Ecological Studies on Seed Dispersal Networks: Insights from a Diverse Tropical Ecosystem a Dissertation Submitted to the Depart

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ECOLOGICAL STUDIES ON SEED DISPERSAL NETWORKS: INSIGHTS FROM A DIVERSE TROPICAL ECOSYSTEM

A DISSERTATION SUBMITTED TO THE DEPARTMENT OF BIOLOGY AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

Camila Iotte Donatti December 2011

This work is licensed under a Creative Commons Attribution- Noncommercial 3.0 United States License. http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/jz498cr4469

ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Rodolfo Dirzo, Primary Adviser

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Carol Boggs

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Fiorenza Micheli

Approved for the Stanford University Committee on Graduate Studies. Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file in University Archives.

iii ABSTRACT

Mutualisms between animals and plants, such as pollination, seed dispersal and ant-plant protection, are key ecological processes in many ecosystems throughout the world. Like any other ecological interaction, plant-animal mutualisms occur in a community context. Therefore, one-to-one interactions are very rare in nature and the majority of species, both animals and plants, have more than one partner. Recently, studies on mutualistic interactions at the community level have used the “network approach” in order to simplify complex interactions and to determine both the pattern of interaction and the properties of species in networks. In this dissertation, I use network theory combined with long-term field work, phylogenetic and multivariate analysis, species extinctions simulations and experimental manipulation to identify the pattern of interaction in a seed dispersal network, to assess the contribution of particular animal species to network stability and robustness, and to address the extent to which seed dispersal interactions can structure plant communities. To do so, I studied a hyper-diverse seed dispersal network sampled in the Brazilian Pantanal, which includes interactions among plant species from 28 families and seed dispersers, both native and exotic species, from 25 families and 4 taxonomic groups. In the first chapter I examine the pattern of interaction in this hyper-diverse seed dispersal network and show that this network has a heterogeneous structure, which is organized around a modular pattern. That is, subsets of species (modules) more frequently interact with each other than with species in other modules. I show that plant and animal trait values are associated with specific modules but phylogenetic signal is limited. I conclude that the observed modularity emerges by a combination of phylogenetic history and trait convergence of phylogenetically unrelated species, shaped by interactions with particular types of dispersal agents. Additionally, my results from this chapter highlight the fact that the absence of large and medium-bodied species may affect the pattern of interaction and, as a consequence, the functioning of this seed dispersal network. In the second chapter I use species extinction simulations to explore how the extinction of large- and medium-bodied species may affect the pattern of interaction in this network and seed dispersal services as a whole. My results show that the removal of large- and medium-bodied species has a large impact on the network pattern and robustness. Although exotic mammalian species usually have negative impacts on native taxa, my results surprisingly show that the exotic feral pig (Sus scrofa) actually plays a critical role in maintaining structural network metrics and in providing seed dispersal services in this community. In the third chapter, I continue to explore the importance of large-bodied species in providing seed dispersal services in the Pantanal plant-animal community. It is well known that defaunation, the contemporary pulse of animal population loss or decline driven by human activities, can compromise the dispersal of large-seeded plants. This is the case because the especially vulnerable large-bodied seed dispersers are extremely important in ingesting and dispersing large-seeded plant species. However, the results from this chapter emphasize the fact that large-bodied animals are also important because

v they disperse such species in high frequencies, and also have the ability to disperse large conspecific seeds, which in turn show high germination rates. The three first chapters of my dissertation focus primarily on the seed dispersal process per se. However, in chapters 4 and 5 I expand my study to a broader view and address the importance of seed dispersal in determining the structure of plant communities. In chapter 4, I describe the properties of species in this network and, in chapter 5, I use one of these species properties, the “maximum dependence”, to address this association, taking into account the complexities related to the effect of multiple seed dispersers on the spatial distribution of animal-dispersed plants. I found that seed dispersal was the main important predictor of the aggregation intensity of individual plants, in comparison with several other biotic and abiotic variables. Therefore, I conclude that, although different variables, such as seed size and edaphic characteristics, can operate at different scales in shaping the distribution and structure of plant communities, seed dispersal appears to be the most important in that respect, even when considering the effects of multiple animal species in dispersing plant species. This study contributes novel information on seed dispersal at the community level, especially because I examined a diverse and relatively complete seed dispersal network, which may provide insights for other diverse systems, especially in the tropics. Besides generating information on the ecology and evolution of plant-animal interactions, this study also shows that not all seed disperser species are equal at the community level; and body size of dispersers seem to be a useful proxy of relative importance for dispersal services. Since contemporary defaunation differentially affects species depending on body size, this work illustrates how human activities, such as hunting, land use and climate change, affect not only taxa, but also crucial processes in which animals of different body size play different roles. This study emphasizes that conservation science needs to look at the conservation of ecological processes driven by species interactions.

vi ACKNOWLEDGMENTS I could not have completed this work without the help and generosity of several people:

My advisor Dr. Rodolfo Dirzo, who has been an incredible source of support and inspiration since the very first day that I arrived at the lab. Dear Rodolfo, you have made me a better person and professional and I will be forever indebted to you. Thank you so much for everything! My committee Dr. Carol Boggs, Dr. Fiorenza Micheli, Dr. Tadashi Fukami and Dr. Lisa Curran My collaborators Dr. Mauro Galetti, Dr. Paulo Guimarães Jr, Dr. Marco Aurélio Pizo, Dr. Alexine Keuroghlian, Ellen Wang, Flávia M. D. Marquitti, Marina Schweizer and Lucas Leuzinger

My friends from the Dirzo Lab Rachel Adams, Eben Broadbent, Posy Busby, Oskar Burger, Yolanda Cachu Pavón, Mauro Galetti, Dennis Hansen, Erin Kuerten, Eduardo Mendoza, Doug McCauley, Katherine Mertes, Beth Pringle, Chelsea Wood and Hillary Young My friends from the Palumbi Lab Dr. Steve Palumbi, who generously offered me a space in his lab, Dan Barshis, Pierre De Wit, Alison Haupt, Hannah Jaris, Jason Ladner, Tom Oliver, Marina Oster, Melissa Pespeni, Carolyn Tepolt and Nina Therkildsen Friends from my cohort Aaron Carlisle, Posy Busby, Henri Folse, Nishad Jayasundara, Jason Ladner, Kevin Miklasz, Malin Pinsky, Beth Pringle, Julie Stewart and Shelby Sturgis

Stanford University staff Pamela Hung, Monica Bernal, Dan King, Valeria Kiszka, Jennifer Mason and Matt Pinheiro

Funding Stanford University Conservation International Zaffaroni Fellowship Fund The State of São Paulo Research Foundation (FAPESP)

My family Caroline, Gustavo, Alice, Rodrigo, João, Karin, Jennifer, Julia and Tom My parents Maria José e José Airton And my husband Jason

vii TABLE OF CONTENTS

LIST OF FIGURES ...... XIII LIST OF TABLES ...... XIIII INTRODUCTION...... 1 STATEMENT ON MULTIPLE AUTHORSHIP ...... 6 REFERENCES...... 7 CHAPTER 1. ANALYSIS OF A HYPER-DIVERSE SEED DISPERSAL NETWORK: MODULARITY AND UNDERLYING MECHANISMS...... 10 ABSTRACT...... 10 INTRODUCTION...... 11 MATERIAL AND METHODS ...... 13 Study sites...... 13 Seed dispersal interactions ...... 13 Sampling robustness ...... 14 The network structure ...... 14 Phylogenetic signal in animal and plant traits and in the network’s pattern...... 16 The role of individual species in the network structure……………………………………...17 RESULTS...... 17 The network structure ...... 17 Modularity...... 18 Phylogenetic signal in animal and plant traits and in the network’s pattern...... 20 The role of individual species in the network structure...... 20 DISCUSSION...... 21 ACKNOWLEDGMENTS ...... 24 REFERENCES ...... 26 FIGURES ...... 29 SUPPORTING INFORMATION...... 34 CHAPTER 2. DEFAUNATION SIMULATIONS REVEAL THE CONSEQUENCES OF SEED DISPERSAL NETWORK DISRUPTIONS AND THE ROLE OF AN EXOTIC SPECIES ON DISPERSAL SERVICES ...... 37 ABSTRACT...... 37 INTRODUCTION...... 38 METHODS...... 40 Study site ...... 40 Seed dispersal interactions ...... 40 Simulation of defaunation steps...... 40 Effects of defaunation on structural network metrics...... 42 Effects on seed dispersal events...... 42 Effects on the robustness of the network...... 43 The importance of particular native and exotic species on network structure and seed dispersal services...... 43 RESULTS...... 44

viii Effects of defaunation on structural network metrics...... 44 Effects on seed dispersal events...... 45 Effects on the robustness of the network...... 46 The importance of particular native and exotic species on network structure and seed dispersal services...... 47 DISCUSSION...... 47 ACKNOWLEDGMENTS ...... 50 REFERENCES ...... 51 FIGURES ...... 55 SUPPORTING INFORMATION...... 57 CHAPTER 3. EFFECTS OF INTRA- AND INTER-SPECIFIC SEED SIZE VARIATION ON SELECTION BY DISPERSERS, GERMINATION AND SEEDLING GROWTH...... 60 ABSTRACT...... 60 INTRODUCTION...... 61 METHODS...... 63 Study site ...... 63 Seed dispersal interactions ...... 63 Diameter of the dispersed seeds - seed disperser body mass interspecific associations.. 64 Diameter of the dispersed seeds - seed disperser body mass intraspecific associations.. 64 Effect of seed size and gut-passage on seed germination of Dipteryx alata...... 65 Effect of seed size and gut passage on seedling survival and growth rates of D. alata in controlled conditions ...... 66 Effect of seed size and gut passage on seedling survival and growth rates of D. alata in field conditions...... 67 RESULTS...... 68 Diameter of the dispersed seeds - seed disperser body mass interspecific associations.. 68 Diameter of the dispersed seeds - seed disperser body mass intraspecific associations.. 68 Effect of seed size and gut passage on seed germination of D. alata ...... 68 Effect of seed size and gut passage on seedling survival and growth rates of D. alata in controlled conditions ...... 69 Effect of seed size and gut passage on seedling survival and growth rates of D. alata in field conditions...... 70 DISCUSSION...... 71 ACKNOWLEDGMENTS ...... 73 REFERENCES ...... 74 TABLES ...... 79 FIGURES ...... 81 CHAPTER 4. THE ROLE OF PLANT AND ANIMAL TRAITS IN DETERMINING NUMBER AND STRENGTH OF INTERACTIONS ACROSS SPECIES IN A SEED DISPERSAL NETWORK ...... 85 ABSTRACT………………………………………………………………………………85 INTRODUCTION...... 85 METHODS...... 88 Properties of species in the seed dispersal network ...... 88 Plant and animal species traits...... 90

ix Phylogenetic signal in traits and properties of animal and plant species in the network 91 RESULTS...... 92 Phylogenetic signal in traits and properties of animal and plant species in the network 92 Observed distribution in the properties of species in the network……………………….93 Association between traits and the properties of species in the network ...... 93 DISCUSSION...... 95 ACKNOWLEDGMENTS ...... 98 REFERENCES ...... 99 TABLES ...... 103 FIGURES ...... 107 CHAPTER 5. THE ROLE OF SEED DISPERSAL INTERACTIONS IN STRUCTURING A PLANT COMMUNITY IN THE BRAZILIAN PANTANAL 110 ABSTRACT...... 110 INTRODUCTION...... 112 METHODS...... 114 Seed dispersal interactions ...... 114 Spatial aggregation of individuals across plant species ...... 115 Consequences of spatial aggregation on seedling and sapling mortality, leaf herbivory and infection by pathogens ...... 117 Seed dispersal interactions and the structure of the plant community ...... 117 RESULTS...... 118 Spatial aggregation of individuals across plant species ...... 118 Consequences of spatial aggregation on seedling and sapling mortality, leaf herbivory and infection by pathogens ...... 119 Seed dispersal interactions and the structure of the plant community ...... 120 DISCUSSION...... 120 ACKNOWLEDGMENTS ...... 123 REFERENCES ...... 124 FIGURES ...... 127

x LIST OF FIGURES CHAPTER 1 Figure 1. Modularity in the seed dispersal network...... 29 Figure 2. Association between body mass and values of fruit trait ...... 30 Figure 3a. The phylogenetic tree of animal species and modules in which each species was assigned...... 31 Figure 3b. The phylogenetic tree of plant species and modules in which each species was assigned ...... 32 Figure 4. Role of each species in the seed dispersal network...... 33 Figure S1. Accumulation curve with the average and the standard deviation of the number of seed dispersal interactions in 1000 iterations, as a function of the number of seed dispersal events ...... 34 Figure S2. Association between K values using branch lengths=1 and K values using branch lengths generated by our simulations……………………………………….36 CHAPTER 2 Figure 1. Values of nestedness and modularity in each defaunation step...... 55 Figure 2. The robustness of the network...... 56 CHAPTER 3 Figure 1. Relationship between the diameter of dispersed seeds and body mass of the seed disperser ...... 81 Figure 2. Diameter of the seeds dispersed by different animal species, from the heaviest to the lightest ...... 82 Figure 3. Number of days from plantation to germination as a function of the diameter of seeds dispersed by tapirs, in controlled conditions ...... 83 Figure 4. Seedling growth as a function of the diameter of seeds dispersed by tapirs, in controlled conditions and in field conditions ...... 84 CHAPTER 4 Figure 1. Association between seed diameter and species degree, maximum dependence and interaction asymmetry of plant species ...... 108 Figure 2. Association between body mass and species degree, maximum dependence and interaction asymmetry of animal species ...... 109 CHAPTER 5 Figure 1. Association between the aggregated distribution of individuals and the value of maximum dependence of the plant on its most important seed disperser .127 Figure 2. Logistic regression between the survival of seedlings and saplings and the distance from the closest conspecific individual ...... 128 Figure 3. Associations between the distance from the closest conspecific adult and: the percentage of the plant with foliar herbivory and the percentage of the plant with foliar attack by pathogens...... 129

xi LIST OF TABLES

CHAPTER 2 Table S1. Animal species removed in each of the defaunation steps in the realistic defaunation simulation, family and their body masses ...... 57 Table S2. Animal species maintained in the network while all other large- and medium-bodied species were removed, and network metrics...... 58 CHAPTER 3 Table 1. Seed disperser species, their body masses and the average diameter of the seeds dispersed by them in a community in the Brazilian Pantanal...... 79 Table 2. Plant species, and their average seed diameter, recorded interacting with seed dispersers in the Brazilian Pantanal...... 80 CHAPTER 4 Table 1. Plant species, plant species traits and properties of plant species in the seed dispersal network...... 103 Table 2. Animal species, animal species traits and properties of animal species in the seed dispersal network...... 105 Table 3a. Results of multiple regression analyses on the associations between species traits and species properties for plant species...... 107 Table 3b. Results of multiple regression analyses on the associations between species traits and species properties for mammal and bird species combined…….107

xii INTRODUCTION

Mutualisms between animals and plants, such as pollination, seed dispersal and ant- plant protection, pervade nature and are key ecological processes in many ecosystems throughout the world (Herrera & Pellmyr 2002). Like any other ecological interaction, plant-animal mutualisms occur in a community context. Therefore, one-to-one interactions are very rare and the majority of species, both animals and plants, have more than one partner (Waser et al. 1996, Bascompte et al. 2003, Memmott et al. 2004, Jordano et al. 2006). The study of those interactions at the community level is crucial for a basic understanding of the ecology and evolution of plant–animal interactions, and for management and conservation of biodiversity (Bronstein et al. 2006, Waser & Ollerton 2006, Rico-Gray & Oliveira 2007). As a mutualistic process, seed dispersal by frugivores is beneficial to both animals and plants. While the animals gain from ingesting fleshy fruits due to their nutritious content, the plants gain from being dispersed by animals through the possibility of exploring and colonizing new or enemy-free habitats, facilitating the regeneration of plant populations and communities, promoting gene flow and genetic intermingling, and, ultimately, contributing to the maintenance of plant diversity (Hubbell 1979, Clark et al. 1998, Connell & Green 2000, Ehrlén et al. 2006). Recently, studies on mutualistic interactions at the community level have used the “network approach” (Memmott 1999, Strogatz 2001, Dicks et al. 2002, Bascompte et al. 2003) in order to simplify such complex interactions and to determine both the pattern of interaction and the properties of species in networks. Seed dispersal networks have already been relatively well described in terms of their pattern, but the majority of those analyzed prior to this study are, in fact, sub-networks that predominantly include interactions between a single taxonomic group of seed dispersers (e.g., birds) and the plants they disperse (Bascompte et al. 2003). In this dissertation, I use network theory combined with long-term field work, phylogenetic and multivariate analysis, species extinctions simulations and experimental manipulation to identify the pattern of interaction in a seed dispersal network, to assess the contribution of particular species to network stability and robustness, and to address

1 the extent to which seed dispersal interactions can structure plant communities. To do so, I studied a hyper-diverse seed dispersal network sampled in the Brazilian Pantanal that includes interactions among plant species from 28 families, and seed dispersers, both native and exotic species, from 25 families and 4 taxonomic groups. The Pantanal, located in central-western Brazil and part of Bolivia and Paraguay, is the world’s largest freshwater wetland, covering an area of 140,000 km2 (Swarts 2000). Due to the low human population density and low hunting pressure on native species (Alho & Lacher 1991, Desbiez et al. 2011, but see Harris et al. 2005), the Pantanal holds one of the highest concentrations of wildlife in the Neotropics (Swarts 2000, Mittermeier et al. 2005). Such concentration of wildlife enabled me to survey seed dispersal interactions for animals from a variety of taxonomic groups. I carried out my research on two private properties, Fazenda Rio Negro and Fazenda Barranco Alto, located in one of the most pristine regions within the Pantanal. The description of the pattern of interaction in a diverse seed dispersal network is an important contribution to the study of seed dispersal, given that networks recorded to date have included only subsets of major frugivore groups, especially birds, and their interactions with plant species that also share similar traits (Rezende et al. 2007). This is the case primarily because most diverse communities worldwide have lost at least some of their vertebrates involved in mutualisms. In the first chapter I examine, for the first time, the pattern of interaction in a hyper- diverse seed dispersal network. I show that this network, in addition to being nested –as is the case of other seed dispersal networks analyzed to date– has also a heterogeneous structure that is organized around a modular pattern. That is, subsets of species (modules) more frequently interact with each other than with species in other modules. In this diverse network, the modular pattern reflects the diversity of taxonomic groups of seed dispersers and of fruit and seed morphological traits. Furthermore, I show that plant and animal trait values are associated with specific modules but phylogenetic signal is limited. I conclude that the observed modularity emerges by a combination of phylogenetic history and trait convergence of phylogenetically unrelated species, shaped by interactions with particular types of dispersal agents. Additionally, my results from this chapter highlight the importance of particular seed dispersers, specifically large-and

2 medium-bodied species, in contributing to the pattern of this network. Whereas large- bodied species can disperse a large number of plant species and, therefore, link species within modules, medium-bodied species can link modules together. Therefore, the absence of large and medium-bodied species may affect the pattern of interaction and, as a consequence, the functioning of this seed dispersal network. In fact, large- and medium-bodied species are the most affected by defaunation, the contemporary pulse of animal local population loss or decline (sensu Dirzo & Miranda 1991) driven by human activities such as hunting, deforestation and fragmentation. In the second chapter I use species extinction simulations to explore how the extinction of these large- and medium-bodied species may affect the pattern of interaction in this network and seed dispersal services as a whole. As large- and medium- bodied species are more affected by human activities than small-bodied species (Bodmer et al. 1997, Cardillo et al. 2005, Cardillo et al. 2006, Peres & Palacios 2007), the simulations developed in this chapter are ecologically realistic. My results show that the removal of large- and medium-bodied species generates the lowest network robustness when compared to simulations that remove species at random and that remove the most- linked species from the community. Furthermore, the loss of just a few species among the largest ones (i.e. species >20 kg) seems to lead to a significant decline of the proportion of the number of seed dispersal events in the community. Another highlight of this study is that, although exotic mammalian species usually have negative impacts on native taxa (Cox 1999, D’Antonio et al. 1999, Cushamn et al. 2004, Busby et al. 2010), my results surprisingly show that the exotic feral pig (Sus scrofa), actually plays a critical role in maintaining structural network metrics and in providing seed dispersal services in this community. Therefore, I posit that network stability will be significantly reduced with defaunation of large- and medium-bodied species, but this effect can be attenuated by the presence of large megafaunal exotic species, if such species are less susceptible to extinction in their novel ecosystems. In the third chapter, I continue to explore the importance of large-bodied species in providing seed dispersal services in the Pantanal plant-animal community. It is well known that defaunation can compromise the dispersal of large-seeded plants (Silva & Tabarelli 2000, Wright 2003, Galetti et al. 2006, Cramer et al. 2007) and this is the case

3 because the especially vulnerable large-bodied seed dispersers are extremely important in ingesting and dispersing large-seeded species (Janzen & Martin 1982, Wheelwright 1985, Chapman et al. 1992, Guimarães et al. 2008, Donatti et al. 2011). However, my data show that large-bodied animals disperse those large-seeded species with high frequencies and have the ability to disperse large conspecific seeds, which in turn show high germination rates. Furthermore, using a set of experiments, I report that, within plant species, seed germination increases with seed diameter and that the combination of a large diameter and gut-passage increases seedling growth, in both controlled and field conditions. The three first chapters of my dissertation focus primarily on the seed dispersal process per se. However, in chapters 4 and 5 I expand my study to a broader view and address the importance of seed dispersal in determining the structure of plant communities. Even though many processes occur between seed dispersal and seedling establishment, such as seed and seedling predation, it is possible to find a “dispersal signal” – an association between patterns of seed dispersal and seedling distribution. In a large area of 50-100 hectares, spatial distributions of seedlings in plant species dispersed by animals are significantly different than those in plants dispersed by gravity or wind (Hubbell 1979, Kinnaird 1998, Hardy & Sonké 2004, Seidler & Plotkin 2006, Russo et al. 2007, Muller-Landau et al. 2008). In a small area of few square meters, the spatial distribution of seedlings can be assigned to the seed dispersal pattern produced by particular animal species (Howe 1989, Herrera et al. 1994, Julliot 1997, Wenny & Levey 1998, Fragoso et al. 2003). However, at the community-level, one-to-one interactions are rare and the majority of species, both animals and plants, have more than one partner (Bakker et al. 1996, Bascompte et al. 2003, Memmott et al. 2004, Strauss & Irwin 2004, Jordano et al. 2006). Thus, the importance of seed dispersal in determining the spatial distribution of plant species, taking into account that the majority of them interact with multiple seed dispersers, has received little attention. Thus, in chapter 4 I describe the properties of species in this network, such as the number of partners of each plant species, and, in chapter 5, I use one of these species properties, the “maximum dependence” to address this association, taking into account the complexities related to the effect of multiple seed dispersers on the spatial distribution of animal-dispersed

4 plants. More specifically, in chapter 4, I describe the variation in the properties of species in networks and explore, in depth, the importance of evolutionary history and species- specific traits in determining these properties. I show that morphological traits of plants and animals are the most important predictors explaining the majority of properties of species in the network. For example, a plant species with large seeds has a high dependence on its most frequent seed disperser species, whereas a plant species with small seeds has a low dependence on its most frequent seed disperser. Due to constraints related to the gape size of seed dispersers and the fruit or seed size, large-seeded species are predominantly dispersed by large-bodied frugivores (Janzen & Martin 1982, Janson 1983, Wheelwright 1985, Chapman et al. 1992, Guimarães et al. 2008, Donatti et al. 2011) that can swallow large seeds, or by medium-bodied frugivores (such as agoutis, Dasyprocta azarae) that can manipulate and carry those seeds. Such dispersers usually disperse seeds in clumps (Howe 1989, Fragoso et al. 2003), either close to or far from the mother plant, possibly generating a highly aggregated distribution of individuals. Then, in chapter 5, I combine the information on the number and the frequency of interactions between particular plants and seed dispersers to place plant species in a “maximum dependence” gradient. This gradient includes, at one extreme, plant species that strongly interact with one seed disperser and, at the other, those that weakly interact with multiple dispersers. I selected eight plant species that were located along this gradient and mapped all conspecific individuals in a 2.6-ha plot. As expected, I found a significant and positive association between the values of maximum dependence of plant species and the degree of aggregation of conspecific individuals. I also found that seed dispersal was the main important predictor of the aggregation intensity of individuals, in comparison with several other biotic and abiotic variables. Therefore, I conclude that, although different variables such as seed size and edaphic characteristics can operate at different scales in shaping the distribution and structure of plant communities, seed dispersal shows to be important in that respect, even when considering the effects of multiple animal species in dispersing plant species. Furthermore, I suggest that, as large- and medium-bodied seed disperser species are highly vulnerable to land use change and hunting, the strongly aggregated distribution of certain plant species can be intensified in a scenario of defaunation, due to increased seedling mortality, and can, perhaps, even lead to a strong

5 fine-scale spatial genetic structuring in the populations of those plant species.

