar interactions—

2443 tends to be rare in empirical networks (Rezende et al., 2007b;

2444 Lind et al., 2015; but see Elias et al., 2013; Fontaine and Thébault,

2445 2015). Further, plants’ roles within networks tend to be less

2446 phylogenetically constrained than those of animals (Rezende et al.,

2447 2007b; Chamberlain et al., 2014c; Rohr et al., 2014; Vamosi et al., 2014;

2448 Lind et al., 2015).

2449 Several mechanisms that might weaken the conservation of

2450 interactions have been identiﬁed in the literature. Pollination and

2451 herbivory may be affected by a wide variety of traits, and not

2452 all of these are likely to be phylogenetically conserved (Rezende

2453 et al., 2007a; Kursar et al., 2009). If, for example, ﬂoral displays

2454 are strongly affected by environmental conditions (Canto et al., 80

2455 2004), then pollinators may not be predicted by plants’ phylogenies.

2456 Even if the traits affecting pollination and herbivory are heritable,

2457 plants may experience conﬂicting selection pressures that weaken

2458 the overall association between plant phylogeny and interaction

2459 partners (Armbuster, 1997; Lankau, 2007; Siepielski et al., 2010; Wise

2460 and Rausher, 2013). For instance, ﬂoral traits that are attractive to

2461 pollinators can also increase herbivory (Strauss et al., 2002; Adler

2462 and Bronstein, 2004; Theis, 2006). Conversely, herbivory can reduce

2463 pollination by inducing chemical defences (Adler et al., 2006) or

2464 altering ﬂoral display or nectar availability (Strauss, 1997). Observed

2465 patterns of similarity in plants’ interaction partners therefore

2466 represent a mixture of environmental effects and various selection

2467 pressures as well as plants’ shared phylogenetic history.

2468 A further complication is the possibility that the relationship

2469 between plants’ relatedness and the similarity of their interaction

2470 partners is not constant across plant clades. Closely-related plants in

2471 one clade might be under strong selection to favour dissimilar sets

2472 of pollinators to avoid exchanging pollen with other species (Levin

2473 and Anderson, 1970; Bell et al., 2005; Mitchell et al., 2009). Similar

2474 pressures could also affect related plants’ defences against herbivores

2475 if congeners tend to grow in the same places such that herbivores

2476 could easily move between them. Unrelated plants might also

2477 converge upon similar phenotypes, attracting a particularly efﬁcient

2478 or abundant pollinator (Ollerton, 1996; Ollerton et al., 2009).

2479 Likewise, herbivores may be able to depredate sets of unrelated

2480 plants if they have evolved similar defences (Pichersky and Gang,

2481 2000). In either case, dissimilarity of interactions among related

2482 species or similarity of interactions among unrelated species could

2483 result in low apparent phylogenetic signal across an entire plant

2484 community. Moreover, all of the aforementioned hypotheses are non-

2485 exclusive; different processes likely affect different clades, and these

2486 processes might be associated with different pressures imposed by

2487 pollination and herbivory.

2488 Here we use a novel approach to investigate how the patterns of

2489 overlap in interaction partners between pairs of plants (henceforth

2490 “niche overlap”) vary over phylogenetic distance. Whereas previous

2491 studies have focused on the presence or absence of phylogenetic

2492 signal across entire networks, we are able to investigate the

2493 relationship between niche overlap and phylogenetic distance in

2494 within networks as well as different plant families. Speciﬁcally, we

2495 test whether niche overlap decreases over increasing phylogenetic

2496 distance in a large dataset of pollination and herbivory networks, 81

2497 whether the plant family composition of a community affects the

2498 relationship between niche overlap and phylogenetic distance in that

2499 community, and whether the relationship between niche overlap and

2500 phylogenetic distance differs across plant families.

2501 Materials and methods

2502 Network data

2503 We studied phylogenetic conservation of interactions within a

2504 set of 59 pollination and 11 herbivory networks. These networks

2505 span a range of biomes (desert to scrub forest to grassland) and

2506 countries (Sweden to Australia), and range in size between 18 and

2507 996 total species (mean 160.93, median 96) with seven to 131 plant

2508 species (mean 38.06, median 28). To ensure that we were analysing

2509 interactions inﬂuenced by similar sets of traits across networks, we

2510 restricted our herbivory networks to insects consuming leaves. This

2511 excluded sap-sucking, leaf-mining, and galling insects as well as seed

2512 predators and xylophagous insects; all of these interactions involve

2513 different plant tissues and means of feeding than leaf consumption

2514 and so may be inﬂuenced by different plant and insect traits. We also

2515 excluded networks which focused on plants from a single genus as

2516 these did not contain sufﬁcient variation in phylogenetic distance

2517 between plants. See Table S3.1, Supporting Information S3 for details on

2518 the original sources of all networks.

2519 Phylogenetic data

2520 In order to ﬁt the plant species in all networks to a common

2521 phylogeny, we ﬁrst compared all species and genus names with the

2522 National Center for Biotechnology Information and Taxonomic Name

2523 Resolution Service databases to ensure correctness. This was done

2524 using the function ‘get_tsn’ in the R (R Core Team, 2014) package

2525 taxize (Chamberlain and Szocs, 2013; Chamberlain et al., 2014a).

2526 Species which could not be assigned to an accepted taxonomic

2527 name (e.g., ‘Unknown Forb’) were discarded, as were those with non-

2528 unique common names and no binomial name given (e.g., ‘Ragwort)

2529 or binomial names that could not be deﬁnitively linked to higher taxa

2530 (e.g., ‘Salpiglossus sp.’). We were left with 2341 unique species in 1027

2531 genera and 195 families. On average, 11.43% of plants were removed

2532 from each network (median 4.60%, range 0-55.10%).

2533 We then estimated phylogenetic distances between species. To

2534 accomplish this, we constructed a phylogenetic tree for our dataset

2535 based on the phylomatic ‘mega-tree’ of higher plants (version 82

2536 20120829; Reveal and Chase, 2011). Where possible, we dated nodes

2537 on the mega-tree according to Wikström et al. (2001). These dates

2538 included divergence times in millions of years (My) between families

2539 and within some families, but did not give dates for divergences

2540 within genera. For those nodes that were not included in the mega-

2541 tree, we used the branch length adjustment algorithm bladj (Webb

2542 et al., 2008) to estimate the ages of all undated nodes. This means

2543 that the ages in our phylogenies are approximations, but the presence

2544 of even a subset of properly dated nodes within a phylogeny

2545 improves upon undated, purely taxonomic approaches (Webb, 2007).

2546 To obtain trees for each network, we pruned the dated mega-tree to

2547 include only species in that network.

2548 Calculating niche overlap within communities

2549 To fully describe the extent to which two plants’ niches overlap, we

2550 deﬁned the overlap between two plants’ sets of interaction partners

2551 by recording the frequencies with which pairs of animals (where

2552 each animal interacted with at least one plant) fall into three unique

2553 patterns (Fig. 12). In the ﬁrst pattern, both plants interact with both

2554 animals, indicating total overlap for that quartet. In the second

2555 pattern, one plant interacts with both animal partners while the other

2556 interacts with only one animal, indicating partial overlap. In the

2557 third pattern, each animal interacts with only one plant, indicating

2558 no overlap. Taken together, the frequencies of these three patterns of

2559 overlap can be used to describe the degree to which two plants have

2560 similar interaction partners.

2561 Using the three patterns deﬁned above provides more detail

2562 than other measures of overlap, such as the proportion of one

2563 species’ partners that are shared with another as given by Jaccard

2564 similarity. In particular, comparing the probability of observing

2565 each pattern rather than one of the other two provides a measure

2566 of indirect interactions between plants by considering pairs of animal

2567 partners rather than each animal separately. For example, a pair

2568 of plants which share two interaction partners are more likely to

2569 inﬂuence each other via these partners than two plants which do

2570 not share interaction partners. Moreover, our measure of overlap has

2571 greater statistical power than Jaccard dissimilarity because it includes

2572 information on the number of shared interaction partners as well as

2573 the proportion. For instance, a pair of plants which together interact

2574 with 100 animals provides more information about shared overlap

2575 than a pair of plants which together interact with only one animal

2576 whereas the Jaccard similarity of both would simply be one. 83

(a) (b) 1 2 3 4 5 Figure 12: Visual depiction of our

A 2 3 decomposition of pairwise niche Total A overlap B overlap of plants’ interaction partners. B (a) First, consider the representation of 1 2 1 3 2 5 3 5 2 4 3 4 Partial C A A A A A A any pollination or herbivory network overlap B B B B B B as an adjacency matrix. Here, ﬁlled D 1 4 1 5 4 5 No A A A cells indicate an interaction between a overlap E B B B particular plant (letters on rows) and an animal (numbers on columns). (b) F For a given pair of plants (e.g., plants A and B), we then considered the set of animals that interact with at least one (c)Total overlap (d)Partial overlap (e) No overlap BA C FED BA C FED BA C FED of the focal plants. Taking each pair of A - 1 1 1 0 0 A - 6 6 6 3 3 A - 3 3 3 0 0 animals in this set in turn, we assigned the resulting quartet (the two focal B 1 - 1 1 0 0 B 6 - 4 4 0 0 B 3 - 1 1 6 6 plants plus two animals) to one of three 1 1 - 1 0 0 6 4 - 0 2 2 3 1 - 0 1 1 C C C patterns of overlap. In the total overlap 1 1 1 - 0 0 6 4 0 - 2 2 3 1 0 - 1 1 D D D pattern, both plants interact with both E 0 0 0 0 - 1 E 3 0 2 2 - 0 E 0 6 1 1 - 0 animals. In the partial overlap pattern, F 0 0 0 0 1 - F 3 0 2 2 0 - F 0 6 1 1 0 - one plant interacts with both animals and the other plant interacts with only one. Finally, in the no overlap pattern each animal interacts with only one plant; note that this includes 2577 Statistical analysis cases where both animals interact with the same plant (e.g., animals 1 and 5 2578 To determine how overlap of interaction partners breaks down over and plant A) as well as cases where 2579 phylogenetic distance, we modelled the probabilities of observing each animal interacts with a different plant (e.g., animals 1 with plant A 2580 each pattern of overlap relative to the other two patterns. We and animal 4 with plant B). (c-e) The 2581 expected that the frequency of the high- and moderate-overlap number of times each pattern occurred was used to summarise the pairwise 2582 patterns would decrease with increasing phylogenetic distance niche overlap between the two plants 2583 between two plants while the frequency of the low-overlap pattern and then related to their phylogenetic 2584 would increase. As we expect pollination and herbivory networks distance.

2585 could show different patterns of overlap, we included effects of

2586 network type and the interaction between network type and distance.

2587 Lastly, to account for the possibility that different communities show

2588 different characteristic relationships, we also included random effects

2589 of network ID on the slope and intercept, giving a mixed-effects

2590 logistic regression of the form

logit(ωpnij) ∝ δij + ρn + δijρn + Nn + δijNn,(1)

2591 where ωpnij is the probability of overlap pattern p occurring between 2592 species i and j in network n, δij is the phylogenetic distance between 2593 plants i and j, ρn is the network type (one in pollination networks, 2594 zero in herbivory networks), and Nn and δijNn are random slope 2595 and intercepts for network n. All models were ﬁt using R function

2596 glmer from package lme4 (Bates et al., 2014). Sample size for these

2597 models was the sum (over all pairs of plants) of the number of

2598 pairs of animals where each plant and each animal has at least one 84

2599 interaction partner. Over all networks, there were 43,288,090 such

2600 sets of plants and animals, with a median of 72 (mean 671 +/- 2247)

2601 pairs of animals per pair of plants and median 58,528 (mean 636,590)

2602 plant-animal sets per network.

2603 Linking network-level trends and community composition

2604 Next, we examined the connection between our network-level

2605 observations and the plant families present in each community.

2606 Speciﬁcally, we tested the hypothesis that varying relationships

2607 between phylogenetic distance and pairwise niche overlap are due

2608 to the different distributions of families across networks. To do this,

2609 we performed a non-parametric permutational multi-variate analysis

2610 of variance (PERMANOVA; Anderson, 2001) using the change in log

2611 odds of observing each pattern of overlap to predict the Bray-Curtis

2612 dissimilarity of networks based on the composition of their plant

2613 component (deﬁned as the proportions of the plant community made

2614 up by each plant family present in the entire dataset). We used Bray-

2615 Curtis dissimilarity because, for a given pair of networks, only those

2616 plant families that appear in at least one network are considered

2617 (Anderson, 2001; Cirtwill and Stouffer, 2015; Chapter 5); that is, the

2618 absence of rare plant families will not make two networks appear

2619 more similar than they actually are.

2620 Note that a PERMANOVA does not assume that the data are

2621 normally distributed, but rather compares the pseudo-F statistic

2622 calculated from the observed data to a null distribution obtained

2623 by permuting the raw data. As pollination and herbivory networks

2624 might have different community composition and the change in log

2625 odds of observing different patterns of overlap, we stratiﬁed these

2626 permutations by network type. That is, the change in log odds for

2627 a pollination network could only be exchanged for that of another

2628 pollination network. Stratifying the permutations in this way ensures

2629 that the null distribution used to calculate the P-value is not biased

2630 by including combinations of changes in log odds and community

2631 composition that would not occur because of inherent differences

2632 in the two network types (e.g., Pinaceae only appeared in herbivory

2633 networks and should not be assigned to pollination networks). We

2634 used 9999 such stratiﬁed permutations to obtain the null distribution

2635 and obtain a P-value.

2636 The PERMANOVA tests whether there is an association between

2637 community composition and network-level patterns but does not

2638 give any information on which plant families have the greatest 85

2639 effects. To address this, we supplemented the PERMANOVA with

2640 three constrained correspondence analyses (CCAs) which placed

2641 plant families along an axis representing the change in log odds of

2642 observing each pattern of overlap. A correspondence analysis (CA) is

2643 similar to other multivariate analyses such as principal components

2644 analysis in that it reduces multivariate data to a set of orthogonal

2645 axes. A subset of axes that explain the majority of variation in the

2646 data can then be interpreted to elucidate trends that were difﬁcult to

2647 interpret in the full multivariate space. A constrained correspondence

2648 analysis (CCA) creates an extra axis based on some constraint - in

2649 this case, the change in log odds of observing each pattern of overlap.

2650 Calculating niche overlap within families

2651 Finally, we wished to compare the breakdown of overlap of

2652 interactions in different plant families. To do this, we used the

2653 same deﬁnitions of overlap and phylogenetic distance as in the

2654 within-network analysis but restricted our regressions to pairs of

2655 plants from the same family and the same network. In order to ﬁt

2656 our regression models, we had to eliminate any family-network

2657 combination where there was no variation in the probabilities of

2658 any pattern of overlap or in phylogenetic distance. This occurred, for

2659 example, when all plant pairs in a given family in a given network

2660 were taken from the same genus (as divergence times in our dataset

2661 were not resolved within genera; Wikström et al., 2001). Unlike in our

2662 previous analysis, we analysed data from pollination and herbivory

2663 networks separately as most well-represented plant families appeared

2664 in only one network type. For those families which appeared in both

2665 network types, we ran separate analyses on each subset of data.

For each plant family, within each network type, we then ﬁt one of two similar sets of models. First, when family f was found in several networks of network type t, we ﬁt mixed-effects logistic

regressions for each pattern of overlap ωpntfij of the form

logit(ωpntfij) ∝ δij + Nn,(2)

where ωptnfij is the probability of overlap pattern p in network n of network type t for plants i and j in plant family f , δij is the phylogenetic distance between plants i and j, and Nn is a random effect of network n. Second, if a plant family was represented in only one network and therefore necessarily in only one network type, we omitted the network-level random effect giving mixed-effects logistic 86

regressions of the form

logit(ωpntfij) ∝ δij.(3)

2666 We ﬁt Eq. 2 using the function ‘glmer’ from the R package

2667 lme4 (Bates et al., 2014) and ﬁt Eq. 3 in R using the function ‘glm’

2668 from the same package.

2669 Results

2670 Within-network conservation of niche overlap

2671 Overlap of interaction partners decreased signiﬁcantly with

2672 increasing phylogenetic distance in pollination networks −1 −1 2673 (βδ+δρ =-17.14 My , P<0.001 for total overlap; βδ+δρ =-9.47 My , 2674 P<0.001 for partial overlap). In herbivory networks, these negative −1 2675 relationships were even stronger (βδ=-40.81 My , P<0.001; −1 2676 βδ=-16.47 My , P<0.001 for total and partial overlap, respectively). 2677 In both cases, the trends for the no-overlap pattern were opposite to

2678 those described above, as expected (see S3.2, Supporting Information

2679 S3 for details). That is, a pair of plants in the same genus was more

2680 likely to share interaction partners than a pair of plants in the same

2681 family in both types of networks, but a pair of congeners would be

2682 less likely to share pollinators than to share herbivores.

2683 Further, plants were slightly less likely to share pollinators than

2684 herbivores regardless of their phylogenetic distance

2685 (βρ=-0.94, P=0.014 and βρ=-0.40, P=0.110 for total and partial overlap,

2686 respectively). This may be due to the greater proportion of specialist

2687 pollinators than specialist herbivores. In our dataset, an average of

2688 48% (+/- 14) of pollinators in a given web were extreme specialists

2689 (i.e., visited only one plant species) compared to 29% (+/- 29) of

2690 herbivores (z=5.62, df=68, P<0.001 for a binomial regression of

2691 specialists and generalists over network type).

2692 Despite these general trends, there was substantial variation

2693 between pollination networks, with overlap of interaction partners

2694 decreasing with increasing phylogenetic distance in some networks

2695 and increasing in others (Fig. 13). For example, the probability of

2696 observing the no-overlap pattern ranged from approximately 0.3

2697 to over 0.95 over the entire range of divergence times observed in

2698 our dataset. Herbivory networks were less variable in the directions

2699 of relationships between overlap and phylogenetic distance, but

2700 there was nevertheless a great deal of variation in probabilities

2701 across networks. In one network, for instance, the probability of 87

2702 observing the no-overlap pattern increased from 0.1 to 0.7 over

2703 800 My of divergence time while in other networks the probability

2704 was much more constant. Overall, overlap of interaction partners

2705 decreased with increasing phylogenetic distance in 77% of pollination 2706 networks (βδ+δρ+δN<0 in 46 of 59 networks for total overlap and in 2707 45 of 59 networks for partial overlap). All herbivory networks, on the

2708 other hand, showed decreasing overlap with increasing phylogenetic

2709 distance (βδ+δN<0 in 11 of 11 networks for total and partial overlap).

2710 Linking network-level trends and community composition

2711 In each PERMANOVA, the change in log odds of observing a given

2712 pattern of overlap in a given network was signiﬁcantly associated

2713 with the composition of the plant community in that network

2714 (F1,68=1.79, P=0.019; F1,68=1.92, P=0.010; and F1,68=1.81, p=0.015 for

2715 total overlap, partial overlap, and no overlap, respectively). In the

2716 CCAs of community composition constrained by the change in log

Plant-herbivore networks Plant-pollinator networks Figure 13: Results of a mixed-effects logistic regression of pairwise niche overlap against phylogenetic distance 0.2 Total overlap for plants in 11 herbivory networks (left; green) and 59 pollination networks (right; purple). In both network types, 0.1 the probability of observing the total and partial overlap patterns tended to decrease as phylogenetic distance increased while the probability of the 0.0 no-overlap pattern tended to increase 0.6 (thick, dark lines). There was also substantial variation between individual Partial overlap networks (thin, pale lines) of both types. 0.4 The model is described in Eq. 1.

0.2

0.0 1.0 Probability of observing pattern 0.8 No overlap

0.6

0.4

0.2

0.0 0 200 400 600 800 0 200 400 600 800 Phylogenetic distance (My) 88

200 Figure 14: Change in log odds of

Total overlap observing different patterns of pairwise niche overlap per million years of 100 divergence time between a pair of plants in 38 separate plant families. 0 Families in pollination networks are

) indicated by dark purple diamonds

-1 while families in herbivory networks -100 are indicated by pale green circles. Note 100 that changes in log odds are analogous

Partial overlap to the slopes of the regression lines from Eq. 2-3 in logit-transformed 50 space and represent the change in the probability of observing a pattern of 0 overlap per million years of divergence time. We also show the slope of the -50 relationship between the log-odds of observing each overlap pattern -100 and phylogenetic distance across 100 all plant families in herbivory (pale, green horizontal line) and pollination No overlap Change in log odds (My 50 (dark, purple horizontal line) networks. Arrows indicate signiﬁcant values 0 for Melastomataceae in herbivory networks which fell outside the plot -50 margins. See Figure S3.1; Supporting Information S3 for more details. The -100 phylogenetic tree below the plots indicates the relatedness between plant families. Error bars represent 95% conﬁdence intervals. Adoxaceae Caprifoliaceae Apiaceae Asteraceae Apocynaceae Rubiaceae Boraginaceae Lamiaceae Orobanchaceae Plantaginaceae Oleaceae Solanaceae Ericaceae Primulaceae Hydrangeaceae Loasaceae Caryophyllaceae Montiaceae Polygonaceae Brassicaceae Cistaceae Malvaceae Geraniaceae Melastomataceae Myrtaceae Euphorbiaceae Malpighiaceae Phyllanthaceae Fabaceae Rosaceae Saxifragaceae Papaveraceae Ranunculaceae Amaryllidaceae Asparagaceae Iridaceae Poaceae Pinaceae

2717 odds of observing each pattern of overlap, the largest decreases in

2718 partial overlap with increasing phylogenetic distance were associated

2719 with Begoniaceae, Gleicheniaceae, Myricaceae, Siparunaceae, and

2720 Apocynaceae (the ordering of plant families was qualitatively similar

2721 for total overlap and no overlap– see S3.3, Supporting Information S3).

2722 The largest increases in partial overlap with increasing phylogenetic

2723 distance were associated with Surianaceae, Malpighiaceae, Goodeniaceae,

2724 Plumbaginaceae, and Resedaceae. 89

Total overlap Partial overlap No overlap Family Table 4: Change in log odds (per Odds Odds Odds P-value P-value P-value million years of phylogenetic distance) ratio ratio ratio of observing total, partial, or no overlap Asteraceae 42.96 0.013 2.04 0.820 -27.95 0.004 in herbivores between a pair of plants Rubiaceae -66.12 <0.001 -24.24 <0.001 55.60 <0.001 in the same family. Melastomataceae -528.51 0.190 -923.08 <0.001 905.82 <0.001 Euphorbiaceae -20.29 <0.001 -7.92 <0.001 17.60 <0.001 Fabaceae 71.05 0.021 32.33 <0.001 -38.87 <0.001 Poaceae -5.91 <0.001 -3.97 <0.001 6.01 <0.001 Pinaceae -47.11 0.351 -13.22 0.340 16.95 0.215 Only seven plant families were sufﬁciently diverse in our dataset to permit this analysis (see Materials and Methods for details). For each pattern of overlap, we show the change in log odds per million years and the associated P-value. Statistically signiﬁcant values are indicated in bold.

2725 Within-family conservation of niche overlap

2726 The relationship between within-family niche overlap and

2727 phylogenetic distance varied widely in both pollination and

2728 herbivory networks. For the families that were well represented in

2729 pollination networks, overlap decreased with increasing phylogenetic

2730 distance in 18 (Table 5). There was no signiﬁcant relationship

2731 between overlap and phylogenetic distance in a further 15 plant

2732 families. Finally, the overlap between pairs of Polygonaceae increased

2733 with increasing phylogenetic distance. Of the seven plant families

2734 that were sufﬁciently well represented in herbivory networks, overlap

2735 decreased with increasing phylogenetic distance in four (Table 4;

2736 Fig. 14). Two families did not show signiﬁcant relationships between

2737 phylogenetic distance and overlap, and in one family, Fabaceae,

2738 overlap of interaction partners increased with increasing phylogenetic

2739 distance.

2740 Discussion

2741 We found broad support for the hypothesis that more closely-related

2742 pairs of plants have a higher degree of niche overlap. Using a novel

2743 method which considers all pairs of plants together, the probability of

2744 two plants sharing the same animal interaction partners decreased

2745 with increasing phylogenetic distance. Considering networks

2746 separately, ≈78% of the pollination and all of the herbivory networks

2747 exhibited the expected trend of decreasing overlap with increasing

2748 distance.

2749 Within families, however, there was much greater variability.

2750 More than half of the plant families in each network type behaved

2751 as we hypothesised, with more closely-related plants having greater

2752 niche overlap than distantly related plants. This relationship between

2753 overlap and phylogenetic distance is consistent with the idea that

2754 traits affecting interactions are heritable (Schemske and Bradshaw,

2755 1999) and changing gradually such that closely related plants

2756 resemble their common ancestor— and each other —more than 90

Total overlap Partial overlap No overlap Family Table 5: Change in log odds (per Odds Odds Odds P-value P-value P-value million years of phylogenetic distance) ratio ratio ratio of observing total, partial, or no overlap Adoxaceae - - -33.37 <0.001 33.87 <0.001 in pollinators between a pair of plants Caprifoliaceae - - 1.04 0.588 -1.23 0.522 in the same family. Apiaceae 11.02 <0.001 -53.50 <0.001 59.57 <0.001 Asteraceae* -4.74 <0.001 -2.00 <0.001 2.38 <0.001 Apocynaceae -8.26 <0.001 -4.48 <0.001 5.67 <0.001 Rubiaceae ------Boraginaceae 26.40 0.470 -24.67 <0.001 23.85 <0.001 Lamiaceae 5.81 0.528 1.90 0.255 -2.10 0.205 Orobanchaceae 241.20 0.998 261.90 0.995 -262.55 0.995 Plantaginaceae -529.93 0.940 -53.81 <0.001 58.36 <0.001 Oleaceae -11.01 0.367 -14.95 <0.001 14.90 <0.001 Solanaceae - - 12.33 0.743 -25.57 0.484 Ericaceae -5.32 <0.001 -4.48 <0.001 5.02 <0.001 Primulaceae -49.15 <0.001 -21.46 <0.001 23.22 <0.001 Hydrangeaceae -7.14 0.002 -1.16 0.027 1.47 0.004 Loasaceae 482.42 0.998 478.88 0.995 -485.71 0.995 Caryophyllaceae -3.42 0.167 -3.63 <0.001 4.09 <0.001 Montiaceae 346.61 0.999 406.10 0.998 -406.90 0.998 Polygonaceae 18.37 <0.001 14.63 <0.001 -14.99 <0.001 Brassicaceae -6.04 0.260 -1.34 0.302 1.57 0.218 Cistaceae -26.90 <0.001 -10.81 <0.001 13.33 <0.001 Malvaceae -1.29 0.558 -4.59 <0.001 5.02 <0.001 Geraniaceae -11.17 0.014 -1.25 0.730 9.96 0.013 Melastomataceae* 47.20 0.998 52.97 0.993 -53.08 0.993 Myrtaceae -70.37 <0.001 -44.38 <0.001 51.83 <0.001 Malpighiaceae -0.83 0.610 -0.26 0.850 0.99 0.513 Phyllanthaceae -389.36 0.995 -24.36 <0.001 24.88 <0.001 Fabaceae* 14.19 0.011 3.18 0.091 -4.60 0.012 Rosaceae -21.45 <0.001 -20.31 <0.001 21.50 <0.001 Saxifragaceae -4.00 0.053 0.40 0.722 0.79 0.474 Papaveraceae -27.67 0.003 -16.16 <0.001 16.80 <0.001 Ranunculaceae 69.01 0.996 -11.70 0.003 10.73 0.006 Amaryllidaceae 0.65 0.933 -1.01 0.465 0.97 0.480 Asparagaceae -73.15 0.003 -33.10 <0.001 35.56 <0.001 Iridaceae 253.09 0.998 1.68 0.773 -2.30 0.691 Poaceae* 343.63 0.996 343.55 0.990 -344.97 0.990 We were able to ﬁt these models to 35 plant families (see Materials and Methods for details). Families marked with an asterisk were also highly diverse in herbivory networks. Statistically signiﬁcant values are indicated in bold. Dashes indicate plant families where the corresponding overlap pattern was either extremely rare or omnipresent and the relevant model was uninformative.

2757 they do distantly related plants. In some families, such as Asteraceae

2758 in pollination networks, the positive slope of this relationship was

2759 very shallow while in others, such as Melastomataceae in herbivory

2760 networks, the positive slope was extremely steep. This could indicate

2761 different rates of phenotypic drift or evolution in different families.

2762 In contrast, Polygonaceae in pollination networks and Fabaceae in

2763 herbivory networks showed the opposite pattern. In these families,

2764 closely-related plants had lower overlap than more distantly-related

2765 pairs of plants. There are several possible reasons a plant family

2766 might display this pattern. First, part of the family may have recently

2767 undergone a period of rapid diversiﬁcation with closely-related

2768 species developing novel phenotypes that attract different animal 91

2769 interaction partners (Linder, 2008; Breitkopf et al., 2015). It is also

2770 possible that the animals have undergone an adaptive radiation to

2771 specialise on their most proﬁtable partner (Janz et al., 2006). Second,

2772 this pattern could be the result of ecological or environmental

2773 ﬁltering (Mayﬁeld et al., 2009; Ackerly, 2003). Closely-related species

2774 which have high degrees of overlap in their interaction partners

2775 might compete too severely to coexist. This is especially likely for

2776 plants sharing pollinators, where the loss of pollen to related species

2777 might severely limit reproductive success (Levin and Anderson, 1970;

2778 Bell et al., 2005; Mitchell et al., 2009). Indeed, animal pollination

2779 and seed dispersal have been shown to act as ﬁlters for several

2780 plant clades (Mayﬁeld et al., 2009). Distantly-related plants with

2781 similar interaction partners, on the other hand, may differ in some

2782 other aspect of their niches and so be able to coexist. Plants sharing

2783 herbivores are unlikely to compete for these interaction partners, but

2784 the presence of both plants in a community could support higher

2785 herbivore populations than could one species alone (Russell et al.,

2786 2007). If the plants compete for some other resource, the combined

2787 impact of herbivory and competition could eliminate the rarer plant

2788 species. Distantly-related plants sharing herbivores, conversely,

2789 would be less likely to compete for vital resources and more likely

2790 to persist.

2791 The remaining families did not show signiﬁcant relationships

2792 in either direction. That is, the niche overlap between two plants did

2793 not vary linearly over phylogenetic distance. Once again, there are

2794 several possible drivers for this trend (or lack thereof). These plants

2795 might be highly specialised on different interaction partners and

2796 therefore have low overlap at all levels of relatedness. In other plant

2797 families with more moderate levels of specialisation, it is possible

2798 that pollination and/or herbivory do not exert large selection

2799 pressures on the plants. If traits affecting pollination or herbivory

2800 are not heritable in these groups (Kursar et al., 2009) and that their

2801 phenotypes are constrained by other factors (e.g., drought tolerance),

2802 then we should not expect a relationship between phylogenetic

2803 distance and overlap of interaction partners. Alternatively, pollination

2804 and/or herbivory might exert large pressures that maintain the clade

2805 within a pollination or defensive syndrome. These syndromes are

2806 commonly believed to predict the pollinators or herbivores with

2807 which a plant will interact (Waser et al., 1996; Fenster et al., 2004;

2808 Ollerton et al., 2009; Johnson et al., 2014). As some recent studies

2809 have suggested that pollination syndromes do not accurately predict

2810 plants’ visitors in all plant families (Ollerton et al., 2009), it may be of

2811 interest for future researchers to test whether syndromes are better 92

2812 predictors in families with weak relationships between overlap and

2813 phylogenetic distance. Lastly, it is possible that the absence of a linear

2814 relationship between niche overlap and phylogenetic distance is

2815 because the data actually exhibit a strongly non-linear one. This

2816 could result, for example, from an early burst of diversiﬁcation

2817 followed by a period of stasis (Davis et al., 2014).

2818 For those families which were well-represented in both

2819 pollination and herbivory networks, we can also contrast the

2820 trends in the two network types. In all ﬁve such cases, there was a

2821 signiﬁcant relationship between overlap and phylogenetic distance

2822 in only one network type (counting the singular relationships in

2823 Rubiaceae in pollination networks as non-signiﬁcant). This may

2824 indicate that one type of interaction places greater constraints upon

2825 these families than the other. Plants may not be able to respond to

2826 selection on both types of interaction simultaneously because traits

2827 affecting pollination can also affect herbivory, and vice versa (Strauss,

2828 1997; Strauss et al., 2002; Adler and Bronstein, 2004; Adler et al.,

2829 2006; Theis, 2006). Associations with pollinators and herbivores

2830 may also be constrained by the larger structure of the community.

2831 In one recent study, plants which are visited by many pollinators

2832 are also consumed by many herbivores (Sauve et al., 2015). This

2833 may be because pairing antagonistic and mutualistic interactions

2834 balances the indirect effects of these interactions, leading to a more

2835 stable community (Sauve et al., 2014). As more networks describing

2836 pollination and herbivory in the same community become available,

2837 it will be interesting to test this hypothesis more thoroughly.

2838 Altogether, our study has revealed a wide variety of

2839 relationships between overlap of interaction partners and

2840 phylogenetic distance between plants in the same family. Regardless

2841 of the precise mechanisms behind these relationships, it is clear that

2842 the differences between families can affect the relationship between

2843 overlap and phylogenetic distance at the network level. Interestingly,

2844 in our analyses the plant families associated with the steepest

2845 relationships between niche overlap and phylogenetic distance at

2846 the network level did not show particularly steep relationships

2847 within themselves. This result suggests that it is not just which

2848 plant families are present but the additional relationships between

2849 the families that affects conservation of interactions at the network

2850 level and is consistent with previous work showing that the shape

2851 of phylogenetic tress, as well as the phylogenetic distances between

2852 species, can affect the strength of phylogenetic signal (Chamberlain

2853 et al., 2014b). Our results emphasise the importance of considering 93

2854 conservation of interactions at multiple scales. We hope that these

2855 results will help to guide future work investigating the genetic and

2856 environmental drivers underpinning these relationships.

2857 Acknowledgements

2858 We would like to thank the authors of the published networks used

2859 in this study. We are grateful for the use of the Edward Percival ﬁeld

2860 station in Kaikoura, New Zealand in May, 2014. This research was

2861 supported by an NSERC PGS-D graduate scholarship (to ARC),

2862 a Marsden Fund Fast-Start grant (UOC-1101) and a Rutherford

2863 Discovery Fellowship, both administered by the Royal Society of

2864 New Zealand (to DBS), a BlueFern HPC PhD scholarship (to NJB),

2865 and by the Allan Wilson Centre (to GVDR).

2866 Author contribution

2867 ARC, DBS, GVDR, and NJB designed the research. ARC, JAT, and

2868 CJW collected published data. ARC and GVDR performed the

2869 analyses. All authors contributed to the manuscript.

2870 Supporting information

2871 S3.1: Original sources for networks

2872 S3.2: Supplemental within-network results

2873 S3.3: Supplemental within-family results 94

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3045 Evolution, 67:1767–1779. 3046 Chapter 4: Are high-arctic plant-pollinator networks

3047 unravelling in a warming climate?

1 2,3 4 3048 Alyssa R. Cirtwill ,Tomas Roslin , Claus Rasmussen , 5 1 3049 Jens Mogens Olesen , Daniel B. Stouffer

1 3050 Centre for Integrative Ecology

3051 School of Biological Sciences

3052 University of Canterbury

3053 Private Bag 4800

3054 Christchurch 8140, New Zealand 2 3055 Department of Ecology

3056 Swedish University of Agricultural Sciences

3057 P.O. Box 7044

3058 SE-750 07 Uppsala, Sweden 3 3059 Spatial Foodweb Ecology Group

3060 Department of Agricultural Sciences

3061 Uiversity of Helsinki

3062 PO Box 27 (Latokartanonkaari 5)

3063 FI-00014 Helsinki, Finland 4 3064 Department of Bioscience

3065 Aarhus University

3066 Ole Worms Allé 1

3067 DK-8000 Aarhus C, Denmark 5 3068 Department of Bioscience

3069 Aarhus University

3070 Ny Munkegade 114

3071 DK-8000 Aarhus C, Denmark

3072 Manuscript prepared in the style of Global Change Biology.

3073 This chapter has been modiﬁed from its original version. It has been

3074 formatted to match this thesis. 100

3075 Abstract

3076 The Arctic is currently experiencing severe climate change, resulting

3077 in substantial changes to plants’ ﬂowering periods and insects’

3078 emergence dates. This has raised concerns that the two groups

3079 of species may be becoming phenologically uncoupled. If this

3080 is the case, networks of plant-pollinator interactions could be

3081 disrupted, with adverse consequences for both plants and insects. We

3082 investigated this possibility using a temporally-replicated network

3083 from a well-studied High Arctic site at Zackenberg, Greenland.

3084 Speciﬁcally, we tested for turnover in community composition and

3085 change in the dates at which species became active in the plant-

3086 pollinator network before and after 15 years of warming. We then

3087 looked for effects of these changes on species’ roles within the

3088 network. Our results suggest that the plant-pollinator network is

3089 beginning to unravel, with changes to the roles of plants active early

3090 in the year and insects late in the year being most pronounced. This

3091 is consistent with phenological uncoupling and suggests that, if the

3092 trends we observed continue, the pollination network at this site may

3093 be disrupted. As the Arctic is the “canary in the coal mine" for the

3094 effects of climate change, we expect that similar changes may also

3095 occur at more temperate sites in the future.

3096 Keywords

3097 pollination, phenological uncoupling, network structure 101

3098 Introduction

3099 Plant-pollinator interactions are currently being strongly inﬂuenced

3100 by climate change (Hegland et al., 2009; Miller-Struttmann et al.,

3101 2015). In particular, the differential effects of climate change on

3102 species’ phenologies are likely to disrupt entire networks of plant-

3103 pollinator interactions (Parmesan, 2006; Tylianakis et al., 2008;

3104 Settele et al., 2014). If plants and their pollinators respond to

3105 climate change in different ways, changes to the active periods of

3106 plants and their pollinators can disrupt pollination— even if the

3107 species themselves remain present in the community (Tylianakis

3108 et al., 2008; Hegland et al., 2009; Petanidou et al., 2014; Forrest,

3109 2015). Advancing phenology in response to global warming has

3110 been reported across biomes (Menzel et al., 2006; Høye et al., 2007;

3111 Hua et al., 2016), raising concerns about just such an uncoupling

3112 of trophic interactions (Both et al., 2006; Thackeray et al., 2010;

3113 Rasmussen et al., 2013; Gezon et al., 2016; Hua et al., 2016; Schmidt

3114 et al., 2016). For example, if early-emerging pollinators respond to

3115 higher temperatures and emerge before plants blossom, they may

3116 have difﬁculty ﬁnding food. Late-emerging pollinators, on the other

3117 hand, may encounter different plant species and may or may not

3118 be able to obtain nectar from or pollinate them. In this context, a

3119 species’ sensitivity to climate change is likely to vary with its range

3120 of alternative resources. Equally, the vulnerability of an interaction is

3121 likely to o depend on the phenologies of the species involved.

3122 Severe effects of climate change on plant-pollinator interactions

3123 are particularly likely in the Arctic— where substantial warming

3124 has already taken place (Høye et al., 2013) —challenging the ability

3125 of organisms, processes, and ecosystems to adapt (Post et al., 2009).

3126 Moreover, the climate of the Arctic is predicted to change faster than

3127 that of most other regions (IPCC, 2013; Settele et al., 2014), making

3128 understanding the effects of climate change on arctic communities

3129 a priority for current research (Settele et al., 2014). Previous work

3130 suggests that rapid climactic shifts in the Arctic have already led

3131 to equally rapid phenological shifts (Høye et al., 2007; Høye and

3132 Forchhammer, 2008b; Høye et al., 2013; Schmidt et al., 2016). Even

3133 more importantly, recent studies suggest that arctic plants and their

3134 pollinators respond differently to climate warming (Høye et al., 2007;

3135 Høye and Forchhammer, 2008b; Høye et al., 2013; Schmidt et al.,

3136 2016).

3137 In a well-studied plant-pollinator community at Zackenberg,

3138 Northeast Greenland, ﬂowering dates in the plant community have 102

3139 been shown to advance along with earlier snowmelt (Høye et al.,

3140 2007). Moreover, although variation in plant phenology across

3141 the landscape is pronounced in the Arctic, this variation tends to

3142 decrease under both natural and experimental warming (Post et al.,

3143 2008; Høye et al., 2013). This means that differences in ﬂowering

3144 time between patches are unlikely to “rescue” pollinators which

3145 require ﬂoral resources later in the summer. To complete this picture

3146 of changing ﬂower availability, late-ﬂowering plants at Zackenberg

3147 have shown greater changes to their phenologies than have early-

3148 ﬂowering plants (Høye et al., 2013; Schmidt et al., 2016). In contrast

3149 to alpine communities, in which early-ﬂowering plants have shown

3150 greater advances in ﬂowering time (Aldridge et al., 2011; Iler et al.,

3151 2013), this suggests that the window of ﬂoral resources in the Arctic

3152 is both changing and shrinking as the climate changes (Høye et al.,

3153 2013; Schmidt et al., 2016). We note, however, that data on individual

3154 pollinators’ responses to changes in the mosaic of ﬂowering plants

3155 are still lacking, meaning that conclusions about the consequences

3156 of change in plants’ phenologies must be considered tentative.

3157 Nevertheless, these changes have the potential disrupt the network

3158 of plant-pollinator interactions at Zackenberg and similar sites,

3159 posing a signiﬁcant challenge to plant-pollinator interactions in the

3160 future (Memmott et al., 2007; Hegland et al., 2009; Post et al., 2009).

3161 As most of the pollinator community at Zackenberg has also

3162 emerged earlier, tracking changes in snowmelt (Høye et al., 2007;

3163 Høye and Forchhammer, 2008b), it is possible that pollination

3164 interactions, and the integrity of the plant-pollinator network, might

3165 be maintained despite climate change. However, phenological

3166 changes vary greatly between taxa (Høye and Forchhammer,

3167 2008b) and more recent studies have found declines in pollinator

3168 populations in the Arctic (Potts et al., 2010; Høye et al., 2013). This

3169 suggests that high-arctic plants and their pollinators may indeed be

3170 vulnerable to phenological uncoupling (Høye et al., 2007; Høye and

3171 Forchhammer, 2008a; Olesen et al., 2011; Rasmussen et al., 2013)

3172 and that pollination networks may be disrupted (Schmidt et al.,

3173 2016). At Zackenberg speciﬁcally, many species also have very short

3174 active periods (4-8 days) (Rasmussen et al., 2013). For these species

3175 in particular, a shift in the phenology of an important interaction

3176 partner could have large effects.

3177 To test whether changes in plants and pollinators’ phenologies

3178 are leading to changes in the interactions between them, we draw

3179 on a set of temporally-replicated plant-pollinator networks from

3180 Zackenberg, Greenland which spans 15 years of warming. Given the 103

3181 substantial phenological change in both plants and pollinators at

3182 this site, we expected (1) that there would be substantial turnover

3183 in plant and/or pollinator communities and (2) that dates at

3184 which pollinators begin visiting plants, and plants begin receiving

3185 visitors, will have changed between decades. If there is substantial

3186 turnover or phenological change, then interactions between plants

3187 and their pollinators may be disrupted. We therefore expect (3)

3188 that the structure of interaction networks will change over time. If

3189 network structure changes over time, we then expect (4) that species’

3190 structural roles within these networks (i.e., the patterns of their

3191 interactions with other species) will also change over time. Moreover,

3192 we expect (5) that species which become active at different times of

3193 the year will have different roles in the plant-pollinator network and

3194 (6) that the roles of species which become active at different times of

3195 the year will change in different ways between decades. Finally, we

3196 expect (7) that the change in a species’ role will depend both on the

3197 magnitude of the change in its phenology and on the direction of that

3198 change. For clarity and later reference, these seven hypotheses are

3199 summarised in Table 6.

3200 Materials and Methods

3201 Study site

3202 At the Zackenberg research station in High Arctic NE Greenland ◦ ′ ◦ ′ 3203 (74 28 N, 20 35 W), the local climate has changed dramatically

Support Table 6: Summary of the hypotheses we Hypothesis Plants Pollinators tested in this study and the strength of evidence for them. Note that the aspect Community composition of phenology we are most interested 1. Weak Strong changed between decades. in throughout this study is the date at which species become active in the Phenology changed between 2. Mixed None plant-pollinator network. decades. Network structure changed 3. Strong between decades. Species’ roles changed between 4. Strong Strong decades. Species’ roles were correlated 5. Strong Strong with their phenology. Change in roles between 6. decades was correlated with None Mixed phenology. Amount of change in roles 7. was correlated with amount of None Mixed change in phenology. 104

3204 during the study period (1996-2011). The average near-surface air

3205 temperature across June, July and August has increased at a rate of ◦ 3206 1.3-1.8 C per decade since 1996, whereas the timing of snowmelt has

3207 advanced at a rate of between 9.8 and 12.8 days per decade (Høye

3208 et al., 2013; Mortensen et al., 2014). Over the same period, the

3209 ﬂowering season of focal plants has shortened at the landscape scale

3210 at a rate of 3.7 days per decade (Høye et al., 2013).

3211 Data collection

3212 We use plant-pollinator data compiled over four summers, in 1996

3213 and 1997 (Olesen et al., 2008) and 2010 and 2011 (Rasmussen et al.,

3214 2013). Each study period lasted from the last snowmelt in spring to

3215 the ﬁrst frost and snowfall in autumn. In 1996 and 1997, this covered

3216 43 and 69 days, respectively, of which 25 in each year had sufﬁciently

3217 ﬁne weather to permit observation (Olesen et al., 2008). In 2010 and

3218 2011, the study period covered 70 and 69 days, respectively, of which

3219 54 and 52 days were spent observing in the ﬁeld (Rasmussen et al.,

3220 2013). All observation days had weather suitable for foraging insects.

3221 During each ﬁeld day (lasting from 09:00 to 17:00), two individuals

3222 of each species of ﬂowering plant were observed for 20 minutes each

3223 (i.e., 40 minutes of observation for each plant species), and all insect

3224 visitors to ﬂowers were recorded as potential pollinators (Olesen

3225 et al., 2008; Rasmussen et al., 2013).

3226 Quantifying species turnover and changing phenologies

3227 We ﬁrst assessed the amount of turnover in plants and pollinators

3228 across years (Hypothesis 1). Using the 1996 community as a baseline,

3229 we calculated the number of plants and pollinators in 1997 that had

3230 also been detected in 1996, the number of new plants and pollinators

3231 observed in 1997, and the number of plants and pollinators observed

3232 in 1996 that were not found in 1997. We then repeated this procedure

3233 between 1997 and 2010 and between 2010 and 2011. As well as

3234 comparing numbers of persistent species, newly-observed species,

3235 and species disappearing from sight from one year to the next, we

3236 also quantiﬁed turnover between all pairs of years using Whittaker’s 3237 beta diversity index (βW; Whittaker, 1972). This index,

βW =(γ − α)/α,(4) 105

3238 compares the total number of species detected across both years (γ)

3239 with the mean number of species detected in one year (α) and varies

3240 between 0 (identical species in both years) and 1 (complete turnover

3241 of the community). We calculated turnover separately for plants and

3242 pollinators.

3243 As well as changes in which species were detected, we were

3244 interested in changes to these species’ phenologies (Hypothesis 2).

3245 For both plants and pollinators, we calculated the change in each

3246 species’ dates of ﬁrst interaction between each pair of networks

3247 from different decades (i.e., 1996-2010, 1996-2011, 1997-2010, and

3248 1997-2011) in which the pollinator was detected. To capture the

3249 phenologies of species which were observed in only one year ina

3250 given decade, we included all between-decade pairs of networks,

3251 thus mimicking our analysis of species turnover above. To explicitly

3252 test whether the phenology of the community has changed over time,

3253 we compared mean dates of ﬁrst interaction between decades (1996

3254 and 1997 vs. 2010 and 2011) using a two-tailed two-sample t-test

3255 and compared variances in emergence times between decades using

3256 an F-test. In addition to comparing the distributions of the entire

3257 communities, we also compared the distributions of newly-arrived

3258 and persistent species.

3259 We note that empirically-observed dates of ﬁrst interaction

3260 are highly dependent on sampling effort, species’ abundances,

3261 and their interaction frequencies; to ensure that our results are not

3262 biased by missing interactions, we repeated all subsequent analyses

3263 using simulated dates of ﬁrst interaction. These simulated dates

3264 were based on the full set of observed interactions for each species

3265 and allowed us to determine how robust our results may be to

3266 noise in the observed ﬁrst dates of interaction. In general, analyses

3267 involving plants’ ﬁrst dates of interaction were more robust than

3268 those involving insects, but in both cases the majority of simulated

3269 datasets led to qualitatively the same conclusions as the observed

3270 data (see Supplemental Information: S4.1 for further details).

3271 Quantifying network structure

3272 To test our remaining hypotheses, we compiled plant-pollinator

3273 networks for each year (1996, 1997, 2010, and 2011) and for each

3274 month of sampling within each year (June, July, August), giving 16

3275 networks in total. Of the observations collected in 1996, 94 were not

3276 precisely dated and were instead associated with a range of tentative

3277 dates. As all of these dates were from late in the summer, they were 106

3278 not likely to affect our estimates of species’ dates of ﬁrst interaction.

