University of Florida Thesis Or Dissertation

DISENTANGLING DRIVING FORCES OF AVIAN COMMUNITY ASSEMBLY ALONG ALTITUDINAL GRADIENTS

FLAVIA A. MONTAÑO CENTELLAS

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2018

To Javier Andrés

ACKNOWLEDGMENTS

First of all, I want to thank my advisor, Bette Loiselle, who did not only fulfil the role of an academic guide, but of role model in every aspect of life. I thank my committee members

John Blake, Emilio Bruna, Benjamin Baiser and Scott Robinson, for their valuable input and advice throughout this research. Everybody at the Department of Wildlife Ecology and

Conservation at the University of Florida who contributed to this research, with their feedback in presentations, their questions in social events and their constant support in distress times. In particular, I am grateful to the crew at “the little white house”, the Tropical Ecology and

Conservation Lab, who provided me with a family during these years of learning.

All this work would not be possible without the incredible participation of my friends in

Bolivia. All of them were crazy enough to grab their backpacks and hike with me chasing flocks, mist-netting in extreme landscapes and learning about this unique ecosystem. In particular, I am grateful to Rhayza, Miguel, Paola, Cesar, Mariela, Camila, Mariano, Darwin, Beatriz, Yara,

Karen, Krystal, Sebastian, Nellsy and Amanda for their extended help in fieldwork, and beyond.

Moving all these people and collecting these data was possible with the help of numerous funding agencies. Grants from the Tropical Conservation Development program (TCD-UF), UF

Biodiversity Institute, Idea Wild, American Ornithologist Union, World Wildlife Fund, The

Rufford Foundation, Neotropical Bird Club, American Philosophical Society and The Field

Museum, made all this possible. I thank the Museo Nacional de Historia Natural, for their support.

Finally, I am grateful to my family, my mom and dad, who not only understood and supported all the crazy decisions I made in life but have tagged along and celebrated successes with me. Especially, I do all this with Javier Andres in my mind and by my side. More than a brother, a guide.

TABLE OF CONTENTS page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 8

LIST OF FIGURES ...... 10

ABSTRACT ...... 14

CHAPTER

1 INTRODUCTION ...... 16

2 BIRD COMMUNITY ASSEMBLY ALONG ELEVATIONAL GRADIENTS: A GLOBAL ANALYSIS ...... 21

Background ...... 21 Methods ...... 26 Bird Elevational Data ...... 26 Patterns of Diversity along Elevational Gradients ...... 27 Deterministic Processes Driving Community Assembly Along Elevational Gradients ...... 30 Results...... 31 Patterns of Diversity along Elevational Gradients ...... 31 Deterministic Processes Driving Community Assembly along Elevation ...... 33 Discussion ...... 34 Patterns of Diversity along Elevational Gradients ...... 34 Deterministic Processes Driving Community Assembly along Elevation ...... 35 Conclusions ...... 40

3 GLOBAL PATTERNS OF BIRD FUNCTIONAL AND PHYLOGENETIC BETA DIVERSITY SHOW THAT MOUNTAIN PASSES ARE HIGHER IN THE TROPICS ....50

Background ...... 50 Methods ...... 54 Bird Elevational Data ...... 54 Phylogenetic and Functional Data ...... 55 Functional and Phylogenetic Beta Diversity ...... 57 Effect of Latitude on Mountain β Diversity Patterns ...... 60 Results...... 61 Avian Assemblages ...... 61 Effect of Latitude on Beta Diversity (βchao) and Speed of Change in Diversity (βsor) .....61 Coupling or Decoupling of Different Dimensions of β Diversity ...... 63 Discussion ...... 64

4 DISENTANGLING DRIVERS OF AVIAN COMMUNITY ASSEMBLY ALONG ELEVATIONAL GRADIENTS IN THE BOLIVIAN ANDES ...... 81

Background ...... 81 Methods ...... 84 Bird Surveys ...... 84 Bird Taxonomic Diversity: Species Richness and Composition ...... 85 Bird Functional and Phylogenetic Dispersion ...... 85 Testing for Deterministic Processes Shaping Bird Assemblages ...... 88 Ecological Predictors of Functional and Phylogenetic Diversity ...... 88 Results...... 91 Discussion ...... 93

5 OPPOSITE PATTERNS OF FUNCTIONAL AND PHYLOGENETIC DIVERSITY OF MIXED-SPECIES FLOCKS ALONG AN ELEVATION GRADIENT PROVIDE EVIDENCE FOR FACILITATION IN COMMUNITY ASSEMBLY ...... 106

Background ...... 106 Methods ...... 111 Mixed Species Flock Surveys ...... 111 Phylogenetic Data ...... 112 Morphological and Ecological Traits ...... 113 Test for Trait Conservatism ...... 114 Functional and Phylogenetic Structure of Flocks ...... 114 Pair-Wise Associations ...... 116 Results...... 118 Discussion ...... 120 Conclusions...... 126

6 INTERACTION NETWORKS OF AVIAN FLOCKS INCREASE IN CONNECTIVITY AND COHESION WITH ELEVATION ...... 136

Background ...... 136 Methods ...... 141 Data Collection ...... 141 Flocking Assemblages: Species Richness and Community Composition ...... 143 Network Structure across Elevations ...... 143 Results...... 147 Flock Richness and Composition ...... 147 Network Structure and Pair-Wise Interactions across Elevations ...... 148 Discussion ...... 149

7 GENERAL CONCLUSIONS ...... 165

APPENDIX

A SUPPLEMENTARY FIGURES ...... 169

B SUPPLEMENTARY TABLES ...... 198

LIST OF REFERENCES ...... 222

BIOGRAPHICAL SKETCH ...... 243

LIST OF TABLES

Table page

2-1 Evidence of deterministic processes in tropical and temperate montane bird assemblages across the globe...... 42

2-2 Coefficients of the two-stage mixed-effect models explaining patterns of variation of functional diversity and phylogenetic diversity along elevational gradients ...... 43

3-1 General linear models for taxonomic (Tβchao), functional (Fβchao) and phylogenetic (Pβchao) beta diversity as a function of latitude ...... 71

3-2 General linear models for the average and the variation of βsor between consecutive elevational bins in 46 mountain systems across the globe ...... 72

4-1 Best models explaining variations in taxonomic, functional and phylogenetic diversity of bird assemblages across elevations in the Andes of Bolivia...... 99

5-1 Regression coefficients of the MRQAP-DSP regression showing the effect of phenotypic and phylogenetic distances on positive species interactions ...... 128

5-2 Regression coefficients of the MRQAP)-DSP regression showing the effect of phenotypic and phylogenetic distances on negative species interactions ...... 129

6-1 Characteristics of the assemblage and network-level properties of mixed-species flocks’ networks at five elevations in the Bolivian Andes ...... 155

6-2 Network dissimilarity (βWN) between mixed-species flock’ networks at consecutive elevations, and its partition into two components βST and βOS...... 156

B-1 Characteristics of the mountain systems included in this study ...... 199

B-2 Mixed-effect model results for effect size of correlations (measured as Fisher’s Z transformed values of Pearson correlations) among diversity metrics...... 201

B-3 Pearson’s moment correlations among dimensions of biodiversity for 46 mountain ranges across the globe...... 202

B-4 Average bird beta diversity values of taxonomic (Tβsor), functional (Fβsor) and phylogenetic (Pβsor) diversity for 46 elevational gradients across the globe ...... 204

B-5 Values of Blomberg’s K for morphological functional traits included in the analyses. ..207

B-6 Pearson moment correlations among environmental variables potentially affecting diversity patterns ...... 208

B-7 Full models of diversity as a function of habitat complexity, arthropod diversity, fruit availability and mean daily temperature in the Bolivian Andes...... 209

B-8 Polynomial regression models of multiple dimensions of diversity mixed species flocks across elevations in the Bolivian Andes...... 210

B-9 Characteristics of flocks at five elevations in the Andes of Bolivia, and number of significant pair-wise associations between species ...... 211

B-10 Regression coefficients of the multiple regression showing the effect of morphological distances on the association strength of positive species interactions .....212

B-11 Regression coefficients of the multiple regression showing the effect of ecological distances on the association strength of positive species interactions ...... 213

B-12 Regression coefficients of the multiple regression showing the effect of morphological distances on the association strength of negative species interactions ....214

B-13 Regression coefficients of the multiple regression showing the effect of ecological distances on the association strength of negative species interactions ...... 215

B-14 Phylogenetic signal (Pagel’s λ) in eight functional traits of 94 bird species participating in mixed-species flocks in the Andes of Bolivia ...... 216

B-15 Generalized linear model with negative binomial errors to contrast species richness per flock, in mixed-species flocks across five elevations in the Bolivian Andes ...... 217

B-16 Pairwise PerManova analyses among flocking assemblages at five elevations in the Bolivian Andes...... 218

B-17 Effects of elevation on species-level metrics of interaction networks of flocking birds across five elevations in the Bolivian Andes...... 219

B-18 Effects of elevation on network-level metrics of interaction networks of flocking birds across five elevations in the Bolivian Andes ...... 220

B-19 Pairwise network beta diversity for all pairs of networks, and their partition in βST and βos...... 221

LIST OF FIGURES

Figure page

2-1 Location of the elevational gradients used in the study...... 44

2-2 Global patterns of bird functional and phylogenetic diversity along elevational gradients...... 45

2-3 Summary of the correlations between dimensions of biodiversity in tropical and temperate montane bird assemblages...... 46

2-4 Global trends of functional and phylogenetic diversity when controlled by species richness ...... 47

2-5 Percentage of tropical and temperate montane bird assemblages showing evidence of underdispersion, in lower, mid and higher elevations...... 48

2-6 Effect of latitude on the patterns of change in functional (SES FRic and SES FDis) and phylogenetic (SES PD and SES MPD) diversity across elevations...... 49

3-1 Schematic representation of the expected patterns of functional (and phylogenetic) β diversity along elevational gradients ...... 73

3-2 Patterns of taxonomic beta diversity (Tβchao), functional beta diversity (Fβchao) and phylogenetic beta diversity (Pβchao) for 46 mountain systems across latitude ...... 74

3-3 Rate of change in taxonomic, functional and phylogenetic diversity across elevations for 46 mountain systems worldwide...... 75

3-4 Variability in the rate of change in taxonomic, functional and phylogenetic diversity across elevations, for 46 mountain systems worldwide ...... 76

3-5 Patterns of taxonomic, functional and phylogenetic beta diversity and its components: richness (βrich) and replacement (βrepl)...... 77

3-6 Comparison of richness (βrich) and replacement (βrepl) components of beta diversity for temperate and tropical mountains ...... 78

3-7 Correlations between dimensions of beta diversity for tropical and temperate mountains ...... 79

3-8 Correlation between functional and phylogenetic beta diversity for tropical and temperate mountains when controlling by taxonomic dissimilarity...... 80

4-1 Hypothetical representation of the mechanisms that drive the predicted results...... 100

4-2 Species richness of 23 assemblages of birds across elevations in the Bolivian Andes ...101

4-3 Species composition of 23 assemblages of birds across elevations in the Bolivian Andes ...... 102

4-4 Ecological predictors of diversity patterns and their variation across elevations along an elevational gradient in the Bolivian Andes ...... 103

4-5 Functional dispersion of 23 bird assemblages across elevations in the Bolivian Andes .104

4-6 Phylogenetic dispersion of 23 bird assemblages across elevations in the Bolivian Andes ...... 105

5-1 Schematic representation of the community assembly framework that considers both positive and negative interactions among species in mixed-species flocks ...... 130

5-2 Theoretical representation of the changes in relative importance of positive and negative interactions among participant species in mixed species flocks...... 132

5-3 Patterns of diversity metrics of mixed species flocks of birds along an elevational gradient in the Bolivian Andes...... 133

5-4 Deviation from null expectations in functional diversity and phylogenetic diversity of mixed-species flocks along an elevational gradient in the Bolivian Andes ...... 134

5-5 Percentage of significant positive and negative pair-wise associations in flocks at different elevations in the Bolivian Andes ...... 135

6-1 Hypotheses tested in this study...... 157

6-2 Changes in mixed-species flocks’ richness across elevation in the Bolivian Andes...... 158

6-3 Non-metric multidimensional scaling ordination of mixed-species flocks of the Bolivian Andes...... 159

6-4 Species-level metrics of networks of flocking birds across elevations in the Andes of Bolivia ...... 160

6-5 Network-level metrics related to network connectivity and with network cohesion, for co-occurrence networks of flocking bird species in the Bolivian Andes...... 161

6-6 Changes in network structure between consecutive elevations, showing the changes in species associations for shared species ...... 162

6-7 Network interactions of species pairs that co-occurred in flocks across all elevations showing changes in association strength with elevation...... 163

6-8 Network interactions of species that were found in more than 25 flocks, showing changes in association strength with elevation ...... 164

A-1 Patterns of functional richness along elevational gradients for 46 mountain systems.. ..170

A-2 Patterns of functional dispersion along elevational gradients for 46 mountain systems .171

A-3 Patterns of phylogenetic richness along elevational gradients for 46 mountain systems ...... 172

A-4 Patterns of phylogenetic dispersion along elevational gradients for 46 mountain systems ...... 173

A-5 Deviations from null expectations of functional richness of bird assemblages across elevations in 46 mountain systems ...... 174

A-6 Deviations from null expectations of functional dispersion of bird assemblages across elevations in 46 mountain systems ...... 175

A-7 Deviations from null expectations of phylogenetic richness of bird assemblages across elevations in 46 mountain systems...... 176

A-8 Deviations from null expectations of phylogenetic dispersion of bird assemblages across elevations in 46 mountain systems ...... 177

A-9 Summary of the effect sizes of correlations between dimensions of biodiversity in tropical and temperate montane bird assemblages...... 178

A-10 Global trends of functional and phylogenetic diversity when controlled by species richness ...... 179

A-11 Effect of latitude on the patterns of change in standardized effect size of functional and phylogenetic diversity ...... 180

A-12 Schematic representation of the pairwise β diversity values extracted to calculate the rate of change in diversity and the accumulated change in diversity ...... 181

A-13 Rate of change in taxonomic diversity between consecutive 200-m elevational bands for 46 mountain systems across the world ...... 182

A-14 Rate of change in functional diversity between consecutive 200-m elevational bands for 46 mountain systems across the world ...... 183

A-15 Rate of change in phylogenetic diversity between consecutive 200-m elevational bands for 46 mountain systems across the world...... 184

A-16 Accumulated change in taxonomic βsor along 46 elevational gradients worldwide, calculated with a benchmark approach ...... 185

A-17 Accumulated change in functional βsor along 46 elevational gradients worldwide, calculated with a benchmark approach...... 186

A-18 Accumulated change in phylogenetic βsor along 46 elevational gradients worldwide, calculated with a benchmark approach ...... 187

A-19 Vertical strata profiles of vegetation for all elevational bands of the studied gradient ...188

A-20 Correlations among predictor variables used in the study...... 189

A-21 Deviations from null expectations in phylogenetic dispersion of mixed-species flocks in the Bolivian Andes, calculated considering only Passeriformes...... 190

A-22 Example of species with significant positive interactions that group with each other within different types of mixed-species flocks ...... 191

A-23 Example of species with significant negative interactions that avoid each other in mixed species flocks ...... 192

A-24 Map of the study site in the Western Andes of Bolivia...... 193

A-25 Species accumulation curves as a function of sample size for avian mixed-species flocks at five elevations in the Bolivian Andes...... 194

A-26 Rank abundance curves showing the frequency distribution of species in flocks across five elevations in the Bolivian Andes ...... 195

A-27 Interaction networks of flocking species at 5 elevations in the Bolivian Andes ...... 196

A-28 Frequency distribution and cumulated degree distributions of positive co-occurrences among species in assemblages of flocking species in the Bolivian Andes...... 197

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

DISENTANGLING DRIVING FORCES OF AVIAN COMMUNITY ASSEMBLY ALONG ALTITUDINAL GRADIENTS

Flavia A. Montaño Centellas

December 2018

Chair: Bette A. Loiselle Major: Wildlife Ecology and Conservation

Understanding spatial patterns of biodiversity and the mechanisms driving them is one of the main challenges ecologists face, and it has become even more critical now as they allow us to predict the potential effects of environmental changes such as those expected by global climate change (Webb et al. 2002, Webb et al. 2010). Elevational gradients have been used as models for exploring patterns of biodiversity and the underlying mechanisms structuring them because they are characterized by considerable environmental changes over relatively small distances (Körner

2007). Here, I examine historical and ecological processes that shape bird communities along elevational gradients. First, I gathered information from bird communities sampled along elevational gradients worldwide and examined whether the relative roles of historical and ecological processes on resulting communities are similar across latitudes (Chapters 2 and 3).

Results suggested a consistent pattern of abiotic filters being stronger forces at higher elevations and biotic filters being stronger forces at lower elevations. However, the effect size of both forces decreased with latitude.

Second, I tested the relative forces of historical and ecological processes in shaping

Bolivian bird assemblages across elevations and tested the importance of ecological variables as drivers of these patterns (Chapter 4). As expected, upslope communities are shaped and

maintained by abiotic barriers, whereas communities at lower elevations were mostly structured by species interactions, potentially including interspecific competition. Temperature and habitat complexity were the strongest predictors of diversity patterns. The unexpected increase of functional diversity with elevation, however, suggests that species interactions are important at a more ecological scale. Potentially both positive and negative interactions at this high elevation assemblages are driving patterns of trait dispersion.

Third, I went beyond the classical environmental filtering-species competition dichotomy and tested the role of positive interactions among species in shaping montane avifaunas. Results suggested that positive interactions explained a significant amount of diversity differences across elevations. Thus, facilitative interactions might play a key role in community structure and resulting diversity patterns. I propose a hypothetical framework to include positive interactions in the analysis of community assembly of biological communities along environmental gradients.

CHAPTER 1 INTRODUCTION

One of the fundamental questions in ecology is why communities are composed of a given set of species and not by another. What are the mechanisms that result in observed patterns of community assembly (i.e., how natural communities are structured and maintained)? The last decade has seen a great advance in our knowledge of the processes by which species from a regional pool colonize and interact to form communities. Building on seminal ideas by Diamond

(1975) , a resurgence in the interest of community assembly grew into the integration of an evolutionary perspective on community assembly, and a merging of knowledge and ideas of different ecological frameworks (HilleRisLambers et al. 2012). Despite the development of a theoretical framework to explain and predict community assembly, and the growing body of empirical studies on the processes that drive community assembly, little is known of the generality of this framework and the mechanisms behind these processes. A promising avenue to address this problem is a ‘trait-based approach’ where organisms are characterized by their biological attributes, such as physiological, morphological or life-history traits (Johnson and

Stinchcombe 2007). Traits can be linked directly to the environment and are independent of species identity, thus facilitating predictions across ecosystems (McGill et al. 2006, Westoby and

Wright 2006). In essence, the trait-based approach asserts that community patterns are shaped and maintained by two main forces that operate on species traits through space and time: the physical environment and species interactions, i.e. environmental filtering and limiting similarity

(Webb et al. 2002). These forces act as filters in such a way that only those species and individuals with appropriate traits to persist under given conditions (e.g., able to persist in cold periods or droughts; adapted to deal with predation or disease) will remain and become part of the community. Analyzing observed patterns of trait diversity allow us to infer which force,

environmental filtering or limiting similarity, plays a stronger role in community assembly

(Webb et al. 2010, Belmaker and Jetz 2013). Recently, Zarnetske et al. (2012) suggested that when some species are affected by climate change, they can trigger a cascade of effects on other species they interact with (for example if a pollinator is lost, a key predator is affected, or a strong competitor removed, species they interact with are affected in positive or negative ways), highlighting the importance of incorporating species interactions when modelling community responses to environmental factors (Zarnetske et al. 2012). Thus, separating the relative importance of these forces and including them as separate factors in modeling is critical to better predicting of climate change consequences (Cahill et al. 2013). In this dissertation, I systematically examined the relative roles of the physical environment and biological interactions as ecological forces driving bird community assembly in elevational gradients.

Why birds? Birds are an ideal group to test my hypotheses because: (1) their taxonomy and evolutionary history are well known, (2) they are diverse and have evolved a broad range of strategies (ecological niches), (3) census methods are standardized allowing for comparison with other studies, (4) they provide important ecological services such as seed dispersal, pollination, and serve as biological controls on plant pests, and (5) their interactions with other species are relatively well studied providing a unique opportunity to incorporate this information in climate change projections.

Why elevational gradients? Elevational gradients represent a gradient of several measurable variables, i.e. temperature, humidity, precipitation regimes and oxygen level. Thus, they can represent stress gradients where higher elevations may impose harsher conditions

(higher stress) for organisms to cope with. On the contrary, at lower elevations biological interactions are expected to be more important than physical conditions for organisms because

conditions are less harsh (Ackerly and Reich 1999, Swenson et al. 2011, Hoiss et al. 2012, Read et al. 2014). Hence, elevational gradients are good models to test how these forces, environmental filtering and limiting similarity, shape community traits (Johnson and

Stinchcombe 2007) and to relate observed traits with specific environmental factors or biological interactions (Lebrija-Trejos et al. 2010).

I first examined the role of environmental filtering and biological interactions as forces shaping avian communities at a global scale (Chapters 2 and 3) and then scaled down to test more specific hypotheses examining bird communities in the Tropical Andes of Bolivia and how they respond to environmental factors that covary across elevations (Chapter 4). Secondly, I propose a new framework to incorporate the role of positive interactions into studies of community assembly and test this framework with data of mixed-species flocks in the

Neotropical Andes (Chapter 5). Finally, I combined community ecology and network theory to test for the mechanisms driving structural characteristics of the interaction networks of mixed- species flocks across elevations (Chapter 6).

In Chapter 2, I presented the first analysis of potential mechanisms governing montane assemblages across latitudes that uses real avian communities worldwide. Gathering data from published and unpublished elevational gradients sampled across the globe, I tested for predictions derived from coexistence theory (HilleRisLambers et al. 2012). I found that the expected pattern of environmental filtering being stronger upslope and biological interactions representing a stronger ecological force for community assembly at lower elevations held across latitudes. However, I did find that the overall strength of environmental filtering decreases with latitude, supporting the early ideas of Daniel Janzen (1967) that mountains in the tropics represent “stronger” physiological barriers for biotas. In Chapter 3, I more directly tested Janzen

(1967)’s seminal ideas by analyzing β diversity patterns in montane ecosystems across the globe.

I used the global data set of bird assemblages to explore functional and phylogenetic β diversity patterns and partitioned these values into their two components: species turnover (βrepl) and species loss (βrich) (Carvalho et al. 2012, Legendre 2014). This partition allowed us to infer the mechanisms driving community changes across elevations and to test if these mechanisms changed with latitude. Combined, results supported the idea that different processes act upon biological communities of tropical and temperate mountains. Whereas environmental filtering was stronger in the highlands of tropical mountains, effects of environmental filtering were more homogeneous across altitudes in temperate mountains. Results, however only partially supported predictions derived from Janzen’s ideas, thus the mechanisms behind these processes remain elusive.

At a local scale, I present data on one full elevational gradient in the Bolivian Andes, a biodiversity-rich and highly threatened ecosystem that is particularly vulnerable to climate change (Urrutia and Vuille 2009, Anderson et al. 2011). The transect dissects forested habitats with no human disturbance (Chapter 4). By combining the trait-based approach with phylogenetic analyses, I tested for the relative roles of the physical environment and the competition/facilitation dichotomy as drivers of community assembly in this complex ecosystem.

Then, I tested for the relationship of taxonomic, functional and phylogenetic diversity and environmental predictors in this system. Building on classic ecology, I selected four putative variables that might affect bird diversity patterns: temperature, habitat structure, fruit availability and arthropod diversity (MacArthur and MacArthur 1961, MacArthur 1964, Terborgh 1971,

1977). I found that temperature is the best predictor for most patterns; however, habitat complexity was also important to predict species richness and phylogenetic diversity. Contrary to

expectations, functional diversity increased with elevation, a pattern that is consistent with species interactions (i.e., biotic filtering) as a main force driving communities at high elevations.

In Chapter 5, I used mixed species flocks along one elevational gradient in the Bolivian

Andes, to further understand how positive and negative species interactions might act as filters to determine species coexistence and communities. Findings from Chapter 5 suggest that whereas environmental filtering and competition remain as strong forces shaping avian communities at a larger temporal and spatial scale, facilitation seems to be an important force at a local scale, counteracting environmental harshness. Signals of facilitation were detected at different elevations, highlighting the need to incorporate positive interactions in our current theorization of community assembly. Finally, in Chapter 6 I use network theory to understand the structure of mixed-species flocks as social networks and how network topology changes across elevations.

Results suggest that upslope networks might be more resilient to disturbances than low-elevation networks, potentially buffering consequences of disturbances on the overall structure of the communities. Based on these results, I discuss the role of facilitation as a strategy promoting diversity under strong environmental stress.

CHAPTER 2 BIRD COMMUNITY ASSEMBLY ALONG ELEVATIONAL GRADIENTS: A GLOBAL ANALYSIS

Background

Understanding spatial patterns of biodiversity along environmental gradients and the mechanisms driving them is a main focus in ecology (Hillebrand 2004, Ricklefs 2004, Swenson

2011b) and is critical to predict the potential effects of global climate change (Cisneros et al.

2014, Quintero and Jetz 2018). One promising approach to better understand the underlying processes behind diversity patterns is examining multiple dimensions of biodiversity and their correlations (Stevens et al. 2013). The simultaneous assessment of phylogenetic and functional dimensions of biodiversity along environmental gradients provides insights into the relative importance of ecological and evolutionary mechanisms structuring assemblages (Cisneros et al.

2014). Whereas the phylogenetic dimension of biodiversity reflects the evolutionary differences among species (Cavender‐Bares et al. 2009), the functional dimension of biodiversity reflects the variability in their ecological attributes (Petchey and Gaston 2006). The examination and comparison of multiple dimensions of diversity, therefore, may better reflect community assembly mechanisms that selectively limit the membership and co-occurrence of species in communities (Mayfield and Levine 2010, Mori et al. 2013, Quintero and Jetz 2018).

Under the classic framework of community assembly, two main niche-based deterministic processes, which are based on species ecological similarity, can be related to diversity patterns and species coexistence within a community: environmental filtering and biotic filtering (Webb et al. 2002). Assuming niches are phylogenetically conserved, phylogenetic and trait similarity among species in a given community can provide insights into the relative role of these two processes (Cavender‐Bares et al. 2009). Whereas the mechanism of abiotic filtering should result in ecologically similar species coexisting, strong biotic interactions (i.e., negative

interactions, such as competition) are expected to lead to co-occurrence of ecologically dissimilar species (MacArthur and Levins 1967, HilleRisLambers et al. 2012). This latter prediction, known as the competition-relatedness hypothesis (Cahill et al. 2008) will be valid only if the niche differences among taxa are important for their coexistence; i.e., if the traits included in the study relate to taxa critical niche dimensions (Mayfield and Levine 2010).

Although other biological interactions have been hypothesized to structure biological assemblages (i.e., facilitation, shared predators), their inclusion in the community ecology framework remains limited (Stachowicz 2001, Morris et al. 2004, Spasojevic and Suding 2012).

Selecting species characteristics that are known to be relevant to interspecific competition is therefore critical to interpret the roles of deterministic processes on community assembly.

To detect the potential role of different mechanisms acting upon communities, one can examine the degree to which species communities are more or less similar than expected from random assemblages (Kraft et al. 2008, Lebrija-Trejos et al. 2010, Trisos et al. 2014, Dreiss et al.

2015). For instance, a pattern of communities with functional overdispersion (i.e., species being more dissimilar to each other than in null expectations) is consistent with community assembly driven by competitive interactions, which result from limiting similarity (MacArthur and Levins

1967, Kluge and Kessler 2011, Presley et al. 2018). Functional underdispersion, on the other hand, is consistent with abiotic filtering. In this latter case, environmental filters related to physiological tolerances, habitat affinities or resource requirements may dominate community assembly, resulting in a less functionally diverse group of coexisting species than would be expected by chance (Cavender‐Bares et al. 2009, Lebrija-Trejos et al. 2010, Presley et al. 2018).

Similarly, if competitive exclusion acts upon taxa that overlap in their niche preferences, the community would show a pattern of phylogenetic overdispersion (i.e., species being less related

than null expectations) (Mayfield and Levine 2010). In contrast, phylogenetic underdispersion is consistent with community assembly driven by environmental filtering or with interclade competition (Mayfield and Levine 2010). An entire clade may have an advantage over other clades because of superior competitive abilities or because of phylogenetically conserved adaptations to local environmental conditions (Kraft et al. 2008, Lebrija-Trejos et al. 2010).

Overall, physical conditions are harsher at higher elevations, thus it is expected that the relative importance of assembly processes changes along the elevational gradient, with abiotic filtering being a stronger force at higher elevations and species interactions a more important factor at lower elevations. Thus, depauperate assemblages in high elevations are assumed to result from strong environmental filtering preventing species to persist or colonize these extreme environments (Hoiss et al. 2012, Graham et al. 2014). Consequently, species reduction is expected to result mainly from species loss towards higher elevations. Similarly, functional diversity and phylogenetic diversity are also expected to decrease with elevation, as only a subset of traits and clades would remain in high elevation assemblages. Although several studies have examined diversity patterns along elevational gradients (McCain and Grytnes 2001, McCain

2009a, Graham et al. 2014), and tested for mechanistic hypotheses on the drivers of these patterns (Ferger et al. 2014, McCain et al. 2018), only few have explored drivers of functional or phylogenetic diversity along elevational gradients (Cadena et al. 2011, Cisneros et al. 2014,

Dreiss et al. 2015). As a consequence, we know little about the relative importance of

deterministic processes underlying biodiversity patterns in montane systems (Graham et al.

2014), and how this importance changes across the globe.

Here, I test for signals of deterministic processes (i.e., abiotic filtering and species interactions) driving community assembly along elevational gradients across the globe to examine the generality of community assembly theory along elevational gradients. Specifically, I use a global data set that included 46 well-sampled elevational gradients of resident birds to address the following questions: 1) Are there general patterns of bird functional and phylogenetic diversity along elevational gradients?; 2) Does the relative role of environmental filtering and limiting similarity, as community assembly drivers, change along elevation in all mountain systems?; 3) Is there a latitudinal signal in the patterns of functional and phylogenetic diversity along elevation; and 4) Is there a latitudinal signal in the strength of environmental filtering and limiting similarity as drivers of community assembly along elevational gradients?

To address these questions, I first examined patterns of bird functional and phylogenetic biodiversity for each one of the gradients and how they vary among mountain systems. Secondly, on each mountain, I tested for signals of deterministic processes driving community assembly and examined if the overall signal of these processes (measured as the effect size of these processes) holds worldwide. Then, I compared differences in the strength of environmental filtering and limiting similarity among tropical and temperate mountains to better understand how the assembly processes driving mountain biotas might change with latitude. Finally, I examined correlations among different dimensions of diversity to gain further insights into the potential mechanisms driving community assembly along elevational gradients (Stevens and

Gavilanez 2015). Whereas high correlations might suggest one or few dominant processes affecting assemblages, low correlations suggest different processes controlling spatial patterns of

variation across different dimensions of biodiversity (Aguirre et al. 2016). In this study I do not intend a full description of the assembly processes driving communities in all these mountain systems; rather, I examined the overall patterns of different dimensions of diversity and the potential differences among them.

Because conditions are harsher for organisms at high elevations, I predicted abiotic filtering to be stronger at higher elevations whereas species interactions, specifically limiting similarity driven by competition, should have a stronger signal at lower elevations (Graham et al.

2014). In any given mountain, stronger abiotic filtering upslope would result in less species-rich assemblages with more homogeneous traits towards the highlands and more speciose and functionally diverse assemblages in lowlands. However, based on the seminal work of Janzen

(1967) and MacArthur (1984), I hypothesized that the signal of environmental filtering would be different for tropical and temperate mountains. Greater temperature stability in tropical latitudes allows for more opportunities for specialization and niche partitioning (MacArthur 1984), whereas species that experience large temperature changes in more seasonal environments (i.e., at higher latitudes) tend to be physiological and ecological generalists (Read et al. 2018).

Seasonal conditions in temperate mountains might result in strong abiotic filtering across elevations, resulting in lower species richness across elevations with few species persisting in seasonal conditions, and less functionally diverse assemblages along the gradient. Thus, the signal of environmental filtering was predicted to be widespread in temperate mountains, and stronger and more prevalent in high elevations in tropical mountains. Further, because different processes were predicted to shape assemblages at different elevations in tropical mountains and environmental filtering is predicted to be more homogeneous across elevations in temperate

systems, I expect higher coupling of different dimensions of biodiversity in temperate mountains compared with tropical mountains.

Methods

Bird Elevational Data

Data on bird assemblages along elevational gradients were extracted from published articles. To do this, I searched the Web of Science using keywords “bird” OR “avian” and

“elevation” OR “altitude”; the resulting articles were examined and selected largely following

McCain (2009a) criteria. First, I pre-selected studies that surveyed all breeding birds, that focused on complete elevational gradients, that sampled at least four elevations and that had adequate sampling effort across elevations (i.e., sampled at least 70% of the forested elevational gradient, similar effort across elevations, multiple visits and/or replicates were conducted at each elevation). I focused on single-gradient data sets (alpha gradients sensu McCain 2009a) because actual coexistence of species within a given assemblage is critical for testing mechanistic processes driving community assembly. After pre-selecting the studies, I either downloaded the species list from the article or supplementary materials and extracted the information if presented in tables or contacted the main author(s) to have access to these data. After this process, I obtained data from 46 elevational gradients across the globe (Fig. 2-1).