In contrast to previous work, here I used a community approach to understand seed dispersal interactions. Therefore, this study contributes novel information on seed dispersal at the community level, especially because we examined a diverse and relatively complete seed dispersal network, which may provide insights for other diverse systems, especially in the tropics. Besides generating information on the ecology and evolution of plant-animal interactions, this study shows that not all seed disperser species are equal at the community level; body size of dispersers seems to be a useful proxy of relative importance for dispersal services. Since contemporary defaunation differentially affects species depending on body size, this work illustrates how human activities, such as hunting, land use and climate change, affect not only taxa, but also crucial processes in which animals of different body size play different roles. This study emphasizes that conservation science needs to look at the conservation of ecological processes driven by species interactions.

STATEMENT ON MULTIPLE AUTHORSHIP

I am the first author and primary contributor to each of my dissertation chapters, including the design, data collection, analysis, and writing. In the paragraphs below I explain the role of each co-author in each chapter. Chapter 1 is published in the journal Ecology Letters (14: 773–781, 2011). Paulo R. Guimarães, Mauro Galetti, Rodolfo Dirzo and I designed research; Mauro Galetti, Marco Aurélio Pizo and I performed research; Paulo R. Guimarães, Flávia M. D. Marquitti and I analyzed data; and Paulo R. Guimarães, Mauro Galetti, Marco Aurélio Pizo, Rodolfo Dirzo and I wrote the paper. In chapter 2, Paulo R. Guimarães, Mauro Galetti, Rodolfo Dirzo and I designed research; Mauro Galetti, Marco Aurélio Pizo and I performed research; Paulo R. Guimarães and I analyzed data; and Paulo R. Guimarães, Mauro Galetti, Marco Aurélio Pizo, Rodolfo Dirzo and I wrote the paper. In chapter 3, Mauro Galetti, Rodolfo Dirzo and I designed research; Mauro Galetti,

6 Marco Aurélio Pizo and I performed research; I analyzed data; and Marco Aurélio Pizo, Mauro Galetti, Rodolfo Dirzo and I wrote the paper. In chapter 4, Rodolfo Dirzo and I designed research; Mauro Galetti, Marco Aurélio Pizo and I performed research; I analyzed data; and Marco Aurélio Pizo, Mauro Galetti, Rodolfo Dirzo and I wrote the paper. In chapter 5, Rodolfo Dirzo, Mauro Galetti and I designed research; I performed research; I analyzed data; and Mauro Galetti, Rodolfo Dirzo and I wrote the paper.

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9 CHAPTER 1

ANALYSIS OF A HYPER-DIVERSE SEED DISPERSAL NETWORK: MODULARITY AND UNDERLYING MECHANISMS Camila I. Donatti, Paulo R. Guimarães, Mauro Galetti, Marco Aurélio Pizo, Flávia M. D. Marquitti & Rodolfo Dirzo

Abstract Mutualistic interactions involving pollination and ant-plant mutualistic networks typically feature tightly linked species grouped in modules. However, such modularity is infrequent in seed dispersal networks, presumably because research on those networks predominantly includes a single taxonomic animal group (e.g. birds). Here, for the first time, we examine the pattern of interaction in a network that includes multiple taxonomic groups of seed dispersers, and the mechanisms underlying modularity. We found that the network was nested and modular, with five distinguishable modules. Our examination of mechanisms underlying such modularity showed that plant and animal trait values were associated with specific modules but phylogenetic effect was limited. Thus, the pattern of interaction in this network is only partially explained by shared evolutionary history. We conclude that the observed modularity emerged by a combination of phylogenetic history and trait convergence of phylogenetically unrelated species, shaped by interactions with particular types of dispersal agents.

10 Introduction In mutualistic interactions, species commonly interact with multiple partners, forming a network of interactions. The pattern of these interactions in a community, i.e., the way interactions are organized, can be described using a network approach, which helps to elucidate the complexity of such interactions (Jordano 1987; Bascompte & Jordano 2007). Mutualistic networks are highly structured, with a prevalence of a nested pattern (Bascompte et al. 2003; Vázquez et al. 2009; Fortuna et al. 2010; Joppa et al. 2010). That is, the interactions of the specialist species tend to be a subset of the interactions observed among the generalists (Bascompte et al. 2003). In addition to being nested, some mutualistic networks are also modular (Dicks et al. 2002 and Olesen et al. 2007: pollination networks; Fonseca & Ganade 1996 and Guimarães et al. 2007: ant- plant networks), whereby subsets of species (modules) more frequently interact with each other than with species in other modules (Olesen et al. 2007). Among mutualisms, modularity has been investigated in depth in pollination networks (Dicks et al. 2002; Olesen et al. 2007; Fortuna et al. 2010), which often include a highly diverse array of animal and plant taxa (e.g. Rezende et al. 2007). In contrast, the majority of seed dispersal networks studied includes mainly seed-dispersing birds, which interact with plant species that share similar traits (Rezende et al. 2007), leading to a highly nested and low modularity pattern of interaction (see Fortuna et al. 2010). The widespread habit of producing fleshy fruits among tropical plant species has been evolutionary associated to the diversification of frugivorous vertebrates (Fleming et al. 1987). Therefore, the diversity of animals that interact with a particular plant species could make them tightly linked within modules. Thus, one can predict networks of interactions in diverse communities, involving plants and several taxonomic groups of seed-dispersing animals, to have low nestedness and high modularity. Here we test if a hyper-diverse seed dispersal network is characterized by low nestedness and high modularity. Beyond such test, we examine the mechanisms organizing this network through a combination of long-term fieldwork, network theory and phylogenetic analysis. We analyze the structure of plant-animal interactions in one of the world’s last remaining species-rich communities involving large vertebrates: the Pantanal (Harris et al. 2005). By investigating the structure of this community of plants

11 and seed dispersers, we are filling an important gap in studies of species networks, given that most similarly diverse communities worldwide have lost at least some of their vertebrates involved in mutualisms and include only subsets of major frugivore groups (but see Gautier-Hion et al. 1985). To the extent that modular patterns reflect a more diversified network of ecological functions and services, the understanding of mechanisms that determine modularity could help uncovering general processes shaping the evolutionary ecology of plant-animal interactions. Modularity in a broad range of ecological networks is associated with habitat heterogeneity (Pimm & Lawton 1980), phylogenetic clustering of closely related species (Lewinsohn et al. 2006), convergence toward syndromes (Corbet 2000), and combinations of these factors (Cattin et al. 2004; Olesen et al. 2007; Rezende et al. 2009). For mutualistic networks, a combination of coevolutionary complementarity and convergence appears to draw other species into the interaction over time, creating a coevolutionary vortex (Thompson 2005) reflected in the structure of the network. Here we i) examine the pattern of interaction in a highly diverse seed dispersal network, including a variety of species from major taxonomic groups of seed dispersers, mammals, birds, fish and reptiles, and the fleshy-fruited species they disperse, and ii) test current hypotheses on mechanisms that may generate the modularity in mutualistic networks. Interactions in this network were sampled in three habitats within a community in the Brazilian Pantanal. We tested the following hypotheses: 1) this network should be modular given the diversity of taxonomic groups of seed dispersers involved, 2) animal and fruit traits, as well as phylogeny should be, therefore, associated to the modularity of this network, and 3) the habitat types where interactions were recorded should not be associated to modules, given that, although several plant species in this community are habitat specialists, the majority of animal species are not. We first illustrate that interactions in this network have a combination of nested and modular patterns. Then, we describe how modules are predominantly associated with fruit and animal traits, and not with the different habitat types where plant species predominantly occur. We show that the modularity in this network is only partially explained by shared evolutionary history because, although modules are related to the different taxonomic groups of animals, phylogeny explains only the assemblage of

12 species in modules associated to birds. We conclude that such modularity likely emerged by a combination of shared phylogenetic history and trait convergence of phylogenetically unrelated species, shaped by interactions with particular types of dispersal agents.

Material and methods

Study sites This study centered on two neighboring locations in the Brazilian Pantanal: Rio Negro (19°34'15"S 56°14'43"W) and Barranco Alto farms (19º34'40"S 56º09'08"W), covering 7,500 ha and 11,000 ha, respectively, of private land (see Appendix S1 in Supporting Information). The main vegetation types in these locations, where seed dispersal interactions were recorded, include gallery forests, savannas and semi- deciduous forests (Prance & Schaller 1982).

Seed dispersal interactions Seed dispersal interactions were recorded using four methodologies. To sample seed dispersal by birds, we carried out focal observations at 14 plant species during 882 hours, recording identity of birds that were unequivocally observed carrying fruits outside the canopy area or swallowed them in situ. Seed dispersal by red-footed tortoises (Geochelone carbonaria), rheas (Rhea americana) and the majority of mammal species, were recorded with camera traps located beneath fruiting trees of 27 plant species, capturing events of fruit ingestion, for a total of 14,800 hours. Some terrestrial and semi- terrestrial bird species were also recorded via camera traps. We analyzed 716 scats of several species of mammals, rheas and red-footed tortoises, and identified the intact seeds in them. To record seed dispersal by the pacu fish (Piaractus mesopotamicus), we caught 80 individuals and identified the intact seeds in their intestine (see Galetti et al. 2008). One event of seed dispersal was considered as such when either: fruits were recorded to have been swallowed or removed from a plant species during focal observations; fruit removal of a particular species by a potential seed disperser was detected with camera

13 traps; a scat pile was found to have at least one intact seed of a particular species in it; or a sampled fish intestine contained at least one intact seed from a particular species.

Sampling robustness To assess if we had recorded enough interactions to describe this network, we generated an accumulation curve with the number of interactions as a function of the number of seed dispersal events sampled (Guimarães et al. 2007, Jordano et al. 2009). We estimated average and standard deviation of the number of interactions for any given fraction of the number of events recorded in 1,000 iterations. After generating this curve, we used the drc package in R (http://www.r-project.org/) and the dose-response model to extrapolate the curve. We then used the Michaelis-Menten equation to assess the asymptotic value of the curve.

The network structure To define the pattern of interaction in the network, we organized a qualitative seed dispersal matrix, collectively using the methodologies previously described. In a matrix of plants in columns and animals in rows, an element representing a seed dispersal interaction received the value of one, and zero otherwise. We then used this matrix to test for nestedness and modularity. Nestedness was analyzed using the NODF metric (Almeida-Neto et al. 2008), through the ANINHADO program (Guimarães & Guimarães 2006). To test if the network is more nested than expected by species richness and heterogeneity of interactions, we compared the recorded NODF value to that of 1,000 random matrices generated by a null model that controls for the number of interactions per species in the network (‘null model 2’, Bascompte et al. 2003). To detect modularity we used the NETCARTO program and an algorithm based on simulated annealing, SA (Guimerà & Amaral 2005) that identifies modules formed by both plants and animals simultaneously (see Olesen et al. 2007). We computed the network modularity index M, which measures the degree to which the network is organized into clearly defined modules, as well as the level of significance of the modularity in this network by comparing its M to that of random networks of similar

14 sizes, generated by the same null model used for the nestedness analysis. As the algorithm is based on an optimization process, the outcomes may vary in different runs. Therefore, to assign each species to a particular module, we ran the analysis 50 times. Species that were assigned to a particular module in > 90% of the runs were included in that module. We compared the nestedness and the modularity of this network with those same parameters calculated for other 24 frugivory networks available in the literature. We associated each plant species to the habitat type (gallery forest, savanna, semi- deciduous forest) in which it was predominantly recorded in a previous 4-year phenological study (C.I. Donatti, unpublished work). We used binomial distributions to test the associations of animal taxonomic groups to particular modules, i.e. if phylogeny explains the coarse network’s topology, and the associations of plant species that predominantly occur in each habitat type to particular modules, i.e. if habitat type explains the coarse topology of the network. We tested if the presence of species from the same taxonomic group or from the same habitat type is over-represented in a particular module when compared with the null expectation that taxonomy or habitat heterogeneity does not affect the organization of modules. For each module, we estimated the probability of getting, by chance alone, a number of species from the same taxonomic group or habitat type equal or higher than that observed in the real network. We used the number of species in a module (number of trials, N), the proportion of species of a given taxonomic group or habitat type in the whole sample (probability of success), and the number of species that belongs to a particular taxonomic group or habitat type that also belongs to a particular module (number of successes), as parameters of binomial distributions. We gathered animal body mass information from the literature, and measured fruit and seed traits (length, diameter and mass) for all plant species, in at least 30 fruits and seeds from at least five individuals. Values of body mass were log transformed and values of plant traits were Box-Cox transformed using JMP v.5.0 (SAS Institute Inc.). We used ANOVA to compare body mass of animal species among modules, MANOVA to compare all fruit and seed traits among modules, and t-tests to compare body mass between modules that represented the same taxonomic animal group.

15 Phylogenetic signal in animal and plant traits and in the network’s pattern We tested whether animal and plant traits had a significant phylogenetic signal, i.e. a quantitative measure of the degree to which phylogeny predicts the ecological similarity of species. To build the animal phylogenetic tree, we followed Bininda- Emonds et al. (2007) for relationships among mammal species and Hackett et al. (2008) for relationships among bird species. In addition, we used published work to resolve relationships within Cracidae (Pereira et al. 2002), Tyrannidae (Tello et al. 2009), and Thraupidae (Klicka et al. 2007). We also used two mitochondrial DNA sequences (Cytochrome b and Cytochrome oxidase subunit 1), available on GenBank, to resolve the relationships between and within the Thraupidae and Icteridae. We then generated a phylogenetic tree for those sequences using the program Méthodes et algorithmes pour la bio-informatique (http://www.phylogeny.fr/) and added those relationships in the tree. The plant phylogenetic tree was built using Phylomatic software (http://www.phylodiversity.net/phylomatic/phylomatic.html). Relationships within Fabaceae followed Wojciechowski et al. (2004), within Rubiaceae followed Bremer & Eriksson (2009) and within Arecaceae followed Asmussen et al. (2006). Since all branches in animal and plant trees were set equal to one, we conducted simulations that showed that using branch lengths equal to one is a conservative approach when values of K are lower than or equal to one (see Appendix S2 in Supporting Information). We assessed K statistic to measure the phylogenetic signal in animal and plant traits, using the function phylosignal in the picante package (Kembel et al. 2010) of R. To assess phylogenetic signal in body mass, we analyzed mammal and bird species independently. The K statistic compares the observed signal in a trait to the signal under a Brownian motion model of trait evolution on a phylogeny (Blomberg et al. 2003). The statistical significance of phylogenetic signal is evaluated by comparing observed patterns of the variance of independent contrasts of a trait to a null model of shuffling taxa labels across the tips of the phylogenetic tree. To test for evidence of phylogenetic signal in modules, i.e. if phylogeny explains the composition of species within modules, we used a function ad hoc in R that corresponds to the "Fixed Tree, Character Randomly Reshuffled" model proposed in Maddison & Slatkin (1991). This function counts the minimum number of transitions needed to get the distribution of modules observed in the

16 real network, randomizes the modules in the phylogeny and then counts the number of transitions in each randomization. The statistical significance of phylogenetic signal is achieved if there are fewer transitions in the real network than in 95% of the randomizations. Phylogenetic signal in modules was tested using the animal and the plant phylogenetic trees independently. For animals, we run the analyses separately for mammals and birds.

The role of individual species in the network structure The SA algorithm also assigns an ecological role to each species in the network based on its interactions within modules (z) and on its interactions among modules (c) (Olesen et al. 2007). Species with low z and low c are considered peripheral species, i.e. they usually interact with species within their own module. Species with either a high z or c were considered generalists, and either i) module hubs, i.e. highly connected within their own module (high z and low c), or ii) connectors, those species that link modules (low z and high c). Species with a high z and a high c were considered supergeneralists, acting as both module hubs and connectors. To define the role of each species, we used the most common values of z and c generated in the 50 times we run the analysis. We used the values of 2.5 for z and of 0.62 for c to define those categories, following Olesen et al. (2007). We performed additional analyses using z and c values in order to assess the correlates of these values with species traits. We analyzed the values of z and c of each animal species as a function of its body mass and the values of z and c of each plant species as a function of its fruit and seed traits using correlations. We compared the c and z values between animal and plant species using t-tests and the c values among modules using ANOVA.

Results

The network structure The network included 46 plant species and 46 animal species. We recorded 2,070 seed dispersal events and 273 seed dispersal interactions. One plant species (Sapindus saponaria) did not show interactions with seed dispersers, probably due to the high level

17 of saponins in the pulp (Pott & Pott 1994). Using the Michaelis-Menten equation we estimated to have sampled 94.5% of the seed dispersal interactions occurring in this community (Figure S1 in Supporting Information). Therefore, we assume that the network described here is robust to additional sampling. The Pantanal seed dispersal network was not only significantly nested (NODF=26.27, expected NODF=18.04, p<0.001) but also significantly modular (M=0.422, expected=0.341, p<0.001), with animal and plant species grouped in five statistically different modules (Fig. 1). Module composition was very robust: we detected five modules in all 50 runs. All but two species were assigned to the same module in 100% of the 50 runs. These species, the bird Crax fasciolata (Cracidae) and the plant Guazuma ulmifolia (Sterculiaceae), were assigned to the same module in 94% of the runs.

Modularity Two modules were exclusively represented by bird species and the plant species they interact with (hereafter bird module 1 and bird module 2; green and blue in Fig. 1, respectively). Two other modules were represented mainly by mammal species and by the plant species they interact with. One of these (hereafter mammal-dominated module 1; red in Fig. 1) also included the tortoise and the rhea, whereas the other one (hereafter mammal-dominated module 2; yellow in Fig. 1) also included a ground-foraging bird (Crax fasciolata). The fifth module (hereafter fish module; purple in Fig. 1) was represented by the fish and plant species it mainly interacts with. The Pantanal seed dispersal network is the second less nested and more modular of the seed dispersal networks so far studied that show a significant pattern (NODF =54.04±17.15, n=24; M =0.31±0.092, n=4). The binomial distributions showed that each module is associated to animal species that belong to a particular animal taxonomic group (bird module 1: p=0.006, n=22; bird module 2: p=0.003, n=22; mammal-dominated module 1: p=0.012, n=25; mammal-dominated module 2: p<0.001, n=18 and fish module: p=0.02, n=4). However, each module does not include plant species that predominantly occur in a particular

18 habitat type (bird module 1: p>0.666, bird module 2: p>0.437, mammal-dominated module 1: p>0.179, mammal-dominated module 2: p>0.659, fish module: p>0.168). Regarding nestedness, the mammal-dominated module 2 showed a significant nested pattern (NODF=64.73, p=0.005), while the other three modules did not (bird module 1: NODF=41.20, p=0.169; bird module 2: NODF=62.83, p=0.061 and mammal- dominated module 1: NODF=60.78, p=0.330). The non-detection of a nested pattern within modules could be an artifact of the low number of species (Guimarães et al. 2006). The fish module could not be tested, given its low species richness. Animal body mass varied across modules (F=64.51, p<0.0001, d.f.=44; mammal- dominated module 1=47.41kg±32.7, mean±SD, mammal-dominated module 2=15.38kg±5.11, bird module 1=0.24 kg±0.07, bird module 2=0.06kg±0.01) and explained 82.52% of the variance among modules. Mean animal body mass significantly differed between the two bird modules (t =2.710, p=0.0129, d.f.=29), but not between the two mammal-dominated modules (t =0.501, p=0.6240, d.f=14), although the low species number may cause low statistical power. Modules also significantly differed when taking into account all fruit and seed traits (MANOVA F=3.4821, p<0.0001, d.f.=44). Modules were, therefore, characterized by particular suites of traits. Fruit mass mainly explained the variance among modules (26.65%), followed by fruit diameter (23.13%), fruit length (17.42%) and seed mass (11.98%). Mean values of all plant traits but fruit length and seed length in each module were positively and significantly correlated with mean body mass of seed dispersers in each module (fruit diameter: F=58.48, p=0.004, r=0.97; fruit mass: F=15.06, p=0.03, r=0.91; seed diameter: F=26.09, p=0.014, r=0.94; seed mass: F=19.63, p=0.021, r=0.93, d.f=4): modules with heavy seed dispersers also had heavy and wide fruits and seeds. When considering all interactions in the network, there were positive associations between the body size of seed dispersers and all fruit and seed traits (Fig. 2) (fruit length: F=50.29, p<0.0001, r=0.39, fruit diameter: F=109.4550, p<0.0001, r=0.53; fruit mass: F=115.95, p<0.0001, r=0.54, d.f.=272; seed length: F=29.9, p<0.0001, r=0.31; seed diameter: F=41.25, p<0.0001, r=0.36; seed mass: F=54.76, p<0.0001, r=0.41, d.f=269).