3279 Our results were qualitatively identical whether these tentatively-

3280 dated observations were included only on the best-guess date of

3281 observation, included only in the yearly networks (i.e., excluded from

3282 the monthly networks), or included for each network covering any

3283 part of the range of tentative dates (see Supplemental Information: S4.2-

3284 S4.3 for details). Thus, we present results based on networks which

3285 included the tentatively-dated observations only in the networks

3286 describing the best guess for the date of observation as this approach

3287 preserves the number of interactions that were actually observed.

3288 We then quantiﬁed the structure of each network based on the

3289 organisation of interactions into two- to six-species “motifs”. These

3290 motifs can be thought of as the building blocks of networks (Milo

3291 et al., 2002, 2004; Stouffer et al., 2007; Baker et al., 2015). Each motif

3292 represents a unique way in which sets of species interact, and hence

3293 a unique contribution to the transfer of energy and other ecosystem

3294 processes (i.e., pollination) within a community. As the number of

3295 individual motifs in a network tends to increase with the number

3296 of species in the network, we converted the counts of each motif to

3297 relative frequencies by dividing by the total number of motifs in the

3298 network. This ensures networks from different years do not appear to

3299 have different structures simply because they have different numbers

3300 of species and/or interactions.

3301 Comparing network structure over time

3302 Having determined the structure of each network, we then aimed to

3303 test whether this structure changed over time (Hypothesis 3). To do

3304 this, we first quantified differences between networks’ motif profiles

3305 (i.e., structures) using Bray-Curtis dissimilarity (Anderson, 2001;

3306 Baker et al., 2015). This dissimilarity measures differences between

3307 networks based only on motifs which appear in at least one of the

3308 networks. Thus, two networks with different structures will not

3309 appear more similar to each other just because they have a large

3310 number of shared “double zeros” (motifs which do not appear in

3311 either network). We then used a non-parametric permutational multi-

3312 variate analysis of variance (PERMANOVA Anderson, 2001) to test

3313 whether network structure varied over time. We were particularly

3314 interested in the change in network structure after several years of

3315 warming and so we compared network structure between decades

3316 (i.e., 1996-1997 to 2010-2011). With PERMANOVA, we achieved this

3317 by comparing the spatial medians of network structures associated 107

3318 with each decade– these median structures can be considered the

3319 “typical" structures for each decade.

3320 Similar to a traditional ANOVA, a PERMANOVA uses a pseudo-

3321 F statistic to compare differences among and within groups. Unlike

3322 an ANOVA, however, the PERMANOVA does not assume that the

3323 data follow any particular distribution. Instead, the raw data are

3324 permuted to obtain the null distribution of the test statistic and

3325 a p-value is computed using this distribution. Where possible,

3326 we used 9999 permutations to calculate the null distribution. In

3327 PERMANOVAs where there were fewer than 9999 possible unique

3328 permutations of the data, we used the maximum number of

3329 permutations possible (as noted below). All PERMANOVAs were

3330 performed using the adonis function in the R (R Core Team, 2014)

3331 package vegan (Oksanen et al., 2014).

3332 We ﬁrst compared the structure of yearly networks across

3333 decades. In this case, there were only 24 possible permutations of

3334 motif proﬁles between decades. We next compared the structure

3335 of monthly networks across decades. As there were many

3336 more monthly than yearly networks, we were able to use 9999

3337 permutations to obtain the null distribution. For these networks, it

3338 was additionally possible that changes in the plants and pollinators

3339 active in each month might drive large amounts of variation in

3340 network structure between decades. To control for the possibility

3341 that such month-to-month variation in network structure might

3342 mask differences in network structure between decades, we stratiﬁed

3343 permutations by month. This stratiﬁcation ensures that motif proﬁles

3344 are only shufﬂed between networks describing the same month (e.g.,

3345 the motif proﬁles of June 1996 and June 2010 could be swapped but

3346 the motif proﬁles of June 1996 and July 1996 could not).

3347 To visualise the change in network structure over time, we also

3348 performed a nonmetric multidimensional scaling (NMDS) analysis

3349 to align the motif proﬁles of all networks along two major axes

3350 explaining the most variation in structure. The NMDS also aligns

3351 the motifs themselves along the same axes, allowing us to interpret

3352 changes in structure based on the motifs which exert the greatest

3353 inﬂuence on these axes. We analysed the structures of yearly and

3354 monthly networks together using the metaMDS function in the R (R

3355 Core Team, 2014) package vegan (Oksanen et al., 2014). 108

AB Figure 15: In this study, we use motifs (unique patterns of 2-6 interacting species) to describe both the structure of networks and species’ roles within them. We show four small networks with different structures. All networks all contain 5 species but some have different numbers of links. However, even those with the same number of links (i.e., B and D) have different arrangements of those links. By CD describing network structures using motifs, we can capture these differences in a way that is not possible with simpler measures of network structure. Along the same line, all of the plants (squares) highlighted in black interact with two pollinators (circles), but their roles within their networks are different. For example, the focal plants in networks A and C interact with two generalist partners, while 3356 Comparing species’ roles over time the focal plants in networks B and D

3357 As with network structure, we used the decomposition of each interact with one specialist and one generalist pollinator. Moreover, by 3358 network into its component motifs to calculate the role of every incorporating indirect interactions, 3359 species within its network (Stouffer et al., 2012; Baker et al., 2015; structural roles based on motifs also allow us to distinguish between a plant 3360 Cirtwill and Stouffer, 2015; Chapter 5), and then to compare these in a network where every species is 3361 roles over time (Hypothesis 4). To do so, we determined the number a generalist (i.e., network A) and one

3362 of times the species appears in the two-species motif, each of which also includes specialists (e.g., network C). As direct and indirect 3363 the two possible three-species motifs, four possible four-species interactions both affect the pollination 3364 motifs, etc. (Baker et al., 2015). As each motif includes one or more service the focal plant receives, and therefore the plant’s population 3365 unique positions that a species might occupy, we next identiﬁed dynamics, structural roles provide a 3366 which position the species took within each motif. There are 74 more comprehensive picture of changes

3367 unique positions that an species can occupy in two- to six-species to species’ roles than simpler measures such as number of interaction partners. 3368 motifs, resulting in vectors of length 74 describing the role of

3369 each species in these plant-pollinator networks (Baker et al., 2015).

3370 These multidimensional roles capture the ways in which species

3371 are embedded into their networks in more detail than simpler

3372 measures like degree (Fig. 15), allowing us to better understand how

3373 pollination is changing over time at Zackenberg.

3374 We were primarily interested in whether species’ roles change

3375 shape over time– that is, whether a species tends to take different

3376 positions within the network in different years (Hypothesis 4) rather

3377 than participating in different numbers of motifs. However, roles

3378 as deﬁned above also vary in magnitude, with species involved in

3379 more interactions also tending to occupy more positions within the

3380 network. This is analogous to networks containing different numbers

3381 of species and interactions having different numbers of motifs. Roles

3382 with different magnitudes may therefore appear different even if the 109

3383 species involved occur with the same frequencies across all motif

3384 positions. To prevent apparent changes in shape driven solely by a

3385 species having different numbers of interaction partners in different

3386 years, we therefore normalised the role vectors of all species by

3387 dividing each role vector by the total number of positions in which

3388 that species occurred. This converts counts of occurrences in different

3389 positions to relative frequencies, and we used these normalised roles

3390 in all subsequent analyses.

3391 We then tested whether species’ roles within networks changed

3392 between decades. As when comparing network structure, we ﬁrst

3393 quantiﬁed differences between roles using Bray-Curtis dissimilarity

3394 (Anderson, 2001; Baker et al., 2015; Cirtwill and Stouffer, 2015;

3395 Chapter 5). Since this ensures that two species’ roles will not

3396 be considered more similar if the species share many “double

3397 zeros”– positions in which neither species occurs. We then used

3398 PERMANOVAs to compare roles between decades. We were able

3399 to use 9999 permutations to obtain the null distribution for all

3400 PERMANOVAs of species’ roles and hence did so.

3401 Testing the effect of emergence date on species’ roles

3402 Next, we tested whether changes to species’ phenologies, particularly

3403 their dates of ﬁrst interaction at the start of each sampling season,

3404 could explain patterns in their roles (Hypothesis 5) or the ways in

3405 which these roles changed over time (Hypothesis 6). To address

3406 Hypothesis 5, we added the effect of date of ﬁrst interaction to

3407 the PERMANOVAs used to compare species’ roles over time. To

3408 address Hypothesis 6, we also included an interaction term between

3409 date of ﬁrst interaction and year or month. As above, we used 9999

3410 permutations to obtain the null distribution of roles in each case.

3411 To test the possibility that changes in roles are driven by changes

3412 in network structure over time, we performed a constrained analysis

3413 of principal coordinates (CAP) that accounted for network structure.

3414 This analysis, similar to a redundancy analysis, measures the

3415 variance in the response (roles) explained by a set of predictors. We

3416 used date of ﬁrst interaction as a predictor and included network

3417 structure as a “conditioning” variable. When testing the ability of

3418 decade to explain variation in pollinators’ roles, the CAP compares

3419 a model including only the conditioning variable (in this case, a

3420 distance matrix based on network structure) with a model including

3421 the conditioning variable and any other predictors. As with the

3422 PERMANOVAs above, we used Bray-Curtis dissimilarities to describe 110

3423 both differences in network structure and differences in species’

3424 roles. We performed the CAP using the capscale function in theR(R

3425 Core Team, 2014) package vegan (Oksanen et al., 2014). These CAP

3426 analyses also allowed us to visualise species’ median roles over time,

3427 as with the NMDS used to visualise network structure.

3428 As well as being interested in the effects of dates of ﬁrst

3429 interaction per se, we were interested in whether the change in

3430 these dates was related to the amount of change in species’ roles

3431 (Hypothesis 7). That is, did pollinators that became active much

3432 earlier in the 2010’s than in the 1990’s have more dissimilar roles

3433 in those years than pollinators that became active at very similar

3434 times in each decade? To test this, we combined the Bray- Curtis

3435 dissimilarities between species’ yearly roles in different decades

3436 (i.e., between 1996 and 2010, 1996 and 2011, 1997 and 2010, and 1997

3437 and 2011) with the differences in species’ dates of ﬁrst interaction

3438 between these years. Negative values for change in date of ﬁrst

3439 interaction indicate that a species became active earlier in the later

3440 network while positive values indicated a shift to becoming active

3441 later in the year.

3442 We then measured the correlation between within-species

3443 differences in emergence date and within-species role dissimilarities.

3444 As we expected that species’ roles might respond differently to

3445 advancing or retreating phenologies, we analysed species which

3446 became active on an earlier date in the 2010’s than in the 1990’s

3447 separately from species which became active on a later date in

3448 the 2010’s than in the 1990’s. In each case, and as in our previous

3449 analyses, we did not assume that this statistic would follow a

3450 normal distribution but rather obtained the null distribution through

3451 permutations. Moreover, as some species’ dates of ﬁrst interaction

3452 were more variable than others, we stratiﬁed permutations to within

3453 species (i.e., the difference in emergence dates for Aedes impiger from

3454 1996 to 2010 could only be swapped with the difference in dates of

3455 ﬁrst interaction for Aedes impiger from 1996 to 2011, 1997 to 2010,

3456 or 1997 to 2011). We used 9999 permutations to obtain the null

3457 distribution.

3458 We followed a similar approach to test the effect of the

3459 magnitude of change in date of ﬁrst interaction on the change in

3460 species’ roles in monthly networks. In this case, we were more

3461 interested in changes across years than within years (i.e., from

3462 June 1996 to June 2010 rather than from June 1996 to July 1996). We

3463 therefore only calculated dissimilarities between networks describing 111

Plants Pollinators 100 Figure 16: From 1996 to 2011, the Species in 1996 A B composition of the Zackenberg 80 Persistent species plant-pollinator community changed 60 New species dramatically. A-B) We show the number Species out of species in each group that were 40 recorded in the previous year (solid), the number of species detected in the

Species 20 previous year that were not observed in 0 the focal year (no ﬁll), and the number -20 of species that were detected in the focal year but not in the previous year (striped ﬁll). The height of the bar C D indicates the total number of plants or pollinators observed each year. The majority of plant species were recorded in all four years. The pollinator interaction

st community, however, both increased in July 1 Aug 1 species richness and showed substantial All species turnover (Table 7). C-D) We show the Persistent species mean date of ﬁrst interaction (±2SE) New species

Date of 1 for plants or their pollinators for each June 1 1996 1997 2010 2011 1996 1997 2010 2011 year. In both communities, mean dates Year of ﬁrst interaction were not signiﬁcantly different between decades or between any two years.

3464 the same month in different years. Using these dissimilarities and

3465 the changes in dates of ﬁrst interaction described above, we once

3466 again tested for correlation between the magnitude of change in dates

3467 and the magnitude of change in roles. As with the yearly roles, we

3468 performed separate tests for species emerging or ﬂowering earlier in

3469 the 2010’s than in the 1990’s and those emerging or ﬂowering later

3470 in the 2010’s. In both cases, we used 9999 permutations to obtain the

3471 null distribution and permutations were stratiﬁed within species.

3472 Results

3473 Did community composition change between decades?

3474 Both the richness and composition of the network varied between

3475 years, partially supporting Hypothesis 1 (Table 6). While numbers

3476 of plant species were relatively constant over time, more pollinator

3477 species were observed in each year from 1996 to 2011 (Fig. 16 A-B).

3478 Similarly, few plant species either appeared or disappeared between

3479 years while there was a great deal of turnover of pollinator species

3480 (Fig. 16C-D, Table 7).

3481 Did species’ phenologies change between decades?

3482 Perhaps more importantly, the dates of ﬁrst interaction did not

3483 vary between decades for either plants or pollinators (F1,126=0.995, 3484 p=0.321 and F1,287=1.52, p=0.219, respectively). This suggests that,

3485 despite the species turnover at Zackenberg, interactions between 112

3486 species were more constant over time and gives no support for

3487 Hypothesis 2 (Table 6). However, for those plants which persisted

3488 between years, dates of ﬁrst interaction were signiﬁcantly earlier

3489 in 2010-2011 (F1,83=6.34, p=0.018). No new plants were detected in

3490 the community in 1997, but those that appeared in 2010 and 2011

3491 had their ﬁrst visitors substantially later than the other plants in the

3492 community. Among the pollinators, dates of ﬁrst interaction did not

3493 differ between decades for either persistent or newly-arrived species

3494 (F1,150=0.299, p=0.591 and F1,75=0.538, p=0.466).

3495 Did network structure change between decades?

3496 The motif structure of yearly networks changed signiﬁcantly

3497 between the mid 1990’s and the early 2010’s (F1,2=6.27, p=0.042 for 3498 a PERMANOVA of structures of yearly networks across decades).

3499 The motif structure of the monthly networks also changed between

3500 the mid 1990’s and the early 2010’s, but only when permutations

3501 were stratiﬁed by month (F1,10=2.32, p=0.064 for an unstratiﬁed 3502 PERMANOVA of structures of monthly networks across decades;

3503 p=0.030 for a similar PERMANOVA stratiﬁed by month). That

3504 is, while network structure did change across the decades, this

3505 change could be masked by the substantial variation in network

3506 structure between months within the same year if the network is not

3507 resolved to ﬁner timescales. Overall, however, these results support

3508 Hypothesis 3 (Table 6).

3509 These trends in network structure for both the yearly and

3510 monthly networks were also visually apparent in the NMDS of

3511 network structure across years. Negative values of the ﬁrst NMDS

3512 axis were associated with several motifs representing tightly knit

3513 groups composed of generalists interacting with other generalists,

3514 while positive values were associated with motifs representing more

3515 loosely connected sets of species involving specialists interacting

3516 with generalists. Moving from negative to positive values of the

3517 second NMDS corresponds to an increase in the relative frequency of

Years Plant turnover Pollinator turnover Table 7: Turnover at Zackenberg 1996 1997 0 000 0 203 (measured using Whittaker’s beta . . diversity index) was higher among 1996 2010 0.169 0.391 insect pollinators than plants. Turnover 1996 2011 0.111 0.432 among pollinators was higher between years in different decades (bolded) 1997 2010 0.169 0.362 than between years in the same decade, 1997 2011 0.111 0.417 while turnover in the plant community was similar across all years. 2010 2011 0.182 0.301 113

3518 ﬁve- and six-species motifs and a decrease in two- and three-species

3519 motifs. From the 1990’s to the 2010’s, the yearly networks increased

3520 along the ﬁrst NMDS axis and decreased along the second NMDS

3521 axis (Fig. S4.5A, S4.5, Supporting Information S4). This suggests that

3522 the yearly networks developed a more ‘open’ structure over time,

3523 with fewer plants sharing all (or almost all) of their pollinators with

3524 other plants and fewer ‘connector’ species connecting small motifs

3525 into larger ones. This trend towards more specialised pollinators

3526 is supported by the lower mean degrees (number of interaction

3527 partners) of pollinators in 2010 and 2011 than in 1996 and 1997 (4.43

3528 and 3.23 partners for the 1990’s and 2010’s, respectively; p=0.007

3529 for an anova of degree by decade). Plants, meanwhile, had similar

3530 numbers of interaction partners in both decades (8.79 and 8.14,

3531 respectively; p=0.573).

3532 In the monthly networks, the amount of change in network

3533 structure varied greatly between months (Fig. S4.5B-D). The June

3534 networks in 1996, 2010, and 2011 had similar structures, but the

3535 1997 network was lower along the ﬁrst NMDS axis and higher along

3536 the second NMDS axis, while the July networks had very similar

3537 structures in each year. In both months, pollinators’ mean numbers

3538 of interaction partners were similar between decades (1.958 and

3539 1.907 partners in June, p=0.862 for an anova of degree by decade;

3540 and 3.857 and 3.270 for July; p=0.216). Plants’ degrees were also

3541 similar between decades in the June and July networks (3.76 and

3542 4.12, p=0.699 for June; 6.87 and 6.26, p=0.524 for July). The August

3543 networks, in contrast, showed greater variation in structure. As

3544 with the yearly networks, they increased along the ﬁrst NMDS axis

3545 and decreased along the second axis. Once again, this corresponds

3546 to the August networks developing a more ‘open’ structure with

3547 fewer species sharing interaction partners, and was associated

3548 with a signiﬁcant decrease in pollinators’ mean degrees (2.786 and

3549 1.856 partners, p=0.006). As with the other months and the yearly

3550 networks, plants’ degrees were not signiﬁcantly different between

3551 decades (4.33 and 5.06 partners, p=0.505).

3552 Did species’ roles change between decades?

3553 The yearly roles of both plants and pollinators varied over time

3554 (F1,126=5.35, p<0.001 and F1,287=12.7, p<0.001, respectively, for 3555 PERMANOVAs of yearly roles against decade; Fig. 17 A). Likewise,

3556 plants’ and pollinators’ monthly roles both varied over time

3557 (F1,230=3.20, p=0.003 and F1,455=8.82, p<0.001, respectively, for

3558 PERMANOVAs of monthly roles against decade). Moreover, the 114

Plants Pollinators Figure 17: The median roles of plants and their insect pollinators differed 0.1 0.3 A B between years. A-B) The median roles 0.0 0.2 of both plants and pollinators in yearly plant-pollinator networks moved towards more negative values along -0.1 1996 2010 0.1

Yearly the first axis of a redundancy analysis -0.2 1997 2011 0.0 (RDA) of species’ roles against year. B) The median roles of pollinators also -0.3 -0.1 moved towards more positive values 0.1 0.3 of the first RDA axis. For both plants C D and pollinators, moving from negative 0.0 0.2 to positive values along the first RDA axis represented a shift towards higher -0.1 0.1 frequencies of positions representing June generalists, while the same transition -0.2 0.0 along the second axis represented -0.3 -0.1 a shift from small to large motifs (Fig. S4.6). C-H) The median roles 0.1 0.3 of plants and pollinators in monthly EF networks showed more variable trends. 0.0 0.2 C-D) In the June networks, the median roles of plants showed similar patterns -0.1 0.1 Second RDA axis July to those in the yearly networks while the median roles of pollinators showed -0.2 0.0 no clear trend over time. E-F) In the -0.3 -0.1 July networks, the median roles of both groups were similar across years, 0.1 G 0.3 H although the roles of plants again showed a more similar trend to that 0.0 0.2 in the yearly networks. G-H) In the August networks, the median roles of -0.1 0.1 plants differed much more between August -0.2 0.0 years in the same decade than across decades while the roles of pollinators -0.3 -0.1 showed a similar trend to that in the -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -2.4 -2.1 -1.8 -1.5 -1.2 yearly networks. First RDA axis

3559 change in monthly roles across decades varied between months

3560 for both plants and pollinators (F2,451=3.75, p<0.001 and F2,226=2.80,

3561 p<0.001, respectively, for the interaction term in a PERMANOVA of

3562 monthly roles against decade, month, and their interaction). This

3563 means that not only are species’ roles changing over time (supporting

3564 Hypothesis 4; Table 6), this change is unevenly distributed across

3565 species that are active in different months.

3566 For both plants and pollinators, moving from negative to

3567 positive along the ﬁrst CAP axis represents a shift from positions

3568 representing specialists who interact with generalists to positions

3569 representing generalists interacting with other generalists, although

3570 the exact motifs involved differed between species groups. The

3571 second axis, meanwhile, represented a shift from positions in small

3572 motifs to positions representing generalists in large motifs. It is 115

3573 noteworthy that, although plants and pollinators were analysed

3574 separately, both groups’ roles diverged along similar axes. As the

3575 roles of plants and pollinators moved towards more negative values

3576 along the ﬁrst axis in 2010 and 2011, both groups participated more

3577 frequently in specialist positions. Combining these results with those

3578 for species’ degrees, described above, it is clear that while plants’

3579 roles shifted towards more specialised positions the addition of

3580 more pollinators to the community has meant that their numbers

3581 of interaction partners have remained stable. Pollinators, on the other

3582 hand, appeared in more specialised positions and interacted with

3583 fewer plants in 2010-2011.

3584 The roles of plants and pollinators in monthly networks,

3585 however, showed different trends. The roles of plants in June

3586 networks followed the same trend as the yearly networks, as did

3587 the July networks (albeit to a lesser extent). Plants’ median roles in

3588 the August networks, meanwhile, showed much greater differences

3589 within each decade than across decades. From the pollinators’

3590 perspective, species’ roles in June varied widely while roles in July

3591 were very similar in all networks. Only in the August networks did

3592 pollinators’ roles follow the same pattern as in the yearly networks.

3593 These differing patterns suggest that, in a network context, plants

3594 and pollinators are not responding to climate change in the same

3595 ways.

3596 Did species’ roles vary with dates of ﬁrst interaction?

3597 As well as varying across decades, plants’ and pollinators’ yearly

3598 roles varied systematically with their dates of ﬁrst interaction

3599 (F1,124=16.1, p=0.004 and F1,285=37.6, p<0.001, respectively, for the date

3600 term in PERMANOVAs of yearly roles against decade, date, and their

3601 interaction). For both groups, the relationship between yearly roles

3602 and date did not vary between decades (F1,124=0.796, p=0.843 and 3603 F1,285=1.38, p=0.233, respectively, for the interaction term in the above

3604 PERMANOVAs). Moreover, date remained a signiﬁcant predictor

3605 even after controlling for changes to network structure between years

3606 (F1,123=14.9, p<0.001 and F1,284=33.8, p<0.001, respectively) in CAPs

3607 of species’ roles against date, conditioned by network structure. Our

3608 results for plants’ and pollinators’ yearly roles therefore support

3609 Hypothesis 5 but not Hypothesis 6 (Table 6).

3610 Support for these two hypotheses from the monthly roles,

3611 however, was mixed. Plants monthly roles did not vary with their

3612 dates of ﬁrst interaction or with the interaction between date and 116

3613 decade (F1,228=7.68, p=0.159 and F1,228=1.02, p=0.382, respectively). 3614 After controlling for changes to network structure, however, date

3615 of ﬁrst interaction did signiﬁcantly predict plants’ monthly roles

3616 (F1,219=7.92, p<0.001). Thus, plants’ roles did vary with their dates

3617 of ﬁrst interaction, although this variation could be obscured by

3618 contrasting changes in network structure. There was, therefore, some

3619 support for Hypothesis 5 but none for Hypothesis 6 from the plants’

3620 monthly roles (Table 6).

3621 Pollinators’ monthly roles, in contrast, did vary with their

3622 dates of ﬁrst interaction (F1,453=17.9, p<0.001), and this relationship

3623 remained signiﬁcant after accounting for network structure

3624 (F1,444=26.8, p<0.001). Unlike pollinators’ yearly roles, this 3625 relationship varied between decades (F1,453=4.78, p=0.004), offering 3626 strong support for Hypotheses 5 and 6. When the roles of pollinators

3627 in monthly networks from the 1990’s and the 2010’s were analysed

3628 in separate PERMANOVAs, date of ﬁrst interaction was a signiﬁcant

3629 predictor of pollinators’ roles in 1996 and 1997 (F1,200=14.0, p=0.011) 3630 but not in 2010 and 2011 (F1,253=9.08, p=0.092). This suggests that 3631 pollinators’ roles may once have been predictable by their dates

3632 of ﬁrst interaction, but that changes to the community have since

3633 undermined this trend.

3634 Was change in dates of ﬁrst interaction related to change in roles?

3635 The magnitudes of changes in plants’ yearly roles were not related to

3636 changes in their dates of ﬁrst interaction for plants with advancing 2 2 3637 or retreating phenologies (R =0.117, p=0.459 and R =0.008, p=0.462,

3638 respectively). For pollinators, in contrast, the relationship between

3639 the change in date of ﬁrst interaction and change in pollinators’

3640 yearly roles differed depending on whether the pollinators’

3641 phenologies advanced or retreated. For pollinators which became

3642 active earlier in 2010 or 2011 than in 1996 or 1997, the amount of

3643 dissimilarity in the pollinators’ yearly roles was not related to the 2 3644 amount of change in date of ﬁrst interaction (R =0.110, p=0.143). For

3645 pollinators which became active later in 2010 or 2011, dissimilarity

3646 in yearly roles increased slightly with increasing differences between 2 3647 dates of ﬁrst interaction (R =0.086, p=0.048). In both cases, the range

3648 of dissimilarities was large for all values of change in date of ﬁrst

3649 interaction (Fig. 18A). Our yearly results, therefore, offer very limited

3650 support for Hypothesis 7 (Table 6).

3651 As with yearly roles, the amount of change in plants’ monthly

3652 roles was not related to the amount of change in plants’ phenologies 117

Yearly networks Monthly networks Figure 18: The relationship between the 1.0 A B magnitude of change in species’ roles between decades and the magnitude 0.8 of change in species’ dates of ﬁrst 0.6 interaction between decades differed between plants and their pollinators. 0.4

Plants A-B) There was no relationship between 0.2 the amount of change in plants’ roles p=0.616 p=0.666 p=0.218 p=0.055 and the amount of change in their dates 0.0 of ﬁrst interactions in either yearly or monthly networks. C-D) Pollinators 1.0 C D with greater changes to their date of ﬁrst interaction also showed greater 0.8 dissimilarities between roles. C) In 0.6 yearly networks, this relationship held for pollinators which became active 0.4 later in the year but not those which

0.2 Pollinators became active earlier. D) The reverse Dissimilarity between roles p=0.152 p=0.047 p=0.007 p=0.589 was true in monthly networks. In all 0.0 panels, change in roles was measured -50 -25 0 25 50 -50 -25 0 25 50 using Bray-Curtis dissimilarity, and Difference between dates of first interaction (days) difference in dates of first interaction is measured in days. The p-values were determined using Mantel tests of Bray-Curtis dissimilarity between 3653 for plants which became active earlier in the year in 2010-2011 roles against absolute difference in 2 3654 (R =0.117, p=0.264), but change in roles was related to change in dates of first interaction. Plants and pollinators were analysed separately, 3655 2010 phenology for plants which became active later in the year in - as were species becoming active earlier 2 3656 2011 (R =0.104, p=0.025, respectively). Pollinators’ monthly roles, and later within each species type. Lines are based on linear regressions of 3657 meanwhile, showed the opposite relationship with the amount of the dissimilarity between roles against 3658 change in dates of first interaction to that in the yearly networks. change in emergence date, sign of 3659 Specifically, for pollinators with retreating phenologies, the amount change, and their interaction, and are indicative only. 3660 of dissimilarity in the pollinator’s monthly roles was not related 2 3661 to the size of the change in its date of first interaction (R =0.046,

3662 p=0.549; Fig. 18B) while for pollinators with advancing phenologies

3663 the amount of dissimilarity increased with the size of the change 2 3664 in date of ﬁrst interaction (R =0.190, p=0.005). Once again, this

3665 constitutes weak support for Hypothesis 7 (Table 6).

3666 Simulating dates of ﬁrst interaction

3667 One other potential explanation for our unexpected results related

3668 to Hypothesis 7 (Table 6) is that our estimates of species’ dates of

3669 ﬁrst interaction may not be entirely accurate. As our networks were

3670 assembled by observing the visitors to focal plants, it is particularly

3671 likely that pollinators’ true dates of ﬁrst interaction may be different

3672 than we observed. To determine how robust our results are to noise

3673 in estimations of species’ dates of ﬁrst interaction, we repeated our

3674 tests for Hypotheses 5-7 (i.e., those which depend upon dates of

3675 ﬁrst interaction; Table 6) using 1000 sets of simulated dates (see S4.3,

3676 Supporting Information S4 for details). In nearly all cases, our results 118

3677 for plants using simulated dates were similar to those obtained using

3678 the observed dates. This indicates that these results are generally

3679 robust to noise in our estimates of date of ﬁrst interaction and means

3680 we can be quite conﬁdent in them. For the pollinators, however,

3681 our results for Hypotheses 5 and 6 using the observed dates were

3682 signiﬁcantly more extreme than those obtained using the simulated

3683 dates. This suggests that our results for the pollinators were more

3684 susceptible to noise in our estimates of dates of ﬁrst interaction, even

3685 though the results for Hypothesis 7 were similar using the observed

3686 and similar datasets.

3687 Discussion

3688 We found evidence in support of most of the hypotheses we tested

3689 in this study, although the degree of support varied between types

3690 of species and network time scales. Testing the hypothesis that there

3691 would be substantial species turnover during 15 years of warming

3692 (Hypothesis 1), we found support from the pollinator community

3693 but not the plants. This may be because the lifecycles of plants and

3694 pollinators occur on different timescales— the plant community

3695 at Zackenberg is perennial while the insects live for only one year.

3696 Plants may also be space-limited such that new species cannot grow

3697 in the study site until a plant present in the previous year dies.

3698 On the surface, it appeared that there was no support for

3699 the idea that dates of ﬁrst interaction would change between

3700 decades (Hypothesis 2), as mean dates of ﬁrst interaction were not

3701 signiﬁcantly different between decades for plants or for pollinators.

3702 However, when examining the patterns at higher resolution we found

3703 that the dates of ﬁrst interaction for plants which persisted in the

3704 community between years did shift earlier between decades, while the

3705 few plants which were ﬁrst observed in 2010-2011 had substantially

3706 later dates of ﬁrst interaction. It therefore appears that dates of

3707 ﬁrst interaction among resident plants are changing in line with

3708 previously reported changes to ﬂowering phenology (Høye et al.,

3709 2013; Schmidt et al., 2016). Neither the dates of ﬁrst interaction for

3710 persistent pollinators nor those of new arrivals differed signiﬁcantly

3711 between decades, indicating that changes in pollinators’ emergence

3712 dates are not reﬂected in their interaction phenologies. While it is

3713 possible that the high turnover in the pollinator community makes it

3714 difﬁcult to obtain a clear signal of changing phenology, these results

3715 contrast with known changes to pollinators’ emergence dates (Høye

3716 et al., 2007; Høye and Forchhammer, 2008b). The lack of change in

3717 pollinators’ dates of ﬁrst interactions also suggests that plants and

3718 their pollinators may indeed be becoming uncoupled as proposed in 119

3719 earlier studies (Høye et al., 2013; Gezon et al., 2016; Hua et al., 2016;

3720 Schmidt et al., 2016).

3721 The possibility that plants’ and pollinators’ phenologies are

3722 diverging is strengthened by our results for Hypothesis 3, where we

3723 found that network structure changed consistently between decades

3724 (Table 6). Speciﬁcally, the network structure shifted towards higher

3725 frequencies of motifs representing plants sharing few pollinators with

3726 each other. This indicates that the network became more open and

3727 loosely connected over time, and that the trend was likely driven by

3728 changes to plants’ roles. Examining the networks for each month

3729 separately, we found that this trend was evident in the August

3730 networks but not those for June or July. Given the relatively constant

3731 size of the plant community over time, these monthly results indicate

3732 that pollinators that are active later in the year were not able to visit

3733 as many plants in 2010-2011 as they were in 1996-1997. This may be

3734 because the dates of ﬁrst interaction for plants have advanced while

3735 those of pollinators have not. Whatever their cause, these changes in

3736 network structure are likely cause for concern, as loosely connected

3737 networks tend to be less robust to species loss (Dunne et al., 2002;

3738 Gilbert, 2009; Kaiser-Bunbury et al., 2010).

3739 We also found support for Hypothesis 4, that species’ roles

3740 would change between decades. In line with the changes in

3741 network structure, we found that the roles of both plants and

3742 pollinators shifted towards higher frequencies of motifs representing

3743 specialists interacting with generalists and lower frequencies of

3744 motifs representing generalists. These changes to species’ roles were

3745 signiﬁcant even after controlling for changes to network structure.

3746 As plants’ mean degrees did not change between decades, these

3747 changes in roles suggest that newly-arrived pollinators in 2010-

3748 2011 tend to interact with relatively few plants and that some of

3749 the persistent pollinators have lost interaction partners such that the

3750 increasing size of the pollinator community did not result in more

3751 interactions per plant. Because all of the positions in plants’ roles

3752 which showed the strongest declines describe generalists interacting

3753 with other generalists, it seems likely that pollinators with many

3754 interaction partners in 1996-1997 lost more interactions in 2010-

3755 2011 than did more specialised pollinators. From the pollinators’

3756 perspective, motifs describing generalists interacting with other

3757 generalists and motifs describing generalists interacting with large

3758 sets of specialists showed large declines. This is consistent with our

3759 picture of specialist pollinators arriving at Zackenberg and persistent

3760 pollinators losing some of their interactions, as is the increase in 120

3761 motifs describing pollinators sharing few plants with many other

3762 pollinators. As specialists are more vulnerable to extinction following

3763 a perturbation to their community (Burkle et al., 2013; Tylianakis,

3764 2013), the changes to species’ roles we have observed suggest plants

3765 and pollinators may be more vulnerable to continued climate change

3766 at Zackenberg. Based on changes to species’ roles in the monthly

3767 networks, it appears that the species most likely to bear the brunt of

3768 future changes are plants that are most active early in the summer

3769 and pollinators that are most active late in the summer.

3770 After establishing that network structure and species’ roles both

3771 changed between decades, we then tested whether species’ roles

3772 were related to their dates of ﬁrst interaction and therefore likely

3773 to be affected by changes in phenology (Hypothesis 5). Plants’ and

3774 pollinators’ roles in the yearly networks were signiﬁcantly associated

3775 with their dates of ﬁrst interactions whether or not we controlled for

3776 network structure, as were pollinators’ roles in monthly networks.

3777 Plants’ roles in monthly networks only varied with their dates of ﬁrst

3778 interaction after we controlled for network structure. We therefore

3779 conclude that species’ dates of ﬁrst interaction are indeed related

3780 to their structural roles in the plant-pollinator networks. For plants,

3781 this relationship did not vary between decades (i.e., there was no

3782 support for Hypothesis 6; Table 6). This suggests that, as plants’

3783 dates of ﬁrst interaction advanced, they merely shifted into roles that

3784 had previously been occupied by other plants. For pollinators, the

3785 relationship between species’ roles and their dates of ﬁrst interaction

3786 did not vary between decades in the yearly networks but seemed to

3787 be stronger in 1996-1997 in the monthly networks. As pollinators’

3788 roles changed between decades but their dates of ﬁrst interaction

3789 did not, it is understandable that the relationship between roles and

3790 phenology is breaking down. Since this breakdown was not detected

3791 in pollinators’ yearly roles, however, it may be quite a subtle effect

3792 (and only detectable with the ﬁner-grained monthly networks).

3793 Lastly, we found limited support for Hypothesis 7 for both

3794 plants and pollinators (Table 6). For plants, there was no relationship

3795 between the change in roles and the change in their dates of ﬁrst

3796 interaction in the yearly networks, and a signiﬁcant relationship in

3797 the monthly networks for plants which became active later in 2010-

3798 2011 in the monthly networks. For pollinators the situation was more

3799 complex. In the yearly networks, there was a signiﬁcant relationship

3800 for pollinators whose dates of ﬁrst interaction retreated between

3801 decades, while in the monthly networks there was a signiﬁcant

3802 relationship for pollinators whose dates of ﬁrst interaction had 121

3803 advanced between decades. From these results, we must conclude

3804 that the amount of change in the dates when species become active

3805 is not a good predictor of the amount of change in their roles.

3806 Other elements of species’ phenologies, such as emergence or

3807 ﬂowering dates, may be better predictors of species’ roles within their

3808 communities, but testing this was beyond the scope of the current

3809 study. In addition, the results of our analyses using simulated dates

3810 of ﬁrst interaction suggest that our results for pollinators may be

3811 more susceptible to noise than those for plants. In future studies at

3812 Zackenberg, this discrepancy could be reduced by complementing

3813 observations of focal plants with analyses of pollinators carrying

3814 pollen are ﬁrst caught, as opposed to pollinators which have emerged

3815 but not yet visited a plant. In the absence of such information, we are

3816 obliged to place more weight upon our results for plants than those

3817 for pollinators.

3818 Putting all of our results together, we have shown that the

3819 plant-pollinator community at Zackenberg has experienced a great

3820 deal of turnover in pollinator species and changes to the timing of

3821 interactions. This is consistent with earlier ﬁndings showing that

3822 plants’ ﬂowering dates have advanced (Høye et al., 2013; Schmidt

3823 et al., 2016) and that species’ ranges are shifting as the climate

3824 warms (Buisson et al., 2008; Flenner and Sahlén, 2008). Along

3825 with these changes, we have shown that the structure of the plant-

3826 pollinator network at this site has changed over time, as have the

3827 roles of species within it. In general, the Zackenberg pollination

3828 network appears to be unravelling, with fewer plants sharing

3829 pollinators and pollinators becoming more specialised. This is

3830 especially true for plants and pollinators active late in the summer.

3831 These species may have difficulty finding enough open flowers to

3832 feed upon (for pollinators) or obtaining sufﬁcient pollination service

3833 (for plants). As feeding and reproduction are both essential to the

3834 maintenance of a population, some of these species may be lost if

3835 these trends continue for long enough. Moreover, both plants and

3836 pollinators are tending to share fewer interaction partners over

3837 time, leading to less redundancy in pollination services and food

3838 resources, respectively. Redundant sets of interaction partners are

3839 believed to provide an ‘insurance policy’ that can sustain species if

3840 their most important interactions are disrupted (Yachi and Loreau,

3841 1999; Memmott et al., 2004; Potts et al., 2010). With severe weather

3842 and other perturbations becoming increasingly likely as climate

3843 change continues (Hassol, 2004; Adger et al., 2007; Steiner et al.,

3844 2015; Benestad et al., 2016), the plant-pollinator communities at high-

3845 arctic sites like Zackenberg may therefore be increasingly vulnerable 122

3846 if species’ roles continue to change in the same ways. Moreover,

3847 as arctic communities have been warming faster than temperate

3848 sites (IPCC, 2013; Settele et al., 2014), they can be seen as the “canary

3849 in the coal mine”, predicting future changes at lower latitudes.

3850 Acknowledgements

3851 The staff at Zackenberg Research Station provided access and assisted

3852 with logistics in Greenland. Heidi Elberling and Jesper B. Mosbacher

3853 also collected data. Insect identiﬁcations were done by Elisabeth Stur

3854 and Torbjørn Ekrem (Chironomidae), Verner Michelsen (all other

3855 ﬂies), Ole Karsholt (Lepidoptera), Gergely Várkonyi (wasps), Paul

3856 H. Williams (bumble bees), and The Barcode of Life Data System

3857 (BOLD), Canada, sequenced all species to conﬁrm identiﬁcations.

3858 We are grateful to the Natural Sciences and Engineering Research

3859 Council of Canada (ARC), INTERACT (projects QUANTIC) under

3860 the European Community’s Seventh Framework Programme (TR),

3861 the Academy of Finland (grant numbers 1276909 and 1285803 to

3862 TR), the Ella and Georg Ehrnrooth Foundation (TR), the Carlsberg

3863 Foundation (CR), the Danish Research Council (JMO), Marsden Fund

3864 grant UOC-1101, administered by the Royal Society of New Zealand

3865 (DBS), and a Rutherford Discovery Fellowship (DBS) for ﬁnancial

3866 support.

3867 Supporting information

3868 S4.1: Simulated dates of ﬁrst interaction

3869 S4.2: Tentatively-dated observations: methods

3870 S4.3: Tentatively-dated observations: results

3871 S4.4: Supplemental ﬁgures 123

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4080 America, 96:1463–1468. 4081 Chapter 5: Concomitant predation on parasites is highly

4082 variable but constrains the ways in which parasites

4083 contribute to food-web structure.

1 1 4084 Alyssa R. Cirtwill & Daniel B. Stouffer

1 4085 Centre for Integrative Ecology

4086 School of Biological Sciences

4087 University of Canterbury

4088 Private Bag 4800

4089 Christchurch 8140, New Zealand

4090 Published in Journal of Animal Ecology (2015).

4091 DOI: 10.1111/1365-2656.12323.

4092 This chapter has been modiﬁed from its original version. It has been

4093 formatted to match this thesis. 130

4094 Summary

4095 1. Previous analyses of empirical food webs (the networks of who

4096 eats whom in a community) have revealed that parasites exert a

4097 strong inﬂuence over observed food-web structure and alter many

4098 network properties such as connectance and degree distributions.

4099 It remains unclear, however, whether these community-level effects

4100 are fully explained by differences in the ways that parasites and

4101 free-living species interact within a food-web.

4102 2. To rigorously quantify the interrelationship between food-web

4103 structure, the types of species in a web and the distinct types of

4104 feeding links between them, we introduce a new methodology

4105 to quantify the structural roles of both species and feeding links.

4106 Roles are quantiﬁed based on the frequencies with which a species

4107 (or link) appears in different food-web motifs– the building blocks

4108 of networks.

4109 3. We hypothesised that different types of species (e.g., top predators,

4110 basal resources, parasites) and different types of links between

4111 species (e.g., classic predation, parasitism, concomitant predation

4112 on parasites along with their hosts) will show characteristic

4113 differences in their food-web roles.

4114 4. We found that parasites do indeed have unique structural roles

4115 in food webs. Moreover, we demonstrate that different types

4116 of feeding links (e.g., parasitism, predation, or concomitant

4117 predation) are distributed differently in a food-web context. More

4118 than any other interaction type, concomitant predation appears to

4119 constrain the roles of parasites. In contrast, concomitant predation

4120 links themselves have more variable roles than any other type of

4121 interaction.

4122 5. Together, our results provide a novel perspective on how both

4123 species and feeding link composition shapes the structure of an

4124 ecological community, and vice-versa.

4125 Keywords

4126 network motifs, species roles, interaction roles, role dispersion, role

4127 diversity 131

4128 Introduction

4129 Food webs– the networks of who eats whom in an ecosystem –

4130 provide ecologists with tools to analyse the structure of ecological

4131 communities (Cohen, 1978; Pascual and Dunne, 2007) and compare

4132 them across space and time (Thompson and Townsend, 2005b; Shurin

4133 et al., 2006; Olesen et al., 2008). Food webs also connect biodiversity

4134 to ecosystem functions by integrating patterns and processes

4135 from individual to community scales (Thompson et al., 2012). In

4136 particular, the overall structure of food webs has been directly tied

4137 to ecosystems’ responses to environmental change (Thompson and

4138 Townsend, 2010, 2005a; Tylianakis et al., 2008) and robustness to

4139 species loss (Dunne et al., 2002b, 2004; Estrada, 2007; Srinivasan et al.,

4140 2007; Gilbert, 2009; Rezende et al., 2009).

4141 The vast majority of food web studies, however, have focused

4142 on networks of predator-prey interactions between free-living

4143 species (Combes, 1996; Huxham et al., 1996; Marcogliese and

4144 Cone, 1997; Lafferty et al., 2006), prompting calls for a broader

4145 and more comprehensive food-web theory (Marcogliese and Cone,

4146 1997; Lafferty et al., 2006; Fontaine et al., 2011; Kéﬁ et al., 2012),

4147 especially where parasites are concerned (Marcogliese and Cone,

4148 1997; Lafferty et al., 2006; Dobson et al., 2008; Lafferty et al., 2008).

4149 Although typically small and difﬁcult to observe, parasites can exert

4150 a strong inﬂuence on their communities (e.g., Huxham, Beaney&

4151 Raffaelli, 1996). They participate in a large proportion of feeding

4152 links (henceforth “links”) (Lafferty et al., 2006; Dunne et al., 2013b)

4153 and exhibit comparable diversity and biomass to free-living species

4154 (Dobson et al., 2008; Kuris et al., 2008). Moreover, parasites’ complex

4155 life histories, which commonly involve different sets of hosts for

4156 different life stages, render them vulnerable to secondary extinctions

4157 and therefore decrease network robustness (Lafferty and Kuris, 2009).

4158 Parasites are also of interest because of the many ways in which

4159 they could potentially inﬂuence food-web structure– the organisation

4160 of links between species (Combes, 1996; Thompson et al., 2005;

4161 Lafferty et al., 2006; Dunne et al., 2013b; Thieltges et al., 2013, Fig. 19).

4162 Like generalist predators, many parasites have multiple potential

4163 hosts which may each support different life stages (Marcogliese and

4164 Cone, 1997; Lafferty et al., 2006; Rudolf and Lafferty, 2011). Parasites

4165 may also have one or more free-living stages which can be important

4166 prey for free-living predators (Combes, 1996; Kuris et al., 2008).

4167 Further, parasites vary in the ways in which they are transmitted

4168 between hosts: they can actively infect new hosts, be ingested as eggs 132

A Free - living B + Parasite C + Concomitant web webs webs Figure 19: Parasites can be incorporated into food webs in several different ways, each of which increases the complexity of the web. (A) Food webs are typically composed of free-living species (circles) and the predator-prey links between them (arrows indicate the direction of energy ﬂow). (B) In “+ parasite” webs, parasites (squares) 4169 or cysts, or be ingested as concomitant prey along with the current parasitize free-living hosts (dotted line). 4170 host (Kuris et al., 2008; Thieltges et al., 2013). They may parasitize one host for their entire life cycle (white square), different hosts (grey square), or be target prey 4171 Because of their plethora of life-history strategies, small body to free-living predators (black square, 4172 sizes, and unusual mode of life, it would appear that the ecological hatched line). Where two parasites infect the same host (black and white 4173 roles of parasites are completely distinct from those of more square), one may kill the other, usually 4174 “traditional” predators and prey (Marcogliese and Cone, 1997; Rudolf consuming it (thick black line). (C) “+ 4175 and Lafferty, 2011). Indeed, at least one study has concluded that concomitant” webs also include links between parasites and the predators of 4176 parasites tend to have broader and less-contiguous prey ranges than their hosts (curved lines). In these links, 4177 free-living species (Dunne et al., 2013b). Despite these important the parasite may simply be digested (white square), or it may infect the 4178 differences, however, that same study has suggested that parasites predator and parasitize it as well (grey 4179 and free-living species can appear to have similar effects on food- square). In some cases, a parasite (black 4180 web structure. For example, when parasites are added to a food square) may be consumed by the same predator both as concomitant prey and 4181 web without including concomitant predation, species richness as target prey. 4182 and number of links necessarily increase, and connectance, link

4183 density, and degree distributions are altered (Dunne et al., 2013b).

4184 Nevertheless, these structural changes are similar to the trends that

4185 emerge when comparing webs with different numbers of free-living

4186 species (Dunne et al., 2013b) and follow known patterns of scaling

4187 with species richness (Riede et al., 2010).

4188 In contrast, the addition of concomitant predation links

4189 resulted in greater structural changes. First, by adding more links

4190 but no additional species, link density and connectance must

4191 necessarily increase (Dunne et al., 2013b). Importantly, this increase

4192 in connectance was observed even when the connectance of webs

4193 excluding concomitant predation was adjusted to account for the

4194 exclusion of this class of links and did not ﬁt the scaling pattern

4195 observed in free-living webs (Dunne et al., 2013b). The higher

4196 connectance of food webs including concomitant links may in turn

4197 drive other trends in food-web structure, especially in properties

4198 such as nestedness which have been observed to increase when

4199 parasites are added to food webs (Lafferty et al., 2006) and are

4200 known to positively correlate with connectance (Dunne et al., 2002a).

4201 In addition to changing connectance, the addition of concomitant

4202 predation altered the frequencies with which different conﬁgurations

4203 of interactions among species occurred. In particular, the overlay of

4204 host-parasite and predator-prey interactions changed the frequencies 133

4205 of two-way feeding interactions (A eats B and B eats A), reﬂecting an

4206 effect of the intimacy between host and parasite on network structure

4207 (Dunne et al., 2013b).