Species data from each gradient were carefully inspected, nomenclature standardized and taxonomy updated when necessary to match that of Jetz et al. (2012). For each data set, I assumed that a species was present between its highest and lowest reported elevation (range interpolation) and created distributional ranges for each species. Following earlier global analyses of taxonomic bird diversity (McCain 2009a), each gradient was then subdivided into

100-m wide elevational bins and all species occurring in each bin were considered as a bird assemblage for analyses. For each assemblage, in each mountain I calculated species richness.

The width of the elevational bins was chosen to have the maximum possible resolution

(minimum width) that balanced the resolution of empirical records while being biologically meaningful. To examine if the decision of using 100m elevational bins affected results I also ran the analyses for bird assemblages in 200m and 400m elevational bins. Overall patterns of functional and phylogenetic diversity did not change with these divisions. However, wider elevational subdivisions resulted in the exclusion of one mountain (with 200m subsections) and

14 mountains (with 400m subdivisions) from the main analyses, as regressions were conducted with a minimum of 5 points in all cases. In this study, I present results only for 100m subdivisions, but overall trends for 200m and 400m subdivisions are presented in Figs. A-10 and

A-11.

Patterns of Diversity along Elevational Gradients

I calculated functional and phylogenetic diversity for each assemblage in each mountain system. I used two metrics to describe functional diversity and two metrics for phylogenetic diversity; in each case, I used one metric to denote richness (functional richness and phylogenetic richness) and one metric of dispersion (functional dispersion and phylogenetic dispersion). I chose these two metrics to fully capture the information contained in functional traits and phylogenetic distances. Further, these metrics were selected because richness metrics are better for detecting the patterns of change, whereas dispersion metrics more accurately measure the signal of deterministic processes by highlighting the extent of variation among traits or tips in a phylogeny while controlling for species richness effects (Chao et al. 2014).

Functional traits for all bird species were compiled from the Wilman et al. (2014) global data set. Traits included in the analyses were (1) diet, (2) foraging strata, and (3) body mass.

Using these traits, I created a species x trait matrix for each bird assemblage (i.e., for each elevational bin within each gradient). In Wilman et al. (2014), diet and foraging strata are

presented as percentages in multiple columns (that add up to 100 for each species), thus they represent non-independent variables. To account for this non-independence and to reduce dimensionality of these variables, I ran two separate Principal Component Analyses (one for diet and one for foraging strata) and kept the first axis to describe each of these two functional traits.

The resulting three traits in each species x trait matrix (diet, foraging strata and body mass) were then standardized to a mean of 0 and ranged from -1 to 1. Functional diversity was calculated for each assemblage as functional richness (FRic) and functional dispersion (FDis). FRic represents the minimum volume occupied by the community in multivariate functional space (Villéger et al.

2008) and FDis represents the mean distance of individual species to the community centroid in trait space (Laliberté and Legendre 2010). FRic and FDis were calculated with functions of the

FD package (Laliberté et al. 2014).

I used the updated version of the bird supertree phylogeny (available at birdtree.org, revised on July 2017) of Jetz et al. (2012), based on the backbone tree by Hackett et al. (2008) to summarize phylogenetic relationships among species for each gradient. The phylogeny in Jetz et al. (2012) results from a MCMC process and is composed of 10000 trees derived from the posterior distribution rather than of a single consensus tree. Thus, for each calculation of phylogenetic diversity for each gradient (and each run in the null models, see below), I randomly selected one of the 10000 trees and pruned to the subset of bird species found at that gradient.

Phylogenetic diversity was then calculated for each assemblage with Faith’s Index (PD) and mean pairwise distance (MPD). Faith’s index relates to phylogenetic richness and sums the quantity of phylogenetic differences present in the assemblage, whereas MPD is a divergence- based phylogenetic diversity metric (Webb et al. 2002, Tucker et al. 2017). Phylogenetic diversity was calculated with functions of the picante package (Kembel et al. 2010).

To examine the patterns of functional and phylogenetic diversity across elevations, I fitted second degree polynomial regressions for each biodiversity metric (FRic, FDis, PD and

MPD) as a function of elevation for each mountain system. Then I assigned these diversity patterns for each mountain to a category based on the overall shape of the polynomial, the signs of the coefficients (positive or negative) and the relative magnitudes of the coefficients.

Following McCain (2009a)’s approach, I used names that could be ecologically interpreted instead of mathematical names for the diversity curves’ shapes. Patterns were classified as

Increasing or Decreasing (if the increase or decrease was monotonic, following a linear trend);

Mid elevation peak or Mid elevation valley if the highest or lowest values of the diversity metric occurred at mid elevations, respectively; Low plateau or High plateau, if there is a peak of diversity metrics that occurs in more than three consecutive elevational bins towards low or high elevations, respectively; Low Valley or High valley, if the lowest values of diversity occurs in more than three consecutive elevation bins towards low or high elevations, respectively. Shapes of these curves are shown in the top panel of Fig. 2-2.

I examined the correlations between functional and phylogenetic diversity, and with species richness, for each mountain by calculating pairwise Pearson’s moments correlation coefficients between each pair of diversity metrics. Resulting correlations coefficients were then transformed to Fisher’s Z scale values to allow for comparisons among mountains. The weighted means of Fisher’s Z values were then compared between tropical and temperate mountains, with a single factor explanatory meta-regression model (with one dummy variable as predictor, tropical = 1 and temperate = 0). For this, I considered every mountain system located between

23.5 and -23.5 degrees of latitude as tropical, whereas mountains with locations above 23.5 and below -23.5 degrees were considered temperate. Calculations of Fisher’s Z values and meta-

regression were performed with the metafor package (Viechtbauer 2010). Finally, I plotted the correlation results to visually search for global patterns of coupling or decoupling between dimensions of diversity.

Deterministic Processes Driving Community Assembly Along Elevational Gradients

I was interested in searching for signals of environmental filtering and limiting similarity as potential processes determining assemblages across elevations. To do this, I needed to test if observed functional and phylogenetic diversities were different than expected given the observed species richness in the assemblage. For each mountain separately, I constructed null models created by randomizing trait and phylogeny tip labels (1000 runs) and tested for clustering or overdispersion by examining the deviation of each observed biodiversity metric from the null model (Ulrich and Gotelli 2013, Swenson 2014). The deviation of the observed value from the average of the null model (i.e., effect size) was standardized to allow comparisons among assemblages (hereafter standardized effect size, SES). The direction of SES (higher or lower than the expected null) was interpreted as overdispersion or underdispersion, and the magnitude of the effect size was interpreted as the strength of the deterministic processes signal on the assemblage

(Swenson 2011b, Swenson 2014). I tested for a global trend in the magnitude and direction of these effect sizes with a second-degree polynomial regression of all values of SES (all mountains grouped together) against normalized elevation (scaled between -1 and 1). I repeated this procedure for each one of the diversity metrics (SES FRic, SES FDis, SES PD and SES MPD).

To test for the latitudinal effect on the relative roles of environmental filtering and limiting similarity, I contrasted the differences in the standardized effect size for each diversity metric (SES FRic, SES FDis, SES PD and SES MPD) among mountains at different latitudes, with a two-stage mixed-effect model (Viechtbauer 2010). Two-stage mixed-effect models are a type of multivariate meta-regression performed in two hierarchical steps. First, I fitted a linear

model of SES as a function of elevation for each mountain separately and extracted the slope of elevation and the variance associated with this slope. Then, I used these two new variables

(slopes and their variances) to calculate weighted values of the slope and used these new values as response variables for a multivariate meta-regression with absolute latitude as predictor and hemisphere as covariate (Koricheva et al. 2013). Meta-regression models were performed with the metafor package (Viechtbauer 2010).

Finally, to further summarize my findings on signals of environmental filtering and limiting similarity, I divided the elevational gradient of each mountain in three sections by dividing all the elevational bins in three equal parts. Then, the upper third of the elevational bins was considered “highlands”, the middle third “mid elevations” and the lower third “lowlands”.

Although I acknowledge this categorization is arbitrary, it allowed for a visual inspection of the signals of deterministic processes within mountains, in a comparable way. For each one of these sections (high, mid and low elevations) I counted the number of mountains that showed a signal of environmental filtering or limiting similarity. I considered a “signal” of these processes if at least half of the elevational bands at a given section had significant deviations from the null expectation. The frequency of mountains showing signals of over or underdispersion were compared visually, because small sample sizes prevented statistical tests to be informative.

Results

Patterns of Diversity along Elevational Gradients

A total of 46 well sampled elevational gradients across the globe located at latitudes between 48.8°N to 24.4° S were included in the analyses. These gradients represent the great variability of mountain regions across the globe (Fig. 2-1, Table B-1), as they are located in regions with different climates, on continental landmasses or on islands and ranging in height from 851 m to 8090 m. Mountain systems included in the analyses also varied greatly in

recorded species richness, from 22 species in Mikura-Jima, Japan to 577 species in Serrania de

Yariguies, Colombia. Altogether, these elevational gradients contained 3522 breeding bird species (~35% of the bird species recognized worldwide).

Most gradients showed a decrease in functional richness with elevation, but the pattern changed across mountains (Fig. 2-2A): most mountains had a mid-elevation peak or a low plateau pattern. Functional dispersion varied more, with no dominant trend across mountains, and roughly half of the mountains showing higher values of functional dispersion upslope (Fig.

2-2B). Phylogenetic richness (PD) showed mostly a decreasing pattern with a low elevation plateau (Fig. 2-2C), whereas phylogenetic dispersion was more variable. Although most mountains showed a pattern of low plateau, mid peak or high valley, a few mountains showed higher values of MPD at higher elevations (Fig. 2-2D). Individual curves for each mountain system are presented in Figs. A-1 to A-4.

No differences in the coupling or decoupling of different dimensions of diversity between tropical and temperate mountains were noted (Table B-2); tropical mountains, however, showed higher variation in correlation values among metrics based on richness (Fig. 2-3). Correlations among different dimensions of diversity varied greatly among mountains (Table B-3, Fig. 2-3; see Fig. A-9 for Fisher’s Z scaled values), with some mountains showing high values of correlation among all diversity metrics (e.g., Mt. Etna, Italy; Mt. Hermon, Israel; Mt. Isarog,

Philippines; and Masoala Peninsula, Madagascar) and others showing strong decoupling, particularly between functional and phylogenetic diversity (e.g., Espinazo del Diablo, Mexico;

Sierra de Tilaran, Costa Rica; and Mt. Wilhelm, Papua New Guinea). As expected, metrics based on richness (species richness, FRic and PD) were overall correlated, whereas dispersion metrics

FDis and MPD showed less consistent patterns.

Deterministic Processes Driving Community Assembly along Elevation

There was no global pattern of increase or decrease in standardized effect sizes among the diversity metrics across elevations (Fig. 2-4), suggesting no consistent global trends in the relative roles of environmental filtering and biotic filtering across elevations. I found, however, that phylogenetic diversity was overall underdispersed, showing a strong effect of environmental filtering on mountain lineages (Fig. 2-4C and D). These patterns remained the same when using

200m and 400m elevational bins (Fig. A-10).

I found evidence of functional and phylogenetic underdispersion in 27% and 94% of all montane bird assemblages respectively, but no significant signal of overdispersion (Table 2-1,

Figs. A-5 to A-8). No differences in the number of mountains with underdispersion were noted between tropical and temperate mountain systems. Underdispersion was more frequent in high and mid elevations in tropical mountains, but otherwise homogeneous across elevations.

Phylogenetic underdispersion was more frequent in temperate mountains but relatively homogeneous across elevations (Table 2-1, Fig. 2-5).

There was a significant yet weak effect of latitude on the variation of SES of functional richness across elevations, with more negative slopes towards tropical latitudes (Table 2-2,

Mixed-effect model, QM = 5.8, P = 0.016). A similar pattern was found for functional dispersion

(Fig. 2-6, Table 2-2, QM=4.47, P=0.034). Changes in phylogenetic richness across elevations were marginally explained by hemisphere but not by latitude (QM=5.29, P=0.062), with Southern hemisphere mountains having more negative slopes. Changes of phylogenetic dispersion across elevations were only explained by latitude, with more positive slopes towards higher latitudes

(QM=7.94, P=0.004; Fig. 2-6C and D, Table 2-2). Overall patterns for these models did not change when calculated for 200m and 400m elevational bins (Fig. A-11).

Discussion

Patterns of Diversity along Elevational Gradients

Bird assemblages at higher elevations were always depauperate and often represented a subset of the overall mountain species pool, as described in previous studies on bird taxonomic diversity (McCain 2009a). These species-poor assemblages in high elevations likely resulted from stronger environmental filtering than lower elevation assemblages, preventing species persistence or colonization of these extreme environments (Hoiss et al. 2012, Graham et al.

2014). Therefore, I expected functional diversity and phylogenetic diversity to also decrease with elevation, as only a subset of traits and clades would remain in high elevation assemblages. As predicted, I found a decreasing pattern of functional richness (FRic) with elevation in most mountain systems, suggesting that the overall trait space occupied by the assemblage decreased with elevation. However, functional dispersion (FDis) showed contrasting patterns, with some mountains increasing functional dispersion with elevation. Higher functional dispersion in high elevations (i.e., the average distance from each species to the centroid of the assemblage, in trait space) may result from two mechanisms: 1) species that are lost towards high elevations are redundant in traits and therefore, species in the extremes of the trait space remain but the average of distances between species increases; or 2) species that replace other species towards high elevations present novel traits, increasing the overall distance among species pairs. These mechanisms need not be mutually exclusive, potentially acting in different mountain systems and even within the same mountain.

Phylogenetic richness (PD) also decreased with elevation, as predicted, supporting the hypothesis of stronger environmental filtering towards high elevations. However, the metric of phylogenetic dispersion (MPD) showed more variable patterns with some mountains showing increased phylogenetic dispersion at higher elevations. MPD values that increase with elevation

can be interpreted as assemblages in higher elevations containing species less related to each other, when compared to assemblages in lower elevation habitats. These results are consistent with Quintero & Jetz (2018), who found higher levels of tip SD (a measure that focuses on the recent diversification events) in the highlands. In their study, Quintero and Jetz (2018) found higher diversification rates at high elevations in mountains worldwide and proposed that rapid diversification of immigrants might be the mechanism behind their results. High elevation immigrants might be more related to taxa from distant regions with similar climates than to other taxa at other elevations in the same mountain range. Then, isolation and heterogeneity of higher elevation landscapes may facilitate the rapid radiation of these immigrant clades. My finding of higher dispersion among high elevation clades in some mountains is consistent with this mechanism. Alternatively, these results of phylogenetic dispersion increasing with elevation can also be interpreted as sister taxa being lost towards high elevations but not entire clades, reducing overall phylogenetic richness, but increasing the average distance among species.

Deterministic Processes Driving Community Assembly along Elevation

I found no global patterns of functional and phylogenetic diversity metrics controlled by species richness effects. The relative role of environmental filtering and limiting similarity were extremely variable across mountains, with both increasing and decreasing patterns of standardized functional diversity (SES FRic and SES FDis). This result suggests that ecological and historical factors shaping functional diversity in montane avifaunas are unique for each system, and no general rules explaining how they change across elevations were found.

Furthermore, this pattern remained when I used different grains of analysis (200m and 400m elevational subdivisions), and when I subsetted the data only to mountains with more than

2000m in length. Effect sizes of elevation on phylogenetic diversity metrics was similarly variable (Fig. 2-4). An overall global pattern of underdispersion, however, was noted, with most

mountains across the globe representing phylogenetically clustered communities. Environmental filtering is, therefore, the main force acting upon montane biotas across elevations.

I hypothesized that the signal of environmental filtering, as shown in the observed patterns of underdispersion, would be different for tropical and temperate mountains. I predicted that it would be widespread in temperate mountains and stronger towards high elevations in tropical mountains (Hoiss et al. 2012, Graham et al. 2014). As predicted, I found signals of environmental filtering in most mountain systems, with significant underdispersion in both functional and phylogenetic diversity (Fig. 2-5). Furthermore, I found a significant effect of latitude on the pattern of change in functional diversity (i.e., the slope of the regression of standardized effect values against elevation, Fig. 2-6). Mountains located at lower latitudes had overall lower and more negative slopes, corresponding to stronger decreases in functional diversity across elevations than temperate mountains. A similar pattern was found for phylogenetic dispersion, with tropical mountains showing stronger decreases in phylogenetic dispersion across elevations. Consequently, tropical mountains showed stronger signals of environmental filtering in high and mid elevations, whereas temperate mountains showed weaker signals. Although these results support the main hypothesis of environmental filtering acting differently in tropical and temperate mountains, it can also reflect differences in speciation rates among tropical and temperate mountains. This results align with a recent study that found that more seasonal mountain systems at higher latitudes are characterized by higher diversification rates, presenting higher values of phylogenetic diversity than tropical mountains (Quintero and

Jetz 2018), despite having lower species richness.

Functional and phylogenetic overdispersion were predicted to occur at lower elevations where community assembly should be dominated by competitive exclusion of closely related

species, resulting in functionally and phylogenetically distant species within assemblages

(Graham et al. 2009, Mayfield and Levine 2010, Dehling et al. 2014). I found no support for functional or phylogenetic overdispersion in any of the 46 mountain systems in this study. The lack of significant overdispersion can be related to random trait selection patterns, resulting from

(1) a balance between mechanisms that promote overdispersion and those that promote underdispersion or (2) a lack of strong effects from any of these mechanisms (Presley et al.

2018). Findings of strong signals of underdispersion but not of overdispersion in any of the mountains suggests environmental filtering as the main driver of mountain bird communities.

These results resemble recent studies of bat assemblages, which also found patterns of underdispersion or random assembly of bat communities across scales (Riedinger et al. 2013,

Patrick and Stevens 2014, Presley et al. 2018).

As temperate mountains were expected to be largely driven by environmental filtering, I expected higher coupling of different dimensions of diversity in temperate compared with tropical mountains. Although I found that correlations among dimensions of diversity were highly variable among mountains, some patterns partially supported my hypothesis. Richness level metrics (FRic and PD) were positively correlated among each other and with species richness (Fig. 2-3A, C, E), with higher variation in tropical mountains, suggesting a great range of mechanisms driving community assembly in the tropics. However, species richness was weakly correlated with FDis, suggesting a highly variable decoupling of taxonomic and functional diversity both in tropical and temperate mountains (Fig. 2-3B). Correlations between functional diversity and phylogenetic diversity were quite variable among mountains, and no differences were evident between tropical and temperate mountains.

Here, I have interpreted the deviations of observed patterns of functional and phylogenetic diversities from the null expectations (standardized effect size) as a metric of the strength of deterministic processes shaping bird assemblages. In particular, underdispersion was interpreted as evidence of environmental filtering as in many other studies (Kraft et al. 2008,

Lebrija-Trejos et al. 2010, Trisos et al. 2014, Dreiss et al. 2015). Other processes, however, can also result in underdispered community patterns (Spasojevic and Suding 2012). For instance, species can indirectly interact by sharing enemies. This interaction, known as apparent competition, is defined as a negative effect of one species on the abundance or persistence of another, mediated through the action of shared enemies (Holt 1977). Apparent competition can occur whether or not the two species compete directly for resources and would result in an augmented signal of direct competition (Holt and Bonsall 2017). Furthermore, equalizing fitness processes might result in competing species coexisting by competing relatively equally instead of partitioning their resources. Positive interactions among species (i.e., facilitation) in stressful environments might also allow for species coexistence resulting in increased functional diversity, a pattern often assumed to result from competition. Either of these processes, however, might result in functional (or phylogenetic) overdispersion, and would not oppose the results of underdispersion as potential evidence of environmental filters. Furthermore, is important to consider that elevation is not the environmental gradient per se but rather is used as a proxy for spatial variation in environmental conditions (Graham et al. 2014).

Our analyses used three main niche axes of birds to calculate functional diversity: body mass, diet and foraging strategy. Other studies examining bird functional diversity at a local scale have used morphological traits as well as ecological guilds to describe bird functional diversity (Dehling et al. 2014, Pigot et al. 2016). Gathering reliable morphological data obtained

“in situ” for the more than 3500 species of birds included in the analyses, however, was virtually impossible. Nonetheless, I believe the three traits used in this study effectively reflect major species characteristics that are related with both the abiotic environment and direct competition pressures. For instance, body mass has implications for life history, and the role of species within ecosystems (Peters and Peters 1986), whereas diet and foraging strategy relate strongly with the ability of using and competing for resources and are strongly correlated with morphological characteristics (Ricklefs and Miles 1994, Pigot et al. 2016). I further reduced the potential bias of using functional groups that treat species within the same guild as identical (i.e., diet or foraging categories), by using information on the percentages of different items in the diet and percentages of different foraging strata used by each species to calculate a unique position of each species in multivariate trait space (by means of principal component analyses).

Several authors have discussed the biases that can arise by the use of empirical data in analyses of biodiversity patterns. In elevational gradient studies, empirical data are sensitive to sample size, differences in human impact along elevation and sampling effort (McCain and

Grytnes 2001, McCain 2004, McCain 2009a). Furthermore, problems with scale and data non- independence among elevational bands have also been discussed in several studies (Nogues-

Bravo et al. 2008). Although I acknowledge that all these biases might affect the data sets included in this study, I have no reason to believe they do it in a similar way across data sets; thus, it is unlikely that they affect the main findings. However, as I was interested in searching for signals of deterministic processes in community assembly, especially environmental filtering as a driver of community assembly along elevation, I needed to examine real assemblages with co-existing species. Local scale data were important as they reflect the scale where species can interact in space and time either directly or indirectly (Graham et al. 2014). Furthermore, the

finding of large variation in taxonomic, functional and phylogenetic diversity patterns among mountains is unlikely a product of sampling biases. On the contrary, this variation among mountains highlights the uniqueness of each elevational gradient and has been previously reported. For instance, Chun & Lee (2017) found different patterns of phylogenetic diversity and functional diversity even in transects along different slopes of the same mountain range sampled with the same protocols and effort. This finding suggests that local factors turn each elevational gradient into a unique scenario for bird assemblages to interact with and emphasizes the need of local scale studies and empirical data to fully understand the processes driving community assembly in each mountain range (Chun and Lee 2017). Future studies might focus on examining the patterns of different dimensions of diversity I describe, and their variation, as a result of changes in environmental variables across elevations and apply this information to better predict how the properties of communities will change in a warming world (Sheldon et al. 2018).

Conclusions

Mountain ranges harbor a great fraction of living organisms on earth, including several taxa that are adapted to the unique environmental conditions in mountain ranges. Unfortunately, a large portion of mountain systems and their biota are currently under threat due to climate change (Colwell et al. 2008, McCain and Colwell 2011). My analyses add to a growing body of work in search of understanding global patterns of biodiversity and the role of deterministic processes in elevational gradients. Here, I presented the first global analysis of multiple dimensions of bird diversity, using the best empirical data sets available that allow for the examination for coexistence and signal of deterministic processes in community assembly. The patterns detected were consistent with the idea that environmental filtering is acting upon high elevation assemblages, reducing species richness and phylogenetic richness, and changing functional and phylogenetic dispersion of local assemblages. I hypothesized that environmental

filtering should be stronger for birds in tropical mountains, and found evidence supporting my hypothesis. Latitude had a significant effect on the slope of functional diversity (richness and dispersion) across elevations, with more negative slopes (i.e., stronger decline in diversity with elevation) in tropical mountains. Furthermore, whereas functional underdispersion was more frequent in tropical highlands it was consistently weak in temperate mountains. I found no evidence of functional or phylogenetic overdispersion, a pattern expected by biological interactions (i.e., competitive forces or facilitation among dissimilar species), highlighting even more the strong role of environmental filters on mountain biotas.

The results showed a great variability of responses to elevation. Furthermore, coupling or decoupling of different dimensions of biodiversity are also variable among mountains, suggesting the relative importance of historical (large scale) and ecological (local scale) processes will also depend on the mountain under study. The brief examination of other characteristics of mountains that might relate to differences in the strength of assembly processes

(i.e., if mountains are on islands or continental masses, or in different hemispheres) provides a glimpse of potential factors to consider in further analysis of biodiversity patterns. There is still much to learn and disentangle in global biodiversity patterns and their drivers, particularly in the face of climate change. Here, I aimed to give the first step towards including other facets of biodiversity in global analysis of elevational gradients and gain insights into the overall weight of potential processes driving bird community assembly worldwide. Understanding the processes that result in mountain bird assemblages are key to retaining the diversity and unique nature of mountain systems worldwide.

Table 2-1 Evidence of deterministic processes in tropical and temperate montane bird assemblages across the globe. Number of mountains that showed significant deviations from expectations from null models are presented for each category (tropical and temperate) and for each section of the mountain (highlands, mid elevations and lowlands). Mountains located between latitudes of 23.5oN and 23.5oS were considered as tropical, and mountains located above 23.5oN and below 23.5oS were considered temperate. FRic stands for functional richness, FDis for functional dispersion, PD for phylogenetic richness and MPD for phylogenetic dispersion. UD = Significant underdispersion, OD = Significant overdispersion, NS = non-significative.

Tropical Mountains (N = 30) Temperate Mountains (N = 16) Elevation FRic FDis PD MPD FRic FDis PD MPD High 6 7 22 11 2 1 14 13 UD Mid 8 7 23 11 2 1 15 14 Low 2 7 21 6 2 1 14 10 High 0 0 0 0 0 0 0 0 OD Mid 0 0 0 0 0 0 0 0 Low 0 0 0 0 0 0 0 0 High 24 23 8 19 14 15 2 3 NS Mid 22 23 7 19 14 15 1 2 Low 28 23 9 24 14 15 2 6

Table 2-2. Coefficients of the two-stage mixed-effect models (meta-regression) explaining patterns of variation of functional diversity and phylogenetic diversity along elevational gradients, as a function of latitude and hemisphere. Models were run for functional richness (FRic), functional dispersion (FDis), phylogenetic richness (PD) and phylogenetic dispersion (MPD), separately. Hemisphere only contributed to the model for SES PD. Model Coefficient Standard error z value P β SES FRic ~ Latitude Intercept -0.249 0.112 -2.236 0.025 Latitude 0.012 0.005 2.409 0.016 β SES FDis ~ Latitude Intercept -0.230 0.137 -1.676 0.094 Latitude 0.012 0.006 2.115 0.034 β SES PD ~ Latitude+hemisphere Intercept 0.432 0.207 2.087 0.037 Latitude 0.002 0.007 0.306 0.759 Hemisphere (South) -0.353 0.189 -1.864 0.062 β SES MPD ~ Latitude Intercept -0.501 0.147 -3.401 0.001 Latitude 0.018 0.007 2.818 0.004

Figure 2-1. Location of the elevational gradients used in the study. Full gradients of bird diversity were obtained for 46 mountain systems ranging from 48.8°N to 24.4°S degrees of latitude.

Figure 2-2. Patterns of bird functional and phylogenetic diversity along elevational gradients. A. Functional richness (FRic), B. Functional dispersion (FDis), C. Phylogenetic richness (Faith’s index, PD), C. Phylogenetic dispersion (Mean pairwise distance, MPD). Each curve represents one of the 46 mountains included in the analysis. Patterns can follow one of eight possible trends (curve shapes) represented below each one of the bars in the frequency distributions of different patterns of diversity. Colors of lines correspond to the best fitted model to describe its pattern (curve shapes).

Figure 2-3. Summary of the correlations between dimensions of biodiversity in tropical and temperate montane bird assemblages. Boxplots show the mean +/- standard deviation. Each point corresponds to an estimated r value calculated with Pearson correlation moments. Panels A and B depict correlations between taxonomic (Richness) and functional diversity (FRic, FDis); panels C and D between taxonomic and phylogenetic diversity (PD, MPD), panels E to H between functional and phylogenetic diversity.

Figure 2-4. Global trends of functional and phylogenetic diversity when controlled by species richness. Each panel shows the standardized effect size of the diversity metric (deviations from null expectations) across elevations, lines at y = 0 represent the null expectations (expected values if assembly would be random across elevations). A. Functional Richness (SES FRic), B. Functional dispersion (SES FDis), C. Phylogenetic richness (SES PD), D. Phylogenetic dispersion (SES MPD). Elevation has been normalized (scaled and centered in 0) to allow comparisons among mountains. Blue curves represent a second-degree polynomial regression of the diversity metric against elevation for each mountain system. The black line represents the second-degree polynomial regression when adding all mountains together.

Figure 2-5. Percentage of tropical and temperate montane bird assemblages showing evidence of underdispersion (significant deviations from null expectations), in lower, mid and higher elevations. FRic= functional richness, FDis= functional dispersion, PD= phylogenetic richness measured as Faith’s index, MPD= phylogenetic dispersion measured as mean pairwise distance. Note the different scale for functional (panels A and B) and phylogenetic diversity metrics (panels C and D).

Figure 2-6. Effect of latitude on the patterns of change in standardized effect size of A. Functional richness (SES FRic), B. Functional dispersion (SES FDis), C. Phylogenetic richness (SES PD) and D. Phylogenetic dispersion (SES MPD) across elevations. For each mountain, the pattern of change across elevation is represented by the slope of a linear regression (β), where more negative values represent decrease in diversity metrics and positive values represent increases. The magnitude of β represents how strong are the changes in diversity patterns across elevations; higher magnitudes of β would correspond to strong changes across elevations, whereas lower magnitudes would be found in mountains where patterns of underdispersion, or overdispersion are homogeneous across elevations. Latitude showed a significant but weak effect on changes in SES FRic (panel A), SES FDis (panel B) and SES MPD (panel D), but not on changes in SES PD. Phylogenetic richness (SES PD) was partially explained by hemisphere, with mountains in the Southern hemisphere showing negative values of β (decrease of SES PD) towards tropical latitudes, but the model was not significant.

CHAPTER 3 GLOBAL PATTERNS OF BIRD FUNCTIONAL AND PHYLOGENETIC BETA DIVERSITY SHOW THAT MOUNTAIN PASSES ARE HIGHER IN THE TROPICS

Background

Elevational gradients have been used as models for exploring patterns of biodiversity and the underlying mechanisms structuring them because they are characterized by considerable environmental changes over relatively small distances (Körner 2007, McCain 2009a, Graham et al. 2014). Variation in environmental characteristics, however, differs with latitude, potentially affecting smaller-scale environmental gradients such as those along elevation. For instance, whereas each elevational band within a temperate mountain system experiences large annual temperature variability, tropical mountain systems might have less variability. Consequently, higher thermal overlap between upslope and downslope sites is expected in temperate mountains.

Based on these observations, tropical ecologist Daniel Janzen presented an idea that continuues to provoke researchers today (Currie 2017, Zuloaga and Kerr 2017, Scheffers and Williams

2018, Sheldon et al. 2018). He proposed that mountains in the tropics might represent physiologically stronger filters for organisms than temperate mountain systems (i.e., tropical mountains are physiologically “higher” ). Janzen (1967) based his statement on the assumption that, having evolved in less variable environments, montane tropical species might have limited acclimation responses and, therefore, smaller elevational ranges than species in temperate mountains. If Janzen’s predictions hold, there should be higher similarity between high and low elevation communities in temperate mountains, whereas higher dissimilarity would be expected among montane assemblages in the tropics as depicted in Fig. 3-1 (Huey 1978, Ghalambor et al.

2006, McCain 2009b). Despite the increasing interest in disentangling the causes of elevational- diversity gradients, and the differences between tropical and temperate montane communities

(Kraft et al. 2011, Tello et al. 2015), five decades after Janzen’s seminal paper, our understanding of the processes shaping mountain biotas is still limited.

The concept of β diversity accounts for the relationship between local and regional diversity and provides information about the dissimilarity among communities (Whittaker 1960,

Tuomisto 2010) Therefore, examining β diversity patterns might provide insights into the mechanisms driving montane communities (McCain and Beck 2016), and a way to test for predictions derived from Janzen’s hypotheses. Several evolutionary and ecological processes can interact to shape β diversity patterns. For instance, β diversity can be influenced by variation in the strength of dispersal limitation (Soininen et al. 2007), species-sorting due to environmental heterogeneity (Qian and Ricklefs 2007), or stochastic processes (Chase and Myers 2011).

However, β diversity can also reflect differences in regional species pools (Chase and Myers

2011). One promising approach to better understand the underlying processes behind β diversity patterns, is to simultaneously examine phylogenetic and functional β diversity along environmental gradients (Kraft et al. 2011, Swenson 2011a, Cisneros et al. 2014). Whereas the phylogenetic dimension of biodiversity reflects the evolutionary differences among species

(Graham and Fine 2008, Cavender‐Bares et al. 2009), the functional dimension of biodiversity reflects the variability in their ecological attributes (Petchey and Gaston 2006). Moreover, how correlated or how decoupled different dimensions of diversity are might highlight the potential role of ecological and historical (geographical and evolutionary) processes driving community assembly (Webb et al. 2002, Stevens and Tello 2014). For instance, if differences in community assembly are driven by limited dispersal of species, two completely different communities may occur even if they share similar environments. However, if the environment plays a dominant role in community assembly, then I expect these two communities to be functionally similar.

