Trends in the Evolution and Ecology of Functional Morphology in Neotropical Cichlids

Jessica Hilary Arbour

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Ecology and Evolutionary Biology University of Toronto

Jessica Arbour

Doctor of Philosophy

Ecology and Evolutionary Biology University of Toronto

2015 Abstract

The uneven distribution of biological diversity has been a subject of great interest in the study of evolutionary biology. Advances in molecular phylogenetics and comparative methods have facilitated an increasing number of studies relating to the impacts of neutral and adaptive processes on morphological diversity, especially those relating to the predictions of adaptive radiation. While such studies have largely focused on restricted, island radiations, such as

African Rift Lake cichlids and Caribbean anoles, recent years have seen an expansion of the analysis of morphological evolution within more broadly-distributed clades. Due to their species-richness, ecological/morphological diversity and age, the continentally-distributed freshwater fish Cichlinae (Neotropical cichlids) represent an ideal system for the study of morphological evolution and its relationship to ecological diversity. Combining data from biomechanics, morphometrics, modern and fossil specimens, and dietary analyses with a comprehensive molecular phylogeny and modern phylogenetic comparative methods I examined functional morphological evolution in Neotropical cichlids and the impact of factors such as selection, adaptation, extinction and ecological opportunity on diversification. Analysis of the functional morphospace of Neotropical cichlids revealed complex selective regimes that have contributed to their modern functional diversity. Rates of functional evolution varied with ii ecological opportunity in Neotropical cichlids, declining in South American through time and increasing upon the colonization of new habitats in Central America. Extinct, fossil cichlid species demonstrate the stability of selective processes on ecomorphological evolution over tens of millions of years. Functional morphology was also significantly correlated with dietary composition. While feeding roles constrained trait evolution along particular morphological axes, dietary specialization increased evolutionary rates, facilitating the evolution of more extreme morphologies. Neotropical cichlid morphological diversity has been influenced by selection and adaptive diversification especially in relation to trade-offs in bentho-pelagic foraging, and patterns of evolution in South America are consistent with a continental adaptive radiation.

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Acknowledgments

Firstly, I would like to thank my supervisor Hernán López-Fernández, who provided me with countless research opportunities, offered valuable perspectives on my research from his expertise in phylogenetics and the natural history of Neotropical fishes, pushed me to aim high, and allowed me the freedom to discover and pursue research questions of my own. His mentorship has helped me to grow as a scientist and as a person. The members of my supervisory committee,

Nathan Lovejoy (U of T), Don Jackson (U of T) and Peter Wainwright (UC Davis), have provided a wealth of advice through my doctoral program. Deborah McLennan (U of T) and

Mark Cadotte (U of T) served on my appraisal committee and I appreciated their constructive comments on my research proposal. David Evans (U of T) and Adam Summers (University of

Washington) brought their enthusiasm and insight to my final exam committee. I am also grateful to the ichthyology curators and staff of the Royal Ontario Museum (Mary Burridge,

Erling Holm, Rick Winterbottom, Marg Zur and Don Stacey), whose assistance with the ROM’s fish collections and support over the last several years has been indispensable. I am also grateful to my undergraduate honours supervisor, Jeffrey Hutchings (Dalhousie), who gave me my first opportunity to study fish morphology and started me down this path in evolutionary research.

Individuals at other institutions have facilitated access to data and specimens for my dissertation research. John Armbruster (AUM), D. Werneke (AUM), Roberto Reis (PUCRS),

Mark Sabaj-Pérez (ANSP) and John Lundberg (ANSP) provided access to cichlid specimens used in morphological and functional analyses. Maria Claudia Malabarba (Universidade Federal do Rio Grande do Sul) provided access to and assistance with fossil cichlid collections. Kirk

Winemiller (Texas A&M), Carmen Montaña (North Carolina State University), Jennifer iv

Cochran-Biedermann (Texas Tech University) and Allison Pease (Winona State University) were kind enough to share with me ecological data from their Neotropical fish research programs. I have also been fortunate enough to receive financial support from NSERC (CGS M and CGS D), the Ontario government (OGS) and various fellowships/award from the University of Toronto.

I would like to thank a number of ROM Ecology and Evolutionary Biology graduate students who have been both wonderful collaborators and friends. I have been fortunate to be able to share and discuss my teaching, research, and field experiences with Sarah Steele and

Frances Hauser. Katriina Ilves was a mentor to me during the later stages of my academic program at U of T, and I value her camaraderie. Viviana Astudillo, Matthew Kolman, Nathan

Lujan and Shannon Refvik contributed to the thoughtful and productive environment in the ROM ichthyology lab. I have also thoroughly enjoyed my opportunities for academic conversation and collaboration with Derek Larson and Caleb Brown of the ROM Palaeontology lab.

I am grateful to my parents, Joseph and Edith Arbour, who encouraged my interest in math and science from a young age. My sister Victoria Arbour has been a crucial source of encouragement and advice through all of my academic years, and I have benefitted greatly from her thoughtfulness, maturity and breadth of experiences. Ginny Arbour, Oliver Arbour and

Penny Staples provided unconditional support during my graduate years. Special thanks go to my partner David Staples, who has always believed in me and in my academic dreams. David, thank you for being there for every step of this journey, and hopefully for many more to come.

Table of Contents

Abstract ...... ii

Acknowledgements ...... iv

Table of Contents ...... vi

List of Tables ...... xi

List of Figures ...... xiv

List of Appendices ...... xviii

General Introduction ...... 1

I.1 Background ...... 1

I.1.1 Morphological diversity ...... 1

I.1.1 Functional morphology ...... 2

I.1.1 Adaptive radiation and ecological opportunity ...... 3

I.1.1 Study system – Neotropical cichlids ...... 4

I.2 Aims and Scope ...... 9

I.3 Overview of the Chapters ...... 10

I.4 Contributions ...... 12

1. Chapter One: Adaptive landscape and functional diversity of Neotropical cichlids:

implications for the ecology and evolution of Cichlinae (Cichlidae; Cichliformes) ...... 13

1.1 Abstract ...... 14

1.2 Introduction ...... 15

1.3 Methods ...... 17 vi

1.3.1 Phylogeny and taxonomic sampling ...... 17

1.3.2 Measuring Cichlinae functional morphology ...... 19

1.3.2.1 Muscle masses ...... 20

1.3.2.2 Lower jaw lever mechanics ...... 21

1.3.2.3 Bite occlusion ...... 22

1.3.2.4 Jaw protrusion ...... 24

1.3.2.5 Lower pharyngeal jaw mass ...... 24

1.3.2.6 Kinematic transmission coefficients ...... 25

1.3.2.7 Suction index ...... 27

1.3.3 Cichlinae functional morphospace ...... 28

1.3.4 Estimating an adaptive landscape of functional morphology ...... 29

1.3.5 Functional disparity and phylomorphospace analyses ...... 32

1.4 Results ...... 34

1.4.1 Functional morphospace of Cichlinae ...... 34

1.4.2 Adaptive landscape of functional morphology ...... 39

1.4.2 Functional disparity and lineage density ...... 47

1.5 Discussion ...... 50

1.5.1 Neotropical cichlid feeding functional morphology ...... 50

1.5.2 Cichlinae functional morphospace and feeding ecology ...... 51

1.5.3 Adaptive landscape and functional evolution ...... 53

2. Chapter 2: Ecological opportunity and ecological release impact functional evolution

in the South and Central American cichlid radiations ...... 58

2.1 Abstract ...... 59

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2.2 Introduction ...... 60

2.3 Methods ...... 63

2.3.1 South and Central American biogeography ...... 63

2.3.2 Ecological opportunity and evolutionary rates ...... 64

2.3.3 Disparity-through-time analyses ...... 66

2.4 Results ...... 70

2.4.1 Central and South American radiations ...... 70

2.4.2 Node Height Tests ...... 72

2.4.3 Disparity-through-time analyses ...... 82

2.5 Discussion ...... 87

2.5.1 Patterns of Neotropical cichlid evolution ...... 87

2.5.2 Ecological opportunity in South America ...... 88

2.5.3 Ecological release in Central America ...... 91

2.5.4 Differences in functional evolution between South and Central America ...... 94

2.5.5 Conclusions ...... 95

3. Chapter Three: Morphological diversification in extant and extinct Neotropical

cichlids ...... 97

3.1 Abstract ...... 98

3.2 Introduction ...... 99

3.3 Methods ...... 101

3.3.1 Morphometrics ...... 101

3.3.2 Phylogenetic canonical correlation analysis ...... 102

3.3.3 Fossil ecomorphology and phylogenetic placement ...... 103

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3.3.4 Fossil disparity and morphospace occupation ...... 110

3.3.5 Gymnogeophagus ecomorphological diversity ...... 111

3.3.6 Cichlid ecomorphological adaptive landscape ...... 112

3.4 Results ...... 113

3.4.1 Phylogenetic canonical correlation analysis ...... 113

3.4.2 Cichlid ecomorphospace ...... 118

3.4.3 Fossil disparity and morphospace occupation ...... 123

3.4.4 Gymnogeophagus diversity ...... 127

3.4.5 Ecomorphospace adaptive landscape ...... 129

3.5 Discussion ...... 132

3.5.1 Cichlid functional morphology and body shape ...... 132

3.5.2 Fossil ecomorphology ...... 133

3.5.3 Fossil cichlids and adaptive landscape dynamics ...... 134

3.5.4 Morphological significance of †Gymnogeophagus eocenicus ...... 135

3.5.5 Phylogenetic and fossil uncertainties ...... 136

3.5.6 Conclusions ...... 137

3.6 Appendices ...... 138

4. Chapter Four: Feeding ecology and functional diversification in Neotropical cichlids .. 145

4.1 Abstract ...... 146

4.2 Introduction ...... 147

4.3 Methods ...... 149

4.3.1 Neotropical cichlid feeding ecology ...... 149

4.3.2 Relationship between functional morphology and diet ...... 151

4.3.3 Functional diversification and feeding roles ...... 152

4.3.4 Functional diversification and specialization ...... 156

4.4 Results ...... 157

4.4.1 Neotropical cichlid diet variation ...... 157

4.4.2 Diet and functional morphology ...... 160

4.4.3 Functional diversification and feeding roles ...... 166

4.4.4 Evolutionary consequences of specialization ...... 176

4.5 Discussion ...... 182

4.5.1 Diet and function ...... 182

4.5.2 Functional diversification and feeding ...... 183

4.5.3 Consequences of dietary specialization ...... 185

4.5.4 Conclusions ...... 186

4.6 Appendices ...... 187

General conclusions ...... 193

C.1 Summary...... 193

C.2 Future Directions ...... 195

References ...... 198

List of Tables

Chapter One ......

Table 1.1: Summary statistics measuring adaptive peaks and convergence in SURFACE

analyses ...... 32

Table 1.2: Phylogenetically-corrected correlation matrix (Pearson’s correlation coefficients)

of 10 functional morphological variables measured from 75 Neotropical cichlid species ...... 36

Table 1.3: Loading factor coefficients for the critical axes of variation in functional

morphology across 75 cichlids species ...... 37

Table 1.4: Summary of adaptive peak shifts and convergence in Cichlinae functional

morphology ...... 41

Table 1.5: Support for BM, OU and SURFACE generated Hansen models under a

multivariate evolutionary assumptions as implemented in functions “mvBM” and

“mvOU” from R package “mvMORPH”...... 45

Chapter Two ......

Table 2.1: Model fitting of null constant rate models of evolution for simulation tests

associated with NHT and DTT analyses ...... 74

Table 2.2: Summary of Node Height Tests of Cichlinae functional morphology for the MCC

tree and 1000 posterior distribution chronograms ...... 77

Table 2.3: Summary of DTT analysis of Cichlinae functional morphology for the MCC tree

and 1000 posterior distribution chronograms ...... 83

Chapter Three ......