19 Phylogenetic signal in animal and plant traits and in the network’s pattern The body mass of closely related mammal species has exactly the amount of signal predicted by Brownian motion (K=1.01, p=0.005). In contrast, the body mass of birds and all seed traits are more divergent than expected under a Brownian model (birds: K=0.778, p=0.001, d.f.=31; seed length: K=0.526, p=0.001; seed diameter: K=0.417, p=0.003; seed mass: K=0.496, p=0.001; d.f.=43). Fruit traits did not show a significant signal (fruit length: K=0.370, p=0.133; fruit diameter: K=0.372, p=0.248; fruit mass: K=0.392, p=0.159; d.f.=44). Modules included phylogenetically related bird species (p=0.006), but not phylogenetically related mammal species (p=0.2) or phylogenetically related plant species (p=0.83) (Fig.3). Although modules are associated to the major animal taxonomic groups (mammals, birds or fish), only modules associated to birds included phylogenetically related bird species.

The role of individual species in the network structure As in pollination networks, the majority of species in this seed dispersal network were peripheral, i.e., they almost always interact with species within their own module (Fig. 4). Three species, the exotic feral pig (Sus scrofa, Suidae, z=2.7869, c=0.5104), the plant Doliocarpus dentatus (Dilleniaceae, z=2.75, c=0.4897) and the tapir (Tapirus terrestris, Tapiridae, z=2.4954, c=0.4099), were considered module hubs, i.e. species with many interactions within their own module. The howler monkey (Alouatta caraya, Cebidae, z=-1.3687, c=0.6666), one plant (Genipa americana, Rubiaceae, z=1.3574, c=0.66463) and the chaco chachalaca (Ortalis canicollis, Cracidae, z=0.55, c=0.625) were considered connectors, i.e. species that had interactions across different modules. None of the species were defined as supergeneralist, indicating a low cohesiveness in this seed dispersal network. Plant and animal species did not differ in both their interactions inside modules (z) and in their interactions among modules (c) (t=0.351, p=0.726, d.f.=44; t=1.035, p=0.303, d.f.=45, respectively). However, we found that the mean participation of species in the whole network (c) differed among modules (F=3.597, p=0.009, d.f.=4; mammal-

20 dominated module 2=0.391±0.05, fish module=0.343±0.11, mammal-dominated module 1=0.326±0.04, bird module 1=0.260±0.04, bird module 2=0.139±0.04). The values of both z and c increased with the body mass of dispersers (z: F=20.2381, p<0.001, r=0.55; c: F=45.654, p<0.001, r=0.7; d.f.=45): a large-bodied seed disperser has more interactions both inside and among modules. In contrast, the values of z decreased with an increment in all fruit and seed traits (fruit length: F=17.74, p=0.0001, r=0.54; fruit diameter: F=20.31, p<0.0001, r=0.56; fruit mass: F=26.65, p<0.0001, r=0.61; d.f.=44; seed length: F=18.81, p<0.0001, r=0.55; seed diameter: F=18.39, p=0.0001, r=0.55, seed mass: F=25.06, p< 0.0001, r=0.61; d.f.=43) and the values of c decreased with an increment in seed traits (seed length: F=7.18, p=0.01, r=0.38; seed diameter: F=4.95, p=0.031, r=0.32; seed mass: F=6.68, p=0.013, r=0.37; d.f.=43): a plant species with small fruit and seed trait values has more interactions inside modules and a plant species with small seed trait values has also more interactions among modules. As the body mass of seed dispersers increases, they are able to disperse a variety of plant species, regardless of fruit and seed sizes, whereas large fruits and/or seeds are restricted to a few animal species able to disperse them. These results could reflect the association between plant and animal species traits and the number of interactions, which increases significantly with body mass of the seed dispersers and decreases significantly with fruit length and all seed traits (body mass: F=65.62, p<0.0001, r=0.77, d.f.=45; fruit length: F=4.64, p=0.036, r=0.31, d.f.=44; seed length: F=13.83, p=0.0006, r=0.49; seed diameter: F=10.69, p<0.002, r=0.45; seed mass: F=18.95, p<0.0001, r=0.55, d.f=43).

Discussion Seed dispersal interactions in this network were nested and modular, as in some pollination and ant-plant mutualistic networks (Fonseca & Ganade 1996, Olesen et al. 2007, Guimarães et al. 2007). The studied network has a heterogeneous structure that is organized around a modular pattern, which reflects a diversity of taxonomic groups of seed dispersers and of fruit and seed morphological traits. In essence, the major taxonomic groups of seed dispersers separated species in five distinct modules. In addition, seed disperser species within those modules varied in their body mass and interacted with plant species that differed in fruit and seed traits. In fact, there were

21 strong correlations between the body size of the seed dispersers and the size of fruits (especially diameter and mass) in modules. Therefore, specific traits of seed dispersers and fruits help explaining the mixing of major taxa of animals among different modules. Similarly, Gautier-Hion et al. (1985) studying a diverse community of fruit and frugivores in an African tropical rainforest found that morphological traits of fruits revealed syndromes associated with consumption by different taxa of vertebrates. In a coarse-level of taxonomic resolution, the phylogenetic effect on network structure was limited to the over-representation of species from a given animal taxonomic group in each module. In a fine-level of taxonomic resolution, modules associated to birds showed significant phylogenetic signal, whereas modules associated to mammals did not. Although the low number of species in mammal-dominated modules could decrease the statistical power of the phylogenetic test, the mammal composition in modules does not show clear evidence that phylogenetically related species belong to same modules. For instance, closely related mammal species, such as artiodactyl ungulates, belonged to different modules. In addition, modules did not include phylogenetically related plant species. Therefore, the pattern in this network is only partially explained by shared evolutionary history in the sense that, although modules are related to the major taxonomic groups of animals, the majority of them do not include phylogenetically related species. Given the consistency of our results, we posit that the modularity of this seed dispersal network is not associated with habitat heterogeneity and that phylogeny only determines the existence of bird, mammal and fish modules, and the assemblage of species in bird modules. Therefore, we suggest that modules emerged by a combination of phylogenetic history and trait convergence of phylogenetically unrelated species, shaped by interactions with particular types of dispersal agents (Van der Pijl 1982). However, we do not necessarily imply here that those tight phenotypic associations between seed dispersers and fruits found in modules are driven by coevolution (e.g., Nuismer et al. 2010). We are simply positing that convergence of species towards a similar and predictable set of traits (Thompson 2005), rather than phylogeny alone, is what explains the way this seed dispersal network is organized. In fact, convergence in these networks might be both an outcome of evolutionary processes

22 such as local adaptation and coadaptation (Thompson 2005) and a consequence of ecological convergence in resource use by subsets of frugivores. In a broader context, the Pantanal seed dispersal network was less nested than all other seed dispersal networks so far studied, with the exception of one sampled in the Brazilian Atlantic forest (Silva et al. 2007), that includes interactions between plants and both mammal and bird species. However, the network analyzed here is more modular than the other four frugivory networks that had significant values of modularity. Modularity of plant-animal networks is expected to increase with trophic specificity, with herbivory and ant-plant networks found to be more modular than pollination and seed dispersal networks (see Fonseca & Ganade 1996; Guimarães et al. 2007; Thebault & Fontaine 2010). Pollination networks are likely to have higher and more prevalent modularity than seed dispersal networks because flowers may restrict the range of visitors through morphological barriers (Santamaría & Rodríguez-Gironés 2007; Stang et al. 2007), whereas fruit traits may lend them to be more open to interaction with multiple visitors (Blüthgen et al. 2007). However, the high degree of modularity in this diverse and well-sampled seed dispersal network suggests that factors other than interaction type are constraining the modularity in frugivory and seed dispersal networks. For instance, the lack of significant modularity in most of these networks could happen because they predominantly include a single taxonomic group. Nevertheless, the phylogenetic diversity only partially explains the modularity of this diverse network and some modules are composed by species from different taxonomic groups. We therefore hypothesize that modularity emerges from the interplay between shared evolutionary history and convergence in patterns of interaction. One of the values of detecting a modular pattern, other than contributing to elucidate the evolutionary ecology of plant-frugivore interactions, is the identification of the role of species in the network (Olesen et al. 2007). This is important because the robustness of the network, i.e. the ability of a species to persist given the extinction of an interacting partner in the community (Jordano et al. 2006) may depend on the role of species in the network. For example, the extinction of connectors may cause the network to fragment into isolated modules, but will have a minor impact on the internal structure of modules. In contrast, the extinction of a module hub may cause its module to fragment

23 with minor cascading impact on other modules. Interestingly, only 6.4% of the species in this network, a lower percentage than that found in pollination networks (Olesen et al. 2007), are connectors or module hubs. Some plant species were important in maintaining the pattern of this network. Genipa americana was considered a connector, linking modules together. This plant species exhibits a typical “megafauna seed dispersal syndrome” (Guimarães et al. 2008) in that their seed dispersers are/were large mammals, yet its fruits are also avidly eaten by several bird species. Consequently, fruits of this species were dispersed by animal species from four modules (two bird modules and two mammal-dominated modules). Although large-bodied seed dispersers such as tapirs and feral pigs showed to be important in linking species within a module, they were not important in linking modules together, maybe because they mainly interact with large- and medium-seeded species, which were not present in all modules of this network. Medium-bodied species, such as howler monkeys and chaco chachalacas, were connectors in this network and, therefore, structurally important because they link modules together. Consequently, we posit that the loss not only of large-bodied seed dispersers, the ones that disperse a high number of plant species, but also of some of the medium-bodied species, may change the pattern of this network, given that their absence could cause the network to fragment into isolated modules. Here, the use of the network approach helped us to understand the structure of a highly diverse seed dispersal network and enabled us to identify the mechanisms that underlie the modular pattern, contributing to elucidate the ecology and evolution of plant- frugivore interactions. In addition, the identification of the modular pattern gave us insights regarding the possible consequences of differential defaunation (cf. Dirzo & Miranda 1991) on the functioning of this seed dispersal network. For example, the presence of few animal species that can link modules together could contribute to the robustness of this network in a scenario of extinction of particular species.

Acknowledgments We would like to thank Jason Ladner, Enrico Rezende and Andrew Rominger for their help with the phylogenetic analysis, Roger Guimerà for providing us the modularity

24 algorithm, Rebecca Terry for helping with the accumulation curve, and Dennis Hansen, Pedro Jordano, John Thompson and three anonymous reviewers for helpful comments on a previous draft. We thank FAPESP (2004/00810-3 and 2008/10154-7), Earthwatch Institute and Conservation International for financial support. CID was supported by Stanford University and PRG by FAPESP and CAPES. We also thank Conservation International, Lucas Leuzinger and Marina Schweizer for their permission to work in their properties.

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28 Figures

Figure 1 Modularity of the Pantanal seed dispersal network. Each module is identified by a different color (bird module 1: green, 22 species, bird module 2: blue, 22 species, mammal-dominated module 1: red, 25 species, mammal-dominated 2: yellow, 18 species and fish: purple, 4 species) in which each species was assigned. Circles in dark shades represent animal species and squares in light shades represent plant species. The size of circles refers to animal body mass (with large circles representing species with body mass≥4.5 kg), whereas the size of squares refers to the fruit diameter (with large squares representing species with fruit diameter ≥95mm). Both body mass and fruit diameters were divided in four size categories exclusively for the purpose of this figure. Fruits and seed sizes are in the same scale in all modules, and represent the relative diameter and length of fruits and seeds in each module. The figure was manually done using the pajek package (http://vlado.fmf.uni-lj.si/pub/networks/pajek/).

Figure 2 Association between body mass and values of fruit traits, illustrating trait complementarity underlying the modular structure of the network (bird module 1: green, bird module 2: blue, mammals-dominated module 1: red, mammal-dominated 2: yellow and fish: purple). a) fruit diameter and b) fruit mass. Body mass is in log scale.

Figure 3 (a) The phylogenetic tree of animal species and modules (identified by different colors) in which each species was assigned (bird module 1: green, bird module 2: blue, mammals-dominated module 1: red, mammal-dominated 2: yellow and fish: purple). Modules associated with birds showed significant phylogenetic signal.

Figure 3 (b) The phylogenetic tree of plant species and modules (identified by different colors) in which each species was assigned (bird module 1: green, bird module 2: blue, mammals-dominated module 1: red, mammal-dominated 2: yellow and fish: purple).

Figure 4 Role of each species in the seed dispersal network. Each symbol describes the within-module degree (z) and the participation coefficient (c) of each species. We used the values of 2.5 for z and of 0.62 for c to assign a role to each species, which could be peripheral species (bottom left), module hub (top left), supergeneralist (top right) or connector (bottom right). Species are color coded according to the module to which they belong (bird module 1: green, bird module 2: blue, mammals-dominated module 1: red, mammal-dominated 2: yellow and fish: purple). Circles represent animal species and squares represent plant species. Lines at z=2.5 and c=0.62 define species roles.

33 Supporting Information

Figure S1. Accumulation curve with the average (in black) and the standard deviation (in grey) of the number of seed dispersal interactions in 1000 iterations, as a function of the number of seed dispersal events. The red line shows the extrapolation of the curve and the dashed line represents the asymptotic value for the number of seed dispersal interactions. We sampled 2,070 events of seed dispersal, 273 seed dispersal interactions and estimated the asymptote for the number of seed dispersal interactions to be ~289.

34 Appendix S1 Description of the study sites The Pantanal, with its 170,000 km2, stretches through Central-West Brazil, Bolivia and into Paraguay. In Brazil, the Pantanal is bordered on the east by one of the world’s richest savannahs, the Cerrado; fringed to the northwest by semi-deciduous forest related to the Amazon, and is contained to the southwest by the dry chaco-like forest of neighboring Bolivia (Prance & Schaller 1982). Within the Pantanal, the study concentrated on the areas surrounding two neighboring farms (Rio Negro and Barranco Alto) that effectively served as field stations. Rio Negro farm (19°34'15"S 56°14'43"W) is a 7,500 ha private property, and Barranco Alto farm (19º34'40"S 56º09'08"W) is an 11,000 ha private property; both located in the Nhecolândia region. Average annual rainfall in this region is 1,192.5 mm and mean monthly temperature is 26°C, ranging from 19°C to 33°C (2004- 2008; D. Eaton, unpublished work). Rio Negro farm has 7,500 ha of protected area, free of cattle since 2001. Barranco Alto Farm has 11,000 ha, in which 3,400 ha are protected areas free of cattle since 1980. Both areas are among the most pristine ones in the Pantanal.

Reference Prance, G.T., G.B.Schaller. 1982. Preliminary study of some vegetation types of the Pantanal, Mato Grosso, Brazil. Brittonia 34(2): 228-251.

Appendix S2 Justification for setting branch lengths equal to one We have tested the assumption that branch lengths equal to one is acceptable for this study. To do so, we created 1,000 phylogenetic trees, simulated trait data using the function rTraitCont in R and calculated the K statistic using Kcal in R (=true K values using “real” branch lengths). Then we re-set all branch lengths equal to one and re- calculated the K statistic. We plotted K values using branch lengths equal to 1 (K value for eddge.lengths=1 in figure S2 below) and K values using “real” branch lengths (True K value in figure S2 below). The data show that K values calculated using branch lengths equal to 1 are consistently greater than K values calculated using “real” branch lengths (see figure S2 below). Therefore, as all K values presented in the manuscript are lower than or equal to one, we can say that setting all branches = 1 is, in the case of this study, a conservative approach.

Figure S2 Association between K values using branch lengths=1 (K value for edge.length=1) and K values using branch lengths generated by our simulations (True K value).

36 CHAPTER 2

DEFAUNATION SIMULATIONS REVEAL THE CONSEQUENCES OF SEED DISPERSAL NETWORK DISRUPTIONS AND THE ROLE OF AN EXOTIC SPECIES ON DISPERSAL SERVICES Camila I. Donatti, Paulo R. Guimarães, Mauro Galetti, Marco Aurélio Pizo & Rodolfo Dirzo

Abstract Given that mutualistic species often interact with multiple partners, the extinction of a species may affect several others, causing cascading effects that may in turn reverberate throughout the entire community. Here, we addressed how a species-rich seed dispersal network changes with selective animal extinction. We compared i) structural network metrics, ii) network robustness and iii) the number of seed dispersal events that are lost in the community, under three distinct defaunation simulations, where the sequence of cumulative removal of species was based on i) the body mass of animals, ii) their number of links or iii) the random loss of animals in the community. Our results show that the removal of large- and medium-bodied species has the largest impact on the network topology and generates the lowest network robustness among the three simulations. Furthermore, the absence of these species significantly increases the number of seed dispersal events that are lost when compared to the simulated extinction of randomly selected species. Surprisingly, our results also show that the exotic feral pig (Sus scrofa) plays a critical, positive role in maintaining structural network metrics and in providing seed dispersal services in this community. Thus, we posit that network stability will be significantly reduced with defaunation of large- and medium-bodied species, but this effect can be attenuated by the presence of large megafaunal exotic species, if such species are less susceptible to extinction in their novel ecosystems.

37 Introduction Defaunation, the contemporary pulse of animal local population loss or decline (sensu Dirzo & Miranda 1991) driven by human activities such as hunting, land use change, and the synergies of both, has been occurring at high rates in tropical forests (Redford 1992, Peres 2000, Dirzo 2001, Corlett 2007, Wilkie et al. 2011), with large-and medium-bodied vertebrate species being the most affected ones (Bodmer et al. 1997, Cardillo et al. 2005, Cardillo et al. 2006, Peres & Palacios 2007). These species are extremely important for several ecological interactions (e.g., seed predation, dispersal and herbivory) and their absence may deeply affect ecological organization and services (Dirzo 2001, Terborgh et al. 2001, Sinclair et al. 2007, Wright et al. 2007, Estes et al. 2011). In seed dispersal interactions, this is likely due to the fact that megafaunal species usually interact with multiple partners (Bascompte et al. 2003, Strauss & Irwin 2004, Jordano et al. 2006, Donatti et al. 2011) and their absence can affect many interacting partners concurrently, causing cascading effects that may reverberate in the entire community. Here, we use network theory and species extinction simulations to address the consequences of the loss of particular animal species on seed dispersal services in a species-rich community. The patterns of interaction among species in a community have been described using the network approach (Memmott et al. 1999, Bascompte & Jordano 2007). Mutualistic networks are characterized by the prevalence of a nested pattern (Bascompte et al. 2003, Vázquez et al. 2009, Fortuna et al. 2010, Joppa et al. 2010), whereby the interactions of the specialist species tend to be a subset of the interactions observed among the generalists (Bascompte et al. 2003). In addition, some mutualistic networks are also modular, whereby subsets of species (modules) more frequently interact with each other than with species from other modules (Olesen et al. 2007, Donatti et al. 2011). Nestedness, modularity and the proportion of all interactions that are realized (connectance), affect network stability and can be used to assess how network topology may change in the absence of particular species (Tylianakis et al. 2010). As large-bodied seed dispersers are less likely to show size constraints between their gape and seed and/or fruit sizes, they are expected to be able to disperse a large number of plant species (Jordano 1995), that is, to be “super-generalists” (Guimarães et

38 al. 2011). The presence of super-generalist species in a community contributes to a high nestedness and connectance of the network (Bascompte et al. 2003, Olesen et al. 2007). Therefore, if these super-generalist species disappear, we should expect a decrease in nestedness and connectance of the network, thus reducing also its robustness (Dunne et al. 2002, Fortuna & Bascompte 2006, Piazzon et al. 2011). To examine these relationships, we developed defaunation simulations to compare the consequences of the absence of selected seed dispersers vs. the absence of randomly selected species from a community that includes native and exotic seed dispersers and the plant species they disperse. We compared structural network metrics, network robustness and the number of seed dispersal events that are lost in the community, under three distinct defaunation simulations, each with 16 defaunation steps related to the cumulative removal of particular species from the network. In a “realistic defaunation simulation”, we removed species based on their body mass, with the heaviest species removed first. In a “most-to-least linked species simulation”, we removed species based on their number of links, with the most linked species removed first. We also developed “random defaunation simulations” where we randomized both the order and the identity of the removed species. Specifically, we hypothesized that the realistic defaunation simulation would lead to: 1) a lower network robustness, whereby the extinction of a few particular animal species leads to a high proportion of the plants losing seed dispersal services, 2) loss of dispersal services to large-fruited and/or large-seeded plant species, given that those plant species are predominantly dependent on large-bodied dispersal agents (Janzen & Martin 1982, Janson 1983, Wheelwright 1985, Chapman et al. 1992, Guimarães et al. 2008, Donatti et al. 2011), and 3) overall, a reduction in the number of events of seed dispersal in the network. We show that the simulated extinctions of large- (i.e. >5kg) and medium- bodied species (i.e. 1-5Kg) lead to a network that is more susceptible to coextinctions, when compared to the intact network and to networks generated by simulated random extinctions and by simulated extinction of most linked species. Moreover, plant species with longer, wider and/or heavier fruits and/or seeds are more likely to lose all seed dispersal potential with the absence of large- and medium-bodied species. Furthermore, we show that the absence of a few, largest species, would significantly decrease the

39 number of seed dispersal events in this community, while an exotic, large-bodied animal species is crucial in maintaining network structure and seed dispersal services. Such a situation of the concurrence of selective defaunation and addition of exotic dispersers is becoming increasingly common in tropical landscapes (Blackburn et al. 2004) and yet has been largely unstudied. We posit that network stability will be significantly reduced with defaunation of large- and medium-bodied species, but this effect would be attenuated by the presence of large megafaunal exotic species, some of which may exert a compensatory role in the face of contemporary defaunation.

Methods

Study site The network of interactions from which we simulated species extinctions was sampled in the Brazilian Pantanal, in two neighboring locations: Rio Negro and Barranco Alto farms, covering 7,500 ha and 11,000 ha, respectively, of private land. More details about the study sites can be found in Donatti et al. (2011).

Seed dispersal interactions Seed dispersal interactions were recorded using four methods: focal observations, camera trap techniques, scat and intestine analyses. One seed dispersal event was noted when either: fruits were recorded to have been swallowed or removed from a plant species during focal observations; fruit removal of a particular species by a potential seed disperser was detected with camera traps; a scat pile was found to have at least one intact seed of a particular species in it; or a sampled fish intestine contained at least one intact seed from a particular species. We have sampled 94.5% of interactions that occur in this community (Donatti et al. 2011) and more details about the methods and the structure and composition of species in this seed dispersal network can be found in Donatti et al. (2011).