4208 This increase in connectance and the trickle-down effects on

4209 food-web structure attributable to higher connectance suggest

4210 that parasites may have their most unique effects on food-web

4211 structure as concomitant prey (Dunne et al., 2013b). This notion was

4212 most strongly supported by an analysis of three-species food-web

4213 motifs from the same study. A food-web motif represents a unique

4214 interaction pattern such as three-species food chains, apparent

4215 competition, or trophic loops (Milo et al., 2002; Kashtan et al., 2004;

4216 Stouffer et al., 2007, 2012), and the frequencies with which different

4217 motifs occur can be used to characterise ﬁne-scale food-web structure

4218 (Stouffer et al., 2007). These frequencies were similar for webs

4219 composed solely of free-living species and webs including parasites

4220 but not concomitant links (Dunne et al., 2013b). This implies that the

4221 roles of free-living species serving as hosts are structurally similar

4222 to those of free-living species serving as prey, and that parasites

4223 as consumers have similar roles to free-living consumers (Dunne

4224 et al., 2013b). When concomitant links were added, the frequencies of

4225 motifs including at least one two-way link changed. This appeared to

4226 be driven by the increase in intraguild predation (predation between

4227 two species that share a common prey/host) as parasites are eaten

4228 along with their host (Dunne et al., 2013b), suggesting that parasites

4229 have different structural effects as resources than free-living species.

4230 Comparisons of whole-network structure such as these,

4231 however, can mask the mechanisms behind the trends they uncover

4232 (Stouffer, 2010) since knowledge of a network-level pattern does

4233 not unambiguously determine how different species contribute to

4234 that pattern (Saavedra et al., 2011; Stouffer et al., 2012). For example,

4235 network-level measures such as connectance are a useful ﬁrst step to

4236 predict predicting overall community stability (Dunne et al., 2002b);

4237 but connectance alone is a poor predictor of variation in species’

4238 degrees (Dunne et al., 2002a) or which species is most critical to

4239 maintain that stability (Dunne et al., 2002b; Olesen et al., 2011). One

4240 way to overcome this drawback is to examine network structure

4241 directly from the perspective of the building blocks of networks:

4242 species and the links between them (Stouffer, 2010; Baker et al., 2015).

4243 Here we use an extension of food-web motifs to quantify species’

4244 “structural roles”– which provide holistic summaries of how they

4245 interconnect with the rest of the web (Stouffer et al., 2012, Fig. S5.1) 134

4246 –and hence to compare the different ways in which parasites and

4247 free-living species are thus embedded in their communities. This

4248 deﬁnition of role is rigorously deﬁned by the relative frequencies

4249 with which species appear across different motifs like apparent

4250 competition, omnivory, or trophic loops. As such, our deﬁnition

4251 of roles incorporates information on a species’ predators and prey,

4252 as well as how that species is indirectly linked to more distant

4253 species. Roles can therefore also be conceptualised as summaries

4254 of the “shape” of species’ biotic niches within a food web. As

4255 a consequence, we can estimate the degree to which species’

4256 contributions to network structure (and hence to energy ﬂows and

4257 other ecosystem functions) are redundant by identifying species with

4258 similar roles. Such species can likely compensate for each other in

4259 the face of disturbances, increasing the network’s robustness (Naeem,

4260 1998; Rosenfeld, 2002).

4261 To understand how roles can vary between species, consider

4262 three hypothetical top predators: one which is a strict specialist that

4263 acts as the top of only one food chain; a second, generalist predator

4264 that acts as the top of several food chains; and a third predator which

4265 forms the top of several food chains and engages in omnivory. The

4266 roles of the ﬁrst two predators are very similar– despite having

4267 different numbers of prey species, both predators only ever appear

4268 in one position in the food web: at the top of a food chain. The third

4269 predator, which is involved in motifs describing omnivory, as well

4270 as three-species food-chains, has a more complex role. One could

4271 therefore argue that the ﬁrst two species make similar structural

4272 contributions to the network while the third predator has a distinct

4273 effect. Moreover, these species likely make different contributions

4274 to the stability and functioning of the community (Stouffer, 2010;

4275 Stouffer et al., 2012).

4276 This argument rests upon the fact that species’ structural roles

4277 describe the ways a species directly and indirectly inﬂuences biomass

4278 and energy ﬂows through a food web. Therefore, the hypothesis

4279 that parasites and free-living species interact with other species in

4280 fundamentally different ways can be directly tested by comparing

4281 their structural roles. Here we focus on the comparison of the roles

4282 of parasites to those of free-living species interacting only with

4283 other free-living species. When concomitant predation is excluded,

4284 parasites have many prey but few consumers and are usually

4285 considered to be the tops of their food chains (Thompson et al.,

4286 2005). We therefore expect the structural roles of parasites excluding

4287 concomitant predation to be similar to the roles of free-living 135

4288 species with no free-living predators (hereafter “top predators”) or

4289 to intermediate consumers with few free-living predators. When

4290 concomitant predation is taken into account, however, parasites have

4291 both prey and many consumers. If parasites have similarly-shaped

4292 niches to those of free-living species, we would then expect the

4293 structural roles of parasites including concomitant predation to be

4294 similar to those of free-living intermediate consumers.

4295 In a similar way, we can examine food webs from the perspective

4296 of the links within them. Just as a species’ structural role summarises

4297 the ways in which it is connected to other species, a link’s structural

4298 role summarises the ways in which an energy transfer between

4299 two species is embedded in the larger food web (Fig. S5.3). The

4300 roles of links, like those of species, vary depending on how many

4301 connections a link has to the rest of the web, and the nature of

4302 species involved in those connections. A link between an unpalatable

4303 basal resource, its specialist herbivore, and a specialist consumer

4304 of that herbivore, for example, would have a role summarised by

4305 a single dimension describing its single position. In contrast, a

4306 link between two generalist intermediate consumers would have a

4307 role with many dimensions corresponding to the many disparate

4308 positions that link appears in across food-web motifs. Note that, as

4309 with species, roles describe the relative frequencies with which a

4310 link occupies different positions rather than the raw count. Thus

4311 a link which appeared in the same position 10 times would have

4312 the same role as a link which only appeared in that position once,

4313 and both would have very different roles to a link which appeared

4314 once in each of 10 different positions. By comparing link roles in this

4315 way, we can determine whether feeding links involving parasites are

4316 organised differently within a food web regardless of whether the

4317 roles of parasites themselves are different. This alternative view is

4318 hinted at by the observation that food-web structure is altered more

4319 by the inclusion of concomitant links than by the simpler addition of

4320 parasites without concomitant predation (Dunne et al., 2013b).

4321 It is more difﬁcult to generate intuitive hypotheses about

4322 differences between the roles of types of links because of a dearth

4323 of previous studies that have directly characterised their roles in food

4324 webs. Nevertheless, predation, parasitism, and concomitant predation

4325 all involve different types of species and have different functional

4326 consequences for the two interacting species. We therefore expect

4327 signiﬁcant differences in the structural roles of these links. Since

4328 adding concomitant predation links changed the motif structures

4329 of food webs (Dunne et al., 2013b), we expect that these links 136

eb A Figure 20: Visualising the distribution of species roles within two hypothetical food webs. In panels (A) and (B), the roles of two trophic groups (e.g., top predators and intermediate consumers) are indicated by circles and triangles, respectively. Because our deﬁnition of roles is multidimensional, they are most easily represented using a correspondence analysis in which roles are compared along major

eodaxisSecond of variation axes of variation rather than axes Trophic group 1 based on particular motifs. Axis one Trophic group 2 might represent, for example, the tendency for roles to contain motifs involving two-way interactions, irst axis of variation while axis two might represent the tendency for roles to contain motifs representing trophic loops. Under 4330 will have different roles from those of links between free-living this representation, dispersion and 4331 species. Conversely, because adding links describing parasitism diversity provide complimentary measures of the distribution of roles 4332 and predation among parasites to food webs does not change motif within communities. Dispersion 4333 structure of food webs, we expect that these links will have similar measures the spread of roles about the median role for a trophic group 4334 roles to those of links between free-living species. (indicated by shapes with thick outlines), while diversity measures 4335 As well as comparing roles of different types of species and links the number of statistically identifiable role “phenotypes” (indicated by dashed 4336 across communities, we aimed to study the variability of different ovals). In hypothetical web A, the roles 4337 roles within communities. Measuring this variability provides of the two types of species have similar levels of dispersion and diversity 4338 a more rigorous analysis of the potential overlap or redundancy despite greater numbers of species in 4339 among the structural roles of species within a type. Specifically, we trophic group 2 being present in the 4340 quantified the within-community dispersion and diversity of roles community. In hypothetical web B, the roles of species in trophic group 1 4341 for each group of species and links. The dispersion of a type of roles are more widely-dispersed and more 4342 is its within-group variance– that is, how similar the role of each diverse than those of trophic group 2.

4343 group of species or links is to the median role for that group in its

4344 community (see Materials and methods). A high role dispersion for a

4345 group of species indicates that each species’ role has limited overlap

4346 with those of other species in the same group. Role diversity, in

4347 contrast, quantiﬁes the observed number of statistically unique role

4348 “phenotypes”– characteristic multidimensional shapes into which

4349 roles can be grouped –occupied by species or links from a particular

4350 group in a community (see Materials and methods). Role diversity

4351 therefore offers a perspective on how different types of species or

4352 links contribute to the overall role diversity of a food web. A high

4353 diversity of roles for a group of species means that these species

4354 occupy a wider range of the potential roles available to all species in

4355 all food webs. Importantly, these two measures are complimentary,

4356 such that a group of species whose roles have high dispersion might

4357 exhibit high or low role diversity (Fig. 20). 137

4358 Once the distributions of species and link roles have been

4359 quantiﬁed within communities, we are able to compare these

4360 distributions across communities. Similar patterns of distribution

4361 across communities can point to general rules in food-web structure

4362 such as the scaling of many food-web properties with species

4363 richness and connectance (Havens, 1992; Dunne et al., 2002a; Riede

4364 et al., 2010). Here we are particularly interested in whether role

4365 dispersion and diversity exhibit scaling relationships with species

4366 richness (or link richness, in the case of link roles). If, for example,

4367 dispersion and diversity increase with species richness, this would

4368 suggest that species roles are increasingly variable in larger webs

4369 and that adding more species does not create redundancy within the

4370 food-web. Such a situation would recall May’s “devious strategies”

4371 by which communities persist, with none acting in the exact same

4372 way as the next (May, 2001). It is also possible that role dispersion

4373 and diversity do not increase with species or link richness; such

4374 saturation of role distributions would indicate high redundancy and

4375 create a community that is robust to perturbations (Petchey et al.,

4376 2008).

4377 Materials and methods

4378 Empirical Data

4379 The food webs studied here describe seven temperate coastal

4380 communities (Huxham et al., 1996; Hechinger et al., 2011b; Mouritsen

4381 et al., 2011; Thieltges et al., 2011a,b, Tables S1-S3) that included

4382 both free-living species and parasites (see S1 for the full deﬁnition

4383 of ‘parasite’). Since we were interested in particular species rather

4384 than whole-network characteristics, we did not aggregate species

4385 with the same predator and prey sets into trophic species as is

4386 common elsewhere (Martinez, 1991; Vermaat et al., 2009; Dunne

4387 et al., 2013b). The links in these food webs describe several different

4388 classes of interaction: predation among free-living species, parasitism

4389 of free-living species, predation among parasites, and target and

4390 concomitant consumption of parasites (Hechinger et al., 2011b).

4391 Using these different link types, we constructed three food

4392 webs describing different interactions among the species in each

4393 community (Fig. 19). The ﬁrst, “free-living” web contains only

4394 free-living species and the predator-prey links between them. The

4395 second, “+ parasite” web includes every species and link in the

4396 free-living web as well as parasites, parasitism of free-living species,

4397 intraguild predation between parasites, and predation by free-living

4398 species upon parasites in which the parasite is target prey (e.g., 138

4399 when a ﬁsh consumes trematode cercariae). The third, and most

4400 complex, “+ concomitant” web contained all of the species and links

4401 in both of the previous webs as well as concomitant links where

4402 parasites are consumed together with their hosts. For each of the

4403 seven communities we therefore have a free-living, parasite, and

4404 concomitant web (giving a total of 21 food webs).

4405 Quantifying Species Roles

4406 We then analysed the role of each species within its community by

4407 quantifying the ways in which the focal species participates the set

4408 of 13 unique three-species building blocks that make up a food web

4409 – network motifs (Milo et al., 2002; Kashtan et al., 2004; Stouffer et al.,

4410 2007, 2012). Of the three-species motifs, ﬁve contain only one-way

4411 interactions (A eats B, B does not eat A) and the remaining eight

4412 contain at least one two-way interaction (A eats B and B eats A). The

4413 two types of motifs tend to occur with different frequencies (Stouffer

4414 et al., 2007) and, by deﬁnition, have different effects on energy ﬂow

4415 throughout a food web. The frequency with which a species appears

4416 in each motif summarises the organisation of its feeding links, as

4417 both predator and prey. Mathematically, the number of times a focal

4418 species i in community s (e.g., the Ythan estuary) in web type w (e.g.,

4419 the “+ parasite” web) appears in each of the 30 unique positions −→w 4420 across the 13 three-species motifs gives a multidimensional vector fsi 4421 that robustly quantiﬁes the species’ role within the food web (Stouffer

4422 et al., 2012, S5.2, Fig. S5.1; Supporting Information S5).

4423 Given a dataset composed of roles for all species in all webs for

4424 each community, we ﬁrst compared the roles for species in different

4425 trophic groups. We divided free-living species into top predators (T),

4426 basal resources (B), and intermediate consumers (I) based on their

4427 interactions with other free-living species (see S1 for more details).

4428 Since food webs have traditionally been composed only of free-living

4429 species and the roles of species have been understood in this context,

4430 we used the roles of free-living species in the free-living webs as a

4431 baseline against which to compare the roles of parasites with (Pc) and

4432 without (P) concomitant links. Although using the free-living species

4433 web as a baseline means comparing the roles of parasites in a larger

4434 web to free-living species in a smaller web, network-level results

4435 suggest that motif frequencies do not change systematically after the

4436 addition of more species, including parasites (Bascompte and Melián,

4437 2005; Stouffer et al., 2007; Dunne et al., 2013b). We therefore do not

4438 expect network size to greatly inﬂuence parasites’ roles compared

4439 to those of free-living species. We included the roles of the same 139

4440 parasite species in both the “+ parasite” and “+ concomitant” webs

4441 in order to determine whether parasites have different roles when

4442 concomitant links are excluded or included. All ﬁve groups of

4443 species were represented in each of the seven webs, giving a sample

4444 size of n = 35 for analysis of species roles.

4445 Quantifying Link Roles

4446 Following an analogous methodology to that used in the

4447 determination of species roles, each link k in web type w at −→w 4448 community s was assigned a role vector fsk based on the frequency 4449 with which it occurred in each of the 24 unique “link positions”

4450 that make up the 13 three-species motifs (S5.2, Fig. S5.2; Supporting

4451 Information S5). As with the roles of species, we used links between

4452 free-living species (F→F) in the free-living webs to set the de facto

4453 baseline since these are the links current food-web theory is based

4454 upon. For consistency with the analysis of species roles, we included

4455 the roles of all other types of links from the least complex web in

4456 which they appeared. That is, we used the roles of parasitism (F→P), t 4457 intraguild predation (P→P), and target predation on parasites (P−→F)

4458 as calculated in the “+ parasite” webs and the roles of concomitant c c 4459 predation (P−→F) links from the “+ concomitant” webs. P−→F links

4460 include those in which the ingested parasite can infect its predator

4461 (i.e., trophic transmission) and those in which the parasite is digested

4462 and killed. Note that predation among parasites and target predation

4463 on parasites were not recorded in the Ythan estuary web. This means

4464 that while analyses of species roles had a sample size of n = 35

4465 (seven sites, 5 types of species roles), analyses of link roles had a

4466 sample size of only n = 33 (seven sites for most link types, six sites

4467 for predation among parasites and target predation on parasites).

4468 Quantifying differences in the Distribution of Roles

4469 Median Roles

4470 We ﬁrst visualised the median roles of parasites with and

4471 without concomitant predation alongside of those of the three free-

4472 living trophic groups. To do this, we performed a correspondence

4473 analysis using the function cca from the package vegan (Oksanen

4474 et al., 2014) in R (R Core Team, 2014). Using correspondence analysis

4475 of species roles also allowed us to examine the axes along which

4476 most variation between roles occurred. We used the same procedure 140

4477 to visualise the median roles of different types of links, and the axes

4478 along which link roles varied.

4479 To compare median roles, we used a non-parametric

4480 permutational multivariate analysis of variance (PERMANOVA)

4481 (Anderson, 2001) across the full set of species (or link) roles. Recall

4482 that as we have deﬁned them here, roles are multidimensional

4483 descriptions; the spatial median of the roles in a given group thus

4484 describes the “typical” role for that group. For species, we compared

4485 median roles across trophic groups (T, I, B, P, and Pc). We conducted

4486 a similar PERMANOVA analysis comparing median roles across t t 4487 link types (F→F, F→P, P→P, P−→F, and P−→F). All comparisons of

4488 median roles were conducted using the adonis function from the

4489 vegan package (Oksanen et al., 2014) in R (R Core Team, 2014).

4490 Like the traditional ANOVA, the PERMANOVA ﬁrst calculates

4491 the distance between all pairs of observations and then compares

4492 among-group distances to within-group distances followinga

4493 pseudo-F statistic (Anderson, 2001). Importantly, a PERMANOVA

4494 does not assume that the data follow any particular distribution.

4495 Instead, a p-value for the test statistic is calculated by directly

4496 permuting the raw data (Anderson, 2001). Since we were most

4497 interested in differences between types of species (or links) and

4498 not between different communities, we stratiﬁed permutations

4499 by community. That is, roles were shufﬂed randomly within a

4500 community but the complete set of roles for that community was

4501 not changed by the permutation process. In this way, we compared

4502 observed distances only to those that could be randomly generated

4503 from the same community, controlling for possible effects of changes

4504 in species richness or other properties between communities.

4505 The distance metric used in a PERMANOVA helps to deﬁne

4506 the null hypothesis being tested (Anderson, 2006). We used Bray-

4507 Curtis dissimilarity between roles as our distance metric because

4508 it has proven useful for other ecological questions (Legendre and

4509 Legendre, 2012) and also has speciﬁc properties that make it well

4510 suited for our purposes. In particular, Bray-Curtis dissimilarity

4511 measures differences between the roles based only on positions in

4512 which at least one of the species (or links) appears and hence is not

4513 affected by “double zeros” in the data (Legendre and Legendre, 2012).

4514 This means that species (or links) that appear in few positions are

4515 not considered more similar to each other due to the large number of

4516 shared zero frequencies. In addition, we wished to avoid a situation

4517 in which two species involved in different numbers of links would 141

4518 be considered to have different roles even if they occurred with the

4519 same frequencies across all motif positions. We therefore calculated

4520 dissimilarities based on relative positional frequencies rather than

4521 absolute frequencies (that is, the number of times a species or

4522 link appeared in each position divided by the number of times it

4523 appeared in any position).

4524 Role Dispersion

4525 In addition to comparing median roles across communities, we

4526 explored the dispersion of roles about these median roles using the

4527 function betadisper from the package vegan (Oksanen et al., 2014)

4528 in R (R Core Team, 2014). As when comparing median roles, we

4529 used Bray-Curtis dissimilarity to measure the dispersion of roles

4530 within a community around their group median. We were then

4531 able to compare the scaling relationships between role dispersion

4532 and species or link richness across communities. We hypothesised

4533 that role dispersion of a given type of species or link could increase

4534 with the number of those species or links observed at an individual

4535 community, indicating that each species and link ﬁlls a novel

4536 structural role. To determine the relationships between the number of

4537 species (or links) of a type at a community and the mean dispersion

4538 of roles for that species type at that community, we used a linear

4539 regression, ﬁt using the function lm in R (R Core Team, 2014).

4540 Role Diversity

4541 We also measured the diversity of unique roles within a

4542 community for each group of species or links. To do this, we used

4543 a heuristic optimisation method to identify clusters of species (or

4544 links) that appear in the same motif positions more often than one

4545 would expect by chance (Guimerà et al., 2007; Sales-Pardo et al., 2007;

4546 Stouffer et al., 2012, S5.3, Supporting Information S5). Each cluster was

4547 interpreted as a unique role phenotype.

4548 As with dispersion, we then compared the scaling relationships

4549 between role diversity and species or link richness across

4550 communities. We expected diversity to increase with species or link

4551 richness, implying that each species or link adds to the niche space

4552 of its food web. To quantify this possible relationship between the

4553 number of species or links and the number of roles in a community,

4554 we used a generalised linear model with a Poisson distribution and 142

Figure 21: The median roles of species A and links vary predictably by type. (A) 2 Within the seven different communities, the different types of species have different median roles, shown here with respect to their location along their 0 first two correspondence analysis axes. The first correspondence analysis axis for species roles described 64.9% of their total variance, and the second axis -2 T described 13.0%. When concomitant I links are excluded, parasites (P) tend B to have roles similar to those of top -4 predators (T). When concomitant links P are added, however, parasites’ (Pc) P roles are much more similar to those c of basal resources (B). Intermediate -6 species’ (I) roles were between those of B and T species. (B) Different types of -1 0 1 2 links also have different median roles, again shown with respect to their first B → two correspondence analysis axes. The 6 F F first correspondence analysis axis for F→P links described 60.7% of their total → variance, and the second axis described P P 15.2%. While there is some overlap 4 P→t F between roles, concomitant predation Second axis of variation →c links and predation between parasites P F mainly varied along the first axis while 2 predation between free-living species, parasitism, and target predation on parasites mainly varied along the second axis. 0

-2 -2 -1 0 1 2 First axis of variation

4555 logarithm link function, ﬁt using the function glm in R (R Core Team,

4556 2014).

4557 Results

4558 Median Roles

4559 We found that both different trophic groups and different link types

4560 have different median roles (see S4 for more details). Both Pc roles c 4561 and the roles of P−→F links were separated from the roles of other

4562 types of species or links, respectively, along the ﬁrst correspondence

4563 analysis axis (Fig. 21). This axis corresponded to a division between

4564 motifs that include only one-way interactions and those that include

4565 at least one two-way interaction (Fig. S5.3), with Pc roles and the 143

B I T P P c Figure 22: The inﬂuence of the number 0.4 of species in a trophic group on the dispersion and diversity of species roles 0.2 differed between free-living species and bc a c ab

Dispersion 0.0 parasites. (Top row) Role dispersion 20 increased with number of species for parasites without concomitant links 10 (p = 0.036). The dispersion of the roles of all other species types did not vary Diversity 0 0 50 100 0 50 100 0 50 100 0 50 100 0 50 100 with species richness (dashed lines). Number of species in trophic group The roles of intermediate consumers were most dispersed, followed by those of parasites with concomitant links, c basal resources, and top predators. 4566 roles of P−→F links being found more often in motifs including at Letters in the lower right of each 4567 least one two-way interaction. panel indicate groups based on mean dispersions of each type of role (Tukey’s HSD test with critical value = 4.11, α=0.05, and df=29). Roles with the 4568 Dispersion & Diversity of Species Roles same letter do not have significantly different mean dispersions. (Bottom 4569 Comparing the underlying variation of species roles, we found that row) Role diversity increased with 4570 dispersion was not affected by species richness for B, I, T, and Pc increasing species richness for all types of species (p = 0.003), and the 4571 = = 22 roles (t28 1.563, p 0.129; Fig. ; for details of the regression estimated rate of increase was the 4572 see S5.5, Supporting Information S5). Pc roles were significantly more same for all species types. For any given species richness, parasites with 4573 dispersed than T roles but had similar dispersion to other types of concomitant links had more diverse 4574 roles (Tukey’s HSD test with critical value = 4.11, α=0.05, and df=29). roles than any other type of species, 4575 Unlike all other types of species roles, dispersion of P roles increased followed by intermediate consumers, parasites without concomitant links, 4576 = = 5 5 with species richness (t29 2.195, p 0.036; S . , Supporting basal resources, and top predators 4577 Information S5). (Tukey’s HSD test with critical value = 4.14, α=0.05, df=26). In both rows, shaded regions represent 95% 4578 The diversity of distinct roles in a trophic group increased confidence regions for the predicted 4579 with the number of species in that group, but the strength of this dispersion or diversity after the removal of statistical outliers (indicated by 4580 22 relationship did not vary across groups (Fig. ). For any given ‘+’s) where applicable. Refer to S5.5, 4581 number of species, Pc roles were significantly more diverse than Supporting Information S5 for details on the regressions. 4582 those of other types of species (z = 5.632, p < 0.001; S5.5, Supporting

4583 Information S5). P roles were signiﬁcantly more diverse than T roles

4584 but their diversity overlapped with those of I and B roles (Tukey’s

4585 HSD with critical value 4.14, α=0.05, and df=26).

4586 Dispersion & Diversity of Link Roles

4587 Dispersion of the roles of P→P links was positively related to the

4588 number of those links in a community (t27 = 4.195, p < 0.001;

4589 Fig. 23B; S5.6, Supporting Information S5) and was independent of

4590 the number of links for all other link types. Of those, the roles of c 4591 P−→F links were the most widely-dispersed, followed by those of t 4592 F→F links, F→P links, and P−→F links (Tukey’s HSD test with critical

4593 value 4.13, α=0.05, and df=27; Fig. 23A). In contrast to the diversity

4594 of species roles, the diversity of unique link roles did not vary with 144

Figure 23: Dispersion of link roles A varied across link types while diversity did not. (A) The roles of concomitant 0.6 c predation links (P−→F) were most r dispersed followed by those of predation among free-living species (F→F), parasitism (F→P), and target s t 0.4 predation on parasites (P−→F). For these st link types, the dispersion of link roles t was not related to the number of links in a community. (B) Dispersion of the roles of links describing predation Dispersion 0.2 between parasites, on the other hand, increased with the number of such links in a community. In (A), the different letters indicate significantly different dispersions and the error bars depict 95% confidence intervals about the 0.0 → → →t→ c mean. Letters above each bar indicate F FF PP FP F groups based on mean dispersions, and types of link with different letters 0.6 have significantly different dispersions B (Tukey’s HSD test with critical value 4.13, α=0.05, df=27). In (B) the shaded region represents a 95% confidence region for predicted dispersion. See S5.5, Supporting Information S5 0.4 for details about the regressions.

Dispersion 0.2

0.0 0 50 100 150 200 Number of P→P Links

4595 the number of links of that type in a community (Fig. S5.6), nor did it

4596 differ across types of links (Tukey’s HSD test with critical value 4.10,

4597 α=0.05, and df=28).

4598 Discussion

4599 Parasites’ unique life histories and ways of feeding suggest that

4600 they should interact with other species differently than free-living

4601 species (Marcogliese and Cone, 1997; Lafferty et al., 2006, 2008;

4602 Warren et al., 2010; Thieltges et al., 2013). Despite these important

4603 morphological and behavioural differences, a previous study

4604 comparing versions of food webs including and excluding parasites 145

4605 found that webs including both types of species but not concomitant

4606 predation have similar structural properties to similarly-sized webs

4607 composed of free-living species only (Dunne et al., 2013b). This

4608 indicates that differences between free-living species and parasites

4609 as consumers do not translate to the network level (Dunne et al.,

4610 2013b). Nevertheless, webs including free-living species, parasites,

4611 and concomitant predation links do indeed have different structures

4612 from other webs, suggesting that it is parasites’ unique positions

4613 as concomitant resources that have the greatest effects on network

4614 structure, including effects on properties such as connectance which

4615 have been linked to robustness (Dunne et al., 2002b, 2013b). In order

4616 to examine this inference in greater detail, here we have examined

4617 food-web structure from the perspective of species and the links

4618 between them. We have thus been able to systematically uncover the

4619 ways in which free-living species, parasites, and the multiple types of

4620 links between them differ in the broader food-web context.

4621 At the species level, our results reafﬁrmed the impact of links

4622 in which parasites are concomitant resources on network structure

4623 (Poulin et al., 2013; Thieltges et al., 2013). The roles of parasites

4624 excluding concomitant predation were most similar to those of top

4625 predators and intermediate consumers. One potential explanation for

4626 the similarity of parasites’ roles to those of free-living intermediate

4627 consumers could be the aggregation of parasite life stages. While

4628 free-living intermediate consumers may experience predation during

4629 any time of life, parasites have very few consumers except during

4630 free-living life stages. Although a stage-speciﬁc analysis is beyond

4631 the scope of the present work, this suggests that the structural roles

4632 of different parasite life stages could range from those of free-living

4633 basal resources (for non-feeding stages with consumers) through to

4634 those of free-living top predators (for parasitic stages that are not

4635 affected by other parasites in the same host). Nevertheless, when

4636 concomitant predation was included, the roles of parasites were

4637 distinct from those of any other type of free-living species. This

4638 suggests that the network-level effects of concomitant predation may

4639 truly be due to changes in the roles of parasites themselves.

4640 In addition to affecting the median roles of parasites, the

4641 inclusion of concomitant predation greatly altered the distribution

4642 of parasites’ roles. Speciﬁcally, adding concomitant predation

4643 increased role variability in parasite-poor communities to a similar

4644 level as that of parasite-rich communities, such that parasites’ roles

4645 appeared saturated when concomitant predation was included and

4646 unsaturated when they were not. This apparent homogenising effect 146

4647 of concomitant predation may arise from the fact that these links

4648 bind the roles of parasites to those of their hosts, creating intimate

4649 structural similarities. In parasite-poor communities, it is likely

4650 that few parasites share common hosts and therefore common

4651 concomitant predation links. As parasites “inherit” role variability

4652 from their hosts via concomitant predation, less overlap in host

4653 ranges among parasites may lead to greater dispersion of their roles.

4654 Unlike role dispersion which was saturated for most trophic

4655 groups, role diversity increased with number of species for all groups.

4656 This implies that, while species roles are similarly predictable on

4657 the basis of species type regardless of the size of the food web, roles

4658 overall do not become more redundant as the number of species in

4659 the web increases. This observation ﬁts in well with the suggestion

4660 that there is no single way to conﬁgure a stable community (May,

4661 2001). Contrary to models of stable ecosystems where greater

4662 diversity begets greater niche overlap in order to use resources

4663 as efﬁciently as possible, in unstable systems each species’ niche

4664 may have to be distinct if it is to withstand disturbances (May,

4665 2001). Beyond this overall lack of saturation, Pc roles were more

4666 diverse than other types of roles for a given number of species

4667 in the trophic group. Lower redundancy in Pc roles despite their

4668 similar dispersion to other role types could be a result of the different

4669 potential outcomes of concomitant predation for the parasite. While

4670 concomitant predation is always fatal for the host, the parasite may,

4671 for certain predators, be able to infect the predator and use it as

4672 its next host. For many parasites, such “trophic transmission” is

4673 an essential part of the life cycle (Thieltges et al., 2013), and it is

4674 possible that the roles of such links differ from those of concomitant

4675 predation links in which the parasite is destroyed. This lack of

4676 redundancy, coupled with the increase in role dispersion resulting

4677 from including concomitant predation, means that parasites should

4678 have widely varying effects on network structure. This in turn

4679 implies that parasites can generate a variety of effects on population

4680 dynamics and energy ﬂows through their communities. In particular,

4681 lack of redundancy means that any effects of ﬂuctuations in the

4682 population of one parasite (e.g., on host mortality) are unlikely to

4683 be compensated for by another parasite with a similar role.

4684 To further clarify the impact of different types of links, we

4685 considered the roles of links directly. The dispersion of link roles

4686 generally appeared to be saturated–that is, independent of the

4687 number of a given type of links present in a network. This suggests

4688 that there were sufﬁcient links in each community to occupy the 147

4689 entire role space for most types of links. Given the saturation of

4690 role dispersion for most types of species, this is not surprising.

4691 The only type of link for which role dispersion was not saturated

4692 was predation among parasites. This type of link includes hyper-

4693 parasitism, predation among free-living stages of parasites, and

4694 attack by one parasite on others within the same host, with or

4695 without consumption (Hechinger et al., 2011b). This variety of types

4696 of feeding and interaction locations might explain the apparent

4697 tendency for links describing predation among parasites to be

4698 increasingly distinct from the group median. Surprisingly, this

4699 variability in link roles does not appear to be linked to a greater

4700 diversity of unique role phenotypes.

4701 Dispersion, conversely, differed among link types with the

4702 roles of concomitant predation links being the most variable. While

4703 concomitant predation ties the roles of parasites to those of their

4704 hosts, the roles of these links are non-trivially tied to the roles of

4705 the predation links that lead to them. Alternatively, it is possible

4706 that the wide variety of outcomes of concomitant predation for both

4707 parasite and consumer (Thieltges et al., 2013) leads to these links

4708 having inherently more variable roles. Were that the case, however,

4709 we could expect a greater diversity of unique roles for these links

4710 as well as greater diversity, which we did not observe. It therefore

4711 appears that, by combining predation with parasitism, concomitant

4712 predation is simply less predictable than other types of interactions.

4713 This may mean that the consequences of concomitant predation for

4714 energy ﬂows or population dynamics are similarly unpredictable.

4715 Conclusions

4716 Our species-centric and link-centric perspectives allow us to robustly

4717 identify how and where the contributions of parasites to network

4718 structure differ from those of different types of free-living species.

4719 Within a complex food web, it is common to characterise species’

4720 structural roles in terms of the organisation of their direct and

4721 indirect links with other species (Luczkovich et al., 2003; Olesen

4722 et al., 2007; Allesina and Pascual, 2009). As we show here, the

4723 structural roles of links can also be characterised by the pair of

4724 species that make them up and, by extension, all other links those

4725 species participate in. Though both perspectives build from the same

4726 fundamental information, our analysis demonstrates that they are

4727 not equivalent and instead provide a complementary picture of the

4728 building blocks of food-web structure. 148

4729 Overall, our results reinforce the idea that concomitant predation

4730 plays a disproportionately important part in determining the

4731 structure of food webs (Dunne et al., 2013b; Poulin et al., 2013)

4732 and that it places considerable constraints on the median roles of

4733 parasites while simultaneously increasing the variability about

4734 these median roles. This implies that concomitant predation not

4735 only affects the ways in which parasites in general affect community

4736 functions and stability but that it decreases the redundancy of each

4737 species’ contribution to those effects. Historically, concomitant

4738 predation has often been omitted from food webs, either because

4739 it is assumed to be energetically insigniﬁcant (Thieltges et al., 2013)

4740 or because it is inherently difﬁcult to directly observe (Marcogliese

4741 and Cone, 1997). The structural implications of these links as shown

4742 here, as well as their prevalence within food webs (Thieltges et al.,

4743 2013), potential energetic implications (Lafferty et al., 2006; Hechinger

4744 et al., 2011a; Thompson et al., 2013), and importance as sources

4745 of either mortality or trophic transmission (Lafferty et al., 2006;

4746 Thieltges et al., 2013) for parasites mean that they should no longer

4747 be ignored. Finally, as concomitant predation links reveal the deep

4748 intimacy between hosts and parasites, they provide a critical lens

4749 through which to examine the many ways in which parasite-host and

4750 predator-prey interactions are linked.

4751 Acknowledgements

4752 We thank Jennifer Dunne, Nicolas Mouquet, Dominique Gravel,

4753 Robert Poulin, Nick Baker, Katie Bowron, Carol Frost, Camille

4754 Coux, Guadalupe Peralta, and Simon Litchwark for comments on

4755 the manuscript. This research was supported by an NSERC-PGS-

4756 M graduate scholarship and a University of Canterbury Doctoral

4757 Scholarship (to ARC) and a Marsden Fund Fast-Start grant (UOC-

4758 1101), administered by the Royal Society of New Zealand (to DBS).

4759 We are thankful to the BlueFern University of Canterbury Super

4760 Computer for computing facilities.

4761 Data accessibility

4762 Food webs used in this study are publicly available

4763 via Dryad data package (Dunne et al., 2013a,

4764 http://dx.doi.org/10.5061/dryad.b8r5c).

4765 Supporting Information

4766 S5.1 Additional References and Description of Food Webs

4767 S5.2 Supplemental methods: quantifying species’ and links’ roles.

4768 S5.3 Supplemental methods: role dispersion & diversity 149

4769 S5.4 DSupplemental results: median roles.

4770 S4.5 Model selection for analysis of dispersion and diversity of

4771 species roles.

4772 S4.6 Model selection for analysis of dispersion and diversity of link

4773 roles. Figure showing link role diversity. 150

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4989 Chapter 6: Taking the scenic route: trophic transmission

4990 of parasites and the properties of links along which they

4991 travel.

1 2 2 4992 Alyssa R. Cirtwill , Clement Lagrue ,Robert Poulin , 1 4993 Daniel B. Stouffer

1 4994 Centre for Integrative Ecology

4995 School of Biological Sciences

4996 University of Canterbury

4997 Private Bag 4800

4998 Christchurch 8140, New Zealand

2 4999 Department of Zoology

5000 University of Otago

5001 340 Great King Street

5002 PO Box 56

5003 Dunedin 9054, New Zealand

5004 Manuscript prepared in the style of Ecology.

5005 This chapter has been modiﬁed from its original version. It has been

5006 formatted to match this thesis. 158

5007 Abstract

5008 Some parasites move from one host to another via trophic

5009 transmission— the consumption of the parasite (inside its current

5010 host) by its future host. As feeding links among free-living species

5011 have different dynamic and structural properties, it seems plausible

5012 that these links will vary in their effectiveness as transmission routes.

5013 Moreover, most parasites are restricted to certain host taxa at each

5014 life stage, so not all links will be possible transmission routes. Here

5015 we test this possibility for parasites and their hosts in four New

5016 Zealand lakes. We use three dynamic properties and one structural

5017 property to measure differences among feeding links and then

5018 test whether each property can predict whether or not a link will

5019 transmit parasites. In each test, we use both an unrestrictive and a

5020 taxonomically-informed null model, allowing us to determine the

5021 extent to which the taxonomy of free-living species affects parasites’

5022 transmission routes. Contrary to our expectations, we found that

5023 parasites tend to be transmitted along dynamically weak links (i.e.,

5024 links that make small contributions to the diets of predators, transmit

5025 little biomass, and involve rare prey). However, the structural

5026 properties of links that transmit parasites reveal that they are likely

5027 to be particularly important to the community because they are

5028 highly central and can therefore affect many free-living species.

5029 By comparing our results against our two null models, we also

5030 found that several of the trends we identify are largely determined

5031 by the restriction of parasites to particular host taxa. This means

5032 that the host speciﬁcity of parasites is a key determinant of their

5033 transmission routes. As a whole, our results suggest that parasites

5034 follow transmission routes that are particularly unlikely to have a

5035 destabilising effect on the community. Dynamically weak links, like

5036 those that transmitted parasites in this study, tend to stabilise food

5037 webs by dissipating perturbations to the community. Structurally

5038 important links, conversely, can have a large impact on food webs.

5039 Parasites therefore appear to strike a balance between the highway

5040 and the scenic route and are transmitted along links that bind their

5041 communities together.

5042 Keywords

5043 concomitant predation, food-web dynamics, network motifs, food-

5044 web structure, interaction roles 159

5045 Introduction

5046 Parasites are increasingly recognised as integral components of

5047 ecological communities (Huxham et al., 1996; Lafferty et al., 2006;

5048 Dobson et al., 2008; Kuris et al., 2008; Hechinger et al., 2011; Thieltges

5049 et al., 2013; Dunne et al., 2013). In some systems, they can reach

5050 similar cumulative biomasses to top predators (Kuris et al., 2008),

5051 and they often act as prey for free-living species during their free-

5052 living life stages (Thieltges et al., 2013). Parasites can also strongly

5053 affect the population dynamics of their hosts (Freedman, 1990;

5054 Marcogliese and Cone, 1997) and inﬂuence the structure of their

5055 communities (Lafferty et al., 2006; Dunne et al., 2013; Cirtwill and

5056 Stouffer, 2015). Many parasites in turn rely on the structure of the

5057 free-living food web to complete their life cycles. These ‘trophically-

5058 transmitted’ parasites move to a new host when their intermediate

5059 host is consumed by an appropriate deﬁnitive host. To complete

5060 their life cycles, these parasites therefore rely on certain feeding links

5061 among free-living species occurring reliably. Feeding links, however,

5062 differ in a number of ways that might affect their suitability as

5063 transmission routes. In particular, we might expect that links which

5064 are more important to the structure and/or functioning of the food

5065 web might occur more reliably than other links. These important

5066 links might therefore be ‘safer bets’ for parasites and more likely to

5067 serve as viable transmission routes. There are, however, a variety of

5068 ways that the importance of a link can be measured, each of which

5069 could be expected to impact parasites for different reasons.

5070 A link might be important because of its dynamic properties— its

5071 contribution to the ﬂow of energy and biomass through the food web

5072 and, by extension, to the maintenance of free-living populations.

5073 Three dynamic properties in particular seem likely to inﬂuence

5074 the suitability of links as transmission routes. First, we might

5075 expect that links which contribute a particularly large proportion

5076 of a a predator’s diet might be more likely to occur and therefore

5077 be a better component of a transmission route than a link which

5078 contributes less to the diet of the predator. This is especially true

5079 for deﬁnitive hosts, which often experience only minor effects from

5080 infections (Lafferty, 1992). Because the cost of infection is low and

5081 infected prey are often easier to catch and kill (Lafferty, 1992), these

5082 hosts have little incentive to avoid consuming infected prey (Lafferty,

5083 1992).

5084 Second, parasites might instead tend to be transmitted along

5085 links involving highly abundant prey, regardless of the contributions 160

5086 these prey make to the diets of deﬁnitive hosts (Canard et al., 2014).

5087 Neutral theory suggests that more abundant prey are more likely to

5088 encounter and be infected by parasites (Canard et al., 2014) and are

5089 more likely to be encountered and consumed by predators (Abrams

5090 and Ginzburg, 2000; Wootton, 2005). Abundant prey may also

5091 represent a more productive niche that can be exploited by more

5092 parasite species (Thompson et al., 2013). Of course, infecting highly-

5093 abundant prey means that the parasite will often be consumed by

5094 predators which are not viable deﬁnitive hosts and killed. Such

5095 losses may be worthwhile, however, if the parasite can still infect its

5096 deﬁnitive host more frequently than if the parasite had a different life

5097 history (Poulin, 2010). Note that while abundant prey can be major

5098 contributors to predators’ diets as described above, this may not be

5099 the case for all predators as some species have strong preferences for

5100 particular prey. The contribution of a link to the predator’s diet and

5101 the abundance of the prey involved therefore provide complementary

5102 information about a the impact of a link on the food web.

5103 Third, parasites’ transmission routes might not be strongly

5104 affected by either the abundance of the prey or the contribution of

5105 the link to the predator’s diet. Instead, parasites might “go with

5106 the ﬂow” and tend to be transmitted along links which transfera

5107 large amount of biomass (Thompson et al., 2013). These energetic

5108 “highways” might involve abundant prey, but they could equally

5109 involve rare but large prey. Similarly, links which contribute large

5110 proportions of the predator’s diet may or may not transfer large

5111 amounts of biomass in the absolute sense, depending on the size

5112 of the predator population and the amount each animal consumes.

5113 Whatever the case may be, links which transfer large amounts of

5114 biomass are likely to be critical to the overall functioning of the

5115 community and therefore may be more reliable than other links.

5116 In addition to their dynamic properties, a link might be

5117 important because of its structural properties— the ways in which the

5118 link contributes to the structure of the food web. In particular, links

5119 which are highly “central”— that is, those which lie on the shortest

5120 paths between many pairs of species (Newman, 2010)—could be

5121 good transmission routes. These links are considered important

5122 because they indirectly affect many species (Jordán et al., 2007; Lai

5123 et al., 2012). As such, variability in central links would have a large

5124 effect on the rest of the web and destabilise the community (Lai et al.,

5125 2012). Central links may therefore be less variable and more reliable

5126 than other links. Supporting this hypothesis, previous research has

5127 shown that highly-central species tend to host more parasite species 161

5128 than do other free-living species (Chen et al., 2008; Thompson et al.,

5129 2013). Highly central hosts also tend to be particularly important for

5130 parasite transmission (Chen et al., 2008). We expect that what is true

5131 for central species will also be true for central links.

5132 Parasites are not always free to follow the best possible

5133 transmission route, however, as each parasite is generally limited to

5134 hosts from certain taxonomic groups at each life stage. For example,

5135 most trematodes use molluscs as hosts for their ﬁrst parasitic

5136 life stage while acanthocephalans always use arthropods as their

5137 intermediate host. Previous analyses of parasites’ transmission routes

5138 have not taken these restrictions into account (e.g., Chen et al., 2008;

5139 Rossiter and Sukhdeo, 2011; Thompson et al., 2013), meaning it is

5140 possible that parasites tend to infect highly-connected species largely

5141 because of the taxonomy of these highly-connected species rather

5142 than because these hosts are the best “stops” for parasites to visit on

5143 their transmission routes. When testing for effects of the properties

5144 of feeding links on the potential for these links to transmit parasites,

5145 it is therefore essential to control for the potential inﬂuence of the

5146 taxonomy of free-living species.

5147 Here we test whether parasites tend to be transmitted along

5148 feeding links that are particularly important to the food web. We also

5149 test which dynamic or structural properties of feeding links most

5150 parsimoniously explain trends in parasite transmission. Speciﬁcally,

5151 we expect that links which transmit parasites would (i) contribute

5152 larger proportions of predators’ diets, (ii) involve more abundant

5153 prey, (iii) transfer more biomass, and (iv) be more central than other

5154 links. We also expect that the inﬂuence of these properties will

5155 depend on the restriction of parasites to particular host taxa. To

5156 investigate this last question, we test each of the above hypotheses

5157 using both an unrestrictive null model and a more conservative,

5158 taxonomically informed null model that explicitly incorporates the

5159 effects of the host speciﬁcity of parasites.

5160 Methods

5161 Dataset

5162 We constructed food webs describing the free-living communities

5163 of four lakes in the South Island of New Zealand: Lake Hayes ◦ ′ ◦ ′ ◦ ′ 5164 (44 58 59.4”S, 168 48 19.8”E), Lake Tuakitoto (46 13 42.5”S, ◦ ′ ◦ ′ ◦ ′ 5165 169 49 29.2”E), Lake Waihola (46 01 14.1”S, 170 05 05.8”E), and ◦ ′ ◦ ′ 5166 Tomahawk Lagoon (45 54 06.0”S, 170 33 02.2”E). To capture the

5167 seasonal variation in each community, we constructed three separate 162

5168 food webs describing each community in September 2012, January

5169 2013, and May 2013 (austral seasons: early spring, mid-summer, and

5170 late autumn). Our dataset thus consisted of 12 food webs in total.

5171 Together, these webs included 2160 links between 110 free-living

5172 species. The lake communities also contained 49 parasite life stages,

5173 13 of which were trophically transmitted. For a detailed description

5174 of sampling methods and reconstruction of feeding links, see S6.1,

5175 Supporting Information s6 and Lagrue and Poulin (2015).

5176 Dynamic and structural properties of links

5177 After assembling the networks, we calculated dynamic and structural

5178 properties of each link in order to test whether any of these

5179 properties predicted the outcome of a link for parasites. To test

5180 whether parasites tend to be transmitted along links that contribute

5181 large proportions of predators’ diets, we deﬁned the contribution

5182 of each link based on the proportion of the predator’s gut contents

5183 accounted for by that link. For this and other properties, we took

5184 the average across all individuals in a species within the same lake

5185 and sampling period. A link which makes a large contribution to

5186 the predator’s diet might represent either rare but large meals or

5187 frequent, small meals. Because the networks in our dataset were

5188 based on gut contents rather than direct observation of interactions,

5189 we did not have information about interaction frequencies that would

5190 allow us to tease these two possibilities apart.

5191 We also expected that parasites might tend to be transmitted

5192 along links involving highly-abundant prey. These links might

5193 make large contributions to the predators’ diets as described above,

5194 but if predators have strong preferences for certain rare prey then

5195 abundant species might contribute relatively little to their diets.

5196 We therefore tested the relationship between prey abundance and

5197 parasite transmission separately from the relationship between

5198 contribution to diet and transmission. We deﬁned abundance as the 2 5199 number of prey individuals per m in each lake. For some resources,

5200 such as terrestrial insects which occasionally fall into the lakes,

5201 we were unable to reliably estimate the standing local abundance

5202 and so we removed these links (see S6.1, Supporting Information

5203 S6 for details). This left us with 1464 links. Because encounter and

5204 consumption rates might depend on the biomass of the prey rather

5205 than its abundance, we also calculated the total biomass of the prey

5206 in each link. We deﬁned prey biomass as the estimated mass of the 2 5207 prey species per m in each lake. As with abundance, we were unable 163

5208 to reliably estimate the standing local biomass of some species and

5209 removed these links from the analysis. This left us with 1627 links.