Table 3.1: Summary of Neotropical fossil cichlids used in ecomorphological analyses ...... 109

Table 3.2: Summary of phylogenetic CCoA of functional morphology and body shape

(ecomorphology) in 74 cichlid species ...... 116

Table 3.3: Mean loading factors from phylogenetic principal component analysis of

ecomorphology and functional morphology of 82 species of extant and extinct Neotropical

cichlids ...... 121

Table 3.4: Summary of model fitting parameters for BM and OU evolution on PC1 and PC2

of ecomorphology ...... 122

Table 3.5: Summary of adaptive peak shifts and convergence in Cichlinae functional

morphology ...... 131

Chapter Four ......

Table 4.1: Taxa assigned to each of four feeding categories for the purpose of fitting

evolutionary models to functional morphology...... 153

Table 4.2: Summary of CCA of diet and functional morphology in 41 species of Neotropical

cichlid, with and without phylogenetic correction...... 160

Table 4.3: Mean loading factor coefficients from phylogenetic principal component analyses

of functional morphology in 41 species of Neotropical cichlid ...... 166

Table 4.4: Summary of BM-OU model fitting results for PC1 (ram-suction morphology)

across 1000 chronograms ...... 168

Table 4.5: Summary of evolutionary rates (σ2) from BM-OU model fitting on PC1 (ram-

suction morphology) for each feeding regime across 1000 chronograms...... 169

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Table 4.6: Summary of adaptive peaks for feeding regimes from BM-OU model fitting on

PC1 (ram-suction morphology) across 1000 chronograms...... 170

Table 4.7: Summary of BM-OU model fitting results for PC2 (biting/crushing morphology)

across 1000 chronograms...... 172

Table 4.8: Summary of evolutionary rates (σ2) from BM-OU model fitting on PC2

(biting/crushing morphology) for each feeding regime across 1000 chronograms...... 173

Table 4.9: Summary of adaptive peaks for feeding regimes from BM-OU model fitting on

PC2 (biting/crushing morphology) across 1000 chronograms...... 174

Table 4.10: Summary of BM-OU model fitting results for functional evolution, based on

feeding specialization (high vs. low) across 1000 chronograms...... 179

Table 4.11: Summary of adaptive peaks from BM-OU model fitting of functional evolution

for the high (H) and low (L) feeding specialization groups across 1000 chronograms ...... 180

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List of Figures

General Introduction ......

Figure I.1: Cichlid ecological and evolutionary diversity ...... 7

Chapter One ......

Figure 1.1: MCC phylogeny of 75 Neotropical cichlids species used in the analysis of cichlid

feeding functional morphology ...... 18

Figure 1.2: Lower jaw biomechanics in Neotropical cichlids ...... 22

Figure 1.3: Bite occlusion patterns in 8 species of Neotropical cichlids with their associated

quadrate offset value ...... 23

Figure 1.4: Measurement points for the oral jaw and hyoid/neurocranium four-bar linkages in

Neotropical cichlids ...... 27

Figure 1.5: Functional Morphospace of 75 species of Neotropical cichlid species from a

phylogenetic principal component analysis of 10 functional morphological traits ...... 38

Figure 1.6: AICc values from 100 SURFACE analyses of Cichlinae functional morphology

from both the forward and backward phase ...... 39

Figure 1.7: Adaptive peaks in functional morphospace ...... 42

Figure 1.8: Results of SURFACE analyses carried out on the MCC chronogram and the first

two PC axis of functional morphology of Cichlinae ...... 43

Figure 1.9: Illustration of the number (Table 1.4, “c”) and pattern of adaptive peak shifts in

functional morphospace from the best supported Hansen model on the MCC chronogram .... 44

Figure 1.10: Adaptive peaks generated under univariate and multivariate model fitting ...... 46

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Figure 1.11: Density plots of functional disparity and lineage density of Cichlinae calculated

across 1000 posterior distribution chronograms ...... 48

Figure 1.12: Phylomorphospace of Cichlinae ...... 49

Chapter Two ......

Figure 2.1: Disparity-through-time curves for a continuous trait evolving across a simulated

phylogeny ...... 69

Figure 2.2: Stochastic character mapping reconstruction of Neotropical cichlid biogeography

for the analysis of functional morphological evolution ...... 71

Figure 2.3: Robust regression weights from node height tests for PC1 and PC2 ...... 75

Figure 2.4: Weights from robust regression analysis of evolutionary rate on relative age of

nodes (time) and biogeography (South America vs. Central America), for PC1 and PC2 ...... 76

Figure 2.5: Changes in evolutionary rates of ram-suction morphology in South and Central

America ...... 78

Figure 2.6: Summary of Node Height Tests of PC1 carried out across 1000 posterior

distribution chronograms ...... 79

Figure 2.7: Changes in evolutionary rates of biting/crushing morphology in South and

Central America...... 80

Figure 2.8: Summary of Node Height analysis of PC2 carried out across 1000 posterior

distribution chronograms ...... 81

Figure 2.9: Disparity-through-time analysis of PC1 (ram-suction morphology) and PC2

(biting/crushing morphology) across 75 species of Cichlinae ...... 84

Figure 2.10: Disparity-through-time analysis of PC1 (ram-suction morphology) and PC2

(biting/crushing morphology) across 48 species of primary South American cichlids ...... 85

Figure 2.11: Disparity-through-time analysis of PC1 (ram-suction morphology) and PC2

(biting/crushing morphology) across 21 species of Central American cichlids ...... 86

Chapter Three ......

Figure 3.1: One of 1000 chronograms used in the following analyses, with the locations of

fossil taxa (all non-contemporaneous tips) randomly sampled as outlined in Table 3.1 and

the methods described ...... 108

Figure 3.2: Phylogenetic canonical correlation analysis (axis 1 and 2) of functional

morphology and body shape in 74 species of Neotropical cichlids, summarized across

1000 posterior chronograms ...... 115

Figure 3.3: Phylogenetic canonical correlation analysis (axis 1 and 3) of functional

morphology and body shape in 74 species of Neotropical cichlids, summarized across

1000 posterior chronograms ...... 116

Figure 3.4: Phylogenetic principal component scores of ecomorphology in 74 species of

modern Neotropical cichlids, and 8 species of extinct, fossil Neotropical cichlids ...... 121

Figure 3.5: Analysis of morphological disparity in PC scores of ecomorphology in fossil and

extant species of Neotropical cichlids ...... 123

Figure 3.6: Convex hulls for extant and extinct, fossil Neotropical cichlid PC scores ...... 126

Figure 3.7: Analysis of morphospace occupation in fossil and extant species of Neotropical

cichlids ...... 126

Figure 3.8: Ecomorphological disparity of Gymnogeophagus ...... 128

Figure 3.8: Adaptive landscape of ecomorphology in modern and extinct Neotropical cichlids.131

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Chapter Four ......

Figure 4.1: Non-metric multidimensional scaling of Neotropical cichlid mean proportional

dietary composition ...... 158

Figure 4.2: Dendrogram of dietary data from 41 species of Neotropical cichlid ...... 159

Figure 4.3: Mean coefficients and scores from canonical correspondence analysis of dietary

composition and functional morphology ...... 162

Figure 4.4: Mean coefficients from canonical correspondence analysis of standardized

independent contrasts of diet and functional morphology ...... 163

Figure 4.5: Procrustes rotation of mean standard and mean phylogenetically-corrected CCA

diet (left) and functional morphology (right) coefficients ...... 165

Figure 4.6: Phylogenetic principal component scores of functional morphology for 41 species

of Neotropical cichlid ...... 168

Figure 4.7: Phenogram of feeding specialization in 41 species of Neotropical cichlids based

on the MCC chronogram ...... 178

Figure 4.8: Feeding specialization in functional morphospace of 41 Neotropical cichlid

species ...... 179

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List of Appendices

Chapter Three ......

Appendix 3.1: Linear morphometric values for 82 species of Neotropical cichlids ...... 139

Appendix 3.1: Biomechanical coefficients from eight extinct species of cichlid ...... 144

Chapter Four ......

Appendix 4.1: Mean volumetric proportional contribution of 12 prey categories to the

stomach contents of 41 species of cichlid ...... 188

xviii 1

I. General Introduction

I.1 Background

I.1.1 Morphology diversity

“The most curious fact is the perfect gradation in the size of the beaks in the different species of Geospiza, from one as large as that of a hawfinch to that of a chaffinch, and… even to that of a warbler… Seeing this gradation and diversity of structure in one small, intimately related group of birds, one might really fancy that from an original paucity of birds in this archipelago, one species had been take and modified for different ends.” – Darwin (1845)

The unequal distribution of morphological diversity, and its relationship to ecology, has fascinated naturalists, morphologists, and evolutionary biologists. Why do some clades produce a diversity of forms while closely related clades do not (Foote 1997; Sidlauskas 2008)? Why do some species adapt to novel ecological strategies compared to their relatives (Losos & Queiroz

1997; Martin & Wainwright 2011)? Evolutionary biologists have since sought to differentiate between adaptively neutral processes that may be responsible for the variety of forms, species and ecological strategies, such as vicariance (Hubbell 2001; McGill et al. 2006) or the accumulation of stochastic processes through time (Hubbell 2001; Collar et al. 2005), and those processes resulting from natural selection to particular biological or environmental conditions

(Collar et al. 2009, 2011; Price et al. 2011; Mahler et al. 2013; Davis et al. 2014). Simpson

(1944, 1953) postulated that “most evolution involves adaptation”, and emphasized the importance of changes in the rate and pattern of evolution. This theme of the “tempo and mode

2 in evolution” (Simpson 1944) has become a subject of considerable research in comparative methods and the study of morphological diversity, especially with improved computing resources, advances in phylogenetics (and associated molecular methods), and the more widespread use of open-source statistical software. For example, studies have sought to identify and associate increases in lineage or morphological diversification with the origin of specific adaptations regarded as “key innovations”, or with the development of ecological strategies

(Alfaro et al. 2009b; Collar et al. 2009, 2011; Price et al. 2010; Davis et al. 2014). Other research has focused on testing for convergence, via modelling adaptive optima in morphology, in ecomorphs across replicate island radiations (Mahler et al. 2013). These and a number of related macroevolutionary topics have expanded our understanding of the mechanisms driving the unequal distribution of diversity.

I.1.2 Functional morphology

One particularly useful topic in the study of morphological diversity is the analysis of functional morphology (Wainwright 2007). Functional morphology comprises the relationship between morphological form and functional output, through methodologies such as comparative anatomy, biomechanical modeling and experimental analysis of kinematics (Ashley-Ross and Gillis 2002).

As functional traits are more explicitly linked to performance capability, evolution in these traits can be more readily related to changes in ecology (Collar & Wainwright 2006; Wainwright

2007). For example, the jaws of most vertebrates, including the cichlid fish featured in this dissertation, operate as a single-lever system (Westneat 1995, 2003). Such systems possess an inherent trade-off in the transmission of force and velocity, which can be characterized by the

3 ratio of the in- and out-lever lengths (i.e., their mechanical advantage). As such, without additional morphological adaptations, an organism possessing such a lever system may have a very strong or very fast bite, but not both. Within fishes, such a trade-off has been correlated with the types of prey that may be consumed, and the mode of prey capture (e.g., Wainwright &

Richard, 1995; Wainwright, 1999). Trade-offs in such inherent functional properties also have significant consequences for the rate of morphological diversification (Holzman et al. 2012). As such, the evolution of such biomechanical systems may help to elucidate the patterns and relationship between ecological and morphological diversity across evolutionary history

(Wainwright et al. 2004; Collar & Wainwright 2006; Wainwright 2007; Collar et al. 2008, 2009;

Alfaro et al. 2009a).