This would result in a decoupling of taxonomic and functional β diversity patterns (Swenson et al. 2011, Aguirre et al. 2016).

Changes in β diversity among assemblages can be related to two different mechanisms: species turnover (the replacement of one species by another species; βrepl) and species loss or gain among assemblages (richness difference; βrich) (Carvalho et al. 2012, Legendre 2014). The same principle can be applied to different dimensions of biodiversity by changing the unit of diversity. For example, differences in the number and identity of the functional groups can produce assemblages that differ in functional diversity due to functional richness or turnover, respectively. If β diversity is mostly explained by changes in βrich, environmental filtering is likely a stronger process limiting the number of species, traits or clades that can exist along a gradient. On the other hand, if changes are explained by βrepl, species interactions resulting in limiting similarity are likely a stronger process shaping assemblages. Alternatively, changes explained by βrepl can result from the gradient containing habitats that can sustain similar species richness but require different strategies (i.e., functional traits) (Kluge and Kessler 2011, Cardoso et al. 2014). Surprisingly, metrics of community dissimilarity that account for species functional or phylogenetic relationships have only been developed recently (Cardoso et al. 2015) and no study has yet to assess the relative importance of these components in dissimilarity patterns at a global scale. Here, I build on classic community ecology and the new analytical advances described above and explore a new approach to disentangle the underlying ecological and evolutionary mechanisms driving β diversity patterns across elevations. I simultaneously examine for functional and phylogenetic β diversity of bird assemblages and its partition into

βrich and βrepl across 46 well-sampled elevational gradients worldwide. Then, inspired in Janzen

(1967)’s seminal work I test for latitudinal differences in the potential processes driving these patterns.

Of all taxa, birds are an ideal group to explore these patterns because their taxonomy and evolutionary history are adequately known, they are diverse and have evolved a broad range of strategies (ecological niches), census methods are standardized allowing for comparisons among studies and there is a fair amount of well sampled elevational gradients of bird diversity across the globe. The main goals of this study are to: (1) describe and compare elevational gradients of phylogenetic and functional beta diversity (hereafter Pβ and Fβ, respectively) of birds at a global scale, and (2) examine potential mechanisms driving diversity patterns in tropical and temperate mountain systems. I accomplish the latter in two ways: first I examined the coupling and decoupling of functional and phylogenetic β diversity and their correlations with taxonomic diversity (hereafter Tβ), and second, I examined the relative importance of the two components of β diversity (β replacement and β richness, hereafter βrepl and βrich) for both functional and phylogenetic diversity.

Taxonomic β diversity along elevational gradients has been found to be highly variable among mountains (McCain and Beck 2016), however some studies found a latitudinal signal on these patterns, with β diversity across elevations decreasing from tropical towards temperate latitudes for different taxa (Stevens and Willig 2002, Kraft et al. 2011). Using 28 independent bird data sets, McCain (2009b) showed that the mean elevational range size of breeding bird species increases with latitude, a pattern that would result in greater taxonomic turnover towards tropical mountains. Latitudinal studies of bird β diversity, however, are still limited to taxonomic diversity, and no global analysis of functional and phylogenetic β diversity of mountain assemblages exist. Following Janzen’s (1967) ideas, I predict differences in the patterns of decay

of phylogenetic β and functional β with elevation between tropical and temperate systems.

According to Janzen, tropical species should have lower rates of dispersal and, consequently, higher allopatric speciation rates than temperate mountains. Thus, tropical montane biotas are expected to have higher Pβ across elevations than temperate mountains. Similarly, environmental variability and particularly harsher winter conditions in the temperate regions might also act as a filter selecting for species with particular traits, limiting the variability among species traits in temperate assemblages (Weiher et al. 2011, Spasojevic and Suding 2012). Therefore, I predicted higher Fβ within tropical montane assemblages. In addition, I expected βrich of taxonomic, functional and phylogenetic β diversity to be a more important component at high elevations and

βrepl to be more important at low elevations in tropical mountains. In contrast, I expected βrich and

βrepl to be less variable along elevational gradients in temperate systems. Finally, if dispersal limitation is, as stated by Janzen (1967), an important process shaping tropical assemblages along elevation, I would expect a stronger decoupling between taxonomic and functional beta diversity in tropical mountains than in temperate mountains. I summarize these predictions in

Fig. 3-1.

Methods

Bird Elevational Data

Elevational data for bird assemblages were extracted from published articles. I searched the Web of Science using “bird” OR “avian” and “elevation” OR “altitude” and the resulting articles were examined and selected. Selection of articles largely followed McCain (2009a) criteria. First, I pre-selected studies that surveyed all birds and breeding birds, that focused on complete elevational gradients, that sampled at least at four elevations and that had adequate sampling effort across elevations (i.e., sampled at least 70% of the forested elevational gradient, similar effort across elevations, multiple visits and/or replicates were conducted at each

elevation). After pre-selecting the studies, I either downloaded the species list from the articles or from their supplementary materials, extracted the information if presented in tables in the original paper or contacted the main author(s) to have access to these data. After this process, I obtained data from 46 elevational gradients across the globe (Table B-1).

Species data from each gradient were carefully inspected, nomenclature standardized and taxonomy updated when necessary to match that of Jetz et al. (2012). I considered only breeding species in the analyses, but herein I will refer to them simply as species. For each data set, I assumed that a species was present between its highest and lowest reported elevation (range interpolation) and created elevational ranges for each species, in each mountain. Each gradient was then subdivided into 200-m wide elevational bins and all species occurring in each bin were considered as a bird assemblage. The width of the elevational bins (200 m) was chosen to balance the resolution of empirical records, the amount of collection effort in each interval and to have a minimum of four assemblages in any given mountain. Considering the resolution of empirical data was critical; if data were collected at larger intervals, then smaller subdivisions would result in turnover values of zero (identical communities in consecutive elevations), affecting overall patterns of pairwise beta diversity.

Phylogenetic and Functional Data

Functional traits for all bird species were compiled from Wilman et al. (2014) and from

La Sorte et al. (2014). I selected traits that can describe species ecological strategies (diet, foraging strata and body mass), and traits that relate with species physiological tolerances: spatial gradients of temperature in latitude (i.e., tropical lapse-rate), and the size, shape and orientation of the spatial distribution. Briefly, diet and foraging strata refer to the relative use of different categories of food (invertebrates, vertebrate endotherms, vertebrate ectotherms, fish, carcasses, fruits, nectar, seeds, other plants) and stratum for feeding (water, water surface, ground,

understory, mid story, canopy, aerial). Body mass is the average weight of an individual of each species, in grams. The tropical lapse-rate describes the variability in temperature conditions within the distributional range of each species, controlled by elevation. The size of the distribution refers to the overall size of the distribution polygon of the species globally, the shape measures the proximity to a circular shape of the polygon (lower values represent elongated ranges whereas higher values indicate circular distributions), and the orientation of the distribution describes the relative position of the distribution polygon in relation to the Equator, with values of 0 representing distributions that run parallel to the Equator and values that approach 90 representing distributions that are perpendicular to the Equator. More details of the calculations for tropical lapse-rate, and size, shape and orientation are presented in La Sorte

(2014) and for diet, foraging strata and body mass in Wilman et al. (2014).

In the original database, diet and foraging strata are presented as percentages in multiple columns (that add up to 100 for each species), thus they represent non-independent variables. To account for this non-independence and to reduce dimensionality of these variables, I ran two separate Principal Coordinate Analyses (PCoA), one for diet and one for foraging strata. After a preliminary inspection of the results, I decided to keep each first axis to describe the two functional traits. Using these scaled seven traits (body mass, diet, foraging strata, tropical lapse- rate, and size, shape and orientation of the spatial distribution), I created a species x trait matrix for each bird assemblage.

I used the updated version of the phylogeny (available at birdtree.org, revised on July

2018) of Jetz et al. (2012) supertree, based on the backbone tree by Hackett et al. (2008) to summarize phylogenetic relationships among species for each gradient. Jetz et al. (2012) phylogeny results from a MCMC process, and is, therefore, composed of 10000 trees derived

from the posterior distribution rather than of a single consensus tree. Thus, when calculating phylogenetic diversity (Pβ) for each gradient, I randomly draw one of these trees and pruned to the subset of bird species found at that gradient. To test the sensitivity of the results to phylogenetic uncertainty, I repeated all computations using 100 trees randomly drawn from the and present the mean of these values as Pβ.

Functional and Phylogenetic Beta Diversity

I was interested in testing how latitude relates to (1) the overall values of Fβ and Pβ in mountain systems and (2) to the rate of change of Fβ and Pβ across elevations. Thus, I calculated

Fβ and Pβ for each elevational gradient separately, following two approaches. First, because I was interested in one unique β value that summarizes how diversity changes for any given mountain (as opposite to a set of pairwise values across elevations), I used the framework proposed by Chao et al. (2014) to partition metrics of phylogenetic and functional diversity. This framework takes advantage of the relationship between local diversity (alpha diversity, α), diversity turnover among localities (beta diversity, β) and regional diversity (gamma diversity,

γ), and results in β diversity values that are independent of the values of α (Hill 1973, Jost 2007).

Recently, Chiu & Chao (2014) and Chiu et al. (2014) extended this idea for diversity decomposition to functional and phylogenetic dimensions of diversity, respectively. Following

Chao et al. (2014) I calculated one unique value of gamma diversity for each elevational gradient that included all species present in that given gradient. A value of alpha diversity was calculated for each elevational bin within each mountain. Beta diversity was then calculated for each mountain based on the multiplicative partitioning of diversity proposed by Jost (2007) where beta diversity is the ratio of gamma diversity and alpha diversity. I used the same approach for taxonomic, functional and phylogenetic dimensions of diversity (see Chao et al. 2014 for a detail derivation of the formulas), and present these values as Tβchao, Fβchao and Pβchao, respectively. A

major advantage of the multiplicative decomposition of beta diversity is that it is independent of the magnitudes of alpha and gamma diversity (Jost 2007, Chao and Chiu 2016), a critical requirement to contrast data sets of different regions. Results from the Chao family of metrics have been found to be highly correlated with other metrics that account for all variability within a group of sites (Sreekar et al. 2018). And thus, represent a valid set of metrics to fully describe overall beta diversity within each mountain, in each dimension of diversity we considered.

Furthermore, metrics derived from Chiu & Chao (2014) and Chiu et al. (2014) are normalized and range between 0 and 1, and thus are directly comparable across communities. Calculations were done with packages hillR (Li 2018) and adiv (Pavoine 2017).

Secondly, I was interested in examining if tropical mountains had “faster” changes in diversity than temperate mountains, i.e., if higher differences were found among assemblages separated by the same elevational distance in tropical mountains than in temperate mountains.

Thus, I tested if the rate of change in Tβ, Fβ and Pβ differed across latitudes. For this, I calculated pairwise differences in Tβ, Fβ and Pβ among consecutive 200-m elevational bins to represent the “rate of change” of each dimension of diversity. For each mountain separately, I calculated pairwise differences in Tβ, Pβ and Fβ among all pairs of 200-m elevational bins, with

Sorensen-family indices between elevations using the frameworks proposed by Podani and

Schmera (2011) and Carvalho et al. (2012). This resulted in one dissimilarity matrix for each dimension of diversity per mountain. My choice of these metrics relies on their mathematical properties, which allow beta diversity partitioning into its two components, and allow these components (βrich and βrepl, see below) to be scaled in the same way. Relativized values of beta diversity components, thus, are comparable across communities and most importantly, ecologically interpretable. A detailed discussion on the benefits of this partition method, in

comparison with other available methods can be found in Cardoso et al. (2014) and Ensing and

Pither (2015). Importantly, I chose these metrics because they have extended beta diversity partitioning to functional and phylogenetic diversity under a unified framework, allowing for direct comparison of these different facets of diversity. To examine the effects of my selection of metrics, I ran analyses of taxonomic diversity using the metrics proposed by Baselga (2010) with functions of the package betapart (Baselga et al. 2018).

Because β is calculated as a pairwise distance, averaging all pair-wise differences in each resulting distance matrix, and contrasting the averages of mountains with different elevational heights might inflate overall differences (by including differences between the extreme elevations). Thus, I extracted only pair-wise distances between consecutive 200-m elevational bins to represent the change in diversity for a 200 m increase in elevation (thus, I refer to it as a

“rate of change”). A schematic representation of this procedure is found in Fig. A-12. I repeated this procedure for each dimension of diversity. I summarized the overall Tβ, Fβ and Pβ of each mountain as the average β (± SD) between neighboring elevational bins, and present these results as Tβsor, Fβsor and Pβsor, to distinguish them from the former analyses. Additionally, I used a benchmark approach to further explore the patterns of change in Tβsor, Fβsor and Pβsor within mountains. For this, I extracted dissimilarity distances between one benchmark elevational band

(the lowest in elevation) and each of the others to observe how Tβ, Fβ and Pβ change with elevational distance. A schematic representation of this benchmark approach is presented in Fig.

A-12.

I assessed if dissimilarity between elevation in terms of Tβsor, Fβsor and Pβsor is mostly explained by replacement (turnover; βrepl) or richness differences (loss or gain of species, clades or functional groups; βrich) using functions developed by Cardoso et al. (2015). This analysis

resulted in two dissimilarity matrices, one for βrepl and one for βrich for each dimension of diversity. I present these results as Tβrepl, Fβrepl and Pβrepl, and Tβrich, Fβrich and Pβrich, respectively. Again, I used the benchmark approach with these new matrices; I extracted dissimilarity values between one benchmark elevational band (the lowest in elevation) and all others, to visually inspect how the relative importance of βrepl and βrich components change with elevation.

Effect of Latitude on Mountain β Diversity Patterns

I used two approaches to compare β diversity patterns of mountain systems across latitudes (N=46). First, I used Generalized Linear Models (GLMs) with Gamma errors to test for the effect of latitude on (1) overall beta diversity (Tβchao, Fβchao and Pβchao), (2) on the rate of change (average of Tβsor, Fβsor and Pβsor calculated between consecutive elevational bins) and (3) on the variability of this changes (standard deviation of Tβsor, Fβsor and Pβsor calculated between consecutive elevational bands). In all cases, I used the absolute value of latitude to describe the position of a mountain system relative to the Equator. Thus, mountains far from the Equator had higher values regardless of their position in the Northern or Southern hemisphere. However, because Northern and Southern hemispheres do not follow the same seasonality patterns, I included a categorical variable hemisphere (two levels: North or South) as a covariate. For overall beta diversity models (βchao) I also included mountain length (as the range in meters between the upper and lower limit of each mountain), as it has been described as an important covariate for taxonomic β diversity (McCain and Beck 2016).

Secondly, I was interested in examining if the correlations among different dimensions of diversity changed between tropical and temperate. For this, I first tested for pair-wise correlations between the Sorensen dissimilarity matrices (Tβsor, Fβsor and Pβsor) by means of

Mantel tests based on Pearson's product-moment correlation (Oksanen et al. 2018). Then, I

extracted the Mantel statistic r estimated in the correlation analysis between Tβ and Fβ, between

Tβ and Pβ, and between Fβ and Pβ for each mountain system. Mantel statistic r values were transformed to Fisher’s Z scale, obtaining values weighted by the number of elevational bands used to calculate each correlation (Koricheva et al. 2013) that were comparable among mountains. Correlation values (transformed r) were compared within tropical and temperate mountains with fixed effect models performed in package metaphor (Viechtbauer 2010). All analyses were conducted within RStudio (RStudio 2018) an environment for R (R Development

Core Team 2018).

Results

Avian Assemblages

obtained 46 well sampled elevational gradients across the globe, located at latitudes between 48.8° N to 24.4° S. These gradients occur in a great diversity of areas characterized by different climates, in different realms either on continental masses or on islands and elevational ranges spanning from 851 m to 8090 m. Altogether, these elevational gradients contained 3522 species of breeding birds (~35% of the bird species recognized worldwide). Mountain systems included in the analyses varied greatly in resident species richness, from 22 species in Mount

Mikura-Jima in Japan to 577 species in Serrania de Yariguies, in Colombia. These gradients represent the great variability of mountain regions across the globe (Table B-1).

Effect of Latitude on Beta Diversity (βchao) and Speed of Change in Diversity (βsor)

Values of overall taxonomic turnover (Tβchao), functional turnover (Fβchao) and phylogenetic turnover (Pβchao) varied greatly among mountains. Tβchao ranged from 0.19 to 0.86

(mean Tβchao = 0.61), Fβchao ranged from 0.22 to 0.88 (mean Fβchao = 0.58) and Pβchao ranged from 0.12 to 0.75 (mean Pβchao = 0.50). Serrania del Aguarague in Bolivia had the lowest values for Tβchao, Fβchao and Pβchao whereas Reserva San Francisco in Ecuador had the highest values of

Tβchao and Pβchao and Serrania de Totare in Colombia had the highest value of Fβchao. Mountain beta diversity (βchao) decreased with latitude for each of the dimensions of biodiversity considered in the analyses (Fig. 3-2, Table 3-1), however no significant effect of latitude was noted.

The rate of change in diversity, measured as the average of the pairwise differences in diversity between consecutive elevational bins, βsor (i.e., average change in 200 m of elevation) also varied greatly among mountains. Averaged values of Tβsor ranged between 0.03 and 0.24,

Fβsor ranged between 0.01 and 0.13 and Pβsor between 0.02 and 0.17. The highest rate of change in Tβsor was in San Francisco Reserve, whereas the lowest was in Serrania del Aguarague.

Surprisingly, the highest rate of change in Fβsor and Pβsor was on Mikura-Jima island, the most species poor mountain. A closer evaluation of the patterns of rate of change showed that overall, changes in Tβsor and Pβsor were faster towards the higher elevational bands with only a few mountains (i.e. Marojejy Reserve, Mt. Trentino) showing fast changes at low elevations (Fig. A-

13 and A-15). In contrast, fast changes in Fβsor at low elevations occurred in several mountains

(e.g. Great Smoky Mountains, Sweet Grass Hills, Trentino, Yaku-Shima island, Marojejy

Reserve, Mt. Chamba, Mt. Hermon; Fig. A-14). Overall, I found no effect of latitude or hemisphere on the average rate of change in diversity across elevation (mean values of βsor;

Table 3-2, Fig. 3-3); however, I did find a weak but significant effect of latitude on the standard deviation SD of rate of change in Tβsor, with higher variation in mountains towards the tropics

(Fig. 3-4, Table 3-3). Similarly, I found a significant effect of hemisphere on the SD of Pβsor

(Table 3-3), with highest variation in mountains of the Northern hemisphere (Fig. 3-4).

I used the benchmark approach to examine visually the accumulated changes in βsor along each elevational gradient. Overall, accumulated Tβsor was high, with most mountains showing a

complete replacement of species (dissimilarity values reaching values above 0.75 between lower and higher elevations, Fig. 3-5A). Accumulated Pβsor was also high, with most mountains replacing more than 75% of their clades along the elevational gradient (Fig. 3-5G). Accumulated

Fβsor, however, was overall lower, with only few mountains surpassing 60% of change in functional characteristics between highest and lowest elevations (Fig. 3-5D). Interestingly, in some cases Fβsor showed a hump-shaped pattern, with lowland assemblages functionally more similar to highland assemblages than to mid-elevation assemblages (e.g. Marojejy Reserve,

Espinazo del Diablo, Mt. Trentino, Undzungwa Scarp, Figs. A-16 to A-18).

The partitioning of components of beta diversity showed interesting patterns. Overall, the richness component (βrich) was more important towards high elevations, suggesting the loss of species, lineages and functional groups is driving changes at higher elevations (Fig. 3-5). In contrast, the replacement component (βrepl) was more important at mid elevations, explaining more of the overall dissimilarity in species composition, functional diversity and phylogenetic diversity (Fig. 3-5). Comparisons of mean values of βrich and βrepl between tropical and temperate mountains (Fig. 3-6), showed differences in the relative importance of these components for functional beta diversity, with βrepl being significantly more important than βrich in tropical mountains. No differences in the importance of these components were noted for taxonomic nor phylogenetic beta diversity (Fig. 3-6).

Coupling or Decoupling of Different Dimensions of β Diversity

Correlations between different dimensions of β diversity varied greatly among mountains but were always positive. As expected, taxonomic β and phylogenetic β were highly correlated, with Pearson’s r values varying between 0.86 for Mikura-Jima island to 0.99 for Amrutganga.

Correlations between taxonomic β and functional β varied the most, oscillating between only

0.19 for Mt. Wilhelm and 0.99 for Serrania del Aguarague. Correlations between functional β

and phylogenetic β oscillated between 0.23 for Mt. Wilhelm to 1 for Serrania del Aguarague. I found no differences in the coupling of different dimensions of diversity between tropical and temperate mountains (Fig. 3-7). However, more variation in the effect sizes of these correlations

(transformed r values to Fisher’s Z values) was found among tropical mountains, with some mountains having very low correlation values (Fig. 3-7). When controlling for compositional turnover with a Partial Manel test, the correlation between functional and phylogenetic beta diversity did not differ between tropical and temperate mountains, but some mountains showed negative correlations, particularly in tropical mountains (Fig. 3-8).

Discussion

Bird assemblages differed along elevational gradients in all dimensions of β diversity. I found partial evidence to support that overall β diversity is lower in temperate mountains compared to tropical mountains, as suggested by other studies (Jankowski et al. 2009, Kraft et al.

2011). The effect of latitude was not equal for different dimensions of diversity. Overall, latitudinal effects were stronger in taxonomic and functional dimensions, and only marginal for phylogenetic βchao diversity. I found no effect of latitude on the rate of change (βsor) of mountains, and only a weak latitudinal effect on the variability of this rate of change (SDβsor).

Although the model had low predictive power, tropical mountains showed more variation in rates of change, partially supporting the hypothesis of stronger differences in the relative role of environmental filtering across elevations than in temperate mountains. Furthermore, examination of the components of dissimilarity (βrepl and βrich) provided additional insights into the mechanisms that may control bird elevational diversity. Changes in bird assemblages are driven by environmental filtering in mountain systems across the globe, however the relative strength changed across elevations. My finding of higher relevance of the replacement component in functional diversity in tropical mountains, suggests that stronger environmental filtering was

concentrated at high elevations in tropical mountains, whereas it was more homogeneous across elevations at higher latitudes. Altogether, these results provide partial support to predictions derived from Janzen’s (1967) original ideas of tropical mountains representing stronger barriers for biotas than temperate mountains.

β diversity is a critical component of biodiversity and has been shown to vary across latitudes, decreasing from tropical to temperate regions (Koleff et al. 2003b). However, how the latitudinal gradient might affect the rate of change in species composition along elevational gradients is not yet understood (Kraft et al. 2011, Tello et al. 2015). I based my expectations on the early work of Janzen (1967) who proposed that mountains represent stronger barriers (are

‘higher’) in the tropics because of species’ narrower physiological tolerances. These narrower tolerances result in part because stronger seasonality in temperate regions selects for broader tolerances. Based on Janzen´s ideas, I expected higher similarity (lower β) between elevational bins in temperate mountains compared with tropical mountains (Ghalambor et al. 2006, McCain

2009b). I found a negative relationship between overall beta diversity of mountain ranges (βchao) and latitude for all dimensions of diversity examined, but the fit of the models was low.

Nevertheless, decreasing patterns of beta diversity across latitudes suggest stronger turnover in species and traits across elevations in tropical mountains. However, the effect of latitude in phylogenetic βchao was only marginal (Table 3-1), suggesting these turnovers of species and functional groups does not correspond to an equal turnover in clades.

Opposite than expected, I found no effect of latitude on the rate of change measured as the average βsor patterns between consecutive elevations, but I did find differences in the variability of taxonomic beta diversity (SD βsor), with higher variability at lower latitudes. This high within-mountain variability suggests the rate of change in species composition is not

constant with elevation; fast and slow rates of change (high and low dissimilarities between neighbor elevational bins) can be found along the same elevational gradient. Stronger differences in βsor between consecutive elevations can be related to different processes affecting bird assemblages at different elevations. Overall, I found the rate of change of species, clades and traits to be highly variable across mountains (Figs. A-10-12), potentially responding to unique gradients of environmental conditions in each mountain range. Furthermore, my finding of very high turnover between neighboring elevational bins for all dimensions of diversity (values up to

1, 0.89 and 0.55 for Tβ, Pβ and Fβ, respectively) suggests that strong habitat filtering might occur at β diversity peaks, driving the overall patterns of turnover across elevations. At least two other studies have found peaks in β diversity along elevation in tropical mountains; in both studies species composition changed more rapidly in middle elevations where vegetation transitioned from one habitat type to another (Navarro 1992, Jankowski et al. 2009). I found this pattern of non-constant rate of change in species composition might be a common phenomenon, occurring also in temperate mountains. Furthermore, results extend this phenomenon to both phylogenetic β and functional β, highlighting the need to further explore the reasons behind elevational peaks in functional and phylogenetic turnover.

Other researchers have examined tropical vs. temperate patterns of beta diversity in mountain systems recently, producing somewhat contradictory results. For instance, Kraft et al.

(2011) compared tree diversity in exhaustively studied forest plots in a tropical (Bolivia) and a temperate (USA) site. Their main conclusion was that, after controlling for differences in species richness, there are no differences in β diversity patterns between tropical and temperate systems.

However, this study has caused debate because of its methods and, more importantly, because of the extrapolation of their results to a global-scale (Qian et al. 2012, Tuomisto and Ruokolainen

2012). In another study, Jankowski et al. (2009) compared the rate of change in bird species composition across elevations between a tropical mountain (Costa Rica) and a temperate mountain (USA), and found a slower rate of change in the temperate mountain. Jankowski et al.

(2009) concluded that these differences suggest that tropical montane species have narrower altitudinal distributions than temperate ones, supporting Janzen´s predictions. Here, analyzing 46 elevational gradients of bird diversity (including both sites compared by Jankowski et al. 2009) I found a negative yet weak effect of latitude on overall β diversity. However, I also found great variation in β diversity both within mountains and among mountains, suggesting different processes drive β diversity patterns in different mountain ranges. Thus, I advise caution when extrapolating such conclusions across tropical and temperate systems. For instance, whereas there seems to be an overall effect of latitude on the observed patterns of functional and phylogenetic β diversity, resulting patterns might be strongly affected by local conditions that do not necessarily correlate with latitude (i.e., precipitation regimes). Results suggest that these local factors drive β diversity peaks, deviating overall patters from expectations derived from latitude.

Partitioning of β diversity into its components provides additional insights into the mechanisms behind β diversity patterns (Legendre 2014). Species replacement (βrepl) is expected to occur along ecological gradients that are sufficiently long to cause simultaneous gain and loss of species, whereas differences in species richness (βrich) are likely to reflect smaller-scale ecological processes (Legendre 2014). Because mountains represent relatively smaller environmental gradients, with strong filtering, I expected βrich to drive species turnover along elevation. I found that overall β diversity was largely explained by the βrich component in each of the three dimensions of diversity, as expected in areas with stronger environmental filtering.

High values of taxonomic βrich might partially reflect the loss of lowland species towards higher altitudes, whereas high values of βrich in the phylogenetic and functional dimensions of β diversity might imply that these changes in species composition translate into loss (or gain) in clades or functional groups. This suggests that environmental variability in mountain systems

(regardless of their latitudinal position) acts as a filter selecting for species with particular traits

(and from particular clades), limiting the variability among species traits (Weiher et al. 2011,

Spasojevic and Suding 2012). Interestingly, βrepl gained importance in tropical mountains explaining a higher percentage of the dissimilarity among tropical assemblages. This result suggests that a rapid replacement of functional traits and clades occurs in the tropics whereas the loss of functional traits is more important at higher latitudes. Further, the differential response between tropical and temperate mountains in βrepl supports Janzen´s ideas of tropical species having narrow altitudinal ranges, and patterns of beta diversity to be dominated by a rapid replacement of species in the tropics.

A deeper inspection of within-mountain patterns of βrich and βrepl showed that βrepl is the most important component of Tβ and Pβ at mid-elevations in most of the mountains, whereas

βrich gains relevance at higher elevations. Peaks in βrepl at mid-elevations suggest that species composition and clades change rapidly probably coinciding with changes in vegetation or habitat types (Navarro 1992, Jankowski et al. 2009). On the contrary, several mountains showed a different pattern in Fβ, with either higher importance of βrepl at higher elevations and mid-peak pattern in Fβrich, or a relatively constant importance of βrepl across elevations (Fig. 3-5). The relative low values of Fβrepl across elevations might indicate that, although there is species replacement along the gradient, environmental filtering is so strong that there is no true functional turnover between assemblages (i.e. similar species are replacing each other).

Altogether, these results provide evidence supporting my previous findings that different processes are acting upon bird assemblages at different elevations. Environmental filtering is the main force shaping mountain bird beta diversity patterns; it promotes strong species replacement but maintains stable functional diversity at mid-elevations whereas at higher elevations, it limits species, trait and clade richness.

I predicted higher decoupling of dimensions of diversity in tropical mountains, as a signal that different processes are acting across elevations (Swenson et al. 2011, Aguirre et al. 2016).

Although I did not find differences in the coupling (i.e. correlations) of different dimensions of diversity between tropical and temperate mountains, more variation in correlation estimates was found among tropical mountains (Fig. 3-7). Two different mechanisms might cause decoupling patterns among different dimensions of diversity. Decoupling can be driven by (1) weak environmental filtering along the gradient and dispersal limitation, where traits remain similar across elevations and differences in composition result from the probability of reaching a given elevation bin, or (2) strong competition in low and mid-elevations, with strong species replacement resulting from the elimination of redundant traits (competitive exclusion) (Freeman

2015). This latter pattern would result in small and slow changes in functional traits but fast and strong changes in taxonomic composition associated with strong changes in phylogenetic composition. In either case, mountain systems that show decoupling of different dimensions of diversity highlight the potential role of different processes across elevations within one mountain range in tropical mountains.

Further evaluation of these data to include environmental covariates (i.e., temperature, humidity, seasonality) might provide additional insights into the potential mechanisms driving β diversity patterns, and result in more interpretable results when searching for mechanisms acting

along latitude (Sheldon et al. 2018). However, at the scale of my analysis and the historical data included in this study, it was virtually impossible to gather accurate environmental data for each mountain system (and its variability across elevations). Altogether, my results highlight the importance of examining multiple dimensions of biodiversity when exploring diversity patterns at large scales, as different patterns might contain non-redundant information about the processes shaping diversity.

Table 3-1. General linear models for taxonomic (Tβchao), functional (Fβchao) and phylogenetic (Pβchao) beta diversity as a function of absolute latitude and hemisphere (North or South) as a covariate. Model Parameter Estimate SE t-value P value

Tβchao~Latitude+HemisphereSouth Intercept 0.725 0.057 12.79 <0.001 Latitude -0.003 0.002 -1.967 0.049 Hemisphere -0.088 0.052 -1.68 0.101

Fβchao~Latitude+HemisphereSouth Intercept 0.778 0.071 11.02 <0.001 Latitude -0.004 0.002 -2.064 0.045 Hemisphere -0.101 0.061 -1.646 0.107

Pβchao~Latitude+HemisphereSouth Intercept 0.637 0.063 10.01 <0.001 Latitude -0.003 0.002 -1.790 0.081 Hemisphere -0.094 0.055 -1.702 0.096

Table 3-2. General linear models for the average and the variation (measured as standard deviation) of βsor diversity metrics between consecutive elevational bins in 46 mountain systems across the globe. Separated models for taxonomic (Tβsor), functional (Fβsor) and phylogenetic (Pβsor) beta diversity as a function of absolute latitude and hemisphere (North or South) as a covariate are presented.

Models Parameter Estimate SE t-value P value

Tβsor~Latitude+HemisphereSouth Intercept 0.121 0.0215 6.045 <0.001 Latitude -0.0003 0.0007 -0.540 0.592 Hemisphere -0.0011 0.0179 -0.640 0.525

Fβsor~Latitude+HemisphereSouth Intercept 0.0005 0.009 5.342 <0.001 Latitude 0.0006 0.0003 0.020 0.984 Hemisphere -0.0003 0.0009 -0.236 0.815

Pβsor~Latitude+HemisphereSouth Intercept 0.085 0.0151 5.613 <0.001 Latitude -0.0001 0.0004 -0.267 0.790 Hemisphere -0.006 0.0136 -0.489 0.628

SDTβsor~Latitude+HemisphereSouth Intercept 0.214 0.0276 7.738 <0.001 Latitude -0.003 0.0008 -3.430 0.0013 Hemisphere -0.059 0.0218 -2.706 0.0097

SDFβsor~Latitude+HemisphereSouth Intercept 0.106 0.0145 7.302 <0.001 Latitude -0.0002 0.0005 -0.509 0.614 Hemisphere -0.0204 0.0135 -1.521 0.136

SDPβsor~Latitude+HemisphereSouth Intercept 0.162 0.0213 7.635 <0.001 Latitude -0.001 0.0007 -1.472 0.149 Hemisphere -0.044 0.0197 -2.232 0.031

Figure 3-1. Schematic representation of the expected patterns of functional (and phylogenetic) β diversity along elevational gradients. Our scheme is derived from Janzen’s (1967) and Ghalambor et al. (2006)’s ideas for taxonomic diversity and extended for predictions of functional diversity. Seasonal changes in air temperature experienced by a low- elevation species (in green) and a high elevation species (in yellow) in temperate and tropical mountains. Seasonality in temperate mountains results in broad physiological tolerance and consequently, high thermal overlap between high and low elevation species. On the contrary, low physiological tolerances result in low thermal overlap between high and low elevation species in the tropical mountain system. The higher overlap in thermal distributions of species in temperate mountains results in low functional β diversity, whereas the narrow thermal distributional ranges in tropical mountains will result in high species and trait turnover. However, because highlands in the tropics experience more thermal variation (on a daily basis) than lower elevations, the expected role of environmental filtering would differ across elevations. Therefore different processes are expected to drive functional β diversity patterns across elevations. This would lead to higher variation in pairwise β values between consecutive elevational bands and to differences in the relative importance of βrich and βrepl as components of functional β diversity. Finally, if traits are phylogenetically conserved, we expect the same responses for metrics of phylogenetic β diversity.