Thirdly, it is possible that parasites “go with the ﬂow” and tend to be transmitted along links that transfer large amounts of biomass. These links may make large contributions to predators’ diets and involve abundant prey, but this depends on the total amount of biomass the predator consumes and the size of each individual prey. We therefore tested the relationship between the amount of biomass transferred along a link and its outcome for parasites independently

of the other properties. We estimated the biomass transfer ωilm for each link i in lake l during sampling period m as

3/4 ωilm ≈ κlm ρilm,(5)

5210 where κlm is the mean biomass of the consumer from link i in 5211 lake l during sampling period m, and ρilm is the proportion of the 5212 predator’s diet contributed by interaction i in lake l during sampling

5213 period m. Following Brose et al. (2008), we used a scaling factor of

5214 3/4 to account for efﬁciencies of scale in larger species. As biomass

5215 transfer, so deﬁned, depends on the predator’s diet and local biomass

5216 but not on the prey’s local biomass or abundance, we were able to

5217 estimate the amount of biomass transfer for all 2160 links.

5218 Finally, because the suitability of a link as a transmission route

5219 might depend on its structural importance as well as its role in the

5220 food web’s dynamics, we tested whether or not the centrality of

5221 a link affected its outcome for parasites. To do this, we calculated

5222 the “betweenness centrality” of each link. This measure represents

5223 the frequency with which a given link lies on the shortest paths

5224 between pairs of species (Newman, 2010) and may be calculated

5225 using weighted (e.g., by the amount of biomass transferred) or

5226 unweighted links. Because we dealt with the dynamic properties

5227 of links separately, we calculated centrality using unweighted links.

5228 Although central links are generally thought to be particularly

5229 important to the structure and functioning of a community, from

5230 the parasite perspective these “highways” are a double-edged sword.

5231 Depending on the broader structure of the network, a central link

5232 has the potential to expose the parasite to many free-living species

5233 that are not suitable hosts. Highly-central links could therefore either

5234 promote transmission or result in losses for the parasite. To get an

5235 idea of these broader structures, and how they affect the outcomes

5236 of links for parasites, we also characterised links’ structural roles

5237 using motifs— unique patterns of interacting species that can be

5238 understood as the building blocks of networks (see S6.2, Supporting 164

5239 Information S6 for details). The results for these structural roles were

5240 qualitatively similar to those for centrality and so are not presented

5241 here.

5242 Outcomes of links for parasites

5243 Next, we categorised the outcomes of feeding links for each parasite

5244 life stage. As a given link might transmit one life stage while killing

5245 another stage of the same species, we performed all of our analyses

5246 at the life-stage level. We therefore expanded our dataset by cross-

5247 referencing the l links included in each food web with the p parasite

5248 life stages observed in that web, resulting in an l×p table of feeding

5249 links and their outcomes for each lake-season combination. Note

5250 that the outcome of a given link for a given parasite life stage was

5251 assumed to be the same in all lakes and sampling periods in which

5252 both the link and the parasite were observed. That is, if a life stage

5253 of the focal parasite was observed in one individual of a free-living

5254 species, that species was considered to be a viable host in all of the

5255 webs in our dataset.

5256 A link was categorised as a “transmission” link if 1) the focal

5257 parasite life stage was known to be trophically transmitted and 2)

5258 the predator and prey in the link were observed as hosts for the focal

5259 parasite life stage and the next stage in the parasite life cycle (Fig. 24).

5260 If the prey was a host for the focal parasite life stage but the parasite

5261 life stage could not be trophically transmitted, or if the predator was

5262 not a host for the next stage in the parasite life cycle, then the link

5263 was categorised as a “loss”. This includes cases where the parasite

5264 is digested along with its host by the predator (e.g. trematode

5265 sporocysts inside a snail host that is eaten by a ﬁsh) as well as cases

5266 where the parasite is killed in an indigestible cyst form (e.g. some

5267 encysted trematode metacercariae when their second intermediate

5268 host is eaten by an unsuitable predator). In rare cases, the parasite

5269 may sometimes be able to reproduce by selﬁng at an earlier life stage

5270 (e.g., trematode metacercariae achieving progenesis in their second

5271 intermediate host; Poulin and Cribb, 2002). Nevertheless, these

5272 parasites should still be under selection to complete their normal life

5273 cycles and reproduce sexually. We therefore assumed that completing

5274 its full life cycle is the best option for the parasite and, for the two

5275 parasites that may be capable of progenesis in our dataset, categorise

5276 links that lead to the normal deﬁnitive hosts as “transmission” and

5277 links leading to other predators as “loss” even if the parasite can

5278 reproduce in an earlier host. The remaining links, where the prey

5279 was not a host for the focal parasite life stage, were categorised as 165

Figure 24: The small subset of species represented here (taken from the dataset used in this study) is used to illustrate the different outcomes of feeding links for parasites. Maritrema poulini uses amphipods and Stegodexamene anguillae uses small fish as intermediate host prey. These parasites are transmitted to their respective definitive hosts along specific trophic links (predator-prey links). Each trophic link may transmit the parasite to the appropriate definitive host (“transmission” link), the parasite may be consumed by a non-host predator and killed (“loss” link), or the parasite may not be affected by the link (“unused” link). Maritrema poulini only uses birds as definitive hosts and is killed (as indicated by the pale, crossed-out symbol) when its amphipod host is consumed by a fish (“loss” link; link 1). For M. poulini, “transmission” is only achieved through link 2. Stegodexamene anguillae does not infect amphipods and thus trophic links including amphipods as prey are “unused” by this parasite (links 1 and 2). For S. anguillae, link 3 is a “loss” link while link 4 is the appropriate “transmission” link to eel definitive hosts; links 3 and 4 are “unused” by M. poulini.

5280 “unused”. These links should not have any impact on the parasite

5281 unless they affect other life stages of the same species.

5282 Throughout our analyses we treated these outcomes as

5283 distinct categories. We note that this ignores the possibility that the

5284 proportion of parasites in an intermediate host that can infect the

5285 predator may vary among the links. For example, some predators

5286 may process their prey (e.g., by chewing) more thoroughly and

5287 thereby kill more parasites than one which consumes the same

5288 prey relatively whole. Alternatively, some predators may simply

5289 be more susceptible to infection than other suitable hosts. In either

5290 case, parasites may exist at different intensities in different hosts,

5291 and changes in intensity of infection between predator and prey

5292 could be used to infer continuous values for parasite transmission.

5293 However, as neither loss nor unused links ever result in the infection

5294 of the predator and the completion of the parasite life cycle, treating

5295 transmission as a continuous variable would obscure the difference

5296 between these two outcomes— a result we chose to avoid. 166

5297 Outcomes of links as a function of dynamic and structural properties

5298 We began by testing what combination of the ﬁve properties

5299 we consider (contribution to predator’s diet, abundance of prey,

5300 biomass of prey, amount of biomass transferred, and centrality)

5301 provided the most parsimonious explanation for the outcome of a

5302 link for a parasite. To do this, we performed a series of canonical

5303 correspondence analyses (CCAs) using the cca function in theR(R

5304 Core Team, 2014) package vegan (Oksanen et al., 2014). Each CCA

5305 relates a matrix of dummy variables describing the outcomes of

5306 links for parasites to a constraining matrix composed of different

5307 combinations of link properties. We performed a CCA for each of the

5308 31 unique linear combinations of predictors. In each case, we scaled

5309 and centred all properties. To provide a baseline, we also performed

5310 a “null” CCA which related the matrix describing outcomes of links

5311 to a unit vector. For each model, we obtained the AIC score using

5312 the function extractAIC, again from vegan (Oksanen et al., 2014).

5313 We then compared these AIC values to ﬁnd the combination of

5314 predictors that most parsimoniously explains the outcomes of links

5315 for parasites (Table S3). To supplement this analysis, we also tested

5316 whether any of the properties were strongly correlated. Clear linear

5317 relationships between properties would mean that they provide

5318 redundant information, potentially biasing our results.

5319 Based on the results of these preliminary tests (see Appendices

5320 S6.3 & S6.4), we chose to explore the relationships between outcomes

5321 of links for parasites and each property (i.e., hypotheses i-iv)

5322 separately. To do this, we began by comparing the mean values of

5323 each property for links with different outcomes using a modiﬁed

5324 Tukey’s Honest Signiﬁcant Difference test. Rather than assuming

5325 equal variances in all links, we used pooled variances for each pair

5326 of outcomes. We then tested whether each property was a signiﬁcant

5327 predictor of links’ outcomes using a modiﬁed ANOVA. Rather than

5328 assume that each property was normally distributed, we obtained

5329 the null distribution of the F statistic by permuting values of the

5330 focal property across the set of links 999 times. In order to control

5331 for the different numbers of intermediate (prey) and deﬁnitive

5332 (predator) hosts for different parasite life stages, we restricted our

5333 permutations to occur within the interaction-outcome combinations

5334 for each parasite.

5335 At ﬁrst, we imposed no further restrictions on the permutations

5336 to control for the host speciﬁcity of parasites. Such an unrestrictive

5337 null model, however, can re-assign transmission links to

5338 physiologically and ecologically inappropriate hosts. As noted 167

5339 previously, parasites are often restricted to hosts from a particular

5340 taxonomic group (Table S6.2, S6.1; Supporting Information S6). To

5341 control for these restrictions, we compared our results to those

5342 obtained using a taxonomically-informed, restrictive null model

5343 where links with a given outcome for a parasite (e.g., transmission)

5344 were only shufﬂed within the set of predator-prey interactions that

5345 could conceivably have that outcome, as determined based on expert

5346 knowledge. Speciﬁcally, we limited the substitution of dynamic

5347 properties for “transmission” links to interactions where the prey was

5348 a potential intermediate host of the parasite— based on the taxonomy

5349 of known intermediate hosts — and the predator was a potential

5350 deﬁnitive host (again based on taxonomy). Similarly, we restricted

5351 the substitution of properties for “loss” links to interactions where

5352 the prey was a potential intermediate host of the parasite but the

5353 predator was not a potential deﬁnitive host. Thirdly, we restricted

5354 the substitution of properties for “unused” links to interactions

5355 where the prey was not a potential intermediate host (regardless

5356 of the predator). For those parasites that relied upon insect hosts, we

5357 considered only aquatic insects to be valid potential hosts. Although

5358 there may be parasites in some systems that infect both terrestrial

5359 insects and ﬁsh, our dataset did not contain any such parasites and

5360 hence transmission could only occur between aquatic insects and

5361 their consumers.

5362 Results

5363 Outcomes & contribution to predator’s diet

5364 The contribution of a feeding link to the predator’s diet was

5365 signiﬁcantly associated with the outcome of the link for parasites

5366 when the host speciﬁcity of parasites was ignored (F2,42019=13.62,

5367 P<0.001), but not when we used the taxonomically-informed null

5368 model (F2,42019=13.62, P=0.999). Surprisingly, transmission links made 5369 up a smaller proportion of predators’ diets than did unused links

5370 (∆Transmission−Unused=-0.072, P<0.001 for a Tukey’s HSD test; Fig 25A) 5371 and made similar contributions to loss links (∆Transmission−Loss=-0.062, 5372 P=0.391). Comparing these results to each of our null models, we

5373 found that transmission links contributed a much lower proportion

5374 of predators’ diets than expected based on the unrestrictive null

5375 model, but made similar contributions to those expected under the

5376 taxonomically-informed null model (Fig. 25A). Loss links, meanwhile,

5377 contributed similar proportions of predators’ diets to those predicted

5378 by the unrestrictive null model but higher proportions than expected

5379 based on the taxonomically-informed null model. Unused links made

5380 similar contributions to those predicted by both null models. 168

a b Figure 25: The dynamic properties 2.5

(%) b of feeding links among free-living ) 0.3 a 2 a 2.0 species affect the consequences of these links for parasites. a) The c b (1000/m 0.2 1.5 contributions of feeding links to the predator’s diet varied across 1.0 links with different outcomes for 0.1 Observed a parasites, but this trend was not Unrestrictive 0.5 T-I significant when the host specificity Prey abundance of parasites was taken into account. Contribution to predator's diet 0.0 0.0 b-c) The local abundance and local c d 50 biomass of the prey species, however, varied significantly among links with 20 a b ) different outcomes whether or not

2 40

(mg) b 15 host speciﬁcity was acknowledged. d) c 30 The amount of biomass transferred

10 along a link showed the same trend 20 as we observed for the contribution of a c 5 a link to the predator’s diet. For each Prey biomass (g/m Biomass transfer 10 property, we show the mean observed value (±2 SE; circles). Different letters 0 0 Unused Loss Transmission Unused Loss Transmission above the observed values represent Outcome for parasite significant differences in Tukey’s HSD tests for each property. Empty symbols (to the left of the observed values) represent the mean value (±2 SE) expected using our unrestrictive null 5381 Outcomes & prey abundance model while symbols with striped fill (to the right of the observed values) 5382 As with the contribution of links to predators’ diets, the abundance represent the mean value (±2 SE) 5383 of prey was significantly associated with the outcome of a link for the expected under our taxonomically- 5384 parasite under the unrestrictive null model (F2,28793=392.875, P<0.001). informed (T-I) null model.

5385 Abundance was also signiﬁcantly associated with different outcomes

5386 when the host speciﬁcity of parasites was taken into account in the

5387 taxonomically-informed null model (F2,28793=392.875, P<0.001).

5388 Contrary to our expectations, transmission links involved prey

5389 with lower abundances than did loss links (∆Transmission−Loss=-737, 5390 P<0.001 for a Tukey’s HSD test; Fig. 25b). However, transmission

5391 links did involve prey with higher abundances than unused links

5392 (∆Transmission−Unused=861, P<0.001). Comparing these observed values 5393 with those in the null models, we found that unused links involved

5394 prey with similar abundances to those expected under both null

5395 models while transmission and loss links behaved differently than

5396 expected (Fig. 25b). Speciﬁcally, transmission links involved prey

5397 with higher abundances than expected based on the unrestrictive null

5398 model, but slightly lower than expected based on the taxonomically-

5399 informed null model. Loss links, in contrast, involved prey with

5400 higher abundances than predicted by either null model.

5401 The relationship between the biomass of prey and the outcomes

5402 of links for parasites was qualitatively identical to that between

5403 abundance and the outcomes of links. The local biomass of the 169

5404 prey species was signiﬁcantly associated with different outcomes

5405 of the link for parasites, whether the host speciﬁcity of parasites

5406 was ignored or taken into account (F2,31832=257.9, p<0.001 and 5407 F2,31832=257.9, p<0.001, respectively). Transmission links involved

5408 prey with lower biomasses than did loss links,

5409 (∆Transmission−Loss=-5.76g, p<0.001 for a Tukey’s HSD test; Fig. 25c). 5410 Both transmission and loss links involved prey with higher biomasses

5411 than did unused links (∆Transmission−Unused=4.47g, p<0.001 and 5412 ∆Loss−Unused=10.2g, p<0.001, respectively). The observed biomass 5413 values for unused links were similar to those expected under both

5414 null models (as with all other link properties we tested). As with

5415 prey abundance, transmission links involved prey with higher

5416 biomasses than expected based on the unrestrictive null model, but

5417 slightly lower than expected based on the restrictive null model.

5418 Loss links, in contrast, involved prey with higher biomasses than

5419 predicted by on either null model.

5420 Outcomes & biomass transfer

5421 Again as with the contribution of links to predators’ diets, the

5422 amount of biomass transferred along a link was correlated with

5423 outcomes for parasites when the unrestrictive null model was used,

5424 but not under the taxonomically-informed null model (F2,42019=8.169, 5425 P=0.001; F2,42019=8.169, P=0.643, respectively). Surprisingly,

5426 transmission links transferred less biomass than did loss or unused 5427 links (∆Transmission−Loss=-18.4mg, P=0.002 and 5428 ∆Transmission−Unused=-29.0mg, P<0.001, respectively, for a Tukey’s 5429 HSD test; Fig. 25d). Again like the contribution to predators’ diets,

5430 and similar to prey abundance, the amount of biomass transferred

5431 by unused links was similar to what was expected under either

5432 null model (Fig. 25d). Both transmission and loss links transferred

5433 less biomass than expected under the unrestrictive null model,

5434 but transmission links also transferred less biomass than expected

5435 under the taxonomically-informed null model. Loss links, in contrast,

5436 transferred more biomass than expected based on the taxonomically-

5437 informed null model.

5438 Outcomes & centrality

5439 Like prey abundance, link centrality was signiﬁcantly correlated

5440 with different outcomes for parasites, whether or not host speciﬁcity

5441 was taken into account (F2,42019=527.5, P<0.001 in both cases). Both

5442 transmission links had much higher centralities than unused links 170

Figure 26: The outcomes of feeding links for parasites vary with their Unused Loss structural properties. Transmission links (circles) and loss links (squares) Transmission b 60 had significantly higher centralities c than unused links (diamonds). Solid fill Observed indicates the observed centralities, no Unrestrictive fill represents the mean value expected Restrictive using our unrestricted null model, and 40 striped fill the mean value expected using our taxonomically-informed null model. For the observed and a expected values, we show the mean centrality (±2 SE), although in some cases the error bars are very small. 20 Note that although transmission and

Centrality (# shortest paths) loss links had similar centralities, they had signiﬁcantly different structural roles. Here we show the median role for each link along the RDA axis that explains the most variation in 0 links’ roles, as well as the motifs most + Direct competition + Apparent competition strongly associated with the axis. Structural roles axis 1 Transmission links appeared more frequently in the apparent competition motif (two predators with one prey) and less frequently in the apparent 5443 (∆Transmission−Unused=22.3, P=0.011 and for a Tukey’s HSD test; Fig. 26). competition motif (two prey with one predator) than did loss links. 5444 Loss links, however, were more central than transmission links For a more detailed discussion of 5445 (∆Transmission−Loss=-4.37, P<0.001). As with the dynamic properties links’ structural roles, see Supporting 6 5446 described above, the centralities of unused links were very similar Information S . Note that, for visual clarity, the expected values for centrality 5447 to those expected under either null model (Fig. 26). Transmission have been staggered along the x-axis 5448 and loss links, meanwhile, were both more central than expected and only their vertical positions are meaningful. 5449 under either null model, although when using the taxonomically-

5450 informed null model transmission links were only slightly (but still

5451 signiﬁcantly) more central than expected based on the null model.

5452 Discussion

5453 Taken together, our results clearly show that the dynamic and

5454 structural properties of links among free-living species affect the

5455 links’ likelihoods of transmitting parasites. The contribution of a

5456 link to the predator’s diet, the abundance of the prey, the amount

5457 of biomass it transfers, and the centrality of a link all signiﬁcantly

5458 predicted whether or not a link would transmit a parasite. However,

5459 these relationships did not always run in the direction we expected.

5460 In particular, parasites tended to be transmitted along links that

5461 would appear to be less important than other links in terms of their

5462 dynamic properties.

5463 Transmission links tended to contribute less to predators’ diets

5464 than other types of links. Predators therefore appear to be “avoiding” 171

5465 prey species which contain parasites that can infect the predator.

5466 After taking the host speciﬁcities of parasites into account, however,

5467 transmission links make similar contributions to predators’ diets to

5468 what would be expected at random. This suggests that taxa which

5469 are potential intermediate hosts for the parasites in this system are

5470 not particularly important prey for parasites’ deﬁnitive hosts. Loss

5471 links, meanwhile, make much greater contributions to predators’

5472 diets than expected based on the taxonomically-informed null model.

5473 This suggests that consuming infected prey is a common strategy

5474 for predators which are not suitable hosts for the focal parasite. This

5475 has previously been observed in other aquatic systems where, for

5476 example, cockles infected with trematodes are mainly consumed

5477 by ﬁsh and whelks and only rarely by the parasites’ bird deﬁnitive

5478 hosts (Mouritsen and Poulin, 2003). The parasite induces changes

5479 in its host that limit burrowing ability and make the cockle more

5480 vulnerable to predation by birds, but other predators also take

5481 advantage of the increased availability of this prey (Mouritsen and

5482 Poulin, 2003). As morphological and behavioural changes that

5483 make parasites’ intermediate hosts more vulnerable to predation

5484 are common (Ness and Foster, 1999; Miura et al., 2006; Mouritsen

5485 and Poulin, 2003; Lefèvre et al., 2009), it is likely that exploitation of

5486 these modiﬁcations by predators other than the deﬁnitive host are

5487 also common.

5488 Our results for prey abundance were quite similar to those for

5489 the contribution of links to predators’ diets. Transmission links

5490 involved more abundant prey than unused links, but less than loss

5491 links. Moreover, prey abundances for transmission links were similar

5492 to (but lower than) what was expected under the taxonomically-

5493 informed null model. This once again suggests that parasites use

5494 abundant intermediate hosts largely because they are restricted to

5495 these host taxa. We expected that abundant hosts might promote

5496 transmission because these species tend to be encountered frequently

5497 and therefore involved in many feeding interactions (Wootton,

5498 2005; Canard et al., 2014). For parasites, however, this means that

5499 an abundant intermediate host is likely to be consumed by many

5500 predators that are not suitable deﬁnitive hosts (Canard et al., 2014).

5501 By infecting rarer intermediate hosts where possible, parasites

5502 may be using prey that are actively sought by their deﬁnitive

5503 hosts (Wootton, 2005), improving the odds of transmission. Without

5504 knowledge of predators’ preferences for different prey, however,

5505 testing this possibility is beyond the scope of this study. 172

5506 Transmission links also transferred less biomass than any other

5507 link type. Unlike the other properties we considered, transmission

5508 links transferred less biomass than expected based on either null

5509 model. Based on this result, predators appear to be obtaining most of

5510 their food either from prey infected with parasites that cannot infect

5511 the predator (i.e., loss links) or uninfected prey (i.e., unused links).

5512 This provides a counterpoint to Thompson et al. (2013)’s ﬁnding

5513 that parasites tend to accumulate in species which participate in

5514 many high-biomass food chains. Thompson et al. (2013) did not,

5515 however, ﬁnd any relationship between parasite diversity and the

5516 amount of biomass ﬂowing into a species— a closer equivalent to

5517 our measure of biomass transfer. It therefore appears that while

5518 parasites may “go with the ﬂow” to the extent that they enter food

5519 chains which transmit large amounts of biomass, they are more

5520 often killed than transmitted to their deﬁnitive hosts along such

5521 chains. As loss links in particular transferred more biomass than

5522 expected under the taxonomically-informed null model, predators

5523 may even preferentially consume infected prey as long as they are

5524 not suitable hosts for the parasite. This is consistent with previous

5525 work suggesting that infected prey are easier to ﬁnd and/or capture,

5526 reducing foraging costs for a predator (Lafferty, 1992; Mouritsen and

5527 Poulin, 2003).

5528 Although transmission links tended to be less important than

5529 other links in terms of their dynamic properties, our results for

5530 centrality supported our hypothesis that transmission links would

5531 be structurally important. Notably, loss links were also highly central.

5532 This is consistent with earlier research that found that more parasite

5533 species infect highly central hosts (Chen et al., 2008) or hosts with

5534 many links to other species (Thompson et al., 2013). Despite loss

5535 and transmission links having similar centralities, our use of motifs

5536 to examine links’ structural properties in more detail indicates that

5537 transmission and loss links tended to be embedded in the food web

5538 in different ways. In particular, it seems that generalist predators

5539 are frequently “dead ends” for parasites while links involving prey

5540 species with many predators more commonly result in transmission.

5541 This demonstrates that, while transmission and loss links are both

5542 structurally important, they nevertheless play different roles within

5543 the food web, just as suggested by our results for links’ dynamic

5544 properties.

5545 Overall, our results highlight the critical importance of taking

5546 host speciﬁcity into account. This outcome may be particularly

5547 striking since we address host speciﬁcity at a relatively coarse level 173

5548 (i.e., classes) when some parasites are known to be specialised to

5549 particular families or genera. It is therefore possible that our null

5550 model may not fully capture the restrictions on some parasite species.

5551 Nevertheless, the dramatic differences in the interpretation of our

5552 results after including even coarse measures of host speciﬁcity in our

5553 analyses demonstrate that, to truly understand trophic transmission

5554 of parasites, host speciﬁcity must be taken into account.

5555 Beyond emphasising the importance of host speciﬁcity, our

5556 results make it clear that parasite transmission is affected by the

5557 structure and dynamics of the free-living community. In particular,

5558 several of our results suggest that weak links— links that make

5559 relatively small contributions to the predator’s diet, transfer little

5560 biomass, etc. —may be the most important for parasites’ transmission

5561 through food webs. Intriguingly, weak links have also been touted

5562 as critical for community stability (McCann et al., 1998; Emmerson

5563 and Yearsley, 2004; Banašek-Richter et al., 2009). Where weak links

5564 are paired with strong ones, perturbations to the community tend

5565 to dissipate. This reduces the likelihood of a permanent change to

5566 the system, stabilising it (McCann et al., 1998; Wootton and Stouffer,

5567 2016).

5568 Weak links’ contribution to community stability might also

5569 explain why they are common transmission routes for parasites.

5570 Due to their complex life cycles and dependence on speciﬁc hosts,

5571 parasites may be unusually vulnerable to perturbations to their

5572 communities (Lafferty and Kuris, 2009). Parasites can also cause

5573 such perturbations by altering the population dynamics of their

5574 hosts (Marcogliese and Cone, 1997) or affecting the strength of

5575 interactions among free-living species (Lefèvre et al., 2009). If

5576 parasites were transmitted along dynamically strong links, the effects

5577 of parasites on their hosts could exacerbate any environmental

5578 perturbations the community experienced. This could lead to

5579 dramatic ﬂuctuations in host populations and the loss of the parasite.

5580 This scenario seems especially likely given our result that parasites

5581 tend to be transmitted along highly central links. As described above,

5582 perturbations to these links are likely to have substantial effects on

5583 the community (Jordán et al., 2007). It may well be that parasites can

5584 only be transmitted along links that are structurally or dynamically

5585 important without destabilising their hosts’ populations. More

5586 work is necessary to determine whether the long-term persistence

5587 of parasites in a community is indeed related to the community’s

5588 overall stability, but our results suggest that this is an avenue worth

5589 following. 174

5590 Acknowledgements

5591 We thank Anne Besson, Isa Blasco-Costa, Manna Warburton, and Kim

5592 Garrett for assistance with ﬁeld collection and laboratory processing

5593 of samples. We thank Bernat Bramon Mora, Melissa Broussard,

5594 Carla Gomez Creutzberg, Matthias Dehling, and Matt Hutchinson

5595 for productive discussions about this study. This study was funded

5596 by two separate Marsden Fund grands, administered by the Royal

5597 Society of New Zealand, to RP and to DBS, and by an NSERC PGS-D

5598 scholarship and UC Doctoral Scholarship to ARC.

5599 Supporting information

5600 S6.1: Detailed methods for data collection

5601 S6.2: Supplemental methods and results for links’ structural

5602 properties

5603 S6.3: Results of model selection

5604 S6.4: Testing for correlations between link properties 175

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5718 Theoretical Ecology, 9:185–195. 5719 General discussion

5720 Summary of results

5721 Over the course of this thesis, I have demonstrated several ways

5722 in which species’ roles in ecological networks can be used to

5723 connect their natural histories to the structure and function of the

5724 communities in which they are embedded. In Chapter 1, my co-

5725 authors and I reviewed several common deﬁnitions of species’ roles

5726 and highlighted the similarities and differences among them. We

5727 framed each deﬁnition of species’ roles in terms of their niches, and

5728 suggested that discussing species’ roles in a niche context will avoid

5729 confusion between role deﬁnitions. Although the remaining chapters

5730 in this review use only a few of the role concepts included in the

5731 review, Chapter 1 also illustrates the range of potential applications

5732 of species’ roles.

5733 Chapters 2 through 6 present original research. In Chapter 2,

5734 we tested whether knowledge about arthropods’ roles in a mainland

5735 food web could be used to improve the predictions of models based

5736 on the Theory of Island Biogeography. In this case we deﬁned

5737 roles as simply the set of prey and arthropod predators for each

5738 species. We found that incorporating information about species’

5739 roles signiﬁcantly improved the predictions of models for both

5740 immigration and extinction. Arthropods’ roles as consumers were

5741 especially informative. This could be because the presence of prey

5742 for a given species was much more variable than the presence of

5743 predators across our dataset, or because the dataset only included

5744 information on arthropods and neglected other taxa (e.g., birds)

5745 that could have large impacts on the arthropods’ ability to colonise

5746 islands.

5747 In Chapter 3, we again deﬁned species’ roles as their sets of

5748 interaction partners. This time, we used plants’ roles in pollination

5749 and herbivory networks to test whether closely-related species have

5750 more similar roles. In general, this was indeed the case. In both 180

5751 network types, dissimilarity in species’ interaction partners increased

5752 as phylogenetic distance increased. Within families, however, there

5753 was a great deal of variability. In some families more closely-related

5754 species had more similar roles, as expected, while in others the

5755 opposite trend emerged. Our results therefore suggest a complex

5756 history of convergent and divergent evolution among plants and their

5757 interaction partners.

5758 In Chapters 4-6, we deﬁned roles more abstractly. Speciﬁcally,

5759 we used motifs to categorise the roles of species (Chapters 4 and 5)

5760 and interactions between species (Chapters 5 and 6). In Chapter 4,

5761 we tested whether the roles of plants and their insect pollinators in

5762 a high-Arctic community changed after 15 years of climate change.

5763 Both groups’ roles did indeed change, as did the structure of the

5764 network overall. In particular, our results suggest that phenological

5765 uncoupling may be occurring in this system. This suggests that,

5766 under continuing climate change, some plants may not receive

5767 adequate pollination services and some pollinators may not ﬁnd

5768 sufﬁcient food.

5769 In Chapter 5, we compared the roles of parasites and free-living

5770 species in order to establish whether parasites’ roles are similar to

5771 those of any free-living group and whether including different types

5772 of interactions (i.e., parasitism, antagonism among parasites, and

5773 concomitant predation on parasites inside their hosts) affect parasites’

5774 roles. When concomitant predation was not included in parasites’

5775 roles, they were similar to those of free-living top predators and

5776 intermediate consumers. When concomitant predation was included

5777 in parasites’ roles, however, these roles were unlike those of any

5778 group of free-living species. By analysing their roles in this way, we

5779 demonstrated that parasites are important both as consumers of free-

5780 living species and as a food source for them.

5781 In this chapter, we also expanded the concept of species’

5782 structural roles to interactions between species. We showed that

5783 different types of interaction (predation between free-living species,

5784 parasitism, antagonism among parasites, and concomitant predation)

5785 had different roles, and that the roles of concomitant predation

5786 interaction were particularly variable. This interaction is contingent

5787 upon parasitism and predation interactions already taking place,

5788 and so likely inherits variation in roles from both of these interaction

5789 types. Concomitant predation is also interesting because it can have

5790 a variety of consequences for parasites. For trophically-transmitted

5791 parasites, consumption of the current host by a suitable host for the 181

5792 parasite’s next life stage is required for the parasite to complete

5793 its life cycle (Marcogliese and Cone, 1997). If the current host is

5794 consumed by an inappropriate predator, however, or if the parasite

5795 is not trophically-transmitted, then the parasite dies. It seems likely

5796 that interactions which have different outcomes for the parasites have

5797 different structural roles. While testing this possibility was beyond

5798 the scope of Chapter 5, it formed the focus of Chapter 6.

5799 In Chapter 6, we deﬁned the roles of feeding links between

5800 free-living species based on their motifs (as in Chapter 5), but also

5801 based on other structural and dynamic properties. Speciﬁcally, we

5802 measured each link’s centrality (the number of times it appears

5803 on the shortest path between two species), its importance to the

5804 predator (i.e., the proportion of the predator’s diet that the link

5805 contributes), the amount of biomass transferred along the link, and

5806 the abundance and biomass of the prey involved in the link. These

5807 measures combined give a comprehensive picture of the way each

5808 link ﬁts into the overall network. We then tested whether any of

5809 our measures of links’ roles were related to the outcome of these

5810 links for parasites. We expected that parasites would tend to be

5811 transmitted along links that were very important to the structure

5812 and dynamics of the network (i.e., highly central, transferring large

5813 amounts of biomass, etc.). We did indeed ﬁnd that parasites tended

5814 to be transmitted along highly central links, transmission links also

5815 tended to be dynamically (e.g., transmit less biomass) weaker than

5816 links resulting in the death of the parasite. As such weak links are

5817 believed to promote community stability by dissipating perturbations

5818 to any one species, while highly central links are believed to transmit

5819 perturbations and could thereby destabilise the community, it

5820 appears that parasites are transmitted along routes that are unlikely

5821 to strongly disrupt the community.

5822 Implications

5823 As the implications of each chapter have been discussed within the

5824 chapters themselves, I will now consider the impact of this thesis as

5825 a whole. In Chapters 2 through 6, my co-authors and I demonstrated

5826 a variety of contexts in which species’ roles can be used to gain a

5827 deeper understanding of ecological communities. In each case, we

5828 used species’ roles either as a bridge between the overall network

5829 structure and species’ traits or to tailor community-level ecological

5830 theory to particular species. Thus, this thesis demonstrates how

5831 species roles can be used to make network ecology directly applicable

5832 for parasitologists, island biogeographers, etc. Over the course of this 182

5833 thesis, I have become ﬁrmly convinced that this type of applicability

5834 is essential for network ecology to achieve its full potential.

5835 Network ecology began as an offshoot of graph theory and,

5836 like other extensions of graph theory in linguistics, neurology,

5837 and sociology, has remained strongly interdisciplinary (Dunne,

5838 2006). Some of the methods I used to determine species’ roles in

5839 Chapters two through six, for example, were ﬁrst developed in the

5840 context of sociology (Jordán et al., 2007; Lai et al., 2012) or statistical

5841 physics (Guimerà et al., 2007). This history has shaped network

5842 ecology into a highly versatile discipline, able to address any type

5843 of interaction in any system one might wish. However, because they

5844 borrow so many terms and methodologies from outside of ecology,

5845 studies of networks can be difﬁcult for non-specialists to understand

5846 and connect to their own work. As demonstrated by Chapters four

5847 and six (which were collaborations with empirically-grounded

5848 researchers), roles are one way to overcome this dilemma.

5849 Because roles are species-level properties, they are easy to

5850 associate with other knowledge about species in a way that analyses

5851 of network structure are not. For example, we can identify the

5852 most central species in a lake food web and determine whether

5853 they are ﬁsh, invertebrates, or algae and how their morphologies

5854 and behaviours differ from less-central species. Thus, a species’

5855 importance to the rest of the network can be explained in typically

5856 ecological terms. A network-level metric like connectance, in contrast,

5857 is more difﬁcult to connect to the particulars of the study system

5858 because it summarises all species and interactions into a single

5859 measure. Instead, network metrics have been studied in the context

5860 of site characteristics like latitude and ecosystem type (Briand, 1983;

5861 Riede et al., 2011; Baiser et al., 2012; Cirtwill et al., 2015) or spatial

5862 scale (Martinez and Lawton, 1995; Thompson and Townsend, 2005).

5863 Similarly, while network-level properties have been used to gauge

5864 the stability of different ecological communities (May, 1972; Dunne

5865 et al., 2002; Gilbert, 2009; Fortuna et al., 2010; Plank and Law, 2012),

5866 such studies are not concerned with the persistence of any particular

5867 species of interest. Roles could be used to ﬁll this gap in the future.

5868 Although species’ roles are easier to integrate with natural

5869 history than network-level metrics, they can still be unintuitive and

5870 difﬁcult to interpret. This is particularly true for high-dimensional

5871 role concepts like structural roles. The length of the vectors used to

5872 deﬁne structural roles is undoubtedly part of the problem; even a

5873 24-dimensional vector is difﬁcult for a human to grasp, and in studies 183

5874 of bipartite networks structural role vectors may be over 100 entries

5875 long. This issue is easily solved by comparing roles statistically and

5876 interpreting differences between them with respect to the motifs

5877 which explain the most variation. However, the motifs themselves

5878 can also be a challenge to interpret. Few of the motifs used to deﬁne

5879 species’ structural roles have been empirically studied (Bascompte

5880 and Melián, 2005). Those that have are small (3-4 species) and

5881 include only one-way interactions. Although one-way feeding links

5882 are more common than two-way links overall, for some types of

5883 species (e.g., ﬁsh whose diets change depending on their age and

5884 size [Rudolf and Lafferty, 2011]) two-way interactions may be both

5885 more common than expected and quite important to the population

5886 dynamics of both species involved. Motifs including two-way

5887 interactions therefore merit further study, particularly in empirical

5888 systems rather than simulations.

5889 One-dimensional conceptions of species’ roles are often easier

5890 to interpret, but can be just as difﬁcult to connect with particular

5891 ecological traits if the traits in question were not the focus of the

5892 study. We saw this in Chapter 3, where our ability to interpret the

5893 changing trends in conservation of plants’ roles across families was

5894 limited by a dearth of information about relevant traits of these

5895 families. This highlights the beneﬁts of collaborations between

5896 network ecologists and researchers with expertise in the study

5897 system being examined. Such collaborations can suggest which of

5898 species’ traits are most relevant to their network roles, but also tend

5899 to suggest interesting questions that might not develop in a group

5900 comprised of only network specialists. I believe that the work in

5901 this thesis argues strongly for intradisciplinary but inter-speciality

5902 collaborations, and I intend to continue along this line in my future

5903 work.

5904 Next steps

5905 Over the course of this thesis we use several different deﬁnitions

5906 of species and link roles. Going forward, it would be useful to

5907 understand which role deﬁnitions are strongly correlated and which

5908 provide unique information about species’ relationships to their

5909 communities. As well as understanding the relationships between

5910 different role concepts in relatively stable communities, it would

5911 also be interesting to investigate how different deﬁnitions of species’

5912 roles change as interaction networks are altered. Interactions can be

5913 lost long before the species involved go extinct (Aizen et al., 2012)

5914 and some deﬁnitions of species roles may be better than others at

5915 tracking the effects of interaction loss across the network. Moreover, 184

5916 different deﬁnitions of roles may vary in their ability to predict

5917 species’ impact on their community and species’ persistence in the

5918 face of perturbations. Identifying which role concepts are best suited

5919 to these types of questions will make roles a much more useful tool

5920 for conservation ecologists and other working on similar questions.

5921 As well as working to understand the relationships between role

5922 concepts, it would be fruitful to investigate the spatial and temporal

5923 variation in species’ roles. Roles are likely to vary over both large

5924 and small scales, but there may be consistent “archetypal” roles in

5925 different ecosystem types, for different taxa, etc. Comparing species’

5926 roles across smaller scales, meanwhile, would indicate how variable

5927 interactions are across species. Those with highly variable roles

5928 might be more able to adapt to climate change or other perturbations,

5929 but they might also be more likely to become invasive if introduced

5930 to a new community. Where spatially replicated communities can be

5931 combined with information about the assembly of the community, it

5932 would also be interesting to test whether the order in which species

5933 colonise a site affects their roles. In addition to comparing the roles

5934 for a single species across sites, it would be interesting to compare

5935 the roles of large collections of species. Speciﬁcally, it may be possible

5936 to group species roles into a small number of ‘archetypal’ roles that

5937 are particularly common across sites. Examining which species share

5938 similar roles could provide a great deal of insight into how similar

5939 network structures develop from disparate communities.

5940 As with spatial variation, temporal variation in species’ roles can

5941 also indicate how species are responding to global change. As my co-

5942 authors and I showed in Chapter 4, comparing species’ roles across

5943 decades can show how communities are responding to global change.

5944 This approach could be used in many other systems to investigate the

5945 effects of different perturbations on entire communities. It would also

5946 be interesting to investigate how species’ roles change throughout a

5947 year. A species that acts as a relatively minor component of a food

5948 web for the majority of the year might assume a keystone role during

5949 one season. This variation is obscured in most food webs which are

5950 either snapshots of a single time period or aggregated over long time

5951 scales, but has the potential to dramatically alter our understanding

5952 of the structure and functioning of ecological communities.

5953 Combining food webs with phylogenies is yet another area ripe

5954 for exploration. Earlier work has shown that species’ roles tend to

5955 be conserved across phylogenies (Stouffer et al., 2012), but my co-

5956 authors and I also show (Chapter 3) that there can be substantial 185

5957 variation about this general trend. Identifying the conditions under

5958 which species’ roles tend to be phylogenetically conserved has the

5959 potential to illuminate the interplay between evolution and ecology

5960 in structuring biological communities. The cases in which species’

5961 roles are not conserved are likely to be particularly interesting as

5962 non-conservation of interactions could be due to convergent or

5963 divergent evolution, the loss of interaction partners, or a number

5964 of other causes. Species with unique roles based on their phylogenies

5965 could also be of particular conservation concern, as these species are

5966 unlikely to be replaced by a relative if they are lost.

5967 Moving from species roles to interaction roles, it would also be

5968 interesting to test whether strong and weak interactions tend to have

5969 different structural roles in networks. The distribution of interaction

5970 strengths within a network has been linked to stability in several

5971 studies (McCann et al., 1998; Emmerson and Yearsley, 2004; Banašek-

5972 Richter et al., 2009; Tang et al., 2014; Nilsson and McCann, 2016), but

5973 a link’s structural role may modulate the effects of its strength on

5974 the rest of the community. In particular, because concepts like motif

5975 roles describe meso-scale or global network structures they have the

5976 potential to capture different links’ abilities to affect species other

5977 than the two that are directly involved.

5978 Apart from continuing to build on role concepts as described

5979 above, studies of species’ roles have much to gain from collaborations

5980 between network ecologists and those with more empirical expertise.

5981 As ecologists ﬁnd more ways in which species’ traits are related to

5982 their roles in ecological networks, species’ roles will become more

5983 and more valuable tools with which to understand their ecology.

5984 In particular, understanding the roles of introduced species in

5985 their native communities may help us to predict which species

5986 will become invasive and how they will spread by helping us to

5987 predict likely interaction partners for the introduced species in its

5988 novel range. Similarly, gaining a better understanding of the roles of

5989 species that are economically important to humans (e.g., food ﬁsh,

5990 crop plants, bees) will help policymakers to manage the impact of

5991 human activities across whole ecological communities rather than

5992 from the perspective of a single metric at a time. These are only

5993 a few ways in which concepts of species roles may be used in the

5994 near future; such a versatile toolkit will surely come to be applied in

5995 myriad ways and in a plethora of systems. 186

5996 Conclusion

5997 I hope that the body of work that this thesis represents is a

5998 convincing argument that species’ roles are a valuable addition

5999 to network ecology; particularly as a bridge between network

6000 structure and species’ traits. Connecting network-level and species-

6001 level information has been named as one of the 100 outstanding

6002 fundamental questions in ecology (Sutherland et al., 2013), and

6003 I am pleased to have made a contribution to solving it. Moving

6004 forward, more attention should be given to the biological meaning

6005 of different role concepts. The studies which make up this thesis

6006 offer several ways to do this— by collaborating with specialists in the

6007 study system from which the web is drawn, by making an important

6008 ecological trait like parasitism the focus of the study, or by drawing

6009 on an extensive literature about the system. I am certain that I and

6010 other researchers will ﬁnd more links between network ecology and

6011 other subdisciplines in the future. 187

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6094 1925.

6095 Appendix: Are parasite richness and abundance

6096 linked to prey species richness and individual feeding

6097 preferences in ﬁsh hosts?

1 1 2 6098 Alyssa R. Cirtwill , Daniel B. Stouffer ,Robert Poulin ,& 2 6099 Clement Lagrue

1 6100 Centre for Integrative Ecology

6101 School of Biological Sciences

6102 University of Canterbury

6103 Private Bag 4800

6104 Christchurch 8140, New Zealand

2 6105 Department of Zoology

6106 University of Otago

6107 340 Great King Street

6108 PO Box 56

6109 Dunedin 9054, New Zealand

6110 Published in Parasitology (2016).

6111 DOI: 10.1017/S003118201500150X 192

6112 Summary

6113 Variations in levels of parasitism among individuals in a population

6114 of hosts underpin the importance of parasites as an evolutionary or

6115 ecological force. Factors inﬂuencing parasite richness (number of

6116 parasite species) and load (abundance and biomass) at the individual

6117 host level ultimately form the basis of parasite infection patterns. In

6118 ﬁsh, diet range (number of prey taxa consumed) and prey selectivity

6119 (proportion of a particular prey taxon in the diet) have been shown to

6120 inﬂuence parasite infection levels. However, ﬁsh diet is most often

6121 characterised at the species or ﬁsh population level, thus ignoring

6122 variation among conspeciﬁc individuals and its potential effects on

6123 infection patterns among individuals. Here, we examined parasite

6124 infections and stomach contents of New Zealand freshwater ﬁsh at

6125 the individual level. We tested for potential links between the

6126 richness, abundance and biomass of helminth parasites and the diet

6127 range and prey selectivity of individual ﬁsh hosts. There was no

6128 obvious link between individual ﬁsh host diet and helminth infection

6129 levels. Our results were consistent across multiple ﬁsh host and

6130 parasite species and contrast with those of earlier studies in which

6131 ﬁsh diet and parasite infection were linked, hinting at a true

6132 disconnect between host diet and measures of parasite infections in

6133 our study systems. This absence of relationship between host diet

6134 and infection levels may be due to the relatively low richness of

6135 freshwater helminth parasites in New Zealand and high host-parasite

6136 speciﬁcity.

6137 Keywords

6138 ﬁsh diet, helminth parasites, infection levels, individual host,

6139 transmission mode. 193

6140 Introduction

6141 Parasites are both important agents of natural selection and factors

6142 contributing to the dynamics of host populations (Ebert et al., 2000;

6143 Albon et al., 2002; Marcogliese, 2004). Within a population, variation

6144 in the degree of parasitism incurred by individual hosts underpins

6145 the importance of parasitism as an evolutionary or ecological force.

6146 Identifying which processes inﬂuence parasite distribution among

6147 hosts, and make some hosts more susceptible to infection than others,

6148 is thus a central question in parasite ecology (Carney and Dick, 1999;

6149 Poulin, 2000; González and Poulin, 2005). Factors inﬂuencing parasite

6150 richness (number of parasite species) and abundance (number of

6151 conspeciﬁc parasite individuals) at the individual host level

6152 ultimately form the basis of parasite infection patterns (Carney and

6153 Dick, 2000). Several ecological factors and host attributes can

6154 inﬂuence the number and diversity of parasites infecting hosts at the

6155 individual level. In ﬁsh, these factors may include age/size, the

6156 number of different prey consumed as well as prey selectivity, habitat,

6157 etc. (Poulin, 2000; Johnson et al., 2004b; Locke et al., 2014). Many

6158 helminth parasites have complex life cycles that are embedded within

6159 food webs, relying on trophic transmission (i.e. consumption of an

6160 infected prey by the predator host) to reach their next host (Simková

6161 et al., 2001). For example, richness and abundance of trophically

6162 transmitted parasites in ﬁsh can thus be largely explained by the

6163 diversity of the prey/intermediate host community upon which

6164 different ﬁsh feed (Carney and Dick, 2000; Bolnick et al., 2003;

6165 Klimpel et al., 2006). Fish with a broad diet, feeding on more species

6166 of prey, may thus have more diverse trophically transmitted adult

6167 parasites (i.e. higher parasite richness) than those with more narrow,

6168 specialised diets (Kennedy et al., 1986; Lo et al., 1998; Locke et al.,

6169 2014). At the same time, a selective diet may not preclude ﬁsh hosts

6170 from accumulating large numbers of parasites (i.e. high parasite

6171 abundance). Trophically transmitted parasites usually utilise limited

6172 numbers (often only 1 or 2) of intermediate host prey taxa, and

6173 parasite abundance in ﬁsh hosts therefore depends on the importance

6174 of these few species in the ﬁsh diet rather than the absolute number

6175 of prey groups consumed; i.e. a ﬁsh feeding mostly on the parasite’s

6176 intermediate host is more likely to accumulate parasites than a ﬁsh

6177 feeding equally on all prey species forming its diet (Kennedy et al.,

6178 1986; Marques et al., 2011). The degree of diet selectivity and the

6179 type/taxa of prey favoured by ﬁsh hosts may thus inﬂuence parasite

6180 infection levels, even in ﬁsh with qualitatively broad diets (Kennedy

6181 et al., 1986; Marques et al., 2011). Shifts in dietary preference with

6182 age/size can also be important determinants of adult helminth 194

6183 richness and abundances in ﬁsh hosts (Johnson et al., 2004b; Poulin

6184 et al., 2011). Prey selection is largely gape-limited, both within and

6185 among ﬁsh species, and the diversity of prey consumed usually

6186 increase with gape size, itself strongly linked to ﬁsh body

6187 size (Wainwright and Richard, 1995; Hyndes et al., 1997; Marcogliese,

6188 2002; Klimpel et al., 2006). Overall, variability in feeding preferences

6189 may thus strongly affect parasite richness and abundance among

6190 sympatric, conspeciﬁc ﬁsh hosts (Knudsen et al., 1997).

6191 On the contrary, prey diversity should have little effect on parasites

6192 that infect ﬁsh directly (Simková et al., 2001). Many larval trematodes

6193 infect ﬁsh through skin penetration and use ﬁsh as intermediate

6194 rather than deﬁnitive hosts (Locke et al., 2013, 2014). Larval

6195 trematodes directly penetrating ﬁsh skin subsequently enter a

6196 dormant stage and wait for the ﬁsh to be consumed by the

6197 appropriate deﬁnitive host predator. Trematode larvae can

6198 accumulate in ﬁsh hosts over time, unlike adult helminths in the

6199 gastrointestinal tract which are shorter lived (Carney and Dick, 2000;

6200 Locke et al., 2014). As a result, larger ﬁsh are expected to have higher

6201 richness and abundances of skin-penetrating trematode

6202 larvae (Zelmer and Arai, 1998; Carney and Dick, 2000; Poulin, 2000).