I.1.3 Adaptive radiation and ecological opportunity

The relationship between species-richness, morphology and ecology may be no more evident than under a model of “adaptive radiation”, which is defined as the rapid divergence of lineages adapting to different ecological niches (Simpson 1953; Schluter 2000; Gavrilets & Losos 2009;

Yoder et al. 2010; Glor 2010). For example, the incredibly diverse African Rift Lake cichlids have diversified along axes of habitat (rock vs. sand) and trophic specialization within habitats

(algae scrapers, scale and eye biters, piscivores, molluscivores, among others) (Streelman &

Danley 2003). Under the “classic view of adaptive radiation”, ecological opportunity, the availability of ecological resources and niches, modulates the rate of speciation and morphological evolution (Gavrilets & Losos 2009). As lineages diverge, morphological traits are selected that better exploit available niches. However, as niches are filled by lineages, new

4 species are less likely to establish and lineages are less likely to adapt to different resources, leading to a reduction in the diversification of lineages, morphology and ecology. This framework of changing ecological opportunity through time makes specific predictions about the rates of evolution, and comparative methods have been developed to test these predictions

(Pybus & Harvey 2000; Harmon et al. 2003; Rabosky & Lovette 2008b, 2009; Slater et al. 2010;

Mahler et al. 2010; Slater & Pennell 2014).

Opinions on the scope and ubiquity of adaptive radiations differ, ranging from unique events in restricted clades, usually in “island” systems, to a process occurring on broad scales and responsible for much of the diversity of life (Simpson 1944, 1953; Schluter 2000; Glor

2010). Disagreement also exists in the literature on the defining features of adaptive radiation, such as “explosive” lineage diversification (McMahan et al. 2013), the development of novel ecological strategies (Martin & Wainwright 2011), the degree of ecological or phenotypic diversity (Losos & Mahler 2010) or patterns of decreasing diversification (Harmon et al. 2003,

2010; Slater et al. 2010; Mahler et al. 2010; Slater & Pennell 2014). Nevertheless, the adaptive radiation model has facilitated the development of a conceptual framework and statistical tools for addressing the link between ecological opportunity, adaptation and diversification, making it a powerful tool in the study of macroevolutionary divergence.

I.1.4 Study system – Neotropical cichlids

Cichlidae is one of the most diverse families of vertebrates. With 1600 described species, cichlids may exhibit some of the highest rates of lineage diversification among acanthomorph fishes (Near et al. 2013; McMahan et al. 2013). Cichlids have a Gondwanan distribution (Africa,

South America with a later colonization of Central America, India, Madagascar and the Middle

East), however their age and pattern of dispersal remains a contentious issue among acanthomorph biologists (Chakrabarty 2004; Sparks & Smith 2005; Smith et al. 2008; López-

Fernández & Albert 2011; Friedman et al. 2013; López-Fernández et al. 2013; Near et al. 2013;

McMahan et al. 2013; Malabarba et al. 2014). Fossil evidence indicates that cichlids have been diversifying in Africa and South America for at least ~40-50 Ma (Murray 2001; Malabarba et al.

2010, 2014). Some cichlids have been used as model systems for the study of speciation and adaptive radiation (Kocher 2004). The study of cichlid diversification has often focused on relatively recent lacustrine radiations, primarily in the African Rift Lakes, where patterns of resource partitioning and morphological adaptation have been used as a model for the patterns of ecological diversification in vertebrate radiations (Streelman & Danley 2003). However, recent years have seen an expansion in the study of the cichlid fauna of South and Central America especially in terms of lineage and morphological diversification.

Neotropical cichlids (subfamily Cichlinae), represent an estimated 600 species in ~60 genera, and are geographically more widespread, ranging from Patagonia to Texas, and likely considerably older than their more frequently studied and more species-rich counterparts in the

African Rift Lakes (López-Fernández et al. 2013; McMahan et al. 2013). While earlier morphological phylogenies placed the African taxon Heterochromis within the Neotropical clade, molecular phylogenies have since repeatedly demonstrated the monophyly of the “New

World” species, as a sister to the Pseudocrenilabrinae, which includes the Lake Victoria, Lake

Malawi and Lake Tanganyika radiations, as well as all African riverine cichlids (Kullander 1998;

Smith et al. 2008; López-Fernández et al. 2010; Ilves & López-Fernández 2014). Current taxonomic and phylogenetic assessments of Cichlinae have established seven tribes (Geophagini,

Heroini, Cichlasomatini, Cichlini, Astronotini, Retroculini and Chaetobranchini, Fig. I.1), with three major groups representing the majority of species diversity (Sparks & Smith 2004; Smith et al. 2008; López-Fernández et al. 2010, 2013; McMahan et al. 2010). The South American

Geophagini are the most species rich (~250 species in ~18 genera), and vary substantially in body shape and size (López-Fernández et al. 2012, 2013). Geophagini includes the two largest genera, Crenicichla (Fig. I.1, right), a clade of more than 90 species of elongate-bodied predators of fish and epibenthic invertebrates (Montaña & Winemiller 2009; Friðriksson et al. 2010), as well as Apistogramma, a group of 100 described species of extremely small-bodied invertebrate pickers with strong sexual dichromatism, and broadly distributed across South America.

Geophagini also includes a large number of substrate-sifting (“earth eating”) genera with convergent gill arch modifications (Fig. I.1, “sifters”; López-Fernández et al. 2005a, 2014). The second largest tribe, Heroini (~150 species in 30 genera), represents the only clade with substantial distributions in South and Central America, as well as one genus (Nandopsis) in Cuba and Hispaniola. Heroini is notable for both its ecological diversity as well as its convergence on body shapes and ecological specializations observed in older geophagin and cichlin lineages

(Cochran-Biederman & Winemiller 2010; López-Fernández et al. 2010, 2013; Winemiller et al.

1995). Cichlasomatini is a smaller (~70 species) but widely distributed clade that comprises moderately-sized, generalist taxa (Musilová et al. 2009; López-Fernández et al. 2010, 2013).

Other smaller tribes include a number of depauperate and often comparably basal lineages (Fig.

I.1, right). Chaetobranchini is a small clade comprised of two genera of planktivores,

Chaetobranchus (2 sp.) and Chaetobranchopsis (2 sp.), Astronotini is a clade of two species of generalist predators within Astronotus (2 sp.), and Retroculini represents three substrate-sifting species within Retroculus. Cichlini is represented by the single genus Cichla (15 species), the

7 peacock basses, a group of comparatively large bodied (nearly 1 m in body length), elongate piscivores (Willis et al. 2007; López-Fernández et al. 2012).

Fig. I.1: Cichlid ecological and evolutionary diversity. Left) Evolutionary relationships and biogeography of cichlid subfamilies and Neotropical cichlids tribes, from López-Fernández et al. (2013). Genera illustrated from top to bottom are: Uaru, Andinoacara, Astronotus, Crenicichla, Chaetobranchus, Cichla, Retroculus, Cyphotilapia, Paratilapia and Paretroplus. Right) Neotropical cichlid ecomorphs, with categories summarized from Winemiller et al. (1995) and Appendix 4.1. Image credits to: J. Arbour, H. López-Fernández and J. Slade, used with permission.

Cichlids represent one of the largest families of freshwater fish among the extremely diverse Neotropical fishes (>7000 species; Reis et al., 2003; Albert & Reis, 2011). Cichlids are common to most aquatic ecosystems in the Neotropics, with communities containing as many as

20 species occupying a variety of habitats and ecological roles (Lowe-McConnell 1991;

Winemiller et al. 1995; Cochran-Biederman & Winemiller 2010; López-Fernández et al. 2012).

Neotropical cichlids have long been the subject of ecomorphological studies (Norton & Brainerd

1993; Winemiller et al. 1995; Montaña & Winemiller 2010, 2013; López-Fernández et al. 2012), and have a number of features which makes them useful for studying phenotypic and functional diversification.

Cichlids show strong convergence in skull morphology with groups such as Centrarchids and Labrids, which have been used extensively as systems for developing and analyzing biomechanical models related to feeding performance (Westneat 1990, 1995; Wainwright et al.

2004; Carroll et al. 2004; Alfaro et al. 2005; Collar et al. 2005; Westneat et al. 2005; Carroll &

Wainwright 2006). Neotropical cichlids also exhibit an extreme diversity in body shape, ranging from nearly cylindrical (Crenicichla and Teleocichla) to disk-shaped and flattened

(Symphysodon and Pterophyllum), and size, ranging from Apistogramma staeki (21mm standard length) to Cichla temensis (990 mm standard length). Numerous Neotropical cichlid species exhibit specialized feeding ecology (piscivores, algivores, detritivores, molluscivores, invertivores, planktivores, etc.; Fig. I1) and behaviours (pickers, scrapers, crushers, sifters, pursuit predators). Many specialized feeding modes are convergent not only between South and

Central American cichlids, but also African lineages (Winemiller et al. 1995). For example, sediment sifters are found in South America (e.g., Geophagus and Gymnogeophagus) in Central

America (ex: Thorichthys and Astatheros robertsoni) and Africa (e.g., Tylochromis and

Lethrinops).

The comparably older divergence times in the Neotropics compared to the Rift Lake radiations in Africa have permitted for more consistent resolution of the phylogenetic relationships of the major lineages of Cichlinae (López-Fernández et al. 2005b, 2010, 2013;

Smith et al. 2008; Musilová et al. 2009; McMahan et al. 2013; Říčan et al. 2013). Neotropical cichlids are also more widely distributed than many more thoroughly studied systems of adaptive radiation, including the African Rift Lake cichlids (Seehausen 2006; Genner et al. 2007b),

Caribbean anoles (Losos et al. 1997; Mahler et al. 2010) and Hawaiian Silverswords (Baldwin

1997), and provide a direct point of comparison with the African Rift Lake radiations. Together, this diversity of forms and ecologies, the existence of reliable molecular phylogenies and the ability to apply well established models of fish feeding biomechanics make Cichlinae an ideal model for studying phenotypic diversification and adaptation.

I.2 Aims and scope

The purpose of this dissertation is to address how factors such as selection, adaptation, extinction and ecological opportunity shape functional diversification in Neotropical cichlids. Does selection across a complex adaptive landscape drive functional diversity in Neotropical cichlids?

Do rates of evolution in Cichlinae reflect changes in ecological opportunity though time? How do fossil cichlids contribute to our understanding of diversification in ecologically-relevant traits? Does adaptation or specialization to particular ecological roles influence functional diversification? I address these and related macroevolutionary topics using a suite of functional morphological traits measured across all major lineages of Cichlinae and through the application of modern phylogenetic comparative methods. These analyses will contribute to our understanding of how disparity and ecological diversity is generated across diverse assemblages,

10 and add to a growing body of literature addressing trait diversification across broadly distributed clades.

I.3 Overview of the Chapters

I.3.1 Chapter One

Chapter one investigates patterns of feeding functional variation and adaptive landscape dynamics in 75 species of Neotropical cichlids. Neotropical cichlids varied primarily along an axis of ram-suction morphology reflective of a trade-off between elongate and deep-bodied forms, similar to other acanthomorph radiations. A high degree of convergence among

Neotropical cichlids could be explained by the complexity of selective constraint in functional morphospace. Adaptive peaks tended to reflect groups of species with similar feeding ecology and behaviour, and an adaptive landscape model was predictive of the functional diversity of this group. I compare patterns of functional evolution with model systems of adaptive radiation and with convergent fish faunas such as centrarchids and labrids. Rapid evolution and complex patterns of selection contribute to the morphological and ecological diversity of cichlids.

I.3.2 Chapter Two

Chapter two addresses the relationship between ecological opportunity and functional diversification. Under a model of adaptive radiation, decreasing ecological opportunity leads to decreasing rates of morphological diversification (Gavrilets & Losos 2009; Mahler et al. 2010).