Figure 3-2. Patterns of taxonomic beta diversity (Tβchao), functional beta diversity (Fβchao) and phylogenetic beta diversity (Pβchao) for 46 mountain systems across latitude. Black lines correspond to the multiple regression of β against absolute latitude with hemisphere (North or South) as a covariate. Results of regressions are presented in Table 3-1. Dark blue points represent mountains in the Southern hemisphere and light blue points mountains in the Northern hemisphere.

Figure 3-3. Rate of change in taxonomic, functional and phylogenetic diversity across elevations, measured as the average of Sorensen dissimilarity (βsor) between neighbor elevational bins (i.e., change in 200 m of elevation), for 46 mountain systems worldwide. Black lines correspond to the multiple regression of β against absolute latitude with hemisphere (North or South) as a covariate. Results of regressions are presented in Table 3-2. Dark blue points represent mountains in the Southern hemisphere and light blue points mountains in the Northern hemisphere.

Figure 3-4. Variability in the rate of change in taxonomic, functional and phylogenetic diversity across elevations, measured as the standard deviation of Sorensen dissimilarity (SD βsor) between neighbor elevational bins, for 46 mountain systems worldwide. Black lines correspond to the multiple regression of β against absolute latitude with hemisphere (North or South) as a covariate. Results of regressions are presented in Table 3-2. Dark blue points represent mountains in the Southern hemisphere and light blue points mountains in the Northern hemisphere.

Figure 3-5. Patterns of taxonomic (A-C), functional (D-F) and phylogenetic (G-I) beta diversity and its components: richness (βrich) and replacement (βrepl) calculated between the lowest elevational band and all others (benchmark approach, see text for details) for 46 mountain systems. Mountains are color coded from light green at latitude 0 to blue at latitude 50. For all dimensions of diversity, βrich gains importance at higher elevations, whereas βrepl is a more important component at mid elevations.

Figure 3-6. Comparison of richness (βrich) and replacement (βrepl) components of beta diversity for taxonomic (A and B), functional (C and D) and phylogenetic (E and F) beta diversity of temperate and tropical mountain systems. Boxplots represent mean ± SD values. Each point represents the percentage of total beta diversity (βsor) explained by the component.

Figure 3-7. Correlations between dimensions of beta diversity for tropical and temperate mountains. Boxplots represent mean ± SD values. Pairwise correlations between bird dissimilarity matrices (for taxonomic, functional and phylogenetic β diversity) were calculated with Mantel tests based on Pearson correlations, and transformed with Fisher’s Z scale for comparisons. Each point summarizes the transformed value for the statistic r from the correlation between two dimensions of diversity in one mountain system. No differences between tropical and temperate mountains were noted (Fixed effect models QM=0.001, P=0.9 for Tβ-Fβ; QM= 0.005, P=0.94 for Tβ- Pβ and QM=0.013, P=0.91 for Fβ-Pβ).

Figure 3-8. Correlation between functional and phylogenetic beta diversity for tropical and temperate mountains when controlling by taxonomic dissimilarity. Boxplots represent mean ± SD values. Correlations were calculated with Partial Mantel tests based on Pearson correlations, and transformed with Fisher’s Z scale for comparisons. Each point summarizes the transformed value for the statistic r from the correlation in one mountain system. No differences between tropical and temperate mountains were noted (Fixed effect models QM=0.001, P=0.98).

CHAPTER 4 DISENTANGLING DRIVERS OF AVIAN COMMUNITY ASSEMBLY ALONG ELEVATIONAL GRADIENTS IN THE BOLIVIAN ANDES

Background

Explaining and predicting community responses to environment have become a priority research area for ecologists and evolutionary biologists (Webb et al. 2010). Community assembly theory hypothesizes that the environment imposes two main ‘filters’ on species that act to shape composition and organization of biological assemblages: the physical environment (i.e. habitat filtering) and biological interactions (e.g., competition) (Pavoine et al. 2011, Spasojevic and Suding 2012). The physical environment may act as a filter such that only species with certain traits will be able to persist, whereas biotic interactions, such as competition, may result in greater spacing of traits among co-existing species (Weiher et al. 2011, Spasojevic and Suding

2012). Analyzing observed patterns of trait diversity allow us to gain insights into the relative strength of these forces (i.e. environment or species interactions) in community assembly (Webb et al. 2010, Belmaker and Jetz 2013).

A trait-based approach to community assembly states that these ecological forces operate on species traits (Weiher et al. 2011) creating observable trait distributions. Consequently, differences in trait diversity and dispersion would indicate which force is acting as an ecological filter and allow us to understand functional community assembly. If both opposing forces are equally strong a community assembly that is random in regard to species traits may result

(Johnson and Stinchcombe 2007, Belmaker and Jetz 2013). However, an organism’s traits might also be determined by its phylogenetic history (i.e. niche conservatism). Therefore, resulting communities represent a subset of a potential species composition filtered by the environment and constrained by their trait history (Cavender-Bares et al. 2004, Cahill et al. 2008, Losos 2008,

Cavender‐Bares et al. 2009). Predictions of community assembly are based under the assumption

that there is a phylogenetic signal in functional and morphological traits, such as when trait dissimilarity correlates with phylogenetic distance (i.e., close relatives are more similar to each other than distant relatives), but only few studies on community assembly test for this assumption. Furthermore, the simultaneous investigation of patterns of phylogenetic and functional structure of assemblages may provide insights into the extent to which evolutionary and ecological processes are driving assembly patterns.

Here, I address the mechanisms that influence bird species coexistence and community assembly along an altitudinal gradient in the Bolivian Andes, a biodiversity-rich and highly threatened ecosystem that is particularly vulnerable to climate change (Urrutia and Vuille 2009,

Anderson et al. 2011). Elevational gradients result in significant change in many conditions (e.g., temperature, precipitation, oxygen levels). Along this gradient, higher elevations may impose harsher conditions for organisms to cope with, whereas biological interactions are expected to be more important for organisms at lower elevations (Ackerly and Reich 1999, Hoiss et al. 2012,

Read et al. 2014). Hence, elevational gradients are good models to test how these forces

(environment, species interactions) shape community traits (Johnson and Stinchcombe 2007) and to relate observed traits with specific environmental factors or biological interactions (Lebrija-

Trejos et al. 2010). Abiotic factors, in particular temperature, have been suggested as the main driver of diversity patterns along elevational gradients (Terborgh 1971, Elsen et al. 2017). For birds, physiological tolerances might constrain species to climatic envelopes, excluding them from areas with environmental conditions outside their climatic tolerances (Laurance et al.

2011). Early studies on tropical avifaunas across elevational gradients have suggested that forest vertical structure also plays a determinant role in bird diversity patterns (MacArthur and

MacArthur 1961, MacArthur 1964, Terborgh 1977). Vertical strata in forested habitats represents

a dimension of potential niches for bird species allowing for species to coexist by partitioning foraging strata (MacArthur and MacArthur 1961, Walther 2002). Fine partitioning of microhabitats among foraging species, as well as feeding resource partitioning (e.g., insects and fruits), have been suggested to be important drivers of coexistence of closely related species

(Naoki and Stouffer 2007). In fact, Naoki (2007) suggests that patterns of sympatry for some often-assumed omnivorous bird species (e.g., genus Tangara) might not be maintained by overall fruit abundance, but by the diversity of these potential resources. Furthermore, studies on bird functional guilds (i.e., frugivores, insectivores) provided evidence that bird diversity and functional diversity might also covary with the diversity of niches available and the availability of resources (Loiselle and Blake 1991, Dehling et al. 2014). All three of these key resources for bird species: vertical strata of forested habitats, arthropod resources and fruit availability vary across elevations, and are plausible mechanisms driving community assembly.

I combine a trait-based with a phylogenetic approach to test if: (1) the diversity and dispersion of functional traits of avian assemblages varies along the altitudinal gradient in response to environmental conditions and species interactions; (2) the phylogenetic diversity and dispersion of the assemblage also changes along the gradient in response to these forces; and (3) observed phylogenetic structure depends on the lability of traits (i.e., niche conservatism or evolutionary change). Assuming physical conditions are harsher at higher elevations I expected them to represent a stronger force shaping communities, whereas biotic forces will have a stronger effect at lower elevations. If this prediction holds, (i) the diversity and variability of observed traits should be smaller in higher elevations, and higher in lower elevations. If traits are phylogenetically conserved, (ii) species coexisting locally at lower elevations will be less related than expected by chance, suggesting competitive interactions influence local assemblages and,

(iii) species are predicted to be more closely related than expected by chance at higher elevations, suggesting environmental filtering is the main force shaping high elevation assemblages (Fig. 4-1). Besides examining deterministic processes shaping community patterns,

I was interested in the mechanisms by which these processes operate. Thus, I tested four main putative drivers of bird diversity: mean temperature, habitat complexity, arthropod diversity and fruit availability to gain further insights into the effects of environmental gradients on assembly processes. Furthermore, I expect that certain traits will be more affected than others by these forces. For instance, variation in body size may be more related with environmental conditions such as vegetation structure and temperature, whereas variation in traits related with foraging strategies and interspecific competition (i.e., bill shape) may be more related with gradients in resource availability.

Methods

Bird Surveys

I examined trait diversity and phylogenetic structure of avian assemblages along an elevational gradient in the Andes of western Bolivia. Between April and August 2014 and April and October 2015, birds were censused along an extensive gradient (1350 – 3650 m asl) in

Cotapata National Park, a protected area in the Andes of Western Bolivia. The landscape along the gradient is dominated by evergreen humid montane and cloud forests, with steep slopes and deep valleys (Ribera 1995, Sevilla Callejo 2010) and largely free of human intervention

(Montaño-Centellas and Garitano-Zavala 2015). Given the extreme topography of the landscape,

I subdivided the elevational gradient in transects that changed 350m in elevation (i.e., from

1350-1700, 1700-2050, etc.), an elevational extension I could evaluate within ~ 3 hours of effort; each 350m elevational transect formed the basis of our daily sampling (Elsen et al. 2017). I surveyed every transect twice each day; one upslope and one downslope, separated by at least 1

hour from each other (Elsen et al. 2017), repeating surveys 8 times per year (N=16). Surveys consisted in walking at a slow constant pace along the 350m transect and registering every encountered bird. For each bird observed, GPS coordinates and elevation, time of the day and distance to observer was noted, as well as the taxonomic identity, and number of individuals observed together.

Bird Taxonomic Diversity: Species Richness and Composition

I pooled all species registered in each 100m elevation band (hereafter “elevation”) to create an assemblage of bird species per elevation. I describe species richness as observed species richness and as Chao II richness estimator (Magurran 2004), and compare species richness among assemblages with individual-based rarefaction and sample-based accumulation curves (Gotelli and Colwell 2001). Species composition among assemblages was described with rank-abundance curves for each elevation and compared visually with a Non-metric

Multidimensional Scaling (NMDS) and statistically with a Permutational Analysis of Variance

(PerManova) and pair-wise a posterior PerManova analyses with Bonferroni corrections

(McCune et al. 2002). All analyses were conducted in R environment (R Development Core

Team 2018); species richness and composition analyses were conducted with base and vegan packages.

Bird Functional and Phylogenetic Dispersion

I used data on ten functional traits to calculate functional diversity: 8 morphological and

2 ecological traits. The morphological traits included: bill length (culmen), bill width (gape), bill depth, tarsus length, Kipp’s distance, wing length, tail length and body mass. These traits were selected because of their association with important dimensions of the avian niche: where bill dimensions relate to feeding, and wing, tarsus and tail lengths and Kipp’s distance relate to flight performance and foraging maneuvers (Dehling et al. 2014, Pigot et al. 2016). Morphological

traits were collected when possible from live specimens captured in the study sites. Mist-netting was conducted at six elevations along the gradient between May to November 2016 and all individuals were measured and color banded. For species that were not captured, morphological data were obtained from museum specimens (The Field Museum, Chicago and Coleccion

Boliviana de Fauna, Bolivia). All measurements included for analyses were collected by a single person (FMC) to avoid measurement biases. In all cases trait data were obtained from a minimum of 4 individuals and a maximum of 12. Following Pigot et al. (2016) I used mean trait values from measured individuals (log-transformed) to describe each morphological trait.

Ecological traits were dietary guild and foraging strata. Dietary guild was based on the proportional use of different items in the diet (fruit, nectar, insects, vertebrates), which were extracted from (Wilman et al. 2014) and modified with field observations. Foraging strata described the strata mostly used for foraging (canopy, midstory or understory) and was based on field observations and supplemented with bibliographic data from Herzog et al. (2016) and del

Hoyo et al. (2005). Ecological traits were included as categorical variables. Using all ten traits, I created a species by species distance matrix using Gower distances for each bird assemblage and used these matrices to calculate functional diversity.

Phylogenetic distances among species were estimated based on existing phylogenetic trees. I constructed a phylogeny using Jetz et al. (2012) supertree based on the Hackett et al.

(2008) bone and the updated version of the phylogeny (available at birdtree.org, revised on

and calculated all phylogenetic measures for each of those trees. In all cases, I present our conclusions based on the average of these 1000 metrics.

I calculated functional and phylogenetic diversity for the bird assemblage at each elevation with two tree-based metrics: mean pairwise dissimilarity (MPD) and mean nearest taxon distance (MNTD) diversity (Webb et al. 2002). An advantage of using the same metrics for functional and phylogenetic diversity is that it makes results of the two facets of diversity directly comparable (Sessa et al. 2018). Furthermore, both MPD and MNTD are considered dispersion metrics (i.e., metrics that describe how different traits or clades are to each other within an assemblage; Webb et al. 2002, Tucker et al. 2017) and dispersion metrics are more accurate to measure the signal of deterministic processes, highlighting the extent of variation among traits or tips in a phylogeny and controlling by species richness effects (Chao et al. 2014).

Although these metrics describe functional and phylogenetic dispersion, respectively, I will refer to them as functional and phylogenetic diversity. For functional diversity, MPD and MNTD

(hereafter referred as MPDfun and MNTDfun) describe species dispersion in trait space. MPDfun represents the average phenotypic distance between pairs of species and will be more influenced by species in the extremes of trait space, whereas MNTDfun represents the average of the minimum phenotypic distances among species, and thus, reflects how densely packed trait space is for the given assemblage. For phylogenetic diversity, MPDphy represents the average of the phylogenetic distances between all pairs of species, and, as such provides information on basal branches; as a consequence, MPDphy is more sensitive to the signal for overdispersion in ecological communities (Mazel et al. 2016). In contrast, MNTDphy represents the average minimum phylogenetic distance between species pairs and, therefore, provides information on the tips of the phylogeny. Thus, MPDphy and MNTDphy provide complementary information, as

they are sensitive to processes operating at different phylogenetic depths (Tucker et al. 2017).

MPD and MNTD for functional and phylogenetic diversity were calculated using functions of the picante package (Kembel et al. 2010).

Most studies of community assembly assume there is a phylogenetic signal in functional traits (i.e., niche conservatism), however only few studies test this assumption (Losos 2008,

Gómez et al. 2010). I tested for niche conservatism on all traits included in the analysis by means of Blomberg’s K, calculated with functions of the package picante (Kembel et al. 2010).

Testing for Deterministic Processes Shaping Bird Assemblages

I used null models to test for signals of deterministic processes on community assembly across elevations. Null models allow comparison with random assemblages of species constructed from the regional species pools to observed assemblages. The regional species pool was obtained by pooling together all species registered in the gradient (Gotelli and Graves 1996,

Ulrich and Gotelli 2013). For each observed assemblage, 1000 random assemblages of the same size were created by randomizing species names in the species/trait matrix, or phylogeny tip labels. Then, observed values of each metric were contrasted with the randomized values and the deviation of each observed metric from the mean obtained from the null models was extracted and standardized to represent the strength of assembly forces (standardized effect size, SES).

Positive values of SES correspond to functionally or phylogenetically overdispersed assemblages, whereas negative values represent underdispersed assemblages. Because SES values are free of effects of species richness, I conducted all further analyses with these metrics.

Ecological Predictors of Functional and Phylogenetic Diversity

Based on classic bird ecology studies I selected four variables to describe major niche dimensions driving bird diversity patterns across elevations: mean temperature, habitat complexity, arthropod diversity and fruit availability (Terborgh 1971, Terborgh and Weske 1975,

Terborgh 1977, Loiselle and Blake 1991, Blake and Loiselle 2000). Mean daily temperature was used as a surrogate of abiotic conditions changing across the elevational gradient. I set 5 data loggers at different elevations across the study gradient (3500, 3000, 2500, 2000 and 1350 m) and collected hourly measurements of temperature during the second period of study (April to

October 2015). I calculated mean daily temperature for these elevations, fitted a linear regression

2 to these values and used the regression model (F1,3 = 41.16, R = 0.93, P<0.001) to predict mean temperature values for all other elevations.

Habitat complexity was measured at each one of the 23 elevations with a modified version of the ‘understory height diversity index (UHD)’ used in MacArthur and MacArthur

(1961). At each elevation, I selected 10 random points separated by at least 100 m from one another. At each point, I set an imaginary 1m-diameter cylinder and noted the presence or absence of vegetation at seven vertical strata (i.e., vegetation height intervals): 0-1 m, 1-2 m, 2-4 m, 4-8 m, 8-16 m, 16-24 m and above 24m. For each elevation, I used data from the 10 replicated cylinders to obtain a proportion for each vegetation height interval. UHD was then calculated as the Shannon-Wiener diversity index H’=Ʃ pi ln(pi), where pi is the proportion of vegetation in each ith height interval. Habitat complexity profiles for each elevation can be found in Fig. A-19.

For estimating arthropod diversity, I sampled arthropods in each one of the 23 elevations using a beating method that consisted of placing a 1m2 cloth supported with a frame under the vegetation and shaking or beating the vegetation to collect the insects that dislodge (Cooper and

Whitmore 1990). Arthropods were collected at the middle of the bird sampling season (July

2016). At each elevation, I randomly selected four points separated at least 50 m from each other. In each point, I collected arthropods by shaking the vegetation at two different heights at

the same time (1 m and 3 m), using the same intensity of beating. Immediately after shaking, cloths with arthropods were manually processed and all insects were conserved in ethanol and labeled. For individuals that might be strongly affected by alcohol (e.g., Lepidoptera larvae) I photographed the individual before conserving it in alcohol. Every captured individual was identified to Family level and catalogued into morphospecies by two trained researchers associated with the University of San Andres (N. Ohara and K. Losantos). For analyses, I grouped all individuals collected at a given elevation and calculated arthropod diversity with the

Shannon-Wiener diversity index using morphospecies and raw numbers as abundance estimates.

Although I acknowledge that because of the great variability of size and energetic contents of different arthropods these data do not equate to resource availability, I consider this metric a good and comparable surrogate of the diversity of available prey for insectivorous species across elevations.

Fruit availability was estimated for each of the 23 elevations as the diversity of plants with bird-dispersed fruits available during the middle of the study period (July 2016). At each elevation a random point was selected, and all possible fruiting plants were censed within a circular plot of ~100 m radius with binoculars. All census and identifications were led by two trained botanists (B. Nieto and Y. Fernandez). The team spent between 3-4 hours per point, walking in search of fruits intensively. For each circular plot, they noted the number of individuals for each species, the size of the crop and the characteristics of the fruits (color, size, fruit type and vertical strata where fruit were available). Samples of all reachable fruiting plants were collected and identified at the Bolivian National Herbarium. We acknowledge that the extreme topography of this landscape prevented us from fully scanning the complete plot, however we standardized the effort for all elevations, repeated the search during two consecutive

visits per site, and registered fruits until no new individuals/species were found. Importantly, in this study I do not pretend to fully describe the fruit diversity nor availability across the gradient, but to provide a comparable estimate of fruit availability (obtained with a similar effort in a systematic design) across elevations. For analyses, I grouped all individual plants for each elevation and calculated fruit diversity with the Shannon-Wiener diversity index. Again, although this metric does not quantify resource availability per se, it represents a good surrogate of the diversity of available fruits for frugivorous species at the time of sample.

I used generalized linear models (GLMs) to test for the effects of the ecological predictors in species richness, functional diversity and phylogenetic diversity metrics. I used binomial regression for species richness and GLMs with Gamma errors for all other metrics.

Predictors were weakly correlated (Fig. A-20, Table B-6), thus I decided to include all four metrics in our full models and used manual variable selection by contrasting models with all possible combinations of predictors with analyses of variance. I report results for the best model in each case, but full models and model comparisons can be found in Table B-7.

Results

I registered a total of 310 bird species between 1350 and 3650 m asl, representing 14 orders and 36 families. Fourteen species were excluded from our database because, although they were registered in our census (perching or feeding at our sites), they are unlikely to respond to ecological factors at the scale of our study (i.e., Psittacidae and Accipitridae). Our data base for analyses was thus composed of 296 species. The estimated species richness for the gradient was 343 (Chao II richness estimator), suggesting I sampled approximately 86% of the local avifauna. There was a significant decrease of species richness with elevation (Fig. 4-2), with only 35 species in the higher elevation assemblage, and 164 species in the richest one. Species richness did not decrease monotonically with elevation, but rather showed a low plateau peak

(sensu McCain, 2009). Model selection found habitat complexity and mean temperature as the two variables best explaining patterns of species richness in the studied gradient (Table 4-1).

Species composition also changed with elevation (Fig. 4-3), with the first and most important axis of the ordination (NMDS1) highly correlated with elevation (r = 0.99, P<0.0001; Fig. 4-3,

Table B-6), and the second one correlated with habitat complexity (r = 0.61, P = 0.002) and fruit availability (r = -0.63, P = 0.001).

I found evidence of functional and phylogenetic clustering across the gradient, with almost all assemblages having values of diversity below those expected by their species richness

(Figs. 4-5 and 4-6). As expected, when environmental filtering plays a stronger role at higher elevations, overall phylogenetic diversity decreased with elevation, with clustered assemblages being found towards the highlands (Fig. 4-6). SES MPDphy however, did not follow a linear pattern but showed a diversity peak ~2000 m. Contrary to expectations, functional diversity measured as SES MPDfun increased with elevation, with more clustered assemblages at lower elevations and more dispersed assemblages at higher elevations (Fig. 4-4A). Combined, these results suggest that despite a loss of species with elevations there was no equivalent loss of functional groups or lineages. On the contrary, opposite patterns of MPDfun and MPDphy suggest that the loss of deeply branched lineages might result in an increase in the extreme values of trait space, thus driving higher values of MPDfun at high elevations. Opposite patterns of MNTDfun and MNTDphy derive from the same process: loss of lineages correlate with the loss of functional groups. However, the small magnitude of changes of MNTD with elevation suggest that the loss of species (tips in the phylogeny) have less impact on the remaining functional trait space in the assemblages. Comparisons of phylogenetic diversity patterns with species richness (Figs. 4-2 and

4-6) show that the species richness peaks (~1600-2300) coincide with the peak of MPDphy,

suggesting that the new species gained at these elevations likely belong to new lineages, maximizing within-assemblage phylogenetic dispersion. Furthermore, these few new species are functionally different resulting in an increase in functional dispersion (MPDfun) observed at these elevations. All traits included in our analyses showed non-random phylogenetic signal, as suggested by calculated values of Blomberg’s K (Table B-5).

Regression models showed that overall functional and phylogenetic diversity were better explained by mean temperature with higher values of SES MNTDfun, SES MPDphy and SES

MNTDphy at higher temperatures and more clustering of traits and lineages at low temperatures

(Table 4-1). SES MPDfun, however, showed an opposite pattern, with more dispersed assemblages towards higher and colder elevations. Habitat complexity had a marginal effect on

SES MNTDfun, opposite to mean temperature, with more dispersed assemblages in less complex habitats. Although this variable was not significant, its inclusion significantly improved the fit of data into the regression model, and thus was included as an informative predictor (Table 4-1).

Full models can be found in Table B-6.

Discussion

Understanding the drivers shaping the spatial distribution of species, and the resulting composition of biological communities is one of the most important goals of ecology. Here, I tested for the relative roles of deterministic processes driving bird community assembly across an elevational gradient in the Bolivian Andes. I found strong evidence of environmental filtering across the elevational gradient, with most assemblages clustered in functional and phylogenetic dispersion. Elevational gradients represent natural experiments to assess the ecological and evolutionary responses of species to environmental conditions (Rahbek 1995, Körner 2007).

However, is not elevation per se that drives community assembly, but the resulting conditions that change across elevations. I selected four ecologically informed putative predictors for

diversity patterns across our gradient and found that temperature was the best predictor for most observed patterns, filtering out lineages and resulting in higher dispersion of species in trait space. Structural complexity also affected patterns of taxonomic, functional and phylogenetic diversity, resulting in contrasting patterns. Assemblages in habitats with greater diversity of vertical strata increased in number of species, with gained species belonging to different lineages but having redundant traits.

In the last decade, there has been an increasing interest in moving beyond the taxonomic diversity approach to a more complete description of diversity that accounts for differences among species. Including different dimensions of diversity not only allows for a better inspection of the diversity patterns in natural environments, but to test for hypotheses related with the processes behind these patterns (Cavender‐Bares et al. 2009). For instance, phylogenetic diversity provides information on the evolutionary and biogeographic history of species within a community (Webb et al. 2002, Webb et al. 2010), whereas functional diversity may provide information on the current ecological processes shaping communities (Swenson 2011b). By studying these different facets of diversity, I found that species richness and phylogenetic diversity decrease with elevation, while functional diversity increased with elevation, such that higher dispersion of functional traits occurred in highland assemblages. At high elevations, temperature appears to filter species out of highland assemblages and excludes species which represent unique lineages. Thus, a decay in species richness appears to correspond to a decay in phylogenetic diversity. In contrast, species filtered out at higher elevations do not represent extreme functional traits and, thus, their exclusion results in an increase in overall pair-wise distances, instead of a reduction. In other words, species able to persist at high elevations are retaining similar trait variability (MPDfun) as that found in lower elevation assemblages.

Alternatively, despite a net species loss with elevation, new species might be added to highland communities through diversification events. If this occurs, our results suggest that these species belong to already present lineages (thus, no increase in overall phylogenetic diversity), but might include extreme trait values, increasing the patterns of functional diversity at high elevations.

These two hypotheses are not exclusive and could both operate to result in the observed diversity patterns at high elevation assemblages.

I found a peak in species richness between 1600 and 2300 m, a band that coincides with the end of the cloud forest and beginning of humid montane forest (Paniagua Zambrana et al.

2003) in our study site. A comparison of the species richness pattern with the hump-shaped pattern of SES MPDphy, suggests that montane lineages are being added at mid-elevations, increasing overall phylogenetic diversity. At these elevations, even if some species are lost compared with the lowland assemblages, most functional groups and phylogenetic lineages are still present (thus increasing the pair-wise distances among species). Above this point, however, species loss will result in an even faster loss of functional groups and lineages. Alternatively, mid-elevations peaks may also result from the gain of species from montane assemblages that are not present below, potentially representing the gain of new functional groups and lineages. These diversity peaks in species richness and SES MPDphy also coincide with the peak of habitat complexity along our gradient. In fact, I also found habitat complexity to be a strong predictor of species richness patterns and SES MPDphy, and a marginal predictor of SES MNTDfun. Vertical structural complexity in forested habitats has been suggested to play a key role in explaining bird species diversity. Seminal studies in bird taxonomic diversity (e.g. MacArthur and MacArthur

1961, MacArthur 1964, Terborgh, 1977) suggest that peaks of bird species richness in habitats

with higher structural complexity might result from the larger number of microhabitats available with more vertical strata.

Our results on functional and phylogenetic diversity responses to habitat structure support this hypothesis. Partitioning of foraging strata might allow the coexistence of birds that are otherwise similar in their use of resources (i.e., that overlap other dimensions of the niche such as diet or body size; Walther, 2002). Fine partitioning of microhabitats and vertical strata has been reported among species assumed to be omnivorous (e.g., genus Tangara) and hypothesized as a mechanism that maintains high levels of sympatry reducing potential competition pressures

(Naoki 2007). If this prediction holds, I would expect an increase in both functional and phylogenetic diversity to coincide with this increase in species richness. Our results partially support this hypothesis. I found that phylogenetic diversity measured as SES MPDphy does increase with habitat complexity, but not when measured as MNTDphy. This suggests that species being added to bird assemblages with high habitat complexity likely belong to different lineages, increasing our metric of phylogenetic MPD but not of MNTD, which is by definition more sensitive to changes in the tips of the phylogeny (e.g., addition of species from the same genus).

Functional diversity measured as MNTD was marginally explained by habitat complexity, providing further information on the mechanisms by which the diversity of vertical strata might affect bird assemblages. Although not statistically significant, habitat complexity had a negative effect on functional MNTD; more complex habitats had lower values of

MNTDfun. Most of the traits I used in this study relate to feeding and foraging behavior (Pigot et al. 2016), and therefore our functional diversity estimations represent those dimensions of bird niche. Lower values of MNTDfun in species-rich assemblages might result from the addition of species that share trait values with species already in the assemblage, thus it is possible that these

species might coexist because they are partitioning microhabitats across vertical strata, as suggested by Naoki (2007).

Other studies have examined multiple dimensions of bird diversity across elevations in the Andes to disentangle the driving forces behind community assembly. Most of these studies, however, focus only on a subset of the avifauna (Graham et al. 2009, Dehling et al. 2014,

Colorado and Rodewald 2015) or used compiled data from species lists and field surveys across years to create assemblages (Pigot et al. 2016). Although these studies have been pivotal for our current knowledge of assembly rules in montane biotas, they overlook a key component of the co-existence framework: co-occurrence. Testing for the role of ecological forces shaping communities requires data on actual co-occurrence in ecological scales of space and time. Here, I use empirical data obtained with a standardized effort across elevations for two consecutive years, which accounted for ~86% of the birds present in the gradient. This is, to our knowledge the first study to examine multiple dimensions of bird diversity in the Neotropical Andes with data collection designed to test for the processes behind community assembly.

Overall, results of this study suggest that environmental filtering is a stronger force filtering out species functional groups and lineages in our montane assemblages, with temperature decreases along the gradient as the most plausible mechanism driving observed patterns. However, ecological forces, which act upon species functional traits, do change in relative importance across elevations. Results on phylogenetic diversity are consistent with stronger competitive forces acting at evolutionary scales, resulting in more phylogenetically dispersed assemblages at mid elevations. This study aligns with previous studies on different facets of diversity along elevational gradients in our finding of environmental filtering as a stronger force shaping montane bird communities. However, Andean assemblages seem to be

composed by highly redundant species in terms of functional traits and evolutionary history. This redundancy results in species loss having a lower impact on functional and phylogenetic diversity. In middle elevations, lineages from lower elevations might coexist with lineages that have evolved in higher elevations, increasing the overall phylogenetic diversity even when assemblages are less species-rich. These results have important implications for conservation, as high elevation lineages might be even more vulnerable than their lowland counterparts.

Table 4-1. Best models explaining variations in species richness and in the standardized effect size of functional diversity (SES MPDfun and SES MNTDfun) and phylogenetic diversity (SES MPDphy and SES MNTDphy) of bird assemblages across elevations in the Andes of Bolivia. Significant predictors of diversity patterns were mean temperature and habitat complexity. Generalized linear models with Gamma errors were applied for all response variables except species richness, which was modeled with binomial errors. Model Coefficient St. Error t value P Species richness Intercept 0.31 0.66 0.47 0.640 Habitat complexity 2.02 0.40 5.07 <0.0001 Mean temperature 0.06 0.01 4.14 <0.0001 SES MPDfun Intercept 0.69 0.77 0.89 0.381 Mean temperature -0.21 0.06 -3.42 0.003 SES MNTDfun Intercept 1.11 1.61 0.69 0.490 Habitat complexity -1.99 1.01 -1.97 0.062 Mean temperature 0.09 0.04 2.12 0.046 SES MPDphy Intercept -9.48 3.87 -2.45 0.023 Habitat complexity 4.59 2.07 2.22 0.037 SES MNTDphy Intercept -4.08 0.05 -7.91 <0.0001 Mean temperature 0.19 0.04 4.61 <0.0001

Figure 4-1. Hypothetical representation of the mechanisms that drive the predicted results. Higher functional diversity is expected at lower elevations due to a stronger influence of limiting similarity compared to environmental filtering force. Lower functional diversity (higher clustering) is expected at higher elevations, due to a stronger effect of environmental filtering.