Simulation of defaunation steps Traditionally, simulations of species extinctions in interaction networks focus on the removal of most-to-least linked species (i.e. species with more connections are

40 removed first) and show drastic changes in the structure and functioning of networks (Solé & Montoya 2001, Dunne et al. 2002, Memmott et al. 2004). However, this extinction simulation is ecologically unrealistic (Srinivasan et al. 2007), given that the order of species loss does not necessarily correlate with their number of interactions in the network. Therefore, as the likelihood of animal extinctions is correlated with demographic and life history traits (Davidson et al. 2009) that are also related to body mass (Bodmer et al. 1997, Jerozolimski & Peres 2003, Peres & Palacios 2007), we also simulated a sequence of extinctions based on the body mass of species, with the heaviest species removed first. Indeed, it is known that species ≥1kg are more likely to be affected by hunting (Peres 2000) and forest fragmentation (Michalski & Peres 2007). Thus, to assess the effect of the absence of particular animal species on a seed dispersal network, we compared network metrics in defaunation steps based on the removal of large- and medium-bodied species from the original network (i.e. the network that includes all sampled interactions) with the same metrics derived from defaunation steps based on the removal of random species. As the removal of the most linked species should be the extreme situation that drastically affects network metrics, we also compared network metrics in defaunation steps based on the removal of large- and medium-bodied species with the same metrics derived from defaunation steps based on the removal of highly linked species from the original network. In the “realistic defaunation simulation” we created 16 defaunation steps based on the cumulative removal of 16 species with body mass >1 kg from the network (Table S1). In each defaunation step we selected one additional species to be removed, from the heaviest to the lightest, until all species with at least 1 kg were no longer present in the network. As a null model, referred to as “random defaunation simulations”, we also created 16 different defaunation steps based on the cumulative removal of 16 random species from the network. Species were randomly assigned to each of the defaunation steps and randomizations were done 1,000 times. In the “most-to-least linked defaunation simulation”, we removed 16 species from the network based on their number of interactions. In this simulation, we created 16 defaunation steps and the most linked species were removed first. In each simulation, the removal of 16 species represents 34% of the animal species in the network.

41 Effects of defaunation on structural network metrics To define the pattern of interaction in each defaunation step of each simulation, we organized qualitative matrices that included all recorded interactions between plant and animal species. In matrices of plants in columns and animals in rows, a cell representing a recorded interaction between a plant and an animal species received the value of one, and “zero” otherwise. We then used these matrices to compute network’s connectance, nestedness and modularity. Details on the nestedness and modularity analyses can be found in Donatti et al. (2011). We compared the connectance of the network (the proportion of all interactions that are realized) across defaunation steps among the realistic, random and the most-to- least linked defaunation simulations. Additionally, we calculated the values of nestedness and modularity, as well as their respective levels of significance, for each of the 16 defaunation steps in the realistic defaunation simulation, for each of the 1,000 randomizations in each of the 16 defaunation steps in random defaunation simulations and for each of the 16 defaunation in the most-to-least linked defaunation simulation. To address whether the network is stable in the sense that its metrics do not drastically change across defaunation steps, we compared the sum of the variation in connectance, nestedness and modularity across defaunation steps among the realistic defaunation simulation, the most-to-least linked defaunation simulation and the average of the sums of the variation across defaunation steps in 1,000 random defaunation simulations.

Effects on seed dispersal events

We also assembled quantitative seed dispersal matrices that included the number of seed dispersal events recorded for each interaction between a plant and an animal species (see Seed dispersal interactions in this section). To address the impact of the absence of large- and medium-bodied species on the number of seed dispersal events, we removed the same 16 animal species from the original quantitative matrix, and computed the number of seed dispersal events that are lost in each defaunation step. Likewise, we computed the same variable in each of the 1,000 randomizations for each defaunation

42 step in random defaunation simulations and in each defaunation step of the most-to-least linked defaunation simulation.

Effects on the robustness of the network Robustness is defined as the propensity for networks to lose species secondarily or, in the case of this study, to leave plant species without seed dispersers. The robustness of the network was thus computed as the percentage of animal species that had to be removed in order for ≥ 50% of the plant species to no longer have seed dispersers (modified from Dunne et al. 2002). We compared the robustness of the network in the realistic defaunation simulation with the robustness of both the network in random extinction simulations and the network in most-to-least linked defaunation simulation. We also compared the robustness of the network in the realistic defaunation simulation, random defaunation simulations and the most-to-least linked defaunation simulation using quantitative data, i.e. the number of animal species that had to be removed in order for ≥ 50% of the number of seed dispersal events to be lost in the community. Additionally, we examined traits of plant species that would most likely lose all their seed dispersers upon removal of animal species. We measured fruit and seed morphological traits (length, diameter and mass) for all plant species in this community, in at least 30 fruits and seeds from at least five individuals. Then we compared the average values of fruit and seed traits of species that would most likely lose all seed dispersers in each of the defaunation steps in the realistic defaunation simulation with those of plant species randomly selected from the species pool. Randomizations were done 1,000 times.

The importance of particular native and exotic species on network structure and seed dispersal services To assess the importance of each of the large-and medium-bodied species in the process of seed dispersal, we ran a new set of 16 simulations with 15 defaunation steps each. To assess the importance of each one of the large- and medium-bodied species, in addition to remove species based on their body masses from the intact network, as

43 previously described, we also maintained one of these species in all defaunation steps of the same simulation. We ran those simulations, each one maintaining one particular species in the community, for all large- and medium-bodied species. Therefore, we could assess changes in connectance, nestedness and modularity, and compare network robustness when a particular species remained in the community while all other large- and medium-bodied species were removed from the community.

Results

Effects of defaunation on structural network metrics Network connectance in all defaunation steps in the realistic defaunation simulation was lower than expected by chance. In addition, the sum of the variation in connectance across all 16 defaunation steps in the realistic defaunation simulation was lower than in the most-to-least linked defaunation simulation and both were significantly higher than the average in random defaunation simulations (realistic defaunation simulation=0.061, most-to-least linked defaunation simulation=0.077, average random defaunation simulation=0.04, p=0.001 for both). This implies that connectance changes more drastically across defaunation steps in the realistic defaunation simulation than with the random removal of species. The absence of large- and medium-bodied species decreases the values of nestedness and increased the values of modularity across defaunation steps, when compared to the removal of randomly selected species (Fig. 1). The low values of nestedness calculated in the realistic defaunation simulation were not due to chance in the majority of defaunation steps. Likewise, modularity in the realistic defaunation simulation was higher than expected by chance in most defaunation steps, the exceptions being the last three steps where species ≤2.8 kg remained in the community. In fact, in these last three defaunation steps of the realistic defaunation simulation, the network was neither nested nor modular, indicating that the organization of the mutualistic network was vanished. The sum of the variation in nestedness across all 16 defaunation steps was higher in the realistic defaunation simulation than in the most-to-least linked defaunation simulation and both were significant higher than the average in random extinction simulations (realistic defaunation simulation=17.88, most-to-least linked defaunation

44 simulation=17.56, average random defaunation simulation=5.85, p<0.0001 for both). The sum of the variation in modularity across all 16 defaunation steps was lower in the realistic defaunation simulation than in the most-to-least linked defaunation simulation and both were significantly higher than the average in random extinction simulations (realistic defaunation simulation=0.19, most-to-least linked defaunation simulation=0.22, average random defaunation simulation=0.09, p=0.004 and p<0.0001, respectively). Therefore, both nestedness and modularity also changed more drastically across defaunation steps in the realistic defaunation simulation than with the random removal of species. The greater change in the topology of the network that results from the realistic loss of species, when compared to the network that results from random extinctions, occurs because large- and medium-bodied species have more interactions in the network. In fact, the number of plant species dispersed increases with the body mass of the seed disperser (F=65.62, p<0.0001, r=0.77, d.f.=45) and this association holds when we analyze our data separately via the different methods used to sample seed dispersal interactions in this community (focal observations, camera trap techniques, scat and intestine analyses; see SI Appendix). Consequently, although we used different methods to assess seed dispersal interactions for large-and small-bodied species, our results showing this positive association are likely not an artifact resulting from sampling protocols. Therefore, significant changes in nestedness and connectance with the absence of large- and medium-bodied species make topological and ecological sense, given that the more plant species an animal species can disperse, the greater the effect of the absence of this animal species on network structure. Indeed, we found that connectance and nestedness decrease even more drastically in the network that faces the extinction of the most-to-least linked species when compared to those in the network that faces the extinction of large- and medium-bodied species.

Effects on seed dispersal events The number of interactions that were lost in the realistic defaunation simulation was higher than expected by chance in most of defaunation steps probably because large-

45 and medium-bodied species also interact with plant species more frequently. We found a positive and significant effect of body mass on the number of seed dispersal events (F= 20.09, r=0.56, p<0.0001, d.f.=45), association that holds across the different sampling methods (see SI Appendix). Thus, although we used different methods to also assess seed dispersal events for large-and small-bodied species, the positive association between body mass and number of events of seed dispersal is likely not an artifact of the methods that we used.

Effects on the robustness of the network In the realistic defaunation simulation, all plant species had at least one seed disperser when species < 6.6 kg were still present in the community. In addition, our results showed significant trends in the representation of morphological traits of plant species that lost all their seed dispersers. Plant species that would most likely lose all their seed dispersers with the absence of large- and medium-bodied species were characterized by having longer, wider and/or heavier fruits and/or seeds than those of plant species randomly selected from the species pool. Surprisingly, with the removal of species based on their number of links, the network was more robust than with the removal of large- and medium-bodied species. A lower percentage of animal species had to be removed in order for ≥ 50% of the plant species to no longer have seed dispersers in the realistic defaunation simulation, followed by the most-to-least linked defaunation simulation and by the average of random defaunation simulations (32.6%, 47.8% and 80%, respectively, Fig. 2). This result indicates that, in the realistic defaunation simulation, some animal species are dispersing plant species that no other species in the network can disperse, even if they do not have a high number of interactions. The quantitative analysis revealed that the network might experience an even earlier collapse when qualitative data are considered, for all three types of simulations. The same percentage of the animal species in the community had to be removed in order for ≥ 50% of the seed dispersal events to be lost in both the realistic and the most-to-least linked defaunation simulations (13%), followed by the average in random defaunation simulations (50%, Fig. 2).

46 The importance of particular native and exotic species on network structure and seed dispersal services Our qualitative data shows that the connectance, nestedness and modularity of the network changed less drastically when all large- and medium-bodied species, except the exotic feral pig, were removed from the network. Furthermore, our quantitative data shows that, although the maintenance of feral pigs or tapirs (Tapirus terretris) in the network decreases the number of plant species that no longer have seed dispersers (Table S2), fewer seed dispersal events are lost in the network if the feral pig remains in the community than if the tapir does. Therefore, in this community, one exotic megafaunal species is the most important player both in maintaining structural network metrics and in providing seed dispersal services.

Discussion This study shows that the absence of large- and medium-bodied animal species will change the pattern of interaction and the relative representation of species traits in species-rich communities, given that several plant species will no longer have seed dispersers. More specifically, with the absence of animal species >2.8 kg, the network becomes less connected, less nested and more modular. Furthermore, with the removal of species ≤2.8 kg, the network becomes neither nested nor modular. Our results also demonstrate that the absence of large- and medium-bodied species decreases the connectance of the network. Therefore, using the definition of stability as a system’s potential to resist a perturbation (see McCann 2000), we posit that network stability will be diminished or not be maintained if large- and medium-bodied species are removed from the community. Several studies have recently shown species networks collapses with the removal of most linked species (Solé & Montoya 2001, Dunne et al. 2002, Memmott et al. 2004). However, our study demonstrates that it is the specific removal of large- and medium- bodied species that has the largest impact on the network topology, since it will no longer be nested or modular. Additionally, the absence of these species significantly increases the number of seed dispersal events that are lost in the network when compared to random defaunation. Therefore, opportunities for seed dispersal in plant communities can

47 decrease considerably as large-and medium-bodied species are removed from the community. Since we were able to collect similar number of scats of species with different body size (T. terrestris: 293 scats, S. scrofa: 331 scats, R. americana: 239 scats), and given that we conducted focal observations for similar number of hours in several plant species, these results are not likely a consequence of any observational bias in our data. All plant species still had all their seed dispersers in defaunation steps where species > 6.6 kg were already removed from the community, because there is redundancy in the use of the majority of plant species by seed dispersers: only 6.5% of the plant species rely on a single seed disperser species. Moreover, as previously mentioned, the pattern of the network is nested even after the removal of all species ≥ 4.5 kg from the network, which could also contribute to its tolerance regarding secondary extinctions. Nevertheless, in defaunation steps in which seed dispersers > 6 kg were no longer present in the network, plant species with longer, wider and heavier fruits and seeds are the first to lose seed dispersers, due to size mismatching between fruit and/or seed sizes and the gape size of animal species that remained in the community (Donatti et al. 2007). However, some small-bodied species (< 1 kg), such as small rodents, could increase their densities in the absence of large-bodied species and, potentially, fill up their roles as seed dispersers, at least for some plant species. Although compensatory density has been shown to occur in areas without large-bodied animals (Peres & Dolman 2000), it is not clear whether small-bodied species could necessarily fill up the ecological gap created by the absence of larger species. Therefore, rare or small-bodied species are probably unable to compensate for declines in ecosystem processes when large, more abundant species are lost (Solan et al. 2004). More specifically, Donatti et al. (2009) showed that small-bodied seed dispersers (i.e. spiny rats and squirrels, both < 1 kg) could not even compensate for seed dispersal rate and distance even in the absence of medium-bodied animals (i.e. agoutis ~2.8 kg). Both the tapir and the agouti play a crucial role in dispersing large-seeded plants (Hallwacks 1986, Fragoso 1997, Galetti et al. 2006) and this study shows that, among the native species, they are the most important in maintaining network robustness. Whereas the tapir can disperse a high number of plant species in this community, the agouti is the

48 only seed disperser of large-seeded plant species left when larger animals are removed from the community. Nevertheless, this study also reveals the importance of some of the large- and medium-bodied species that have currently invaded tropical ecosystems, such as the exotic feral pig, on the process of seed dispersal. Since it is well known that exotic mammalian species can have direct, negative impacts on native taxa (Cox 1999, D’Antonio et al. 1999, Cushman et al. 2004, Busby et al. 2010) the importance of feral pigs in maintaining both network topology and seed dispersal services described in this study is surprising. However, because the plant community in the Pantanal includes several species that were presumably dispersed by extinct Pleistocene megafauna (Donatti et al. 2007, Guimarães et al. 2008), the ability of feral pigs to disperse some of these fruits, combined to the relatively high density of pigs in this system (6.35 Ind/km2, C.I. Donatti, unpublished data), make them extremely important analogue seed dispersers in this community. Therefore, here we report a situation where an exotic species has a positive role in a mutualistic process, perhaps because it, along with some native species such as tapirs and agoutis, is filling up the empty niche left by the extinct Pleistocenic megafauna (Janzen & Martin 1982). We argue that the effects of differential defaunation on the process of seed dispersal reported here are compelling given that we have sampled 94.5% of interactions that occur in this community (Donatti et al. 2011). In addition, our network comprises interactions in one of the few communities that still harbor intact contingents of large- bodied mammals and birds (Harris et al. 2005). Our goal here was to address how a seed dispersal network behaves when it is altered via animal extinction. We showed that the selective defaunation of large- and medium-bodied species affects network stability more drastically than the defaunation of random species. Therefore, changes found in the structure of the network are not simply an effect of the loss of an indiscriminate number of species, but are rather dependent on their specific identities (McCann 2000, Worm and Duffy 2003). Consequently, a focus on conserving species diversity alone may not necessarily conserve network structure and functioning (Bascompte et al. 2006, Bastolla et al. 2009). Our results raise conservation concerns because we have demonstrated significant negative effects of the absence of large- and medium-bodied species even in a network

49 that shows a high seed dispersal redundancy among animal species and is highly nested, which should provide a buffer against coextinctions. Even though not all large- and medium-bodied species are already extinct in many tropical areas, the loss of just a few among the largest ones (i.e. the six species >20 kg in this case) would decrease by more than 50% the number of seed dispersal events in the community. As the selective loss of large-bodied species and their disproportionate ecological importance is a global phenomenon (Ceballos & Ehrlich 2002, Estes et al. 2011, Dirzo 2001, Wilkie et al. 2011), we predict that some of the consequences of differential defaunation on seed dispersal services shown here are likely to be happening already in many tropical ecosystems. However, some exotic species may exert a compensatory role as seed dispersers in the face of contemporary defaunation, provided they are less susceptible to extinction in their novel ecosystems.

Acknowledgments We would like to thank Roger Guimerà for providing us the modularity algorithm, and Jim Estes and members of the Dirzo lab for helpful comments on a previous draft. We thank FAPESP (2004/00810-3 and 2008/10154-7), Earthwatch Institute and Conservation International for financial support. CID was supported by Stanford University and the Zaffaroni Fellowship Fund, PRG by FAPESP and CAPES, and MG and MAP by a CNPq fellowship. We also thank Conservation International, Lucas Leuzinger and Marina Schweizer for their kind permission to work in their properties.

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53 consequences for plant regeneration. Biotropica 39: 289–291.

54 Figures

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! # $ % 2 " 3 4 5 6 #! ## #$ #% #2 #" #3 /'89:+9);<+,()'=( Figure 1. Values of nestedness (top) and modularity (bottom) in each defaunation step. Red circles represent those in realistic defaunation simulation, blue circles represent those in the most-to-least linked defaunation simulation and box plots represent those in random defaunation simulations.

Figure 2. The robustness of the network in terms of the number of plant species that would lose all seed dispersers in each of the defaunation steps (top) and the number of seed dispersal events that would be lost in the network in each of the defaunation steps (bottom). Red circles represent those in the realistic defaunation simulation, blue circles those in the most-to-least linked defaunation simulation, and box plots those in random defaunation simulations.

56 Supporting Information

Table S1. Animal species removed in each of the defaunation steps in the realistic defaunation simulation, family and their body masses. Note that each defaunation step (except defaunation step one) also contains species that were/was already removed in the previous defaunation step.

Defaunation steps Additional species removed Family Body mass (in kg.) 1 Tapirus terrestris Tapiridae 240 2 Sus scrofa Suidae 50 3 Mazama spp. Cervidae 36 4 Tayassu pecari Tayassuidae 35 5 Pecari tajacu Tayassuidae 26 6 Rhea Americana Rheidae 20 7 Cuniculus paca Cuniculidae 9.1 8 Geochelone carbonaria Testudinidae 6.6 9 Euphractus sexcintus Dasypodidae 6 10 Cerdocyon thous Canidae 5.7 11 Alouatta caraya Cebidae 5.4 12 Nasua nasua Procyonidae 5.1 13 Procyon cancrivorus Procyonidae 4.5 14 Dasyprocta azarae Dasyproctidae 2.8 15 Crax fasciolata Cracidae 2.8 16 Aburria pipile Cracidae 1

57 Table S2. Animal species maintained in the network (listed by body mass) while all other large- and medium-bodied species were removed, the sum of the variation for the Nestedness (NODF), Modularity (M) and Connectance (C), the number of plant species that would lose all their seed dispersers in each simulation, and the number of seed dispersal events lost. Note that in the simulation where the feral pig (Sus scrofa) is maintained in the network, there is less change in network metrics, less seed dispersal events are lost and less plant species no longer have seed dispersers when compared to the other simulations.

Sum of the Sum of the Sum of the Plant species Seed dispersal Species maintained variation (NODF) variation (M) variation (C) without dispersers events lost Tapirus terrestris 15.42 0.1302 0.045 8 1310 Sus scrofa 12.89 0.1288 0.044 8 1065 Mazama spp. 18.17 0.1917 0.055 22 1460 Tayassu pecari 14.98 0.1411 0.046 11 1247 Pecari tajacu 13.38 0.1787 0.053 17 1455 Rhea americana 14.89 0.1545 0.054 18 1336 Agouti paca 16.21 0.2176 0.047 22 1499 Geochelone carbonaria 14.98 0.1793 0.050 15 1471 Euphractus sexcintus 17.61 0.2029 0.056 22 1487 Cerdocyon thous 16.97 0.1520 0.050 14 1409 Alouatta caraya 17.45 0.2160 0.056 23 1497 Nasua nasua 16.09 0.1561 0.048 13 1457 Procyon cancrivorus 18.18 0.1944 0.056 21 1487 Dasyprocta azarae 15.47 0.1596 0.048 13 1439 Crax fasciolata 17.81 0.1928 0.055 21 1487 Aburria pipile 16.78 0.1735 0.055 25 1439

58 Appendix S1. Association between both the number of plant species dispersed and the number of seed dispersal events and the body mass of seed dispersers across different methods

The positive association between the number of plant species dispersed and the body mass of the seed disperser holds when we analyzed data collected through camera traps and focal observations separately (camera trap: F=4.8, p=0.045, r2=0.25, n=16; focal observations: F=10.4, p=0.003, r2=0.27, n=30), but not when analyzing data collected through scats only (F=2.02, p=0.205, n=8), the one sampling method in which our sample size is limited. However, when pooling data from camera traps and focal observations together, we still get the same positive and significant relationship between body mass and number of interactions (F=43.9, p<0.0001, r2=0.53, n=40). Likewise, the association between the number of seed dispersal events and the body mass of seed dispersers holds when analyzing data collected through camera traps and focal observations separately (camera traps: F=5.15, p=0.039, r2=0.26, n=16; focal observations: F=7.4, p=0.01, r2=0.2, n=30), but not when analyzing data collected through scat analyses only (F=1.7, p=0.2398, n=8). However, when pooling data from camera traps and focal observations, we still get the positive and significant association between body mass and number of seed dispersal events (F=20.62, p<0.0001, r2=0.34, n=40).