Additionally, the colonization of new territories may increase trait variation via ecological

11 release (Yoder et al. 2010). I test these hypotheses in the South and Central American radiations of cichlids using phylogenetic comparative methods. I found that along an axis of ram-suction morphology that the rate of diversification decreased through time, especially within South

America, and subsequently increased upon the colonization of Central America. Patterns of diversification in performance-related traits are consistent with 1) a continental adaptive radiation in South America, and 2) ecological release from basal cichlid lineages and possibly ostariophysan fishes upon the colonization of Central America.

I.3.3 Chapter Three

Chapter three examines the ecomorphological diversity of extinct, fossil lineages of Neotropical cichlids. Neotropical cichlids are represented in the fossil record ranging as far back as the

Eocene, including one species placed as a member of an extant genus (Malabarba et al. 2010). I examine morphological diversity and evolution in fossil cichlids using a combination of traditional linear morphometrics and feeding biomechanics, while accounting for the uncertainty associated with fossil phylogenetic positions. Fossil cichlids were as disparate as modern taxa after accounting for sample size and phylogenetic position, and were found to be evolving towards adaptive peaks occupied by modern taxa. †Gymnogeoghagus eocenicus was morphologically similar to its modern relatives, and such similarity was best explained by its phylogenetic position in this modern genus. Overall, ecomorphology in fossil cichlids emphasize the stability of macroevolutionary processes in Neotropical cichlids over tens of millions of years.

I.3.4 Chapter Four

Ecology and morphology are often correlated; however, even in classic systems of adaptive radiation ecology may influence morphological diversification in unexpected ways. Chapter four quantifies the relationship between feeding ecology and functional diversification among

Neotropical cichlids. Using dietary composition data from stomach content analysis compiled from previous studies for 41 species of cichlids I test for a relationship between feeding biomechanics and feeding ecology. I also test whether particular feeding roles or dietary specialization are related to changes in the tempo and mode of functional diversification.

Functional morphology and diet were significantly correlated both with and without phylogenetic correction, and these relationships were reflective of the primary axes of cichlid functional diversity. The relationship between feeding and function in Neotropical cichlids is characterized by trade-offs in diversification along axes of benthic and pelagic foraging and feeding specialization.

I.4 Contributions

Chapter 1 was published in the “Journal of Evolutionary Biology”. Chapters 2-4 will be submitted for publication in a form similar to that presented here and I will be the first author on all of these papers. Dietary data was provided by Carmen Montaña (North Carolina State

University), Kirk Winemiller (Texas A&M), Jennifer Cochran-Biedermann (Winona State

University) and Allison Pease (Texas Tech University). Access to fossil cichlid specimens was granted by Maria Claudia Malabarba (Universidade Federal do Rio Grande do Sul).

1. Chapter One

Adaptive landscape and functional diversity of Neotropical

cichlids: implications for the ecology and evolution of

Cichlinae (Cichlidae; Cichliformes)

Jessica Hilary Arbour1 and Hernán López-Fernández1,2

1 Department of Ecology and Evolutionary Biology, University of Toronto, 25 Wilcocks St.,

Toronto, Ontario M5S 3B2, Canada

2 Department of Natural History, Royal Ontario Museum, 100 Queen’s Park, Toronto, Ontario

M5S 2C6, Canada

Published As: Arbour, J. H. & López-Fernández, H. 2014 Adaptive landscape and functional diversity of Neotropical cichlids: implications for the ecology and evolution of Cichlinae

(Cichlidae; Cichliformes). Journal of Evolutionary Biology 27: 2431–42.

1.1 Abstract

Morphological, lineage and ecological diversity can vary substantially even among closely related lineages. Factors that influence morphological diversification, especially in functionally- relevant traits, can help to explain the modern distribution of disparity across phylogenies and communities. Multivariate axes of feeding functional morphology from 75 species of

Neotropical cichlid and a stepwise-AIC algorithm were used to estimate the adaptive landscape of functional morphospace in Cichlinae. Adaptive landscape complexity and convergence, as well as functional diversity of Cichlinae were compared with expectations under null evolutionary models. Neotropical cichlid feeding varied primarily between morphologies associated with ram-feeding vs. suction-feeding/biting, and secondarily with oral jaw muscle size and pharyngeal crushing capacity. The number of changes in selective regimes and the amount of convergence between lineages was higher than expected under a null model of evolution, but convergence was not higher than expected under a similarly complex adaptive landscape.

Functional disparity was compatible with an adaptive landscape model. The continentally- distributed Neotropical cichlids have evolved relatively rapidly towards a number of adaptive peaks in functional trait space. Selection in Cichlinae functional morphospace is more complex than expected under null evolutionary models. The complexity of selective constraints in feeding morphology has likely been a significant contributor to the diversity of feeding ecology in this clade.

1.2 Introduction

The evolution of morphological traits and their contribution to ecological and lineage diversification is a subject of increasing interest in explaining the geographic and phylogenetic distribution of biodiversity. Even closely related groups of species can differ dramatically in morphological diversity (Foote 1997; Collar et al. 2005; Sidlauskas 2008), and analyzing patterns of morphological evolution can be extremely useful in our understanding of what processes drive these differences in diversity. For example, morphological convergence among replicate “island” radiations such as those observed in Anolis lizards and African Rift Lake cichlids, has been used to examine factors such as determinism and historical contingency in adaptive evolution (Cooper et al. 2010; Losos 2010). Functional traits, in particular, can be extremely informative for examining factors driving ecological diversification, as they link morphological adaptation to ecological performance (Wainwright 2007).

While it is expected that clades, especially of different ages, can differ in functional and morphological diversity as a result of random processes or time since divergence, factors such as selective constraints (Hansen 1997), innovations leading to evolutionary rate shifts (Alfaro et al.

2009b), competitive exclusion and ecological opportunity (Losos 2010; Mahler et al. 2010), among others, can influence the rate and extent to which disparity accumulates. For example, under a random-walk evolutionary process (i.e., Brownian motion evolution) variance in a continuous trait is expected to increase with time, and therefore older clades are expected to show greater disparity than younger ones (Garland 1992). However, when morphological traits evolve under selective constraint towards an optimum value, clades of varying ages may show much more similar (or equal) disparity. Since functional morphology is so strongly linked to

16 ecological characteristics, the rate and mode of functional evolution can be a determining factor in the extent and pattern of ecological diversification.

A macroevolutionary adaptive landscape, in which changes in phenotypic characters are driven by selection towards local adaptive optima, has long been used as a concept to explain convergence in phenotype (Simpson 1944, 1953; Ingram & Mahler 2013; Mahler et al. 2013).

Even under a random walk process, some species are likely to evolve towards similar trait values

(Stayton 2008). However, to understand adaptation and its link to ecology, it is important to separate those species that have evolved similar traits at random, from those that have evolved towards similar adaptive regimes. Recent developments in phylogenetic comparative methods have introduced a stepwise algorithm for estimating a multivariate phenotypic adaptive landscape (Ingram & Mahler 2013). These adaptive landscape methods have been used to investigate convergence in Anolis ecomorphs across islands (Mahler et al. 2013), adaptive evolution in body shape across geographic regions in rockfish (Ingram & Kai 2014) and antwrens (Bravo et al. 2014), and divergent selection of swimming morphology in geophagin cichlids (Astudillo-Clavijo et al., In Review). Applying adaptive landscape estimation methods to radiations of varying sizes and geographic ranges will help us to examine factors that influence the distribution of morphological diversity. The objective of this chapter is to characterize the feeding functional morphospace of Neotropical cichlids, estimate its adaptive landscape, and examine how factors such as selection and convergence have influenced Cichlinae functional diversity.

1.3 Methods

1.3.1 Phylogeny and taxonomic sampling

All measurements described in this chapter were obtained from 1 to 6 preserved specimens for each of 75 species (Fig. 1.1), representing all seven tribes and all but four of the currently described Cichlinae genera (Tahuantinsuyoa, Chaetobranchopsis, Tomocichla and the recently described Nosferatu). The 75 species include 26 from Geophagini, 10 from Cichlasomatini and

34 from Heroini, two from Cichlini (Cichla ocellaris and Cichla temensis), one from Retroculini

(Retroculus lapidifer), one from Astronotini (Astronotus ocellatus) and one from

Chaetobranchini (Chaetobranchus flavescens). Most species measured in the current dataset match those used in previous phylogenetic analysis or are single representatives of a described genus (López-Fernández et al. 2010, 2013). Two exceptions are Crenicichla saxatilis and Cichla ocellaris, which replaced Crenicichla sveni and Cichla monoculus respectively, and belong to the same species group within each genus as the taxa in the original phylogenetic analysis (Willis et al. 2007; Kullander et al. 2009).

All comparative analyses within this and subsequent chapters were based on a time- calibrated molecular phylogeny from López-Fernández et al. (2010 and 2013) based on 3868 aligned base pairs of DNA sequences from two nuclear and three mitochondrial genes of 154

Neotropical cichlid species and several old-world cichlid outgroup taxa, with three fossil calibrations (Fig. 1.1, Maximum Clade Credibility, MCC, chronogram). A distribution of 1000 randomly-sampled chronograms from the posterior distribution of the MCMC search in BEAST

Fig. 1.1: MCC phylogeny of 75 Neotropical cichlids species used in the analysis of cichlid feeding functional morphology. Coloured boxes indicate species in the tribes Geophagini (dark blue), Cichlasomatini (orange), Heroini (green), Astronotini (grey), Chaetobranchini (light blue), Cichlini (yellow) and Retroculini (purple). Red text indicates heroin species from Central America. Abbreviations to the right of the phylogeny correspond to those used in all subsequent figures.

(Drummond & Rambaut 2007) were used to quantify uncertainty associated with branch-length and topological variation.

While the divergence times of Neotropical cichlids are likely to vary from that used here

(López-Fernández et al. 2013) with increasing molecular, taxonomic and fossil sampling, this remains the most comprehensively sampled multi-locus phylogeny for Cichlinae. The majority of analyses presented in this and subsequent chapters are based on the chronograms scaled to relative time, and while variation in internal branch lengths may vary somewhat with future phylogenies, it is unlikely to bias our results (e.g., higher or lower convergence) compared to any other current phylogenetic hypothesis of cichlid evolutionary relationships (Friedman et al. 2013;

McMahan et al. 2013; Říčan et al. 2013).

1.3.2 Measuring Cichlinae functional morphology

Variation in feeding function was quantified using a series of ten biomechanical variables and functional traits and included 1) adductor mandibulae mass (AM mass), 2) sternohyoideus mass

(ST mass), 3) lower jaw closing mechanical advantage (lower jaw MA) 4) lower jaw opening mechanical advantage, 5) bite occlusion via quadrate offset, 6) maximum jaw protrusion, 7) fifth ceratobranchial mass (CB5), 8) oral jaw four-bar linkage kinematic transmission coefficient, 9) hyoid/neurocranium four-bar linkage kinematic transmission coefficient, 10) suction index.

These variables quantified the production and distribution of force, and the transmission of force and movement, through the oral jaws, pharyngeal jaws and expansion of the buccal cavity during suction feeding, and are described in detail in the following paragraphs. All functional measurements were means of three measurements from each specimen to minimize measurement

20 error. Data associated with this chapter and the resulting publication (Arbour & López-

Fernández 2014) have been archived through the online repository “Dryad”

(doi:10.5061/dryad.j04r6).