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Figure 4-2. Species richness of 23 assemblages of birds across elevations in the Bolivian Andes. Black line represents the second-degree polynomial regression of species richness as a function of elevation. Grey shade is the 95% CI for the predicted values of the model.

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Figure 4-3. Species composition of 23 assemblages of birds across elevations in the Bolivian Andes. Assemblages are color coded by elevation. NMDS 1 is strongly correlated with elevation (r = 0.99, P<0.0001), and NMDS 2 is correlated with habitat complexity (r = 0.61, P = 0.002) and fruit availability (r = -0.63, P = 0.001).

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Figure 4-4. Ecological predictors of diversity patterns and their variation across elevations along an elevational gradient in the Bolivian Andes. Black points represent observed at each elevation. Black lines represent the second-degree polynomial regressions of observed values against elevation. Grey area represents the 95% CI of the predicted regression. Temperature refers to the mean daily temperature during the months of bird surveys in 2016. Habitat complexity was calculated with the understory height diversity index (UHD)’. Arthropod diversity and fruit availability were calculated as the diversity of arthropods and fruiting plants available during the middle portion of bird surveys (July), using the Shannon-Wiener diversity index.

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Figure 4-5. Functional dispersion of 23 bird assemblages across elevations in the Bolivian Andes. Each point represents the standardized effect size (i.e., deviations from null expectations) for mean pair distance (SES MPDfun) and mean nearest taxon distance (SES MNTDfun) of species in trait space. Point size is relative to species richness in the assemblage. Black lines represent a second-degree polynomial regression of each functional dispersion metric against elevation.

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Figure 4-6. Phylogenetic dispersion of 23 bird assemblages across elevations in the Bolivian Andes. Each point represents the standardized effect size (i.e., deviations from null expectations) for mean pair phylogenetic distance (SES MPDphy) and mean nearest taxon phylogenetic distance (SES MNTDphy) of species within each assemblage. Point size is relative to species richness in the assemblage. Black lines represent a second-degree polynomial regression of each phylogenetic dispersion metric against elevation.

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CHAPTER 5 OPPOSITE PATTERNS OF FUNCTIONAL AND PHYLOGENETIC DIVERSITY OF MIXED-SPECIES FLOCKS ALONG AN ELEVATION GRADIENT PROVIDE EVIDENCE FOR FACILITATION IN COMMUNITY ASSEMBLY

Background

The assembly of communities is hypothesized to be shaped by deterministic processes imposed by the physical environment (i.e., habitat filtering) and the biological environment (i.e., species interactions) (Pavoine et al. 2011, Spasojevic and Suding 2012). The evolutionary history of species may further constrain trait evolution (i.e., niche conservatism) and, thus, leave a phylogenetic signal on community assembly (Losos 2008, Cavender‐Bares et al. 2009).

Numerous studies have used patterns of phenotypic and phylogenetic structure to infer the role of environmental filtering and biological interactions in community assembly (Cavender-Bares et al. 2004, Cavender‐Bares et al. 2009, Graham et al. 2014). From a trait-based perspective, the physical environment may act as a filter, such that only species with certain traits will be able to persist. In contrast, biotic interactions may result in either greater spacing of traits among coexisting species or, the opposite pattern, clustered traits (Webb et al. 2010, Spasojevic and

Suding 2012). Interactions among species can be either positive or negative, yet most studies on community assembly focus on negative interactions (e. g., competition) as the prevalent biotic force, neglecting the potential role of positive interactions (i.e., facilitation) and limiting their inferences to the competition-habitat filtering dichotomy (Gross 2008).

In terms of trait dispersion, local communities that are structured by competition are expected to be phenotypically overdispersed, i.e., consist of species less similar than expected by chance (Cavender‐Bares et al. 2009, Webb et al. 2010). However, positive species interactions can result in different patterns. Positive interactions between phenotypically similar species, as observed in cases of social information exchange, can result in phenotypic clumping. Facilitation

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between dissimilar species, such as that between fly-catching and foliage-gleaning birds, however, can lead to community patterns that are indistinguishable from those that result from competition. Therefore, accounting for both competitive (negative) and positive interactions in studies of community assembly are critical to fully understand the processes that shape community patterns (Gross 2008, Cavender‐Bares et al. 2009, Pausas and Verdú 2010, Sridhar et al. 2012).

Surprisingly, social systems such as mixed-species flocks of birds (hereafter “flocks”) have rarely been assessed under the assembly framework even though their structural patterns are likely the outcome of species interactions (Sridhar et al. 2012). In fact, a recent review suggests that facilitation can be as important as competition in determining flock composition and spatial dynamics (Goodale et al. 2015). However, the few existing studies of flock assembly focus on competition as the main biological force (Graves and Gotelli 1993, Gomez et al. 2010,

Colorado and Rodewald 2015; but see Péron 2017), whereas tests of the role of positive interactions in flock assembly are still scarce (Sridhar et al. 2012, Péron 2017). When joining a flock, individuals may gain benefits as a result of decreased predation and/or increased foraging efficiency (Terborgh 1990, Sridhar et al. 2009). Therefore flocking behavior might be more prevalent when resources are scarce and competition is more likely (Develey and Peres 2000,

Greenberg 2000, Sridhar et al. 2012). Flocks are an ideal system to test community assembly theory; the spatial scale at which flocks occur is small enough such that any individual in the community could potentially colonize any flock (Graves and Gotelli 1993), and the decision of joining the flock would depend purely on the potential benefits gained (Sridhar et al. 2009) and the costs of competition when joining (Gross 2008, Sridhar et al. 2012).

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A promising venue to disentangle the driving forces shaping flock assembly is to combine community assembly framework with a pair-wise analysis of co-occurring species along an environmental gradient (Farine et al. 2012). Inferring biological interactions from co- occurrence matrices has been shown to be particularly valuable for analysis of community dynamics across gradients that focus on non-trophic associations (such as facilitation and competition) (Freilich et al. 2018). At the scale of this study, the decision of any given individual to join a flock depends purely on the balance of costs and benefits of joining. Several studies have found that the benefits (and costs) gained by flocking individuals will result from joining the flock as a unit and not only from joining a given individual or species within it (Sridhar et al.

2009, Sridhar and Shanker 2014, Freeberg et al. 2017). Thus, for these analyses, I assume that all individuals co-occurring within a given flock are potentially engaged in real interactions (either positive, negative or neutral) among each other (Sridhar et al. 2012, Mokross et al. 2013).

Pairwise analysis of species co-occurrence allows for a species-by-species examination of whether a given pair of species is aggregated, segregated or random in occurrence and determine if and how species co-occurrence changes along environmental gradients (Sfenthourakis et al.

2006, Sridhar et al. 2012, Veech 2014). Whereas the community assembly framework helps us separate the potential role of environmental filtering from biological interactions, it will provide information only on a net effect of facilitation and competition (Bruno et al. 2003). The pairwise analyses of co-occurrence will allow for a deeper inspection of the nature of the associations shaping observed community patterns, and test for signals of facilitation and competition separately. For instance, examining the proportion of negative and positive interactions across the gradient, and the proportion of positive interactions between similar and dissimilar species

(Sridhar et al. 2012), can help tease apart the community patterns shaped by competition or

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facilitation. Furthermore, examining the phenotypic and phylogenetic nature of species that are found to be positively associated (i.e., defined here as those species that co-occur more than expected by chance) can help us detect the signal of facilitation in community assembly. If phenotypically dissimilar species are positively associated, they could result in functionally overdispersed assemblages; a pattern often interpreted as competition. On the contrary, if phenotypically similar species are found to be positively associated, they can result in underdispersed assemblages; a pattern often interpreted as environmental filtering.

Complementing community assembly patterns based on trait-based and phylogenetic-based approaches with information on pair-wise associations and pair-wise phenotypic and phylogenetic distances might allow for a better detection of the role of facilitation in mixed- species flock assembly.

Here, I combine information on phenotypic variation, phylogenetic relationships and species composition and co-occurrence in mixed species flocks across elevations to gain insights into the processes governing community assembly. First, I develop predictions of potential driving forces in flock assembly based on the community assembly framework and the expected pair-wise association patterns that might be found if these predictions hold. My predictions account both for the signal of environmental filtering and of positive and negative species interactions, and how the relative importance of these driving forces is expected to change across elevations (Fig. 5-1A). Then, I test these predictions using a data set of mixed-species flocks along a continuous elevational gradient in the Andes of Bolivia. Elevational gradients are good models to test how deterministic forces shape community traits (Johnson and Stinchcombe 2007) because higher elevations may impose harsher conditions for which organisms need to adapt

(Hoiss et al. 2012).

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Specifically, I use analyses of functional and phylogenetic structure, and a pair-wise analysis of species associations to test three hypotheses: (1) The relative importance of habitat filtering, and species interactions will change along the elevational gradient, with habitat filtering being a stronger force at higher elevations and species interactions more important at lower elevations. Then, I predict reduced functional trait richness and trait dispersion at higher elevations, while increased functional richness with more regular trait dispersion is expected at lower elevations (Fig. 5-1B, upper panel). Furthermore, if traits are conserved, a similar pattern is expected for phylogenetic richness and phylogenetic divergence. The second hypothesis states that (2) if species interactions shape community assembly, then positive interactions are more important than negative interactions in structuring flock assembly at higher elevations (Develey and Peres 2000, Greenberg 2000, Sridhar et al. 2012). Flocking behavior is reported to be more prevalent where conditions are harsher, with fewer resources and more extreme abiotic characteristics (Greenberg 2000) and, thus, a higher benefit can be gained by facilitation in extreme abiotic conditions (Callaway 1998, Stachowicz 2001). Assuming physical conditions are harsher at higher elevations, positive interactions (i.e., facilitation) are expected to be more frequent in high-elevation flocks, whereas negative interactions (i.e., competition) are expected to be more common at lower elevations. If this holds, I expect a higher proportion of significantly positive interactions at higher elevations compared to lower elevation flocks (Fig.

5-2). Finally, I address the hypothesis that (3) facilitation is driving functional and phylogenetic structure in flocks by examining phenotypic and phylogenetic distances between pairs of species that have positive associations. If facilitation is driving community assembly, then I predict that phenotypic and phylogenetic distances between interacting species correlate with the association strength of species pairs (Fig. 5-1C). For example, a signal of facilitation in overdispersed

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assemblages (Fig. 5-1A, quadrants a and b) that corresponds with positive interactions between dissimilar species, would result in high phenotypic (and phylogenetic) distances between species pairs with high positive association strengths. On the contrary, a signal of facilitation in clustered communities that corresponds with facilitation among similar species (Fig. 5-1A, quadrants c and d) would result in a negative relationship between phenotypic (and phylogenetic) distances and association strength; lower phenotypic and phylogenetic distances should correspond to stronger associations. Furthermore, if the role of facilitation changes across elevations I predict the relationship between phenotypic and phylogenetic differences and association strength to change with elevation (Fig. 5-1 B and C). I summarize the predictions for different scenarios in Fig. 5-1.

Methods

Mixed Species Flock Surveys

Avian mixed-species flocks were studied along the Sillutinkara trail, a continuous elevational gradient in Cotapata National Park, a protected area in the Andes of western Bolivia.

The trail is a one-person path (~1-1.5 m-wide) where not even pack animals have access. This trail runs between the bottom of the valley, at 1300 m to treeline at 3550 m asl, however two small human settlements (~30 and 20 families, respectively) are located at 1300 m and 1900 m.

Because I was interested in eliminating human disturbance effects that could obscure our results,

I only included flocks registered between 2100 and 3550 m, where no human impacts were noted

(Montaño-Centellas and Garitano-Zavala 2015). The landscape along the gradient is dominated by evergreen humid montane and cloud forests, with steep slopes and deep valleys (Ribera 1995,

Sevilla Callejo 2010).

Between May and November 2016, mixed-species flocks were systematically surveyed along the gradient. Surveys consisted of walking at a slow constant pace and registering every encountered flock. The gradient was surveyed 8 times during this period, and each given

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elevation was registered both in morning and afternoon hours, where flock activity was highest.

Detected flocks were followed as long as possible (data collection ranged from 5 – 45 min) and for every detected flock GPS coordinates and elevation, as well as the taxonomic identity, number of species and individuals per species were recorded (Colorado and Rodewald 2015). I included as part of the flock only individuals that were observed less than 10 meters from other flocking individuals and that were actively moving with the flock. I recorded the whole encounter until the flock was gone; species remaining in the site after the flock left were removed from the flock data.

Neotropical avian flocks are known to be relatively stable in time and space (Martínez and Gomez 2013, Munoz 2016) thus flocks observed less than 400 m from each other, during the same trip were considered non-independent and pooled together for analysis. This decision was also supported by resightings of color-banded individuals (see morphological and ecological traits) in flocks within this distance in consecutive days. Because species richness is known to be correlated with observation time in mixed-species flocks, I further standardized effort by including only flocks that were observed between 15 and 20 minutes. Although this reduced sample size significantly, it allowed for more comparable sampling of the communities for analyses. Finally, I eliminated all flocks that had less than 4 species, as calculations of functional and phylogenetic metrics require a minimum number of different species. Data were then organized into presence-absence matrices with independent flocks as columns and species as rows.

Phylogenetic Data

Phylogenetic distances among species were estimated based on existing phylogenetic trees. I constructed a phylogeny using Jetz et al. (2012) supertree based on the Hackett et al.

(2008) bone and the updated version of the phylogeny (available at birdtree.org, revised on

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August 2018). Jetz et al. (2012) phylogeny results from a MCMC process, and is, therefore, composed of 10000 trees derived from the posterior distribution rather than a single consensus tree. Thus, I randomly selected 1000 samples of the potential 10000 trees to conduct analyses and calculated all phylogenetic measures for each of those trees. In all cases, I present results and conclusions based on the average of these 1000 metrics.

Morphological and Ecological Traits

Data on ten functional traits (8 morphological and 2 ecological) were used for the analysis. The morphological traits included: bill length (culmen), bill width (gape), bill depth, tarsus length, Kipp’s distance, wing length, tail length and body mass. These traits were selected because of their association with important dimensions of the avian niche: where bill dimensions relate to feeding, and wing, tarsus and tail lengths and Kipp’s distance relate to flight performance and foraging maneuvers (Dehling et al. 2014, Pigot et al. 2016). Morphological traits were collected when possible from live specimens captured along the Sillutinkara trail.

Mist-netting was conducted at six elevations along the gradient between May to November 2016 and all individuals from flocking species were measured and color banded. For species that were not captured, morphological data were obtained from museum specimens (The Field Museum,

Chicago and Coleccion Boliviana de Fauna, Bolivia). All measurements included for analyses were collected by a single person (FMC) to avoid measurement biases. In all cases trait data were obtained from a minimum of 4 individuals and a maximum of 12. Following Pigot et al.

(2016) I used mean trait values from measured individuals (log-transformed) to describe each species trait.

Ecological traits were dietary guild and foraging strata. Dietary guild was based on the proportional use of different items in the diet (fruit, nectar, insects, vertebrates), extracted from

Wilman et al. (2014) and modified with field observations. Foraging strata described the strata

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mostly used for foraging when in mixed species flocks (canopy, midstory or understory), and was based on field observations. Ecological traits were included as categorical variables for data analyses.

Test for Trait Conservatism

Studies of community assembly based on trait dispersion often assume trait conservatism, and thus have similar predictions for both phylogenetic and functional dispersion. However, assuming trait conservatism can obscure conclusions on the role of deterministic processes

(Gómez et al. 2010). To assess to what extent phylogeny predicts the ecological similarity of species, given my choice of traits, I measured the phylogenetic signal of each of the traits using

Pagel’s λ statistic in R package phytools (Revell 2012). Pagel’s λ evaluates if traits on a phylogeny are overdispersed, independent, consistent with a model of Brownian motion or conserved. Pagel’s λ is a robust statistic even with incompletely resolved phylogenies and suboptimal branch-length information (Molina-Venegas and Rodríguez 2017).

Functional and Phylogenetic Structure of Flocks

Functional diversity and phylogenetic diversity were calculated for each observed flock.

I used two metrics to describe functional diversity and two metrics for phylogenetic diversity; in each case, I used one metric to denote richness and one metric of dispersion. Whereas richness metrics work better to account for the patterns of change, dispersion metrics are more accurate to measure the signal of deterministic processes, highlighting the extent of variation among traits or tips in a phylogeny and controlling by species richness effects (Chao et al. 2014). Functional diversity was calculated as functional richness (FRic) and functional divergence (FDiv). FRic represents the minimum volume occupied by the community in multivariate functional space and

FDiv quantifies how species diverge in their distances from the center of gravity in the functional space (Villéger et al. 2008). Both FRic and FDiv have been proposed to perform the best across

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metrics of functional diversity to discriminate assembly rules, even with poor species assemblages (Mouchet et al. 2010). Similarly, phylogenetic diversity was calculated with Faith’s

Index (PD) and mean pairwise distance (MPD). Faith’s index relates to phylogenetic richness and sums the quantity of phylogenetic differences present in the assemblage, whereas MPD is a divergence-based phylogenetic diversity (Webb et al. 2002, Tucker et al. 2017). Both PD and

MPD have been suggested as ‘anchor’ metrics providing complementary information on phylogenetic diversity (sensu Tucker et al. 2017). FRic and FDiv were calculated with functions of the FD package (Laliberté et al. 2014), and PD and MPD with functions of the picante package in R environment (Kembel et al. 2010). To test how the patterns of diversity change along the gradient, I regressed each metric of diversity (species richness, FRic, FDiv, PD and

MPD) against elevation using second degree polynomial regressions.

To examine how traits and phylogenetic diversity of flocks are structured, I compared

FRic, FDiv, PD and MPD against null models obtained for random flocks of species constructed from the regional species pool (Gotelli and Graves 1996, Ulrich and Gotelli 2013). Regional species pools were created by grouping together all flocking species registered in the gradient, but not including non-flocking species. This approach ensures that the MPD values are not affected by the fact that flocking behavior is itself phylogenetically clustered (Péron 2017). For each observed flock, 1000 random flocks of the same size were created by randomizing species names in the species/trait matrix, or phylogeny tip labels. Then, observed values of each metric were contrasted with the randomized values and the deviation of each observed metric from the mean obtained from the null models was extracted and standardized to represent the strength of assembly forces (standardized effect size, SES). Positive values of SES correspond to functionally or phylogenetically overdispersed flocks, whereas negative values represent

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underdispersed flocks. I used second-degree polynomial regressions to test for the effects of elevation in the SES of each of the functional and phylogenetic diversity metrics. Additionally, to assess the possibly disproportionately large contribution of the few non-passerine species to observed SES PD and SES MPD values, I followed the same procedure both on the complete dataset and on the dataset from which all non-passerines were excluded (Péron 2017).

Pair-Wise Associations

A pair-wise approach was used to examine effects of elevation on (1) the number of significant positive and negative species interactions in flocks and (2) the overall phenotypic and phylogenetic distance between species with positive associations. I subdivided the gradient in

300 m elevational subsections (hereafter “site”) and grouped all flocks within these subsections as samples of a flock that occurs in each site. Data from each site were organized in a species by flock matrix and used to calculate pair-wise index of association among species within a site, based on their co-occurrence frequency. Because rare species in pair-wise analyses can bias outcomes, I eliminated all species that occurred in only one flock per site before the analysis. I used the probabilistic model proposed by Veech (2013) to calculate the association strength index between each pair of species per site, and to test if the observed value differs from expected by chance (Veech 2014). The model allows for the extraction of a standardized effect size of association strength for each pair-wise association (i.e., a value of deviation from co- occurrence by random association, hereafter referred as “association strength”), that ranges from

-1 to 1 while controlling for sample size (number of flocks per site). The probability for this value to be observed can be interpreted as a significance value (P value). Because I had relatively few flocks per site, I considered as significant any association that had a probability of less than 0.1 to occur (alpha = 0.1). First, I examined how the importance of positive and negative interactions changes across elevations (Fig. 5-2) by counting the number of significant

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positive and significant negative interactions in each site, calculating the percentage of significant associations (out of all pairs analyzed) and plotting them against elevation.

Secondly, to examine the phenotypic and phylogenetic signal in positive pair-wise associations, I tested for the relationship between association strength and phenotypic and phylogenetic distances between species pairs. Because I are interested in testing if species that potentially facilitate each other are similar or dissimilar (Fig. 5-1C), I extracted all positive associations (association strength > 0) from the pair-wise analysis for each site and organized the data in a species by species matrix. The matrix of positive associations was then related with a pair-wise phenotypic distance matrix and a phylogenetic distance matrix with a Multiple

Regression Quadratic Assignment Procedure (MRQAP) with Double-Semi-Partialing (Farine

2017). MRQAP is a multiple regression method for matrices that is appropriate for non- independent observations, such as pair-wise interactions where the same species is repeated in several pairs (Farine 2017). For this analysis, pairwise phenotypic distances were calculated with

Gower distances (Gower 1966) because of the inclusion of categorial traits. To further explore these patterns, I also ran the analyses with morphological traits only and ecological traits only.

As the results were remarkably similar, I present results for the multi-trait distance matrix only

(additional regressions are included as Supplementary material). Finally, to examine for signals of competition I repeated this analysis including only negative interactions. For this, I extracted all negative associations (association strength < 0) from the pair-wise association matrix for each site and used these values as a response variable. Then, I regressed these values with phenotypic and phylogenetic distance matrices as predictors, as explained above. MRQAP-DSP regressions were conducted in the asnipe package, with function mrqap.dsp and 999 permutations (Farine

2013).

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Results

A total of 135 independent flocks were registered in the study period; however, only 69 flocks comprising 94 bird species fulfilled selection criteria and were included in the analyses.

The species that were more common in the flocks were Myioborus melanocephalus, Diglossa cyanea, Anisognathus igniventris and Basileuterus luteoviridis, Margarornis squamiger and

Pyrrhomyias cinnamomeus, all occurring in more than 30 flocks. Examples of species that participate in flocks at different elevations that have significant positive and negative interactions are found in Figs. A-22 and A-23, respectively. Species richness per flock varied between 4 and

28 species (mean=12.3). Twenty-five species were registered only in one flock. Some species were common in the area, yet they were registered actively following a flock only once during the study period (e.g., Andigena cucullata, Zimmerius bolivianus, Pheucticus aureoventris,

Chiroxiphia boliviana). In contrast, some other species that were frequently found in flocks at lower elevations than those sampled here occurred at their upper distribution limit (Montaño-

Centellas in prep.) and were rarely registered in the study area during the sampling period (e.g.

Diglossa caerulescens, Lepidocolaptes lacrymiger, Tangara punctata).

Flock functional richness showed a hump-shaped pattern across elevations, with higher values towards the cloud forest (~2800-3000 m), whereas functional dispersion increased with elevation, with higher divergence of species in trait space in the highland flocks (Fig. 5-3, Table

B-8). Phylogenetic richness also showed a humped-shape pattern, with higher values at mid elevations (~ 2900-3000 m), whereas phylogenetic divergence showed a decreasing pattern, with higher values at lower elevations (Fig. 5-3, Table B-8). As expected, FRic and PD resembled species richness as both metrics are highly correlated with assemblage richness (Fig. 5-3).

When testing for deviations from null expectations, I found that both SES FRic and SES

FDiv were higher in high elevation flocks whereas greater functional clustering was found at

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lower elevations (Fig. 5-4A and B); the models, however, did show considerable variability (R2

2 = 0.07, F1,67 = 5.01, P=0.03 for FRic; and R = 0.25, F1,67 = 21.71, P<0.0001 for FDiv). There was an overall clumping of species in trait space for SES FRic (i.e., most values were below expected), particularly at lower elevations, a pattern consistent with both facilitation among similar species and habitat filtering as important processes shaping trait richness within flocks

(see Fig. 5-1). SES FDiv followed an increasing pattern with highland flocks showing positive values. In contrast, phylogenetic diversity (both SES PD and SES MPD) decreased along the elevational gradient (Fig. 5-4 C and D), with the models also showing high variability (R2 =

2 0.37, F1,67 = 39.12, P<0.0001 for PD; and R = 0.36, F1,67 = 38.01, P<0.0001 for MPD). There was higher underdispersion in SES MPD, particularly at higher altitudes, a pattern concordant with habitat filtering and facilitation among similar species driving phylogenetic divergence within flocks. The overall patterns of SES PD and SES MPD did not change when calculated only for Passeriformes (Fig. A-21), however higher values of SES PD in lower elevations were found when including non-passeriform species.

The pair-wise analysis showed that overall, positive interactions were more common among species in mixed-species flocks than negative interactions across elevations (Fig. 5-5,

Table B-9). In addition, there was an increase in the proportion of significantly positive associations with elevation, suggesting facilitation might be a more important driver of flock assembly at higher elevations. However, no evidence of more frequent negative interactions towards lower elevations was found, contrary to expectations (compare Figs. 5-2 and 5-5).

Examples of species that were positively and negatively associated can be found in Figs. A-22 and A-23, respectively.

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The MRQAP-DSP regressions of association strength for positively associated species

(i.e., putatively facilitating species) as a function of phenotypic and phylogenetic distances showed a strong effect of phenotypic distance and a weaker, but significant, effect of phylogenetic distance on positive associations (Table 5-1). At all elevations, the association strength for positively interacting species increased with greater phenotypic distance, suggesting dissimilar species have stronger patterns of association (Fig. 5-1C). Furthermore, these relationships persisted when only a subset of traits were included in the analyses (Tables B-10 and B-11). The relationship between association strength and phylogenetic distance was also positive, with greater phylogenetic distance between strongly associated species. In contrast, the association strength of negatively associated species (i.e., putatively competing species) was negatively associated with phenotypic and phylogenetic distances, suggesting stronger competitive interactions among similar species (Table 5-2). As before, this relationship remained when only a subset of traits was included in the analyses (Tables B-12 and B-13).

All traits showed phylogenetic signal, with values between 0 and 1 (closer to 1) suggesting that the effect of phylogeny is weaker than in the Brownian model (Table B-14).

Discussion

In this study, I used mixed-species flocks as model systems to gain insights into the potential mechanisms driving community assembly along elevational gradients in the Andes of

Bolivia. Results suggest that environmental filtering, competition and facilitation are important forces in flock assembly, yet they operate at different scales. Whereas environmental filtering and competition act at an evolutionary scale (driving phylogenetic diversity patterns), a balance of competitive and facilitation forces drive flock assembly at an ecological scale, which drives functional diversity patterns and pair-wise associations. I found evidence that positive associations among dissimilar species were predominant among species within flocks, and

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facilitation is likely a more important driver of community assembly than competition in montane bird assemblages, a pattern described previously for other taxa (Callaway 1995,

Bertness et al. 1999, Bruno et al. 2003). Moreover, I found facilitation to be more important at higher elevations where abiotic conditions are harsher. This study exemplifies how accounting for positive interactions might provide a more complete picture and help advance our understanding of the complex interaction of ecological and evolutionary forces shaping communities.

I used patterns of phenotypic and phylogenetic structure to infer the role of environmental filtering and biological interactions across elevations (Cavender-Bares et al. 2004,

Cavender‐Bares et al. 2009, Graham et al. 2014). Functional and phylogenetic richness (SES

FRic and SES PD) provided information on assemblage size in terms in functional space, and on phylogenetic diversity, in relation to the species pool. In terms of functional space, the size of the trait space occupied by species within a given flock increased with elevation, however, almost all values were below zero (underdispersed). This result suggests that flocks represent only subsets of the potential species pool of flocking species and these species are clumped in trait space, i.e., flocks occupy smaller volumes in trait space than expected from null models (Fig. 5-4A).

However, these subsets of species include more extreme trait values (species with larger differences) towards high elevations. In terms of phylogenetic richness, I found an opposite pattern: a lower SES PD was found at high elevations (Fig. 5-4C). PD represents the sum of the phylogeny branch lengths and, thus, it measures an accumulated phylogenetic diversity. Our finding of lower SES PD at higher elevations is consistent with higher environmental filtering at these elevations, where species are filtered and only some lineages can survive and/or colonize these elevations. For example, the great diversity of tanagers (Thraupidae) in the tropical Andes

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has resulted from high rates of diversification in montane areas where phylogenetically old species have persisted (Fjeldså and Rahbek 2006).

Functional and phylogenetic dispersion metrics (SES FDiv and SES MPD) are more informative for my hypotheses as they focus on differences among species within flocks. In terms of trait space, I found an even stronger pattern of increase of SES FDiv with elevation

(Fig. 5-4B), supporting my prediction of species being more phenotypically dissimilar in higher elevation flocks. This result is consistent with biological forces driving the assembly of highland flocks, with both competition and facilitation among dissimilar species as the main forces, whereas facilitation among similar species appears to be the main driver of flock assembly at lower elevations (Fig. 5-1A). In contrast, SES MPD decreased with elevations (Fig. 5-4D), suggesting these species, despite being phenotypically different, belong to more closely related lineages than species in flocks at lower elevations.

These results also suggest a pattern of phylogenetic underdispersion, with most values of

SES MPD falling below the null expectation (Fig. 5-4D). This pattern is consistent with relaxed competition, as expected in mutualisms (Gómez et al. 2010, Sridhar et al. 2012) and with a predominant role of facilitation in community assembly across elevation (Nuismer and Harmon

2015). In a global study, Perón (2017) also found mixed-species flocks to be phylogenetically clustered, but the clustering was less evident for flocks that were more stable, suggesting that competitive interactions can be more important in stable flocks. Neotropical flocks can be notably stable in space and time, as found in resurveys of flocks over two decades apart

(Martínez and Gomez 2013). In this study, color-banded individuals were observed in the same flocks during consecutive days, months and years, suggesting Andean flocks are also stable in space and time, a pattern also noted by (Munoz 2016) in the Peruvian Andes. Although I caution

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that while competition might not necessarily prevent species from joining flocks in this study system, some species may respond to competition by changing their behavior, foraging ecology or spatial position to reduce competition costs when joining flocks (Goodale et al. 2015).

Altogether, the contrasting results on functional and phylogenetic diversity patterns provide evidence of environmental filtering and likely competition occurring at larger spatial and temporal scales. These two forces shape phylogenetic diversity of flocks across elevations, whereas ecological processes (i.e., competitive and facilitation forces) are suggested to drive the actual decision of each individual to join (or not) the flock. Furthermore, at least at high elevations results support the importance of facilitation above competition as an assembly force.

Species that are associated in flocks in the highlands are closer to each other than expected by null expectations (lower SES MPD), a pattern opposite to that expected if competition would be the main process driving flock assembly. Simulation studies on the relationship between phylogenetic information and interspecific interactions suggest that in communities where interactions are generally mutualistic or facilitative, the rate at which benefits are exchanged among community members is maximized when species are all closely related (low phylogenetic diversity) (Nuismer and Harmon 2015). My approach of examining positive and negative pair- wise interactions separately allows for a deeper inspection of the potential role of competition and facilitation in shaping flock assembly. Considering the small temporal and spatial scale of flock formation, species with positive interactions are actively taking the “decision” to participate in the flock when a given set of species is already present. Joining flocks with dissimilar species might reflect two non-exclusive mechanisms: (1) species might be avoiding competition costs by joining flocks with dissimilar species only and/or (2) species might gain larger benefits by joining dissimilar species (Sridhar et al. 2009). If species are avoiding

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competition costs, then I should find a decreasing amount of negative pair-wise interactions (i.e., species that co-occur less than expected by chance) with elevation, assuming competition is stronger at low elevations. Furthermore, these negative interactions should occur between phenotypically similar species as competition is expected to be stronger among them. If, on the other hand, facilitation is a stronger force, I expect more significant positive than negative interactions to be found between flocking species, and I expect these interactions to be positively related with phenotypic and phylogenetic distance between species pairs.

Pair-wise analyses had three main findings related to these predictions: First, positive species interactions were more common than negative interactions, and they were even more frequent in high-elevation flocks, supporting my prediction that positive interactions might provide more benefits to participating species when conditions are harsher (Stachowicz 2001). A higher prevalence of positive compared to negative associations in flocks align with other studies, and simulation studies have shown that positive interactions, even among competitors, might be common and stable in time (Gross 2008), relaxing the pressures of competition in interacting species. This reduced competition may result in fewer significant species interactions, even when competitors are present in the same assemblages. Secondly, although fewer significant negative interactions were found, their relative frequency was consistent across the gradient, suggesting competition may also have a role in montane flock assembly at an ecological scale, as suggested by previous studies (Graves and Gotelli 1993, Gómez et al. 2010,

Colorado and Rodewald 2015). None of these studies, however, considered facilitation in their framework. Lastly, I found that the strengths of both positive and negative interactions are significantly related with phenotypic and phylogenetic distances. As expected, pairs of species that have stronger negative associations (i.e., putatively competing species) are phenotypically

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similar to each other, whereas species that have stronger positive interactions (i.e. putatively facilitating species) are more dissimilar. Furthermore, the positive relationship between association strength and phenotypic distance held when phenotypic distances were calculated only with a subset of traits (Tables B-10 to B-13), suggesting that this is a consistent pattern, not driven by one -or few- traits. As with most trait-based studies, I cannot rule out the possibility that some other traits that were not included in this study might be driving decisions to join flocks. However, traits included in this study are known to be related to key dimensions of ecological niche in birds (i.e., diet, foraging maneuver and foraging substrates), and which are likely filtered by species interactions such as competition and facilitation (Pigot et al. 2016). For instance, size and diet -both traits included in our analyses- can play a role in species participating in flocks, either determining which species associate or even the role of the species within the flock (Sridhar et al. 2012, Goodale et al. 2017). Moreover, phylogenetic trees should capture patterns of trait similarity and differences that might not be considered and may influence species interactions (Nuismer and Harmon 2015).