59 CHAPTER 3

EFFECTS OF INTRA- AND INTER-SPECIFIC SEED SIZE VARIATION ON SELECTION BY DISPERSERS, GERMINATION AND SEEDLING GROWTH Camila I. Donatti, Mauro Galetti, Marco Aurélio Pizo & Rodolfo Dirzo

Abstract Seed dispersal can help plants explore and colonize new or enemy-free habitats, facilitate the regeneration of populations and communities, and contribute to the maintenance of plant diversity. However, several studies have shown that seed dispersal can be affected by defaunation, the contemporary pulse of animal population loss or decline driven by human activities, which primarily affects large- and medium-bodied animal species. Studies have found that, especially for large-seeded plants, the rate of seed removal and dispersal decreases in defaunated areas. As fruit size frequently varies within and among species, and as seeds can be selected by dispersers according to their body (gape) size, we hypothesized that large-bodied animals would selectively disperse large-seeded plant species and conspecific seeds with large sizes. We tested this hypothesis by looking at the association between the size of the disperser and the size of dispersed seeds, both at the interspecific and intraspecific levels, in the Pantanal ecosystem. Furthermore, for one plant species, we assessed the significance of seed size variation on germination, seedling performance and survival. Within plant species, our results show that, both at the interspecific and intraspecific levels, the diameter of the dispersed seeds increases with the size of the seed disperser. In addition, at the intraspecific level, seed germination increases with seed diameter and the combination of a large diameter and gut-passage increases seedling growth, in both controlled and field conditions. We conclude that large-bodied animals are not only important because they disperse large-seeded species, but also larger conspecific seeds, leading to increased germination.

60 Introduction Seed dispersal by frugivores is beneficial to both animals and plants (Herrera & Jordano 1981, Howe & Smallwood 1982, Herrera 1987). While the animals gain from ingesting fleshy fruits due to their nutritious content, the plants gain from being dispersed by animals mainly in four ways: the escape from the high density-dependent mortality in the vicinity of the mother plant (Janzen 1970, Connell 1971), the chance of reaching sites that are favorable for seed germination (Wenny & Levey 1997), the expansion of plant species local distributions (Howe & Smallwood 1982) and the increment in seed germination rate and speed after gut-passage (van der Pijl 1982, Traveset &Verdú 2002). Thus, seed dispersal can facilitate the regeneration of populations within natural communities and, ultimately, can contribute to the maintenance of plant diversity (Hubbell 1979, Clark et al. 1998, Connell & Green 2000, Ehrlén et al. 2006). The process of seed dispersal has been recently reported to be indirectly affected by defaunation, the contemporary pulse of animal population loss or decline (sensu Dirzo & Miranda 1991) driven by human activities such as hunting and land use change (Redford 1992, Peres 2000, Dirzo 2001, Corlett 2007, Wilkie et al. 2011). As a result of defaunation, several fleshy-fruited species may have their dispersal services reduced or lost due to the absence or low abundances of large- and medium-bodied species, those that are more vulnerable to the risk of extinction (Bodmer et al. 1997, Cardillo et al. 2005, Cardillo et al. 2006, Peres & Palacios 2007). Defaunation studies have found that, at least for large-seeded plants, the rate of seed removal and dispersal decreases (Silva & Tabarelli 2000, Wright 2003, Galetti et al. 2006, Cramer et al. 2007) and the number of seeds that remains beneath the mother plant increases in defaunated areas (Galetti et al. 2006, Forget & Jansen 2007, Holbrook & Loiselle 2009). The reason for those differences is primarily that the large-bodied seed dispersers are both especially vulnerable and extremely important in ingesting and dispersing species with large fruits and/or seeds (Janzen & Martin 1982, Wheelwright 1985, Chapman et al. 1992, Guimarães et al. 2008, Donatti et al. 2011), given the relation between gape and body size (Lord et al. 2002). Additionally, large-bodied seed dispersers can also increase the chances of seed dispersal because they spend more time foraging on plants (Gross-Camp et al. 2009), have larger seed loads per scat (Fragoso 1997, Galetti et

61 al. 2001, Bizerril et al. 2005) and disperse seeds further from the mother plant (Fragoso et al. 2003) than do small-bodied species. Thus, the absence or low abundance of large- bodied species is critical, especially for the dispersal of large-seeded plants, since for many of those plants large-bodied species provide seed dispersal services that no other remaining species is able to provide (Peres & Roosmalen 2002, Poulsen et al. 2002). Furthermore, as fruit and seed size usually vary within individual plants (Gorchov 1985, Wheelwright 1993, Mazer & Wheelwright 1993) and as large conspecific seeds can be selected by large dispersers or conspecific dispersers with larger sizes (Kaspari 1996, Rey et al. 1997, Alcántara et al. 2000), one can hypothesize that large-bodied animal species not only are effective dispersers of large-seeded species but also selectively disperse large conspecific seeds. In this study, we tested these relationships by examining the association between the size (proxied by body mass) of the seed disperser and the size of the dispersed seeds, both at the plant intraspecific and interspecific levels, in a plant- animal community in the Brazilian Pantanal. Furthermore, within a plant species, we assessed the importance of seed size on seed germination, seedling performance and survival. Here, we tested the following hypotheses: 1) at the plant interspecific level, large- bodied species more frequently interact with large-seeded plants, 2) at the plant intraspecific level, animal species with the largest body mass disperse seeds with the largest diameters, 3) seeds that experience gut-passage have an advantage in terms of germination and survival over seeds that do not experience gut-passage, 4) conspecific seeds with large diameters have an advantage in terms of germination and survival over seeds with small diameters, and 5) seedlings emerging from large seeds that experience gut-passage have an advantage in terms of growth and survival over seedlings emerging from small seeds that do not experience gut-passage. We show that, at the interspecific level, the diameter of the dispersed seed increases with the body mass of the seed disperser. Likewise, at the intraspecific level, the diameter of dispersed seeds of a sample of species increases with the body mass of the seed dispersers. Our results also show that, for one particular plant species, seed germination increases with seed diameter and the combination of a large diameter and gut-passage increases seedling growth, both in controlled and in field conditions. We thus conclude that large-bodied animals are not

62 only important because they disperse large-seeded species (Janzen & Martin 1982, Janson 1983, Wheelwright 1985, Chapman et al. 1992, Guimarães et al. 2008, Donatti et al. 2011), but also because they interact with these plant species more frequently and disperse larger conspecific seeds, leading to increased germination.

Methods

Study site The Pantanal, located in central western Brazil and part of Bolivia and Paraguay, is the world’s largest freshwater wetland, covering an area of 140,000 km2 (Swarts 2000). Due to the low human population density and low hunting pressure on native species (Alho & Lacher 1991, Desbiez et al. 2011, but see Harris et al. 2005), the Pantanal still holds the highest concentration of wildlife in the Neotropics (Swarts 2000, Mittermeier et al. 2005), which makes it easy to survey interactions between seed dispersers and fleshy- fruited plant species. Within the Pantanal, this study concentrated on the areas within two neighboring farms (Rio Negro and Barranco Alto). Rio Negro Farm (19°34'15"S 56°14'43"W) has 7,500 ha and Barranco Alto Farm (19º34'40"S 56º09'08"W) has 11,000 ha, and they are both located at the Nhecolândia region, one of the most pristine regions within the Pantanal.

Seed dispersal interactions Events of seed dispersal interaction were surveyed using three different methods. To sample seed dispersal by birds, we carried out focal observations in trees, recording the identity of bird species that visited the plants, as well as the number of fruits that were ingested “in situ” or removed outside the tree canopy area. To record seed dispersal by several species of mammals, rheas (Rhea americana) and red-footed tortoises (Geochelone carbonaria), we collected their scats, and identified, counted and measured, the intact seeds in them. To record seed dispersal by the pacu fish (Piaractus mesopotamicus), we caught them and identified, counted and measured the intact seeds in the intestine. We used our seed collection as a reference to identify seeds found in scats and intestines, and our data base on fruit morphology to assess the average seed diameter

63 of each plant species. Using a caliper we measured, to the nearest 0.01 mm, seeds collected directly from plant species and all intact seeds found in scats of the mammal, bird and reptile species, and in intestines of the fish species. In our analysis, we used seed diameter to represent seed size and the smallest measurement of seed size to represent seed diameter if the seed was rounded.

Diameter of the dispersed seeds - seed disperser body mass interspecific associations To compute the average diameter of all seeds (regardless of the species) dispersed by each animal, we first counted the number of seeds from each plant species that were dispersed by each animal species. Then, we multiplied the number of seeds of each plant species by its average seed diameter, and averaged them based on the total number of seeds dispersed by each animal species. Finally, we used the average diameter of all seeds dispersed by each animal species to test the association between the average diameter of the dispersed seeds and body mass of seed dispersers using a linear regression. We gathered animal body mass information from the literature.

Diameter of the dispersed seeds - seed disperser body mass intraspecific associations We compared the diameter of conspecific seeds of seven plant species found in the scats or intestine of various species, including three mammal species (Tapirus terrestris, 240 kg; Sus scrofa, 50 kg and Cerdocyon thous, 5.7 kg), one bird species (Rhea americana, 20 kg), one reptile species (Geochelone carbonaria, 6.6 kg) and one fish species (Piaractus mesopotamicus, 1.1 kg). We identified and measured all intact seeds found in scats of the mammal, bird and reptile species, and in intestines of the fish species. The plant species for which seeds could be found in the scats and/or in the intestines of two or more seed disperser species were the following: Dipteryx alata (Fabaceae, average seed diameter ± SE=32.41±0.21), Attalea phalerata (Arecaceae 22.48±0.21), Acrocomia aculeata (Arecaceae, 21.70±0.45), Copernicia alba (Arecaceae, 13.40±0.14), Bactris glaucescens (Arecaceae, 11.88±0.06), Byrsonima orbignyana (Nyctaginaceae, 6.32±0.1) and Vitex cymosa (Verbenaceae, 8.46±0.13). All those plant species produce single-seeded fruits and, therefore, fruits have to be swallowed whole for

64 seeds to be dispersed. As there are positive correlations between seed diameter and fruit diameter in all these plant species (Dipteryx alata: F=36.44, p<0.0001, R2=0.4587, n=38; Attalea phalerata: F=193.01, p<0.0001, R2=0.71, n=75; Acrocomia aculeata: F=39.29, p<0.0001, R2=0.81, n=21; Copernicia alba: F=129.9, p<0.0001, R2=0.73, n=50; Bactris glaucescens: F=19.28, p<0.0001, R2=0.19, n=81; Byrsonima orbignyana: F=33.19, p<0.0001, R2=0.43, n=45 and Vitex cymosa: F=36.75, p<0.0001, R2=0.52, n=35;), by assessing seed diameter we are indirectly assessing fruit diameter.

Effect of seed size and gut-passage on seed germination of Dipteryx alata For one of these plant species, D. alata, we tested the effect of seed diameter, the presence of pulp and the gut-passage on the percentage and speed of seed germination. We planted 50 whole fruits (including seed and pulp) collected directly from trees (estimated range in seed diameter: 27.52-37.85 mm), 100 seeds collected directly from trees that had the pulp manually removed (range in seed diameter: 25.42-39.56 mm) and 96 seeds collected from tapir scats that had the pulp removed as they passed through the animal gut (range in seed diameter: 24.16-34.78 mm). All fruits and seeds were planted with 1cm-depth from the soil surface, in black plastic bags. Before planting, all seeds were subjected to a flotation test, following Vallejo et al. (2006) whereby seeds were submerged in a mixture of water and detergent to discard the ones that floated and would probably be empty or damaged and not able to germinate. All planted seeds and fruits were kept at the same weather conditions and we watered them every three days. We checked all planted seeds daily to record seed germination speed. We compared seed germination (number of seeds that germinated) among treatments (seeds with pulp, seeds without pulp and seeds dispersed by tapirs) using a chi-square test, and seed germination speed (number of days from plantation to germination) among the three treatments using ANOVA. To test the effect of gut-passage (seeds dispersed by tapir vs. control) on seed germination, we pulled together seeds that were planted with pulp and seeds that had the pulp manually removed, considering all of them as the control treatment. We then tested the association between seed diameter and seed germination, for all treatments together and for the tapir and the control treatments

65 independently, using logistic regressions. Likewise, we tested the effect of seed diameter on germination speed, also for all treatments together and for the tapir and the control treatments independently, using linear regressions.

Effect of seed size and gut passage on seedling survival and growth rates of D. alata in controlled conditions After germination of D. alata, we monthly recorded the height, basal diameter, leaf area and the number of leaflets of each seedling. Height was recorded from the collet (the external demarcation between the stem and the root) to the petiole of the youngest leaf. The basal diameter was recorded on the collet. Since there was a positive and significant correlation between leaf area and the sum of the product of leaflet width and leaflet length (Pearson's r=0.93, p<0.0001, n=50), we recorded, once a month, the diameter and the length of every leaflet and used the sum of the product of diameter and length of each leaflet as a proxy for seedling leaf area. As not all seeds germinated on the same day, we used seedling length in mm/day, basal diameter in mm/day, leaf area in mm2/day and number of leaflets/day to assess seedling growth. We then tested the associations between seed diameter and each of the variables that assessed seedling growth, for all treatments together and independently for the tapir and the control treatments, using linear regressions. We also used another approach to address differences in seedling growth as a function of seed size. We defined small seeds as those with diameter <29 mm and large seeds those with diameter > 31mm. We binned seeds using those values to get about the same number of seeds in each category and in each treatment. Using t-tests, we then compared growth rate variables in seedlings emerging from small and large seeds using both all data together (i.e. pooling tapir and control treatments) and data from the tapir and the control treatments independently. To test the effect of the treatment, we used t-tests to compare the seedling growth variables between seedlings emerging from seeds from the tapir and the control treatments. We also tested the association between seed diameter and the survival of seedlings, regardless of the treatment, using logistic regressions calculated for every month from seed germination to seedling transplantation to the field. Similarly, we tested

66 the association between seed diameter and the survival of seedlings, independently for the tapir and the control treatments, using logistic regressions calculated for every month from seed germination to seedling transplantation to the field. To compare seedling survival between tapir and control treatments every month before transplantation, we used chi-square tests.

Effect of seed size and gut passage on seedling survival and growth rates of D. alata in field conditions Since there are differences in seedling growth between controlled and field conditions (see Marshall 1986), we also measured seedling growth in the field. Approximately six months after seeds were planted, we transplanted seedlings to the field and followed their fate and growth in natural conditions. We planted all seedlings in the field, from both control and tapir treatments, around one single adult of D. alata. After transplanting seedlings to the field, we recorded seedling survival and measured height, basal diameter, leaf area and number of leaflets for every seedling on a monthly-basis, for 12 months after transplantation. We then tested the associations between seed diameter and both seedling growth (measured as seedling length in mm/day, basal diameter in mm/day, leaf area in mm2/day and number of leaflets/day) for all seedlings together and independently for the tapir and control treatments, using linear regressions. Additionally, we compared seedling growth between large and small seeds (as previously defined), combining all data together (i.e. pooling tapir and control treatments) and considering data for the tapir and control treatments independently, using t-tests. Associations between seed diameter and the survival of seedlings were assessed through logistic regressions, calculated for every month from seedling transplantation to the field to the end of the experiment. Logistic regressions were calculated for all data together and for the tapir and the control treatments independently. We compared seedling survival between tapir and control treatments every month after transplantation, using chi-square tests.

67 Results

Diameter of the dispersed seeds - seed disperser body mass interspecific associations We recorded interactions between 34 seed disperser species (Table 1) and 40 plant species (Table 2). We found a positive and significant association between the average diameter of the dispersed seeds and the body mass of seed dispersers (F=64.09, p<0.0001, r=0.81, d.f.=33, Fig. 1). Thus, animal species with large body mass disperse seeds with large diameters more often than do small-bodied species.

Diameter of the dispersed seeds - seed disperser body mass intraspecific associations We measured 7,983 seeds of seven plant species that could be identified in the scats of two or more seed disperser species. Three of these plant species showed a positive association between body mass of the seed disperser and the diameter of the dispersed seeds, i.e. large-bodied species disperse large conspecific seeds. We found that, for these three plant species, the largest animal species were able to disperse seeds with the largest diameters (Dipteryx alata: F=50.82, p<0.0001; Copernicia alba: F=44.22, p<0.0001; Vitex cymosa: F=10.27, p<0.0001; Fig. 2). Three other plant species showed a difference in the diameter of seeds dispersed by the different animal species. However this difference was not associated with the body mass of the seed dispersers (Attalea phalerata: F=51.29, p<0.001; Acrocomia aculeata: F=4.9087, p=0.0022, Byrsonima orbignyana: F=30.60, p<0.0001), i.e. large-bodied seed dispersers did not necessarily disperse large conspecific seeds. One plant species did not show a significant difference in the diameter of the seeds dispersed by different animal species (Bactris glaucescens: F=8.98, p=0.0001).

Effect of seed size and gut passage on seed germination of D. alata There was a difference in seed germination among the three treatments (X2=10.985, p=0.041, d.f.=229). Germination in seeds collected in tapir scats was 33.75%, compared to 20% in seeds that had the pulp manually removed and to 10% in seeds that were planted with pulp. Therefore, pulp removal increases seed germination,

68 but this is even higher in seeds that passed through the animal’s digestive tract. Seeds dispersed by tapirs germinated faster than those that had the pulp manually removed and those that were planted with pulp (F=5.81, p=0.005, n=52; 41.8 days, 49 days and 58 days, respectively). Thus, seeds dispersed by tapirs, those that combine pulp removal and gut passage, showed the highest percentage of germination and germination speed among the three treatments. Only for seeds dispersed by tapirs was there a negative and significant effect of seed diameter on seed germination speed (F=4.33, p=0.047, r= 0.38, n=27, Fig. 3). That is, the number of days necessary for germination decreases with the diameter of the seed. There was no such effect for seeds in the control treatment. When pooling all seeds together, germination increased with the diameter of seeds (X2=3.79, p=0.049, n=230) but there was no effect when considering tapir and control treatments independently (X2=0.05, p=0.82, n=80 and X2=1.21, p=0.27, n=150; respectively).

Effect of seed size and gut passage on seedling survival and growth rates of D. alata in controlled conditions About six months after seed plantation and only for seeds dispersed by tapirs, seedling growth measured as seedling basal diameter in mm/day increased with seed diameter (F=4.72, p=0.042, r=0.46, n=19, Fig. 4). Likewise, large seeds dispersed by tapirs generated seedlings that showed higher growth (basal diameter in mm/day) than seedlings generated by small seeds that were also dispersed by tapirs (t-test=2.86, p=0.0124, n=16). However, the opposite trend was found for seedlings generated by seeds in the control treatment. Seedlings generated from large seeds showed a lower growth (basal diameter in mm/day) than seedlings generated by small seeds in the control treatment (t-test=2.54, p=0.027, n=13). When analyzing both treatments together, seedling survival did not increase with seed diameter in any month before seedling transplantation to the field. However, in two time-points (two and five months after seed plantation), survival in seedlings generated by seeds dispersed by tapir was higher than those generated by seeds from the control treatment (X2=3.44, p=0.046, n=180 and X2=4.06, p=0.033, n=180; respectively).

69 Effect of seed size and gut passage on seedling survival and growth rates of D. alata in field conditions Three months after transplantation, the diameter of the seeds dispersed by tapirs showed a positive effect on seedling growth (basal diameter in mm/day) (F=4.93, p=0.0412, r=0.48, n=18). In addition, growth (basal diameter in mm/day) in seedlings generated from large seeds that were dispersed by tapirs was higher than the growth in seedlings generated by small seeds that were also dispersed by tapirs (t-test=2.192, p=0.04, n=14). Six months after seedling transplantation, the diameter of the seeds dispersed by tapirs still showed a positive effect on seedling growth (in terms of basal diameter/day) (F=4.47, p=0.05, r=0.49, n=16, Fig. 4). The effect of seed diameter on seedling growth among seeds dispersed by tapir continued to occur twelve months after we transplanted those seedlings to the field (F=11.56, p=0.042, r=0.88, n=5). However, there was a negative effect of seed diameter on the growth (basal diameter in mm/day) of seedlings generated from seeds in the control treatment (F=7.39, p=0.0216, r=0.65, n=7). Although all variables measured in seedlings were correlated, we could not find an effect of seed size and/or gut passage in any other variables. Likewise, there were no significant differences in seedling growth between the tapir and the control treatments. Approximately nine months after seedling transplantation to the field, there was a positive and significant association between seed diameter and the survival of seedlings generated by those seeds (X2=4.85, p=0.0276, n=40) when considering all seedlings regardless of the treatment. Likewise, eight, nine and ten months after seedling transplantation to the field, seedling survival increased with seed diameter, but only if we consider seedlings dispersed by tapirs (X2=3.93, p=0.047, n=21; X2=4.53, p=0.033, n=21, X2=4.53, p=0.033, n=21, respectively). Twelve months after seedling transplantation to the field, 26.25% of the seedlings generated from seeds dispersed by the tapir were alive, followed by 17% of the seedlings generated from seeds planted without pulp and by 6% of the seedlings from seeds planted with pulp (F=4.45, p=0.0126, n=230). However, when considering only seedlings that were transplanted to the field, there was no difference in seedling survival between tapir and control treatments in any month until the end of the experiment. Likewise, there was no effect of seed diameter on seedling survival at the end of the experiment, i.e. twelve months after seedling transplantation.

70 Discussion Our data show that, at both the interspecific and intraspecific levels, animal species disperse seeds non-randomly with respect to seed diameter. At the plant interspecific level, there is a positive and significant association between the body size of seed dispersers and the diameter of the dispersed seeds, that is, large-bodied species tend to interact more often with large-seeded species. Likewise, at the plant intraspecific level, there is also a positive and significant association between the body size of seed dispersers and the diameter of the dispersed seeds, that is, the largest animal species tend to interact more often with the largest seeds of a given plant species. Although previous studies have shown a positive association between the body mass of dispersers and the average seed diameter of plant species dispersed by those animals (see Donatti et al. 2011), and that seeds dispersed by birds and mammals can be different than those collected directly from trees (Howe &Vander Kerckhove 1980, Wheelwright 1993, Wheelwright 1985, Rey et al. 1997, Stevenson et al. 2005, Côrtes et al. 2009, Traba et al. 2006), to our knowledge this is first study that reports the association between the animal body mass and the diameter of the dispersed seeds within plant species (but see Kaspari 1996 for a similar study with ants). In our study, as all seed dispersers that interacted with a given plant species could potentially disperse seeds of all sizes, differences in the diameter of conspecific seeds dispersed by the different animal species cannot be explained by morphological mismatching. Instead, our results show that seed disperser species may be competing for conspecific fruits. Therefore, we suggest that, at least for some plant species, different animal species are competing, with the result that they select conspecific seeds of particular diameters, which correspond to their body mass. However, this association between the diameter of the dispersed seeds and the body mass of the seed disperses was not diagnosed for all seven plant species studied here. In this particular community, competition may occur only in plant species that produce fruits outside the peak of fruit availability in the comunity. Dipteryx alata does not produce fruits at the peak of the fruiting season, Copernicia alba produces fruits at the end of the fruiting season and Vitex cymosa at the beginning of the fruiting season, three periods when fruits are scarce in this community (C.I. Donatti, unpl. data).