1.3.2.1 Muscle masses

Force production by muscles is directly proportional to the physiological cross-sectional area, which is determined as , with θ being the angle between pennation and major muscle tendon (direction of action). If structural variation in muscle is reasonably low, force production will thus tend to scale with muscle mass2/3 (Wainwright et al. 2004), and in other vertebrates muscle mass has been shown to be a strong predictor of bite force. Across several species of bats exhibiting overall high cranial facial diversity, in vivo bite force was well correlated with jaw muscle mass, and jaw muscle mass had higher explanatory power than fibre length (explained 63% vs 13% of variation in bite force; Herrel et al. 2008). Jaw muscle mass and bite force were also strongly correlated in a study of 36 species of finches (r = 0.95; Van der

Meij and Bout 2004). The mass of the major jaw opening and closing muscles was used as a proxy for force production during biting and oral jaw/buccal expansion (Wainwright et al. 2004;

Collar et al. 2009). The major jaw closing muscle group in cichlids is the adductor mandibulae complex, which directly attaches to the lower jaw, primarily along the coronoid process of the articular. The sternohyoideus is the primary jaw opening muscle, and drives buccal expansion by direct depression of the hyoid as well as indirectly acts to rotate the lower jaw via the interopercular-mandibular ligament.

1.3.2.2 Lower jaw lever mechanics

Force and movement produced by the facial muscles are transmitted with varying efficiency into jaw movement and biting force. The lever property know as mechanical advantage (MA) describes the transmission efficiency of force and velocity in simple lever systems such as the lower jaw of cichlids and structurally similar fishes (Wainwright et al. 2004; Wainwright 2007;

Hulsey et al. 2010b). Mechanical advantage (MA) is calculated as the ratio between the in-lever length to the out-lever length. Output force scales with MA whereas output velocity scales with the inverse of MA (Wainwright 1999). The mechanical advantage of the lower jaw of fishes has been shown to have significant performance and ecological consequences, with ram feeders

(those fish that accelerate their body to overtake and engulf prey) and suction feeders (those species that use a pressure gradient to draw food into the buccal cavity with limited forward body movement) exhibiting consistently lower MA (velocity optimized) than oral-manipulators

(species using biting, nipping or shearing movements of the jaws), which require higher bite force (Wainwright & Richard 1995; Wainwright 1999).

The out-lever length for all lower jaw MAs was the distance between the quadrate- articular joint and the tip of the anteriormost tooth (Fig. 1.2, left). The lower jaw closing in-lever was measured as distance of the midpoint of the A2 attachment (on the coronoid process of the articular) to the quadrate-articular joint, and the out-lever as the distance of the joint to the anteriormost tooth (Fig. 1.2, left). The lower jaw opening in-lever was measured as the distance between the interopercular-mandibular ligament attachment on the angular to the quadrate- articular joint (Fig. 1.2, left).

Fig. 1.2: Lower jaw biomechanics in Neotropical cichlids. Left: lever lengths used in the calculation of lower jaw opening and closing mechanical advantage, as taken from ‘Cichlasoma’ urophthalmus. Right: measurement of quadrate offset as the distance from the lower jaw fulcrum, perpendicular to a line passing through the dentition, as taken from ‘Cichlasoma’ salvini.

1.3.2.3 Bite occlusion

Patterns of bite occlusion can help to quantify how force is distributed and directed during biting.

For example: species with more even jaw occlusion are able to distribute force over the length of the jaw (and the tooth row), and the force vectors during biting are better aligned for gripping or crushing (Ramsay & Wilga 2007; Anderson 2009). Species with less even jaw occlusion have jaws that contact progressively from the posterior to the anterior (or more rarely the reverse, such as in Squalus acanthius (see Ramsay & Wilga 2007), which concentrates pressure over a smaller region of the jaws and can be useful for shearing. Jaw occlusion can be quantified by measuring quadrate offset, which is calculated as the perpendicular distance from the tangent line of the tooth row, to the quadrate-articular joint (Fig. 1.2, right), divided by the length of the lower jaw

(Anderson 2009; Arbour & López-Fernández 2013).

Neotropical cichlids (and likely many other structurally similar fishes such as labrids and centrarchids) tend to have jaw joints which are offset below the tangent of the tooth row, unlike systems in which quadrate offset has been previously used to quantify bite occlusion. Among

Neotropical cichlids, species with a higher quadrate offset (the joint is more ventral to the tooth row) were observed to possess more un-even jaw occlusion and greater lower and upper jaw overlap, during biting (Fig. 1.3, bottom row), while those with the jaw joint closer to the tangent of the tooth row (lower quadrate offset) had more even jaw occlusion (Fig. 1.3, top row).

Fig 1.3: Bite occlusion patterns in 8 species of Neotropical cichlids with their associated quadrate offset value. The angle of jaw occlusion increases steadily with increasing quadrate offset. Taxa illustrated from left to right are: (top row) Crenicichla sp. “Orinoco-wallaci”, Acaronia nassa, Petenia splendida, ‘Geophagus’ steindachneri, (bottom row) Andinoacara rivulatus, Uaru amphicanthoides, Symphysodon aequifasciatus and Herotilapia mutlispinosa.

1.3.2.4 Jaw protrusion

The ability to protrude the upper jaw has been suggested to have contributed to the trophic diversity of many teleost lineages (Schaeffer & Rosen 1961; Lauder 1982; Motta 1984). Jaw protrusion may aid in prey capture by increasing ram distance (starting distance from fish to prey) and velocity (Wainwright et al. 2001; Waltzek & Wainwright 2003). Jaw protrusion may also benefit feeding by increasing fluid acceleration around prey during suction feeding

(Holzman et al. 2008). Jaw protrusion has been linked to ecological performance, for example; in Heroin cichlids, jaw protrusion has been correlated with the amount of evasive prey items

(fish and crustaceans) in a species diet (Hulsey & Garcia De Leon 2005). I quantified jaw protrusion by measuring the distance between the posterior margin of the orbit and the tip of the anterior-most tooth on the pre-maxilla with the jaws closed and again after rotating the lower jaw until the oral jaws were in their maximally protruded position (Hulsey & Garcia De Leon 2005).

1.3.2.5 Lower pharyngeal jaw mass

Cichlids are among several families of fishes which possess internal gill arches that have been modified into a secondary set of jaws (Liem 1973). Cichlids also belong to one of at least two disparate clades that have evolved convergent, highly-specialized pharyngeal jaws that share

(among other features) fused fifth ceratobranchials (CB5s) that form a lower jaw plate.

Assuming that structural variation is low, the compressive strength of bone tends to scale with its mass (Currey 1984; Turingan et al. 1995). Therefore, more massive CB5s are likely better able to resist the forces produced when crushing hard prey (Hulsey et al. 2006). The mass of the

CB5s for each specimen examined was obtained and used as an indicator for the ability to consume hard food items.

1.3.2.6 Kinematic transmission coefficients

Four-bar linkages are complex lever systems composed of four joined elements, which possess the property that rotation of one link (compared to a fixed link) results in a known rotation of all other links in the system (Suh & Radcliffe 1978; Muller 1987, 1996; Westneat 1990; Alfaro et al. 2009a). The kinematic transmission coefficient (KT), a mechanical property of four-bar linkages, is the ratio between the rotation of an output link compared to the rotation of an input link. Similar to simple, single-lever systems, there is an inherent trade-off between the transmission of force and velocity in four-bar linkages (Wainwright et al. 2004). High KT values are associated with efficient velocity transmission, where as low KT values are associated with efficient force transmission.

At least two systems within the cichlid feeding apparatus can be modelled as four-bar linkages: the oral jaw four-bar linkage and the hyoid (/neurocranium) four-bar linkage. In the oral jaw four-bar linkage, rotation of the lower jaw is transmitted to rotation of the maxilla and nasal bones, which protrudes the pre-maxilla (Wainwright et al. 2004; Hulsey & Garcia De Leon

2005). The lengths of the oral jaw links were measured as follows (and see fig. 1.4, left): 1) the fixed link was the distance between the quadrate-articular joint to the attachment of the nasal on the neurocranium, 2) the input link was the distance between the quadrate-articular joint and the ligamentous connection between the maxilla and the dentary, 3) the ouput link was the distance between the maxilla-dentary connection and the ligamentous connection between the maxilla and

26 nasal bones 4) the coupler link was the length of the nasal (from its attachment points on the maxilla and neurocranium respectively). Output rotation for oral jaw KT was calculated as the rotation of the output link compared to the coupler link following a 30º rotation of the lower jaw from a closed mouth position (i.e., input link compared to the fixed link; Wainwright et al.

2004).

The hyoid four-bar linkage has been shown to accurately predict hyoid depression as a function of dorsal rotation of the neurocranium by the epaxials and contraction of the sternohyoideus (Westneat 1990). Thus the hyoid four-bar linkage KT quantifies the trade-off of force and velocity transmission in dorso-ventral expansion of the buccal cavity. The lengths of the hyoid four-bar linkage were measured as follows (and see Fig. 1.4, right): 1) the fixed link was the distance between the post-temporal–supracleithrum joint (as the major axis of neurocranium elevation following Carroll et al. 2004) and the ventral tip of the cleithrum, 2) the output link was the distance from the ventral tip of the cleithrum to the connection of the hyohyal and urohyal 3) the coupler link was the length of the hyoid bar from the connection of the urohyal and hyohyal to the joint between the interhyal and hyomandibula 4) the input link was the distance from the interhyal-hyomandibula joint to the post-temporal–supracleithrum. Output rotation for hyoid KT was calculated as the rotation of the output link compared to the fixed link following a 5º dorsal rotation of the input link (neurocranium) relative to the fixed link and a shortening of the output link by 10% to account for contraction of the sternohyoideus muscle during hyoid depression (Muller 1987; Westneat 1990; Wainwright et al. 2004).

Fig. 1.4: Measurement points for the oral jaw (left) and hyoid/neurocranium (right) four-bar linkages in Neotropical cichlids. Labels refer to the names of links as described in the text. Taxa illustrated are Mazarunia charadrica (left) and Pterophyllum scalare (right)

1.3.2.7 Suction index

Suction feeding is one of the primary mechanisms of prey capture in fish (Lauder 1980). Suction index (see equation below) was used to measure variation in suction feeding ability among

Neotropical cichlids (Carroll et al. 2004; Carroll & Wainwright 2006; Wainwright et al. 2007).

Suction index quantifies the ability of a fish to transmit force generated by the epaxial muscles, across the post-temporal–supracleithrum (PT-S) joint, to expansion of the buccal cavity via dorsal neurocranium elevation, and has been shown to accurately predict maximum suction pressure in several centrarchid species (Carroll et al. 2004). Force production potential by the epaxial muscles was inferred by estimating the cross-sectional area of the muscle directly above the PT-S joint (CSAepx). The area of the buccal cavity over which the force of the epaxial

28 muscles is projected was calculated as gape width * buccal length (measured along the roof of the mouth from the pre-maxilla to in-line with the posterior-most basibranchial). As maximum suction force may occur before the buccal cavity is fully expanded, the buccal area was scaled by

0.67 following Carroll et al. (2004). The mechanical advantage of epaxial rotation of the neurocranium (MAPT-S) was calculated as the ratio between the epaxial in-lever to the buccal out- lever. In-lever length was the vertical distance from the PT-S joint to the centroid of the epaxial cross section and out-lever length was the distance from the PT-S joint to the mid-point of buccal length.

1.3.3 Cichlinae functional morphospace

All muscle and bone masses were cube-rooted and subsequently all size-dependent variables

(AM mass, ST mass, CB5 mass and Jaw Protrusion) were log-transformed and regressed on log

( ) (Wainwright et al. 2004) using phylogenetic size-correction (function

"phyl.resid" in R package "phytools"; Revell 2009, 2012). The residuals of these regressions were used in all subsequent analyses. The major axes of variation in Neotropical cichlid functional morphology were summarized using phylogenetically-corrected principal component analysis (function “phyl.pca” from R package “phytools”; Revell 2009, 2012). The principal component (PC) analysis used a correlation matrix (see Table 1.2) to account for the different

29 scales at which variables were measured (e.g. ratios and masses). The number of critical PC axes was determined using average parallel analysis (Horn 1965), which has been previously shown to produce reliable estimates for the number of relevant axes (Peres-Neto et al. 2005), and was computationally simple to carry out over a distribution of phylogenies. Parallel analysis provides cutoff values for critical eigenvalues based on a normally-distributed, random dataset with the same dimensions as the observed data, and accounts for the fact that even randomly distributed data will have components with eigenvalues >1 (i.e., the Kaiser-Guttman criterion).