6203 Overall, among conspeciﬁc ﬁsh, larger individuals may harbour

6204 higher adult and larval helminth richness and abundances because

6205 they tend to consume a greater number of prey; they should be

6206 exposed to an increasing variety of potential intermediate hosts,

6207 being less gape-limited, and have been accumulating more larval

6208 parasites than their smaller conspeciﬁcs (Bell and Burt, 1991; Poulin,

6209 1995; Morand et al., 2000; González and Poulin, 2005; Dick et al., 2009;

6210 Zelmer, 2014).

6211 Phylogenetic effects relating to host speciﬁcity can also structure

6212 parasite communities among ﬁsh species that have similar diets but

6213 are phylogenetically distinct (Poulin, 1995). A broad diet may bring a

6214 ﬁsh into contact with a wide diversity of parasite species, though

6215 only a small subset of these may infect the host for evolutionary

6216 reasons (e.g. host-parasite compatibility; Kennedy et al., 1986).

6217 Ingestion of larval helminths by ﬁsh is frequent in most ﬁsh species

6218 due to the abundance and diversity of these parasites in aquatic

6219 ecosystems (Marcogliese, 2002; Parker et al., 2003). However, while

6220 different, co-occurring ﬁsh species can be exposed to the same

6221 helminths, host-parasite compatibility may subsequently modulate

6222 parasite infection patterns among ﬁsh host species (Lagrue et al.,

6223 2011). Overall, similarities or differences in parasite richness and

6224 abundance among sympatric ﬁsh species should be largely 195

6225 inﬂuenced by the combination of host diet and species-speciﬁc

6226 host-parasite compatibility (Lile, 1998; Knudsen et al., 2008; Lagrue

6227 et al., 2011).

6228 Despite the potential for effects on parasite infection patterns, ﬁsh

6229 diet is most often characterised at the species or population level,

6230 thus ignoring potential variation among individuals (Fodrie et al.,

6231 2015). Diet variation and ‘individual specialisation’ among

6232 conspeciﬁc individuals is common in natural populations, including

6233 ﬁsh (Bolnick et al., 2002, 2003; Araújo et al., 2011; Layman et al., 2015;

6234 Rosenblatt et al., 2015). Species assumed to be dietary generalists and

6235 exhibiting broad population-level diets can actually specialise at the

6236 individual level, inducing intraspeciﬁc differences in risk of

6237 parasitism (Curtis et al., 1995; Wilson et al., 1996). Combining data on

6238 individual ﬁsh stomach contents (number of prey groups and relative

6239 abundance in ﬁsh diet) and parasites (richness and speciﬁc

6240 abundances) may therefore provide a more accurate picture of the

6241 link between host diet and infection levels. Numerous ﬁsh species are

6242 considered opportunistic omnivores consuming a wide variety of

6243 prey taxa, though as individuals, ﬁsh can display contrasting dietary

6244 preferences that may yield differences in parasite richness and

6245 abundance among conspeciﬁc hosts. An individual host typically

6246 harbours a small sample of the local parasite community that reﬂects

6247 its individual diet range (i.e. number of prey groups consumed) and

6248 prey selectivity (Locke et al., 2013). Usually, parasites are aggregated

6249 among available hosts (Poulin, 2007; Poulin et al., 2013). This is often

6250 due to differences in the rate of parasite acquisition among hosts. For

6251 trophically transmitted helminths, differences in diet among

6252 conspeciﬁc hosts can generate heterogeneity in exposure to parasites

6253 and ultimately produce such aggregated distributions (Knudsen

6254 et al., 2004; Poulin, 2007).

6255 Here, we used ﬁeld sampling to quantify and analyse the richness

6256 and abundance of all helminth parasites as well as stomach contents

6257 of individual ﬁsh of 11 species. Stomach contents reﬂect short-term

6258 feeding patterns, but may still capture the causal link between diet

6259 and helminth richness and abundance among but also within ﬁsh

6260 species (i.e. among conspeciﬁc ﬁsh individuals; (Johnson et al.,

6261 2004b). Individual ﬁsh feeding preferences are likely consistent over

6262 time, at least seasonally, and even a single stomach content sample

6263 should reﬂect fairly accurately individual ﬁsh diet. Strong overlap in

6264 parasite infection (richness and abundance), or lack thereof, among

6265 unrelated ﬁsh species may reﬂect similarities or differences in diet,

6266 habitat and host speciﬁcity (or a combination of these factors) that 196

6267 are sometimes difﬁcult to tease apart due to phylogenetic

6268 effects (Carney and Dick, 1999). Here, by comparing parasite richness

6269 and abundance among sympatric conspeciﬁcs, we eliminated these

6270 potential phylogenetic and geographical effects. Our main goal was

6271 to determine whether differences in parasite richness and abundance

6272 among fish species and among conspecific fish individuals can be

6273 linked to variations in the number of prey groups consumed, feeding

6274 preferences and/or ﬁsh size. These factors should have contrasting

6275 inﬂuences on trophically compared with directly transmitted

6276 parasites. We thus tested the potential effects of diet range and

6277 selectivity on parasite infection levels in individual ﬁsh host

6278 separately for the 2 parasite categories. Trophically transmitted

6279 parasite richness should increase with diet range in ﬁsh diet and

6280 specific parasite abundance be more influenced by individual fish

6281 feeding preferences. In contrast, directly transmitted parasites should

6282 not be inﬂuenced by ﬁsh host diet. Overall, differences in feeding

6283 preferences among individuals may be reﬂected in differences in

6284 parasite infections. Ideally, individual feeding preferences would be

6285 assessed at multiple time points; however, for obvious reasons (the

6286 need to sacriﬁce ﬁsh to recover gut contents and parasites), this is not

6287 possible, and we must rely on a single measurement.

6288 Material and Methods

6289 Data collection

6290 Field sampling ◦ 6291 Fish were sampled in 4 lake ecosystems. Lake Hayes (44 58’59.4"S, ◦ ◦ ◦ 6292 168 48’19.8"E), Lake Tuakitoto (46 13’42.5"S, 169 49’29.2"E), Lake ◦ ◦ 6293 Waihola (46 01’14.1"S, 170 05’05.8"E) and Tomahawk Lagoon ◦ ◦ 6294 (45 54’06.0"S, 170 33’02.2"E; South Island, New Zealand) were

6295 selected to provide a variety of lake types (size, depth and altitude),

6296 freshwater communities (coastal vs alpine, trophic state and tidal or

6297 not; see Table SA.1 for details). Within each lake, 4 sampling sites

6298 were selected along the littoral zone to cover all microhabitat types

6299 (substrate, macrophytes, riparian vegetation, etc.) present within each

6300 lake. The 4 lakes were sampled in early spring, summer and late

6301 autumn (austral seasons: September 2012, January and May 2013).

6302 Fish were captured at each site and in each lake to assess potential

6303 spatial variability within and among lakes in ﬁsh gut contents (prey

6304 richness and selectivity) and infection levels (parasite richness and

6305 abundance). We used a combination of ﬁsh catching gear types so

6306 that accurate cross-sections of ﬁsh species and size classes were

6307 sampled from each site. Two fyke nets and 10 minnow traps were set 197

6308 overnight in each site, when some ﬁsh species are more active (i.e.

6309 eels and common bully), as they are passive sampling methods

6310 relying on ﬁsh to willingly encounter and enter traps (Hubert, 1996).

6311 The next day, trapped ﬁsh were recovered and set aside for later

6312 dissection. Sampling was then complemented using two 15m long

6313 multi-mesh gillnets. Gillnets were benthic-weighted sets with top

6314 ﬂoats, 1.5m high and comprised 3 panels of 25, 38 and 56mm meshes,

6315 each 5m long. Gillnets covered the whole water column and were

6316 used to capture highly mobile, mainly diurnal ﬁsh (i.e. trout, perch

6317 and mullet). Fish caught in the nets were removed immediately to

6318 avoid excessive accumulation and the potential visual deterrence to

6319 incoming ﬁsh (Lagrue et al., 2011). Finally, ﬁsh sampling was

6320 completed using a standard, ﬁne-mesh (5mm mesh size) purse seine

6321 net. As an active sampling method, seine netting captures small

6322 and/or sedentary ﬁsh species (i.e. galaxiids, smelt and juvenile ﬁsh

6323 of most species) that are not captured by passive gear like fyke nets

6324 or gillnets (Thorogood, 1986). All ﬁsh were killed immediately to

6325 inhibit the digestion process and stored on ice to preserve internal

6326 tissues, stomach contents and parasites for future identiﬁcation,

6327 count and measures. In the laboratory, ﬁsh were identiﬁed to species,

6328 measured to the nearest millimetre (fork length), weighed to the

6329 nearest 0.01g and then dissected. The gastrointestinal tract, from

6330 oesophagus to anus, and all internal organs (heart, liver, gall bladder,

6331 gonads, swim bladder, etc.) of each ﬁsh were removed and preserved

6332 in 70% ethanol for later diet and parasite analyses. Fish bodies were

6333 frozen separately for later parasite analyses as ethanol preservation

6334 renders muscle tissues difﬁcult to screen for parasites.

6335 Parasites

6336 Complete necropsies of all ﬁsh were conducted under a dissecting

6337 microscope. The head, gills, eyes, brain and spine of each ﬁsh were

6338 examined using ﬁne forceps to pull apart ﬁsh tissues and obtain an

6339 accurate, total parasite count for all helminth species in each

6340 individual ﬁsh. Soft tissues (muscle and skin) were removed from the

6341 spine, crushed between 2 glass plates and examined by transparency

6342 to identify and count parasites. Internal organs and the

6343 gastrointestinal tract were ﬁrst rinsed in water to wash off the

6344 ethanol. The digestive tract was then separated from other organs.

6345 Liver, swim bladder, gall bladder, gonads and other organs and

6346 tissues from the body cavity (fat, mesentery, kidneys, heart, etc.) were

6347 all screened for parasites. Finally, the digestive tract was dissected.

6348 Stomach and intestine contents were removed, screened for parasites

6349 and then set aside for later diet examination. Oesophagus, stomach, 198

6350 pyloric caeca (when present), intestine and rectum were then

6351 examined for gastrointestinal parasites. All parasites were identiﬁed

6352 and counted. For each ﬁsh individual, helminth parasite richness

6353 (total number of species) and speciﬁc abundances (total number of

6354 individuals per parasite species) were determined. The life stage

6355 (adult or larval) and infection mode (directly or trophically

6356 transmitted) of all individuals was also recorded. Note that no

6357 external parasite (copepods, monogeneans or leeches) were recovered

6358 from any of the ﬁsh examined and are thus not considered here.

6359 Fish diet contents

6360 Food items from the stomach and intestine of all ﬁsh were identiﬁed

6361 under a dissecting microscope to determine the diet range of each

6362 individual (number of different prey taxa). Prey items were also

6363 counted to estimate the relative importance of each prey taxa in

6364 individual ﬁsh gut contents. Relative importance of each prey

6365 (number of a speciﬁc prey divided by the total number of prey items

6366 in the ﬁsh diet contents) was used as an estimate of diet selectivity of

6367 individual ﬁsh hosts.

6368 Analyses

6369 Parasite richness

6370 As different mechanisms are expected to affect the number of directly

6371 and trophically transmitted parasite species acquired by a given ﬁsh

6372 host, we ﬁrst divided the parasite community within each ﬁsh based

6373 on transmission mode (considering each life stage separately for

6374 parasites with complex life cycles). We then tested for a potential

6375 relationship between the richness of each group of parasites and host

6376 diet range (here deﬁned as the number of prey taxa found in the ﬁsh

6377 host’s gut contents), size (log of weight in grams) and their

6378 interaction. To account for the possibility that the richness of a host’s

6379 parasite community was lower or higher because of its environment,

6380 we also included nested random effects of lake and site within lake.

6381 These random effects allow us to control for additional variation in

6382 parasite richness that can be explained by lake and site-within-lake 199

6383 without sacriﬁcing the degrees of freedom that would be lost if they

6384 were ﬁxed effects. This gave us the model:

Σi = β0 + β0t +(β1 + β1t)ωi +(β2 + β2t)ρi +(β3 + β3t)ωiρi + Li + Si + ǫi (6)

6385 where Σi is the number of parasite species with a given transmission 6386 mode (direct or trophic) in an individual host i, ωi is the log of the 6387 weight of the ﬁsh host, ρi is the host’s diet range, Li is a random 6388 effect of lake, Si is a nested random effect of site within lake, and ǫi is 6389 a residual error term. Note that β0, β1, β2, and β3 refer to 6390 directly-transmitted parasites while β0t, β1t, β2t, and β3t are

6391 ‘adjustments’ to these β’s when considering trophically transmitted

6392 parasites. As we were not interested in seasonal variations in this

6393 study, we analysed data from all 3 seasons together.

6394 As richness, deﬁned here as the number of parasite species per ﬁsh

6395 host, can take integer values only, and because many potential hosts

6396 did not contain any parasites, we ﬁt these models as zero-inﬂated

6397 Poisson processes where the ﬁxed effects described above applied to

6398 the Poisson components of the model only. That is, the zero-inﬂated

6399 component consisted of a ﬁxed probability of having a parasite

6400 richness of zero, modulated by different random effects of lake and

6401 site within lake. In addition to having separate random effects,

6402 separate variance terms were ﬁt to the zero-inﬂated and Poisson

6403 components of the model with no covariance between them. Because

6404 the number of parasites infecting a host varied among ﬁsh species,

6405 we ﬁt separate models for each host species. We also restricted our

6406 analyses to ﬁsh host species in which at least 1 individual was

6407 infected with at least 1 parasite and to host species represented by at

6408 least 11 individuals (to give the necessary degrees of freedom to ﬁt

6409 the model above). Individuals of Anguilla australis and Anguilla

6410 dieffenbachi were pooled under Anguilla spp. to increase sample size

6411 and ﬁt a single model at the genus level. Both species are biologically

6412 and functionally similar, feeding on the same prey and acquiring the

6413 same parasites, and often co-exist (McDowall, 1990). We ﬁt all models

6414 using the function MCMCglmm in the R (R Core Team, 2014)

6415 package of the same name (Hadﬁeld, 2010). 200

6416 Abundance and biomass of trophically transmitted

6417 parasites

6418 We next tested whether feeding preferences of individual ﬁsh hosts

6419 showed any relationship with the abundance and biomass of

6420 trophically transmitted parasites with which they were infected. For

6421 each ﬁsh host species and each trophically transmitted parasite

6422 species found in that host, we determined the proportion ηiq of host 6423 i’s gut contents (by abundance) accounted for by intermediate host q.

6424 We used abundance (rather than biomass or volume) to determine

6425 proportions because, while prey species deliver different amounts of

6426 energy to the predator depending on their size, each intermediate

6427 host acts as a single ‘packet’ of parasites delivered to the deﬁnitive

6428 host. While addressing the richness of ﬁsh parasite communities, we

6429 ﬁt separate models for each observed combination of ﬁsh host and

6430 parasite species.

6431 Using these data, we constructed parallel models for the abundance

6432 of each parasite species in each individual ﬁsh host. When a host i

6433 had 2 intermediate host preys q and r, we ﬁt the model:

Υij = β0 + β0t + β1ωi + β2ηiq + β3ηir + β4ωiηiq + β5ωiηir + Li + Si + ǫij (7)

6434 where Υij is the number of individuals of parasite species j observed 6435 in a ﬁsh host i and all other symbols are as in equation 6 or as

6436 deﬁned above. Where only 1 intermediate host prey taxon was

6437 observed for a given ﬁsh host-parasite combination, β3 and β5 were

6438 omitted from the model. We then ﬁt an equivalent model for the total

6439 biomass of parasites,

Mij = β0 + β0t + β1ωi + β2ηiq + β3ηir + β4ωiηiq + β5ωiηir + Li + Si + ǫij (8)

6440 where Mij is the biomass of parasite species j observed in host 6441 species i and all other symbols are as above.

6442 We ﬁt both of these models to each ﬁsh host-parasite combination

6443 with sufﬁcient sample size (the minimum required sample size varied

6444 depending on the number of intermediate hosts and levels of random

6445 effects). We also excluded combinations where none of the parasite’s

6446 potential intermediate hosts were observed in the diet of ﬁsh hosts as 201

6447 the effect of diet could not be measured in these cases. As parasite

6448 abundances were integer values, we ﬁt the models of parasite

6449 abundances as Poisson processes, and we ﬁt the model of parasite

6450 biomass as a Gaussian process. We therefore ﬁt equation 7 using the

6451 function glmer in the R (R Core Team, 2014) package lme4 (Bates

6452 et al., 2014) and ﬁt equation (Venables and Ripley, 2002) using the

6453 function lmer in the R package lmerTest (Kuznetsova et al., 2014)

6454 (Kuznetsova et al. 2014). After ﬁtting the full models, we ﬁt the suite

6455 of all possible reduced models for each full model using the R (R

6456 Core Team, 2014) function dredge from package MuMIn (Bartón,

6457 2014) and then averaged across all models (weighting by AIC) using

6458 the function model.avg, also from the package MuMIn.

6459 Results

6460 Across all samples, 614 ﬁsh representing 11 species were examined,

6461 and 12 species of parasites were identiﬁed (see Table A1 for details).

6462 A total of 309 546 parasites with different transmission modes (direct

6463 vs trophic) and prey hosts were recovered (see Table A2 for details).

6464 Note that the trematodes Stegodexamene anguillae and Telogaster

6465 opisthorchis use ﬁsh, albeit different species, as both intermediate and

6466 deﬁnitive hosts and were found as either directly transmitted

6467 metacercariae (i.e. trematode parasites larval stage) or trophically

6468 transmitted adults (Table A2). The different life stages of these 2

6469 parasite species were thus considered separately in the models.

Table A1: Details of the fish species, status, life-history strategy and numbers examined for our study with the parasite species identified from each fish species. Fish species Status L.S. nTot n1-n2-n3-n4 Parasite species Aldrichetta forsteri Nat. M.v. 15 0-0-15-0 H. spinigera Anguillicola sp., C. parvum, H. spinigera, Anguilla spp. Nat. Cat. 38 4-11-15-8 S. anguillae, T. opisthorchis, Nematoda sp. Galaxias argenteus Nat. Amp. 1 0-0-1-0 A. galaxii, Eustrongylides sp., S. anguillae, Galaxias maculatus Nat. Amp. 70 0-12-15-43 T. opisthorchis Apatemon sp., C. parvum, Deretrema Gobiomorphus cotidianus Nat. F.r. 268 60-24-68-116 sp., Eustrongylides sp., S. anguillae, T. opisthorchis, Tilodelphys sp., Cestoda sp. Onchorhynchus mykiss Int. F.r. 4 0-0-0-4 A. galaxii, C. parvum, Eustrongylides sp., Perca fluviatilis Int F.r. 179 50-46-47-36 H. spinigera Eustrongylides sp., H. spinigera, Cestoda Retropinna retropinna Nat. Amp. 23 0-10-13-0 sp. Rhombosolea retiaria Nat. Amp. 2 0-0-2-0 A. galaxii, C. parvum, H. spinigera Salmo trutta Int. F.r. 14 3-1-10-0 A. galaxii, C. parvum, Eustrongylides sp.

Nat., native; Int., introduced; L.S., life-history strategy; M.v., marine visitor; Cat., catadromous; Amp., amphidromous; F.r., freshwater resident; nTot, total number of ﬁsh examined; number of ﬁsh examined from lakes Hayes (n1), Tuakitoto (n2), Waihola (n3) and Tomahawk Lagoon (n4). 202

Table A2: Details of the parasite phylum/class, numbers, life stage, transmission mode, and prey host species used for transmission for each parasite species. (B) Parasite Transmission Species Phylum/class Life stage nTotal Mode Preyhost(s) Acanthocephalus galaxii Acanthocephala Cyst. 26 Trophic Amphipodsp.A Anguillicola sp. Nematoda Ad. 9 Trophic Copepodsp. Apatemon sp. Trematoda Mc. 270 666 Direct Coitocaecum parvum Trematoda Ad. 721 Trophic Amphipod spp.A,B Deretrema sp. Trematoda Ad. 14 Trophic Decapodsp. Eustrongylides sp. Nematoda L. 231 Trophic Oligochaete sp. Hedruris spinigera Nematoda Ad. 645 Trophic Amphipodsp.B Stegodexamene anguillae Trematoda Mc. 28 469 Direct S. anguillae Trematoda Ad. 1791 Trophic Fish Telogaster opisthorchis Trematoda Mc. 5029 Direct T. opisthorchis Trematoda Ad. 1112 Trophic Fish Tilodelphys sp. Trematoda Mc. 600 Direct Unnamedsp. Cestoda L. 4 Direct Unnamed sp. Nematoda Ad. 229 Unknown

Cyst., cystacanth; Ad., adult; Mc., metacercaria; L., larva; Prey host(s): Paracalliope ﬂuviatilis (Amphipoda sp.A), Paracorophium excavatum (Amphipoda sp.B), Tenagomysis chiltoni (Decapod sp.), Gobiomorphus cotidianus and Galaxias maculatus (Fish).

6470 Overall, 2 224 096 prey items belonging to 53 different taxa were

6471 found in stomach contents of ﬁsh, identiﬁed and counted.

6472 Parasite richness

6473 We were able to ﬁt our models in 6 ﬁsh taxa: Aldrichetta forsteri

6474 (n=15), Anguilla spp. (n= 38), Gobiomorphus cotidianus (n=268), Perca

6475 ﬂuviatilis (n=179), Galaxias maculatus (n=70) and Salmo trutta (n=14).

6476 As hypothesised, there was no signiﬁcant effect of host diet range on

6477 the richness of directly transmitted parasites in A. forsteri (β2=2.30, 6478 P=0.165), Anguilla spp. (β2=1.21, P=0.106), G. maculatus (β2=-1.74, 6479 P=0.182), G. cotidianus (β2=0.101, P=0.459), P. ﬂuviatilis (β2=-0.299, 6480 P=0.454) or S. trutta (β2=-3.25, P=0.221). In G. maculatus, there was a 6481 signiﬁcant interaction between diet range and host size (β3=2.61,

6482 P<0.001), but in all other ﬁsh species the interaction was

6483 non-signiﬁcant (β3=-0.194, P=0.967; β3=0.727, P=0.518; β3=-0.209, 6484 P=0.133; β3=-0.062, P=0.761; and β3=1.24, P=0.649 for A. forsteri,

6485 Anguilla spp., G. cotidianus, P. ﬂuviatilis and S. trutta, respectively).

6486 There was thus no overall effect of ﬁsh gut contents on directly

6487 transmitted parasite richness in any of the 4 ﬁsh taxa mentioned

6488 above; in the case of G. maculatus the effect of the interaction between

6489 host mass and diet range was small relative to the variability between

6490 MCMCglmm ﬁts (Fig. A1; Table A3). 203

Aldrichetta forsteri Anguilla spp. Figure A1: Marginal effects of fish host 10 10 diet range on the richness of directly A B transmitted parasites found in the 5 5 6 fish taxa for which models could be fitted; (A) Aldrichetta forsteri, (B) 0 0 Anguilla spp., (C) Galaxias maculatus, (D) Gobiomorphus cotidianus, (E) Perca -5 -5 fluviatilis and (F) Salmo trutta. Marginal effects are obtained by summing the -10 -10 effect of host diet range with the effect 2 3 1 2 3 10 10 10 10 10 of the interaction between host mass Galaxias maculatus Gobiomorphus cotidianus and diet range across the observed 10 10 range of fish host masses. A marginal C D effect of zero indicates that there is 5 5 no overall effect of host diet range on parasite richness. Marginal effects 0 0 greater than zero indicate that parasite richness increases with increasing host -5 -5 diet range, and marginal effects below zero indicate that parasite richness -10 -10 decreases as host diet range increases. 0 1 -1 0 1 10 10 10 10 10 Horizontal lines indicate that the effect of host diet range does not vary with Perca fluviatilis Salmo trutta host size, while sloped lines indicate 10 10 that the effect of host diet range differs E F among hosts of different sizes. We 5 5 show mean marginal effects (mean over 10 000 MCMCglmm iterations; black 0 0 line) along with the marginal effects estimated in 100 of the MCMCglmm -5 -5 iterations with below-average deviances (grey lines). -10 -10

Marginal effect of diet range on directly-transmitted parasite richness 0 1 2 3 1 2 3 10 10 10 10 10 10 10 Host mass (g)

6491 Contrary to our expectations, there was no effect of host diet range

6492 on the richness of trophically transmitted parasites in A. forsteri,

6493 Anguilla spp., G. maculatus, G. cotidianus, P. ﬂuviatilis and S. trutta

6494 (β2 + β2t=-0.227, P=0.780; β2 + β2t=0.291, P=0.651; β2 + β2t=-0.779, 6495 P=0.445; β2 + β2t=-0.268, P=0.175; β2 + β2t=-0.267, P=0.436; and 6496 β2 + β2t=1.61, P=0.437, respectively). Furthermore, there was no

6497 signiﬁcant interaction between host size and diet range in any of the

6498 above ﬁsh (β3 + β3t= 0.044, P=0.928; β3 + β3t=-0.615, P=0.524; 6499 β3 + β3t=-0.622, P=0.532; β3 + β3t= 0.279, P=0.089; 6500 β3 + β3t=-0.242, P=0.778; and β3 + β3t=-0.154, P=0.957, respectively).

6501 There was therefore no overall effect of diet range on the richness of

6502 trophically transmitted parasites at any host size in these ﬁsh (Fig.

6503 A2; Table A3). 204

Table A3: Estimated ﬁxed effects in equation 6 (with P-values in parentheses). β1, β2 and β3, represent the effects of host mass, diet range and their interaction (respectively) on the richness of directly transmitted parasites, while β1t, β2t and β3t are adjustments to these effects for trophically transmitted parasites. β1 + β1t therefore represents the main effect of host mass acting on the richness of trophically transmitted parasites. Effects are means over 1000 MCMC iterations.

Species β1 β1 + β1t β2 β2 + β2t β3 β3 + β3t Aldrichetta -0.718 0.118 2.30 -0.227 -0.194 0.044 forsteri (0.366)(0.582)(0.165)(0.780)(0.967)(0.928) Anguilla -0.324 1.67 1.21 0.291 0.727 -0.615 spp. (0. 532) (<0.001)(0.106)(0.651)(0.518)(0.524) Galaxias -1.68 0.971 -1.74 -0.779 2.61 -0.622 maculatus (0.303)(0.474)(0.182)(0.445) (<0.001)(0.532) Gobiomorphus 0.332 0.101 0.067 -0.268 -0.209 0.279 cotidianus (0.005) (<0.001)(0.459)(0.175)(0.133)(0.089) Perca 0.390 0.846 -0.299 -0.267 -0.062 -0.242 ﬂuviatilis (0.590)(0.025)(0.454)(0.436)(0.761)(0.778) Salmo 2.42 -0.870 -3.25 1.61 1.24 -0.154 trutta (0.429)(0.483)(0.221)(0.437)(0.649)(0.957)

6504 Abundance and biomass of trophically transmitted parasites

6505 We were able to ﬁt our models to the abundance and biomass of 3

6506 trophically transmitted parasites in 3 ﬁsh host taxa: Hedruris spinigera

6507 in A. forsteri, Coitocaecum parvum in P. ﬂuviatilis, and both

6508 Eustrongylides sp. and C. parvum in G. cotidianus. In the ﬁrst 3 cases,

6509 only 1 prey species is used by the parasite as an intermediate host.

6510 Hedruris spinigera uses the amphipod Paracorophium excavatum for

6511 transmission to A. forsteri, C. parvum uses the amphipod Paracalliope

6512 ﬂuviatilis only for transmission to P. ﬂuviatilis and Eustrongylides uses

6513 oligochaete sp. to reach G. cotidianus. Two prey species, the

6514 amphipods P. excavatum and Pa. ﬂuviatilis are used as intermediate

6515 hosts by C. parvum to be transmitted to and infect G. cotidianus. As

6516 expected, the abundance of H. spinigera in A. forsteri (i.e. number of

6517 parasites per individual ﬁsh host) tended to increase as the

6518 proportion of the intermediate host P. excavatum in the diet of an

6519 individual ﬁsh increased (β2=15.6, P=0.005). This effect interacted 6520 negatively with host mass (β4=-10.7, P<0.001) such that in smaller A.

6521 forsteri (roughly <300mm) the abundance of H. spinigera increased

6522 sharply with the proportion of P. excavatum in the diet but in the

6523 largest A. forsteri the abundance of H. spinigera decreased (Fig. A3-A;

6524 Table A4). Note that ‘small’ and ‘large’ here refer to opposite ends of

6525 the continuum of A. forsteri lengths and not to explicit groups.

6526 The abundances of C. parvum in P. ﬂuviatilis and Eustrongylides sp. in

6527 G. cotidianus did not vary with the proportion of intermediate hosts 205

6528 (the amphipod Pa. ﬂuviatilis and an unnamed oligochaete,

6529 respectively) in the diets of the ﬁsh hosts (β2=0.010, P=0.989 and 6530 β2=0.006, P=0.723, respectively). There was no signiﬁcant interaction

6531 between ﬁsh host size and the proportion of intermediate hosts in

6532 ﬁsh host diets (β4=0.025, P=0.966 and β4=0.002, P=0.839,

6533 respectively). As such, there was no overall effect of the proportion of

6534 intermediate hosts in ﬁsh diet contents on parasite abundance for

6535 these 2 parasite-host combinations (Fig. A3-B, C; Table A4).

6536 Likewise, the abundance of C. parvum in G. cotidianus did not vary

6537 with the diet of ﬁsh hosts. Parasite abundance was not signiﬁcantly

6538 associated with the proportion of either intermediate host (the

6539 amphipods Pa. ﬂuviatilis and P. excavatum; β2=-0.087, P=0.383 and 6540 β3=-0.127, P=0.283, respectively). Further, there were weak

6541 interactions between the proportions of each intermediate host in the

6542 diet and ﬁsh host size (β4=-0.034, P=0.955 and β5= 0.307, P=0.610,

2 Aldrichetta forsteri Anguilla spp. Figure A : Marginal effects of fish host 10 10 diet range on the richness of trophically A B transmitted parasites found in the 6 fish 5 5 taxa for which models could be fitted; (A) Aldrichetta forsteri, (B) Anguilla spp., 0 0 (C) Galaxias maculatus, (D) Gobiomorphus cotidianus, (E) Perca fluviatilis and -5 -5 (F) Salmo trutta. Marginal effects are obtained by summing the effect of -10 -10 host diet range with the effect of the 2 3 1 2 3 interaction between host mass and diet 10 10 10 10 10 range across the observed range of fish Galaxias maculatus Gobiomorphus cotidianus host masses. We show mean marginal 10 10 effects (mean over 10 000 MCMCglmm C D iterations; black line) along with the 5 5 marginal effects estimated in 100 of the MCMCglmm iterations with below- 0 0 average deviances (grey lines). See Fig. A1 for details about the interpretation -5 -5 of marginal effects.

-10 -10 0 1 -1 0 1 10 10 10 10 10 Perca fluviatilis Salmo trutta 10 10 E F 5 5

0 0

-5 -5

-10 -10 0 1 2 3 1 2 3 10 10 10 10 10 10 10 Marginal effect of diet range on trophically-transmitted parasite richness Host mass (g) 206

Hedruris spinigera in Aldrichetta forsteri Coitocaecum parvum in Perca fluviatilis 60 Figure A3: Marginal effects of the A 3 B 50 proportion of intermediate hosts in fish 40 2 stomach contents on the abundance 30 1 of trophically transmitted parasites in 4 20 individual fish hosts in the parasite- 0 fish host taxon combinations for which 10 -1 models could be fitted; (A) Hedruris 0 -2 spinigera in Aldrichetta forsteri, (B) -10 Coitocaecum parvum in Perca fluviatilis, Paracorophium excavatum -3 Paracalliope fluviatilis -20 (C) Eustrongylides sp. in Gobiomorphus 2 1 2 10 10 10 cotidianus and (D) C. parvum in G. Eustrongylides sp. in Gobiomorphus cotidianus Coitocaecum parvum in Gobiomorphus cotidianus cotidianus. Intermediate host prey C 2 D taxa are also identified within each 0.10 panel. Marginal effects are obtained 0.05 1 by summing the effect of proportion of intermediate host with the effect of the 0.00 0 interaction between fish host mass and proportion of intermediate hosts across -0.05 -1 the observed range of fish host masses.

Marginal effect of fish host diet on parasite abundance -0.10 Paracalliope fluviatilis Oligochaete sp. -2 Paracorophium excavatum We show mean marginal effects ( black lines) with 95% conﬁdence intervals -1 0 1 -1 0 1 10 10 10 10 10 10 (grey). See Fig. A1 for details about the Fish host mass (g) interpretation of marginal effects.

6543 respectively). Overall, the abundance of C. parvum did not vary

6544 signiﬁcantly with the diet of G. cotidianus (Fig. A3-D; Table A4). In

6545 general, relationships between parasite biomass and proportions of

6546 intermediate hosts in the diet of ﬁsh hosts were similar to the

6547 relationships with parasite abundances described above (see

6548 Supplementary Material for details).

6549 Discussion

6550 Conspeciﬁc individuals are often treated as ecologically equivalent

6551 although individual specialisation in habitat or resource use is a

Table A4: Estimated fixed effects in equation 7 (with P-values in parentheses). β1 indicates the effect of fish host mass on the abundance of the parasite, β2 and β3 the effects of the proportions of 2 intermediate hosts in the diet of the fish host, and β4 and β5 the effects of the interaction between proportion of intermediate host and fish host mass. NA indicates that only 1 intermediate host was found in the gut contents of the fish host. Estimates are based on averages over the full equation 7 and all possible reduced models, weighted by AIC.

Fish host Parasite β1 β2 β3 β4 β5 Aldrichetta Hedruris 0.257 15.6 NA -10.72 NA forsteri spinigera (<0.001)(0.005) (<0.001) Perca Coitocaecum 0.119 0.010 NA 0.025 NA ﬂuviatilis parvum (0.862)(0.989)(0.966) Gobiomorphus Eustrongylides 0.441 0.006 NA 0.002 NA cotidianus sp. (<0.001)(0.723)(0.839) Gobiomorphus Coitocaecum 0.375 -0.087 -0.127 -0.034 0.307 cotidianus parvum (<0.001)(0.383)(0.283)(0.955)(0.610) 207

6552 widespread phenomenon with potentially broad ecological

6553 implications (Bolnick et al., 2003). Inter-individual variation in diet

6554 can influence infection risk among conspecific fish when exposure to

6555 parasites varies with prey type (Curtis et al., 1995; Wilson et al., 1996).

6556 Fish that consume more species of prey should have more diverse

6557 trophically transmitted parasites (Locke et al., 2014). Comparatively,

6558 exposure to directly transmitted parasites should not depend on host

6559 diet (Simková et al., 2001; Locke et al., 2013, 2014). We indeed found

6560 no clear relationship between ﬁsh gut contents and the richness of

6561 directly transmitted parasites in individual hosts. Results indicate

6562 that infection levels of directly transmitted helminth larvae are highly

6563 variable among ﬁsh species, indicating high host speciﬁcity and

6564 potential phylogenetic constraints in these parasites.

6565 In contrast with our predictions, we also did not ﬁnd clear

6566 relationships between host diet range and the richness of trophically

6567 transmitted parasites in ﬁsh hosts. Although broader diet range has

6568 been linked with higher parasite richness in ﬁsh, this pattern is only

6569 observed when a wide variety of prey species is utilised by a diverse

6570 array of parasite species for transmission (Carney and Dick, 1999). If

6571 only a few species in the ecosystem are actually used by local

6572 parasites for trophic transmission, then parasite richness in ﬁsh host

6573 is unlikely to increase with diet range (Kennedy et al., 1986). In lakes

6574 sampled here, the number of ﬁsh parasite species using trophic

6575 transmission is relatively low (8 species overall with a maximum of 7

6576 in any 1 lake/season combination) and the overall number of prey

6577 taxa used by these parasites limited to 7, divided into only 3 groups

6578 (ﬁsh, crustaceans and oligochaetes). Comparatively, 53 different prey

6579 taxa were found in ﬁsh gut contents with a maximum of 26 prey taxa

6580 in any 1 site/lake/season combination. It is thus possible that, as

6581 long as the few prey taxa used by parasites are consumed by ﬁsh,a

6582 broader diet range does not further increase the richness of parasites

6583 found in individual hosts (Kennedy et al., 1986). Usually, larger ﬁsh

6584 harbour higher parasite diversities because large individuals have a

6585 higher feeding rate and are also less gape-limited (and thus less

6586 restricted in prey choice) than small ﬁsh (Poulin and Cribb, 2002;

6587 González and Poulin, 2005). Generally, our results indicate that

6588 individual ﬁsh size did not have major effects on the relationship

6589 between host diet range and parasite richness in ﬁsh species captured

6590 in the present study.

6591 Interspeciﬁc differences in diet range and host-parasite compatibility

6592 among ﬁsh species may add extra layers of complexity to the factors

6593 determining parasite richness in individual ﬁsh hosts (Knudsen et al., 208

6594 1997, 2008; Lagrue et al., 2011). Fish species sampled here have

6595 contrasting life-history strategies, varying from freshwater resident to

6596 marine visitors, potentially affecting their parasite fauna (Bouillon

6597 and Dempson, 1989; Kristoffersen et al., 1994). However, apart from

6598 A. forsteri, all other ﬁsh species examined in our study are permanent

6599 freshwater residents as adults (McDowall, 1990). Although the larvae

6600 of the catadromous and amphidromous ﬁsh sampled here are

6601 oceanic, their freshwater parasite fauna could not have been

6602 inﬂuenced by different life-history strategies. Aldrichetta forsteri is a

6603 marine ﬁsh that migrates inland into freshwater during the summer

6604 months and usually remains freshwater bound for several months,

6605 feeding exclusively on freshwater prey. However, it is possible that

6606 recently immigrated ﬁsh individuals may lack freshwater parasites

6607 due to their recent arrival from the sea, potentially inﬂuencing

6608 diet-parasite links. Unfortunately, this cannot be determined from

6609 our data as we cannot determine residence time of ﬁsh in freshwater.

6610 Parasites can also be highly host-speciﬁc and may never be found in

6611 some ﬁsh species even though prey taxa used for transmission are

6612 consumed by that particular ﬁsh species. Alternatively, some

6613 parasite-carrying prey may never be consumed by a given ﬁsh

6614 species, further reducing parasite richness in any particular

6615 host (Kennedy et al., 1986; Lagrue et al., 2011); for example, parasites

6616 transmitted through ﬁsh prey consumption can only infect large

6617 piscivorous ﬁsh predators. Finally, gut contents may also provide a

6618 biased representation of individual diet range (Svanbäck et al., 2015).

6619 Apparent differences in diet among individual ﬁsh may reﬂect

6620 short-term foraging activities, with observed diets being only

6621 snapshots of actual diet ranges; all ﬁsh within a population may

6622 actually be feeding on the same range of available prey (Curtis et al.,

6623 1995). Comparatively, parasites likely remain in ﬁsh for longer than

6624 the prey used for transmission and thus provide a clearer signature

6625 of prey consumed over extended time periods than stomach

6626 contents (Johnson et al., 2004a; Valtonen et al., 2010). For example, in

6627 our study, prevalence of H. spinigera in A. forsteri was 100% although

6628 only 40% of ﬁsh were found with the intermediate host prey P.

6629 excavatum in their gut contents, indicating that all ﬁsh individuals

6630 were feeding on P. excavatum even though the prey was not found in

6631 stomach contents. Similarly, only around 10% of G. cotidianus

6632 individuals infected with Eustrongylides sp. larvae had eaten

6633 oligochaetes recently. However, on the other end of the spectrum,

6634 only around 10% of G. cotidianus individual infected by C. parvum

6635 had not consumed the host Pa. ﬂuviatilis, while all infected P.

6636 ﬂuviatilis had the prey intermediate host in their stomachs. These 209

6637 differences are likely explained by the speciﬁc persistence time (i.e.

6638 lifespan) of each parasite in ﬁsh hosts. Eustrongylides sp. larvae

6639 remain in the ﬁsh until transmission to the bird deﬁnitive host and

6640 thus potentially for the life time of the ﬁsh. Hedruris spinigera is a

6641 large nematode that attaches to the stomach epithelium of the ﬁsh

6642 host, needing to achieve signiﬁcant growth and to ﬁnd a mate before

6643 reproduction, and likely remain in the ﬁsh for longer than the small,

6644 fast maturing, hermaphrodite C. parvum adult (Lagrue et al., 2011).

6645 On the other hand, although intestinal parasites were found in

6646 introduced ﬁsh host species (Table A1), a previous study on the same

6647 system showed that their abundance and size are signiﬁcantly lower

6648 in introduced hosts (Lagrue et al., 2011). Despite feeding heavily on

6649 intermediate host prey, these ﬁsh harboured low abundances of small

6650 parasites, hinting at a quick turnover with parasites remaining in ﬁsh

6651 host for a short amount of time due to host-parasite incompatibility.

6652 As a result, infection levels in introduced species may be more closely

6653 linked to recent, short-term ﬁsh host diet. Overall, stomach content

6654 data represent only a very limited window of time unless stomach

6655 contents are repeatedly sampled from the same individual using

6656 non-lethal methods like stomach ﬂushing (Araújo et al., 2011).

6657 However, this is logistically very difﬁcult to achieve and cannot

6658 document parasite richness and abundance simultaneously as

6659 parasite identiﬁcation and count require host dissection. Overall, the

6660 utility of the stomach contents data when assessing ﬁsh diet range

6661 and selectivity and their link with parasite richness and abundance

6662 will likely be inﬂuenced by species-speciﬁc host-parasite

6663 characteristics.

6664 While diet range did not seem to inﬂuence parasite richness, diet

6665 specialisation among ﬁsh individuals may still inﬂuence their

6666 exposure to trophically transmitted parasites (Bolnick et al., 2003).

6667 Among individuals, variation in diet is common in natural

6668 populations (Svanbäck et al., 2015). Intraspeciﬁc differences in diet

6669 preferences (i.e. individual diet specialisation; Layman et al., 2015;

6670 Rosenblatt et al., 2015) should thus translate in abundance variations

6671 of trophically transmitted parasites among conspeciﬁc ﬁsh

6672 hosts (Curtis et al., 1995; Wilson et al., 1996). Diet range may be

6673 limited, but ﬁsh feeding intensively on the few prey taxa used by

6674 local parasites for transmission should carry heavy parasite loads,

6675 and vice versa for ﬁsh feeding preferentially on prey taxa devoid of

6676 parasites (Kennedy et al., 1986; Dick et al., 2009). Differences in prey

6677 selectivity among sympatric ﬁsh should thus cause differences in

6678 parasite acquisition, and potential patterns of parasite segregation

6679 and aggregation among hosts (Crofton, 1971; Knudsen et al., 1997, 210

6680 2004, 2008). However, our results showed no clear link between the

6681 proportion of prey intermediate hosts in individual ﬁsh diet contents

6682 (i.e. individual diet preference) and the abundance of parasites in ﬁsh

6683 hosts. Furthermore, relationships between diet preferences and

6684 parasite abundance were differentially inﬂuenced by ﬁsh size and

6685 species as well as prey and parasite species. In particular, the

6686 relationship between the abundance of H. spinigera in A. forsteri and

6687 the proportion of the intermediate host in the diet of A. forsteri was

6688 stronger in smaller ﬁsh. It is important to note, however, that feeding

6689 observations over short time frames (e.g., stomach content analyses)

6690 may overestimate the degree of diet specialisation and thus inﬂuence

6691 documented relationship between parasite loads and host

6692 diet (Novak and Tinker, 2015). As mentioned previously, the

6693 temporal scale of study, as well as the number of independent

6694 observations, can greatly inﬂuence estimates of the degree and

6695 persistence over time of diet range and preferences (Curtis et al., 1995;

6696 Fodrie et al., 2015). Dietary variations among individuals can also be

6697 caused by temporal or spatial patchiness in prey distribution rather

6698 than individual specialisation and may not be reﬂected in parasite

6699 loads if individual hosts are mobile enough to move among prey

6700 patches (Rosenblatt et al., 2015). Again, potential links between

6701 feeding specialisation and variation in parasite loads among

6702 individual ﬁsh hosts should be conﬁrmed through repeated diet and

6703 parasite sampling, if at all feasible.

6704 Overall, there was no clear relationship between diet range, estimated

6705 as the number of prey taxa in ﬁsh stomach contents, and parasite

6706 richness or between diet preferences (i.e. the proportion of prey

6707 species used for parasite transmission in individual ﬁsh diet contents)

6708 and parasite loads among individual ﬁsh hosts. Whether this lack of

6709 clear patterns was due to stomach sampling method limitations or

6710 accurately represents host-parasite relationships in the study systems

6711 is a question that should be tested further, but is technically and

6712 logistically challenging. Sampling repeatedly and concomitantly

6713 stomach contents and parasite abundances overtime in the same ﬁsh

6714 individuals would be ideal but is difﬁcult if not impossible in wild

6715 ﬁsh. Although the methods used here are only a proxy of overall ﬁsh

6716 diet and parasite surveys, our results are roughly consistent across

6717 several host and parasite species, and contrast with those of earlier

6718 studies using similar methods in which diet and parasite infection

6719 were linked (Curtis et al., 1995; Knudsen et al., 1997, 2003; Bertrand

6720 et al., 2008). This pattern hints at a true disconnect between host diet

6721 (at least as measured here) and measures of parasite infections

6722 although host-parasite species-speciﬁc patterns may vary. Inherent 211

6723 characteristics of New Zealand lake systems (low parasite species

6724 richness, limited numbers of prey species used for trophic

6725 transmission, high host-parasite speciﬁcity) likely limit the inﬂuence

6726 of diet range and individual diet specialisation on parasite richness

6727 and abundance patterns. Repeated diet sampling over a longer time

6728 period, by maintaining ﬁsh in enclosure and using non-lethal

6729 stomach ﬂushing to document individual ﬁsh diet for example,

6730 would help conﬁrm or invalidate the utility of gut content data as

6731 well as the role of variation among individuals in diet specialisation

6732 and its effects on parasite loads among sympatric ﬁsh. Our results

6733 and those of previous studies conﬁrm that, although parasite

6734 acquisition is obviously related to host diet, other factors that vary

6735 widely among ecosystems, hosts and parasites likely inﬂuence how

6736 parasite richness and load are linked to host diet.

6737 Acknowledgements

6738 We thank Anne Besson, Isa Blasco-Costa, Manna Warburton and Kim

6739 Garrett for assistance with ﬁeld collection. Animal sampling was

6740 carried out in accordance with Otago University animal ethics

6741 guidelines (Animal Ethics Approval ET 10/12) and under the New

6742 Zealand Department of Conservation and Fish and Game ﬁshing

6743 permit (NZ - DOC permit OT-34204-RES). We thank three

6744 anonymous referees and the editor for positive and constructive

6745 comments on previous versions of the manuscript.

6746 Financial Support

6747 This study was funded by a grant from the Marsden Fund (Royal

6748 Society of New Zealand) to RP. ARC was supported by an NSERC

6749 PGS-D Doctoral Scholarship and a University of Canterbury Doctoral

6750 Scholarship, and DBS by a Marsden Fund grant (UOC-1101)and a

6751 Rutherford Discovery Fellowship (both administered by the Royal

6752 Society of New Zealand). 212

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6952 Supporting Information S2

6953 Supporting information for Chapter 2:

6954 Knowledge of predator-prey interactions improves

6955 predictions of immigration and extinction in island

6956 biogeography

6957 Alyssa R. Cirtwill & Daniel B. Stouffer 220

6958 S2.1: Full models

Table S2.1: Main effects included in the full initial immigration, repeat immigration, and extinction models. Note that each model also included all possible interaction terms between the ﬁxed effects indicated below, plus random effects of census and source population. These models were simpliﬁed to give the models in Tables S2.4–S2.6. See Tables S2.2-S2.3 for a complete list of terms included in each model. TIB refers to models based on the Theory of Island Biogeography– that is, excluding any trophic interactions.

Model Main effects (a) Initial and Distance Island Interval Species Presence Ability to Presence repeat from diameter between richness of consume of immigration mainland censuses predators basal resources animal prey Null TIB X X X Species-richness X X X X Top-down XXX X Top-down & X X X X X Species-richness Bottom-up X X X X X Bottom-up & XXXX X X Species-richness Top-down & XXX XX X Bottom-up

(b) Extinction Null TIB X X Species-richness X X X Top-down XX X Top-down & X X X X Species-richness Bottom-up X X X X Bottom-up & XXX X X Species-richness Top-down & XX XX X Bottom-up 221

Table S2.2: Symbols used in mathematical description of the statistical models.