71 Although we have measured thousands of seeds recovered from scats, we acknowledge the possibility that many of them may belong to a single mother plant but were taken as independent sample points in our analyses. Given that some of the large- bodied species show long retention times in the gut and/or disperse a large number of seeds per scats, it is difficult to know how frequently this occurred. However, because we collected hundreds of scats from each of the animal species, we assume that seeds we measured were collected from several adult plants and, therefore, are a good representation of the seed size that different animal species can disperse. We experimentally assessed the combined effects of seed size and gut passage on seed germination in controlled conditions and seedling growth and survival in both controlled and in field conditions. Large seeds show a higher germination rate than small seeds probably because they have more food reserves, which stimulate germination (Howe & Richter 1982, Dunlop & Barnett 1983, Tripathi & Kahn 1990). This positive effect of seed size on seed germination rate was also found in other studies (Pizo et al. 2006, Venkatesh & Nagarajaiah 2010, Seemi & Shaukat 2010, Mut et al. 2010). Our results also showed that gut passage increases seed germination. The high germination rate in seeds dispersed by tapirs likely resulted from the removal of pulp and both mechanical and chemical abrasion during consumption and transit through the gut (Barnea et al. 1991, Traveset &Verdú, 2002). Furthermore, our study showed that a combination of a large size with gut passage enables seeds to germinate faster. Rapid germination has been associated with enhanced seedling survival, due to lower sibling competition (Hyatt & Evan 1998) and, therefore, a higher availability of resources, especially light (Ross & Harper 1972, Seiwa 1998) and nutrients (Ross & Harper 1972), as well as reduced incidence of pathogens and seed predators (Schupp 1993, Seiwa 1998). In addition, early rooting can increase the ability of seedlings to withstand water stress (Seiwa 2000), which in the Pantanal coincides with the period of fruit production of Dipteyx alata. A positive relationship between seed size and seedling growth has also been reported for other plant species (Weis 1982, Stanton 1984, Weller 1985). By aggregating more reserve tissues, large seeds can also improve the growth (Venkatesh & Nagarajaiah 2010, Nik et al. 2011, Muhammad et al. 2011) and vigor of seedlings (Pizo et al. 2006,

72 Howe & Richter 1982, Silva e Silva & Carvalho 2008), turning them into stronger survivors and competitors with respect to other seedlings under distinct environmental adversities (Howe & Richter 1982, Leishman et al. 2000, Alcántara & Rey 2003, Pizo et al. 2006). Therefore, a combination of large size and faster germination may enable seeds and seedlings to maximize their ability to utilize environmental resources (Seiwa 2000). Here, we assessed the importance of body size of animal species in the process of seed dispersal at both interspecific and intraspecific levels, and discussed about the effects that animal extinctions may have on the early stages of plant development and, ultimately, on plant populations. The combination of a large diameter and the gut passage seems to be advantageous to the seed, in terms of seed germination rate and speed, as well as seedling growth. Therefore, if seed dispersal is a process that increases plant fitness and given that seed size is a heritable trait (Leishman et al. 1995), an evolutionary change should be expected with the absence of the largest seed dispersers from the community. For example, if seedlings generated from large seeds that also experienced gut passage have a high survivorship, and if individual trees are consistently producing seeds of similar diameters in consecutive years, we expect individual plants that produce large-sized seeds to be negatively affected if the largest seed dispersers are locally extinct. We conclude that large-bodied animals are not only important because they disperse large-seeded species, but also larger conspecific seeds, leading to increased germination. Since contemporary defaunation differentially affects animal species depending on body size, this work illustrates how human activities, such as hunting, deforestation and climate change, my affect not only taxa, but also crucial processes in which animals of different body size play different roles.

Acknowledgments We would like to thank Lee Love-Anderegg, Adilson Braga Samuel and Luisa Haddad for their help with the experiment. We thank FAPESP (2004/00810-3 and 2008/10154-7), Earthwatch Institute and Conservation International for financial support. CID was supported by Stanford University and the Zaffaroni Fellowship Fund, and MG and MAP by a CNPq fellowship. We also thank Conservation International, Lucas Leuzinger and Marina Schweizer for their permission to work in their properties.

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78 Tables

Table 1. Seed disperser species, their body masses and the average diameter of the seeds dispersed by them in a community in the Brazilian Pantanal.

Mean diameter Seed disperser Body mass (kg) of dispersed seeds (mm) Aburria pipile 1 5.18 Alouatta caraya 5.4 7.25 Turdus sp. 0.05 4.98 Casiornis rufa 0.02 3.3 Columba sp. 0.14 4.79 Crypturellus sp. 0.08 4.82 Cyanocorax chrysops 0.14 1.73 Cyanocorax cyanomelas 0.2 5.48 Geochelone carbonaria 6.6 5.07 Guira guira 0.07 6.83 Icterus croconotus 0.07 4.36 Myiarchus ferox 0.024 3.6 Myiodinastes maculatus 0.043 4.82 Nasua nasua 5.1 9.12 Ortalis canicollis 0.6 4.08 Piaractus mesopotamicus 1.14 9.02 Pitangus sulphuratus 0.068 6.28 Paroaria coronata 0.03 4.36 Psarocolius decumanus 0.25 4.9 Pteroglossus castanotis 0.27 7.64 Ramphastos toco 0.54 8.71 Ramphocelus carbo 0.025 2.68 Rhea americana 20 12.97 Saltator coerulescens 0.052 5.67 Sus scrofa 50 10.23 Tachyphonus rufus 0.033 6.01 Tapirus terrestris 240 17.9 Tayassu pecari 35 9.57 Thraupis palmarum 0.036 5.11 Thraupis sayaca 0.03 3.77 Trogon curucui 0.07 4.69 Turdus rufiventris 0.07 4.06 Tyrannus melancholicus 0.039 4.36 Tityra cayana 0.07 4.82

79 Table 2. Plant species, and their average seed diameter, recorded interacting with seed dispersers in the Brazilian Pantanal.

Plant species Mean seed diameter (mm) Acrocomia aculeata 21.34 Attalea phalerata 22.08 Agonandra brasiliensis 14.63 Alibertia sessilis 5.08 Annona cornifolia 4.66 Annona dioica 8.27 Bactris glaucescens 8.53 Byrsonima obrgnyana 6.06 Copernicia alba 14.16 Curatella americana 5.00 Dipteryx alata 35.73 Doliocarpus dentatus 4.37 Diospyros hispida 12.93 Genipa americana 6.01 Guazuma ulmifolia 1.78 Hancornia speciosa 10.87 Inga laurina 9.92 Syzydium cumini 9.96 Licania parvifolia 8.33 Melicoccus lepidopetalus 11.20 Mouriri elliptica 13.27 Ocotea diospyrifolia 4.82 Phoradendron 0.50 Pouteria gardneri 7.21 Pouteria ramiflora 15.05 Protium heptaphyllum 9.54 Psidium nutans 3.41 Psittacanthus cordatus 8.50 Psittacanthus calyculatus 8.50 Rhamnidium elaeocarpum 4.83 Garcinia brasiliensis 10.12 Sideroxyllum obstusifolium 8.62 Sterculia apetala 14.35 Swartzia jorori 10.67 Syagrus flexuosa 13.70 Tocoyena formosa 6.44 Vitex cymosa 8.97 Zanthoxyllum rigidum 3.30

80 Figures

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Figure 1. Relationship between the diameter of dispersed seeds (regardless of plant species identity) and body mass of the seed disperser. Body mass is in log scale.

Figure 2. Diameter (average±SE) of the seeds dispersed by different animal species, from the heaviest to the lightest. Top) Dipteryx alata, n=324, 627 and 109 seeds measured, respectively. Center) Copernicia alba, n=168, 26 and 674 seeds measured, respectively. Bottom) Vitex cymosa, n=381, 842, 167 and 64 seeds measured, respectively.

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Figure 3. Number of days from plantation to germination as a function of the diameter of seeds dispersed by tapirs (n=27), in controlled conditions.

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Figure 4. Seedling growth (in mm of basal diameter/day) as a function of the diameter (in mm) of seeds dispersed by tapirs, in controlled conditions (top) and in field conditions (bottom).

84 CHAPTER 4

THE ROLE OF PLANT AND ANIMAL TRAITS IN DETERMINING NUMBER AND STRENGTH OF INTERACTIONS ACROSS SPECIES IN A SEED DISPERSAL NETWORK Camila I. Donatti, Mauro Galetti, Marco Aurélio Pizo & Rodolfo Dirzo

Abstract Recent studies of mutualistic networks have uncovered emergent properties of species interactions, such as the nested pattern and the interaction asymmetry between species. Even though those properties of mutualistic interactions have been validated in several studies, the importance of evolutionary history and species traits in determining these properties across species in networks has seldom been investigated at the community level. Here we address the extent to which evolutionary history and/or species traits account for the variation in number and strength of interactions across species in a plant- animal interaction network. We studied a seed dispersal network that includes a broad range of animals from different taxonomic groups and plants from different families. Our results show that phylogenetic history of both plant and animal species do not explain the variation in their properties in the network, which are mainly explained by species morphological traits. We found that plant species with small fruits and/or seeds have many interactions and are more important to the animal species set, whereas animal species with large body mass and/or high densities have many interactions and are more important to the plant species set. Therefore, we suggest that species-specific morphological traits of plants and animal species should be considered in studies aimed at evaluating the ecological and evolutionary consequences of plant-animal interaction networks.

85 Introduction Plant–animal mutualistic networks describe not only the trophic relationships among species (Jordano 1987), but also the complexities of the interactions that drive coevolutionary processes (Thompson 1999, Jordano et al. 2003, Montoya et al. 2006). Such interactions among species at the community level can be simplified using the network approach (Guimera & Amaral 2005, Olesen et al., 2007, Clauset et al. 2008, Rooney et al. 2008), in which the architecture of the system is reduced to elements (interacting species) and the interactions between such elements are represented by links (Ben-Naim et al. 2004). Recent studies of mutualistic networks have uncovered some general properties of the pattern of species interactions, such as the prevalence of the nested pattern (Bascompte et al. 2003, Jordano et al. 2003, Guimarães et al. 2006, Vázquez et al. 2009, Fortuna et al. 2010, Joppa et al. 2010), in which interactions of the specialist species tend to be a subset of the interactions observed among the generalists. In addition to being nested, some mutualistic networks are also modular (Dicks et al. 2002 and Olesen et al. 2007: pollination networks; Donatti et al. 2011 and Mello et al. 2011: seed dispersal networks), with subsets of species (modules) more frequently interacting with each other than with species in other modules (Olesen et al. 2007). Given that species do not interact with the same intensity and all interactions are therefore not equally important (Vázquez et al. 2005), studies that incorporate a measurement of interaction strength have found that interactions between mutualistic partners are typically asymmetric (Bascompte et al. 2003, Vázquez & Aizen 2004, Stang et al. 2006, Stang et al. 2007, Vázquez et al. 2007). That is, whereas a focal species strongly relies on a partner species, this partner weakly relies on the focal species. Nevertheless, even though those characteristics of mutualistic interactions have been identified in several studies, the importance of evolutionary history and species-specific traits in determining the properties of species in networks, especially quantitative ones such as species strength (i.e. how important a species is to its set of partners), has seldom been investigated at the community level (Waser et al. 1996, Thompson 2005, Vázquez 2005, Jordano et al. 2006, Stang et al. 2006, Vázquez et al. 2007). This is likely due to missing evolutionary and/or species-specific trait (e.g., morphological) information for the majority or for the whole studied community (Olesen & Jordano 2002, Vázquez

86 2005) and to the difficulties attendant on collecting quantitative information about species interactions in large assemblages of species (Berlow et al. 2004). A recent study by Rezende and collaborators (2007), analyzing both seed dispersal and pollination networks, found a phylogenetic signal in the degree of species in networks (i.e., number of interactions that a given species has) for a third of the analyzed networks, but not in the species strength (i.e., how important a species is to the set of partners). Thus, for some networks, phylogenetically related species tend to have a similar number of seed dispersers or pollinators. However, phylogeny is often not sufficient to explain the variation in species strength, which may be due to variables that respond to more local ecological factors, such as the density of species (Bascompte & Jordano 2007). Additionally, other studies have suggested that, in the absence of an evolutionary explanation, two main processes may determine some of the properties of species in networks: interaction neutrality and trait matching. Under interaction neutrality frameworks, all individuals have the same probability of interaction regardless of their taxonomic identity (Dupont et al. 2003, Ollerton et al. 2003, Vázquez 2005, Vázquez et al. 2007, Krishna et al. 2008). That is, common species would interact more frequently with common species than with rare species. Therefore, the abundance of species is predicted to primarily determine the properties of species in networks. In contrast, trait matching depends primarily on the identities of species in the community and their traits, with species interacting either in a complementary fashion (e.g., more nutritious fruits are preferred by frugivores) or as a barrier (e.g., plant species with large fruits exclude seed dispersers with small gape) (Jordano et al. 2003, Rezende et al. 2007, Santamaría & Rodríguez-Gironés 2007, Stang et al. 2007). Therefore, species-specific traits such as fruit size and nutritious content in the fruit pulp are predicted to primarily determine such properties of species in seed dispersal networks. Here we address the extent to which evolutionary history and/or species-specific traits account for the properties of species in a seed dispersal network that includes a broad range of animals from different taxonomic groups and plants from different families. To do so, we asked: i) if the properties of species in the network (i.e. species degree, species strength, the value of maximum dependence of a focal species on its most important partner and interaction asymmetry between species) have a phylogenetic

87 signal, and ii) which processes (interaction neutrality or trait matching) most likely determine the variation in these properties across species in this seed dispersal network. The analysis of both morphological constraints and density of species can offer explanatory power to address the magnitude of the relationship between species traits and properties in the network. In addition, the analysis of properties of both animals and plants is also not common in such associations. This study adds a novel approach by uncovering important information on the properties of species, both animals and plants, in a highly diverse seed dispersal network. As interactions between fleshy-fruited plants and seed dispersers are not random in this network (which in fact is highly nested and modular; Donatti et al. 2011), we expect trait matching to be more important than interaction neutrality in explaining the properties of plants and animals in this network. We found that species traits per se, rather than evolutionary history, explain the properties of species in this network. Furthermore, we found that morphological traits (seed or fruit size and the body mass of seed dispersers) explain the highest variation in the majority of quantitative and qualitative properties of species. Therefore, we suggest that such species-specific traits of species should be considered in studies that evaluate the ecological and evolutionary consequences of plant-animal interaction networks.

Methods

Properties of species in the seed dispersal network Seed dispersal interactions were recorded using four methods. To sample seed dispersal by birds, we carried out focal observations in fruiting trees and palm trees, recording identity of birds that were unequivocally observed carrying fruits outside the canopy area or swallowed them in situ. Seed dispersal by red-footed tortoises (Geochelone carbonaria), rheas (Rhea americana) and the majority of mammal species, were recorded with camera traps located beneath fruiting trees to capture events of fruit ingestion. Some terrestrial and semi-terrestrial bird species were also recorded via pictures taken with camera traps. We analyzed scats of several species of mammals, rheas and red-footed tortoises, and identified the intact seeds in them. To record seed dispersal by the pacu fish (Piaractus mesopotamicus), we caught individuals and identified the

88 intact seeds in their intestine. We were able to record interactions between 45 plant species (Table 1) and 46 animal species (Table 2). We carried out focal observations of 20 plant species for 984 hours, and we recorded frugivore activity (via camera traps located beneath fruiting trees of 27 plant species) for a total of 27,800 hours. We analyzed 1,030 scats of several species of mammals, rheas and red-footed tortoises, and identified the intact seeds in them. Seed dispersal by the pacu fish (Piaractus mesopotamicus), was determined from 80 caught individuals, from which we identified and counted the intact seeds in their intestine (see Galetti et al. 2008). Using this data set on species interactions we assembled a qualitative seed dispersal network, in which an element representing a seed dispersal interaction received the value of one, and zero otherwise. Using the same data set, we also assembled a quantitative seed dispersal network, in which we included how many times each interaction between a plant and an animal species was recorded. One event of seed dispersal was considered as such when either: i) fruits were recorded to have been swallowed or removed from a plant species during focal observations; ii) fruit removal of a particular species by a potential seed disperser was detected with camera traps; iii) a scat pile was found to have at least one intact seed of a particular species in it; or iv) a sampled fish intestine contained at least one intact seed from a particular species. We then used the quantitative and qualitative data to assess the properties of species in the network. We used the qualitative network to assess the number of seed dispersal interactions recorded for every plant and animal species, hereafter “species degree” and the quantitative seed dispersal network to calculate the other three species properties: the importance of a species to its partners, hereafter “species strength”, the dependence of a species on its most important partner, hereafter “maximum dependence” and the asymmetry in the interaction strength between species, hereafter “interaction asymmetry”. All four properties of species were calculated for all plant and animal species in the network. The value of importance of a focal species to a partner species (interaction strength) is calculated as the number of seed dispersal events recorded between those two species, divided by the sum of the seed dispersal events recorded for the partner. The

89 species strength of the focal species is then calculated as the sum of all values of interaction strength of this focal species (Bascompte et al. 2006). The value of dependence of a focal species to a partner species is calculated as the number of seed dispersal events recorded between those two species divided by the sum of the seed dispersal events recorded for the focal species. Thus, the value of maximum dependence of the focal species is the maximum value calculated for this focal species. Following Vázquez et al. (2007), we deﬁned interaction asymmetry as the relative difference between the interaction strength exerted by a focal species on the partner species and the interaction strength exerted by the partner species on the focal species. Therefore:

J − ∑ (sij s ji) Interaction asymmetry of species i = j=1 Ni where Ni is the degree of species i, sij is the interaction strength of species i on species j and sji is the interaction €strength of species j on species i.

Plant and animal species traits For each plant species, we measured fruit and seed diameter in at least 30 fruits and seeds from five different individuals. We also collected the pulp of at least 15 fruits per plant species and measured the milligram of protein, carbohydrates, and lipids in one gram of dry fruit, later combined as an “energy content of fruit” (kcal/gram of dry pulp), which refers to the amount of energy that frugivores can obtain from a gram of dry fruit. In addition, we sampled the density of adults of each plant species in the habitat type in which it predominantly occurred in a previous 4-year phenological study (C.I. Donatti, unpubl. data). We surveyed species that mainly occur in the semi-deciduous forest, in the savannah and in the gallery forest, in 1-ha plots set up in each one of those habitat types. In addition, we counted the number of fruits produced by at least five individuals of each plant species and assessed the number of months with mature fruits of every plant species based on our previous phenological study (C.I. Donatti, unpubl. data). We then combined the density of a given plant species with its fecundity and its phenology (C.I. Donatti, unpubl. data) into a “fruit availability index” (density of a plant species/ha X average number of months with mature fruits/year X average number of fruits

90 produced/individual plant). For each animal species, we gathered information on the animal body mass from the literature and assessed the density of mammals and large- bodied bird species in a previous census in the area (C.I. Donatti, unpubl. data). We then tested the associations betwee those independent variables and the properties of plant and animal species in the network. For the plant species, we used a regression tree model from the R package tree (http://www.r-project.org/) to select the three most important variables that were later used in a linear regression model. Variables were Box-Cox transformed using JMP v 5.0 (SAS Institute Inc.) to ensure that the assumptions of the linear model were met. For the animal species, we just used the linear regression model, as we had only two variables (body mass and animal species density). Body mass was log transformed and species density was Box-Cox transformed to ensure that the assumptions of the linear regression model were met.

Phylogenetic signal in traits and properties of animal and plant species in the network We tested whether traits and properties of animal and plant species in the network had a significant phylogenetic signal, i.e. a quantitative measure of the degree to which phylogeny predicts the similarity of species regarding their traits and properties in the network. To build the animal phylogenetic tree, we followed Bininda-Emonds et al. (2007) for relationships among mammal species and Hackett et al. (2008) for relationships among bird species. In addition, we used published work to resolve relationships within Cracidae (Pereira et al. 2002), Tyrannidae (Tello et al. 2009), and Thraupidae (Klicka et al. 2007). We also used two mitochondrial DNA sequences (Cytochrome b and Cytochrome oxidase subunit 1), available on GenBank, to resolve the relationships between and within the Thraupidae and Icteridae. We then generated a phylogenetic tree for those sequences using the program Méthodes et algorithmes pour la bio-informatique (http://www.phylogeny.fr/) and added those relationships in the tree. The plant phylogenetic tree was built using Phylomatic software (http://www.phylodiversity.net/phylomatic/phylomatic.html). Relationships within Fabaceae followed Wojciechowski et al. (2004), within Rubiaceae followed Bremer & Eriksson (2009) and within Arecaceae followed Asmussen et al. (2006). Since all branches in animal and plant trees were set equal to one, we conducted simulations that

91 showed that using branch lengths equal to one is a conservative approach when values of K are lower than or equal to one (see Donatti et al. 2011). We assessed the K statistic to measure the phylogenetic signal in traits and properties of animal and plant species in the network, using the function phylosignal in the picante package (Kembel et al. 2010) of R. The K statistic compares the observed signal in a trait to the signal under a Brownian motion model of trait evolution on a phylogeny (Blomberg et al. 2003). The statistical significance of phylogenetic signal is evaluated by comparing observed patterns of the variance of independent contrasts of a trait to a null model of shuffling taxa labels across the tips of the phylogenetic tree. To assess phylogenetic signal in the body mass and in the properties of animal species, we analyzed them for mammal and bird species independently. However, the phylogenetic signal in animal species density was calculated for mammal and bird species combined.

Results

Phylogenetic signal in traits and properties of animal and plant species in the network Plant species traits, fruit diameter, the availability of fruits and energy content were found not to show a significant phylogenetic signal (K=0.372, p=0.248, d.f.=44, K=0.2796, p=0.518, d.f.=40; K=0.3037, p=0.177, d.f.=41, respectively), and seed diameter is more divergent than expected under a Brownian model (K=0.417, p=0.003, d.f.=43). We did not detect phylogenetic signal in the values of species degree, species strength, maximum dependence and interaction asymmetry in plant species (K=0.2919, p=0.323; K=0.3109, p=0.146; K=0.3534, p=0.386; K=0.3384, p=0.166; d.f.= 44; respectively). The body mass of closely related mammal species has exactly the amount of signal predicted by Brownian motion (K=1.01, p=0.005, d.f.=12). In contrast, the body mass of birds is more divergent than expected under a Brownian model (birds: K=0.778, p=0.001, d.f.=31). The density of mammals and birds does not have a phylogenetic signal (K=0.4936, p=0.364, d.f=14). Likewise, we did not detect phylogenetic signal in the values of species degree, species strength, maximum dependence and interaction asymmetry in bird species (K=0.2654, p=0.191; K=0.2151, p=0.367; K=0.1958, p=0.367;

92 K=0.2067, p=0.323; d.f.=31, respectively) nor mammal species (K=0.5215, p=0.631, K=0.66, p=0.257, K=0.439, p=0.917; K=0.6617, p=0.365; d.f.=12; respectively). Therefore, shared evolutionary history does not explain the variation in traits nor in the four properties across species in this network.