Phylogenetic-correction increased the variation explained by the first axes of randomized data compared to standard (non-phylogenetically-corrected) parallel analysis. Therefore, transformation by branch-lengths during phylogenetic-correction imparts the appearance of more structure to randomly distributed values, and therefore a phylogenetic correction (by using phylogenetic PCA to calculate cutoff values) was incorporated into the determination of critical axes to avoid the retention of trivial axes.

1.3.4 Estimating an adaptive landscape of functional morphology

A step-wise model-fitting approach was implemented to estimate an adaptive landscape for feeding functional morphology across the 75 species of Neotropical cichlids examined. A

Brownian motion (BM) model represents a random-walk evolutionary process under a constant rate of evolution (parameter σ2), while an Ornstein-Uhlenbeck (OU) process models a random- walk under a constant rate, but also incorporates a parameter for the strength of selection towards an adaptive peak (α) (Hansen 1997; Collar et al. 2009, 2011). Complex OU models, often referred to as Hansen models, can allow different selective regimes to be “painted” on different

30 parts of a phylogeny for model-fitting purposes (Hansen & Martins 1996; Butler & King 2004).

Ingram & Mahler (2013) developed a function that uses a stepwise Akaike Information Criterion

(AIC) procedure to fit Hansen models (without an a priori hypothesis regarding trait space) to estimate a multi-dimensional adaptive landscape in trait space. The procedure “SURFACE” is comprised of a “forward” search phase that sequentially adds adaptive peaks to the phylogeny, and a “backward” phase that collapses similar peaks together if this improves the fit (based on

AIC) of the Hansen model. In these analyses, the forward phase begins with a model including a single adaptive peak, and maximum likelihood model fitting is used to solve for the rate of evolution (σ2), the selection parameter (α), and the optimal trait value (θ) (Butler & King 2004;

Ingram & Mahler 2013). During each “step” in the analysis a single new adaptive regime is added, with all candidate positions (nodes) in the phylogeny compared using sample-size corrected AIC (Ingram & Mahler 2013). The addition of adaptive peaks fits additional θs (the location of the adaptive peaks), but does not fit different evolutionary rates or selective constraint per peak, which can currently only be implemented in a hypothesis-testing framework (ex: see

“OUwie” package in R; Beaulieu & O’Meara 2013). I applied the function “runSurface” from the R package “surface” (Ingram & Mahler 2013; Ingram 2014), on the MCC chronogram and associated phylogenetically-corrected PC scores. Algorithm-path dependence limited the selection of well-supported models when only the best fitting model was chosen at each step of the forward phase (Mahler et al. 2013). Therefore, the phylogenetic location of the new adaptive peak was randomly sampled from all peak shifts with ΔAICc < 2 compared to the absolute best- fitting model at each step (Burnham & Anderson 2002), using the ‘sample_shifts = TRUE’ option (Ingram 2014). Following Mahler et al. (2013), a distribution of Hansen models with

ΔAICc up to 10 was used to examine uncertainty in the location of peak shifts and extent of convergence.

I measured the extent of adaptive peak shifts and convergence (Table 1.1) following

Ingram & Mahler (2013). In the following results and discussion, convergence in the SURFACE model (c, Table 1.1) is quantified as described by Ingram & Mahler (2013) and Mahler et al.

(2013), i.e., evolution towards the same adaptive peak. This differentiates between convergence resulting from deterministic adaptation to specific ecological conditions, and convergence occurring by chance under simple random-walk processes (Stayton 2008). To determine to what extent convergence in the adaptive landscape of functional morphology in Neotropical cichlids could have occurred by chance under a non-convergent process (i.e., all adaptive peaks have each only been colonized by one lineage) character histories were simulated 99 times under two null models of evolution (Ingram & Mahler 2013; Mahler et al. 2013). The first null model was a single peak OU process, and therefore did not include adaptive peak shifts or convergence. The second model was a Hansen model with the same number of adaptive peaks as the end of the forward phase of SURFACE from the best supported Hansen model, and therefore included peak shifts but assumed no convergence between lineages. Convergence summary statistics (see

Ingram & Mahler 2013, and Table 1.1) were determined from each of the 99 simulations for each null model and the significance of the observed results were determined as the frequency of combined simulated and observed values greater than or equal to that of the best supported

Hansen model (Ingram & Mahler 2013; Mahler et al. 2013).

Table 1.1: Summary statistics measuring adaptive peaks and convergence in SURFACE analyses.

Variable Description k The number of adaptive peak shifts (forward phase of SURFACE) k’ The number of unique peaks after collapsing all similar peaks (backward phase of SURFACE) Δk k-k’, the reduction in landscape complexity after accounting for convergence k’conv The number of peaks reached by independent lineages c The number of shifts to convergent peaks c/k The proportion of adaptive peak shifts that are to convergent peaks

For computational reasons, SURFACE independently estimates model parameters for each trait axis provided. To verify that the results of SURFACE are consistent with those estimated under a multivariate evolutionary model (Bartoszek et al. 2012), the R package “mvMORPH” (Clavel et al. 2014) was used to fit BM, single-peak OU, and both the forward (non-convergent) and backward (convergent) phases of SURFACE based on the number and phylogenetic position of adaptive peak shifts estimated in the best-supported model.

1.3.5 Functional disparity and phylomorphospace analyses

Multivariate functional disparity of Cichlinae was calculated using mean squared pairwise

Euclidean distances between species PC scores, using the function “disparity” from the R package “geiger” (Harmon et al. 2008). The distribution of morphological evolution was also determined using lineage density following Sidlauskas (2008) and Arbour & López-Fernández

(2013), which summarizes the efficiency of morphospace expansion by a clade. The total of all morphometric branch lengths (distances between nodes in morphospace, see Fig. 1.12) for each clade was compared to the volume of morphospace it occupied (LD = total distance/volume), based on the volume of convex hulls surrounding all principal component scores.

I tested whether the observed functional disparity and lineage density was different from that expected under BM, OU and adaptive landscape (Hansen model) evolution. BM and OU models were fit for each axis individually using the maximum likelihood function

“fitContinuous” in the R package “geiger” (Harmon et al. 2008). Character histories were generated under an adaptive landscape framework, using the best fit Hansen model from the

SURFACE analysis described above. For each of the 1000 posterior distribution chronograms,

99 simulated character histories were generated for each model (BM, OU and Hansen), using the functions “sim.char” and “rescale.phylo” from R packages “geiger” and “ape” (for BM and OU models), and ‘surfaceSimulate’ from package “surface” (for Hansen models). Functional disparity and lineage density was calculated for each of these simulated character histories and compared to the observed values for a given chronogram. For a given model, I tested whether the observed value fell (on average across all chronograms) within the upper or lower 2.5% percentiles of the combined simulated and observed values. The p-value for these tests was calculated as twice the frequency (two-tailed test, alpha = 0.05) of values in this combined set that were either ≥ observed value (if obs was in the lower tail) or ≤ observed value (if obs was in the upper tail).

1.4 Results

1.4.1 Functional morphospace of Cichlinae

Based on phylogenetically-correlated regressions (using “phyl.resid”, package phytools), several significant correlations were observed between functional elements of the Neotropical cichlid feeding apparatus. In particular, suction index was associated with a number of other functional variables. Suction index was negatively correlated with jaw protrusion, AM mass and CB5 mass, and positively correlated with both lower jaw closing MA and quadrate offset (Table 1.2). The two lower jaw mechanical advantages were strongly and significantly positively correlated (r =

0.452), while lower jaw closing mechanical advantage showed a very high correlation (r = 0.785) with quadrate offset.

Phylogenetically-corrected principal component analysis revealed two critical axes of variation in Cichlinae functional morphology, explaining 34.1% and 15.2% of functional variation, respectively (Table 1.3). The first principal component (PC1) was most strongly loaded by suction index, quadrate offset and lower jaw closing MA (Fig. 1.5 and Table 1.3). PC1 was moderately influenced by lower jaw opening MA (+), maximum jaw protrusion (-), hyoid

KT (-), AM mass (-), and CB5 mass (-). Those fish on the negative extreme of PC1 (e.g.

Crenicichla multispinosa, ‘Cichlasoma’ salvini and Cichla ocellaris) possessed fast lower jaw opening and closing, were poor at transmitting force during these movements, but had proportionately higher force production for the oral jaws (Fig. 1.5). Those fish on the positive extreme of PC1 had higher suction potential, lower velocity transmission in lower jaw movements, and produced less force but were more efficient at transmitting it to biting and oral jaw opening. Both Geophagini and Heroini included taxa with scores on the negative extreme of

PC1 (Fig. 1, blue and green respectively), however, Heroini largely dominated the positive extreme of PC1. Velocity-optimized heroins were predominantly from Central America and the

Greater Antilles, such as Parachromis spp., Petenia splendida and Nandopsis spp. (Fig. 1.5).

Cichlasomatini occupied a narrow region of morphospace along PC1 with the exception of two taxa: Acaronia nassa and Cleithracara maronii. PC2 was strongly loaded by both AM and ST mass, and moderately loaded by CB5 mass (Table 1.3). Proportional to body size, bite force production (although not necessarily transmission) and pharyngeal crushing potential was maximized for those species with negative values on PC1 (high CB5 mass) and PC2 (high AM and CB5 mass), such as Parachromis friedrichsthalii and Nandopsis haitiensis (Central

American and Caribbean heroins, respectively). Suction ability was optimized for taxa with positive scores on PC1 and negative scores on PC2 (high suction index and ST mass).

Table 1.2: Phylogenetically-corrected correlation matrix (Pearson’s correlation coefficients) of 10 functional morphological variables measured from 75 Neotropical cichlid species, summarized across 1000 posterior distribution trees. Bold values indicate significant correlations

after Bonferroni correction (α = 0.0011).

ST Mass ST

AM Mass AM

Hyoid KT Hyoid

CB5 Mass CB5

Lower Jaw Jaw Lower Jaw Lower

Closing MA Closing

Opening MA Opening

Oral Jaw KT Jaw Oral

Jaw Protrusion Jaw Quadrate Offset Quadrate Jaw Protrusion AM Mass 0.026 ST Mass -0.021 0.388 CB5 Mass 0.047 0.460 -0.005 Lower Jaw -0.302 -0.272 0.088 -0.242 Closing MA Lower Jaw -0.171 -0.082 -0.004 0.020 0.452 Opening MA Quadrate Offset -0.227 -0.31 0.079 -0.390 0.785 0.366 Hyoid KT 0.164 0.123 0.123 0.032 -0.171 -0.208 -0.29 Oral Jaw KT -0.025 0.097 0.033 -0.096 -0.026 -0.206 0.122 -0.242 Suction Index -0.403 -0.430 0.257 -0.527 0.597 0.329 0.592 -0.264 -0.092

Table 1.3: Loading factor coefficients for the critical axes of variation in functional morphology across 75 cichlids species. Coefficients were averaged across 1000 posterior distribution trees. Bold values indicate the strongest loadings on each axis.