Combined, results obtain from the community assembly and pair-wise analyses suggest that facilitation within mixed species flocks is a stronger force at higher elevations. I predicted this result based on the hypothesis that flocking propensity is higher where abiotic conditions are more challenging and resources are limited (Callaway 1995, Stachowicz 2001). Most of the species participating in flocks in montane assemblages are either insectivores or frugivore- insectivores, which feed on arthropods and fruits while flocking. The diversity of arthropods and fruits available for birds to feed might decrease with elevation and therefore, high elevations might represent habitats where species face not only harsher abiotic conditions, but depleted resource availability (Lessard et al. 2011, Dehling et al. 2014, Ferger et al. 2014, Smith et al.

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2014). In such conditions, facilitation might represent a larger benefit for flocking individuals and, thus, species might have a higher propensity to flock at higher elevations (Goodale et al.

2015). Alternatively, predation-related benefits gained by joining a flock can also result in higher prevalence of flocking at high elevations (Thiollay 2003, Goodale et al. 2017). Although the diversity of predators is expected to decline with elevation, more open habitats in highland forests might represent riskier areas to forage, particularly for species with foraging habits that make them more vulnerable to predators. Studies of perceived predation risk (landscapes of fear) along environmental gradients might provide more detailed insights to tease apart the potential benefits of facilitative associations among species in flocks (Bleicher 2017). Moreover, testing for the relationship between specific traits that might directly relate to predation-related benefits

(e.g., color patterns) might also prove useful to better understand the mechanisms behind individual decisions of joining mixed-species flocks. For instance, convergence in color patterns among flocking species has been hypothesized to increase cohesion among flock members while reducing the costs of learning visual cues (Moynihan 1962), or by reducing the potential cost of the “oddity effect”, where individuals that are phenotypically different than the rest of the members might have higher predation costs (Moynihan 1968). Alternatively, it has been suggested that color convergence might allow subordinate species to feed without being attacked by dominant species (Diamond 1982), potentially reducing the costs of competition among flocking individuals.

Conclusions

This study highlights the hidden, yet determinant role of positive interactions in the generation and maintenance of Andean diversity. Facilitation was found to be a main force driving community assembly in mixed-species flocks, and a balance between competition and facilitation determine the decision of individual birds to join a flock and engage in pair-wise

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associations. As expected for mountain assemblages, I found signals of environmental filtering driving the patterns of community assembly across elevations, a result that aligns with several other studies on elevational gradients. Competition likely operates at both evolutionary and ecological scales; however, facilitation can be a more determinant force for flock assembly at a local scale (Goodale et al. 2015), particularly at higher elevations. Mixed-species flocks represent a great proportion of avian species in the Neotropics, particularly in the Andes, therefore the driving forces in flock assembly might impact the overall community patterns in these systems. Including facilitation in the theoretical framework of ecological studies might provide a better understanding of the processes acting upon natural systems (Bruno et al. 2003).

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Table 5-1. Regression coefficients of the Multiple Regression Quadratic Assignment Procedure (MRQAP) with Double-Semi-Partialing (DSP) showing the effect of phenotypic and phylogenetic distances on the association strength of positive species interactions. Association strength was calculated with Veech (2013) probabilistic method. All positive interactions (with association strength above 0) were included in each site’s association matrix for analyses. Phenotypic distance matrices were obtained by calculating Gower distances between species including eight morphological traits and two ecological traits. N is the number of associations used in each regression, and R2 is the adjusted R squared value of the regression. Models Coefficients P Site 1: 2050 -2350 (N=101, R2=0.68) Intercept 0.0013 0.2202 Phenotypic distance 0.1493 0.0001 Phylogenetic distance 0.0002 0.0001 2 Site 2: 2350 - 2650 (N=131; R =0.57) Intercept 0.0021 0.2072 Phenotypic distance 0.0755 0.0001 Phylogenetic distance 0.0004 0.0001 Site 3: 2650 - 2950 (N=247; R2=0.47) Intercept 0.0041 0.0631 Phenotypic distance 0.0692 0.0001 Phylogenetic distance 0.0002 0.0001 Site 4: 2950 - 3250 (N=303; R2=0.50) Intercept 0.0028 0.1081 Phenotypic distance 0.0503 0.0001 Phylogenetic distance 0.0003 0.0001 Site 5: 3250 - 3550 (N=66; R2=0.60) Intercept 0.0034 0.1932 Phenotypic distance 0.0740 0.0020 Phylogenetic distance 0.0006 0.0001

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Table 5-2. Regression coefficients of the Multiple Regression Quadratic Assignment Procedure (MRQAP) with Double-Semi-Partialing (DSP) showing the effect of phenotypic and phylogenetic distances on the association strength of negative species interactions. Association strength was calculated with Veech (2013) probabilistic method. All negative interactions (with association strength below 0) were included in each site’s association matrix for analyses. Phenotypic distance matrices were obtained by calculating Gower distances between species including eight morphological traits and two ecological traits. N is the number of associations used in each regression, and R2 is the adjusted R squared value of the regression. Models Coefficients P Site 1: 2050 -2350 (N=66; R2 = 0.51) Intercept -0.0016 0.1982 Phenotypic distance -0.0781 0.0000 Phylogenetic distance -0.0001 0.0010 2 Site 2: 2350 - 2650 (N=87; R = 0.62) Intercept -0.0011 0.2432 Phenotypic distance -0.0158 0.1061 Phylogenetic distance -0.0004 0.0001 Site 3: 2650 - 2950 (N=140; R2 = 0.53) Intercept -0.0008 0.2903 Phenotypic distance -0.0574 0.0000 Phylogenetic distance -0.0002 0.0001 Site 4: 2950 - 3250 (N=176; R2 = 0.50) Intercept -0.0007 0.2472 Phenotypic distance -0.0913 0.0000 Phylogenetic distance -0.00004 0.0230 Site 5: 3250 - 3550 (N=48; R2 = 0.49) Intercept -0.0017 0.3113 Phenotypic distance -0.1372 0.0000 Phylogenetic distance -0.0002 0.0080

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Figure 5-1. Schematic representation of the community assembly framework that considers both positive and negative interactions among species in mixed-species flocks (A) and the hypotheses tested in this study (B and C). (A) A hypothetical set of points representing standardized effect sizes (SES, i.e., deviations from null expectations) of functional or phylogenetic diversity of mixed-species flocks are shown in grey. Flocks can be either overdispersed (higher than the null, represented as a dashed line) or underdispersed (lower than the null) in functional or phylogenetic diversity. Assuming conditions are harsher at higher elevations, SES patterns can be better explained by the main forces described for each panel. For example, overdispersed flocks at lowland elevations (panel a) can be explained by competition forces and facilitation between dissimilar species. (B) Hypothetical representation of the four different patterns of SES of functional or phylogenetic diversity that can be observed across elevations. The main forces driving these patterns can be found in A. Black lines depict the overall trends across elevations. (C) Expected relationship between a matrix of positive pair-wise association strength (denoted as association) and matrices of phenotypic and phylogenetic distances between interacting species (denoted as distance). Stronger associations and higher distances are represented as dark cells in the matrices, and weaker associations and lower distances are represented as light grey cells. Each one of the four cases correspond to a case in B. For example, if community assembly patterns show that competition and facilitation among dissimilar species are potential drivers at low elevations (upper panel B, overdispersion in lowland flocks), then positive interactions should be stronger among dissimilar species, resulting in a positive relationship between association and distance (denoted by association ~ distance). On the contrary, if community assembly patterns show environmental filtering and facilitation among similar species (upper panel B, underdispersion in highland flocks), then a negative relationship between association and distance is expected (denoted by association ~ 1/distance).

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Figure 5-1. Continued

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Figure 5-2. Theoretical representation of the changes in relative importance of positive and negative interactions among participant species in mixed species flocks across elevations. Assuming abiotic conditions are harder at high elevations, and competition forces are stronger at low elevations, I expect significant positive interactions to be more frequent in highland flocks, whereas more negative interactions are expected in lowland flocks.

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Figure 5-3. Patterns of diversity metrics of mixed species flocks of birds along an elevational gradient in the Bolivian Andes. (A) Species richness (B) functional richness, FRic, (C) functional divergence, FDiv, (D) phylogenetic richness, PD (Faith’s index) and (E) phylogenetic dispersion, MPD (mean pairwise distance). Grey lines represent second degree polynomial regressions of each metric against elevation, model coefficients and significance can be found in the Table B-7.

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Figure 5-4. Deviation from null expectations in functional diversity and phylogenetic diversity of mixed-species flocks along an elevational gradient in the Bolivian Andes. Each point represents the standardized effect size (SES, deviation from the null); point size is relative to the number of species in the flock. Black points show significant deviations from the null (with alpha = 0.05), whereas grey points do not differ significantly. Black lines represent second-degree polynomial regression models of SES values against elevation. (A) SES of functional richness does not change with elevation (SES FRic; R2 = 0.07, P = 0.08) and is overall underdispersed, (B) SES of functional divergence increases with elevation (FDiv; R2 = 0.25, P = 0.00001), (C) SES of phylogenetic decreases with elevation richness (SES PD; R2 = 0.38, P = 0.00001), and (D) SES of phylogenetic dispersion decreases with elevation (SES MPD; R2 = 0.36, P = 0.00001). Potential mechanisms related with these patterns are explained in Fig. 5-1.

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Figure 5-5. Percentage of significant positive (white dots) and negative (grey dots) pair-wise associations in flocks at different elevations in the Bolivian Andes, showing how more positive interactions are found towards higher elevations. Significant associations were calculated using the probabilistic method by Veech (2013). Associations with P values lower than 0.1 were considered significant. Percentages are calculated as number of significant associations divided by the total pairs analyzed (see Table B-8). Dots are plotted in the middle point of each elevational bin.

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CHAPTER 6 INTERACTION NETWORKS OF AVIAN FLOCKS INCREASE IN CONNECTIVITY AND COHESION WITH ELEVATION

Background

Network theory represents an useful conceptual framework to understand the structural complexity of ecological communities (Poisot et al. 2016). Ecological communities are composed of species that interact with other species, often in complex mutualistic (i.e., positive) or antagonistic (i.e., negative) relationships (Thébault and Fontaine 2010). Network theory quantifies these interactions and examines properties of the community as a whole. Thus, network theory potentially provides an understanding of how local processes drive group-level properties, by taking into account the environment experienced by individuals (Bascompte 2007,

Farine and Whitehead 2015, Tylianakis and Morris 2017). By definition, networks consist of nodes connected by edges, where nodes represent the interacting entities (e.g., individuals, species, etc.) and the edges represent the interaction between two nodes (i.e., how two entities relate). In community ecology, networks are a description of observed patterns of associations or interactions among species (Bascompte 2007), therefore nodes are represented by species and edges represent the interactions among species. Edges in community networks can have numeric values, describing the strength of the association between pairs of interacting species, or the probability of encounter among them (Farine and Whitehead 2015).

Recently, the need to understand how communities respond to environmental change has led to an increasing interest in how networks change across environmental gradients (Cagnolo et al. 2011), which promises to advance our understanding of mechanisms driving network structure (i.e., network topology) (Bascompte 2007, Tylianakis et al. 2009, Lurgi et al. 2012,

Tylianakis and Morris 2017). For instance, studies along anthropogenic disturbance gradients found that the topology of biological networks can be affected by specific characteristics of the

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environment (e.g., habitat heterogeneity, patch size). In fact, environmental drivers might affect the frequency at which species interact even though community composition remains constant

(Tylianakis and Morris 2017). Consequently, changes in network topology have proven useful to detect community responses to environmental gradients that classic community ecology (i.e., species richness and species composition analyses) might not detect (Poisot et al. 2016).

Non-trophic associations, such as competition and facilitation, are known to influence community structure and function (Callaway 1998, Stachowicz 2001, Kéfi et al. 2015).

However, most ecological network studies have focused on trophic-interaction networks such as food webs (Pascual and Dunne 2006) and bipartite (two-group) mutualistic networks (Bascompte and Jordano 2007), and little is known about how non-trophic interaction networks (i.e., networks where species interact with non-trophic associations, such as competition or facilitation) respond to environmental changes (Valiente-Banuet et al. 2015). Furthermore, the few studies in non-trophic networks along gradients have focused mostly on anthropogenic environmental change (Mokross et al. 2013, Goodale et al. 2015, Borah et al. 2018, Kay et al.

2018). In contrast, how networks change across natural gradients, such as elevation, has seldom been studied (Tylianakis and Morris 2017).

The structure of ecological networks is expected to change along environmental gradients via changes in species richness, species composition and frequency of interactions (Tylianakis et al. 2007, Hoiss et al. 2015). Patterns of species richness and changes in composition have been extensively studied along elevational gradients. Whereas species richness decreases with elevation, species composition can change rapidly and result in different communities across elevation (McCain and Grytnes 2001). A few studies of mutualistic networks (e.g., plant- pollinator, ant-plant) found an effect of elevation on network topology (Ramos-Jiliberto et al.

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2010, Hoiss et al. 2015, Maglianesi et al. 2015, Plowman et al. 2017). These studies report changes in the number of connections species have within the network, with connections being more even and random at higher elevations, potentially with higher opportunistic association, and a consequent decrease in specialization with elevation. These changes result in higher network connectedness, which potentially may confer increased robustness of mutualistic networks at high elevations (Cuartas-Hernández and Medel 2015). Co-occurrence networks, such as mixed-species flocks, have received less attention, and to my knowledge no study has examined how network topology of mixed-species flocks changes across elevations (Tylianakis and Morris 2017). Here, I use networks of bird species associated in mixed-species flocks to examine the mechanisms by which networks change across an extensive elevational gradient.

Mixed-species flocks of birds (hereafter referred as “flocks”) are one of the most complex multi-species aggregations among terrestrial biota, and are particularly well developed and widespread in forested habitats (Greenberg 2000). Flocks are networks of non-trophic associations that consist of two or more species that associate to gain benefits; these benefits are most likely related to reduced predation or increased foraging opportunities (Sridhar et al. 2009,

Farine et al. 2012). Enhanced foraging efficiency might result from increased access to prey flushed by other participants in the flock, information on location of feeding resources or by exploiting new areas that were unavailable before (Sridhar et al. 2009, Goodale et al. 2017).

Enhanced anti-predator benefits might result from complementary vigilance from different species, from information about predator location and type and from dilution and confusion effects (Morse 1977, Thiollay 1999). At the same time, participating in flocks can also represent a cost for interacting individuals, a cost related with competition and aggression (Munn and

Terborgh 1979, Colorado and Rodewald 2015). Thus, different species are expected to associate

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with each other in flocks when benefits outweigh the potential cost of associating. Potentially, this will happen when resources are scarce and/or predation risk is higher, and flock participation might become a more beneficial strategy (Develey and Peres 2000, Srinivasan and Quader

2012). Because the benefits gained by joining a flock will vary among species and will depend upon habitat context and flock (Mokross et al. 2013, Borah et al. 2018), changes in network topology of flocks are expected across environmental gradients. In fact, recent studies of flock responses to anthropogenic environmental changes have found that flock size, richness and network structural complexity (connectivity, cohesion and stability) decrease with increased fragmentation (reviewed in Goodale et al. 2015). Natural and human-induced environmental gradients may influence networks via similar mechanisms, thus flock network changes across elevation may resemble flock responses to anthropogenic change (Tylianakis et al. 2009,

Tylianakis and Morris 2017).

Compared to the lowlands, very little is known about montane flocks, especially in the

Neotropics. Early descriptions suggest Neotropical montane flocks differ from lowland flocks in several aspects. For instance, montane flocks may be more dynamic, composed of very loosely associated species, and less stable than Amazonian flocks (Poulsen 1996). In Ecuador, individual birds in montane flocks tended to join flocks whenever one was available inside their home range, regardless of flock specific location within the home range (Pomara et al. 2007), whereas

Amazonian flocks tended to have more obligate participants. In addition, montane flocks often included members from all vertical levels as opposed to lowland flocks where the use of strata was more segregated (Poulsen 1996, Guevara et al. 2011, Marín-Gómez and Arbeláez-Cortés

2015), thus they are composed of more generalist species than flocks at lower elevations. Besides

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these few descriptions, no study has yet assessed how mixed-species flocks change across elevations in the Neotropics.

Based on the responses of mixed-species flocks to anthropogenic disturbances and on the effects of elevational changes for mutualistic networks, here I combine co-occurrence network analyses with traditional community richness and compositional analyses to explore four main hypotheses (Fig. 6-1). Elevation should result in (H1) lower species richness and in (H2) changes in species composition, towards smaller assemblages of flocking species at higher elevations. At a network level, these changes are hypothesized to (H3) result in differences in network metrics related with either network connectivity (i.e. how interactions are organized around single nodes) or network cohesion (i.e. how aggregated are nodes among each other forming subgroups within the network). Fewer and more even interactions are expected at high elevations resulting in increased connectedness of the network and a consequent decrease in network cohesion, as found for mutualistic networks (Cuartas-Hernández and Medel 2015). Additionally, these changes are hypothesized to result in (H4) differences in the relevance of each species within the network and differences in the overall strength of species pair-wise associations. Any species within the network can lose, gain or restructure co-occurrence links across elevations, changing its importance for the overall network (Araújo et al. 2011, Tylianakis and Morris 2017, Kay et al.

2018). Comparing both network properties and how the shared species among them interact provides complementary information on network responses to environmental changes and a better understanding of the ecological meaning of such changes (Tylianakis and Morris 2017,

Delmas et al. 2018).

Finally, I evaluated the relevance of different components of network dissimilarity (i.e., changes in network structure resulting from differences in species richness and composition and

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those resulting from differences in the interactions among shared species) by calculating pair- wise network dissimilarity measures (network beta diversity, βWN). Network dissimilarity metrics allow for the decomposition of βWN into its two additive components: network differences due to changes in species composition βST, and network changes due to restructuring of within network connections, βOS (Poisot et al. 2012). This partitioning provides insights into the relative roles of these mechanisms in driving network changes across elevations. In this study, I test hypotheses

H1 to H4 and partitioning of network dissimilarities using a data set of mixed-species flocks along an extensive elevational gradient in the Western Andes of Bolivia. The extreme topography in the study region prevents human settlements, largely reducing the impact of anthropogenic effects on community responses.

Methods

Data Collection

Avian mixed-species flocks were studied for two consecutive years (2016 and 2017) along an elevational gradient (~1350 – 3500 m asl, Fig. A-24) in Cotapata National Park, a protected area in the Andes of western Bolivia. The landscape along the gradient is dominated by evergreen humid montane and cloud forests, with steep slopes and deep valleys (Ribera 1995,

Sevilla Callejo 2010) and largely free of human intervention (Montaño-Centellas and Garitano-

Zavala 2015), with only one small human settlement (~20 families) occurring at 1950 m. In order to eliminate anthropogenic disturbance effects in the study, all flocks registered between

1700 and 2100 m asl were eliminated.

Between May and November 2016 and May and August 2017, flocks were systematically surveyed along the gradient. Given the extreme topography of the landscape, I subdivided the elevational gradient into transects of 350 m of change in elevation (i.e., from 1350 to 1700 m, from 1700 to 2050m, etc.), an elevational extension I could evaluate within ~ 3 hours of effort;

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each 350 m transect formed the basis of my daily sampling (Elsen et al. 2017). Every transect was surveyed twice each day; one upslope and one downslope, separated by at least 1 hour from each other (Elsen et al. 2017), repeating surveys 8 times per year (N=16). Surveys consisted of walking at a slow constant pace along the 350 m elevational transect and registering every encountered flock. Detected flocks were followed as long as possible (data collection ranged from 5 – 45 min) and for every detected flock GPS coordinates and elevation, as well as the taxonomic identity, number of species and individuals per species were recorded (Colorado and

Rodewald 2015). In this study, I considered a flock as a gathering of at least two species foraging near to each other (less than 10 meters from each other) and moving in the same direction. Flock data were collected with the “gambit of the group’ method, where several individuals observed at the same time and place are assumed to be associated, then the association rate represents the propensity of each pair of individuals to co-occur in the same group (Farine and Whitehead

2015). I consider this assumption valid, because at the scale of this study, the decision of any given individual of joining a flock depends purely on the balance of costs and benefits of joining.

Several studies have found that the benefits (and costs) gained by flocking individuals will result from joining the flock as a unit and not only from joining a given individual or species within it

(Sridhar et al. 2009, Sridhar and Shanker 2014, Freeberg et al. 2017). Thus, for this study system, and scale, I assume that all individuals co-occurring within a given flock are potentially engaged in real interactions (either positive, negative or neutral) among each other (Sridhar et al.

2012, Mokross et al. 2013). Furthermore, inferring biological interactions from co-occurrence matrices has been shown to be particularly valuable for analysis of community dynamics across gradients that focus on non-trophic associations (such as facilitation and competition) (Freilich et al. 2018). I recorded the whole encounter until the flock was unreachable and excluded all

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species that remained in the study areas (feeding, perching and/or vocalizing) after the flock was gone, to diminish the bias or registering non-flocking individuals that were active in the same area at the same time. Each detected flock represents a snapshot of the realized associations resulting from individual birds deciding to join (or not) the flock, thus flocks observed during different days were considered independent replicates of mixed-species flocks in each 350 m elevational transect (hereafter “elevation”). Species richness is known to be correlated with observation time; thus, I further standardized my effort by including only flocks that were observed between 10 and 20 minutes; flocks that were followed for less time were eliminated.

Data were organized into presence-absence matrices for each elevation, with flocks as columns and species as rows.

Flocking Assemblages: Species Richness and Community Composition

All species registered in flocks at a given elevation were pooled to create an assemblage of flocking species per elevation. Species richness was described with the observed species richness and with Chao II richness estimator (Magurran 2004), and compared among assemblages with individual-based rarefaction and sample-based accumulation curves (Gotelli and Colwell 2001). Species composition among assemblages was described with rank-abundance curves for each elevation and compared visually with a Non-metric Multidimensional Scaling

(NMDS) ordination and statistically with a Permutational Analysis of Variance (PerManova) and pair-wise a posteriori PerManova analyses with Bonferroni corrections (McCune et al. 2002).

Additionally, I compared the average species richness per flock across elevations with a

Generalized Linear Model with Negative Binomial errors.

Network Structure across Elevations

Weighted networks were constructed for each elevation following Mokross et al. (2013) , where the frequency of each pair-wise interaction represented the association strength between

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those two species (Farine and Whitehead 2015). I calculated species-level metrics related with each species’ connectivity and position within the network (degree, weighted degree, and centrality) and network-level metrics related with network connectivity (density, skewness and connectance) and network cohesion (transitivity and modularity) and examined the effects of elevation on each of these metrics. First, I calculated degree (i.e., number of connections each species has) and weighted degree (i.e., the sum of the frequency of interspecific associations for each node) for each species in each network. Because degree is sensitive to species richness per network, I calculated normalized degree by dividing degree values by the number of possible interspecific associations in each flock (n-1, where n is the number of species) to compare networks. Similarly, I calculated a normalized weighted degree by dividing each calculated value by the maximum of each data set (Ioannis and Eleni 2007). Then, I calculated centrality (the number of shorter paths that cross a given node), a measure of the position of each species within the network. Again, centrality is sensitive to species richness, thus I normalized it by dividing by n-1 to compare networks. In all cases, I report normalized and non-normalized values for species-level metrics, but I conducted analyses with normalized values only. I used Generalized

Linear Models (GLMs) with Gamma errors and elevation as a factor, to examine if normalized degree and normalized weighted degree change across elevations, and GLMs with Tweedie distributions (Dunn and Smyth 2005) for normalized centrality, as data were zero inflated, but values were continuous.

Network-level metrics related with network connectivity included density, skewness and connectance. Connectance is a measure of network connectivity that summarizes species degree while accounting for network size; it is calculated for each network as k/n2, where k is the accumulated degree (k, sum of degree values for all species) and n is the number of species in

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the network (Gilbert 2009). Connectance varies between 0 and 1, and is considered a good estimate of community sensitivity to perturbation, with more sensitive networks characterized by lower connectance (Montoya et al. 2006). Network skewness quantifies the level of asymmetry in frequency distribution of the number of connections of species within a network, i.e. degree frequency distributions (sensu Tylianakis et al. 2010). Network density is the portion of potential connections in a network that are realized (Wasserman and Faust 1994). The more skewed the distribution of species connections, the lower the overall density of the network, therefore skewness and density are negatively correlated.

Network-level metrics associated with cohesion were transitivity and modularity.

Network transitivity refers to the degree to which all nodes in a network tend to cluster together.

It estimates how likely is it that two connected nodes are part of a larger highly connected group and can be measured at a network level with a clustering coefficient (Soffer and Vázquez 2005).

Whereas transitivity gives information on the grouping of nodes with their neighbors, modularity gives similar information at a larger scale (Delmas et al. 2018) Network modularity quantifies the strength of division of a network into modules, thus transitivity and modularity are negatively correlated (Wasserman and Faust 1994). Modularity has been suggested to promote stability by containing perturbations within a module (Stouffer and Bascompte 2011, Delmas et al. 2018).

All network-level metrics were calculated for each network as a whole, thus, I used bootstrapping to generate a distribution of values associated with each empirical metric. In each iteration (N=1000) I resampled the observed flocks, generated a new network and recalculated the network-level metric. To test for elevation effects on the distribution of these values, I used

GLMs with Gaussian errors, with elevation as a factor.

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Finally, I was interested in examining how the relative importance of changes in species composition and changes in species interactions were driving network differences across elevations. To do this, I followed Poisot et al. (2012) to calculate an overall network beta diversity (βWN) and to partition this value into two main components: dissimilarity from differences in species composition (βST) and dissimilarity in interactions among shared species

(βOS). Poisot (2012)’s framework allows for the usage of different network beta diversity metrics, and is based on an additive partitioning, where βWN = βST + βOS. I used Lennon et al. (2001)’s proposed dissimilarity metric (βSIM), a metric that focuses more on compositional turnover than on species richness and thus, is less sensitive to potential richness differences resulting from sampling biases. Furthermore, simulation studies found that βSIM performed better than other metrics under different sets of scenarios and is appropriate to test for differences along environmental gradients (see Koleff et al. 2003 for a full discussion). Network dissimilarity was calculated between networks at consecutive elevations using the betalink function of package betalink (Poisot 2015).

Changes in the strength of specific pair-wise interactions across elevations were examined by calculating association strength (measured with the simple association index, SRI,

Ginsberg and Young 1992) between pairs of species that were shared in consecutive elevations, and then counting the number of associations that increased or decreased in strength. To visually examine the effects of elevation on association strength, I plotted networks for 1) the species that were present in flocks across all elevations (14 species), and 2) the most common species

(species registered in more than 25 flocks during the study, 33 species).

All analyses were conducted in R environment (R Development Core Team 2018); species richness and composition analyses were conducted with base and vegan packages

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(Oksanen et al. 2018), network analyses and representation with packages asnipe (Farine 2013) and igraph (Csardi and Nepusz 2006), generalized linear models with packages MASS (Ripley et al. 2018) and statmod (Smyth et al. 2015).

Results

Flock Richness and Composition

A total of 358 flocks were included in the analyses with 3375 individuals of 164 species

(Table 6-1). Accumulation curves approached asymptotes for all five elevations suggesting sample effort was adequate to describe flocking assemblages (Fig. A-25). Flocking assemblages were composed of several frequent species and few rare species that were mostly facultative members (Fig. A-26). The most common species in the flocks in this study system were

Anisognathus igniventris, A. somptuosus, Myioborus melanocephalus, M. miniatus, Atlapetes rufinucha, Diglossa cyanea, D. mystacalis, Cinnycerthia fulva, Delothraupis castaneoventris and

Margarornis squamiger; these species were present in more than 80% of the flocks registered.

However, which species were dominant (i.e., more frequent) in flocks varied among elevations

(Fig. A-26).

Species richness per flock ranged between 2 and 24 (mean = 8.7 +/- 4.4 SD). Species richness of flocking assemblages decreased with elevation (ChiSq, P = 0.023 of model comparison, Table B-15), with approximately twice the number of species in the lower elevation assemblage compared with the higher elevation one (Fig. 6-2). Species composition also changed among assemblages (Fig. 6-3, PerManova test, F4,340 = 27.218, P<0.0001) with assemblages segregating along the first axis of NMDS; this axis was strongly correlated with elevation (r

=0.94, P<00001). Furthermore, the significance of the model was not driven by differences between two or few networks; all networks were significantly different from each other. Ad hoc

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pair-wise analysis of variance also found significant differences among all assemblage combinations even after Bonferroni corrections (Table B-16).

Network Structure and Pair-Wise Interactions across Elevations

Network topology changed across elevations both in species-level and network-level metrics (Fig. A-27). At a species-level, elevation had a negative effect on normalized degree

(Fig. 6-4A) but did not affect weighted degree (Fig. 6-4B), suggesting the number of connections, but not their strength, decreased with elevation. Similarly, no significant change in normalized centrality was detected, suggesting the overall structure of nodes within interaction networks remained similar across elevations with very few highly connected species functioning as hubs (Fig. 6-4C, Table B-17).

At the network level, I found a significant effect of elevation on connectivity metrics

(Table B-18), with overall more connected networks at higher elevations. Frequency distributions of the number of links per species showed very few species highly connected, whereas many more were loosely associated (degree distributions, Fig. A-28). All cumulative distributions fitted power-law distributions. The skewness of these distributions decreased with elevation (Fig. 6-5A) whereas network density increased (Fig. 6-5B), suggesting a larger proportion of weak links in the lowland network and a higher number of realized connections towards higher elevations. Similarly, elevation had a positive effect on network cohesion (Table

B-18), with more cohesive networks in high elevations. Network transitivity (i.e., the degree to which nodes within the network tend to cluster together measured with the global clustering index) increased with elevation (Fig. 6-5C), whereas network modularity decreased (Fig. 6-5D).

Notably, whereas the overall effect of elevation was consistent (Table B-18), the network at the highest elevation showed an opposite pattern in all metrics, indicating a potential threshold in

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network properties (Fig. 6-5). This result suggests that above ~3150 m, there is a rapid decay of network-level cohesion and connectivity.

Network dissimilarity (βWN) between consecutive elevations ranged between 0.29

(between the two higher elevations) and 0.74 (between the two lower elevations). In all cases, changes in network interactions (βOS) was a main component of dissimilarity, accounting for 58-

66% of the total dissimilarity, whereas dissimilarity resulting from changes in species composition (βST) accounted for the 34-42% of total dissimilarity (Table 6-2). This partition suggests that in montane bird flocking networks, restructuring of species associations drive network differences more than changes in species richness and composition. All other pairwise values of network dissimilarity can be found in Table B-19.

Pair-wise analyses showed that relatively few species associations remained constant across consecutive elevations (Fig. 6-6), with most shared species increasing in association strength towards the highlands and creating more connected networks at high elevations. This was also evident when observing the 14 species that were present in flocks across all elevations

(Fig. 6-7); the same set of species was weakly associated in lower elevation networks and increased their association strength towards mid elevations. The most common species in flocks were also the ones with higher centrality in the networks when they were present, and they showed high (yet also variable) values of association strength across elevations (Fig. 6-8).

Discussion

In this study, I tested four hypotheses related to the potential mechanisms behind avian flock network changes across elevations. Network topology changes can be related to (a) changes in species richness and composition or (b) changes in the interaction frequencies and the nature of pairwise interactions and (c) changes in coevolutionary processes and patterns

(Tylianakis et al. 2017). Whereas (c) is more easily related with trophic and mutualistic

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coevolutionary networks (Guimarães Jr et al. 2011), (a) and (b) hold importance for co- occurrence networks such as mixed-species flocks. To examine which of these mechanisms drives network differences across elevations, I first tested if the assemblages of flocking bird species changed in richness and composition, then I tested for differences in network topology and the nature of pair-wise associations. Finally, I used network dissimilarity metrics to further explore the relative role of these mechanisms (a and b) driving network changes. The results of this study suggest that networks of flocking bird species change across elevations both in composition and in the nature of species’ associations. The restructuring of pair-wise associations, however, seems to be a more important driver of network differences than changes in species composition. Pair-wise interactions changed rapidly across elevations, with shared species changing in position and importance within the network. Overall, high elevation networks have higher connectivity and cohesion, whereas flocks in the lowlands tend to have connections that are less even and higher modularity. However, I also found a threshold in these patterns, with flocks above ~3150 m asl rapidly decaying in connectivity and cohesion.