Observed distribution in the properties of species in the network The frequency distributions of species degree, species strength and interaction asymmetry for both plant and animal species were right-skewed and were statistically distinguishable from a normal distribution (Shapiro-Wilk W=0.93, p=0.0136, W=0.74, p<0.0001, W=0.81, p<0.0001, n=45, respectively for the plant species; W=0.8, p<0.0003, W=0.73, p<0.0001, W=0.768, p<0.0001, n=46, respectively for the animal species). Therefore, for both plant and animal species sets, many species have few interactions, many species have low importance to the set of partners, and the majority of species is as important to a partner as the partner is important to them. In addition, although the majority of values of interaction asymmetry is weak for both plant and the animal species sets, animal species show high positive values more frequently than plant species, indicating that consumers (animal species) are more important to the set of hosts (plant species) than vice-versa. The frequency distributions of the value of maximum dependence of both plant and animal species were not skewed but could be still distinguished statistically from a normal distribution (W=0.92, p=0.009, n=44; W=0.89, p=0.0003, n=46, respectively). For the plant species, the distribution was relatively symmetrical, meaning that plant species show a wide variation in this property and values are relatively similar across this variation, whereas for the animal species, the distribution had two very pronounced peaks around values of 0.5 and 1. That is, animal species either rely to a low and similar intensity on several plant species or strongly rely on a single plant species.

Association between traits and the properties of species in the network For both plant and animal species sets, species strength and interaction asymmetry showed a strong positive association with species degree (plants: F=55.65, p<0.0001,

93 r2=0.54; F= 53.89, p<0.0001, r2=0.54, d.f.=44; animals: F=221.82, p<0.0001, r2=0.83; F=29.78, p<0.0001, r2=0.7, d.f.=45; respectively), whereas the value of maximum dependence of both plant and animal species showed a strong negative association with species degree (F=29.51, p<0.0001, r2=0.41, n=44; F=41.57, p<0.0001, n=46; respectively). Thus, since species strength and the value of maximum dependence are correlated to species degree for both the plant and the animal species set, we used the ratio of species strength and species degree and the ratio of maximum dependence and species degree to test the associations between these species traits and these two properties. For the interaction asymmetry, we did not divide the values by species degree to test the association between interaction asymmetry and species traits, since the formula to assess this value already accounts for the number of interactions in each species. For plant species degree, the regression tree model selected seed diameter as the best predictor for this attribute, and the linear regression model was negative and 2 significant (F=10.36, p=0.002, r =0.19, d.f.=43). For plant species strength, the regression tree model selected fruit diameter as the best predictor for this attribute, and the linear regression model was also negative and significant (F=26.74, p<0.0001, r2=0.38, d.f.=43). For maximum dependence of plant species, the regression tree model shows that seed diameter is the only predictor that explains this attribute, and the linear regression model was positive and significant (F=4.185, p=0.046, r2=0.07, d.f=43). For the interaction asymmetry of plant species, the regression tree model shows that seed diameter is the only predictor that explains this property, and the linear regression model was negative and significant (F=7.936, p=0.0072, r2=0.16, d.f.=43, Table 3a). In summary, plant species degree, plant species strength and interaction asymmetry in plant species decreased with an increment in seed diameter or in fruit diameter, whereas the value of maximum dependence of plant species on seed dispersers increased with the increment in seed or fruit diameter (Fig. 1). Although the interaction asymmetry is positively associated with seed diameter, values of interaction asymmetry in plant species were usually negative. Thus, as seed diameter increases, there is more asymmetry between the interaction strength of a plant and the interaction strength of an interacting animal species. For animal species degree, the multiple regression was significant (F=3.767, p=

94 0.048, r2=0.51, d.f.=13), with the body mass of animals mainly explaining the variance in this attribute (27.62%). For animal species strength, the multiple regression was significant (F=7.166, p=0.007, r2=0.67, d.f.=13), with the density of animal species explaining the highest variance in this attribute (33.07%), followed by body mass (18.28%) and the association between those two variables (16.89%). For maximum dependence, the multiple regression was significant (F=4.192, p=0.036, r2=0.54, d.f.=13), with body mass explaining the highest variance in this attribute (32.68%). For interaction asymmetry, the multiple regression was significant (F=73.82, p=0.0067, r2=0.69, d.f.=12), with the interaction between body mass and density explaining the majority of the variance in this attribute (26.96%), followed by body mass (26.72%) and by density (16.5%) (Table 3b). Therefore, animal species degree increased with an increment in the body mass of seed dispersers, whereas animal species strength and interaction asymmetry of animal species increased with an increment in both the body mass and the density of seed dispersers. On the other hand, the value of maximum dependence of an animal on particular plant species decreased with the body mass of seed dispersers (Fig. 3).

Discussion Recent work has greatly improved our knowledge of the patterns and properties of interactions that characterize mutualistic networks, and the challenge now is to understand the ecological and evolutionary processes that contribute to them. Our results suggest that both quantitative and qualitative species properties in networks are determined by ecological processes rather than by evolutionary ones. That is, phylogenetic history of both plant and animal species did not explain the variation in their properties, which were mainly explained by species morphological traits. The lack of phylogenetic signal in properties of species in this network is not surprising given that the variables that explain such properties do not have a phylogenetic signal. Furthermore, it has been recently shown that the pattern of this network is only partially explained by shared evolutionary history (Donatti et al. 2011). Therefore, the properties of plant species in this network are solely explained by morphological traits, specifically fruit and seed diameter. Plant species with small fruits or seeds have more interactions, are more important to seed dispersers, show a lower

95 interaction asymmetry with animals and are less dependent on a single seed disperser than species with large fruits or seeds. Thus, our data show that in this seed dispersal network, trait matching as the result of exploitation barriers was the main process by which plant species showed variation in all four properties studied here. Although several other studies have also found that trait complementarity (such as phenological matching and/or exploitation barriers) were important in explaining plant species degree in networks and, ultimately, in indirectly determining the pattern in mutualistic interactions (Stang et al. 2007, Jordano et al. 2006, Rezende et al. 2007, Santamaría & Rodríguez-Gironés, 2007), just a few studies looked at species properties derived from quantitative networks. These studies either show a combined effect of species morphology and abundance (Stang et al. 2006, Stang et al. 2007), or of species abundance, in partially or entirely explaining the properties of species in mutualistic networks (Vázquez et al. 2007, Vázquez et al. 2009). In contrast, the properties of animal species in this network were explained by two processes: (1) trait matching as a result of exploitation barriers, where seed dispersers only interact with fleshy-fruited plants if they are big enough to swallow or carry the fruits, and (2) neutrality, where the probability of an interaction between an animal and a plant species depends on the density of animals. Animal species with large body mass have more interactions, are less dependent on a single plant species and show a higher interaction asymmetry with plants than species with small body mass. Additionally, species with large body mass and high densities are more important to the set of plant species. Therefore, although our data show that body mass is the most important variable that explains animal species properties in this network, animal species density has also an important effect in determining species strength. Our ﬁndings are consistent with the existing literature in mutualistic networks that suggests an important role of gape size and of body mass in constraining the number of interactions established by animals (Jordano 1987, Benkman 1999, Herrera 2002, Böhning-Gaese et al. 2003, Cohen et al. 2003, Abzhanov et al. 2004, Vázquez et al. 2007, Carnicer et al. 2009, Donatti et al. 2011), or of animal abundance in determining properties derived from interaction frequencies such as species strength in mutualistic networks (Vázquez et al. 2007, Vázquez et al. 2009: pollination networks; Carnicer et al. 2009, Schleuning et al. 2011: seed dispersal

96 networks). The few studies that tested associations between ecological processes and properties of species in seed dispersal networks have highlighted the importance of species abundance in explaining species properties. However, in the present study, we have shown that the density of animal species and the availability of fruits have a minor (in the case of the animal species set) or no effect at all (in the case of the plant species set) in explaining the majority of properties of species in this network. This is the case probably because interactions between species do not occur randomly in this seed dispersal network (see Donatti et al. 2011). Furthermore, the importance of species abundance in explaining species properties found in previous studies could be due to the low diversity in such networks, which includes mainly seed-dispersing birds that interact with plant species that share similar traits (Rezende et al. 2007). In this way, since fruit and animal morphological traits do not vary considerably in other fruit-frugivore communities, the importance of the abundance of species in explaining species properties may have stood out in those studies. Conceivably, the lack of importance of fruit availability in explaining plant species properties in this network may lie on the fact that our plant community is very diverse. Thus, since we have species with a variety of fruit and seed sizes, this variation is probably large enough to enable the association between seed (or fruit) size and the properties of species to arise. On the other hand, even though our original animal community is also very diverse, we were not able to include all species of this network in our analysis, as density data for all animal species is not available. Thus, it is possible that if we had included census data for all 46 animal species from the network, intrinsic species traits, such as body mass, could have been an important predictor of species properties in this network. A previous study has shown that the pattern of this seed dispersal network emerges mainly by trait convergence of phylogenetic unrelated species, with phylogeny showing a limited effect (Donatti et al. 2011). Likewise, here, we have shown that species-specific traits also determine the quantitative properties of species in the network, such as species strength and interaction asymmetry between species. Finally, we posit that the results that we found here may contribute to understanding coevolution among interacting species. More specifically, our results imply that large-seeded fruits and large-bodied animals will

97 have limited opportunity for coevolution because they are asymmetrically influenced by their interaction partners. Thus, as interactions with strong reciprocal effects may have the greatest potential for coevolution (Vázquez et al. 2007), interactions involving small- seeded plant species and small-bodied animal species may establish the template for coevolution to take place in seed dispersal networks, if such interactions occur in high frequencies and are common through time. Therefore, we argue that species-specific morphological traits of plants and animal species should be considered in studies that evaluate the ecological and evolutionary consequences of plant-animal interaction networks.

Acknowledgments We would like to thank Reese Rogers, Lee Love-Anderegg, Adilson Braga Samuel and Luisa Haddad for their help with data collection. We thank FAPESP (2004/00810-3 and 2008/10154-7), Earthwatch Institute and Conservation International for financial support. CID was supported by Stanford University and MG and MAP by a CNPq fellowship. We also thank Conservation International, Lucas Leuzinger and Marina Schweizer for their permission to work in their properties.

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102 Tables

Table 1. Plant species in the seed dispersal network, seed diameter (mm), fruit diameter (mm), availability of fruits in the area, energy content in fruit pulp (in Kcal/g of dry fruit), species degree, species strength, maximum dependence and interaction asymmetry.

Fruit Seed Availability Energy Species Species Maximum Interaction Species diameter diameter of fruits content degree strength dependence asymmetry Ocotea diospyrifolia 5.852 4.824 15.48 3.279 11 3.607 0.326 0.237 Anonna dioica 75.067 8.27 n/a 1.501 6 0.187 0.694 -0.135 Zanthoxyllum rigidum 3.3 3.3 208.8 n/a 7 3.033 0.286 0.29 Protium heptaphyllum 15.695 9.536 6122.592 3.169 1 0.64 0.194 -0.036 Melicoccus lepidopetalus 20.635 11.2 3.432 1.248 8 0.974 0.24 -0.003 Sterculia apetala 17.564 14.346 163.17 n/a 4 1.481 0.806 0.12 Guazuma ulmifolia 22.068 1.770 45 0.743 8 0.357 0.25 -0.08 Mouriri eliptica 29.273 13.267 0.96 1.578 9 1.323 0.646 0.036 Psidium nutans 26.439 3.411 6 2.827 11 0.596 0.25 -0.037 Eugenia desynterica 25.841 11.003 n/a 2.01 3 0.188 0.571 -0.271 Dipteryx alata 39.905 35.734 270.289 2.295 4 0.371 0.735 -0.157 Swartzia jorori 14.875 10.785 n/a 3.148 3 0.96 0.667 -0.013 Inga laurina 18.807 9.921 89.64 3.653 5 0.153 0.417 -0.169 Enterolobium contortisiliquum 78.763 10.092 48 2.492 5 0.722 0.371 -0.056 Hymenaea stigonocarpa 41.704 17.02 0.9 2.623 2 0.235 0.811 -0.383 Rhamnidium elaeocarpum 7.498 4.829 18115.2 2.251 5 1.219 0.429 0.044 Ficus pertusa 7.099 0.1 4262.4 0.884 13 3.198 0.233 0.169 Ficus gomelleira 20.930 0.1 7992 1.033 6 0.169 0.462 -0.139 Cecropia pachystachia 8.563 0.1 27.594 0.765 9 2.005 0.256 0.112 Salacia elliptica 46.048 13.618 157.248 1.792 3 0.073 0.667 -0.309 Licania parvifolia 12.458 8.333 3648 3.156 1 0.182 1 -0.818 Couepia uiti 21.860 18.800 1733.04 1.836 5 0.368 0.316 -0.126 Garcinia brasiliensis 22.585 10.255 n/a 7.545 5 0.201 0.625 -0.16 Caryocar brasiliense 65.15 31.545 18 1.96 1 0.072 1 -0.928 Byrsonima verbascifolia 18.126 9.852 3.6 1.67 2 0.048 0.5 -0.476 Byrsonima orbignyana 11.837 6.055 18048 2.464 14 1.651 0.39 0.046 Doliocarpus dentatus 5.453 4.368 6 1.641 15 5.945 0.265 0.33 Curatella americana 6 5 7920 1.46 11 3.292 0.219 0.208 Dulacia egleri 17.122 9.775 6 3.081 5 0.671 0.567 -0.066 Agonandra brasiliensis 24.227 14.627 81.696 1.504 7 0.253 0.286 -0.107 Psittacanthus calyculatus 9.677 7 24.9 1.308 3 0.496 0.667 -0.168

103 Psittacanthus cordatus 12.5 8.502 24.9 1.308 1 0.289 1 -0.711 Pouteria ramiflora 31.901 15.054 1.26 1.903 3 0.087 0.455 -0.304 Pouteria gardneri 11.893 7.209 2.76 2.381 4 0.125 0.4 -0.219 Diospyrus hispida 45.636 12.931 88.8 1.356 5 0.545 0.35 -0.091 Vitex cymosa 16.412 8.974 1534.464 3.130 8 0.710 0.796 -0.036 Hancornia speciosa 28.823 10.868 4.32 3.416 12 2.061 0.18 0.088 Genipa Americana 60.019 6.013 3.12 3.334 18 3.742 0.256 0.152 Tocoyena Formosa 34.863 6.438 239.616 1.338 2 0.027 0.8 -0.486 Alibertia sessilis 26.67 5.084 272.448 2.316 8 0.358 0.544 -0.08 Atallea phalerata 35.106 22.081 3914.400 2.271 7 0.981 0.486 -0.003 Syagrus flexuosa 15.884 13.701 0.9 n/a 3 0.131 0.667 -0.29 Bactris glaucescens 18.1 8.535 7056 2.536 5 0.818 0.852 -0.036 Acrocomia aculeata 32.135 22.08 344.52 3.008 6 0.617 0.407 -0.064 Copernicia alba 16.350 14.16 293.04 1.823 8 0.837 0.867 -0.02

104 Table 2. Animal species in the seed dispersal network, body mass (kg), species density, species degree, species strength, maximum dependence and interaction asymmetry.

Body Density Species Species Maximum Interaction Species mass (inds/km2) degree strength dependence Asymmetry Piaractus mesopotamicus 1.14 n/a 4 2.136 0.787 1.136 Geochelone carbonaria 6.6 n/a 14 1.465 0.125 0.465 Euphractus sexcintus 6 n/a 5 0.149 0.437 -0.426 Alouatta caraya 5.4 2.02 4 0.157 0.5 -0.084 Agouti paca 9.1 0.1 3 0.112 0.5 -0.148 Dasyprocta sp 2.8 1.84 17 2.573 0.144 0.524 Cerdocyon thous 5.7 n/a 14 2.831 0.261 0.108 Nasua nasua 5.1 1.7 16 1.114 0.269 0.114 Procyon cancrivorus 4.5 n/a 5 0.417 0.437 -0.032 Tapirus terrestris 240 n/a 21 4.416 0.273 1.708 Mazama americana 36 1.49 7 0.948 0.347 -0.002 Sus scrofa 50 6.35 26 5.59 0.239 1.53 Pecari tajacu 26 3.69 9 1.635 0.298 0.03 Tayassu pecari 35 9.63 18 4.216 0.346 1.608 Crypturellus sp 0.08 n/a 1 0.143 1 -0.061 Rhea americana 20 n/a 17 3.224 0.371 0.171 Aburria pipile 1 11.71 8 1.065 0.492 0.032 Ortalis canicollis 0.6 36.28 13 1.581 0.171 0.042 Crax fasciolata 2.8 4.02 7 0.941 0.854 -0.059 Columba sp 0.2 n/a 3 0.092 0.5 -0.454 Guira guira 0.07 n/a 2 0.087 0.5 -0.304 Trogon curucui 0.07 2.03 2 0.146 0.714 -0.142 Ramphastos toco 0.54 4.85 10 1.769 0.781 0.384 Pteroglossus castanotis 0.27 1.26 5 0.41 0.526 -0.197 Brotogeris versicolurus 0.066 n/a 2 0.093 0.5 -0.907 Aratinga leucophthalmus 0.166 n/a 1 0.071 1 -0.232 Aratinga aurea 0.1 n/a 1 0.093 1 -0.302 Tytira cayana 0.07 n/a 1 0.186 1 -0.163 Pitangus sulphuratus 0.068 n/a 6 2.028 0.289 0.514 Tyrannus melancholicus 0.039 n/a 1 0.132 1 -0.124 Myiodinastes maculatus 0.043 n/a 2 0.055 0.5 -0.189 Casiornis rufa 0.02 n/a 1 0.286 1 -0.238 Myiarchus ferox 0.024 n/a 3 0.212 0.5 -0.046 Cyanocorax cyanomelas 0.2 n/a 8 1.066 0.414 0.009 Cyanocorax chrysops 0.14 n/a 3 0.201 0.5 -0.266 Turdus rufiventris 0.07 n/a 2 0.095 0.5 -0.181 Turdus sp 0.05 n/a 3 0.378 0.7 -0.622 Saltator coerulescens 0.052 n/a 3 0.839 0.666 -0.161 Poroaria coronata 0.03 n/a 1 0.015 1 -0.985 Thraupis palmarum 0.036 n/a 3 0.352 0.571 -0.162 Thraupis sayaca 0.03 n/a 2 0.376 0.947 -0.078 Psariocollius decumanus 0.25 n/a 7 0.682 0.26 -0.045 Icterus croconotus 0.07 n/a 2 0.127 0.75 -0.109

105 Gnomiropsar chopi 0.07 n/a 1 0.023 1 -0.977 Ramphocelus carbo 0.025 n/a 6 0.458 0.52 -0.034 Tachyphonus rufus 0.033 n/a 1 0.016 1 -0.109

106 Table 3a. Results of multiple regression analyses on the associations between species traits and species properties for plant species.

Species degree r2=0.19 Species strength r2=0.38 Maximum dependence r2=0.07 Interaction asymmetry r2=0.16 Model terms SS P SS P SS P SS p Seed diameter 134.58 0.002* 0.2309 0.046* 0.4743 0.0072* Fruit diameter 0.2107 <0.0001* Residuals 558.62 0.3311 2.3732 2.5703

Table 3b. Results of multiple regression analyses on the associations between species traits and species properties for mammal and bird species combined.

Species degree r2=0.51 Species strength r2=0.67 Maximum dependence r2=0.54 Interaction asymmetry r2=0.69 Model terms SS P SS P SS P SS p Log Body mass 137.915 0.05* 0.009 0.037* 0.0373 0.021* 1.2063 0.015* Density 135.800 0.054 0.016 0.009* 0.0245 0.052 0.6863 0.05* Log Body Mass:Density 49.482 0.21 0.008 0.043* 0.0018 0.563 1.2172 0.014* Residuals 286.017 0.015 0.0563 1.4042

107 Figures

Figure 1. Association between seed diameter (mm) and species degree (top), maximum dependence (center) and interaction asymmetry (bottom) of plant species.

108

Figure 2. Association between body mass (kg) and species degree (top), maximum dependence (center) and interaction asymmetry (bottom) of animal species. Body mass is in log scale.

109 CHAPTER 5

THE ROLE OF SEED DISPERSAL INTERACTIONS IN STRUCTURING A PLANT COMMUNITY IN THE BRAZILIAN PANTANAL Camila I. Donatti, Mauro Galetti & Rodolfo Dirzo

Abstract The study of seed dispersal is crucial to our understanding of the structure of plant communities, especially in tropical forests, where dispersal limitation is prevalent. Even though additional relevant processes occur between seed dispersal and seedling establishment (e.g., seed and seedling predation), it is possible to find an association between patterns of seed dispersal and seedling distribution. However, the importance of seed dispersal in determining the spatial distribution of plant species, taking into account that the majority of them interact with multiple seed dispersers, has received little attention. Here, our goal is to examine if a “dispersal signal” in terms of an association between seed dispersal and spatial distribution of animal-dispersed plants can be detected in a natural setting where a multiplicity of plants and dispersal agents interact. We assessed spatial distribution of plant species that are located in a gradient of dispersal- dependence that includes, at one extreme, those that strongly interact with one single seed disperser and, at the other, those that weakly interact with multiple dispersers. Species that strongly rely on a single seed disperser species showed a highly aggregated distribution pattern of individuals, and higher than the aggregation found for plant species that weakly rely on multiple dispersers. This highly aggregated distribution of individuals could contribute to an observed high mortality of seedlings and saplings. In addition, seed dispersal was the main important predictor of the aggregation intensity of conspecific individuals in comparison with several other variables. We conclude that seed dispersal interactions represent an important factor in determining plant spatial distribution even when multiple animal species operate as dispersal agents of a particular plant.

110 Furthermore, we suggest that the intensely aggregated distribution of certain plant species can be intensified in a scenario of defaunation, where large- and medium-bodied species are absent.