PC1 PC2 Percent Variation Explained 34.1% 15.2%

Jaw Protrusion -0.571 0.270 Adductor Mandibulae Mass -0.525 -0.670 Sternohyoideus Mass 0.076 -0.620 Fifth Ceratobranchial Mass -0.508 -0.497 Lower Jaw Closing MA 0.825 -0.191 Lower Jaw Opening MA 0.515 -0.335 Quadrate Offset 0.838 -0.022 Hyoid KT -0.377 -0.043 Oral Jaw KT 0.004 0.131 Suction Index 0.861 -0.066

(Next Page) Fig. 1.5: Functional morphospace of 75 species of Neotropical cichlid species from a phylogenetic principal component analysis of 10 functional morphological traits. Points show PC1 and PC2 scores averaged over 1000 posterior distribution trees (blue: Geophagini, green: Heroini, orange: Cichlasomatini, black: Retroculini, Chaetobranchini, Cichlini and Astronotini). Black labels: South American taxa, red labels = Central American taxa. Abbreviations correspond to those listed in Fig. 1.1. Image Credits to J. Arbour, H. Lopez-Fernandez and K. Alofs.

39 1.4.2 Adaptive landscape of functional morphology

Model parameters for each axis from the single best-fit model (and calculated over the MCC

2 2 chronogram scaled to a total length of 1) were: evolutionary rates - σ PC1= 7.30, σ PC2= 242.22, and 2) selective constraint - αPC1= 8.12, αPC2= 323.03. A total of 53 of 100 Hansen models generated by randomly-sampling between all well-supported peak shifts (see methods) were found to be within 10 AICc units of the best-fitting model, and were used to assess uncertainty in peak shifts. All 53 best-supported Hansen models estimated 7 adaptive peaks (Table 1.3, k; Fig.

1.6 and 1.7), with 3 peaks including convergent shifts in the best-supported model (Fig. 1.9).

Fig. 1.6: AICc values from 100 SURFACE analyses of Cichlinae functional morphology (solid lines) from both the forward (increasing numbers of adaptive peaks) and backward phase (decreasing numbers of adaptive peaks). Dashed lines show the AIC value of a single rate BM processes (no selective constraint) and a single peak OU process.

The ancestral adaptive regime was largely reconstructed as peak 1, with heroins later diversifying to peak 4, or as peak 4 with geophagins + chaetobranchins having later shifted to peak 1 (Fig. 1.8, see pie charts on nodes). Peak 5 always included the South American genera

Uaru, Mesonauta, Heros and Pterophyllum, and in a few models (including the best supported model) also the Central American species Cryptoheros chetumalensis (Fig 1.7 and 1.8). Peak 2, which represented the most negatively positioned peak on PC1 (i.e., the most velocity-optimized and suction poor peak), was colonized by two South American lineages Cichla and Crenicichla, across all best supported models (Fig. 1.7 and 1.8). Peak 7, the most positive on PC1 (high suction-feeding potential, but poor velocity transmission), was unique to the member of the

“Discus fish” genus, Symphysodon aequifasciatus, across all models (Fig. 1.8). A shift to adaptive peak 3 occurred either at the origin of the geophagin clade formed by Biotodoma-

Dicrossus-Crenicara (Fig. 1.8), or in 13 of 53 Hansen models occurred at the origin of the

Geophagus-‘Geophagus’-Dicrossus (GGD) clade sensu Arbour & López-Fernández (2013), thus also including Geophagus, ‘Geophagus’, Gymnogeophagus and Mikrogeophagus. Adaptive peak

6 was highly convergent (3 shifts, Fig. 1.9) across all models and included both South American

(cichlasomatin Cleithracara maronii) and Central American taxa (Paraneetroplus spp. and

Herotilapia multispinosa).

Compared to character histories simulated under a null model without convergence or adaptive peak shifts (a single peak OU), Neotropical cichlids showed significantly more adaptive peaks, both before (k, p = 0.01) and after collapsing convergent peaks (k’, p = 0.03), and significantly greater convergent peak shifts (c, p = 0.05; Table 1.4). This is despite the fact that

SURFACE estimated as many as 11 adaptive peaks (and up to 7 “backwards phase” adaptive peaks) from data simulated from a single peak OU model. However, neither the number of peaks

41 nor the number of convergent shifts differed significantly from that generated under a Hansen model without convergence (from the forward phase of the best supported Hansen model, see methods). Additionally, the proportion of convergent shifts compared to number of adaptive peaks (c/k) did not differ from expectations under either null models (OU1 or Hansennon-conv;

Table 1.4). Therefore, at least some of the peaks in the adaptive landscape of Cichlinae represent multiple, non-convergent peaks, and the adaptive landscape is likely more complex than shown in Fig. 1.7 and 1.9.

Table 1.4: Summary of adaptive peak shifts and convergence in Cichlinae functional morphology. k = adaptive peaks, k’ = unique adaptive peaks, Δk = reduction in landscape complexity with convergence, c = number of shifts to convergent peaks, k’conv = number of convergent peaks, c/k = convergent shifts proportionate to the number of adaptive peaks. Values are given for the best supported Hansen model from SURFACE. For the distribution of well supported Hansen models, the OU1 simulated data, and the Hansen non-convergent simulated data (see methods for more details), values are given as: median (range).

Best Sampled Model Hansennon- pHansen(non- Statistic Hansen OU1 pOU1 Distribution conv conv) Model k 12 11 (10, 12) 7 (3, 11) 0.01* 11 (7, 15) 0.32 k' 7 7 (7, 7) 4 (2, 7) 0.03* 7 (4, 9) 0.52 Δk 5 4 (3, 5) 2 (0, 6) 0.08 4 (2, 8) 0.32 c 8 7 (5, 8) 4 (0, 9) 0.05* 7 (3, 13) 0.38 k'conv 3 3 (2, 3) 2 (0, 3) 0.14 3 (1, 6) 0.73 c/k 0.67 0.64 (0.5, 0.67) 0.60 (0, 0.89) 0.40 0.67 (0.38, 1) 0.51

Fig. 1.7: Adaptive peaks in functional morphospace. Large numbered circles indicate the position of the 7 adaptive peaks estimated from SURFACE analyses. Smaller circles indicate the position of PC scores for individual species in trait space and are coloured by their estimated adaptive peak based on the best fitting Hansen model calculated on the MCC chronogram. Photographs illustrate taxa that were assigned to a given adaptive peak based on the best supported model. Image Credits to J. Arbour and H. Lopez-Fernandez.

(Next Page) Fig. 1.8: Results of SURFACE analyses carried out on the MCC chronogram and the first two PC axis of functional morphology of Cichlinae. Branches are coloured according to the selective regime (see Fig. 1.7 and 1.9 for corresponding locations of adaptive peaks in morphospace) estimated for each node from the best supported Hansen model. Asterisks indicate the location of adaptive regime shifts based on the best supported model. Pies on nodes indicate the percentage of 53 best supported models (all within ΔAIC < 10 of the best supported model) in which nodes were estimated as evolving towards a particular adaptive regime.

Fig. 1.9: Illustration of the number (Table 1.4, “c”) and pattern of adaptive peak shifts in functional morphospace from the best supported Hansen model on the MCC chronogram. Arrows each represent a single lineage evolving towards a new adaptive peak and are coloured by their ancestral peak. Adaptive peaks arrived to by multiple lineages are convergent (k’conv).

Multivariate evolutionary model-fitting using package “mvMORPH” (Clavel et al. 2014) yielded similar AIC results to those generated by SURFACE. Both BM and OU models were poorly supported compared to the SURFACE-generated Hansen models, and AIC values improved considerably upon collapsing convergent adaptive peak shifts together (Table 1.4).

Adaptive optima estimated by “mvMORPH” were similar to those reconstructed by SURFACE

(Fig. 1.10).

Table 1.5: Support for BM, OU and SURFACE generated Hansen models under a multivariate evolutionary assumptions as implemented in functions “mvBM” and “mvOU” from R package “mvMORPH”. Multivariate models were fit based on the number and phylogenetic position of adaptive peaks from the best supported SURFACE run and the MCC chronogram. Convergent refers to the model from the backward phase of the SURFACE analysis and non-convergent refers to the forward phase model. The variable k is the number of parameters in each model.

Model Adaptive Peaks LogLik k AICc BM 0 -218.9 5 448.7 OU 1 -209.9 8 438.0 Hansen (non-convergent) 12 -139.4 30 381.0 Hansen (convergent) 7 -146.4 20 348.4

Fig. 1.10: Adaptive peaks generated under univariate and multivariate model fitting. Small circles illustrate PC scores from 75 species of Neotropical cichlid. Large filled circles show the location of adaptive peaks as estimated by SURFACE. Large, open circles show the location of adaptive peaks as estimated by function “mvOU” from package “mvMORPH” , based only on the number and phylogenetic position of adaptive peak shifts generated by the best-fit SURFACE model.

47 1.4.3 Functional disparity and lineage density

As would be expected, BM evolution resulted in higher functional disparity than evolution towards a single adaptive peak (OU), and was associated with greater variation in estimated functional disparity than both OU and Hansen models (Fig. 4, top). Evolution under a single peak OU model produced the lowest disparities. The functional disparity of Cichlinae showed a nearly identical distribution to that generated under a Hansen model (Fig. 4, top), and was not generally significantly different from Hansen model expectations (mean p = 0.528). The functional disparity of Cichlinae was significantly different from that expected under a single peak OU model (mean p = 0.0487). Cichlinae functional diversity was not significantly different

(mean p = 0.0721) than that expected under BM simulations, which also produced the most variable simulated values of functional disparity (Fig. 1.11, top). Lineage density in Cichlinae

(Fig. 1.12) was not significantly different from that expected under a single peak model (OU, p =

0.754), but was significantly different from that expected under a BM or Hansen model (both mean p = 0.02; Fig. 1.11, bottom).

Fig. 1.11: Density plots of functional disparity and lineage density of Cichlinae calculated across 1000 posterior distribution chronograms. Distributions for simulated character histories (BM, OU and Hansen) incorporate both phylogenetic uncertainty and variation in evolutionary simulations.

Fig. 1.12: Phylomorphospace of Cichlinae. Points are PC scores summarized across 1000 posterior distribution chronograms. Lines connect PC scores to the calculated ancestral PC values for progressively more basal nodes. Branch lengths are colour coded according to time since root node.

1.5 Discussion

1.5.1 Neotropical cichlid feeding functional morphology

Several relationships observed between the variables examined in this study were informative to the overall function of the cichlid (and other acanthomorphs) feeding apparatus. The significant negative correlation between maximum jaw protrusion and suction index and association with velocity-optimized traits on PC1 is consistent with previous studies suggesting jaw protrusion is tied strongly to ram-feeding (accelerating to overtake and engulf prey) through factors such as increased ram velocity (Lauder & Liem 1981; Wainwright et al. 2001; Waltzek & Wainwright

2003). Suction index (Carroll et al. 2004; Carroll & Wainwright 2006) was significantly correlated with more functional traits than any other trait examined, and appears to be a good predictor of a number of features of cichlid feeding function. One such correlation was the positive relationship between quadrate offset and suction index. A higher quadrate offset resulted in higher overlap between the pre-maxilla and dentary as the oral jaws open and close (see Fig.

1.3), and in addition to concentrating bite force, (Ramsay & Wilga 2007; Anderson 2009) may be useful in regulating the mouth aperture. As suction feeding is maximized when water is drawn through a smaller opening (Wainwright et al. 2001), fine tuning of the gape during feeding may maximize suction force without greater limitations on maximum gape size (Wainwright &

Richard 1995). Oral jaw KT and Hyoid KT were the only variables not significantly correlated with at least one other aspect of functional morphology, which may relate to the non-linear mapping of morphology and functional output in four-bar linkages, an aspect which has been well studied under the scope of “many-to-one mapping” (Wainwright et al. 2005).

Neotropical cichlids showed strong overlap in functional morphology to other anatomically similar fishes. For example: lower jaw closing MA varied from 0.16 to 0.34 in

51 Cichlinae, and 0.13 to 0.33 in wrasses (non-scarine labrids; Wainwright et al. 2004). Among the taxa examined, only a few cichlid’s oral jaw KTs ranged higher than the 1.52 maximum in wrasses (Wainwright et al. 2004), and varied up to a maximum of ~2.26 (particularly among elongate species). Conversely, several wrasse species exhibited higher maximum hyoid KT (up to 4.53), compared to Neotropical cichlids (maximum 3.56 in Crenicichla saxatilis).