Species richness and composition changed across elevations with a less rich assemblage of flocking species towards high elevations, supporting the first prediction (Fig. 6-1). Species composition also changed with elevation, with only 14 (8.5%) species being shared across the whole gradient. Along elevation, changes in species composition are non-random and often result from environmental changes imposing energetic constraints on species persistence

(Graham et al. 2014). Non-random selection of species across elevations likely alters the architecture of the network (Ekloff et al. 2013, Dehling et al. 2014, Tylianakis 2017). In the

Andes of Bolivia, the richness of flocking species decreased with elevation, and species composition showed a pattern of turnover highly correlated with elevation (Fig. 6-3). If natural

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and anthropogenic gradients affect networks with the same mechanisms (as suggested by

Tylianakis et al. 2009), high elevations might result in the extinction of rare species and specialists, as well as extinction of their interactions (Aizen et al. 2012, Vidal et al. 2014,

Valiente-Banuet et al. 2015). The loss of specialists may accelerate increases in connectance, which generally correlates with declining network size (Banašek-Richter et al. 2009, Tylianakis and Morris 2017). These results align to early observations on montane flocks that describe them as composed mostly of generalist species (Pomara et al. 2007).

As predicted, network connectivity increased with elevation, which may have been enhanced by the ability of species to restructure their associations (i.e., rewire). Degree distribution skewness decreased with elevation, suggesting that connections are more even at high elevations. Accordingly, a higher proportion of all potential pair-wise associations per network occurred at higher elevations, as shown by a higher network density. Combined, these results showed that lower-elevation networks had lower connectivity, whereas looser, less structured flocks were found towards the highlands. Metrics of cohesion also provided evidence of the effects of elevation on network topology, with more cohesive networks at higher elevations. Whereas the strength of division within the network (i.e., modularity) decreased with elevation, transitivity increased. Transitivity refers to the probability that, if two nodes are related to each other, and one of them is related to a third species, then this third species is likely related with the second member of the pair. In other words, connections among species in high elevation flocks are more even, conferring overall higher cohesion to the network.

Higher connectivity and cohesion have been related to higher resiliency to environmental change in networks (Dunne et al. 2002), thus my findings suggest that high-elevation flocks might be robust and more resilient to species loss, an important feature under current threats such

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as climate change. However, results also show a rapid decay of network connectivity and cohesion above 3150 m asl, suggesting flocks above this critical point might be less robust to local extinctions. A potential explanation for this pattern of decay might result from the natural fragmentation in forested areas at these elevations. The tree line in this study system is around

~3500 m, however forests at these high elevations contain more open habitats and are naturally more heterogeneous. This natural fragmentation of open areas and closed forest might affect flocks in the same way as anthropogenic fragmentation, causing an overall loss of network connectivity (Mokross et al. 2013). Further studies might contrast the effect sizes of natural and anthropogenic disturbances at high elevations to fully understand the potential threats faced by high elevation species and their interactions networks.

Pair-wise and species-level network analyses showed a high potential of species in montane flocks to restructure their associations (i.e., rewire). Species-level metrics (degree and centrality) showed that very few species were core nodes for the whole network, whereas many more were loosely connected. While flocks changed in species composition and richness, pair- wise results showed that most species that were shared between consecutive elevations changed their associations, either gaining or losing connections, or changing the strength of the connection. Most of the shared species increased the strength of their associations at higher elevation networks (Fig. 6-6). Furthermore, partition of network dissimilarity suggests that restructuring of species associations is the main mechanism driving network differences, having more importance than changes in species richness and composition.

Variation in the level of connections and position of each species within networks across elevations may be related to a change in the role species perform within the networks. Species that occupy a nuclear position in any given elevation might have a less central position in a

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higher-elevation flock, or vice versa. For example, in this study system dominant species that would be good candidates to be considered “nuclear” or “leading” are Chlorospingus flavopectus, Anisognathus igniventris and Myioborus melanocephalus (Moynihan 1962). These species maintain monospecific groups and give constant contact calls, as should be expected from leading species in mixed-species flocks (Goodale and Beauchamp 2010, Goodale et al.

2010). Both the normalized degree and centrality values for these species, as well as the strength of their pair-wise associations, changed across the gradient. These changes likely reflect how the balance of costs and benefits of participating in mixed-species flocks change across different environments within each species’ distribution. This variability in centrality metrics challenges the definition of fixed “species roles” within montane mixed-species flocks of birds. High potential of rewiring has been called as an important feature to stabilize mutualistic networks against extinction cascades (Schleuning et al. 2016). In networks of mixed-species flocks, this ability of species to modify their associations might also confer robustness to the network, making montane flocks, particularly at higher elevations, less sensitive to local extinctions.

Flocks have been recently proposed as conservation targets, as they are easily monitored and susceptible to anthropogenic disturbance (Zou et al. 2018). In the region of this study, flocking species represent a high proportion of the local avifauna, encompassing close to 55% of the local avifauna (Montaño-Centellas in prep). Using network theory to study and monitor mixed-species flocks seems a promising avenue for future conservation planning in the tropical

Andes (Gilman et al. 2010), where forested areas are being lost at a fast rate (Urrutia and Vuille

2009, Anderson et al. 2011). Furthermore, this study exemplifies how shifting the focus from networks onto interactions, and combining both approaches, can provide more informative

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results on the responses of species and assemblages to environmental gradients (Delmas et al.

2018).

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Table 6-1. Characteristics of the assemblage and network-level properties of mixed-species flocks’ networks at five elevations in the Bolivian Andes. Number of flocks refer to the number of replicated flocks included in the analysis. Species richness is the observed number of species in each assemblage. Chao II is the estimated species richness per assemblage, as calculated by the Chao II predictor (Magurran 2004). Average degree is the average value of all species degree values within the network. Connectance is the sum of all connections in the network, divided by the number of species squared (k/n2). Density is the proportion of realized connections out of all potential connections in the network. Skewness is a measure of the asymmetry of the degree distribution for each network. Transitivity is the degree to which nodes in a network tend to cluster together, measured with the global clustering index. Modularity is the strength of division of a network into modules.

Characteristics Site1: 1500 Site2: 2250 Site3: 2600 Site4: 2950 Site5: 3300 Number of flocks 86 69 63 92 47 Species Richness 114 88 73 66 51 Richness Chao II 139.00 144.25 84.64 74.00 57.72 Network connectivity Average degree 33.79 30.82 31.37 29.70 19.18 Connectance 0.30 0.35 0.43 0.45 0.38 Density 0.30 0.35 0.44 0.46 0.38 Skewness 0.61 0.47 0.31 0.06 0.65 Network cohesion Transitivity 0.59 0.63 0.65 0.70 0.60 Modularity 0.28 0.25 0.24 0.17 0.22

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Table 6-2. Network dissimilarity (βWN) between mixed-species flock’ networks at consecutive elevations, and its partition into two components: dissimilarity that originates from differences in species composition (βST) and dissimilarity in interaction structure of shared species (βOS). Dissimilarity values were calculated following Poisot et al. (2012) using the βSIM dissimilarity matrix proposed by Lennon et al. (2001) and based on Simpson (1943). Networks Network beta diversity components compared βWN βST βOS 1500 2250 0.74 0.27 0.47 2250 2600 0.55 0.19 0.36 2600 2950 0.39 0.13 0.26 2950 3300 0.29 0.12 0.17

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Figure 6-1. Hypotheses tested in this study. Species richness of flocking assemblages will decrease with elevation. Community composition will change with elevation. Network-level and species-level metrics will change with elevations, networks will have fewer and more even links at high elevations, with lower modularity and transitivity. As a result, degree distributions will be more skewed in lowland networks, with few species highly connected and several species poorly connected. Restructuring of pairwise associations will change with elevations, with some pair- wise associations being lost and some others being gained.

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Figure 6-2. Changes in mixed-species flocks’ richness across elevation in the Bolivian Andes. (A) Individual-based rarefaction curves for flocking assemblages. Dashed line shows the point for species richness comparisons. (B) Species richness per flock across elevations. Colors palette runs from lower elevation flocks dark green (1500 m asl) to high elevation flocks in orange (3300 m asl) in both panels.

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Figure 6-3. Non-metric multidimensional scaling (NMDS) ordination of mixed-species flocks of the Bolivian Andes. Color points represent a given flock and they are color coded by elevational bin. Crosses represent species scores.

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Figure 6-4. Species-level metrics of networks of flocking birds across elevations in the Andes of Bolivia. A. Normalized degree (number of edges connected to a given node, number of connections per species), B. normalized weighted degree (node strength, sum of weights of edges connected to the node) and C. normalized centrality (number of shortest paths that cross a given node).

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Figure 6-5. Network-level metrics related to network connectivity (A and B) and with network cohesion (C and D), for co-occurrence networks of flocking bird species at five elevations in the Bolivian Andes. A. Skewness of the distribution of positive links among species. B. Network density. C. Transitivity (as measured with the global clustering index). D. Modularity.

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Figure 6-6. Changes in network structure between consecutive elevations, showing the changes in species associations for shared species. Species in each pair of networks are plotted in the same position, as a blue node. Node size is proportional to the number of connections it has (degree). Width of grey lines (pairwise links) are proportional to the association strength. Note how the importance of each species for the network (degree) and the strength of particular associations (weighted degree) changes with elevation. In A and B, there is an increase in the number of existing associations, with high numbers of lost associations and gain of new associations. In C and D, there is a decrease in overall number of links, with several links being lost and several others becoming stronger.

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Figure 6-7. Network interactions of species pairs that co-occurred in flocks across all elevations (14 species, 87 realized interactions) showing changes in association strength with elevation. Species are plotted in the same position in each network. Links between pairs of species (grey lines) are proportional to the association strength and the size of the nodes is proportional to the number of connections each species has (degree). Note that these are not the most important species in these networks, but those that participated in flocks along the whole elevational gradient of these study. See Fig. 6-8 for comparison.

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Figure 6-8. Network interactions of species that were found in more than 25 flocks (30 species), showing changes in association strength with elevation among the most common participants in flocks. Links between pairs of species (grey lines) are proportional to the association strength and the size of the nodes is proportional to the number of connections each species has (degree). A higher evenness in species degrees in high elevation networks is noticeable. Note that different species (out of the 30 most common) might be present in different networks, and thus they are not depicted in the same order.

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CHAPTER 7 GENERAL CONCLUSIONS

McCain and Colwell 2011). Analyses in this study add to a growing body of work that seeks to understand global patterns of biodiversity and the role of deterministic processes in elevational gradients. However, unlike previous studies, I advance beyond the environmental filtering- competition dichotomy and propose a framework to incorporate positive interactions (i.e. facilitation) as a potential driver of community assembly. In this dissertation, I combined trait- based and phylogenetic approaches to examine drivers of community assembly at a large geographic scale and at a local scale. Then I focused on field data collected at a smaller spatial scale, on a subset of the avian community, to further separate the relative roles of positive and negative interactions. To this end, I combined community assembly framework with network analyses and use avian mixed-species flocks as study systems.

I present the first global analysis of multiple dimensions of bird diversity, using the best empirical data sets available that allows us to examine for coexistence and signal of deterministic processes in community assembly. The patterns detected are consistent with the idea that environmental filtering is acting upon high elevation assemblages, reducing species richness and phylogenetic richness, and changing functional diversity and phylogenetic dispersion of local assemblages. I hypothesized that environmental filtering signal should be stronger for birds in tropical mountains, and found evidence supporting my hypothesis. Whereas functional

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underdispersion was more frequent in tropical highlands it was consistently weak in temperate mountains. In contrast, phylogenetic underdispersion was consistently stronger in temperate mountains. I found no evidence of functional or phylogenetic overdispersion, a pattern expected by biological interactions (i.e., competitive forces or facilitation among dissimilar species), highlighting even more the strong role of environmental filters on mountain biotas. But what is the mechanism by which environmental filtering acts upon avian assemblages? In Chapter 3, I examine this question by testing for elevation effects on beta diversity and its components. I found that environmental variability in mountain systems (regardless of their latitudinal position) acts as a filter selecting for species with particular traits (and from particular clades), limiting the variability among species traits (Weiher et al. 2011, Spasojevic and Suding 2012). Interestingly, species replacement gained importance over species loss as components of functional and phylogenetic β diversity at lower latitudes. This suggests that a rapid replacement of functional traits and clades occurs in the tropics whereas the net species gain, or loss, seem more important driving community patterns at higher latitudes. Combined, results partially support Janzen’s

(1967) ideas of tropical species having narrow altitudinal ranges, and patterns of beta diversity to be dominated by a rapid replacement of species in the tropics. Furthermore, these results highlight the importance of examining multiple dimensions of biodiversity when exploring macro-ecological patterns, as different facets of diversity might contain non-redundant information about the processes shaping diversity.

The role of biological interactions has long been recognized as a main driver of bird community assembly (Terborgh and Weske 1975). However, most ecological theory emphasizes negative interactions (i.e., competition and predation) as the main forces driving diversity patterns (Swenson 2011b, Jankowski et al. 2012, Wisz et al. 2013). Despite the growing

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recognition that facilitation can be at least as important as competition on population and community level patterns (Stachowicz 2001, Bruno et al. 2003, Gross 2008), studies accounting for facilitation as a potential driver of communities are still scarce. The last two chapters of this dissertation focus on testing for the role for facilitation in structuring bird communities using mixed-species flocks along elevational gradients in the Andes of Bolivia, as model systems.

Mixed-species flocks represent a great proportion of avian species in the Neotropics, particularly in the Andes; therefore, the driving forces in flock assembly might affect the overall community patterns in these systems. Results of this study suggest that competition is an important force driving flock assembly that operates at both evolutionary and ecological scales. However, at a local scale facilitation can be a more determinant force especially at high elevations (Goodale et al. 2015). Examining the resulting structure of interaction networks of mixed-species flocks provided further evidence of the mechanisms by which a balance of competition and facilitation act upon flocking bird species. I found that, overall, networks of montane flocking birds have high connectance and cohesion and are potentially resilient to the loss of species. Network resilience results from the ability of montane flocking species to re-structure pair-wise associations, suggesting montane flocks are more opportunistic and loose than lowland flocks, a pattern not described before but suggested by early descriptive studies (Munn 1984, Poulsen

1996). My findings challenge the definitions of species roles within mixed-species flocks and open a new avenue of research questions regarding the paradigms behind flocking behavior in birds. In combination, results of this dissertation highlight the hidden, yet determinant role of positive interactions in the generation and maintenance of Andean diversity. Including facilitation in the theoretical framework of ecological studies might provide a better understanding of the processes acting upon natural systems (Bruno et al. 2003).

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Combined, all studies in this dissertation contribute to advancing different bodies of ecological science. Chapter 1 and Chapter 2 advanced the knowledge of community assembly along environmental gradients and their mechanisms operating at different scales. Initially I challenge the generality of community assembly rules across elevational gradients and conclude that particular characteristics of each mountain will determine how elevation affects its biota.

However, there is a signal of latitudinal effects in how communities respond to elevation. At a local scale, I add to the framework of community assembly, by proposing a framework to incorporate facilitation as a key component. The two studies focusing on mixed-species flocks

(Chapter 5 and Chapter 6) contribute to our knowledge of these complex social systems. I include, in this dissertation, the first analysis of flock assembly that accounts for facilitation and competition. This study is also the first to present empirical data of mixed-species flocks along a non-disturbed elevational gradient. Finally, this dissertation contributes to network theory in ecology, by providing the first analysis of interaction networks along an extensive natural gradient: elevation.

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APPENDIX A SUPPLEMENTARY FIGURES

This section contains the supplementary figures for all chapters. The figures are references in the main text.

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Figure A-1. Patterns of functional richness (FRic) along elevational gradients for 46 mountain systems. Curves are predicted shapes for second-degree polynomial regresions of FRic as a function of elevation.

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Figure A-2. Patterns of functional dispersion (FDis) along elevational gradients for 46 mountain systems. Curves are predicted shapes for second-degree polynomial regresions of FDis as a function of elevation.

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Figure A-3. Patterns of phylogenetic richness (Faith’s index, PD) along elevational gradients for 46 mountain systems. Curves are predicted shapes for second-degree polynomial regresions of PD as a function of elevation.

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Figure A-4. Patterns of phylogenetic dispersion (mean pairwise distance, MPD) along elevational gradients for 46 mountain systems. Curves are predicted shapes for second-degree polynomial regresions of MPD as a function of elevation.

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Figure A-5. Deviations from null expectations (standarized effect size) of functional richness (SES FRic) of bird assemblages across elevations in 46 mountain systems. Purple lines describe overall tendencies and correspond to simple linear regressions of SES Fric as a function of elevation. Black points represent assemblages that are significantly different than null expectations (represented as a dotted grey line). Grey points represent assemblages that do not differ from what is expected by chance.

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Figure A-6. Deviations from null expectations (standarized effect size) of functional dispersion (SES FDis) of bird assemblages across elevations in 46 mountain systems. Purple lines describe overall tendencies and correspond to simple linear regressions of SES FDis as a function of elevation. Black points represent assemblages that are significantly different than null expectations (represented as a dotted grey line). Grey points represent assemblages that do not differ from what is expected by chance.

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Figure A-7. Deviations from null expectations (standarized effect size) of phylogenetic richness (SES PD) of bird assemblages across elevations in 46 mountain systems. Purple lines describe overall tendencies and correspond to simple linear regressions of SES PD as a function of elevation. Black points represent assemblages that are significantly different than null expectations (represented as a dotted grey line). Grey points represent assemblages that do not differ from what is expected by chance.

176

Figure A-8. Deviations from null expectations (standarized effect size) of phylogenetic dispersion (SES MPD) of bird assemblages across elevations in 46 mountain systems. Purple lines describe overall tendencies and correspond to simple linear regressions of SES MPD as a function of elevation. Black points represent assemblages that are significantly different than null expectations (represented as a dotted grey line). Grey points represent assemblages that do not differ from what is expected by chance.

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Figure A-9. Summary of the effect sizes of correlations (calculated as Fisher’s Z transformed correlation values) between dimensions of biodiversity in tropical and temperate montane bird assemblages. Boxplots show the mean +/- standard deviation. Each point corresponds to an estimated value of Fisher Z, calculated from a Pearson correlation and weighted based on sample size. Panels A and B depict correlations between taxonomic and functional diversity; panels C and D between taxonomic and phylogenetic diversity, panels E to H between functional and phylogenetic diversity.

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Figure A-10. Global trends of functional and phylogenetic diversity when controlled by species richness. Each panel shows the standardized effect size of the diversity metric (deviations from null expectations) across elevations, lines at y = 0 represent the null expectations, i.e. the expected values if assembly would be random across elevations. Elevation has been normalized (scaled and centered in 0) to allow comparisons among mountains. Blue curves represent a second-degree polynomial regression of the diversity metric against elevation for each mountain system and the black line represents the second-degree polynomial regression when adding all mountains together. Panels A to D are metrics calculated for 45 mountains subdivided in 200m- elevational bins. Panels E to H are metrics calculated for 32 mountains subdivided in 400m-elevational bins.

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Figure A-11. Effect of latitude on the patterns of change in standardized effect size of functional richness (SES FRic), functional dispersion (SES FDis), phylogenetic richness (SES PD) and phylogenetic dispersion (SES MPD) across elevations. For each mountain, the pattern of change across elevation is represented by the slope of a linear regression (β), where more negative values represent decrease in diversity metrics and positive values represent increases. The magnitude of β represents how strong are the changes in diversity patterns across elevations; higher magnitudes of β would correspond to strong changes across elevations, whereas lower magnitudes would be found in mountains where patterns of underdispersion, or overdispersion are homogeneous across elevations. Patterns are presented for 45 mountains subdividedin 200m-elevational bins, and for 32 mountains subdivided in 400m-elevational bins.

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Figure A-12. Schematic representation of the pairwise β diversity values extracted to calculate (A) the rate of change in diversity and (B) the accumulated change in diversity using the benchmark approach, across elevations. In the scheme, the β pairwise diversity matrix represents a βsor matrix for any given dimension of diversity (i.e., taxonomic, functional and phylogenetic). In this hypothetical example, distances are calculated for a mountain system with five elevational bins, from 0-200 m, 200-400m, 400- 600m, 600-800m and 800-1000 m. Color in cells represent different values of dissimilarity between two elevations, running from black when the values are zero to white, when values are maximum. From the pairwise βsor matrix of distances, we extracted only the values between consecutive elevations (A) and used these values to calculate the average and SD of the rate of change (i.e., change in diversity by a 200 m elevational distance). From the same matrix of distances, we extracted only the values between a benchmark elevation (the lowest in each gradient) and every other elevational bin. These values where then used to create accumulated curves of change across the gradient.

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Figure A-13. Rate of change in taxonomic diversity between consecutive 200-m elevational bands for 46 mountain systems across the world. Higher values of Tβsor represent faster changes in taxonomic diversity between consecutive elevational bins. Flatter curves represent more even changes across elevations, whereas more curvilinear trends show variation in the speed of change across the elevational gradient.

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Figure A-14. Rate of change in functional diversity between consecutive 200-m elevational bands for 46 mountain systems across the world. Higher values of Fβsor represent faster changes in functional diversity between consecutive elevational bins. Flatter curves represent more even changes across elevations, whereas more curvilinear trends show variation in the speed of change across the elevational gradient.

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Figure A-15. Rate of change in phylogenetic diversity between consecutive 200-m elevational bands for 46 mountain systems across the world. Each point represents the change in diversity between two elevational bins. Higher values of Pβsor represent faster changes in phylogenetic diversity. Flatter curves represent more even changes across elevations, whereas more curvilinear trends show variation in the speed of change across the elevational gradient.

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Figure A-16. Accumulated change in taxonomic βsor along 46 elevational gradients worldwide, calculated with a benchmark approach. Each point represents the change in diversity between the lowest elevational band (benchmark) and each one of the other bands for each mountain.

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Figure A-17. Accumulated change in functional βsor along 46 elevational gradients worldwide, calculated with a benchmark approach. Each point represents the change in diversity between the lowest elevational band (benchmark) and each one of the other bands for each mountain.

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Figure A-18. Accumulated change in phylogenetic βsor along 46 elevational gradients worldwide, calculated with a benchmark approach. Each point represents the change in diversity between the lowest elevational band (benchmark) and each one of the other bands for each mountain.

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Figure A-19. Vertical strata profiles of vegetation for all elevational bands of the studied gradient. The proportion of cylinders with vegetation (out of 10 replicates) at each vertical stratum is depicted. Vertical strata were 0-1 m, 1-2 m, 2-4 m, 4-8 m, 8-16 m, 16-24 m and >24.

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Figure A-20. Correlations among predictor variables used in the study. Each correlation was calculated with Pearson’s correlations moments, between values for the 23 sites across an elevational gradient. Arthropods refers to arthropod diversity measured with a Shannon-Wiener index, Complexity refers to the vertical structural complexity, measured with an understory height diversity indes (UHD), Fruits refer to the diversity of fruiting plants available, NMDS1 and NMDS2 refer to the two main axis of the non-metric multidimensional scaling of species composition among the 23 studied communities, and Temperature refers to main daily temperature. Correlation coefficients and significance values are presented in Table B-5.

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Figure A-21. Deviations from null expectations in phylogenetic dispersion of mixed-species flocks along an elevational gradient in the Bolivian Andes, calculated considering only species within the order Passeriformes. Each point represents the standardized effect size (SES, deviation from the null); point size is relative to the number of species in the flock. Black points show significant deviations from the null (with alpha = 0.05), whereas grey points do not differ significantly. (A) SES of phylogenetic richness decreases with elevation richness (SES PD; R2 = 0.38, P = 0.00001), and (B) SES of phylogenetic dispersion decreases with elevation (SES MPD; R2 = 0.36, P = 0.00001).

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Figure A-22. Example of species with significant positive interactions (pair-wise analysis) that group with each other within different types of mixed-species flocks. Species are grouped in blue shades that represent given modules within mixed species flocks. Photo credits: Flavia Montano-Centellas, Cesar Mayta Rocabado.

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Figure A-23. Example of species with significant negative interactions (pair-wise analysis) that avoid each other in mixed species flocks (i.e., tend not to flock together). Species that are grouped in orange shades avoid each other, and those with a white frame represent altitudinal replacements likely driven by interspecific competition. Photo credits: Flavia Montano-Centellas, Cesar Mayta Rocabado.

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Figure A-24. Map of the study site in the Western Andes of Bolivia. The yellow line demarks the gradient where mixed species flocks were surveyed. The picture in the top was taken from the trail at ~3200 m asl and shows the overall landscape of the study region.

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Figure A-25. Species accumulation curves as a function of sample size (number of flocks) for avian mixed-species flocks at five elevations in the Bolivian Andes.

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Figure A-26. Rank abundance curves showing the frequency distribution of species in flocks across five elevations in the Bolivian Andes. Silhouettes of the five more frequent species are shown for each assemblage.

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Figure A-27. Interaction networks of flocking species at 5 elevations in the Bolivian Andes. Each node represents a species (yellow circles) in the flocking assemblage, and the size of the node is relative to the number of species that it is connected to (degree). Each connection line represents the co-occurrence between two species (edges, grey lines) and the width of the line represents the strength of this association.

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Figure A-28. A. Frequency distribution and B. cumulated degree distributions of positive co- occurrences among species in assemblages of flocking species at five elevations in the Bolivian Andes.

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APPENDIX B SUPPLEMENTARY TABLES

This section contains the supplementary tables referenced throughout the text.

198

Table B-1. Characteristics of the mountain systems included in this study. Absolute latitude and longitude are in decimal degrees. Hem. stands for hemisphere, with N= Northern and S=Southern. Isl. stands for island, with values of 1 if the mountain is located on an island and 0 if it is located on a continental mass. Min. elev. and Max. elev. refer to the elevations at the bottom of the valley and at the summit of the peak, respectively, and are presented in meters above sea level. Data source column shows the original paper where the data were extracted. Abs Abs Min Max Mountain Hem Isl Data Source lat long elev elev Aguarague, Bolivia 21.4 63.6 S 0 400 1900 Martinez et al. 2011 Amrutganga, India 30.7 78.9 N 0 1500 3600 Dixit et al. 2016 Hawkins and Goodman D’Andohahela, Madagascar 24.7 46.7 S 1 0 2000 1999 Annapurna, Nepal 28.3 84.1 N 0 100 8091 Thiollay 1980 Sierra de Atoyac, Mexico 17.5 100.2 N 0 600 3550 Navarro 1992 El Avila, Venezuela 10.5 66.9 N 0 120 2765 Sainz-Borgo 2012 Sierra de Bahoruco, Dominican 18.2 71.6 N 1 20 1750 Latta et al. 2003 Republic Mallet Rodriguez et al. Bocaina, Brazil 23.0 44.6 S 0 0 1700 2015 Blake and Loiselle Braulio Carrillo, Costa Rica 10.3 84.0 N 0 36 2906 2000 Poulsen and Lambert Buru, Indonesia 3.3 126.3 S 1 0 2428 2000 Mt Canigo, France 42.5 2.5 N 0 0 2784 Prodon et al. 2002 Mt Chamba Kailash, India 32.5 76.3 N 0 600 4000 Mahabal 1992 Mt Cinto, France 42.1 9.1 N 1 0 2706 Prodon et al. 2002 Cosnipata, Peru 13.1 71.6 S 0 800 3400 Jankowski et al. 2013 Cordillera de Cutucu, Ecuador 2.7 78.1 S 0 1000 2300 Robbins et al. 1987 Medina-Macias et al. Espinazo del Diablo, Mexico 23.5 105.7 N 0 200 2900 2010 Mt Etna, Italy 37.8 15.0 N 0 0 3329 Massa et al. 1989 Poulsen and Lambert Halmahera, Indonesia 0.9 127.9 N 1 0 1635 2000 Nathan and Werner Mt Hermon, Israel 33.4 35.9 N 0 300 2814 1999. Goodman and Gonzales Mt Isarog, Philippines 13.7 122.3 N 1 0 1966 1990 Mallet Rodriguez et al. Itatiaia, Brazil 22.4 44.5 S 0 100 2500 2015 Mt Karimui, Papua New Guinea 6.6 144.7 S 1 0 2652 Diamond 1972 Diamond and Le Croy Karkar, Papua New Guinea 4.6 145.9 S 1 0 1854 1979 Mt Kilimanjaro, Tanzania 3.0 37.2 S 0 800 5891 Cordeiro 1994 Marojejy NP, Madagascar 14.4 49.8 S 1 0 2100 Goodman et al. 2000 Safford and Duckworth Marojejy W, Madagascar 14.5 49.7 S 1 75 2133 1988 Thorstrom and Watson Masoala, Madagascar 15.5 50.2 S 1 20 1224 1997 Mikura-Jima, Japan 33.9 139.6 N 1 0 851 Nishiumi 2002

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Table B-1. Continued Abs Abs Min Max Mountain Hem Isl Data Source lat long elev elev Mt Mulu, Malaysia 4.1 114.9 N 1 0 2450 Burner et al. 2016 Romdal and Rahbek Mt Mwanihana, Tanzania 7.7 36.7 S 0 300 2100 2003 Mallet Rodriguez et al. Serra dos Orgaos, Brazil 22.4 42.9 S 0 100 2200 2015 Piedmont, Italy 45.1 7.1 N 0 1700 3100 Chamberlain et al. 2016 San Francisco Reserve, Ecuador 3.9 79.1 S 0 950 3350 Paulsch 2007 Sibuyan Island, Philippines 12.4 122.6 N 1 0 2057 Goodman et al. 1995 Cerros del Sira, Peru 9.4 74.7 S 0 690 2220 Socolar et al. 2013 Kendeigh and Fawver Great Smoky Mts, USA 35.5 83.5 N 0 200 2100 1981 Sweet Grass hills, USA 48.8 111.4 N 0 900 2200 Thompson 1978 Sierra de Tilaran, Costa Rica 10.3 84.8 N 0 0 3600 Jankowski et al. 2009 Totare, Colombia 4.5 75.2 N 0 270 3642 Martinez 2014 Trentino, Italy 46.2 11.2 N 0 1300 2750 Pedrini and Brambilla Udzungwa Scarp, Tanzania 6.1 37.5 S 0 500 2576 Rombdal 1998 Vilcabamba, Peru 12.5 73.6 S 0 400 3600 Terborgh 1977 Mt Wilhelm, Papua New Guinea 5.8 145.2 S 1 200 4509 Tavardikova 2014 Yaku-Shima, Japan 30.4 130.5 N 1 0 1935 Eguchi et al. 1989 Serrania de los Yariguies, 6.7 73.45 N 0 100 3200 Donegan et al. 2010 Colombia Grinnell and Storer Yosemite, USA 37.8 119.5 N 0 0 4200 1924

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Table B-2. Mixed-effect model results for effect size of correlations (measured as Fisher’s Z transformed values of Pearson correlations) among diversity metrics. Model coefficient estimate se z P Rich-FRic~tropical (QM = 0.1141, P = 0.735) intercept 1.19 0.17 6.98 <0.0001 tropical 0.07 0.21 0.34 0.7355 Rich-FDis~tropical (QM = 0.1141, P = 0.735) intercept 0.12 0.21 0.56 0.57 tropical 0.21 0.26 0.8 0.43 Rich-PD~tropical (QM =0.00006, P = 0.98) intercept 2.59 0.13 20.39 <0.0001 tropical 0.004 0.16 0.02 0.98 Rich-MPD~tropical (QM =1.584, P = 0.21) intercept 0.68 0.196 3.49 0.0005 tropical -0.3 0.242 -1.26 0.21 FRic-FDis~tropical (QM =0.379 P = 0.538) intercept 0.68 0.22 3.1 0.002 tropical 0.17 0.27 0.62 0.54 FRic-PD~tropical (QM =0.567, P =0.452) intercept 1.24 0.177 7.004 <0.0001 tropical 0.16 0.218 0.75 0.452 FRic-MPD~tropical (QM =0.629, P =0.4274) intercept 0.702 0.191 3.67 0.002 tropical -0.187 0.236 -0.793 0.427 FDis-PD~tropical (QM =0.722, P =0.395) intercept 0.204 0.219 0.93 0.3515 tropical 0.23 0.271 0.85 0.3953 FDis-MPD~tropical (QM =0.043, P =0.835) intercept 0.649 0.183 3.5486 0.004 tropical 0.047 0.226 0.208 0.835 PD-MPD~tropical (QM =1.89, P =0.169) intercept 0.864 0.201 4.28 <0.001 tropical -0.342 0.249 -1.375 0.169