Symbol Description

Cijk+1 Probability of immigration for species i on island j between census k and census k + 1 Xijk+1 Probability of extinction for species i on island j between census k and census k + 1 δj Distance of island j from the mainland (meters) λj Diameter of island j (meters) τk+1 Time between census k and census k + 1 (days) Σk Species richness during census k ρijk Presence of predators of species i on island j during census k: ρijk=1 if predators of species i were observed on island j during census k, ρijk=0 otherwise

ηi Ability of species i to eat plants: ηi=1 if species i is able to eat basal resources, ηi=0 otherwise αijk Presence of animal prey for species i on island j during census k: αijk=1 if prey of species i were observed on island j during census k, αijk=0 otherwise Ek+1 Random effect of period between censuses k and k + 1 Si Random effect of species i Random effect of source population (i.e., the interaction between species i, island j, and W ijq event window q)

ǫijk+1 Residual error for species i on island j between census k and census k + 1 222 + + + + + + + + + + + + + + + + i i 1 i k 1 ijk ijk ijk η ijk ijk ijk ijk ijk S ijk η ijk + + Σ 1 1 k α ρ α α ρ α α α α k α 1 j i i i k i 1 + 1 1 + τ E + k η + η j η k η λ + ijk + + Σ k j j 1 k 1 τ k τ k k 1 λ j ρ + τ δ δ ijk j + + τ τ τ Σ j 1 + j j k j k j λ δ ρ k j + + λ τ + 1 + λ E Σ λ λ τ j k + δ j i j j 1 + τ + i δ λ δ k η δ j + ijk + + ijk + j + η τ k ρ λ j 1 α λ + + ijk 1 τ + + ijk j ijk j ijk + ijk λ ρ i ) as in the full extinction + ijk δ k α α + k α ijk ρ k λ ijk i i ijk η ρ i τ j 1 τ j + 1 α i ρ ijq η Σ η ρ + δ η i j δ + 1 k 1 η + k 1 + k η δ k ijk + + + W Σ 1 τ + + Σ + ijk k τ k k j + j α k j 1 j + i τ ρ τ + i Σ τ λ j k λ j ijk + 1 λ η j j ijk Σ j 1 η j τ k α λ 1 λ δ j + δ ρ λ j ijk δ + τ els was identical except that the ijk k j 1 δ j ijk + k α λ τ ρ k δ + + + τ ρ + + j k λ + 1 j τ k j + j λ δ Σ τ k + + δ ijk + ijk ijk + ijk j k λ ijk α ρ ρ i Σ δ α τ + ijk k + 1 i 1 + 1 j + α η ijk + i j δ α η + + + Σ α + i k j η k k k ijk 1 j λ ijk ijk j η i ijk τ τ τ Σ + Σ α + j α j λ α j λ η 1 ρ 1 i 1 k i j δ + k + λ λ τ + η η + δ + ijk j j j + k k k + k Σ δ δ α + j ijk i τ τ ijk ijk λ τ i + Σ j j δ iogeography, without any trophic effects. η α ρ ρ ijk 1 η j + δ δ + j j 1 ijk + + α ijk + δ k λ ρ i + + i ijk λ k j α + k + ijk η η ρ Σ τ 1 ijk j τ j j λ 1 k i 1 + α + ijk + j i + ρ η λ λ + Σ + k j Σ δ k α j j + η 1 1 k k 1 1 τ ijk k δ i λ Σ τ τ j + λ + + j + j + + j α η k k Σ k i λ k 1 δ λ τ τ + 1 λ j + + τ τ j j η j ijk j + δ j + + 1 δ j δ k α λ + k δ + ijk λ ijk i + τ τ Σ + 1 α k 1 α j η j ijk + + i i + + + τ k + + + α λ j λ η η k ijk k i j i ijk Σ 1 δ 1 ijk τ ρ τ ijk δ η η ijk 1 j ijk ρ j 1 + ρ + 1 + 1 α k α k k α + 1 λ i λ + + j j + + j + k τ 1 τ k Σ + η k j k δ j δ τ λ k 1 k τ + j τ τ + λ ijk ijk λ k τ j δ + j j j Σ + + 1 τ ρ + α k δ j + δ j δ + δ 1 ijk i τ k + δ i i + λ ijk η α + η η + + Σ + j j ijk + k j ijk ρ + + 1 δ 1 τ ijk k δ ǫ λ α ijk + ijk + k ijk α + ijk ρ Σ ijk ijk k i k α + + + α + 1 1 + Σ α τ i ρ α η ijk τ j i i i 1 1 1 n species, island, and colonisation interval ( j + + k η ijk ρ η k k S η ijk λ δ 1 + j + + + j ijk j ρ Σ 1 τ τ k k k ρ δ j + δ j 1 k ρ δ j τ τ τ + + k j δ j + j λ + k + Σ λ τ δ j 1 k + j + τ + i λ λ + δ j τ + + j k i η λ j ijk + i + k δ + + + η η α Σ Σ λ 1 + E j ijk i j j 1 j k + ijk k ijk + + ρ δ δ ijk λ η δ + α 1 α k Σ j j 1 Σ + ijk j k i α 1 τ ijk 1 δ + j + α τ λ 1 j η + + λ + k i j + k α δ j + j + i τ k λ 1 k k δ τ η + 1 δ j k λ + j τ η 1 τ + + τ Σ + j δ λ k j + i + 1 k + + ijk ijk + ijk λ τ k + τ η λ j + j ρ ρ j j i + τ α j + ijk k j j δ 1 ijk δ δ δ η λ + k τ ijk ρ j j λ ijk j + α λ j j j α k Σ δ ijk 1 1 + δ ρ + λ δ i + j ijk δ + j δ j τ α j + + 1 j η δ ρ 1 i δ j k j λ + + k + λ dels. Mathematical structure of the repeat immigration mod + η λ + + τ + ijk j i λ k j j λ j + k 1 j Σ ǫ δ + 1 η + τ δ j τ ijk λ δ j 1 λ + k j i ijk j + δ k + k δ α + k + + δ η α Σ + λ τ k + Σ τ i + k j 1 + j j τ ijk + i ijk + δ S j ijk + + Σ + ijk j λ η α λ ρ k δ 1 i j j ijk ijk α j 1 1 k α λ τ + + i δ η δ j ρ + ρ j + + λ 1 ijk Σ j k + η 1 δ 1 k δ k + j α + τ j τ λ τ + + + + + i j + + λ j j ijk j k k + λ + 1 ijk η ijk 1 δ δ λ j ρ τ k λ E j ǫ ijk j + ρ 1 ijk ijk δ + + ijk j ijk δ Σ δ k α + ρ + + i α + + i α ijk + α λ + τ k 1 j + k η i j j ǫ η + i + τ + k + ijk δ k ijk η + λ + Σ λ ijk i k S k j j . TIB refers to a model based on the original Theory of Island B j ρ i Σ 1 α + + Σ 1 τ ρ η + δ j i 2 δ λ Σ j η i k k + + . + j + + 1 η λ k + S j λ k δ + 2 ijk i + 1 Σ Σ ijk 1 τ + + + δ 1 τ 1 1 η 1 α k 1 α + + + 1 + + + i + k τ + 1 + k + + j k + + ijk + i k ijk ijk k η τ k 1 + k + E k τ δ ijk τ τ ρ ijk η ρ τ j α τ + j j j j τ i ijk 1 ijk α k ijk α + λ + λ η + 1 λ λ + λ ρ 1 k α + ǫ + + j E + j j j j + k 1 j + + j j i δ Σ δ δ j λ τ ijk k + λ ijk j + + + λ + j + η λ λ τ λ k ijk α ρ j j j λ 1 + i j + λ τ ǫ + + j + δ ijk δ j + ijk ijk η + λ + + + δ j λ ρ δ j k j α j ijk j j α + ijk δ + ijk ijk δ i ijk k 1 δ δ E δ δ ρ + + i α α + η α ρ + k i = + = Σ i i ijk i S 1 1 1 i + = η = η = Σ η ρ η = = = η ijk + k + + k k ijk 1 1 1 1 + + 1 k k k 1 1 1 1 1 1 1 1 ǫ 1 Σ α + + + + Σ τ Σ τ τ i 1 + + + + + + + + + 1 1 j j j j j k k k k + k η + + τ τ τ τ + + λ τ λ λ λ λ k ijk ijk ijk ijk ijk ijk ijk ijk j j j j k i j j j j ijk j j k k E C C C C δ S C δ C λ C δ Σ λ δ τ ǫ C τ λ δ λ δ ) was replaced with a random effect of the interaction betwee i S ). All symbols are as in Table S 4 . 2 : Mathematical structure of the full initial immigration mo 3 . 2 Top-down Species-richness Top-down & Species-richness Bottom-up random effect of species ( TIB Table S ModelNull Mathematicalstructure Bottom-up & Species-richness models (Table S Top-down & Bottom-up 223 + + + + + + + + i i k η η ijk ijk ijk ijk Σ ijk k 1 α α 1 α α α 1 Σ i 1 i + i + k 1 η η + + η k j τ k 1 k + k τ j j k τ τ λ + Σ λ τ k j λ + τ + + λ j 1 + + i i + + λ η η 1 ijk ijk 1 + ijk 1 ijk ijk α + α + + α ǫ + ρ i k i k k η τ ijk ijk τ η j ijk + Σ ijk α ρ ǫ i j λ + α + + η ijq λ + k + 1 + ijk ijk ijk W + Σ ρ ρ ijk + k ijq 1 α 1 ijk 1 ρ τ 1 i + + α j + 1 + W k η k + k k 1 λ + k τ τ τ 1 k Σ + j τ ijk + j k τ + ρ λ 1 + 1 + + λ j E + + + ijk + λ k i ijk + + ǫ ijk 1 + k ijk ijk η i E α α + i j ǫ 1 j α + Σ η + ijk η k 1 k λ + + ijk λ ρ + k Σ + j ǫ Σ ijk ijq ijk 1 τ k j 1 + + ρ Σ α + τ ijq i i + λ + W 1 1 1 k + k η η τ + + + j W j τ + j + k + k k ijq j λ λ τ ijk λ τ τ 1 λ j + j j ijk W α ijk + j λ 1 + + λ ρ λ + α k j λ + + 1 + k E k λ 1 + ijk ijk + 1 + + Σ i 1 E k j ρ + + k ρ + + i j η + τ 1 + k i λ k j Σ η ijk ijk j λ + η + 1 E 1 ijk λ ǫ j Σ ijk α k Σ + i + . TIB refers to a model based on the original Theory of Island 1 ρ + ǫ j + τ λ ijk k 2 + k η k 1 + + . + λ 1 τ α τ 2 k i i + Σ + j + ijk + + τ ijk k η 1 η ijq k j + λ + ρ 1 τ 1 α 1 + τ ijq j ijk i λ k 1 j k W + + + ρ η λ k k τ + k + j + Σ W λ j j τ k τ τ + + j λ ijk j j τ λ + λ + + j 1 ρ ijk ijk λ λ λ 1 + 1 ρ + λ α + ijk + ijk i k 1 ijk + + 1 + + + α α k 1 η k α E + + i k + ijk 1 τ k + E k j η ijk ρ Σ ijk + k τ + + τ + k j k ijk 1 λ α j k τ i α i + i 1 j Σ ρ τ λ Σ + η λ η η ijk 1 + k λ + 1 + 1 α τ + + + + i + j + + + i + ijk + k k + ijk k η k ǫ k η λ τ τ k k α j τ ijk symbols are as in Table S ijk Σ i Σ ijk ijk ijk λ + ρ Σ Σ ρ + + η + α α α + 1 i + i + 1 + + + + ijk η + ijq ijk ijk η 1 ijk 1 + k ijk ρ 1 1 1 1 1 α ρ k ρ + τ i + W 1 α 1 j k τ k + + + + + + k i η k k k k + k τ + τ λ η Σ k + τ k τ τ τ τ j j + τ + ijk τ 1 + λ λ + + j α + + + + + j j + + λ ijk j j j j k + + + λ ijk ijk λ α E λ λ λ λ i ρ α k ijk ijk i ijk ijk η = α = Σ = η α = = = = = i ρ α + k k ijk 1 1 η 1 1 1 1 1 1 1 1 + i Σ ρ Σ + + i + + + + + + + + 1 1 ijk 1 η k k η τ ρ τ + + + ijk ijk ijk ijk ijk ijk ijk ijk k j j j ijk k k k X X X X X X X X τ τ λ λ λ ρ τ Σ : Mathematical structure of the full extinction models. All 4 . 2 Bottom-up TIB Biogeography, without any trophic effects. Top-down & Bottom-up Table S ModelNull Mathematical structure Top-down & Species-richness Top-down Bottom-up & Species-richness Species-richness 224

6959 S2.2: Best-ﬁt models

Table S2.5: Mathematical structure of the best-ﬁtting initial immigration models after model simpliﬁcation. All symbols are as in Table S2.2. TIB refers to a model based on the original Theory of Island Biogeography, without any trophic effects. Model Mathematical structure TIB Cijk+1 = δj + λj + τk+1 + δjλj + Ek+1 + Si + ǫijk+1 Σ Σ Species-richness Cijk+1 = δj + λj + τk+1 + k+1 + δjλj + λj k+1 + Ek+1 + Si + ǫijk+1

Cijk+1 = δj + λj + τk+1 + ρijk+1 + δjλj + δjρijk+1 + τk+1ρijk+1 + Ek+1 + Top-down Si + ǫijk+1

Top-down & Species-richness Equivalent to Top-down model

Cijk+1 = δj + λj + τk+1 + αijk+1 + δjλj + δjαijk+1 + λjαijk+1 + δjλjαijk+1 + Bottom-up Ek+1 + Si + ǫijk+1

Cijk+1 = δj + λj + τk+1 + Σk+1 + αijk+1 + δjλj + λjΣk+1 + λjαijk+1 + Bottom-up & Species-richness Ek+1 + Si + ǫijk+1

C = δ + λ + τ + ρ + α + δ λ + λ ρ + λ α + Top-down & Bottom-up ijk+1 j j k+1 ijk+1 ijk+1 j j j ijk+1 j ijk+1 τk+1ρijk+1 + Ek+1 + ǫijk+1

Table S2.6: Mathematical structure of the best-ﬁtting repeat immigration models after model simpliﬁcation. All symbols are as in Table S2.2. TIB refers to a model based on the original Theory of Island Biogeography, without any trophic effects.

Model Mathematical structure TIB Cijk+1 = λj + τk+1 + λjτk+1 + Ek+1 + Wijq + ǫijk+1 Species-richness EquivalenttoTIBmodel Top-down EquivalenttoTIBmodel Top-down & Species-richness Equivalent to TIB model

Bottom-up Cijk+1 = λj + τk+1 + ηi + λjτk+1 + λjηi + τk+1ηi + Ek+1 + Wijq + ǫijk+1 Bottom-up & Species-richness Equivalent to Bottom-up model Top-down & Bottom-up Equivalent to Bottom-up model 225

Table S2.7: Mathematical structure of the best-ﬁtting extinction models after model simpliﬁcation. All symbols are as in Table S2.2. TIB refers to a model based on the original Theory of Island Biogeography, without any trophic effects.

Model Mathematical structure TIB Xijk+1 = λj + τk+1 + λjτk+1 + Ek+1 + Wijq + ǫijk+1

Xijk+1 = λj + τk+1 + Σk + λjτk+1 + λjΣk + τk+1Σk + λjτk+1Σk + Ek+1 + Species-richness Wijq + ǫijk+1

Top-down Equivalent to TIB model

Top-down & Species-richness Equivalent to Species-richness model

Bottom-up Xijk+1 = τk+1 + ηi + αijk + τk+1ηi + ηiαijk + Wijq + Ek+1 + ǫijk+1

Xijk+1 = λj + τk+1 + Σk + ηi + αijk + λjΣk + τk+1Σk + τk+1ηi + τk+1αijk + Bottom-up & Species-richness Σkηi + Ek+1 + Wijq + ǫijk+1

Top-down & Bottom-up Equivalent to Bottom-up model 226

6960 S2.3: Summary tables for best-fit models Table S2.8: Summary tables of the best-fit Theory of Island Biogeography (TIB), Species-richness (SR), Top-down (TD), Bottom-up (BU), Bottom-up & Species-richness (BU & SR), and Top-down & Bottom-up (TD & BU) models for probability of initial immigration. The best-fit Top-down & Species-richness model was identical to the best-fit species- richness model and is not shown. Standardised effects (βs) and intercepts shown refer to the same scale as the logit- transformed data (e.g., a 1 day increase in time between censuses or a 1m increase in distance from the mainland). Models are as in Table S2.5. The intercept of the null model was -3.84 (p<0.001). Standardised effects of 0 were not included in the best-fit versions of each model. An empty cell indicates that the term was not part of the full model and hence could not appear in the best-fit version.

TIB SR TD Bottom-up BU&SR TD&BU Fixed effect β p-value β p-value β p-value β p-value β p-value β p-value Intercept -3.77 <0.001 -3.78 <0.001 -3.98 <0.001 -3.87 <0.001 -3.84 <0.001 -4.02 <0.001 Distance -55.7 <0.001 -56.5 <0.001 -57.1 <0.001 -61.7 <0.001 -55.3 <0.001 -56.3 <0.001 Diameter 0.977 0.015 0.938 0.016 -0.122 0.865 -0.041 0.938 0.425 0.353 -0.711 0.34 Timesince 11.1 <0.001 10.7 <0.001 17.8 <0.001 11.1 <0.001 10.7 <0.001 18.1 <0.001 Species 1.19 0.07 1.1 0.103 Predators 0.251 0.081 0.239 0.104 Animal Prey 0.22 0.112 0.125 0.368 0.119 0.387 Distance:Diameter 327 <0.001 304 <0.001 322.7 <0.001 205 0.045 314 <0.001 333 <0.001 Distance:Animals 12.7 0.568 0 NA 0 NA Diameter:Species 7.31 0.026 6.59 0.048 Diameter:Predators 1.29 0.08 1.29 0.077 Diameter:Animals 2.27 0.002 1.13 0.027 1.32 0.009 Time:Predators -9.01 0.108 -9.32 0.099 Distance:Diameter:Animals 285 0.05 0 NA 0 NA

Table S2.9: Summary tables of the best-fit Theory of Island Biogeography (TIB) and Bottom-up models for probability of repeat immigration. The best-fit Top- down, Species-richness, and Top-down & Species-richness models were identical to the best-fit TIB model, while the best-fit Bottom-up & Species-richness and Top-down & Bottom-up models were identical to the best-fit Bottom-up model and are not shown. Standardised effects (βs) and intercepts shown refer to the same scale as the logit-transformed data (e.g., a 1 day increase in time between censuses or a 1m increase in distance from the mainland). The best-fitting Species-richness, Top-down, and Top-down & Species-richness models were identical to the best-fitting TIB model, and the best-fit Bottom-up & Species- richness and Top-down & Bottom-up models were identical to the Bottom-up model. Models are as in Table S2.6. The intercept of the null model was -2.77 (p<0.001). Standardised effects of 0 were not included in the best-fit versions of each model. An empty cell indicates that the term was not part of the full model and hence could not appear in the best-fit version.

TIB Bottom-up Fixed effect β p-value β p-value Intercept -2.91 <0.001 -2.82 <0.001 Diameter -0.671 0.486 0.504 0.637 Time -46.8 0.137 -76.8 0.027 Basal resources -0.164 0.431 Diameter:Time -464 0.052 -431 0.073 Diameter:Basal -2.52 0.025 Time:Basal 51.5 0.024 227

Table S2.10: Summary tables of the best-fit Theory of Island Biogeography (TIB), Species-richness, Top-down, Bottom- up, and Bottom-up & Species-richness (BU & SR) models for extinction probability. The best-fit Top-down model was identical to the best-fit TIB model, the best-fit Top-down & Species-richness model was identical to the best-fit Species- richness model, and the best-fit Top-down & Bottom-up model was identical to the best-fit Bottom-up model. None are shown here. Standardised effects (βs) and intercepts shown refer to the same scale as the logit-transformed data (e.g., a 1 day increase in time between censuses or a 1m increase in distance from the mainland). The best-fitting Top-down model was identical to the best-fitting TIB model, the best-fitting Top-down & Species-richness model was identical to the best-fitting Species-richness model, and the best-fitting Top-down & Bottom-up model was identical to the best-fitting Bottom-up model. Models are as in Table S2.7. The intercept of the null model was -0.587 (p<0.001). Standardised effects of 0 were not included in the best-fit versions of each model. An empty cell indicates that the term was not part of the full model and hence could not appear in the best-fit version.

TIB Species-richness Bottom-up BU & SR Fixed effect β p-value β p-value β p-value β p-value Intercept -0.462 <0.001 -0.59 <0.001 -0.174 0.863 1.22 0.007 Diameter 0.437 0.419 -0.836 0.276 0 NA 0.009 0.987 Time 60.0 <0.001 23.9 0.252 91.9 <0.001 117 <0.001 Species-richness 4.00 0.001 4.75 <0.001 Basal resources -0.470 0.646 -1.87 <0.001 Animals 0.201 0.844 -1.19 0.003 Diameter:Time 140 0.008 -209 0.210 0 NA 0 NA Diameter:Species -2.55 0.652 -9.02 0.031 Time:Species 546 0.017 1660.016 Time:Basal -57.7 <0.001 -69.4 <0.001 Time:Animals -20.4 0.146 Species:Basal -3.70 0.036 Basal:Animals -1.64 0.135 0 NA Diameter:Time:Species 2.74x103 0.037 0 NA 228

6961 S2.4: Details of models not described in the main text

6962 Initial immigration models 0.06 Figure S2.1: Predicted per-species A probabilities of initial immigration, repeat immigration, and extinction as a function of island diameter in 0.04 models based on the classic Theory 2 m of Island Biogeography. (A) Predicted 163 m initial immigration probability was 533 m affected by island diameter, distance 0.02 from the mainland, and interval between censuses. For islands that were relatively far from the mainland, Probability of initial immigration a species’ immigration probability increased with island size. The opposite 0.00 trend occurred for islands close to the mainland. Predictions are shown for an B island close to the mainland (2m, light, 0.12 dashed line), a moderate distance from the mainland (163 m, solid line), and far 10 days from the mainland (533 m, dark, dotted 0.08 69 days line). All predictions used the mean 172 days observed interval between censuses of 37 days and were based on a sample size of 18,420 opportunities for initial 0.04 immigration. Predicted probability of initial immigration increased linearly

Probability of repeat immigration with an increasing interval between censuses (not shown). (B) Predicted 0.00 repeat immigration probability 0.80 varied with island diameter and C interval between censuses. For short 0.70 to moderate intervals between censuses, 0.60 repeat immigration probability increased with island diameter. 0.50 When the interval between censuses was short, immigration probability 0.40 decreased with increasing island 0.30 diameter. (C) Predicted extinction 10 days probability increased with increasing Probability of extinction 0.20 42 days island diameter, and this increase was 0.10 113 days steeper when the interval between censuses was long. In panels (B) and 0.00 (C), predicted per-species probabilities 0 5 10 15 20 25 of immigration and extinction are Island diameter (m) shown for the minimum observed interval between censuses of 10 days (dashed line), a moderate interval of 28 days (solid line), and a large interval 6963 The best-ﬁtting TIB model for initial immigration included main between censuses (76 days). Predictions were based on N=1,674 and N=1,943 6964 effects for diameter, distance from the mainland, interval between opportunities for repeat immigration 6965 censuses, and interactions between diameter and both distance and and extinction, respectively. 6966 interval between censuses (Table S2.5). This model signiﬁcantly 2 6967 improved upon the AIC of the null model (χ =52.0, df=4, p<0.001;

6968 Table 3A, main text). As we expected, species were less likely to

6969 immigrate to more isolated islands (βDistance=-55.7), although this 6970 effect was reversed on large islands (βDistance:Diameter=327). More 229

6971 intuitively, probability of immigration also increased when the

6972 interval between censuses was long (βTime=11.1; Fig. S2.1).

6973 The best-ﬁtting species-richness model also improved on the ﬁt of the 2 6974 null model (χ =60.9, df=6, p<0.001). As in the TIB model, a species’

6975 probability of immigration increased with increasing island diameter

6976 and interval between censuses, and decreased with increasing

6977 distance from the mainland. All effect sizes were very similar to

6978 those in the TIB model (Table S2.8). Contrary to our expectations,

6979 species’ probability of immigration also increased with increasing

6980 species richness (βSpecies=7.31).

6981 The best-fitting top-down model also significantly improved upon the 2 6982 fit of the null model (χ =60.7, df=7, p<0.001). As with the

6983 species-richness model, in the top-down model a species’ probability

6984 of immigration decreased with increasing distance from the

6985 mainland, except on large islands (Table S2.8). Species with predators

6986 present were more likely to immigrate, especially on large islands

6987 (βPredators=0.251, βDiameter:Predators=1.29). However, for these species

TIB Species-richness Top-down Bottom-up TD & BU Figure S2.2: Hypothesis comparison for AUC=0.5 AUC=0.513 AUC=0.516 AUC=0.519 AUC=0.528 0.006 Theory of Island Biogeography (TIB),

0.004 species-richness, top-down, bottom-up,

TIB and top-down & bottom-up (TD & BU) 0.002 initial immigration models. Row names

0.000 indicate the model from which test data

AUC=0.514 AUC=0.5 AUC=0.519 AUC=0.535 AUC=0.534 was generated; column names indicate 0.006 the model used to ﬁt the test data. 0.004 Each plot shows the histogram of log- likelihoods of obtaining test data from 0.002 one model using another, based on

Species-richness 0.000

Probability density 10,000 randomly-generated test datasets.

AUC=0.515 AUC=0.519 AUC=0.5 AUC=0.535 AUC=0.514 0.006 Dotted curves indicate the success of a given model at predicting itself, as do 0.004 plots on the diagonal. The grey shaded

0.002 regions indicate overlap between the Top-down two models, where a given dataset 0.000 was equally likely to have come from AUC=0.520 AUC=0.529 AUC=0.535 AUC=0.5 AUC=0.526 0.006 either model. All pairs of models have 0 5 0.004 AUC’s close to . , indicating that the likelihood of observing a given dataset 0.002

Bottom-up was approximately equal assuming

0.000 either model were true.

AUC=0.528 AUC=0.529 AUC=0.513 AUC=0.521 AUC=0.5 0.006

0.004

0.002 TD & BU

0.000 -2400 -2100 -1800 -2400 -2100 -1800 -2400 -2100 -1800 -2400 -2100 -1800 -2400 -2100 -1800 Log-likelihood 230

Species-richness Species-richness & Bottom-up Bottom-up Figure S2.3: Hypothesis comparison AUC=0.5 AUC=0.508 AUC=0.535 for species-richness, species-richness 0.006 & bottom-up, and bottom-up initial immigration models. Row names 0.004 indicate the model from which test data was generated; column names 0.002 indicate the model used to ﬁt the test Species-richness data. Each plot shows the histogram of log-likelihoods of obtaining test data 0.000 from one model using another, based AUC=0.509 AUC=0.5 AUC=0.511 on 10,000 randomly-generated test

0.006 Probability density datasets. Dotted curves indicate the success of a given model at predicting 0.004 itself, as do plots on the diagonal. The grey shaded regions indicate overlap 0.002 between the two models, where a given dataset was equally likely to have come from either model. Data generated by Species-richness & Bottom-up 0.000 each model was ﬁt well by either of AUC=0.529 AUC=0.518 AUC=0.5 the other models. This indicates that 0.006 all three models capture very similar variation in the data. 0.004 Bottom-up 0.002

0.000 -2400 -2100 -1800 -2400 -2100 -1800 -2400 -2100 -1800 Log-likelihood

6988 likelihood of immigration increased less with increasing interval

6989 between censuses than for other species (βTime=17.8, 6990 βTime:Predators=-9.01).

6991 The bottom-up model, which also signiﬁcantly improved upon the 2 6992 null model (χ =63.7, df=8, p<0.001), also included similar terms for

6993 distance from the mainland, interval between censuses, and the

6994 interaction between distance and island diameter (Table S2.8).

6995 Contrary to our expectations, the bottom-up model did not include

6996 any terms for the ability to consume basal resources. However,

6997 species with animal prey present were more likely to immigrate than

6998 those without animal prey present (βAnimals=0.22). This effect was 6999 stronger on larger islands, islands farther from the mainland, and

7000 especially on large, isolated islands (βDistance:Animal=12.7, 7001 βDiameter:Animal=2.27, βDistance:Diameter:Animal=285).

7002 The best-ﬁt top-down & species-richness model was identical to the

7003 best-ﬁt species-richness model. The best-ﬁt bottom-up &

7004 species-richness model, however, included terms for species richness

7005 and the presence of animal prey similar to those in the 231

7006 species-richness and bottom-up models in addition to similar terms

7007 to those in the TIB model (Table S2.8). Despite combining features of

7008 the species- richness and bottom-up models, the bottom-up &

7009 species-richness model did not signiﬁcantly improve upon the ﬁt of 2 2 7010 either (χ =5.92, df=2, p=0.052 and χ =0, df=0, p>0.999). Each of the

7011 species-richness, bottom-up, and bottom-up & species-richness

7012 models all ﬁt data generated by any of the other models extremely

7013 well (Fig. S2.3).

7014 Repeat immigration models

7015 The best-ﬁtting TIB model did not signiﬁcantly improve upon the 2 7016 null model (χ =6.09, df=3, p=0.107). The TIB model included terms

7017 for island diameter, interval between censuses, and their interaction,

7018 but no terms relating to distance from the mainland (Table S2.6). In

7019 this model, a species’ probability of re-immigration decreased with 7020 increasing island diameter (βDiameter=-0.671), an effect which was 7021 strengthened when the interval between censuses was large

7022 (βDiameter:Time=-464; Fig. S2.1). The species-richness and top-down 7023 models both reduced to the best-ﬁtting TIB model, indicating that the

7024 number of species or presence of predators on an island explained

7025 little variation in the data. Unsurprisingly, the combined model

7026 including species-richness and top-down effects also reduced to the

7027 best-ﬁtting TIB model.

7028 Extinction models 2 7029 The best-ﬁtting TIB model for extinction was the full model (χ =59.8,

7030 df=3, p<0.001; Table S2.7), and this model had a lower AIC than the

7031 null model (Table 3C, main text). Contrary to our expectations, the

7032 data indicated that extinction probability increased on larger islands

7033 (βDiameter=0.437). More intuitively, extinction probability also 7034 increased with increasing intervals between censuses (βTime=60.0). 7035 The larger the island, the larger this effect (βDiameter:Time=140; Fig. 7036 S2.1).

7037 Similarly, the best-ﬁtting species-richness model was the full model,

7038 which had a lower AIC than both the null and TIB models (Table 3C,

7039 main text). Unlike the TIB model, the species-richness model

7040 predicted that extinction probability would decrease on larger islands

7041 (βDiameter=-0.836), and that this effect would be stronger with large 7042 census intervals (βDiameter:Time=-209). As expected, probability of 7043 extinction increased with species richness (βSpecies=4.00), although 232

Null TIB Bottom-up 0.020 Figure S2.4: Hypothesis comparison for AUC=0.5 AUC=0.524 AUC=0.589 null, Theory of Island Biogeography 0.015 (TIB), and bottom-up repeat immigration models. Note that the best-fitting species-richness and 0.010 Null top-down models were identical to the best-fitting TIB model while the 0.005 best-fitting top-down & bottom-up model was identical to the best-fitting 0.000 0.020 bottom-up model. Row names indicate AUC=0.523 AUC=0.5 AUC=0.574 the model from which test data was Probability density generated; column names indicate 0.015 the model used to fit the test data. Each plot shows the histogram of log- 0.010 TIB likelihoods of obtaining test data from one model using another, based on 0.005 10,000 randomly-generated test datasets. Dotted curves indicate the success of a 0.000 given model at predicting itself, as do 0.020 AUC=0.594 AUC=0.569 AUC=0.5 plots on the diagonal. The grey shaded regions indicate overlap between the 0.015 two models, where a given dataset was equally likely to have come from 0.010 either model. All pairs of models have

Bottom-up AUC’s close to 0.5, indicating that the 0.005 likelihood of observing a given dataset was approximately equal assuming 0.000 either model were true. -600 -500 -400 -300-600 -500 -400 -300-600 -500 -400 -300 Log-likelihood

7044 this effect was weaker on larger islands (βDiameter:Species=-2.55). 7045 Because of a strong three-way interaction between diameter, species

7046 richness, and time between censuses, any of the above relationships

7047 could be reversed when both species richness and the interval

7048 between censuses were sufﬁciently large (or when both were small)

7049 (βDiameter:Time:Species=2740). Nevertheless, overall the species-richness 7050 model generated very similar predictions to those of the TIB model

7051 (Fig. S2.5). The best-ﬁtting top-down model was identical to the TIB

7052 model while the best-ﬁtting top-down & species-richness model was

7053 identical to the species-richness model.

7054 The bottom-up & species-richness model provided signiﬁcant

7055 statistical improvement over both the species-richness and bottom-up 2 2 7056 models (χ =44.8, df=3, p<0.001 and χ =19.5, df=5, p=0.002,

7057 respectively). In this model, as in the bottom-up model, extinction

7058 probabilities were lower for species with animal prey available or

7059 able to consume basal resources (βAnimals=-1.19, βBasal=-1.87). Further, 7060 the increase in probability of extinction with increasing interval

7061 between censuses was weaker for these species. Unlike the bottom-up

7062 model, the bottom-up & species-richness model also included 233

Null TIB Species-richness Bottom-up Figure S2.5: Hypothesis comparison for AUC=0.5 AUC=0.729 AUC=0.744 AUC=0.896 0.020 null, Theory of Island Biogeography

0.015 (TIB), species-richness and bottom- up extinction models. The top-down

Null 0.010 model was identical to the TIB model

0.005 while the top-down & bottom-up was identical to the bottom-up model. Row 0.000 names indicate the model from which AUC=0.741 AUC=0.5 AUC=0.580 AUC=0.728 0.020 test data were generated; column names indicate the model used to ﬁt the test 0.015 data. Each plot shows the histogram of

TIB 0.010

Probability density log-likelihoods of obtaining test data

0.005 from one model using another, based on 10,000 randomly-generated test 0.000 datasets. Dotted curves indicate the

AUC=0.749 AUC=0.575 AUC=0.5 AUC=0.781 0.020 success of a given model at predicting itself, as do plots on the diagonal. The 0.015 grey shaded regions indicate overlap 0.010 between the two models, where a given

0.005 dataset was equally likely to have come

Species-richness from either model. 0.000

AUC=0.835 AUC=0.678 AUC=0.739 AUC=0.5 0.020

0.015

0.010 Bottom-up 0.005

0.000 -1000 -900 -800 -700 -600-1000 -900 -800 -700 -600-1000 -900 -800 -700 -600-1000 -900 -800 -700 -600 Log-likelihood

7063 positive effects of species-richness and the interaction between

7064 species-richness and census interval on probability of extinction

7065 (βSpecies=4.75 and βTime:Species=166). Despite these additional terms, 7066 the bottom-up & species-richness model captured similar variation in

7067 the data to the bottom-up model (average pairwise AUC = 0.618,

7068 Fig. S2.6). In addition, the parameters of the combined and

7069 bottom-up models were qualitatively similar (Table S2.10), suggesting

7070 that the statistical gains of the combined model may be due to

7071 over-ﬁtting. 234

Species-richness Species-richness & Bottom-up Bottom-up Figure S2.6: Hypothesis comparison AUC=0.5 AUC=0.736 AUC=0.781 0.020 for species-richness, species-richness & bottom-up, and bottom-up extinction 0.015 models. Row names indicate the model from which test data was 0.010 generated; column names indicate the model used to ﬁt the test data. Species-richness 0.005 Each plot shows the histogram of log- likelihoods of obtaining test data from 0.000 one model using another, based on AUC=0.699 AUC=0.5 AUC=0.616 0.020 10,000 randomly-generated test datasets. Probability density Dotted curves indicate the success of a 0.015 given model at predicting itself, as do plots on the diagonal. The grey shaded 0.010 regions indicate overlap between the two models, where a given dataset 0.005 was equally likely to have come from either model. Data generated by the Species-richness & Bottom-up 0.000 species-richness model was poorly ﬁt by AUC=0.739 AUC=0.619 AUC=0.5 0.020 the bottom-up and combined models, and vice versa. In contrast, pairings of 0.015 the bottom-up and combined models had AUC’s close to 0.5. This indicates 0.010 that adding species-richness effects to

Bottom-up the bottom-up model did not capture 0.005 any variation not already explained by bottom-up effects. 0.000 -900 -800 -700 -900 -800 -700 -900 -800 -700 Log-likelihood 235

7072 S2.5: Cumulative species richness plots for islands not shown

7073 in the main text

Figure S2.7: Initial immigrations, repeat 40 A B immigrations, extinctions, and species 8 richness over time for island E1, (11m 30 in diameter, 533m from the mainland). (A)-(D) We show the cumulative values 20 4 for the observed experiment (white 10 circles) along with the equivalent values as predicted by the the best-ﬁtting Initial immigrations 0 Repeat immigrations 0 models for initial immigration, repeat immigration, and extinction (i.e., top- 40 25 C D down & bottom-up, bottom-up, and 20 bottom-up models, respectively). We 30 obtained the model predictions for 15 total species richness at each census 20 by adding predicted immigrants and 10 subtracting predicted extinctions. In

Extinctions 10 all panels, the solid line indicates the 5

Species richness mean prediction while the shaded area 0 0 corresponds to one standard deviation. 0 200 400 600 0 200 400 600 Days since defaunation

30 Figure S2.8: Initial immigrations, repeat 120 A B 25 immigrations, extinctions, and species 100 richness over time for island E2 (12m 20 80 in diameter, 2m from the mainland). (A)-(D) We show the cumulative values 60 15 for the observed experiment (white 40 10 circles) along with the equivalent values 20 5 as predicted by the the best-ﬁtting Initial immigrations

Repeat immigrations models for initial immigration, repeat 0 0 immigration, and extinction (i.e., top- 120 down & bottom-up, bottom-up, and C 50 D 100 bottom-up models, respectively). We 40 obtained the model predictions for 80 total species richness at each census 30 60 by adding predicted immigrants and subtracting predicted extinctions. In 40 20

Extinctions all panels, the solid line indicates the 20 10 mean prediction while the shaded area Species richness corresponds to one standard deviation. 0 0 0 200 400 600 0 200 400 600 Days since defaunation 236

25 Figure S2.9: Initial immigrations, repeat A B 80 immigrations, extinctions, and species 20 richness over time for island E3 (12m 60 15 in diameter, 172m from the mainland). (A)-(D) We show the cumulative values 40 10 for the observed experiment (white circles) along with the equivalent values 20 5 as predicted by the the best-ﬁtting Initial immigrations

Repeat immigrations models for initial immigration, repeat 0 0 immigration, and extinction (i.e., top- 40 down & bottom-up, bottom-up, and C D 80 bottom-up models, respectively). We 30 obtained the model predictions for 60 total species richness at each census 20 by adding predicted immigrants and 40 subtracting predicted extinctions. In

Extinctions 10 all panels, the solid line indicates the 20 mean prediction while the shaded area Species richness corresponds to one standard deviation. 0 0 0 200 400 600 0 200 400 600 Days since defaunation

100 35 Figure S2.10: Initial immigrations, A B 30 repeat immigrations, extinctions, and 80 25 species richness over time for island E7 (25m in diameter, 15m from the 60 20 mainland). (A)-(D) We show the 40 15 cumulative values for the observed 10 experiment (white circles) along with 20 5 the equivalent values as predicted by Initial immigrations

Repeat immigrations the the best-ﬁtting models for initial 0 0 immigration, repeat immigration, and 100 extinction (i.e., top-down & bottom-up, C D 40 bottom-up, and bottom-up models, 80 respectively). We obtained the model 30 60 predictions for total species richness at each census by adding predicted 40 20 immigrants and subtracting predicted

Extinctions extinctions. In all panels, the solid line 20 10 indicates the mean prediction while Species richness the shaded area corresponds to one 0 0 0 200 400 0 200 400 standard deviation. Days since defaunation 237

20 Figure S2.11: Initial immigrations, A B 80 repeat immigrations, extinctions, and 15 species richness over time for island 60 E9 (18m in diameter, 379m from the 10 mainland). (A)-(D) We show the 40 cumulative values for the observed experiment (white circles) along with 20 5 the equivalent values as predicted by Initial immigrations

Repeat immigrations the the best-ﬁtting models for initial 0 0 immigration, repeat immigration, and extinction (i.e., top-down & bottom-up, 70 C 40 D 60 bottom-up, and bottom-up models, respectively). We obtained the model 50 30 predictions for total species richness 40 20 at each census by adding predicted 30 immigrants and subtracting predicted Extinctions 20 10 extinctions. In all panels, the solid line

10 Species richness indicates the mean prediction while 0 0 the shaded area corresponds to one 0 200 400 0 200 400 standard deviation. Days since defaunation

80 25 Figure S2.12: Initial immigrations, A B 70 repeat immigrations, extinctions, and 20 60 species richness over time for island 50 15 ST2 (11m in diameter, 154m from 40 the mainland). (A)-(D) We show the 30 10 cumulative values for the observed 20 experiment (white circles) along with 5 the equivalent values as predicted by

Initial immigrations 10

Repeat immigrations the the best-ﬁtting models for initial 0 0 immigration, repeat immigration, and 30 extinction (i.e., top-down & bottom-up, C D 80 bottom-up, and bottom-up models, respectively). We obtained the model 60 20 predictions for total species richness at each census by adding predicted 40 immigrants and subtracting predicted 10

Extinctions extinctions. In all panels, the solid line 20 indicates the mean prediction while Species richness the shaded area corresponds to one 0 0 0 200 400 600 0 200 400 600 standard deviation. Days since defaunation

7074 Supporting Information S3

7075 Supporting information for Chapter 3:

7076 Conservation of interaction partners between related

7077 plants varies widely across communities and between

7078 plant families.

7079 Alyssa R. Cirtwill, Giulio V. Dalla Riva, Nick J. Baker,

7080 Joshua A. Thia, Christie J. Webber, Daniel B. Stouffer 240

7081 S3.1. Original sources for networks

Table S3.1: Original sources for all networks used in this analysis. PH indicates a plant-herbivore network, and PP a plant-pollinator network. Network Network type Source 1 PH (Basset and Samuelson, 1996) 2 PH (Blüthgen et al., 2006) 3 PH (Bodneretal., 2010) 4 PH (Cagnolo et al., 2011) 5 PH (Coley et al., 2006) 6 PH (Ibanezetal., 2013) 7 PH (Novotny et al., 2012) 8 PH (Otte and Joern, 1976) 9 PH (Peraltaetal., 2014) 10 PH (Sheldon and Rogers, 1978) 11 PH (Ueckert and Hansen, 1971) 12 PP (Arroyo et al., 1982) 13 PP (Arroyo et al., 1982) 14 PP (Arroyo et al., 1982) 15 PP (Barrett and Helenurm, 1987) 16 PP (Clements and Long, 1923) 17 PP (Dicks et al., 2002) 18 PP (Dicks et al., 2002) 19 PP (Dupont et al., 2003) 20 PP (Elberling and Olesen, 1999) 21 PP Elberling, H. & Olesen, J. M. (unpubl.). 22 PP (Olesen and Jordano, 2002) 23 PP Olesen, J. M. (unpubl.). 24 PP (Ollerton et al., 2003) 25 PP (Hocking, 1968) 26 PP (Petanidou, 1991) 27 PP (Herrera, 1988) 28 PP (Memmott, 1999) 29 PP Olesen, J. M. (unpubl.). 30 PP (Inouye and Pyke, 1988) 241

Table S3.1, continued. Network Network type Source 31 PP (Kevan, 1970) 32 PP (Katoetal., 1990) 33 PP (Medanetal., 2002) 34 PP (Medanetal., 2002) 35 PP (Mosquin and Martin, 1967) 36 PP (Motten, 1982) 37 PP (McMullen, 1993) 38 PP (Primack, 1983) 39 PP (Primack, 1983) 40 PP (Primack, 1983) 41 PP (Ramirez and Brito, 1992) 42 PP (Ramirez, 1989) 43 PP (Schemske et al., 1978) 44 PP (Small, 1976) 45 PP (Smith-Ramírez et al., 2005) 46 PP (Percival, 1974) 47 PP Olesen, J. M. (unpubl.). 48 PP (Montero, 2005) 49 PP (Montero, 2005) 49 PP (Stald, 2003) 50 PP (Ingversen, 2006) 51 PP (Ingversen, 2006) 52 PP (Philipp et al., 2006) 53 PP (Montero, 2005) 54 PP (Kato, 2000) 55 PP (Lundgren and Olesen, 2005) 56 PP (Bundgaard, 2003) 57 PP (Dupont et al., 2009) 58 PP (Dupont et al., 2009) 59 PP (Bek, 2006) 60 PP (Stald, 2003) 61 PP (Vázquez,D.P., 2002) 62 PP (Witt, 1998) 63 PP (Yamazaki and Kato, 2003) 64 PP (Kakutani et al., 1990) 65 PP (Kato and Miura, 1996) 66 PP (Katoetal., 1993) 67 PP (Inoue et al., 1990) 68 PP (Bartomeus et al., 2008) 69 PP (Bezerraetal., 2009) 242

7082 S3.2. Supplemental within-network results

7083 The frequency of the no-overlap pattern increased signiﬁcantly with

7084 decreasing phylogenetic distance in both pollination and herbivory

7085 networks (βδ+δρ = 11.21; P<0.001 and βδ = 26.96; P=0.006). In both 7086 cases, this indicates that overlap of interaction partners decreases

7087 with increasing phylogenetic distance. This is the same trend as

7088 observed in the other patterns of overlap (see Results, Chapter 3). 243

7089 S3.3. Supplemental within-family results

7090 Families associated with the largest changes in overlap

7091 The largest decreases in total overlap with increasing phylogenetic

7092 distance were associated with Apocynaceae, Lacistemataceae, Olacaceae,

7093 Sapotaceae, and Chrysobalanaceae. The largest increases in the no

7094 overlap pattern with increasing phyloganaetic overlap were

7095 associated with Apocynaceae, Begoniaceae, Gleicheniaceae, Myricaceae,

7096 and Siparunaceae. The largest increases in total overlap with

7097 increasing phylogenetic distance were associated with Malpighiaceae,

7098 Plumbaginaceae, Surianaceae, Cactaceae, and Goodeniaceae. The largest

7099 decreases in the no overlap pattern with increasing phylogenetic

7100 distance were associated with Malpighiaceae, Surianaceae,

7101 Plumbaginaceae, Goodeniaceae, and Cactaceae. These orders were similar

7102 to those for the partial overlap pattern (see Results, Chapter 3). 244

7103 Expanded ﬁgure 14 1000 Figure S3.1: Change in log odds of

Total overlap observing different patterns of pairwise niche overlap per million years of 0 divergence time between a pair of plants in 38 separate plant families. -1000 Families in pollination networks are

) indicated by dark purple diamonds

-1 while families in herbivory networks -2000 are indicated by pale green circles. Note 1000 that changes in log odds are analogous

Partial overlap to the slopes of the regression lines from Eq.2-3 (Results, Chapter 3) in logit- 0 transformed space and represent the change in the probability of observing a pattern of overlap per million years -1000 of divergence time. We also show the slope of the relationship between the -2000 log-odds of observing each overlap pattern and phylogenetic distance 2000 across all plant families in herbivory

No overlap (pale, green horizontal line) and Change in log odds (My 1000 pollination (dark, purple horizontal line) networks. The phylogenetic tree below the plots indicates the 0 relatedness between plant families. Error bars represent 95% conﬁdence intervals. -1000 Adoxaceae Caprifoliaceae Apiaceae Asteraceae Apocynaceae Rubiaceae Boraginaceae Lamiaceae Orobanchaceae Plantaginaceae Oleaceae Solanaceae Ericaceae Primulaceae Hydrangeaceae Loasaceae Caryophyllaceae Montiaceae Polygonaceae Brassicaceae Cistaceae Malvaceae Geraniaceae Melastomataceae Myrtaceae Euphorbiaceae Malpighiaceae Phyllanthaceae Fabaceae Rosaceae Saxifragaceae Papaveraceae Ranunculaceae Amaryllidaceae Asparagaceae Iridaceae Poaceae Pinaceae 245

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7275 Supporting information S4

7276 Supporting information for Chapter 4:

7277 Are high-arctic plant-pollinator networks unravelling

7278 in a warming climate?

7279 Alyssa R. Cirtwill, Tomas Roslin, Claus Rasmussen, Jens Mogens

7280 Olesen, & Daniel B. Stouffer 252

7281 S4.1 - Simulated dates of ﬁrst interaction

7282 Methods

7283 To test whether our results are vulnerable to small errors in the

7284 estimation of species’ dates of ﬁrst interaction, we repeated all

7285 analyses that included date of ﬁrst interaction as a predictor using

7286 1000 simulated dates of ﬁrst interaction for each species. The

7287 simulations were designed to give reasonable dates of ﬁrst interaction

7288 based on the distribution of observed interactions for each species,

7289 and for the community as a whole. We obtained separate sets of

7290 dates for each species for each year. Within each year, we also

7291 simulated dates for plants and insects independently. As we did not

7292 want to alter the number of interactions in the networks, and as our

7293 results did not vary depending on the method used to account for

7294 the tentatively-dated species (see S4.2-3), we used the single

7295 best-guess dates when creating simulated datasets.