111 Introduction Understanding mechanisms that regulate the structure of ecological communities is a central goal of community ecology. Ecological theory recognizes a variety of abiotic and biotic factors that shape the structure and dynamics of terrestrial plant communities. Environmental heterogeneity, disturbance, and biotic interactions may all play key roles in determining plant community composition and diversity in space and time (Tilman & Pacala 1993, Rosenzweig 1995, Van der Heijden 1998, Hubbell 2001, Ricklefs 2004). Among those, the study of seed dispersal is crucial to understand the structure of plant communities, especially in tropical forests where dispersal limitation is prevalent (Clark et al. 1999, Hubbell et al. 1999). Even though many processes occur between seed dispersal and seedling establishment, such as seed and seedling predation, it is possible to find a “dispersal signal” – an association between patterns of seed dispersal and seedling distribution. In a large area of dozens of hectares, spatial distributions of seedlings in plant species dispersed by animals are significantly different than those in plants dispersed by gravity or wind (Hubbell 1979, Kinnaird 1998, Hardy & Sonké 2004, Seidler & Plotkin 2006, Russo et al. 2007, Muller-Landau et al. 2008). In a small area of few square meters, the spatial distribution of seedlings can be associated with the seed dispersal by particular animal species (Howe 1989, Herrera et al. 1994, Julliot 1998, Wenny & Levey 1998, Fragoso et al. 2003). However, at the community-level, one-to-one interactions are rare and the majority of species, both animals and plants, have more than one partner (Bakker et al. 1996, Bascompte et al. 2003, Memmott et al. 2004, Strauss & Irwin 2004, Jordano et al. 2006). Thus, the importance of seed dispersal in determining the spatial distribution of plant species, taking into account that the majority of them interact with multiple seed dispersers, has received little attention. One way to summarize the importance of multiple animal species for the dispersal of a particular plant species is to combine the number and the frequency of interaction between a particular plant and its seed dispersers. Collectively, the number and strength of interactions enable one to place plant species in a gradient that includes, at one extreme, those that strongly interact with one seed disperser and, at the other, those that weakly interact with multiple dispersers.

112 In our study site, it has been previously shown that the variation in the number and frequency of interactions across plant species is primarily explained by fruit and seed diameter (Donatti et al. unpub. data). Thus, a plant species with large seeds has a high dependence on its most frequent seed disperser whereas a plant species with small seeds has a low dependence on its most frequent seed disperser. Due to constraints between the gape size of seed dispersers and the fruit or seed size, large-seeded species are predominantly dispersed by large-bodied frugivores (Janzen & Martin 1982, Janson 1983, Wheelwright 1985, Chapman et al. 1992, Guimarães et al. 2008, Donatti et al. 2011) that can swallow large seeds, or by medium-bodied frugivores (such as agoutis, Dasyprocta azarae) that can manipulate and carry those seeds. Such animals usually disperse seeds in clumps (Howe 1989, Fragoso et al. 2003), either close or far from the mother plant, possibly generating a highly aggregated distribution of individuals. In contrast, small-seeded plant species are visited by several seed dispersers that vary in body size (given that many of the disperser species can swallow small fruits or seeds). Such dispersers can disperse seeds in clumps (for example by large-and medium-bodied mammals) or scattered in the area (for example by small-bodied birds; Howe 1989), possibly generating a random or less aggregated distribution of individuals. Thus, the combined information on the number of seed disperser species and the frequency of interactions with these partners shown by a particular plant species should explain, at least partly, the spatial distribution of conspecific individuals. Although there is still a need for basic studies that emphasize natural history in seed dispersal, the challenge now is to understand such natural history knowledge in light of the processes that shape plant populations and communities (Howe & Miriti 2004, Volvov et al. 2009). Here, our goal is to examine if the association between seed dispersal and spatial distribution of animal-dispersed plants can be detected even when multiple animal species operate as seed dispersal agents of a particular plant species. We assessed seed dispersal interactions and the spatial distribution of plants in a 2.6-hectare plot in a semi-deciduous forest in the Brazilian Pantanal. More specifically, we evaluate: a) the association between the spatial distribution of plant species and the relative dependence of those plants on their most important seed disperser species, b) the impact of an aggregated distribution of individuals for seedling and sapling mortality, c) if such

113 mortality is related to high herbivory and attack by pathogens, and d) how much the value of maximum dependence on seed dispersers, which represents interactions between plants and seed dispersers, in contrast with other variables, contributes to plant spatial distribution. Our hypotheses are that: 1) the aggregated distribution of a plant species will be positively associated with the value of maximum dependence on its most important seed disperser, 2) the distance from a conspecific individual will significantly affect seedling and sapling mortality, 3) such high mortality will be related to the high rates of herbivory and attack by pathogens in individuals located close to a conspecific, and 4) the value of maximum dependence will be an important predictor of the variation in the aggregated distribution of individuals across plant species. All these hypotheses were corroborated, but the high seedling and sapling mortality observed in individuals close to any other conspecific was not related to high herbivory or attack by pathogens, which was only true for seedlings and saplings located close to a conspecific adult. Therefore, the positive association between seedling mortality and proximity to conspecific individuals may be a result of competition for abiotic factors. We conclude that, although different mechanisms such as dispersal mode and edaphic characteristics can operate at different scales in shaping the distribution and structure of plant communities, seed dispersal interactions also represent an important factor in this respect even when multiple animal species participate in the dispersal of a particular plant. Furthermore, plant species that strongly rely on few seed disperser species show strongly aggregated distributions, which can be intensified in a scenario of defaunation, thus increasing seedling and sapling mortality.

Methods

Seed dispersal interactions To sample both the number and the frequency of interactions between animal and plant species, seed dispersal interactions were recorded using four complementary methods: 1) focal observations, 2) scats analysis, 3) camera trap techniques and 4) intestinal analysis. More details about these methods are presented in Donatti et al. (2011). One event of seed dispersal was considered as such when 1) a fruit was recorded

114 to have been removed from a plant species during focal observations, 2) a visit of a seed disperser to a plant species with fruit removal was detected with camera traps, 3) a scat pile was found to have at least one intact seed of a particular species in it, or 4) a sampled fish intestine contained at least one intact seed in it from a particular plant species. We used the number of seed dispersal events recorded for each interaction between each plant and animal species to calculate a value of dependence of a particular plant species on its most important seed disperser species. To determine the frequency of interaction between particular plant species and all animal dispersers, we divided the number of seed dispersal events recorded between a plant species and each seed disperser species by the total number of dispersal events recorded for this particular plant. For each plant species, the highest value of interaction frequency was then considered the dependence of a plant species on its most important seed disperser, hereafter “maximum dependence”.

Spatial aggregation of individuals across plant species We set up a 2.6-ha plot in a semi-deciduous forest to assess the spatial distribution of eight plant species that vary in their values of “maximum dependence” on seed dispersers. Besides the variation in the gradient of maximum dependence that these eight plant species display, we also choose to work with these species because there are clear morphological differences among them in all life stages and because they do not propagate vegetatively. We mapped all individuals to assess the spatial aggregation of individuals across species, permanently tagged them to follow their fate, and measured the basal diameter or the diameter at breast height (1.3 m; DBH) to categorize their size classes/stages. Seedlings were considered as those individuals with ≤10 mm basal diameter, representing those individuals that were predominantly up to 50 cm tall. Individuals were considered saplings if they were 10.1≤15 mm in basal diameter or ≤5 mm in DBH, representing those that were predominantly 50-150 cm tall (Webb & Peart 1999). Juveniles were considered individuals > 15 mm in basal diameter or >5mm in DBH, representing those >150 cm in height that did not yet have signs of flowers or fruits. Adults were considered those with signs of flowers or fruits (Scariot 1999). After

115 mapping and marking individuals for the first time in July 2008, we followed their fate and marked new individuals on four consecutive times (January 2009, July 2009, January 2010 and January 2011), to be able to record seedling and sapling mortality and to accurately assess spatial distribution. Spatial aggregation of individuals within each plant species was assessed through Ω, an index that measures aggregation of individuals (hereafter “aggregation” or “Ω”) around other conspecifics based on the ratio of local density (within annuli located in every 5 meters from the focus individual) to overall population density (Condit et al. 2000). More specifically, we evaluated spatial aggregation of each species by counting individuals in annuli around conspecifics. We counted the number of conspecifics between x and x + ∆x meters around each individual of each species for several annuli inside the plot, as well as calculated the area inside the plot of each of these annuli. Then, the number of neighbors Nx in each annulus at distance x were summed over all individuals and divided by the sum of the area Ax in each annulus at distance x of over all individuals. To calculate Ω of a given species in a given annulus, we then divided this ratio (i.e. number of conspecific/area) by the density of this species in the whole plot. A great advantage of this method is that it is sample-size independent and different species can be compared regardless of their densities. In a perfectly random distribution, Ω = 1 for all distances x. Aggregation is indicated when Ω> 1 for short distances, whereas Ω < 1 indicates spacing at some scale, or hyperdispersion (Condit et al. 2000). We calculated the aggregation of individuals across plant species in different annuli and tested the associations between theses values of aggregation and those of maximum dependence using linear regressions. For each plant species, aggregation was calculated for seedlings around seedlings, seedlings and saplings combined around seedlings and saplings combined, seedlings around adults, saplings and seedlings combined around adults, and individuals (regardless of the stage) around conspecific individuals (regardless of the stage). As many annuli of particular plants included areas outside of the plot, an edge correction was used so that only the area inside the plot was considered in the calculation of Ω.

116 Consequences of spatial aggregation on seedling and sapling mortality, leaf herbivory and infection by pathogens To address the consequences of aggregation of individuals on seedling and sapling mortality, we tested the survival of seedlings and saplings as a function of the distance to the closest conspecific individual, using logistic regressions. Additionally, in our last survey (January 2011), we also assessed the standing level of herbivory and attack by pathogens. To do so, we estimated the percentage of damage by herbivores and pathogens in all leaves of every seedling and sapling, which resulted in an estimated percentage of the plant with foliar damage by herbivores and pathogens (modified from Dirzo & Domínguez 1995). We tested the effect of the distance from conspecific individuals on the percentage of each seedling and sapling that were attacked by herbivores or damaged by pathogens using linear regressions.

Seed dispersal interactions and the structure of the plant community

Spatial distributions of tropical trees often correlate with local (i.e., microhabitat) environmental conditions in terms of particular light, soil, moisture and topographic characteristics, suggesting the potential importance of niche differentiation in structuring tropical forest tree communities (Clark et al. 1999, Svenning 1999, Webb & Peart 2000, Harms et al. 2001, Russo et al. 2005). Thus, to tease apart the importance of some of these and other abiotic and biotic characteristics in explaining the spatial aggregation of individuals across plant species, we divided the 2.6-ha plot in 64 subplots of 20m X 20m (two subplots were not included in the analyses due to the presence of bee hives inside them), and collected data on soil properties, canopy openness and terrestrial Bromelia cover for each subplot. In July 2009, we collected soil samples (from 0-20 cm of depth) from five random locations inside each subplot. We mixed those five sub-samples into a bulk sample for soil analyses. Canopy openness was assessed through hemispherical photographs taken with a fisheye lens. Photographs were taken at a height of one meter using a horizontally leveled digital camera and a ﬁsheye lens of 180° ﬁeld of view. All photographs were taken either before dawn, after sunset, or at other times of the day when the sun was blocked by clouds to ensure homogeneous illumination of the canopy

117 and a correct contrast between canopy and sky. Canopy openness in each photograph was assessed using the software Gap Light Analyzer (GAP, Simon Fraser University, Vancouver, Canada, http://www.ecostudies.org/gla/). As the average value of canopy openness did not significantly differ if two or four hemispherical photographs were taken in each subplot (t-test: 0.619, p=0.54, n=34) and as the average canopy openness of two and of four photographs were significantly correlated (Pearson's r=0.89, p<0.0001, n=17), we took only two photographs in each subplot and averaged them to measure canopy openness in that subplot. Therefore, for each subplot, we had values of terrestrial Bromelia cover, canopy openness and soil pH, organic matter, K, Ca, Mg and S in the soil. Additionally, for each subplot we also had values of maximum dispersal dependence of plant species if such species was recorded in this particular subplot. With this information we then tested the importance of biotic (value of maximum dependence, terrestrial Bromelia cover) and abiotic variables (canopy openness, soil pH, organic matter, K, Ca, Mg and S), in explaining the aggregation of individuals across species within subplots. We calculated the aggregation of individuals (regardless of the stage) within 10 meters of conspecific individuals. Therefore, for each subplot of 20 X 20 meters we also had one value to represent the aggregation of individuals of a given plant species, if such species was recorded in this particular subplot. We used a regression tree model from the package tree of R (http://www.r-project.org/) to select the most important independent variables that were later used in a linear regression model with the values of aggregation (Ω) as a dependent variable. Variables were Box-Cox transformed using JMP v 5.0 (SAS Institute Inc.) to ensure that the assumptions of the linear model were met.

Results

Spatial aggregation of individuals across plant species All individuals of the eight plant species, which were located along the gradient of maximum dependence on seed dispersers, were mapped and permanently tagged, totaling 3377 plants (3078 alive and 299 dead in our last survey in January 2011). All plant

118 species showed a value of aggregation (Ω) greater than one in the majority of annuli. That is, all plant species show an aggregated distribution in the majority of annuli, but such value of aggregation varies across plant species. We found a significant and positive association between the maximum dependence of a plant on its most important seed disperser and the spatial aggregation of conspecific individuals for individuals located within five meters from any other conspecific (three meters: F=5.51, p=0.05, r=0.67; four meters: F=6.86, p=0.03, r=0.68; five meters: F=7.97, p=0.03, r=0.75; n=8, Fig. 1). That is, plant species exhibiting high values of dependence on a single disperser show an intense aggregation of conspecific individuals. In this way, plant species that strongly rely on a few disperser species have their individual plants mainly concentrated around conspecific individuals when compared to plant species that weakly rely on many seed disperser species. However, we did not find the same association for any plant stage in specific.

Consequences of spatial aggregation on seedling and sapling mortality, leaf herbivory and infection by pathogens We also evaluated the possible consequences of high clumpiness on seedling and sapling mortality. Our results show that the survival in seedlings increases with distance from other conspecific seedling (F=10.36, p=0.0013, R2=0.0072, n=1780), that the survival of seedlings and saplings combined increases with the distance of any conspecific seedling or sapling (F=11.27, p=0.0008, R2=0.0065, n=2218), and with the distance from any conspecific individual (F=15.42, p<0.001, R2=0.0088, n=2218, Fig. 2). In other words, seedlings and saplings are more likely to survive the farther they are from other conspecific individuals. We evaluated if this association could be due to high herbivory and infection by pathogens in areas with high aggregation of conspecific individuals. We found a trend suggesting that the herbivory and infection by pathogens in seedlings and saplings are higher in those plants located close to conspecific adults (F=16.5, p<0.0001, R2=0.09, n=1902; F=38.74, p<0.0001, R2=0.17, n=1902, respectively, Fig 3). However, herbivory in seedlings is higher in those located far from other seedlings (F=33.27, p<0.0001, r=0.14, n=1515). When considering seedlings and

119 saplings combined, herbivory is higher in those located far from any other conspecific (F=50.51, p<0.0001, r=0.16, n=1902). Therefore, our data show that herbivory and infection by pathogens may explain the mortality of individuals located close to conspecific adults but not necessarily to conspecific individuals in general.

Seed dispersal interactions and the structure of the plant community The regression tree model that tested the importance of several biotic and abiotic variables on the aggregated distribution of individuals in subplots was significant (F=2.3, p=0.027, R2=0.0676, n=404). Among the significant variables, the maximum dependence, the dependence of a plant species on its most important seed disperser, and the interaction between maximum dependence and the percentage of terrestrial Bromelia were those that predicted the highest percent of variation in the aggregated distribution of individuals across subplots (1.9 % and 2% respectively). As expected, the aggregated distribution of individuals (within a 10-meter radius) is positively affected by values of maximum dependence. In contrast, the aggregated distribution of individuals is negatively affected by the percentage of terrestrial Bromelia cover. That is, an increase in the dependence of a plant on its most important seed disperser increases the aggregation of individuals, whereas an increase in the percentage of terrestrial Bromelia cover decreases the aggregation distribution of individuals. Therefore, seed dispersal, here represented by the value of maximum dependence, is the most important variable to explain the degree of aggregation of individuals across species, among those we assessed.

Discussion Seed dispersal fundamentally influences plant populations and community dynamics but such association is difficult to disentangle and to be quantified directly (Russo et al. 2006), especially because the seed dispersal of a plant species often involves interactions with multiple dispersers. In this study, we show that seed dispersal interactions can affect the spatial distribution of a plant species even when multiple dispersal agents add to the complexity of the interaction. More specifically, in this study we show that a plant species that strongly relies on a single seed disperser has a more

120 intense aggregation of individuals within short distances (five meters) than a plant species that weakly relies on many dispersers. Furthermore, we show that aggregations of individuals do not only occur around adults, but also around conspecific individuals as a whole. Therefore, plant species that strongly rely on a single seed disperser show a strongly aggregated pattern that is not solely caused by limited seed dispersal around adults but also by seed dispersal to particular areas caused by the interaction with particular animal species (i.e., large-and medium-bodied seed dispersers). Although seed diameter explains a high percentage of variation in the value of maximum dependence (Donatti et al. in prep.), seed diameter alone was not significantly associated with the values of aggregation in different annuli across the plant species we studied. Likewise, Russo et al. (2007) found that the effect of seed size on the intensity of aggregation was relatively small for animal-dispersed plants when taking into account spatial scales of 0-20 meters. Therefore, the aggregated distribution across species may in fact be determined by the patterns of dispersal generated by particular animal species that interact with those seeds. For instance, although seeds of Sterculia apetala are not extremely large, this species is almost exclusively dispersed by the toco toucan (Ramphastos toco) (Pizo et al. 2008), a medium-bodied bird species. As a result, the value of maximum dependence in this species is very high (i.e., 0.8). Seed dispersal interactions of plant species, here summarized by the value of maximum dependence on seed dispersers, was the most important predictor of the intensity of spatial aggregation across plant species, which is somewhat surprising for at least two reasons. First, the existence of many other processes that occur between seed dispersal and seedling establishment, such as seed and seedling predation, and second, the strong effects that abiotic factors may have on plant distribution (Clark et al. 1999, Svenning 1999, Webb & Peart 2000, Harms et al. 2001, Russo et al. 2005). However, the percentage of variation explained by seed dispersal was very small (~2%) and other factors not explored in this study, such as topography or proximity of the water table, also likely play an important role in explaining the spatial distribution of plants. We also found that the mortality of seedlings and saplings increases with the proximity of other conspecific individuals, which was also demonstrated by studies that specifically looked at density-dependent mortality in seedlings (Webb & Peart 1999,

121 Harms et al. 2000, Bell et al. 2006, Comita & Hubbell 2009). In such studies, the density-dependent mortality in seedling and saplings was likely caused by host-specific enemies such as herbivores and pathogens, as predicted by the Janzen-Connell hypothesis (Janzen 1970, Connell 1971). Although we anticipated finding high levels of herbivory and attack by pathogens in seedlings and saplings located close to conspecific individuals, this was the case only for individuals located close to a conspecific adult. Although the mortality of seedlings and saplings was not associated with the proximity of adults when all species were analyzed together, there was a tendency for this association in particular plant species, especially the less abundant in this community (i.e. Diperyx alata, Salacia elliptica and Sterculia apetala). Thus, we suggest that, for this plant community, a combination of host-specific enemies and intraspecific competition for abiotic resources may contribute to the high mortality of seedlings and saplings that are established at short distances from other conspecific individuals. Regardless of the mechanisms that determine seedling and sapling mortality in this community, we have shown here that such mortality increases as individuals get more aggregated. Density-dependent mortality has been acknowledged to open up space for individuals of other species that may not be vulnerable to enemies that are specialized in certain plant species. In this way, it has been suggested that seed dispersal would contribute to plant community diversity especially as a result of seed dispersal limitation (Harms et al. 2000, Muller-Landau et al. 2002). Although we recognize that the high mortality of aggregated seedlings and saplings may open opportunities for enhancing or maintaining plant species diversity, we also recognize that the intense aggregation of individuals of particular plant species may compromise the populations of these plants. As seed dispersal affects seedling recruitment and distribution, this process may also continue to impact fitness by subsequently influencing demography and, ultimately, the spatial genetic structure of plants (Howe & Smallwood 1982, Howe 1989, Wang & Smith 2002, Hamilton & Miller 2003). Therefore, seed dispersal limitation can have far- reaching consequences not only for the demography of plants and plant community composition, but also for the fine-scale patterns of genetic structure and for plant population differentiation (Vekemans & Hardy 2004). Evidently such repercussions of seed dispersal warrant further examination.

122 Here, we show that species that strongly rely on a single seed disperser species show a high aggregated distribution of individuals, which could contribute to a high mortality of seedlings and saplings. We also found that such mortality could be due to a high level of herbivory and attack by pathogens in seedlings and saplings that are located close to conspecific adults. The reasons for the high mortality of seedlings and saplings located close to other conspecific plants could be competition for abiotic resources, but those were not assessed in this study. Furthermore, seed dispersal was the main important predictor of the aggregation intensity of individuals in comparison with several other variables. Therefore, we conclude that, although different variables such as seed size and edaphic characteristics can operate at different scales in shaping the distribution and structure of plant communities, seed dispersal interactions appear to be important in that respect, even when considering the effects of multiple animal species in dispersing particular plant species. Furthermore, we suggest that, as large- and medium-bodied seed disperser species are highly vulnerable to deforestation and hunting, the intense aggregated distribution of certain plant species can be intensified in a scenario of defaunation, increasing seedling mortality and, perhaps, leading to a strong fine-scale spatial genetic structure in those plants.

Acknowledgments We would like to thank Reese Rogers, Jorge Guedes, Lee Love-Anderegg, Adilson Braga Samuel and Luisa Haddad for helping with field work. We thank FAPESP (2004/00810-3 and 2008/10154-7), Earthwatch Institute and Conservation International for financial support. CID was supported by Stanford University and the Zaffaroni Fellowship Fund. We also thank Conservation International, Lucas Leuzinger and Marina Schweizer for their permission to work in their properties.

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126 Figures

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#4$ #4% #4& #4' #41 #42 #43 5,67)8)-9*:*;9*;<* Figure 1. Association between the aggregated distribution (Omega) of individuals and the value of maximum dependence of the plant on its most important seed disperser. Top: Value of aggregation for individuals located within three meters of conspecifics. Center: Value of aggregation for individuals located within four meters of conspecifics. Bottom: Value of aggregation for individuals located within five meters of conspecifics.

127

Figure 2. Logistic regression between the survival of seedlings and saplings and the distance from the closest conspecific individual (m).

128

Figure 3. Associations between the distance (m) from the closest conspecific adult and a) the percentage of the plant with foliar herbivory, and b) the percentage of the plant with foliar attack by pathogens.

129