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Table B-3. Pearson’s moment correlations among dimensions of biodiversity for 46 mountain ranges across the globe. Pearson’s r statistics and p values are presented for each pair-wise correlation between different metrics of taxonomic diversity (TD), functional diversity (FD) and phylogenetic diversity (PD). S= Species richness, FRic= Functional richness, FDis=Functional dispersion, PD=Phylogenetic richness (Faith’s index), MPD= Phylogenetic dispersion (mean pairwise distance). Taxonomic - Functional Taxonomic - Phylogenetic Functional -Phylogenetic S- S- S- S- FRic- FRic- FDis- FDis- Mountain Fric P FDis P PD P MPD P PD P MPD P PD P MPD P Aguarague 0.60 0.04 0.36 0.24 0.97 0.00 0.35 0.26 0.78 0.00 0.67 0.02 0.87 0.00 0.54 0.07 Amrutganga 0.85 0.00 0.58 0.02 0.99 0.00 0.66 0.01 0.84 0.00 0.90 0.00 0.82 0.00 0.66 0.01 D’Andohahela 0.35 0.23 -0.34 0.23 1.00 0.00 -0.44 0.11 0.73 0.00 0.37 0.19 0.66 0.01 -0.33 0.25 Annapurna 0.44 0.00 -0.84 0.00 0.99 0.00 0.32 0.02 -0.73 0.00 0.34 0.02 -0.67 0.00 -0.82 0.00 Sierra de Atoyac 0.67 0.00 -0.43 0.03 0.88 0.00 0.02 0.91 0.33 0.11 0.91 0.00 0.53 0.01 -0.01 0.96 El Avila 0.87 0.00 0.02 0.96 0.90 0.00 -0.89 0.00 0.51 0.13 1.00 0.00 -0.54 0.11 0.45 0.19 Sierra de Bahoruco 0.86 0.00 0.79 0.00 0.98 0.00 0.22 0.40 0.94 0.00 0.87 0.00 -0.21 0.42 0.77 0.00 Bocaina 0.73 0.00 0.14 0.58 0.99 0.00 0.56 0.02 0.64 0.01 0.81 0.00 0.93 0.00 0.26 0.32 Braulio Carrillo 0.98 0.00 0.80 0.00 1.00 0.00 0.12 0.62 0.81 0.00 0.98 0.00 0.15 0.51 0.85 0.00 Buru 0.89 0.00 -0.61 0.00 0.99 0.00 0.04 0.85 -0.55 0.01 0.93 0.00 0.01 0.95 -0.62 0.00 Mt Canigo 0.81 0.00 0.55 0.00 1.00 0.00 0.72 0.00 0.88 0.00 0.81 0.00 0.53 0.00 0.56 0.00 Mt Chamba Kailash 0.81 0.00 0.50 0.01 0.98 0.00 0.48 0.01 0.87 0.00 0.88 0.00 0.64 0.00 0.62 0.00 Mt Cinto 0.97 0.00 -0.13 0.53 0.99 0.00 0.95 0.00 -0.30 0.13 0.97 0.00 0.94 0.00 -0.15 0.46 Manu 0.83 0.00 0.71 0.00 0.98 0.00 0.48 0.01 0.83 0.00 0.88 0.00 0.65 0.00 0.81 0.00 Cordillera de Cutucu 0.72 0.01 0.03 0.93 0.99 0.00 0.63 0.02 0.52 0.07 0.78 0.00 0.71 0.01 0.11 0.73 Espinazo del Diablo 0.58 0.00 -0.25 0.22 0.97 0.00 0.03 0.90 0.56 0.00 0.52 0.01 -0.34 0.09 -0.24 0.24 Mt Etna 0.99 0.00 0.97 0.00 1.00 0.00 0.90 0.00 0.99 0.00 0.99 0.00 0.91 0.00 0.97 0.00 Halmahera 0.92 0.00 -0.30 0.27 0.99 0.00 -0.76 0.00 -0.35 0.20 0.93 0.00 -0.80 0.00 -0.37 0.17 Mt Hermon 0.92 0.00 0.77 0.00 0.97 0.00 0.66 0.00 0.78 0.00 0.94 0.00 0.71 0.00 0.83 0.00 Mt Isarog 0.95 0.00 0.73 0.00 1.00 0.00 0.68 0.00 0.81 0.00 0.94 0.00 0.68 0.00 0.75 0.00 Itatiaia 0.72 0.00 -0.80 0.00 0.99 0.00 0.39 0.08 -0.52 0.02 0.77 0.00 0.13 0.58 -0.78 0.00 Mt Karimui 0.84 0.00 0.70 0.00 1.00 0.00 0.93 0.00 0.78 0.00 0.86 0.00 0.88 0.00 0.74 0.00 Karkar 0.91 0.00 0.94 0.00 1.00 0.00 -0.14 0.58 0.86 0.00 0.89 0.00 -0.22 0.38 0.94 0.00 Mt Kilimanjaro 0.92 0.00 0.61 0.00 0.99 0.00 0.73 0.00 0.84 0.00 0.93 0.00 0.85 0.00 0.64 0.00 Marojejy NP 0.98 0.00 0.26 0.34 0.99 0.00 -0.59 0.02 0.38 0.17 0.98 0.00 -0.48 0.07 0.34 0.22

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Table B-3. Continued Taxonomic - Functional Taxonomic - Phylogenetic Functional -Phylogenetic S- S- S- S- FRic- FRic- FDis- FDis- Mountain Fric P FDis P PD P MPD P PD P MPD P PD P MPD P Marojejy West 0.94 0.00 0.08 0.73 1.00 0.00 -0.11 0.62 0.34 0.14 0.94 0.00 0.05 0.82 0.08 0.73 Masoala 0.98 0.00 0.96 0.00 1.00 0.00 0.99 0.00 0.99 0.00 0.98 0.00 0.95 0.00 0.97 0.00 Mikura-Jima 0.87 0.01 0.82 0.02 0.98 0.00 0.87 0.01 0.90 0.01 0.85 0.02 0.87 0.01 0.85 0.02 Mt Mulu 0.71 0.00 -0.05 0.82 0.98 0.00 0.44 0.05 0.56 0.01 0.82 0.00 0.91 0.00 0.11 0.65 Mt Mwanihana -0.27 0.33 -0.72 0.00 0.99 0.00 0.17 0.53 0.63 0.01 -0.35 0.20 -0.64 0.01 -0.77 0.00 Serra dos Orgaos 0.81 0.00 -0.76 0.00 1.00 0.00 0.52 0.02 -0.85 0.00 0.82 0.00 0.05 0.84 -0.78 0.00 Piedmont 0.90 0.00 0.84 0.00 0.99 0.00 0.19 0.56 0.89 0.00 0.93 0.00 0.31 0.32 0.88 0.00 San Francisco 0.83 0.00 0.53 0.01 0.99 0.00 0.71 0.00 0.85 0.00 0.85 0.00 0.89 0.00 0.57 0.00 Sibuyan 1.00 0.00 0.61 0.03 0.98 0.00 0.49 0.09 0.62 0.02 0.98 0.00 0.51 0.07 0.74 0.00 Cerros del Sira 0.87 0.00 0.81 0.00 0.99 0.00 0.75 0.00 0.94 0.00 0.91 0.00 0.85 0.00 0.86 0.00 Great Smoky 0.72 0.00 -0.35 0.21 0.93 0.00 0.64 0.01 0.33 0.23 0.89 0.00 0.90 0.00 -0.11 0.69 Sweet Grass hills 0.72 0.01 -0.71 0.01 0.98 0.00 0.82 0.00 -0.28 0.41 0.67 0.02 0.42 0.19 -0.69 0.02 Tilaran 0.64 0.00 0.40 0.02 0.98 0.00 -0.23 0.17 0.95 0.00 0.57 0.00 -0.28 0.10 0.31 0.06 Totare 0.96 0.00 0.47 0.00 0.98 0.00 0.65 0.00 0.56 0.00 0.97 0.00 0.69 0.00 0.61 0.00 Trentino 0.46 0.14 -0.84 0.00 0.99 0.00 -0.77 0.00 -0.24 0.45 0.40 0.20 -0.24 0.45 -0.87 0.00 Udzungwa Scarp 0.17 0.54 -0.08 0.77 0.99 0.00 -0.86 0.00 0.92 0.00 0.30 0.28 0.24 0.39 0.05 0.87 Vilcabamba 0.66 0.00 0.54 0.00 0.98 0.00 0.81 0.00 0.92 0.00 0.73 0.00 0.83 0.00 0.61 0.00 Mt Wilhelm 0.07 0.68 -0.11 0.53 0.99 0.00 0.85 0.00 0.72 0.00 0.13 0.45 0.17 0.34 -0.02 0.90 Yaku-Shima 0.82 0.00 0.65 0.00 0.99 0.00 0.94 0.00 0.89 0.00 0.79 0.00 0.77 0.00 0.63 0.01 Yariguies 0.77 0.00 0.52 0.00 0.97 0.00 0.19 0.31 0.86 0.00 0.88 0.00 0.66 0.00 0.70 0.00 Yosemite 0.88 0.00 -0.90 0.00 0.97 0.00 0.55 0.00 -0.70 0.00 0.86 0.00 0.67 0.00 -0.85 0.00

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Table B-4. Average bird beta diversity (mean ± SD) values of taxonomic (Tβsor), functional (Fβsor) and phylogenetic (Pβsor) diversity between consecutive 200m-elevational bands for 46 elevational gradients across the globe, and the percentages explained by its components, richness (βrich) and replacement (βrepl). Separate columns are presented for taxonomic, functional and phylogenetic dimensions of diversity.

Taxonomicβsor Functional βsor Phylogenetic βsor

β ± SD Tβrich Tβrepl β ± SD Tβrich Tβrepl β ± SD Tβrich Tβrepl Aguarague 0.03 ± 0.05 31.98 67.44 0.01 ± 0.01 25.81 74.19 0.02 ± 0.03 29.41 70.59

Amrutganga 0.23 ± 0.32 74.72 25.28 0.09 ± 0.15 82.07 17.78 0.17 ± 0.24 77.95 21.96

D’Andohahela 0.07 ± 0.06 81.89 18.31 0.04 ± 0.05 86.73 13.27 0.05 ± 0.04 89.91 10.09 Annapurna 0.11 ± 0.20 47.69 52.23 0.02 ± 0.05 57.17 42.83 0.07 ± 0.13 55.62 44.38 Sierra de Atoyac 0.21 ± 0.14 47.87 52.13 0.11 ± 0.08 51.14 49.02 0.15 ± 0.08 54.08 45.80 El Avila 0.14 ± 0.17 58.03 41.97 0.07 ± 0.11 55.39 44.91 0.09 ± 0.12 43.35 56.88 Sierra de Bahoruco 0.11 ± 0.14 67.11 32.89 0.09 ± 0.10 79.69 20.31 0.08 ± 0.10 77.32 22.68 Bocaina 0.24 ± 0.32 84.11 15.73 0.12 ± 0.17 94.03 5.97 0.19 ± 0.29 88.33 11.67 Braulio Carrillo 0.30 ± 0.35 53.67 46.36 0.12 ± 0.15 59.37 40.72 0.21 ± 0.24 57.57 42.43 Buru 0.15 ± 0.20 92.02 7.90 0.08 ± 0.10 94.04 5.96 0.11 ± 0.17 95.77 4.23 Canigo 0.12 ± 0.13 85.04 14.83 0.04 ± 0.07 86.89 12.93 0.09 ± 0.09 88.15 11.68

Mt Chamba Mt 0.22 ± 0.12 65.54 34.42 0.08 ± 0.12 90.37 9.53 0.16 ± 0.08 70.38 29.67

Mt Cinto 0.14 ± 0.10 80.81 19.25 0.05 ± 0.03 84.33 16.01 0.10 ± 0.09 83.69 16.23 Manu 0.29 ± 0.15 63.67 36.31 0.14 ± 0.08 64.11 35.78 0.20 ± 0.12 69.42 30.58 Cordillera de Cutucu 0.26 ± 0.16 68.44 31.49 0.14 ± 0.08 83.25 16.63 0.20 ± 0.11 77.53 22.47

Espinazo del Diablo 0.19 ± 0.14 68.00 31.95 0.08 ± 0.07 83.59 16.41 0.13 ± 0.09 71.09 29.04

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Table B-4. Continued

Taxonomicβsor Functional βsor Phylogenetic βsor

β ± SD Tβrich Tβrepl β ± SD Tβrich Tβrepl β ± SD Tβrich Tβrepl Mount Etna 0.14 ± 0.22 87.15 12.85 0.08 ± 0.15 91.49 8.51 0.10 ± 0.18 90.97 9.03

Halmahera 0.20 ± 0.20 100.00 0.00 0.06 ± 0.04 100.00 0.00 0.15 ± 0.16 100.00 0.00

Hermon Transect 0.24 ± 0.14 51.17 48.92 0.12 ± 0.14 79.02 20.89 0.18 ± 0.16 57.40 42.67 Mt Isarog 0.20 ± 0.16 89.08 10.92 0.12 ± 0.10 90.31 9.69 0.16 ± 0.13 89.53 10.47 Itatiaia 0.18 ± 0.13 75.44 24.56 0.09 ± 0.08 84.48 15.63 0.14 ± 0.12 78.59 21.34 Mt Karimui 0.18 ± 0.09 62.76 37.18 0.08 ± 0.07 77.78 21.97 0.13 ± 0.08 67.98 32.02 Karkar 0.17 ± 0.18 80.92 18.95 0.07 ± 0.07 89.51 10.19 0.13 ± 0.13 85.01 14.91

Mt Kilimanjaro 0.13 ± 0.11 75.09 24.96 0.06 ± 0.07 88.04 12.08 0.11 ± 0.10 73.84 26.16

Marojejy NP 0.11 ± 0.12 66.31 33.69 0.07 ± 0.08 72.92 27.08 0.09 ± 0.11 51.82 48.03

Marojejy reserve 0.21 ± 0.21 87.75 12.25 0.12 ± 0.12 88.12 11.88 0.17 ± 0.17 91.40 8.60 Masoala 0.07 ± 0.05 76.31 23.69 0.07 ± 0.07 91.12 8.88 0.05 ± 0.04 87.65 12.35 Mikura Jiima 0.29 ± 0.16 50.70 49.30 0.22 ± 0.14 73.23 27.08 0.26 ± 0.21 71.89 28.11

Mount Mulu 0.31 ± 0.24 61.05 38.91 0.14 ± 0.14 66.34 33.74 0.22 ± 0.21 71.37 28.78

Mt Mwanihana 0.17 ± 0.13 58.71 41.22 0.07 ± 0.07 77.00 23.38 0.11 ± 0.09 58.71 41.18

Orgios 0.15 ± 0.12 83.52 16.48 0.07 ± 0.05 87.19 12.81 0.12 ± 0.10 83.48 16.44 Piedmont 0.29 ± 0.16 75.77 24.23 0.21 ± 0.14 81.46 18.47 0.22 ± 0.12 65.71 34.15 RBSF 0.35 ± 0.27 74.41 25.59 0.15 ± 0.17 88.04 12.07 0.26 ± 0.20 78.68 21.29 Sibuyan Island 0.21 ± 0.25 98.64 1.36 0.10 ± 0.15 99.49 0.51 0.16 ± 0.21 99.09 0.91

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Table B-4. Continued

Taxonomicβsor Functional βsor Phylogenetic βsor

β ± SD Tβrich Tβrepl β ± SD Tβrich Tβrepl β ± SD Tβrich Tβrepl Cerros del Sira 0.23 ± 0.09 40.85 59.01 0.10 ± 0.05 56.59 43.20 0.16 ± 0.06 46.90 52.97

Great Smoky 0.14 ± 0.11 73.76 26.24 0.11 ± 0.11 89.97 10.03 0.10 ± 0.09 89.22 10.93

Sweetgrass hills 0.31 ± 0.15 57.47 42.60 0.11 ± 0.09 82.23 17.77 0.21 ± 0.10 68.46 31.54 Tilaran 0.06 ± 0.12 53.15 46.75 0.03 ± 0.07 65.64 34.36 0.04 ± 0.07 53.93 46.35 Totare 0.27 ± 0.28 39.20 60.82 0.13 ± 0.13 50.95 48.92 0.18 ± 0.18 43.41 56.59 Trentino 0.33 ± 0.17 86.07 14.01 0.18 ± 0.11 91.38 8.62 0.26 ± 0.16 87.72 12.33 Udzungwa Scarp 0.14 ± 0.05 61.95 38.14 0.07 ± 0.09 88.73 11.09 0.09 ± 0.03 61.86 38.00

Vilcabamba 0.18 ± 0.06 44.82 55.25 0.09 ± 0.07 57.63 42.37 0.12 ± 0.04 45.08 54.92

Mt Wilhelm 0.14 ± 0.14 71.83 28.17 0.05 ± 0.11 94.99 4.88 0.11 ± 0.10 71.37 28.63

Yaku Shima 0.12 ± 0.07 65.47 34.53 0.09 ± 0.08 81.30 18.82 0.10 ± 0.08 76.96 22.93 Yarigues 0.22 ± 0.23 65.80 34.26 0.10 ± 0.07 66.06 34.21 0.16 ± 0.17 67.21 32.75 Yosemite 0.11 ± 0.07 75.50 24.44 0.03 ± 0.02 81.33 19.16 0.07 ± 0.06 81.04 18.96

206

Table B-5. Values of Blomberg’s K for morphological functional traits included in the analyses. Values of K that are greater than 1 suggest strong phylogenetic conservatism on traits, values that are close to 1 suggest some degree of conservatism, and values close to zero suggest random or convergent patterns of evolution. Traits with P values <0.05 have significant non-random phylogenetic signal. Trait Blomberg's K P Body mass 3.281 0.001 Bill length 0.810 0.001 Bill height 0.982 0.001 Bill width 0.879 0.001 Wing length 0.684 0.001 Tail length 0.861 0.001 Kipp's distance 0.896 0.001 Tarsus length 0.734 0.001

207

Table B-6. Pearson moment correlations among environmental variables potentially affecting diversity patterns. Arthropods refers to arthropod diversity measured with a Shannon-Wienner index, Complexity refers to the vertical structural complexity, measured with an understory height diversity index (UHD), Fruits refer to the diversity of fruiting plants available, NMDS1 and NMDS2 refer to the two main axis of the non-metric multidimensional scaling of species composition among the 23 studied communities, and Temperature refers to mean daily temperature. Arthropods Fruits Temperature NMDS1 NMDS2 r P r P r P r P r P Habitat complexity -0.17 0.446 -0.20 0.358 0.65 0.001 -0.70 0.000 0.61 0.002 Arthropods -0.17 0.429 -0.23 0.289 0.24 0.273 0.16 0.478 Fruits 0.40 0.061 -0.35 0.106 -0.63 0.001 Temperature -0.99 0.000 -0.09 0.689 NMDS1 0.00 1.000

208

Table B-7. Full models of species richness, functional diversity and phylogenetic diversity as a function of habitat complexity, arthropod diversity, fruit availability and mean daily temperature along an elevational gradient in the Bolivian Andes. Species richness was models with binomial generalized linear model, whereas all other response variables were modeled with generalized linear models assuming Gamma errors. Model Coefficient St. Error z value P Species richness Intercept 0.64 1.03 0.62 0.535 Habitat complexity 1.66 0.47 3.52 <0.001 Arthropod diversity 0.11 0.10 1.06 0.289 Fruit availability -0.14 0.10 -1.37 0.172 Mean temperature 0.08 0.02 4.24 <0.0001

SES MPDfun Intercept 3.27 6.00 0.55 0.592 Habitat complexity -1.86 2.67 -0.70 0.496 Arthropod diversity 0.02 0.63 -0.37 0.971 Fruit availability 0.22 0.69 0.32 0.752 Mean temperature -0.17 0.12 -1.47 0.159

SES MNTDfun Intercept 4.00 3.02 1.33 0.219 Habitat complexity -2.44 1.34 -1.82 0.086 Arthropod diversity -0.43 0.32 -1.38 0.185 Fruit availability -0.17 0.35 -0.47 0.641 Mean temperature 0.10 0.06 1.68 0.110

SES MPDphy Intercept -3.72 8.27 -0.45 0.658 Habitat complexity 0.71 3.68 0.19 0.850 Arthropod diversity 0.04 0.86 0.04 0.967 Fruit availability -0.62 0.96 -0.65 0.526 Mean temperature 0.22 0.16 1.39 0.181

SES MNTDphy Intercept -5.04 4.06 -1.24 0.230 Habitat complexity -0.30 1.80 -0.17 0.869 Arthropod diversity 0.20 0.42 0.48 0.636 Fruit availability 0.32 0.47 0.68 0.508 Mean temperature 0.18 0.08 2.35 0.030

209

Table B-8. Polynomial regression models of species richness (A), functional richness (B), functional divergence (C), phylogenetic richness (D) and phylogenetic dispersion (E) against elevation, for mixed species flocks along an elevational gradient in the Bolivian Andes. Model Estimate St. error t P Richness Intercept 12.300 0.644 19.087 <0.0001 elevation 4.750 5.348 0.889 0.377 elevation2 -18.099 5.348 -3.384 0.001 FRic Intercept 0.016 0.001 13.513 <0.0001 elevation 0.022 0.010 2.149 0.035 elevation2 -0.03 0.10 -3.40 0.001 FDiv Intercept 0.815 0.005 117.02 <0.0001 elevation 0.173 0.038 4.53 <0.0001 elevation2 0.024 0.038 0.63 0.530 PD Intercept 401.04 14.46 27.73 <0.0001 elevation -115.73 120.15 -0.96 0.339 elevation2 -381.90 120.15 -3.18 0.002 MPD Intercept 101.68 1.40 72.55 <0.0001 elevation -63.66 11.64 -5.47 <0.0001 elevation2 -0.21 11.64 -0.02 0.986

210

Table B-9. Characteristics of flocks at five elevations in the Andes of Bolivia, and number of significant pair-wise associations between species. Association strength values were calculated following Veech (2013)’s probabilistic method. Number of species and number of flocks refer to those that were included in the analyses after eliminating species that were observed in only one flock per site. Number of positive associations and number of negative associations refer to statistically significative associations. I considered as significant any association that was observed with less than 0.1 probability (P<0.1), any other association was considered as random. Unclassifiable associations were those that were not calculated by the model, because of low power related with small sample size (i.e., associations between rare species). Elevational range <2300 2300-2600 2600-2900 2900-3200 >3200 Number of species 31 31 33 39 22 Number of flocks 14 12 17 17 9 Number of positive associations 8 8 17 30 7 Number of negative associations 3 3 4 3 2 Random associations 165 236 356 419 98 Unclassifiable associations 14 10 10 27 16 Number of analyzed pairs 190 257 387 479 123

211

Table B-10. Regression coefficients of the Multiple Regression Quadratic Assignment Procedure (MRQAP) with Double-Semi-Partialing (DSP) showing the effect of morphological distances on the association strength of positive species interactions. Morphological distance matrices were obtained by calculating Euclidean distances between species including eight morphological traits. Association strength was calculated with Veech (2013) probabilistic method. All positive interactions (with association strength above 0) were included in each site’s association matrix for analyses. N is the number of associations used in each regression, and R2 is the adjusted R squared value of the regression. Models Coefficients P R2 Site 1: 2050 -2350 (N= 101) 0.59 Intercept 0.003 0.099 Morphological distance 0.096 0.0001 Site 2: 2350 - 2650 (N= 131) 0.45 Intercept 0.005 0.04 Morphological distance 0.070 0.0001 Site 3: 2650 - 2950 (N=247) Intercept 0.009 0.001 0.34 Morphological distance 0.049 0.0001 Site 4: 2950 - 3250 (N=303) Intercept 0.007 0.006 0.34 Morphological distance 0.058 0.0001 Site 5: 3250 - 3550 (N=66) Intercept 0.007 0.088 0.39 Morphological distance 0.115 0.0001

212

Table B-11. Regression coefficients of the Multiple Regression Quadratic Assignment Procedure (MRQAP) with Double-Semi-Partialing (DSP) showing the effect of ecological distances on the association strength of positive species interactions. Ecological distance matrices were obtained by calculating Gower distances between species including only two ecological traits. Association strength was calculated with Veech (2013) probabilistic method. All positive interactions (with association strength above 0) were included in each site’s association matrix for analyses. N is the number of associations used in each regression, and R2 is the adjusted R squared value of the regression. Models Coefficients P R2 Site 1: 2050 -2350 (N= 101) 0.50 Intercept 0.005 0.0170 Ecological distance 0.089 0.0001 Site 2: 2350 - 2650 (N= 131) 0.32 Intercept 0.008 0.0060 Ecological distance 0.070 0.0001 Site 3: 2650 - 2950 (N=247) 0.30 Intercept 0.011 0.000 Ecological distance 0.053 0.000 Site 4: 2950 - 3250 (N=303) 0.28 Intercept 0.009 0.001 Ecological distance 0.052 0.0001 Site 5: 3250 - 3550 (N=66) 0.34 Intercept 0.008 0.06 Ecological distance 0.078 0.001

213

Table B-12. Regression coefficients of the Multiple Regression Quadratic Assignment Procedure (MRQAP) with Double-Semi-Partialing (DSP) showing the effect of morphological distances on the association strength of negative species interactions Morphological distance matrices were obtained by calculating Euclidean distances between species including eight morphological traits. Association strength was calculated with Veech (2013) probabilistic method. All negative interactions (with association strength below 0) were included in each site’s association matrix for analyses. N is the number of associations used in each regression, and R2 is the adjusted R squared value of the regression. Models Coefficients P R2 Site 1: 2050 -2350 (N= 101) 0.42 Intercept -0.003 0.105 Morphological distance -0.049 0.0001 Site 2: 2350 - 2650 (N= 131) 0.54 Intercept -0.002 0.111 Morphological distance -0.057 0.0001 Site 3: 2650 - 2950 (N=247) Intercept -0.002 0.091 0.43 Morphological distance -0.047 0.0001 Site 4: 2950 - 3250 (N=303) Intercept -0.001 0.096 0.47 Morphological distance -0.037 0.0001 Site 5: 3250 - 3550 (N=66) Intercept -0.003 0.2312 0.40 Morphological distance -0.084 0.0001

214

Table B-13. Regression coefficients of the Multiple Regression Quadratic Assignment Procedure (MRQAP) with Double-Semi-Partialing (DSP) showing the effect of ecological distances on the association strength of negative species interactions. Ecological distance matrices were obtained by calculating Gower distances between species including eight morphological traits. Association strength was calculated with Veech (2013) probabilistic method. All negative interactions (with association strength below 0) were included in each site’s association matrix for analyses. N is the number of associations used in each regression, and R2 is the adjusted R squared value of the regression. Models Coefficients P R2 Site 1: 2050 -2350 (N= 101) 0.44 Intercept -0.003 0.1110 Ecological distance -0.054 0.0001 Site 2: 2350 - 2650 (N= 131) 0.29 Intercept -0.005 0.0380 Ecological distance -0.055 0.0001 Site 3: 2650 - 2950 (N=247) 0.38 Intercept -0.003 0.034 Ecological distance -0.049 0.000 Site 4: 2950 - 3250 (N=303) 0.36 Intercept -0.002 0.032 Ecological distance -0.040 0.0001 Site 5: 3250 - 3550 (N=66) 0.45 Intercept -0.003 0.1921 Ecological distance -0.064 0.001

215

Table B-14. Phylogenetic signal (Pagel’s λ) in eight functional traits of 94 bird species participating in mixed-species flocks in the Andes of Bolivia. Lambda (λ) values of zero indicate that evolution of traits is independent of phylogeny; λ values of 1 indicate that the distribution of traits in a phylogeny is consistent with a model of Brownian motion. Values of λ between 0 and 1 indicate that traits have evolved according to a process in which the effect of phylogeny is weaker than in the Brownian model. Functional trait Pagel’s λ P Blomberg's K P Bill length 0.98 0.0001 0.91 0.001 Bill width 0.96 0.0001 0.54 0.001 Bill depth 0.99 0.0001 1.02 0.001 Tarsus length 0.988 0.0001 0.57 0.001 Kipp's distance 0.954 0.0001 0.13 0.117 Wing length 1 0.0001 0.54 0.001 Tail length 0.983 0.0001 0.369 0.001 Body mass 0.99 0.0001 1.03 0.001

216

Table B-15. Generalized linear model with negative binomial errors to contrast species richness per flock, in mixed-species flocks across five elevations in the Bolivian Andes. Elevation was included as a factor with five levels. Overall elevation was significant when compared with the null model, with lower species richness at higher elevations. Coefficients b SE z P intercept 2.22 0.05 42.42 <0.0001 elevation 2250 -0.04 0.79 -0.45 0.656 elevation 2600 0.03 0.80 0.33 0.745 elevation 2950 -0.10 0.74 -1.34 0.181 elevation 3300 -0.27 0.09 -2.89 0.004

217

Table B-16. Pairwise PerManova analyses among flocking assemblages at five elevations in the Bolivian Andes. Adjusted P values after Bonferroni corrections. Assemblage pairs F R2 P Adjusted P 3300-2950 3.87945 0.028977 0.001 0.01 3300-2600 10.51632 0.091038 0.001 0.01 3300-2250 27.03861 0.194468 0.001 0.01 3300-1500 42.14917 0.247719 0.001 0.01 2950-2600 5.012901 0.033416 0.001 0.01 2950-2250 30.27129 0.166078 0.001 0.01 2950-1500 55.22769 0.247405 0.001 0.01 2600-2250 14.84074 0.10463 0.001 0.01 2600-1500 41.13574 0.223399 0.001 0.01 2250-1500 25.46556 0.145131 0.001 0.01

218

Table B-17. Effects of elevation on species-level metrics of interaction networks of flocking birds across five elevations in the Bolivian Andes. Elevation had significant effects on normalized degree, but not in normalized weighted degree nor normalized centrality. Generalized Linear Models (GLMs) with Gamma errors and link function log were used for normalized degree and normalized weighted degree. For normalized centrality, a zero inflated tweedie model was used, as the response variable was continuous, but several nodes had zero shortest paths crossing through them. In all cases, elevation was included as a factor. Species level metric b SE t P A) Normalized degree intercept -1.207 0.057 -21.01 <0.0001 elevation 2250 0.169 0.087 1.95 0.052 elevation 2600 0.376 0.092 4.09 <0.0001 elevation 2950 0.424 0.094 4.47 <0.0001 elevation 3300 0.249 0.103 2.48 0.016 B) Normalized weighted degree intercept -0.760 0.056 -13.49 <0.0001 elevation 2250 -0.062 0.085 -0.73 0.468 elevation 2600 0.025 0.090 0.28 0.780 elevation 2950 -0.094 0.093 -1.01 0.313 elevation 3300 -0.093 0.101 -0.92 0.357 C) Normalized Centrality intercept -0.60 0.271 -2.20 0.028 elevation 2250 -0.02 0.411 -0.05 0.961 elevation 2600 -0.09 0.436 -0.22 0.830 elevation 2950 -0.04 0.449 -0.09 0.927 elevation 3300 -0.16 0.491 -0.33 0.739

219

Table B-18. Effects of elevation on network-level metrics of interaction networks of flocking birds across five elevations in the Bolivian Andes. Generalized Linear Models (GLMs) were used to run the regressions Gaussian errors were assumed for all models. In all cases, elevation was included as a factor. Elevation had significant effects on all network-level metrics; whereas skewness and modularity decreased with elevation, network density and transitivity increased. Network level metric b SE t P Connectivity metrics A) Skewness intercept 0.73 0.004 195.47 <0.0001 elevation 2250 -0.28 0.005 -52.41 <0.0001 elevation 2600 -0.47 0.005 -88.62 <0.0001 elevation 2950 -0.60 0.005 -114.03 <0.0001 elevation 3300 -0.13 0.005 -24.69 <0.0001 B) Density intercept 0.23 0.001 291.73 <0.0001 elevation 2250 0.04 0.001 39.94 <0.0001 elevation 2600 0.12 0.001 107.70 <0.0001 elevation 2950 0.15 0.001 136.06 <0.0001 elevation 3300 0.07 0.001 64.37 <0.0001 Cohesion metrics C) Transitivity intercept 0.58 0.001 1000.52 <0.0001 elevation 2250 0.06 0.001 70.16 <0.0001 elevation 2600 0.07 0.001 91.66 <0.0001 elevation 2950 0.12 0.001 141.89 <0.0001 elevation 3300 0.21 0.001 25.82 <0.0001 D) Modularity intercept 0.34 0.001 353.43 <0.0001 elevation 2250 -0.05 0.001 -33.53 <0.0001 elevation 2600 -0.05 0.001 -39.73 <0.0001 elevation 2950 -0.13 0.001 -98.79 <0.0001 elevation 3300 -0.06 0.001 -45.30 <0.0001

220

Table B-19. Pairwise network beta diversity for all pairs of networks, and their partition in βST and βos. S values refer to the dissimilarity in species composition among assemblages.

network 1 network 2 S βWN βOS βST 1500 2250 0.32 0.74 0.47 0.27 1500 2600 0.45 0.91 0.56 0.35 1500 2950 0.50 0.95 0.61 0.34 1500 3300 0.63 0.97 0.71 0.26 2250 2600 0.21 0.55 0.36 0.19 2250 2950 0.32 0.67 0.37 0.30 2250 3300 0.41 0.72 0.33 0.38 2600 2950 0.18 0.39 0.26 0.13 2600 3300 0.22 0.39 0.19 0.20 2950 3300 0.16 0.29 0.17 0.12

221

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BIOGRAPHICAL SKETCH

Flavia Montaño Centellas is a Bolivian ecologist deeply committed to the study and conservation of the Andes. She was born and raised in La Paz, Bolivia. She holds a Bachelor of

Science degree in biology and a Master of Science degree in ecology from the Universidad

Mayor de San Andrés, in her home city. During her academic years as an undergraduate, and then as a Graduate student in Bolivia, she volunteered with the Programa para la Conservation de

Murciélagos de Bolivia (PCMB). During these years, she worked for local NGO’s in conservation projects that involved local communities and biodiversity management. After receiving her master’s degree in 2007, Flavia worked as a professor at her alma mater and left that position a few years later to travel the world. On return, she pursued another academic dream and joined the University of Florida family, in 2013, to obtain a PhD in Wildlife Ecology and Conservation. Her research has focused on understanding the mechanisms driving community structure in the Neotropics, using birds and bats as model organisms. In particular, she is interested in species interactions, having studied seed dispersal systems and mixed-species flocks. She received her PhD from the University of Florida in Fall 2018.

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