7296 For each species type (plant or insect) in each year (1996, 1997, 2010,

7297 or 2011), we ﬁrst ﬁt a linear regression of interaction dates against

7298 species identity, with no intercept. This gave us the mean values of

7299 the normal distributions that best described the observed interactions

7300 for each species. In order to account for the varying amounts of

7301 information we had about different species, we weighted the

7302 regression using the number of observed interactions for each species.

7303 Thus, the conﬁdence intervals of the ﬁtted means were narrower for

7304 species with many observed interactions and wider for species with

7305 few observed interactions.

7306 To obtain simulated dates of ﬁrst interactions, we simulated 1000 sets

7307 of interaction dates using the linear regression described above, and

7308 then took the earliest date for each species as its simulated date of

7309 ﬁrst interaction. Note that simulating interactions in this way

7310 generated datasets of the same size and structure as the observed

7311 dataset, such that species with only one observed interaction also had

7312 only one simulated interaction. We then used these sets of simulated

7313 earliest interactions to repeat our tests for Hypotheses 5, 6, and 7 (i.e.,

7314 that species active at different times of the year will have different

7315 roles, that their roles will change in different ways between decades,

7316 and that the magnitude of change in species’ roles will depend on the

7317 magnitude and direction of change in their dates of ﬁrst interaction).

7318 We present the results of these repeated analyses below. 253

7319 Hypothesis 5

7320 Species with different dates of interaction had different roles in both

7321 the observed data (see main text) and in the majority of simulated

7322 datasets. Speciﬁcally, plants with different dates of ﬁrst interaction

7323 had signiﬁcantly different roles in 602/1000 simulated datasets for

7324 the yearly networks and 765/1000 datasets for the monthly networks.

7325 Pollinators, on the other hand, had signiﬁcantly different roles in

7326 655/1000 simulated datasets for the yearly networks and 659/1000

7327 datasets for the monthly networks. In general, however, the results

7328 for the observed data were signiﬁcantly more extreme than the

7329 simulated datasets. For plants, this was true in the yearly networks,

7330 while for the monthly networks the result from the observed dataset

7331 were similar to those from the simulated datasets (p=0.004 for the

7332 yearly networks and p=0.354 for the monthly networks; Fig. S4.1).

7333 For insects, on the other hand, the F-statistic from the observed

7334 dataset were more extreme than those obtained from the simulated

7335 datasets in both monthly and yearly datasets (p<0.001 for both

7336 monthly and yearly webs). This indicates that our results for

7337 pollinators were more susceptible to observation error in dates of ﬁrst

7338 interaction than were our results for the plants. However, as the

7339 majority of our simulation results remained signiﬁcant we can still be

7340 conﬁdent that different dates of ﬁrst interaction are indeed associated

7341 with different roles. This is also the case for plants’ roles in yearly

7342 webs, although we can be more conﬁdent in this case because the

7343 values for the observed and simulated datasets were more similar.

Plants Pollinators Figure S4.1: Values of the F-statistics A p=0.007 B p<0.001 40 for the main effect of date of ﬁrst interaction in a PERMANOVA test Yearly webs 30 of species’ roles against date of ﬁrst interaction, decade, and their

20 interaction (Hypothesis 5). In each panel we show the F-statistics for 1000

10 simulated dates of ﬁrst interaction (circles) as well as the value of the

0 F-statistic for the observed dataset (horizontal line). We also give the C p=0.330 D p<0.001 probability that the F-statistic from 40 the observed dataset was signiﬁcantly Monthly webs

F-statistic (main effect) larger than the F-statistics from the 30 simulated datasets.

0 0 250 500 750 1000 0 250 500 750 1000 254

7344 In addition to testing whether species with different dates of ﬁrst

7345 interaction had different structural roles, we repeated our CAP

7346 analysis testing whether this difference could be explained by

7347 changes in network structure. As with the PERMANOVA described

7348 above, the relationship between species’ roles and their dates of ﬁrst

7349 interaction remained signiﬁcant after accounting for network

7350 structure in most of the simulated datasets (984/1000 for plants’ roles

7351 in yearly networks, 996/1000 for plants’ roles in monthly networks,

7352 573/1000 for pollinators’ roles in yearly networks, and 819/1000 for

7353 pollinators’ role in monthly networks). Our results for plants’ roles

7354 were similar in the observed and simulated datasets for both the

7355 yearly and monthly networks (p=0.075 and p=0.546, respectively; Fig.

7356 S4.2). As with the PERMANOVA results, this suggests that our

7357 results for plants’ roles are relatively robust to noise in our estimates

7358 of ﬁrst date of interaction. The F-statistics we observed for insects’

7359 roles, however, were signiﬁcantly greater than those we obtained

7360 from the simulated datasets (p<0.001 for both network types). This

7361 suggests that our results for insects’ roles are much more sensitive to

7362 potential errors in estimates of species’ dates of ﬁrst interaction.

7363 Nevertheless, as the majority of simulated datasets also gave

7364 signiﬁcant results, we remain conﬁdent in our results.

7365 Hypothesis 6

Plants Pollinators 40 Figure S4.2: Value of the F-statistic for A p=0.075 B p<0.001 a CAP analysis of species’ roles against date of ﬁrst interaction, constrained 30 Yearly webs by network structure (Hypothesis 5). In each panel we show the F-statistics 20 for 1000 datasets with simulated dates of ﬁrst interaction (circles) as well as 10 the F-statistic for the observed dataset (horizontal line). In each panel, we

0 also give the probability that the F- statistic from the observed dataset was 40

F-statistic C p=0.546 D p<0.001 more extreme than the values from the simulated datasets. 30 Monthly webs

0 0 250 500 750 1000 0 250 500 750 1000

Simulated dataset 255

Plants Pollinators 10 Figure S4.3: Values of the F-statistics for A p=0.774 B p=0.233 the interaction term in a PERMANOVA 8 test of species’ roles against date of Yearly webs ﬁrst interaction, decade, and their 6 interaction (Hypothesis 6). In each panel we show the F-statistics for 1000 4 simulated dates of ﬁrst interaction (circles) as well as the value of the 2 F-statistic for the observed dataset

0 (horizontal line). We also give the probability that the F-statistic from 10 C p=0.713 D p<0.001 the observed dataset was signiﬁcantly larger than the F-statistics from the 8

Monthly webs simulated datasets.

F-statistic (interaction effect) 6

0 0 250 500 750 1000 0 250 500 750 1000

7366 The PERMANOVA we used to test Hypothesis 5 was also used to test

7367 Hypothesis 6, that relationships between species’ roles and their

7368 dates of ﬁrst interaction would change between decades. Consistent

7369 with our observed result that this relationship did not change

7370 between decades for plants, this relationship was signiﬁcant in only

7371 317/1000 simulated datasets for yearly webs and 101/1000 simulated

7372 datasets for the monthly webs, and the F-statistics we obtained from

7373 the simulated datasets were not signiﬁcantly different from those we

7374 found in the observed datasets in both cases (p=0.790 for the yearly

7375 webs and p=0.724 for the monthly webs; Fig. S4.3). Likewise, our

7376 results for insects’ roles in yearly webs were similar for the observed

7377 and simulated datasets (p=0.220), with only 31/1000 simulated

7378 datasets showing a signiﬁcant change in the relationship between

7379 species’ roles and their dates of ﬁrst interaction between decades. The

7380 results for simulated datasets in the monthly networks were similar,

7381 with only 20/1000 datasets showing a signiﬁcant change in the

7382 relationship. This contrasts strongly with the signiﬁcant result in the

7383 observed dataset. Moreover, the F-statistic we obtained from the

7384 observed data for insects’ roles in monthly networks was signiﬁcantly

7385 larger than the results we obtained from the simulated datasets

7386 (p<0.001). This indicates that this result may be more susceptible to

7387 errors in estimation of species’ dates of ﬁrst interaction.

7388 Hypothesis 7

7389 Finally, we compared the correlations between the magnitude of

7390 change in species’ roles and the magnitude of change in dates of ﬁrst 256

7391 interaction in the observed dataset with the correlations in the

7392 simulated datasets. As in the main text, we analysed species with

7393 advancing and retreating phenologies separately. For plants’ roles in

7394 yearly webs, 98/1000 simulated datasets had signiﬁcant results for

7395 species with advancing phenologies and 78/1000 had signiﬁcant

7396 results for species with retreating phenologies. This is consistent with

7397 the non-signiﬁcant results for the observed dataset, and indeed the

7398 observed correlations were not signiﬁcantly different from those in

7399 the simulated datasets (p=0.515 for species becoming active earlier in

7400 the year and p=0.633 for species becoming active later; Fig. S4.3).

7401 This was also the case for plants’ roles in monthly networks, with few

7402 simulated datasets yielding signiﬁcant results (494/1000 for plants

7403 becoming active earlier and 231/1000 for plants becoming active

7404 later) and the observed correlations not signiﬁcantly different from

7405 those obtained using simulated datasets (p=0.475 and p=0.549,

7406 respectively). Likewise, most of the simulated datasets yielded

7407 non-signiﬁcant results for insects’ roles in yearly webs (84/1000 for

7408 those active earlier and 22/1000 for those active later) and the

7409 observed correlations were similar to those from the simulated

7410 datasets (p=0.526 and p=0.278, respectively). This was also true for

7411 insects’ roles in monthly networks for species becoming active earlier

7412 (67/1000 simulated datasets with signiﬁcant results, p=0.191 for the

7413 observed correlation being different from those in the simulated

7414 datasets) and those becoming active later (107/1000, p=0.45).To

7415 reiterate, in all cases the correlations in our observed dataset were not

7416 signiﬁcantly different from the correlations in our simulated datasets.

A Plants B Pollinators 2 1.0 Figure S4.4: R values for correlations p=0.515 p=0.633 p=0.526 p=0.278 between the change in species’ roles and change in their dates of 0.5

Yearly webs ﬁrst interaction between decades (Hypothesis 7). As we expected that 0.0 species with advancing and retreating phenologies might show different trends, we analysed each group -0.5 separately. In each panel, we show the values for 1000 datasets using simulated -1.0 dates of ﬁrst interaction (circles) as well C D as the value from the observed dataset 1.0

Correlation p=0.475 p=0.549 p=0.191 p=0.354 (horizontal lines). We also show the probability that the R2 value from the 0.5 Monthly webs observed dataset is more extreme than the values from the simulated datasets.

0.0

-0.5

-1.0 Became active earlier Became active later Became active earlier Became active later 257

7417 This indicates that all of these results are quite robust to

7418 mis-estimation of species’ dates of ﬁrst interaction. 258

7419 S4.2 - Tentatively-dated observations: methods

7420 There were 94 interactions in our dataset which could not be ascribed

7421 to a deﬁnite date. Of these, 41 were observed in 1996 and the

7422 remaining 53 were observed in 1997. The interactions involve ﬁve

7423 insect species visiting 15 plant species. Boloria chariclea visited all 15

7424 plants, Colias hecla visited four plant species, Limnophyes brachytomus

7425 visited three, and Paraphaenocladius impensus and Syngrapha parilis

7426 each visited one. In addition to visiting the most plant species, Boloria

7427 chariclea was observed far more often than any of the other insects (79

7428 of the 94 tentatively-dated observations).

7429 Each interaction is associated with a range of possible dates where

7430 the plant had been observed ﬂowering and the insect had been

7431 observed at the site. Within this range, we used the earliest date that

7432 was not associated with a deﬁnitively-labelled interaction as the

7433 best-guess date for the interaction. This date was used to include the

7434 interaction in the monthly networks described in the main text.

7435 Because of the uncertainty regarding these dates, we repeated our

7436 analyses using two other methods of assigning these interactions.

7437 First, we excluded these interactions from the monthly networks

7438 entirely. As each interaction was deﬁnitively associated with a

7439 particular year, however, we included the interactions in the yearly

7440 networks. This method underestimated the number of interactions in

7441 the 1996 and 1997 monthly networks but presented no risk of

7442 assigning an interaction incorrectly. Second, we included the

7443 interaction in all networks describing any part of the range of

7444 potential dates. This included the yearly networks, as in the other

7445 methods, and any relevant monthly networks. This method

7446 over-estimates the number of interactions in the 1996 and 1997

7447 monthly networks, but does not exclude any of the pollination

7448 interactions that occurred. These three methods of assembling the

7449 monthly networks cover a range of conservatism and all have

7450 different attendant biases. As described below, all results were

7451 qualitatively identical regardless of the method use. 259

7452 S4.3 - Tentatively-dated observations: results

7453 Change in network structure

7454 When tentatively-dated observations were not included in the

7455 monthly webs, change in network structure between decades was

7456 very similar to the change in network structure when

7457 tentatively-dated observations were only included on their most

7458 likely date. That is, the structure of monthly networks did not change

7459 between decades (F1,10=2.13, p=0.091 for a PERMANOVA of monthly 7460 network structure against decade) except when controlling for

7461 differences between months (F1,10=2.24, p=0.042 for a PERMANOVA

7462 of monthly network structure against decade, stratiﬁed by month).

7463 When tentatively-dated observations were included for all dates

7464 within the probable range, however, the structure of monthly

7465 networks differed between decades regardless of whether differences

7466 between months were taken into account (F1,10=4.03, p=0.002 for a

7467 PERMANOVA of monthly network structure against decade;

7468 F1,10=4.27, p=0.002 for a similar PERMANOVA, stratiﬁed by month). 7469 Despite this minor difference, all three methods of accounting for

7470 tentatively-dated observations agree that, if the differences between

7471 networks describing June, July, and August in different years are

7472 taken into account, network structure undoubtedly changed between

7473 the 1990’s and the 2010’s.

7474 Change in species’ roles

7475 Changes in plants’ and pollinators’ roles in the monthly networks

7476 were similar regardless of the way in which the tentatively-dated

7477 interactions were included. Plants’ roles changed between decades

7478 regardless of whether these interactions were included only in the

7479 yearly networks or for the full range of possible dates (F1,227=2.28, 7480 p=0.017 and F1,247=5.78, p<0.001, respectively, for a PERMANOVA of

7481 monthly roles against decade, stratiﬁed by species). This was also the

7482 case for pollinators’ roles (F1,458=13.5, p<0.001 and F1,455=7.96,

7483 p<0.001, respectively). In all cases, these results were qualitatively

7484 identical to those presented in the main text.

7485 The extent of change in species’ roles varied between months

7486 regardless of how the tentatively-dated interactions were treated.

7487 That is, the interaction term in a PERMANOVA of species’ roles

7488 against decade, month, and their interaction was signiﬁcant whether

7489 the tentatively-dated observations were included only in the yearly

7490 networks or for the full range of possible dates (F1,223=2.13, p=0.011 260

7491 and F1,243=4.65, p<0.001, respectively for plants and F2,451=2.78, 7492 p=0.003 and F2,454=4.85, p<0.001, respectively, for pollinators).

7493 Effect of date of ﬁrst interaction on species’ roles

7494 Our results relating species’ roles to their dates of ﬁrst interaction

7495 were also robust to different ways of including the tentatively-dated

7496 observations. Plants’ roles initially did not appear to be related to

7497 their dates of ﬁrst interaction when tentatively-dated observations

7498 were included in only the yearly networks (F1,225=7.36, p=0.126 in a

7499 PERMANOVA of PERMANOVA of plants’ roles against decade, date

7500 of ﬁrst interaction, and the interaction between them). After

7501 controlling for network structure, however, plants’ roles were related

7502 to their dates of ﬁrst interaction, as in the Main Text (F1,216=7.63, 7503 p<0.001 for a CAP of plants’ roles against their date of ﬁrst

7504 interaction, conditioned by network structure). When the

7505 tentatively-dated observations were included across the full range of

7506 possible dates, plants’ roles varied with their dates of ﬁrst interaction

7507 whether or not network structure was taken into account (F1,245=11.1,

7508 p=0.016 for a PERMANOVA similar to that described above, and

7509 F1,236=11.7, p<0.001 for a CAP as described above). The relationship

7510 between plants’ roles and their dates of ﬁrst interaction did not vary

7511 between decades regardless of how tentatively-dated interactions

7512 were included, again as in the Main Text (F1,225=1.08, p=0.344 when

7513 these interactions were included in the yearly webs only and

7514 F1,245=0.761, p=0.660 when they were included across the range of

7515 possible dates).

7516 Pollinators’ roles were likewise associated with their dates of ﬁrst

7517 interaction regardless of whether the tentatively-dated interactions

7518 were included in the yearly webs only or in all possible dates

7519 (F1,453=15.9, p=0.001 and F1,456=20.5, p<0.001, respectively, for the

7520 main effect of date in PERMANOVAs of pollinators’ monthly roles

7521 against decade, date of ﬁrst interaction, and their interaction). This

7522 relationship remained signiﬁcant when accounting for network

7523 structure (F1,447=19.3, p<0.001 and F1,444=14.791, p<0.001, respectively,

7524 in the CAPs described above). Unlike plants’ roles, but consistent

7525 with the results we present in the Main Text, the relationship between

7526 pollinators’ roles and their dates of ﬁrst interaction changed between

7527 decades whether we included the tentatively-dated observations in

7528 the yearly webs only or for the full range of potential dates

7529 (F1,444=14.8, p<0.001 and F1,447=27.4, p<0.001, respectively, for the

7530 interaction term in the PERMANOVAs described above). As with our

7531 other results, these are identical to the results presented in the main 261

7532 text where tentatively-dated interactions were included only on their

7533 most probable date.

7534 Magnitude of change in roles and change in dates of ﬁrst interaction

7535 The magnitude of change in plants’ roles was not related to the

7536 magnitude of change in their dates of ﬁrst interaction for species 2 7537 which became active earlier in the year (R =0.094, p=0.440 when

7538 tentatively-dated observations were included in the yearly webs only 2 7539 and R =0.014, p=0.620 when these observation were included for the

7540 full range of potential dates). For plants which became active later in

7541 the year, on the other hand, change in roles was related to change in 2 2 7542 dates of ﬁrst interaction (R =0.107, p=0.017 and R =0.084, p0.034,

7543 respectively).

7544 For pollinators, these patterns were reversed. Change in roles was

7545 related to change in dates of ﬁrst interaction for species which 2 7546 became active earlier in 2010-2011 than in 1996-1997 (R =0.028, 2 7547 p=0.012; and R =0.028, p=0.020, respectively). For species which

7548 became active later in 2010-2011, this relationship was not signiﬁcant 2 2 7549 (R <0.001, p=0.292; and R =0.016, p=0.310, respectively). Once again,

7550 these results are all qualitatively identical to those in the main text.

7551 This indicates that including using the best-guess dates for the

7552 tentatively-dated observations did not affect our results. 262

7553 S4.4 - Supplemental ﬁgures

0.4 0.4 A - Yearly B - June Figure S4.5: The structure of plant-

0.2 0.2 pollinator networks at Zackenberg, Greenland changed between years. A) 0.0 0.0 Yearly networks generally increased along the first NMDS axis and -0.2 -0.2 decreased slightly along the second 1996 1997 NMDS axis. The structures of the 1996 -0.4 2010 -0.4 and 1997 webs were very similar, with 2011 larger changes from 1997 onwards. The -0.6 -0.6 -1 0 1 2 3 -1 0 1 2 3 changes in the structure of monthly networks was more variable. B-D) In 0.4 0.4 C - July D - August June, the 1996, 2010, and 2011 networks were fairly similar while the 1997 web Second NMDS axis 0.2 0.2 was lower along the first NMDS axis; 0.0 0.0 the July networks were very similar in all four years; and the August networks -0.2 -0.2 increased along the first NMDS axis in every year and showed a hump- -0.4 -0.4 shaped trend along the second NMDS axis. Moving from negative to positive -0.6 -0.6 -1 0 1 2 3 -1 0 1 2 3 values along the first axis represented First NMDS axis a shift from high frequencies of motifs representing tightly-knit groups to high frequencies of more loosely-connected motifs. Moving from negative to positive values of the second NMDS axis corresponds to an increase in larger (five or six species) motifs and a decrease in smaller motifs.

Plants Pollinators 2.0 Figure S4.6: Here we show the ﬁve A B motifs most strongly associated with 1.0 the two axes of a constrained analysis of principal coordinates (CAP) of 0.0 species’ roles conditioned by the overall -1.0 network structure. As plants and their pollinators never occupy the same -2.0 positions within motifs, we analysed the two groups separately. In both -3.0 cases, however, the axes had similar interpretations. A-B) Moving from negative to positive values along the 2.0 C D ﬁrst axis represented a shift from high 1.0 frequencies of positions that tend to Second CAP axis represent specialists to high frequencies 0.0 of positions that tend to represent generalists. C-D) Moving from negative -1.0 to positive values along the second axis, -2.0 meanwhile, represented a shift from high frequencies of positions in small -3.0 motifs to high frequencies of positions in large motifs. Positions that were -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 strongly associated with an axis are First CAP axis indicated in black. Small dots indicate the exact location of each position with respect to the two axes. 7554 Supporting Information S5

7555 Supporting information for Chapter 5:

7556 Concomitant predation on parasites is highly variable

7557 but constrains the ways in which parasites contribute to

7558 food-web structure

7559 Alyssa R. Cirtwill & Daniel B. Stouffer 264

7560 S5.1. Additional References and Description of Food Webs

Table S5.1: Locations and original sources for food-web datasets. The Ythan web used is version 3 from Huxham et al. (1996). Following Huxham et al. (1996), species 100 in this web was removed as it is an animal with no recorded resources in the food web. This also resulted in the removal of one link 100 → 85 where species 100 appeared as a resource. Site Source Location Bahia Hechinger et al. (2011) Bahia Falsa, Baja California Mexico Carpinteria Hechinger et al. (2011) Carpinteria Salt Marsh, California USA Estero Hechinger et al. (2011) Estero de Punta Banda, Baja California Mexico Fjord Thieltges et al. (2011a) Flensburg Fjord, Baltic Sea Germany/Denmark Otago Mouritsen et al. (2011) Otago Harbour New Zealand Sylt Thieltgesetal.(2011b) Sylt Tidal Basin, North Sea Germany/Denmark Ythan Huxham et al. (1996) Ythan Estuary, Scotland UK

7561 Trophic groups of free-living species were deﬁned based on the

7562 free-living webs. Top predators (T) were deﬁned as species with prey

7563 but no predators, basal resources (B) as species with predators but no

7564 prey, and intermediate consumers (I) were all remaining species (that

7565 is species with both predators and prey). Cannibalistic species were

7566 considered to be intermediate consumers, as some individuals serve

7567 as prey to their conspeciﬁcs even if they are not prey to other species

7568 (Williams and Martinez, 2000). Parasites were deﬁned by the authors

7569 of the original food webs, and included species ranging from

7570 apicomplexan and ciliate protozoans to nematode, trematode, and

7571 cestode worms to parasitic copepods (Dunne et al., 2013; Huxham

7572 et al., 1996; Hechinger et al., 2011; Mouritsen et al., 2011; Thieltges

7573 et al., 2011b,a). Any species with both parasitic and free-living life

7574 stages was considered a parasite. 265

Table S5.2: Representation of each type of species across the different food webs. Type “free-living” refers to webs with free-living species only while type “par & con” refers to “parasite” and “concomitant” webs which include parasites and free-living species. S refers to the total species richness in each web. %F,%T,%I, %B, and %P refer to the proportion of species that are free-living, top predators, intermediate consumers, basal resources, and parasites, respectively. Site Type S %F %T %I %B %P Bahia free-living 119 100 7 79 14 0 Bahia par & con 171 70 5 55 10 30 Carpinteria free-living 107 100 5 84 11 0 Carpinteria par & con 165 65 3 55 7 35 Estero free-living 138 100 7 83 10 0 Estero par & con 214 64 4 54 6 36 Flensburg free-living 77 100 12 80 8 0 Flensburg par & con 123 62 7 50 5 38 Otago free-living 123 100 26 71 3 0 Otago par & con 142 87 23 61 3 13 Sylt free-living 126 100 21 74 5 0 Sylt par & con 161 78 17 58 3 22 Ythan free-living 91 100 34 62 4 0 Ythan par & con 133 68 23 42 3 32

Table S5.3: Frequency of different types of links across the different food webs. L refers to the total number of links in each web while F → F, P → F, P → P, t c F −→ P, and F −→ P to the number of links describing predation among free- living species, parasitism, predation between parasites, target predation of free- living species on parasites, and concomitant predation on parasites, respectively. t Note that neither F −→ P nor P → P links were observed in the Ythan web. t c Site Type LF → FF → PP → PP −→ FP −→ F Bahia free-living 1075 1075 0 0 0 0 Bahia parasite 2232 1075 807 165 185 0 Bahia concomitant 3765 1075 807 165 185 1533 Carpinteria free-living 963 963 0 0 0 0 Carpinteria parasite 2180 963 755 166 296 0 Carpinteria concomitant 3762 963 755 166 296 1582 Estero free-living 1647 1647 0 0 0 0 Estero parasite 3324 1647 835 169 673 0 Estero concomitant 5805 1647 835 169 673 2481 Fjord free-living 577 577 0 0 0 0 Fjord parasite 966 577 271 40 78 0 Fjord concomitant 1428 577 271 40 78 462 Otago free-living 1200 1200 0 0 0 0 Otago parasite 1481 1200 173 19 89 0 Otago concomitant 1852 1200 173 19 89 371 Sylt free-living 1047 1047 0 0 0 0 Sylt parasite 1944 1047 552 69 276 0 Sylt concomitant 3033 1047 552 69 276 1089 Ythan free-living 416 416 0 0 0 0 Ythan parasite 593 416 177 0 0 0 Ythan concomitant 1268 416 177 0 0 675 266

7575 S5.2. Supplemental methods: quantifying species’ and links’

7576 roles

2 4 6 7 8 8 11 12 12 15 16 18 20 22 24 25 26 28 30 Figure S5.1: Three-species motifs with unique positions numbered. 1 3 5 7 7 9 10 10 13 14 14 17 19 21 23 25 25 27 29 29

7577 Interactions between species are a direct consequence of the motif

7578 structure of a food web. Motifs are the set of 13 three-species

7579 subwebs describing all possible interaction patterns of three species

7580 (Milo et al., 2002; Stouffer et al., 2007, Fig. S5.1). Each motif contains

7581 one or more unique positions, indicating a unique way in whicha

7582 species’ interactions are organised in that motif (e.g., the top predator,

7583 intermediate consumer, and resource in a three- species food chain)

7584 (Stouffer et al., 2012). In the 13 three-species motifs, there are 30 such

7585 positions (Kashtan et al., 2004; Stouffer et al., 2012). Similarly, there

7586 are 24 unique link types connecting species (Fig. S5.2). By counting w 7587 the frequency cij with which each species i in community s in web 7588 type w (i.e., free-living, parasite, or concomitant) occurs in each −→w 7589 position j, we obtained a vector fsi describing the overall role of that 7590 species within its food web,

−→w w fsi = {ci1, ci2,..., ci29, ci30}s .(9)

7591 The same process was used to determine the roles of links between

7592 species, giving a vector

−→w w fsl = {cl1, cl2,..., cl23, cl24}s .(10)

−→w 7593 that describes the role fsl for each link l in community s in web type 7594 w.

5 10 13 18 22 Figure S5.2: Three-species motifs with 1 2 3 4 6 6 7 7 8 8 9 9 11 11 14 15 17 19 20 20 21 23 24 24 unique links numbered. 6 12 16 20 267

7595 S5.3. Supplemental methods: role dispersion & diversity

7596 As described in Chapter 5, we quantiﬁed the distribution of species’

7597 and links’ roles by their role dispersion and role diversity (Fig. 20,

7598 Chapter 5). In order to quantify role diversity, we ﬁrst needed to

7599 identify subsets of species (or links) that have statistically-similar

7600 motif-based roles; that is, clusters of species (or links) that appear in

7601 the same motif positions more often than one would expect by

7602 chance. To perform a clustering of this nature, we followed a

7603 recently-proposed method that is an extension of community

7604 detection algorithms for complex networks to the case of detecting

7605 groups of nodes in bipartite networks with weighted edges

7606 (Sales-Pardo et al., 2007; Stouffer et al., 2012). Here, the bipartite

7607 network consists of each species (or link) in our dataset on one side

7608 and the different motif positions on the other. Each edge in this w 7609 network is weighted by the frequency csij with which the species or 7610 link i in community s in web type w occupies position j. The

7611 clustering algorithm consists of maximising an objective function M

7612 (referred to as “modularity”) that is high when nodes in the same

7613 cluster tend to occupy the same positions with similar frequencies

7614 and low otherwise (Stouffer et al., 2012).

7615 We used a stochastic and heuristic optimisation method known as

7616 simulated annealing (Kirkpatrick et al., 1982) to cluster nodes (species

7617 or links) while maximising modularity (Sales-Pardo et al., 2007;

7618 Girvan and J., 2002). Since this procedure is not always guaranteed to

7619 ﬁnd a global optimum, and since we are most interested in the

7620 expected variety of clusters per group as a proxy for role diversity,

7621 we performed this modularity maximisation 100 separate times for

7622 roles of species and links in each community. As with dispersion, we

7623 included the roles of free-living species from the “free-living” web as

7624 well as the roles of parasites from both the “parasite” and

7625 “concomitant” webs. We then calculated the weighted average

7626 number of clusters containing each type of species (or link) across the

7627 100 modularity-maximised clusterings following

Mi Mk Nˆ j = ∑ piNij , pi = e / ∑ e ,(11) i k

Mk 7628 where Mi is the modularity of a given clustering i, ∑k e is the sum 7629 of modularities over all k clusterings, and pi is the relative probability 7630 of obtaining a clustering i weighted by its modularity; Nij is the 7631 number of clusters containing species type j in clustering i, and Nˆ j is 268

7632 the weighted average of the number of clusters containing species (or

7633 link) type j (Sales-Pardo et al., 2007). We assume that each cluster

7634 represents a unique structural role, therefore this average number of

7635 clusters provides an estimate of the role diversity for each type of

7636 species and links. 269

7637 S5.4. Supplemental results: median roles

Figure S5.3: The major axes of variation A 26 for median roles demonstrated key 2.0 29 27 differences in the roles of different 30 22 19 types of species and links. (A) The 28 25 24 1 first major axis of variation for species 17 12 14 7 23 20 roles corresponded to a split between 21 15 4 positions in motifs containing only 18 10 one-way interactions and positions in 16 5 8 motifs containing at least one two-way 0.0 13 interaction. This axis separates the 11 6 2 roles of parasites including concomitant predation from other types of roles (Fig. 21A, Chapter 5). The second major axis was largely defined by positions 3 representing the base of a three-species -2.0 9 food chain (3) and a species with two predators which do not eat each other. These positions are most common in -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 the roles of basal resources. (B) The first 8.0 major axis of variation for link roles also corresponds to a split between B positions in motifs that contain only one-way interactions and those in 6.0 10 motifs containing at least one two- 12 way interaction. Positions associated with two-way interactions were more frequent in the roles of concomitant

Second axis of variation 4.0 21 predation links than in other role 20 types (Fig. 21B, Chapter 5). The second 3 4 11 axis is largely determined by two 2.0 positions representing mutual predation 6 9 between species with a common prey 5 13 18 22 or common predator. These positions 23 are most common in the roles of links 0.0 1 15 24 describing predation between parasites. 2 8 19 16 7 1417 -2.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 First axis of variation

7638 When comparing across different types of species, we found that

7639 trophic group was a signiﬁcant predictor of median roles, as

7640 hypothesised (F4,1432 = 218.15, p= 0.001; Fig. 21A, Chapter 5). The P 7641 roles were between those of I and T free-living species, and they

7642 slightly overlapped with each. The Pc roles, in contrast, were distinct

7643 from all other role types. They were separated from T, I, and P roles

7644 along the ﬁrst correspondence analysis axis (which accounted for

7645 64.9% of total variance in species roles) and separated from B roles

7646 along the second correspondence analysis axis (which explained

7647 13.0% of total variance). 270

7648 The ﬁrst axis corresponds mainly to a split between positions in

7649 motifs containing only one-way interactions and positions in motifs

7650 with at least one two-way interaction (Fig. S5.3A). T, I, and P roles are

7651 associated with a greater frequency of one-way motifs, while Pc roles

7652 are associated with a greater frequency of two-way motifs. The

7653 second axis was largely deﬁned by the frequencies of positions 3 and

7654 9 (Fig. S5.2). Position 3 represents the base of a three-species food

7655 chain, while position 9 represents a species which is preyed upon by

7656 two other species (apparent competitors). These positions are more

7657 frequent in B roles and less frequent in other types of roles.

7658 When comparing different types of links, we found that link type

7659 signiﬁcantly predicted median roles (F4,20908 = 1018.75, p < 0.001;

7660 Fig. 21B, Chapter 5). There was a great deal of overlap between the

7661 median roles of F→P and F→F links while the median roles of P→P

7662 links were highly variable across communities. In general, the roles c 7663 of P−→F and P→P links showed more variation along the ﬁrst

7664 principal-component axis (which accounted for 60.7% of total

7665 variance in link roles) while the roles of F→F links, F→P links, and t 7666 P−→F links showed more variation along the second

7667 principal-component axis (which accounted for 15.2% of total

7668 variance).

7669 As with species roles, the ﬁrst correspondence axis corresponds to a

7670 split between one-way interactions and two-way interactions (Fig.

7671 S5.3B). Two-way interaction positions were more frequent in the roles

7672 of concomitant predation links and less frequent in other groups. The

7673 second axis corresponds to mainly to link positions 10 and 12, which

7674 represent species with a common prey that consume each other and

7675 species which consume each other and have a common predator,

7676 respectively (Fig. S5.3). These link positions are most common in

7677 links describing predation among parasites. 271

7678 S5.5. Supplemental results: species roles

7679 Dispersion

7680 We determined the overall relationship between species-richness and

7681 role dispersion using the model

σgs = β1Bg + β2 I + g + β3Tg + β4Pg + β5Pcg + β6Ngs + β6PgNgs .(12)

7682 where σgs is the dispersion of group g (B, I, T, P, or Pc) in community 7683 s (e.g., Ythan), Bg, Ig, Tg, Pg, and Pcg are dummy variables that equal 7684 1 if g is the corresponding group type (i.e., Bg=1 if g represents the 7685 roles of basal resources), Ngs is the number of species N in group g at 7686 community s, and PgNgs represents the number of species N in group

7687 g at community s if g represents the roles of parasites without

7688 concomitant predation links.

7689 We then removed the non-signiﬁcant overall effect of species richness

7690 (Table S5.4), leaving the model,

σgs = β1Bg + β2 Ig + β3Tg + β4Pg + β5Pcg + β6PgNgs ,(13)

7691 which was used to compare the dispersions of B, I, T, and Pc roles as

7692 well as the slope of P role dispersion over species richness.

Table S5.4: Standardised effects, t-values, and p-values for all terms included in models 1 and 2, as well as the F-statistic, degrees of freedom, and p-value of each model overall Model 1 Model 2 Parameter Effect t-value p-value Effect t-value p-value B 0.251 11.903 <0.001 0.261 12.703 <0.001 I 0.255 3.961 <0.001 0.352 17.133 <0.001 T 0.213 9.050 <0.001 0.233 11.344 <0.001 P 0.189 3.157 0.004 0.189 3.081 0.005 Pc 0.268 6.825 <0.001 0.320 15.611 <0.001 Ngs 0.001 1.563 0.129 NA Pg Ngs 0.002 1.128 0.269 0.003 2.195 0.036 F-statistic 160.6 178.1 Degrees of freedom 7, 28 6, 29 Overall p-value <2.2e-16 <2.2e-16 272

Table S5.5: Standardised effects, z-values, and p-values for all terms included in models 3 and 4, as well as the AIC and degrees of freedom of each model overall Model 3 Model 4 Parameter Effect t-value p-value Effect t-value p-value Intercept NA 0.189 0.802 0.422 B 0.291 0.901 0.368 NA I 0.566 0.892 0.372 NA T -0.128 -0.320 0.749 NA P 0.617 1.407 0.159 NA Pc 1.558 4.081 <0.001 1.151 5.632 <0.001 N 0.007 1.000 0.317 0.012 2.968 0.003 AIC 122.56 108.83 Degrees of freedom 26 29

7693 Diversity

7694 We tested the effect of species richness on role diversity using the

7695 model,

δgs = β1Bg + β2 Ig + β3Tg + β4Pg + β5Pcg + β6Ngs ,(14)

7696 where δgs is the role diversity of trophic group g in community s and 7697 all other symbols are as in the dispersion models above. Only Pc

7698 roles had a diversity signiﬁcantly different from zero and there was

7699 no signiﬁcant effect of species richness. This model was also used in

7700 the Tukey’s HSD test of mean diversities across groups, as the

7701 reduced model used to establish the mean diversity of Pc roles,

δgs = β0 + β1Pcg + β2Ngs ,(15)

7702 did not include intercepts for other role types (Table S5.5). 273

7703 S5.6. Link roles

7704 Dispersion

7705 We examined the effect of link richness on the dispersion of link roles

7706 using the model,

t c σls = β1F → Fl + β2F → Pl + β3P −→ Fl + β4P −→ F + β5P → Pl + β6Nls + β7P → Pl Nls ,(16)

7707 where σls is the dispersion of the roles of link type l in community s, t c 7708 F → Fl, F → Pl, P −→ Fl, P −→ Fl, and P → Pl are dummy variables 7709 that are equal to 1 if link type l is the relevant type (i.e., F → Fl=1 for 7710 F → F links) and 0 otherwise, Nls is the number of links of type l in 7711 community s, and P → Pl Nls is an additional effect of link richness 7712 speciﬁc to P → P roles, only the model above which includes the

7713 interaction between link richness and P → P roles showed any

7714 signiﬁcant effect of link richness on link role dispersion. This model

7715 was used to conclude that link richness does not affect the dispersion t c 7716 of F → Fl, F → Pl, P −→ Fl, and P −→ Fl roles.

7717 We then used the reduced model,

t c σls = β1F → Fl + β2F → Pl + β3P −→ Fl + β4P −→ Fl + β5P → Pl + β7P → Pl Nls ,(17)

7718 which includes an effect of link richness for P → P roles only, to

7719 calculate the conﬁdence intervals in Fig. 23, Chapter 5). The best

7720 parameter estimates returned by the two models were very similar

7721 (Table S5.6).

7722 Diversity

7723 Finally, we determined that there was no effect of link richness on

7724 link role diversity using the model

t c Nls = β1F → Fl + β2F → Pl + β3P −→ Fl + β4P −→ Fl + β5P → Pl + β7Nls ,(18)

7725 where Nls is the role diversity for link type l in community s and all 7726 other symbols are as above. We then used the model 274

Table S5.6: Standardised effects, t-values, and p-values for all terms included in models 5 and 6, as well as the F-statistic, degrees of freedom, and p-value of each model overall. Model 5 Model 6 Parameter Effect t-value p-value Effect t-value p-value F → Fl 0.345 12.066 <0.001 0.359 19.342 <0.001 F → Pl 0.295 13.524 <0.001 0.302 16.282 <0.001 t P −→ Fl 0.264 12.504 <0.001 0.267 13.338 <0.001 c P −→ Fl 0.450 14.179 <0.001 0.466 25.095 <0.001 P → Pl 0.262 6.709 <0.001 0.262 6.783 <0.001 Nls <0.001 0.640 0.528 NA P → Pl Nls 0.001 4.095 <0.001 0.001 4.195 <0.001 F-statistic 260.3 310.4 Degrees of freedom 7, 26 6, 27 Overall p-value <2.2e-16 <2.2e-16

t c δls = β1F → Fl + β2F → Pl + β3P −→ Fl + β4P −→ Fl + β5P → Pl ,(19)

7727 to generate conﬁdence intervals in Fig. S5.4. Although the estimated

7728 diversities for each link type differed between models (Table S5.7),

7729 the standard errors on these estimates were large, such that different

7730 types of links did not have signiﬁcantly different role diversities.

Table S5.7: Standardised effects, z-values, and p-values for all terms included in models 7 and 8, as well as the AIC and degrees of freedom of each model overall. Model 7 Model 8 Parameter Effect t-value p-value Effect t-value p-value F → Fl 12.147 2.676 0.013 8.616 2.886 0.007 F → Pl 9.022 3.465 0.015 7.201 2.412 0.023 t P −→ Fl 9.483 3.350 0.009 8.533 2.646 0.013 c P −→ Fl 14.730 2.929 0.007 10.553 3.535 0.001 P → Pl 7.507 2.316 0.028 7.133 2.212 0.035 Nls -0.004 -1.032 0.311 NA AIC 237.35 236.63 Degrees of freedom 27 28 275

Figure S5.4: Diversity of unique roles 21 was not related to the number of links in a community for any link type. Diversity of unique roles did not differ 18 across link types.

Diversity 9

0 F→FF→PP→PP→tFP→ c F 276

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7779 Supporting Information S6

7780 Supporting information for Chapter 6:

7781 Taking the scenic route: trophic transmission of

7782 parasites and the properties of links along which they

7783 travel.

7784 Alyssa R. Cirtwill, Clement Lagrue, Robert Poulin, & Daniel B.

7785 Stouffer 280

7786 S6.1: Detailed methods for data collection

7787 Study lakes and sampling sites

7788 Detailed ﬁeld data on food web composition and structure, including

7789 parasites, was obtained from four lake ecosystems. Based on existing

7790 knowledge and accessibility, Lake Hayes, Lake Tuakitoto, Lake

7791 Waihola, and Tomahawk Lagoon (South Island, New Zealand) were

7792 selected to provide a variety of lake types (size, depth, altitude; Table

7793 S6.1) and freshwater communities (coastal versus alpine, oligotrophic

7794 versus eutrophic, tidal or not, etc.). Within each lake, 4 sampling sites

7795 were selected along the littoral zone. Site selection was partly

7796 restricted by accessibility and sampling permit speciﬁcation (New

7797 Zealand Department of Conservation permit OT-34204-RES and Fish

7798 and Game New Zealand permit to capture ﬁsh for research

7799 purposes), but was ultimately made to represent all habitat types

7800 (substrate, macrophytes, riparian vegetation, etc.) present within each 2 7801 lake. Sampling sites consisted of 225m square areas (15m × 15m)

7802 with one side of the square following the lake shore line (Figure S6.1).

7803 Distances between sampling sites varied within and among lakes

7804 according to lake size and shape as well as sampling site distribution

7805 (Table S6.1; Figure S6.1). The four lakes were sampled in early spring,

7806 mid-summer, and late autumn (austral seasons: September 2012,

7807 January and May 2013). In each lake and in each season (4 lakes × 3

7808 seasons = 12 full sets of samples), ﬁsh, benthic and demersal

7809 invertebrates, plankton, periphyton, and macrophytes were sampled

7810 in each sampling site to determine their local species composition,

7811 density and/or biomass as well as that of their parasites, and

7812 potential temporal and spatial variability within and among lakes. In

7813 all cases, we averaged values across the four sites within a lake and

7814 sampling period prior to any analysis.

Table S6.1: Geographical locations and characteristics of the four study lakes (South Island of New Zealand), and distance between sampling sites (straight lines). GPS Surface area Depth (m) Altitude Dist. between sites (m) Lake coordinates (km2) Mean Max (m) Min Mean Max 44◦58’59.4"S 2.76 3.1 33 329 314 1190 2250 Hayes 168◦48’19.8"E 46◦13’42.5"S 1.32 1.0 3 15 417 794 1590 Tuakitoto 169◦49’29.2"E 46◦01’14.1"S 6.35 1.3 2 4 1330 1620 2020 Waihola 170◦05’05.8"E Tomahawk 45◦54’06.0"S 0.10 1.0 1 15 124 253 438 Lagoon 170◦33’02.2"E 281

Figure S6.1: Location, size (see scale bars) and shape of the four study lakes on the South Island of New Zealand. The position of the 4 sampling sites per lake is indicated by shaded squares (not drawn to scale).

7815 Field sampling

7816 Fish

7817 Fish were sampled once per season at each sampling site in each lake

7818 (1 sample × 4 sites × 3 seasons = 12 replicates per lake). We used a

7819 combination of ﬁsh-catching gear types following a standardised

7820 protocol so that samples represented accurately ﬁsh diversity and

7821 density (Hayes, 1989). First, two fyke nets and ten minnow traps

7822 were set in the evening. Fyke nets were positioned perpendicularly to

7823 the shore at either edge of the sampling site (i.e., 15m apart) to stop 2 7824 and capture ﬁsh swimming in and out of the focal 225m area. Fyke

7825 nets consist of a cylinder of netting (2m length, 15mm mesh size)

7826 wrapped around a series of hoops to create a trap. Fish enter through

7827 the mouth of the trap and are retained by a series of funnel-shaped

7828 constrictions. One leader (or wing) is attached to the mouth and used

7829 to direct ﬁsh into the fyke net. The leader (3m length, 50cm height,

7830 15mm mesh size) has a ﬂoat-line at the top and lead-line at the

7831 bottom to keep it upright in the water and in close contact with the 282

7832 substrate. To prevent ﬁsh from swimming around it, the end of the

7833 leader was securely anchored to the lake shore. Along with the two

7834 fyke nets, 10 minnow traps were set overnight in each sampling site.

7835 Traps were set diagonally across the sampling area at regular

7836 intervals (i.e., ≈1.7m apart). Minnow traps are small ﬁsh traps that

7837 typically consist of two funnel-shaped entrances (25mm entrance

7838 diameter) at either end of a mesh box (40 × 25 × 25cm, 2mm mesh

7839 size). Fyke nets and minnow traps were set during the night, when

7840 ﬁsh are more active, as they are passive sampling methods relying on

7841 ﬁsh to willingly encounter and enter traps (Hubert, 1996). The next

7842 day, all trapped ﬁsh were recovered from the nets and a subsample of

7843 ﬁsh from each species was set aside for later dissection. Remaining

7844 individuals were identiﬁed to species, counted and measured to the

7845 nearest mm (fork length). These ﬁsh were then released at least a

7846 hundred meters away from the sampling site.

7847 Fish sampling was then complemented using two 15m long

7848 multi-mesh gillnets. Gillnets were benthic weighted sets with top

7849 ﬂoats, 1.5m high and comprised 3 panels of 25, 38 and 56mm meshes,

7850 each 5m long. Nets were set 15m apart similarly to fyke nets,

7851 perpendicularly to the shore line and anchored to the lake shore on 2 7852 the edge of the 225m sampled area with the ﬁner mesh panel closer

7853 to shore on one side and further from shore on the other. Gillnets

7854 covered the whole water column in all cases and were checked every

7855 15 min for an hour. Fish caught in the nets were removed

7856 immediately to avoid excessive accumulation and potential visual

7857 deterrence to incoming ﬁsh (Lagrue et al., 2011). Fish caught in fyke

7858 nets and gillnets were either entering or exiting the sampling site and

7859 thus considered as site “users/occupants". All ﬁsh were identiﬁed,

7860 counted, and measured. Again, a subsample was kept for later

7861 dissection and the remaining ﬁsh released away from the sampling

7862 site.

7863 Finally, ﬁsh sampling was completed using a standard, ﬁne-mesh

7864 purse seine net. As an active sampling method, seine netting captures

7865 small and/or sedentary (i.e., resident) ﬁsh that are not captured by

7866 passive gear like fyke nets or gillnets (Thorogood, 1986). The seine

7867 net was 20m long and 1.5m high (5mm mesh size), thus covering the

7868 whole water column, and dragged by two people across the whole

7869 sampling area, catching virtually all small, sedentary fish remaining 2 7870 in the 225m area. A final subsample of fish was kept for dissections

7871 and all other ﬁsh captured in the seine net were identiﬁed, counted,

7872 measured, and immediately released. All ﬁsh set aside for later

7873 dissection were killed immediately following University of Otago 283

7874 Animal Ethics Committee guidelines (permit ET 10/12) to inhibit the

7875 digestion process and stored on ice to preserve internal tissues,

7876 stomach contents, and parasites for future identiﬁcation, counts, and

7877 other measures.

7878 Plankton

7879 Four plankton samples were taken per site and per season in each

7880 lake (4 samples × 4 sites × 3 seasons = 48 replicates per lake).

7881 Sampling was done at night when planktonic organisms migrate up

7882 from the shelter of the substrate to the top water layers of the

7883 lake (Iwasa, 1982; Haney, 1988; Rhode et al., 2001). Samples were

7884 taken using plankton net tows. The net used was a conical device

7885 (25cm mouth diameter) made of ﬁne nylon mesh (90µm mesh size)

7886 pulled through the water for a set distance. Since we sampled the

7887 littoral zone of shallow lakes, water depth was always less than a

7888 meter. We thus used a three meter horizontal pull repeated four

7889 times within each sampling area (i.e., four samples per site). Samples 2 7890 were distributed haphazardly across the 225m area. Animals

7891 captured at the bottom of the net were rinsed into a storage jar and

7892 ﬁxed in 70% ethanol for later identiﬁcation and count. The amount of

7893 water from which zooplankton are removed was estimated as length

7894 of tow (3m) times mouth diameter of the net (25cm). Plankton

7895 density and biomass could thus be later determined using the sample

7896 count, volume of water ﬁltered, and water depth at the sampling site.

7897 Demersal and benthic invertebrates

7898 Six demersal and six benthic invertebrate samples were taken per site

7899 and per season in each lake (6 samples × 4 sites × 3 seasons = 72

7900 replicates per lake for each sample type). Benthic sampling was done 2 7901 using a standard Surber sampler net with a 0.1m horizontal metal

7902 frame (0.33 × 0.3m) ﬁtted with a 250µm mesh collecting net (Surber,

7903 1937; Fenchel, 2011). Samples were taken by embedding the Surber’s

7904 metal frame into the lake bottom. Substrate and macrophytes

7905 enclosed within the frame were manually scooped up into the net to

7906 a depth of 5cm so that animals living on or within (hyporheic habitat)

7907 the substrate were captured into the net. Demersal invertebrates

7908 living on or near the substrate but either too fast or too rare to be

7909 captured in Surber nets were sampled using a rectangular dip net

7910 (i.e., a 30cm wide and 22cm high frame ﬁtted with a 250µm mesh net

7911 and attached to a long pole). Each demersal sample consisted of a

7912 fast, two meter long sweep of the net along the lake bottom without 284

7913 dredging the substrate. Again, the 12 samples (6 benthic and 6 2 7914 demersal) were distributed haphazardly across the 225m sampling

7915 area so that none overlapped. Substrate, wood debris, and

7916 macrophytes contained in the net (Surber or dip net) were placed into

7917 a bucket of water and stirred, shaken, and/or scrubbed to dislodge

7918 attached invertebrates, and then transferred into another bucket.

7919 Animals and substrate remaining in the ﬁrst bucket were transferred

7920 onto a sieve (250µm mesh size) so ﬁne sediment could be rinsed off.

7921 Samples were then stored individually in jars ﬁlled with 70% ethanol

7922 for later sorting, identiﬁcation, count, and measurement of

7923 invertebrates. Benthic and demersal invertebrate density and biomass

7924 were then determined using sample counts and sampling surface

7925 area.

7926 Periphyton

7927 Periphyton growing on hard substrate (rocks, gravels) was brushed

7928 off rocks with a toothbrush and rinsed with lake water into a

7929 container. We used a 3.9cm diameter PVC pipe as a template to 2 7930 standardise sampling surface (11.9cm ; Hughes et al., 2012).

7931 Periphyton from soft sediment bottom (sand or mud) was sampled

7932 from the top 5mm layer of sediments. The top half of a Petri dish 2 7933 (9cm in diameter, 63.6cm sampling surface) was pushed into the lake

7934 bottom sediment and a small spatula was slipped under, sealing the

7935 sample inside the Petri dish. Then the sample was lifted and rinsed

7936 with lake water into a container. Five samples of periphyton, 2 7937 distributed haphazardly across the 225m area, were taken per

7938 sampling site. The number of periphyton samples from soft and hard

7939 substrate parts of each sampling site was representative of the

7940 relative proportion of each substrate type within each sampling area.

7941 Samples were preserved in Lugol’s solution and stored in the dark

7942 for later identiﬁcation and count (Wood et al., 2012).

7943 Macrophytes

7944 Macrophytes recovered in benthic invertebrate samples were used to

7945 examine macrophyte diversity and abundance within sampling sites.

7946 During benthic sampling, macrophytes transferred into Surber nets

7947 with substrate and invertebrates were recovered, rinsed to dislodge

7948 invertebrates and wash off all sediment, and bagged into zip-lock

7949 bags. Macrophyte samples were frozen for later sorting, identiﬁcation

7950 and biomass assessment. 285

7951 Birds

7952 Birds could not be sampled for dissections (permission was not

7953 granted by the New Zealand Department of Conservation). However,

7954 species composition and relative species abundances of the bird

7955 communities foraging at each sampling site of each lake and during

7956 each season were assessed by visual counts carried out from shore

7957 with binoculars. Once per site and per season, birds present around

7958 each sampling area were identiﬁed to species (Heather and

7959 Robertson, 1996). Birds were observed over a one hour period and

7960 every bird present or passing through a 200m radius zone centred on

7961 the sampling site was counted. Given the small size of Tomahawk

7962 Lagoon, all birds present on the lake were identiﬁed and counted.

7963 Note that bird counts were done during the day and did not account

7964 for highly secretive and/or nocturnal bird species like the

7965 Australasian bittern (Botaurus poiciloptilus) or marsh crake (Porzana

7966 pusilla). However, these birds are rare and represent a negligible

7967 fraction of the bird populations in our study lakes.

7968 Laboratory analyses

7969 Fish

7970 In the laboratory, ﬁsh were identiﬁed to species, measured to the

7971 nearest mm (fork length), weighed to the nearest 0.01g and then

7972 dissected. Their gastrointestinal tract, from esophagus to anus, and

7973 all internal organs (heart, liver, gall bladder, gonads, swim bladder,

7974 etc.) were removed and preserved in 70% ethanol for later diet and

7975 parasite analyses. Fish bodies were frozen individually.

7976 All ﬁsh bodies were later examined for parasites. The head, gills,

7977 eyes, brain, and spine of each ﬁsh were examined under a dissecting

7978 microscope using ﬁne forceps to pull apart ﬁsh tissues to obtain an

7979 accurate overall parasite count for each ﬁsh. Soft tissues (muscle and

7980 skin) were removed from the spine, crushed between two glass plates,

7981 and examined by transparency under a dissecting microscope to

7982 identify and count parasites. Internal organs and gastrointestinal

7983 tract were ﬁrst rinsed in water to wash off the ethanol. The digestive

7984 tract was then separated from other organs. Liver, swim bladder, gall

7985 bladder, gonads, and other organs and tissues from the body cavity

7986 (fat, mesentery, kidneys, heart, etc.) were all screened for parasites.

7987 Finally, the digestive tract was dissected and stomach contents were

7988 removed and examined. Prey items were counted and identiﬁed to

7989 genus or species when possible to assess diet composition and the 286

7990 dietary importance of each prey taxon. Esophagus, stomach, pyloric

7991 ceca (when present), intestine, and rectum were then examined for

7992 gastrointestinal parasites. All parasites were identiﬁed, counted, and

7993 a subsample of 20 individuals per genus/species (or all individuals

7994 when less than 20 were found in a ﬁsh) were measured to the nearest

7995 0.01mm (diameter for spherical parasites; length, width, and

7996 thickness for ﬂattened ellipsoids; length and width for

7997 cylinder-shaped parasites).

7998 Plankton

7999 Plankton samples were examined under a dissecting microscope. All

8000 individuals were counted, identiﬁed to genus, and a subsample of 20

8001 individuals per genus per sample (or all individuals when less than

8002 20 were found in a sample) was measured to the nearest 0.01mm

8003 (body length) to assess potential within genus variations in body size

8004 across sites, seasons, and/or lakes. Planktonic crustaceans were

8005 examined for parasites by crushing subsamples of individuals from

8006 each genus between two glass plates, but no metazoan parasite could

8007 be detected in any sample.

8008 Demersal and benthic invertebrates

8009 Demersal and benthic samples were sorted under a dissecting

8010 microscope. All invertebrates were separated from debris and

8011 sediment, identiﬁed to genus or species when possible (using

8012 identiﬁcation keys; see Winterbourn et al., 1989; Moore, 1997;

8013 Chapman et al., 2011), and counted. Again, a subsample of 20

8014 individuals per taxon (genus or species) and per sample (or all

8015 individuals when less than 20 were found in a sample) were

8016 measured to the nearest 0.01mm (body length) to assess potential

8017 within-taxon variations in body size across sites, seasons, and/or

8018 lakes. Invertebrates were then dissected under a dissecting

8019 microscope using ﬁne forceps and examined for parasites. For

8020 abundant invertebrate taxa (chironomid larvae, gastropods,

8021 amphipods, etc.), subsamples of 20 to 80 individuals per sample were

8022 dissected. All parasites were identiﬁed, counted, and a subsample of

8023 20 individual parasites per genus/species (or all individuals when

8024 less than 20 were found in a sample) were measured to the nearest

8025 0.01mm (diameter for spherical parasites; length, width, and

8026 thickness for ﬂattened ellipsoids; length and width for cylinder

8027 shaped parasites). Stomach contents of carnivorous invertebrates

8028 (odonate larvae, leeches, Trichoptera larvae, etc.) were also examined. 287

8029 Prey items were counted and identiﬁed to genus or species when

8030 possible to assess diet composition and the dietary importance of

8031 particular prey taxa.

8032 Periphyton

8033 Periphyton samples were topped up with distilled water to

8034 standardise sample volume to 50ml and stored in the dark until

8035 analysis. Samples were then homogenised and, using a compound

8036 microscope and a Palmer-Maloney counting chamber, algae, diatoms,

8037 and cyanobacteria cells were identiﬁed and counted. An aliquot of

8038 the homogenised sample was ﬁrst transferred into the counting

8039 chamber and cells were allowed to settle at the bottom. Cells were

8040 then counted and identiﬁed following standard protocols for

8041 quantitative periphyton analysis (Biggs and Kilroy, 2000). Because of

8042 their small size, periphyton cells were not measured. Mean body

8043 sizes of the different taxa recorded were obtained from the literature

8044 and used to calculate body volumes for each taxon and for later

8045 biomass estimation (Biggs and Kilroy, 2000).

8046 Macrophytes

8047 Macrophytes from each sample were sorted by species and

8048 identiﬁed (Clayton and Edwards, 2006). Plants were patted dry to

8049 eliminate excess moisture and weighed to determine the fresh weight

8050 of each species (all individuals combined) within each sample.

8051 Body mass

8052 Body mass was calculated/measured differently for different types of

8053 organisms. Parasites were too small to be individually weighed and

8054 body measurements indicated that they varied little in size within

8055 each life stage of each taxonomic species. We thus calculated body

8056 volume for the subsamples of parasite individuals measured during

8057 host dissection based on the most appropriate formula for each

8058 species’ shape (e.g. adult nematodes and acanthocephalans,

8059 trematode rediae and sporocysts = cylinder, adult trematodes =

8060 ﬂattened ellipsoid, encysted juvenile trematodes [metacercariae] =

8061 spheres). Body volume was then calculated for each life stage of each

8062 species and their volume was converted to mass assuming their

8063 density equalled that of water. We could thus calculate a mean(± SE)

8064 individual body mass for each life stage of each parasite species. In

8065 the case of trematodes in their snail ﬁrst intermediate host, since 288

8066 rediae or sporocysts are the product of clonal multiplication, all

8067 rediae or sporocysts have the same genotype (with infrequent

8068 exceptions) and are issued from the same larva hatched from a single

8069 egg. Individual parasite body mass was thus considered as the sum

8070 of all rediae/sporocysts present in a snail host. Although rediae and

8071 sporocysts size (length and width) and volume (cylinder) were

8072 measured or calculated for each redia/sporocyst for convenience,

8073 individual parasite body mass for that life stage was reported as the

8074 total body mass of all rediae/sporocysts present in a snail host.

8075 Most free-living invertebrates were large enough to be weighed

8076 individually (isopods, chironomids, odonates, large Trichoptera

8077 larvae, adult hemiptera, molluscs, leeches, etc.). Invertebrates varied

8078 little in size within taxonomic species or genus and by weighing a

8079 subsample of individuals for each taxon (to the nearest 0.01mg) we

8080 could calculate the mean body mass of an individual for all

8081 invertebrate taxa. For small free-living invertebrates, which varied

8082 little in size intraspeciﬁcally (amphipods, small Trichoptera larvae,

8083 oligochaetes, planktonic crustaceans, etc.), we pooled 5, 10, or 20

8084 conspeciﬁc individuals (depending on individual body size) from

8085 random subsamples, weighed them as a group, and from the total

8086 mass calculated the average body mass of one individual.

8087 For ﬁsh, each individual was weighed individually and ﬁsh body

8088 mass could be directly inferred from the data. Consequently ﬁsh

8089 body mass data for a given species varied across lakes and seasons,

8090 while the body mass of smaller organisms was treated as constant for

8091 each genus/species (or life stage of parasites within a taxonomic

8092 genus/species).

8093 Similarly to parasites, periphyton cells were too small to be weighed.

8094 Taxon-speciﬁc sizes and shapes were obtained from the literature and

8095 used to calculate body volume (Biggs and Kilroy, 2000). Body volume

8096 was then converted to body mass assuming their density equalled

8097 that of water.

8098 Density 2 8099 Density of organisms (number of individuals per m and its variance)

8100 was calculated for all taxa except macrophytes for which only 2 8101 biomass (mg per m ) was estimated. For ﬁsh, we obtained a single

8102 estimate of abundance (number of ﬁsh per species) per sampling site

8103 per season. Since we used a combination of passive and active gear

8104 types and virtually captured all ﬁsh individuals present in (sedentary 289

8105 individuals) or passing through (user/occupant) each sampling area,

8106 we considered the number of ﬁsh captured as representative of the

8107 ﬁsh community present at and/or using the site. Fish density was

8108 thus calculated as the total number of ﬁsh captured divided by the 2 8109 surface of the entire sampling area (225m ). One value of ﬁsh density

8110 was thus obtained per sampling site per season per lake and for each

8111 species present.

8112 Densities of benthic and demersal invertebrates were simply

8113 calculated as the number of individuals of each taxon captured in a

8114 sample divided by the surface of the lake bottom sampled, regardless

8115 of water depth since these organisms live in, on and/or close to the 2 2 8116 substrate. Sample surface was 0.1m for benthic and 0.6m (0.3m net

8117 width × 2m sweep of the net) for demersal invertebrates.

8118 Invertebrate densities were calculated for all samples and could then

8119 be used to estimate mean densities per site, season and/or lakes.

8120 Plankton density in each sample was ﬁrst expressed as the number of 3 8121 individuals per m of water ﬁltered by dividing the number of

8122 individuals captured in a sample by the volume of the sample 3 3 8123 (0.15m ; 0.25m net diameter and 3m net tow). Density per m was 2 8124 then converted to density per m by projection of the number of 3 8125 individuals per plankton taxon contained in 1m of lake water onto 3 8126 the ﬂat surface necessary to contain that 1m of water according to

8127 water depth at each sampling site.

8128 Parasite populations are usually quantiﬁed as individuals per host

8129 rather than per surface area. Here, we calculated parasite densities 2 8130 (individuals per m ) to provide a common metric for all free-living

8131 and parasite taxa. Also, because distinct life stages of parasites with

8132 complex life cycles exploit completely different host species, we

8133 estimated parasite densities separately for each life stage of these

8134 parasites (trematodes, nematodes, acanthocephalans, etc.). Parasite

8135 abundance (mean number of parasites per individual host) was ﬁrst

8136 calculated for each parasite taxon in each host species from dissection

8137 data. Parasite abundance was then multiplied by host density 2 8138 (number of hosts per m ) to obtain parasite density. Parasite densities

8139 were also estimated in all individual samples. In the case of

8140 trematode parasites in their snail host, we did not count each

8141 individual redia or sporocyst as separate individual parasites, since

8142 these are the product of clonal multiplication. All rediae or

8143 sporocysts are issued from the same larva hatched from a single egg

8144 and were considered as a single individual. Density of these life 290

8145 stages was thus estimated as the number of infected snail hosts per 2 8146 m .

8147 Density of periphyton was calculated from the number of cells

8148 counted in the volume of the subsample contained in a

8149 Palmer-Maloney counting chamber (0.05ml). By multiplying the

8150 number of periphyton cells found in the subsample by 1000 we

8151 obtained an estimation of the number of cells in a whole sample. 2 8152 That number was then divided by sampling surface (11.9cm for hard 2 8153 substrate and 63.6cm for soft sediments) to obtain periphyton 2 8154 density (cells per m ) in each sample. Mean density per site, season

8155 and/or lake could then be estimated.

8156 Density of birds was estimated per species from the number of

8157 individuals identiﬁed during bird counting. Density (number of 2 8158 individuals per m ) was thus calculated as the number of birds

8159 counted per species divided by the area sampled. Area sampled

8160 corresponded to the whole lake for Tomahawk Lagoon or circular

8161 sector centred on each sampling site and delimited by two 150m

8162 shoreline radii and an arc within which birds were counted.

8163 Biomass 2 8164 Biomass of organisms (mg fresh weight per m ) was calculated for all

8165 taxa. For ﬁsh, only one biomass estimate could be calculated per site

8166 in each season (4 biomass estimates per season in each lake) because

8167 only one density estimate was obtained per site. First we calculated a

8168 mean body mass for each ﬁsh species in each sampling site. Mean

8169 body mass of each species was then multiplied by the species density 2 8170 (number of individuals per m ) in the same sampling site, giving the

8171 biomass of each species in each sampling site for all seasons and

8172 lakes.

8173 For invertebrates and parasites, biomass was simply the product of

8174 the mean individual body mass of each taxon by the density (number 2 8175 of individuals per m ) of that particular taxon in each sample. We

8176 thus obtained biomass estimates for all individual samples.

8177 Biomass of macrophytes was calculated as the mass of each species

8178 (mg of fresh weight per sample) recovered in Surber nets during 2 8179 benthic samples divided by the surface sampled (0.1m with Surber

8180 nets). Since 6 replicates were taken in each site, a mean macrophyte

8181 biomass per site could be calculated. 291

8182 Biomass of birds was calculated for each species as the product of the 2 8183 density (number of individuals per m ) of each species observed at

8184 each sampling site by the mean individual body mass obtained from

8185 the literature.

8186 Weighted trophic links

8187 Because we recorded diet of predatory taxa both qualitatively and

8188 quantitatively, we could calculate weighted trophic links. While the

8189 diets of primary consumers were estimated from the literature and

8190 the actual food sources available in each sampling site, stomach

8191 contents recorded during dissections of predator taxa were used to

8192 calculate the proportion of each prey taxon in the diet of predators,

8193 both numerically and in terms of biomass/energy transfer. First, we

8194 calculated the proportional contribution of each resource taxon, in

8195 terms of biomass, to the total diet of a consumer taxon, and assigned

8196 a fraction (between 0 and 1) to each resource-consumer link such that

8197 the sum of all trophic links toward any consumer species equalled 1.

8198 This was done for all consumers.

8199 The diet of grazers and detritivores could not be quantiﬁed from

8200 stomach contents. Instead, we assumed that the diet of grazers

8201 consisted of a mixture of periphyton taxa proportional to their local

8202 abundance at the site and season of sampling. The diet of detritivores

8203 was assumed to consist entirely of detritus (not measured in the

8204 present study).

8205 Many of the top predators in the 4 lake food webs considered here

8206 are birds. Because we were not allowed to sample birds, we used

8207 published information on their diet (O’Donnell, 1982; Sagar, P.M.,

8208 Schwarz, A.-M., Howard-Williams, 1995; Wakelin, 2004) to establish

8209 the relative composition of their diet in terms of the main groups of

8210 ﬁsh or invertebrates or macrophytes. We assumed the diet of the

8211 birds at out study site matched that of the same bird species studied

8212 elsewhere, and used (where necessary) the species available locally to

8213 reconstruct the most likely diet of each bird species.

8214 The ‘diet’ of each parasite taxon consists of the range of host species

8215 they use. For host-speciﬁc parasites, i.e., those occurring in only one

8216 host species at a given stage of their life cycle, the diet consists only

8217 of that host (a single trophic link of value 1 going to the parasite). For

8218 parasite species or life stages using more than one host species, we

8219 calculated the proportional contribution of each host taxon, in terms

8220 of the proportion of the parasite population harboured by each host, 292

8221 to the total diet of the parasite. Each link from a particular host was

8222 then assigned a fraction (between 0 and 1) such that the sum of all

8223 trophic links toward any parasite equalled 1.

8224 Finally, many parasites are consumed by non-host predators that

8225 capture and eat their current host, a phenomenon known as

8226 concomitant predation on parasites. This creates trophic links in

8227 which these parasites become resources for the non-host predators.

8228 From stomach content analysis of all predator taxa, we estimated the

8229 contribution of concomitant predation on parasites to each predator’s

8230 diet. Furthermore, we determined whether parasites consumed by

8231 non-host predators were digested and thus assimilated to the

8232 predator’s diet or simply lost in the faeces without being digested;

8233 trematode metacercariae protected by thick cysts are often passed

8234 through the faeces intact and should not be included in the

8235 predator’s diet. For each parasite life stage of each species, the mean

8236 number of parasites per prey item was multiplied by the mean

8237 number of individual prey consumed by unsuitable hosts for that

8238 parasite. For parasites actually digested by the predator, after

8239 converting this number of parasites eaten into biomass, these new

8240 links were added to the more traditional prey-predator links going to

8241 a consumer, and as above assigned a fraction (always very small)

8242 representing their contribution to the total diet of the consumer. 293

8243 Potential host taxa for parasite life stages Table S6.2: Potential host taxa for the parasite life stages observed in this dataset. For each life stage, we identify the host taxa for both the focal life stage and the next life stage in the parasite life cycle. If the next life stage is free-living or the current life stage is the adult ( final) stage in the parasite’s life cycle, there are no future hosts (indicated by a ‘-’). In our null model which accounted for parasites’ host specificity, only those links where the prey was a potential current host and the predator was a potential future host were included as possible “transmission” links; links where the prey was a potential current host but the predator was not a potential future host were considered possible “loss” links; and all other links were categorised as “unused” (see Material and Methods, Chapter 6 for details). Parasite Life stage Host for focal stage Host for next stage Acanthocephalus galaxii Cystacanth Amphipod Fish Acanthocephalus galaxii Adult Fish - Anisakidae sp. Larva Unknown Fish Apatemon sp. Metacercaria Fish Bird Apatemon sp. Sporocyst Gastropod - Aporocotylid sp. I Sporocyst Gastropod - Coitocaecum parvum Metacercaria Amphipod or Mysid Fish Coitocaecum parvum Sporocyst Gastropod - Coitocaecum parvum Adult Fish - Deretrema sp. Adult Fish - Eustrongylides sp. Larva Fish Bird Gymnocephalous sp. I Redia Gastropod - Hedruris spinigera Larva Amphipod Fish Hedruris spinigera Adult Fish - Hydracarina sp. Larva Insects (aquatic) - Lepocreadiidae sp. Metacercaria Leech Bird Maritrema poulini Metacercaria Amphipod or Isopod Bird Maritrema poulini Sporocyst Gastropod - Microphalloidea sp. Metacercaria Trichoptera Bird Microphallus livelyi Metacercaria Gastropod Bird Microphallus sp. Metacercaria Amphipod or Isopod Bird Neoechinorhynchus sp. Adult Fish - Notocotylus sp. Metacercaria Mollusc Bird Notocotylus sp. Redia Gastropod - Plagiorchioid sp. Sporocyst Gastropod - Pronocephaloid sp. I Metacercaria Mollusc Bird Pronocephaloid sp. I Redia Mollusc - Pronocephaloid sp. IV Metacercaria Mollusc Bird Pronocephaloid sp. IV Redia Mollusc - Stegodexamene anguillae Metacercaria Fish Fish Stegodexamene anguillae Redia Mollusc - Stegodexamene anguillae Adult Fish - Telogaster opisthorchis Metacercaria Fish Fish Telogaster opisthorchis Redia Mollusc - Telogaster opisthorchis Adult Fish - Tylodelphys sp. Metacercaria Fish Bird Virgulate sp. I Sporocyst Mollusc - Unidentified “Apatemon sp.” Metacercaria Odonate Bird Unidentified cestode sp. Larva Fish Bird Unidentified nematode sp. Adult Fish - Unidentified trematode sp. Metacercaria Mollusc Bird Unidentified trematode sp. A Adult Fish - Unidentified trematode sp. B Adult Fish - 294

8244 S6.2: Supplemental methods and results for links’ structural

8245 properties

8246 Methods

8247 In addition to calculating each link’s centrality, we also deﬁned their

8248 structural roles to get a richer picture of the ways in which species

8249 are embedded in their networks. These roles describe the link’s

8250 position in the network in terms of “motifs”— unique patterns of 3

8251 interacting species that can be understood as the building blocks of

8252 networks (Milo et al., 2002; Kashtan et al., 2004; Stouffer et al., 2007).

8253 Each motif has different implications for the ﬂow of energy and

8254 biomass through the network (Stouffer et al., 2007; Stouffer and

8255 Bascompte, 2010). For example, the populations of three species in a

8256 direct competition motif (two predators with one prey) will affect

8257 each other differently from those of the three species in an apparent

8258 competition motif (two prey with one predator). Moreover, each

8259 unique position in each motif has different implications (Cirtwill and

8260 Stouffer, 2015). For example, in the omnivory motif the top predator

8261 consumes both an intermediate consumer and a basal species that is

8262 also eaten by the intermediate consumer, and each of these links will

8263 almost certainly provide the top predator with different amounts of

8264 biomass and energy, and the top predator will in turn affect the

8265 intermediate and basal species differently. By tracking the frequency

8266 with which a link appears in each position in each motif, we thereby

8267 obtain a rich picture of the way each link is embedded in the

8268 network.

8269 To calculate a link’s structural role, therefore, we counted the

8270 frequency with which the link appears in each of the 24 unique

8271 positions in the 3-species motifs. We were interested in comparing

8272 the shapes of links’ roles rather than their sizes (i.e., the number of

8273 times the link appeared across all motifs). To ensure that different

8274 role sizes did not inﬂuence our analyses, we normalised each role

8275 vector by dividing by the total number of positions in which the link

8276 appears.

8277 After obtaining these normalised role vectors for each link, we tested

8278 whether links with different outcomes had different typical roles. We

8279 ﬁrst visualised the median roles for each outcome using a canonical

8280 correspondence analysis conducted using the function cca from the

8281 package vegan (Oksanen et al., 2014) in R (R Core Team, 2014). The

8282 median roles for each outcome as determined by this analysis

8283 describe the outcomes’ “typical” roles. This visualisation is

8284 equivalent to the Tukey’s HSD tests performed for the univariate 295

8285 properties above. We then statistically compared these typical roles

8286 with a non-parametric permutational multivariate analysis of

8287 variance (PERMANOVA Anderson, 2001) by using the adonis

8288 function from the package vegan (Oksanen et al., 2014) in R (R Core

8289 Team, 2014).

8290 Like our modiﬁed ANOVA in Chapter 6, the PERMANOVA compares

8291 between-group differences to within-group differences following a

8292 pseudo-F statistic (Anderson, 2001). As when testing for correlations

8293 between links’ roles and other structural or dynamic properties, we

8294 deﬁned differences between links’ roles using Bray-Curtis

8295 dissimilarity, calculated using relative frequencies of positions within

8296 each role (see above). Once again, we did not assume a particular

8297 distribution of the data and computed p-values from null

8298 distributions based on permutations of the data (Anderson, 2001). As

8299 in our modiﬁed ANOVA tests, we used both the unrestrictive and

8300 taxonomically-informed null models.

8301 Results

8302 Links with different outcomes were associated with different

8303 structural roles, whether we used the unrestrictive or the

8304 taxonomically-informed null model (F2,42019=126.5, p<0.001 in both

8305 cases). Transmission links, on average, had more positive values on

8306 both axes than loss links, and loss links in turn had more positive

8307 values on both axes than unused links (Fig. S6.5A).

8308 To put these results into context, positive values of the ﬁrst RDA axis

8309 were most strongly associated with frequent participation in the

8310 direct competition motif, where one prey has two predators, followed

8311 by the lower link in a three-species food chain (Fig. S6.5B). Negative

8312 values of this axis were strongly associated with frequent

8313 participation in the apparent competition motif, where one predator

8314 has two prey. Positive values of the second RDA axis were associated

8315 with frequent participation in both direct and apparent competition,

8316 while negative values were associated with both links in a

8317 three-species food chain. These motifs were more strongly associated

8318 with the RDA axes than any others by at least an order of magnitude.

8319 Transmission links therefore tended to appear more frequently in the

8320 bottom of food chains and in direct competition links. 296

A Figure S6.5: Feeding links between 0.005 free-living species with different outcomes for parasites also had different structural roles. A) For each outcome, we show the median role across all parasite life stages (±2SE) with respect to the ﬁrst two axes of a redundancy analysis that, together, explain 76.5% of variation in links’ structural roles. B) We also show the 0.0025 four structural role positions that were most strongly associated with the two RDA axes. The dashed box indicates the location of panel A, while the lines indicate the strict relationship Unused between the frequency of each position Loss (highlighted in black) and the two RDA Transmission axes. Note that all four lines extend far beyond the borders of panel B. The 0 four positions were: the single unique -0.001 0 0.001 position in the direct competition motif (top right), the single unique position in the apparent competition motif (top B left), the upper link in the three-species food chain (bottom centre, black link), and the lower link in the three-species

RDA Axis 2 (20.4%) 0.01 food chain (bottom right, black link).

-0.01

-0.02

-0.03

-0.01 -0.005 0 0.005 0.01 RDA Axis 1 (56.1%) 297

8321 S6.3: Results of model selection

Table S6.3: AIC scores for CCAs of outcomes of links for parasites against different combinations of dynamic and structural properties. In the table below, an ‘X’ indicate that a property was included in the model. The models have been ranked from lowest to highest AIC. A line separates the best two models from those with AIC’s signiﬁcantly greater (∆AIC>2) than the most parsimonious model. Properties Model Contribution to Prey Prey Biomass Centrality AIC score predator’s diet abundance biomass transfer 1 X XXX 18151.36 2 X X XXX 18152.12 3 X X X 18154.81 4 X XX X 18155.54 5 X X X 18179.01 6 X X X X 18180.02 7 X X 18184.66 8 X X X 18185.60 9 X X X 18562.44 10 X X XX 18563.40 11 X X 18566.94 12 X X X 18567.89 13 X X 18595.06 14 X X X 18596.25 15 X 18600.21 16 X X 18601.35 17 X X XX 18894.91 18 X X X 18900.35 19 X X X 18989.52 20 X X 18995.31 21 X X X 19192.71 22 X X 19198.19 23 X X 19311.62 24 X 19318.33 25 X X X 19451.30 26 X X 19459.78 27 X X 19565.75 28 X 19570.78 29 X X 19803.15 30 X 19811.87 31 X 19947.92 32 19953.55 298

8322 S6.4: Testing for correlations between link properties

8323 Methods

8324 To control for the possibility that relationships between outcomes of

8325 feeding links and dynamic properties might be similar because of

8326 hidden relationships between the properties, we ﬁrst tested for

8327 correlations between them. We did this using the R (R Core Team,

8328 2014) function cor.test from the stats package (R Core Team, 2014).

8329 When testing for correlation between links’ contributions to

8330 predators’ diets and the amount of biomass they transfer, we

8331 included all links (n=2160). When testing for correlations between

8332 prey biomass or prey abundance and any other property, however,

8333 we restricted our sample to those links where the local prey biomass

8334 (n=1627) or abundance (n=1464) could be estimated.

8335 We also tested for correlations between links’ structural roles and the

8336 other predictors. To do this, we performed a series of non-parametric

8337 t-tests for multivariate independence, using the function dcor.ttest in

8338 the R (R Core Team, 2014) package energy (Rizzo and Szekely, 2014).

8339 Once again, we included only those links where biomass or

8340 abundance had been estimated when testing for correlations

8341 involving prey biomass or abundance. This function tests for

8342 correlations between the inter-point distances in two datasets. In our

8343 case, these were the sets of structural roles for each link and the set of

8344 links’ contributions to predators’ diets (or any other univariate

8345 predictor we considered). We deﬁned differences between links’ roles

8346 using Bray-Curtis dissimilarity (Anderson, 2001; Baker et al., 2015;

8347 Cirtwill and Stouffer, 2015) since it measures differences between

8348 roles based only on positions in which at least one of the links

8349 appears. That is, this dissimilarity is not affected by “double zeros”

8350 such that links which appear in few positions are not considered

8351 more similar to each other due to the large number of shared zeros

8352 frequencies. We also wished to avoid a situation in which two links

8353 involved in different numbers of positions would be interpreted as

8354 having different roles even if they occurred with the same frequencies

8355 across all positions; we therefore calculated the dissimilarities based

8356 on positions’ relative frequencies (that is, the number of times a link

8357 appears in a position divided by the number of times it appeared in

8358 any position). As all of the other properties we tested were univariate

8359 and Bray-Curtis dissimilarity could not be used, we calculated

8360 Euclidean distances between links for these properties. 299

8361 Results

8362 As we expected, there were signiﬁcant correlations among many of

8363 the dynamic properties we investigated. The contribution of a link to

8364 the predator’s diet was signiﬁcantly and positively correlated with 2 8365 the local abundance of the prey species (R =0.073, p=0.005), the local 2 8366 biomass of the prey species (R =0.198, p<0.001), and the amount of 2 8367 biomass transferred along the link (R =0.238, p<0.001). However, not

8368 all of these properties were correlated amongst themselves. In

8369 particular, prey abundance and prey biomass were not signiﬁcantly

8370 correlated with the amount of biomass transferred along a link 2 2 8371 (R =0.016, p=0.537 and R =0.024, p=0.326, respectively). Prey

8372 abundance and biomass were strongly correlated with each other 2 8373 (R =0.521, p<0.001). It is worth noting that, even though many of

8374 these properties were signiﬁcantly correlated, the correlations tended

8375 to be both weak and potentially non-linear (Fig. S6.2). We therefore

8376 present the results for each property separately.

8377 Centrality was signiﬁcantly and positively correlated with the

8378 contribution of a link to the predator’s diet, the abundance of the 2 8379 prey, and the amount of biomass transferred along a link (R =0.088, 2 2 8380 p<0.001; R =0.071 , p=0.007; and R =0.133, p<0.001, respectively; Fig.

8381 S6.3). Centrality was not, however, correlated with prey biomass 2 8382 (R =0.030, p=0.227). Links’ structural roles, meanwhile, were strongly

8383 correlated with each of the link’s contribution to the predator’s diet,

8384 the biomass of the prey, the abundance of the prey, and the amount

8385 of biomass transferred along a link (t2158=685, p<0.001; t1625=57.9, 8386 p<0.001; t1462=69.5, p<0.001; and t2158=69.5, p<0.001, respectively).

8387 Finally, centrality and link’s structural roles were also signiﬁcantly

8388 correlated t2158=35.4, p<0.001). Once again, however, the correlations 8389 between centrality and dynamic properties were weak and non-linear,

8390 while the linearity of correlations between structural roles and other

8391 properties is difﬁcult to assess. We therefore present all results

8392 independently but note the potential for confounding effects between

8393 properties. 300

3 Figure S6.2: The contribution of a feeding link to the predator’s diet ) 2 2 was positively correlated with the local abundance and biomass of the

(1000/m 1 prey (p<0.001 in both cases) and with Prey abundance 0 the amount of biomass transferred along the link (p<0.001). Likewise, 80 70 prey abundance was correlated with 60 prey biomass (p<0.001). The amount

) 50 2 of biomass transferred along a link, 40

(g/m 30 however, was not correlated with the 20 abundance or biomass of the prey Prey biomass 10 (p=0.537 and p=0.326, respectively). For 0 each pair of properties, we show the

300 best-ﬁt loess regression (red line) with 250 a 95% conﬁdence interval (shaded area) 200 together with the means (±2 SE) of the

(mg) 150 observed property for 10 bins ( sizes of 100 bins vary depending on the regression). 50 Biomass transfer Note that even when the correlations 0 0 0.2 0.4 0.6 0.8 1 1e-08 1e-06 0.0001 0.01 1 1 10 100 between two dynamic properties were Contribution to Prey abundance Prey biomass 2 2 predator's diet (%) (1000/m ) (g/m ) signiﬁcant, the correlations were weak and appeared non-linear. We therefore present the results for all four dynamic properties.

1 4 Figure S6.3: A link’s centrality was

0.8 signiﬁcantly correlated with its 3

(%) contribution to the predator’s diet ) 0.6 2 (p<0.001), the abundance of the prey 2 (p=0.007), and the amount of biomass 0.4 (1000/m transferred along the link (p<0.001). Prey abundance

Contribution to predator's diet 1 Centrality was not, however, correlated 0.2 with the biomass of the prey (p=0.227).

0 0 For each pair of properties, we show the best-ﬁt loess regression (red line) 40 700 with a 95% conﬁdence interval (shaded 35 600 area) together with the means (±2 30 SE) of the observed property for 10 500 25

) bins (sizes and numbers of bins vary 400 2 20

(mg) depending on the regression). Note that (g/m 300 15 even when the correlations between 200 Prey biomass Biomass transfer 10 two properties were signiﬁcant, the

100 5 correlations were weak and appeared

0 0 non-linear. As with the dynamic 0 100 200 300 400 500 600 0 100 200 300 400 500 properties, we therefore present the Centrality (shortest paths) results for centrality separately. 301

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8483 Supporting Information SA

8484 Supporting information for Appendix:

8485 Are parasite richness and abundance linked to prey

8486 species richness and individual feeding preferences in

8487 fish hosts?

8488 Alyssa R. Cirtwill, Daniel B. Stouffer, Robert Poulin, & Clement

8489 Lagrue 306

8490 S7.1. Supplemental results: biomass of trophically transmitted

8491 parasites

Hedruris spinigera in Aldrichetta forsteri Coitocaecum parvum in Perca fluviatilis Figure SA.1: Marginal effects of the 6000 A B proportion of intermediate host prey 0.10 in the diet of ﬁsh hosts on the total 4000 0.05 biomass of trophically-transmitted parasites in individual hosts in the four 2000 0.00 parasite-ﬁsh host taxon combinations

0 -0.05 for which models could be ﬁtted; (A) Hedruris spinigera in Aldrichetta -0.10 -2000 Paracorophium excavatum Paracalliope fluviatilis forsteri, (B) Coitocaecum parvum in

2 1 2 Perca fluviatilis, (C) Eustrongylides sp. 10 10 10 in Gobiomorphus cotidianus and (D) C. Eustrongylides sp. in Gobiomorphus cotidianus Coitocaecum parvum in Gobiomorphus cotidianus 0.8 1.5 parvum in G. cotidianus. Intermediate C D host prey taxa are also identified 0.6 1.0 0.4 within each panel. Marginal effects 0.5 are obtained by summing the effect of 0.2 0.0 proportion of intermediate host with 0.0 the effect of the interaction between -0.2 -0.5 fish host mass and proportion of -0.4 -1.0 Paracalliope fluviatilis intermediate hosts across the observed Oligochaete sp. Paracorophium excavatum -0.6 range of fish host masses. We show Marginal effect of definitive host diet on parasite biomass (mg) -1.5 -1 0 1 -1 0 1 mean marginal effects (black lines) with 10 10 10 10 10 10 95% confidence intervals (grey). See Fish host mass (g) Fig. A1, Appendix for details about the interpretation of marginal effects. 8492 In general, relationships between parasite biomass and proportions of

8493 intermediate hosts in the diet of ﬁsh hosts were similar to the

8494 relationships with parasite abundances described above (Fig. S7.1).

8495 The main distinction was that, unlike the abundance of H. spinigera in

8496 A. forsteri, the biomass of the parasite did not vary signiﬁcantly with

8497 the proportion of P. excavatum in the ﬁsh’s diet, and there was no

8498 signiﬁcant interaction with the size of A. forsteri (β2 = 758, P = 0.607; 8499 β3 =-560, P = 0.456). As such, host diet did not affect the biomass of

8500 H. spinigera for A. forsteri of any size (Fig. S7.1A; Table S7.2). Also

8501 unlike abundance, the biomass of Eustrongylides sp. in G. cotidianus

8502 increased with the proportion of intermediate hosts in the ﬁsh’s diet

8503 (β2 = 2.96, P<0.001). However, there was no signiﬁcant interaction 8504 with ﬁsh host size (β3 = 0.009, P = 0.874) and the high degree of

8505 variance associated with this interaction meant that, overall, the

8506 biomass of Eustrongylides sp. did not vary with the diet of G.

8507 cotidianus (Fig. S7.1C). More similarly, neither the biomass of C.

8508 parvum in P. ﬂuviatilis nor the biomass of C. parvum in G. cotidianus

8509 varied with the proportion of intermediate hosts in the ﬁshes’ diets −4 8510 (β2 =-2.80x10 , P = 0.960 and β2 =-0.029, P = 0.557; β3 =-0.048, P =

8511 0.434, respectively). Further, there were no signiﬁcant interactions

8512 between proportions of intermediate hosts and ﬁsh host size (β3 = −6 8513 2.09x10 , P = 0.999 and β4 = 0.038, P = 0.906; β5 = 0.134, P = 0.676).

8514 Therefore there was no overall effect of the proportion of either

8515 intermediate host on C. parvum biomass (Fig. S7.1B, D; Table S7.2). 307

Table SA.1: Geographical locations and characteristics of the four lakes sampled for G. cotidianus (South Island of New Zealand). Surface GPS Depth (m) Altitude Trophic Lake area Tidal coordinates Mean - Max (m) status (km2) 44◦58’59.4"S Hayes 2.76 3.1 - 33 329 Mesotrophic No 168◦48’19.8"E 46◦13’42.5"S Tuakitoto 1.32 0.95 - 3 5 Mesotrophic Yes 169◦49’29.2"E 46◦01’14.1"S Waihola 6.35 1.3 - 2.2 4 Eutrophic Yes 170◦05’05.8"E Tomahawk 4◦54’06.0"S 0.096 1.0 - 1.2 15 Eutrophic No Lagoon 170◦33’02.2"E

Table SA.2: Estimated fixed effects in equation 3 (with P-values in parentheses). β1 indicates the effect of fish host mass on the biomass of the parasite, β2 and β3 the effects of the proportions of two intermediate hosts in the diet of the fish host, and β4 and β5 the effects of the interaction between proportion of intermediate host and fish host mass. NA indicates that only one intermediate host was found in the gut contents of the fish host. Estimates are based on averages over the full equation 2 and all possible reduced models, weighted by AIC.

Fish host Parasite β1 β2 β3 β4 β5 Aldrichetta Hedruris 14.0 758 NA -560 NA forsteri spinigera (0.224)(0.607)(0.456) Perca Coitocaecum 0.003 -2.80x10−4 NA 2.09x10−6 NA ﬂuviatilis parvum (0.823)(0.960)(0.999) Gobiomorphus Eustrongylides 2.96 0.036 NA 0.009 NA cotidianus sp. (<0.001)(0.775)(0.874) Gobiomorphus Coitocaecum 0.004 -0.029 -0.048 0.038 0.134 cotidianus parvum (0.948)(0.557)(0.434)(0.906)(0.676)

Table SA.3: Estimated fixed effects in equation 2 (with P-values in parentheses) where proportions of intermediate hosts were determined using masses of intermediate hosts. β1 indicates the effect of fish host mass on the abundance of the parasite, β2 and β3 the effects of the proportions of two intermediate hosts in the diet of the fish host, and β4 and β5 the effects of the interaction between proportion of intermediate host and fish host mass. NA indicates that only one intermediate host was found in the gut contents of the fish host. Estimates are based on averages over the full equation 2 and all possible reduced models, weighted by AIC.

Fish host Parasite β1 β2 β3 β4 β5 Aldrichetta Hedruris 0.208 44.6 NA 0.991 NA forsteri spinigera (<0.001)(0.002)(0.898) Perca Coitocaecum 0.113 0.007 NA 0.024 NA ﬂuviatilis parvum (0.839)(0.991)(0.964) Gobiomorphus Eustrongylides 0.438 0.287 NA 0.028 NA cotidianus sp. (<0.001)(0.075)(0.725) Gobiomorphus parvum 0.131 -0.847 1.07 6.10 -4.41 cotidianus parvum (0.191)(0.358)(0.229)(0.250)(0.405) 308

Table SA.4: Estimated fixed effects in equation 3 (with P-values in parentheses) where proportions were determined based on the masses of each intermediate host. β1 indicates the effect of fish host mass on the biomass of the parasite, β2 and β3 the effects of the proportions of two intermediate hosts in the diet of the fish host, and β4 and β5 the effects of the interaction between proportion of intermediate host and fish host mass. NA indicates that only one intermediate host was found in the gut contents of the fish host. Estimates are based on averages over the full equation 2 and all possible reduced models, weighted by AIC.

Fish host Parasite β1 β2 β3 β4 β5 Aldrichetta Hedruris 10.4 2.88x103 NA 3.45x102 NA forsteri spinigera (0.415)(0.632)(0.287) Perca Coitocaecum 0.003 -2.62x10−4 NA 1.79x10−6 NA ﬂuviatilis parvum (0.823)(0.962)(0.998) Gobiomorphus Eustrongylides 3.27 5.19 NA 1.49 NA cotidianus sp. (<0.001) (<0.001)(0.138) Gobiomorphus parvum 0.042 -0.273 0.442 4.83 -4.38 cotidianus parvum (0.571)(0.694)(0.516)(0.320)(0.365)

Table SA.5: Estimated fixed effects in equation 2 (with P-values in parentheses) using absolute counts of intermediate hosts consumed rather than proportions. β1 indicates the effect of fish host mass on the abundance of the parasite, β2 and β3 the effects of the counts of two intermediate hosts in the diet of the fish host, and β4 and β5 the effects of the interaction between number of intermediate host individuals consumed and fish host mass. NA indicates that only one intermediate host was found in the gut contents of the fish host. Estimates are based on averages over the full equation 2 and all possible reduced models, weighted by AIC.

Fish host Parasite β1 β2 β3 β4 β5 Aldrichetta Hedruris 0.130 1.18 NA 0.549 NA forsteri spinigera (<0.001) (<0.001)(0.002) Perca Coitocaecum 0.095 5.76 NA -2.77 NA ﬂuviatilis parvum (0.922)(0.885)(0.993) Gobiomorphus Eustrongylides 0.411 0.007 NA -0.009 NA cotidianus sp. (<0.001)(0.842)(0.889) Gobiomorphus Coitocaecum 0.891 0.842 -0.156 4.59 -0.423 cotidianus parvum (<0.001)(0.187) (<0.001)(0.013)(0.015)

Table SA.6: Estimated fixed effects in equation 3 (with P-values in parentheses) using absolute counts of intermediate hosts consumed rather than proportions. β1 indicates the effect of fish host mass on the abundance of the parasite, β2 and β3 the effects of the counts of two intermediate hosts in the diet of the fish host, and β4 and β5 the effects of the interaction between number of intermediate host individuals consumed and fish host mass. NA indicates that only one intermediate host was found in the gut contents of the fish host. Estimates are based on averages over the full equation 2 and all possible reduced models, weighted by AIC.

Fish host Parasite β1 β2 β3 β4 β5 Aldrichetta Hedruris 8.43 78.9 NA 48.5 NA forsteri spinigera (0.212)(0.076)(0.222) Perca Coitocaecum 0.003 0.025 NA -9.42x10−4 NA ﬂuviatilis parvum (0.824)(0.933)(0.999) Gobiomorphus Eustrongylides 2.91 0.106 NA -0.225 NA cotidianus sp. (<0.001)(0.809)(0.823) Gobiomorphus Coitocaecum 0.081 0.017 -9.14x10−4 0.003 2.16x10−4 cotidianus parvum (0.388)(0.903)(0.911)(0.974)(0.974) 8516 Acknowledgements

8517 I would like to thank my primary supervisor, Daniel Stouffer, for

8518 always pushing me to do the best possible science. Your energy and

8519 attention to detail are an inspiration. Thanks also to my secondary

8520 supervisor Dominique Gravel for providing key feedback that has

8521 shaped the course of this thesis.

8522 Many thanks to Nick Baker, Katie Bowron, Camille Coux, Carol Frost,

8523 Simon Litchwark, Lupe Peralta, and Kate Wootton for making me

8524 feel at home when I arrived in New Zealand. Special thanks to Nick

8525 for all of the advice and assistance during my ﬁrst attempts at

8526 programming.

8527 Thanks also to the members of the Stouffer and Tylianakis labs, past

8528 and present, who provided feedback on drafts, support when times

8529 were tough, and smiles in the good times. I feel very lucky to have

8530 been part of such a great group of people.

8531 Special thanks to my co-authors Clement Lagrue, Jens Olesen, Robert

8532 Poulin, Tomas Roslin, and Claus Rasmussen for sharing their data

8533 and their empirical expertise. This thesis is much stronger because of

8534 you. I would also like to thank Nick Baker, Malyon Bimler, Fernando

8535 Cagua, Camille Coux, Matthias Dehling, Marilia Gaiarsa, Giulio Dalla

8536 Riva, Daniel Stouffer, Josh Thia, and Christie Webber for their

8537 contributions to papers.

8538 Funding for this thesis was provided by the National Science and

8539 Engineering Research Council of Canada and the University of

8540 Canterbury. Additional funds supporting conference attendance was

8541 provided by a Marsden Fund grant (to Daniel Stouffer), administered

8542 by the Royal Society of New Zealand. I also appreciate the use of

8543 computation services provided by the BlueFern University of

8544 Canterbury Super Computer and Abacus biology cluster. 310

8545 I would like to thank everyone who has given me feedback on drafts

8546 and talks, supported me at conferences, and shared their research

8547 with me. These conversations are a huge part of what makes science

8548 worthwhile.

8549 I would like to thank Karen Adair, Melissa Broussard, Carla Gomez

8550 Creutzberg, Matt Hutchinson, Bernat Bramon Mora, and Johanna

8551 Voinopol-Sassu for their advice, support and friendship.

8552 Last but certainly not least, thanks to Dan Cirtwill, Max Cirtwill, and

8553 Susanne Collins for encouraging me to chase my dreams and to

8554 James Litchwark for making sure I always had something to smile

8555 about.