Additionally, suction index among predatory species in Crenicichla, Cichla, Parachromis,

Petenia, and others overlapped considerably with that observed among the similarly elongate, piscivorous, ram-feeding centrarchid Micropterus salmoides (~0.05 to 0.10 values for suction index). Similarly, invertebrate pickers/suction-feeders from the centrarchid genus Lepomis (~0.3 to 0.5 suction index values) overlapped with similar deeper-bodied cichlids, such as

Paraneetroplus spp., Archocentrus centrarchus and Guianacara dacrya. Both Pterophyllum scalare and Symphysodon aequifasciatus, possessed suction index values that were higher than that observed among the centrarchids examined by Carroll et al. (2004). Both these cichlid species also possessed more extreme body shape modifications (highly compressed, with disk- like lateral profiles) than is exhibited among the comparably younger centrarchid radiation.

Convergence in ecomorphs between acanthomorph radiations such as cichlids, centrarchids, and labrids (as well as others) likely represents similar functional underpinnings despite their comparably ancient common ancestor.

1.5.2 Cichlinae functional morphospace and feeding ecology

The largest axis of variation in functional morphology corresponded to a gradient between ram- feeding (rapid jaw movement, high protrusion and evenly occluding jaws) and suction-feeding strategies (high suction potential, efficient force transmission). Body-shape variation along PC1 was also congruent with a trade-off between ram- and suction-feeding ecomorphs (Norton &

52 Brainerd 1993). Taxa showing the most extreme ram-feeding functional traits included highly predatory species with elongate bodies, such as the South American Crenicichla and Cichla, and

Central American Parachromis and Petenia (Winemiller et al. 1995; Hulsey & Garcia De Leon

2005; López-Fernández et al. 2012). Comparatively, strong suction feeders corresponded to laterally-compressed fishes with deep bodies and small mouths, and included taxa that consumed a large percentage of detritus and vegetation, such as Symphysodon aequifasciatus (Crampton

2008), Herotilapia multispinosa (Baylis 1976) and Paraneetroplus species (Winemiller et al.

1995). The functional overlap on PC1 between presumed suction-feeding (high SI) and biting morphologies (high force transmission, uneven bite occlusion) among detritivores/herbivores/deep-bodied invertebrate-pickers (e.g. Paraneetroplus, Mesonauta,

Heros, Herotilapia), parallels adaptations observed among benthic feeding wrasses (Ferry-

Graham et al. 2002) and with previous analyses of centrarchids and Neotropical cichlids

(Montaña & Winemiller 2013). Species with moderate values along the first PC axis comprised predominantly benthic/epibenthic invertivores and generalist feeders (Winemiller et al. 1995;

López-Fernández et al. 2012; Montaña & Winemiller 2013).

Sediment-sifters within Geophagini (from the genera Geophagus, Gymnogeophagus,

Mikrogeophagus, Biotodoma, Acarichthys and Satanoperca), Central American heroins

(Thorichthys meeki and Astatheros robertsoni) and Retroculini (Retroculus lapidifer) showed considerable functional variability (Fig. 1.5). While cichlid substrate-sifters are morphologically and behaviourally specialized, the variability of functional morphology is consistent with the considerable variation in diet composition (Winemiller et al. 1995; López-Fernández et al.

2012). Previously observed convergence in morphological and behavioural characteristics are likely more related to efficient processing of food at the interface of the sediment and water column than to functional specialization for food types (López-Fernández et al. 2014).

53 In general the primary axis of cichlid functional diversity characterizes a gradient between elongate-bodied, predatory fishes capable of feeding on prey in the water column, to deeper-bodied fishes, reliant on increasingly immobile food sources (invertebrates to vegetation) on, or in, the benthos. These adaptations are common amongst very recent divergence associated with resource polymorphisms in post-glacial lakes, such as in Arctic char (Salvelinus alpinus),

Coregonus whitefish and Gasterosteus stickleback (Skulason et al. 1989; Malmquist et al. 1992;

Schluter & McPhail 1993). McGee et al. (2013) reported significant divergence between benthic and limnetic stickleback morphs in traits such as lower jaw mechanical advantage, suction index and jaw protrusion. Cooper et al. (2010) also found a similar pattern in bentho-pelagic divergence in head morphology among African Rift Lake cichlid radiations, between long-jawed and headed predators and deeper-headed aufwachs-feeders and benthic invertivores with compact jaws.

1.5.3 Adaptive landscape and functional evolution

An adaptive landscape with seven peaks in Cichlinae functional morphospace was estimated using SURFACE (but see discussion of convergence below). Most geophagins were estimated to be evolving towards adaptive peaks with comparatively better velocity transmission than force- transmission, possibly driven by the predominance of sediment-sifters in these clades, which use rapid and complex movements to “winnow” food particles from the benthos. Peak 3 was shared by the strongly benthivorous Biotodoma and Dicrossus (López-Fernández et al. 2012), with particularly high PC2 scores being found in Dicrossus and Crenicara, two small, elongate taxa, possessing small, sub-terminal mouths and large eyes, which perhaps imposes structural constraints on muscle and bone size (López-Fernández et al. 2005a; Hulsey et al. 2007; Hulsey

& Hollingsworth, Jr 2011). Peak 2 was strongly velocity-optimized, with small oral jaw muscles

54 (overall poor force production and transmission), and occupied by the elongate-bodied predators in Cichla and the geophagine Crenicichla. Peaks dominated by heroin cichlids (peaks 4- 7) possessed a combination of large oral jaw muscles and lower pharyngeal jaws (low PC2), and/or unevenly occluding jaws with high force transmission and high suction-feeding potential (high

PC1). While many non-heroines have small, conical oral jaw teeth (Chakrabarty 2007), more specialized dentition is common among heroines, especially the Central American clade, for example: tricuspid teeth in Herotilapia, fang-like pairs of teeth in Parachromis, ‘Cichlasoma’

(salvini) and Nandopsis (Chakrabarty 2007), truncate or spatulate teeth in Hypsophrys and

Tomocichla (Bussing 1998), and very long and closely set in Uaru. The location of the heroin- dominated adaptive peaks may be related to higher variability in dentition and perhaps to an increased reliance on oral manipulation compared to other Neotropical cichlids.

The adaptive landscape of Cichlinae functional morphology was significantly more complex than expected and included significantly more convergent peak shifts than expected under evolution towards a central optimum value. Interestingly, the number of convergent shifts as determined by best-fit model (c, Table 1) appears to have been largely driven by the large number of adaptive optima (k, Table 1). Replicate adaptive radiations among Anolis lizards in the Greater Antilles also exhibited a complex adaptive landscape in ecologically-relevant trait space (Mahler et al. 2013). However, the number of convergent shifts to adaptive peaks in these replicate Anolis radiations was significantly higher than expected, even when simulating under a model with numerous peak shifts, unlike in Neotropical cichlids. The difference in the occurrence of convergent peak shifts in these island and continental radiations may relate to factors such as: differences in the strength of spatial isolation, (e.g. cichlid dispersal through floodplain or channel portals; Lovejoy & De Araújo 2000; Winemiller et al. 2008; Winemiller &

Willis 2011; de Souza et al. 2012), area-size effects (e.g. within Anolis endemic, non-convergent

55 peaks were more common on larger islands; Gavrilets & Vose, 2005; Mahler et al., 2013), or alternatively the lower sample size/number of traits analyzed (Ingram & Mahler 2013; Mahler et al. 2013).

The strength of selective constraint can be informative regarding the patterns of diversification along different trait axes or within different clades. Phylogenetic half-life, which is the time to move one half the distance from an ancestral state towards an adaptive peak, is calculated as t1/2 = ln 2 / α (Hansen 1997; Hansen et al. 2008), and is informative of the speed of adaptation. Estimates of the age of cichlids are extremely variable, ranging from 39.9 Ma

(absolute minimum age of fossils from Argentina; del Papa et al. 2010; Malabarba et al. 2014) to

124.4 Ma (López-Fernández et al. 2013). Based on this range, phylogenetic half lives in adaptive trait space were t1/2 = 3.41 to 10.6 Ma on PC1 and t1/2 = 0.0856 to 0.267 Ma on PC2. The half- life on PC1 is lower than that observed along a similar axis of functional variation in centrarchids

(13.1 Ma; Collar et al. 2009) indicating that cichlids may be able to adapt more rapidly in functional trait space, perhaps contributing to their higher species richness. Lineage-specific effects on diversification are also likely to be more prominent on PC1 than PC2 (higher t1/2 on

PC1 represents slower adaptation compared to PC2), which may promote greater morphospace partitioning among lineages. Trophic polymorphism and phenotypic plasticity in traits such as pharyngeal jaw structure may contribute to rapid adaptation along PC2 (Stauffer & van Snick

Gray 2004; Muschick et al. 2011).

Adaptive shifts occurred more frequently in force-transmission/suction-optimized space

(positive PC1) than within ram-optimized space (negative PC1; Fig. 1.9) and occurred among fairly recent nodes and represented few lineages (Fig. 1.8). Neotropical cichlids may be inherently more likely to evolve towards and diversify in either generalized or ram-feeding adapted morphospace (on PC1), due to physiological, anatomical or other constraints. The

56 lineage density of Cichlinae was higher than expected under the best-fit Hansen model, but was similar to that under evolution towards a single adaptive peak (Fig. 1.11), which may reflect higher adaptive constraint towards a few adaptive peaks. Alternatively, several of the traits examined here represent biomechanical trade-offs (e.g. high mechanical advantage improves force transmission at the expense of speed, low MA results in the opposite), and such trade-offs are known to bias the rate and pattern of morphological evolution (Holzman et al. 2012), which may influence the distribution of evolutionary change in Cichlinae.

It is also possible that competition with other Neotropical fish lineages has contributed to the apparent low diversification of taxa evolving towards adaptive peaks in suction-feeding morphospace. Winemiller et al. (1995) hypothesized that algivores and detritivores were relatively rare among South American cichlids due to the presence of some better “pre-adapted”

South American ostariophysans. Strongly negative PC2 values (representing large oral jaw muscles and high crushing potential) were more common among Central American taxa, and such trait values may be beneficial to behaviours such as scraping, shearing/nipping and food processing. However, low PC2 values in Central American taxa were not exclusive to suction- feeding space on PC1, and only peak 6 (Fig. 1.7), was dominated by detritivorous Central

American taxa (Paraneetroplus spp. and Herotilapia multispinosa).

The functional disparity of Cichlinae was nearly identical to that generated under an adaptive landscape model, even when both phylogenetic branch length uncertainty and variation across evolutionary simulations were taken into account (Fig. 1.11). Overall, the functional disparity of Cichlinae appears to be best explained by the complexity of selective constraint (i.e., number of adaptive peaks) in functional morphospace. Since ecological performance is linked to functional traits (Wainwright 2007), such adaptive landscape complexity (whether driven by physiological, ecological, environmental or other factors) may contribute to the overall

57 ecological diversity of cichlids and other similar radiations. Further comparisons to other

Neotropical fish radiations will improve our understanding of what factors contribute to morphological and ecological diversity.

2. Chapter Two

Ecological opportunity and ecological release impact

functional evolution in the South and Central American

cichlid radiations

Jessica Hilary Arbour1

1 Department of Ecology and Evolutionary Biology, University of Toronto, 25 Wilcocks St.,

Toronto, Ontario M5S 3B2, Canada

2.1 Abstract

Ecological opportunity and ecological release, the availability of and the response to niches respectively, are thought to drive patterns of phenotypic diversification during adaptive radiations. Phylogenetic comparative methods were used to test two predictions of functional diversification regarding ecological opportunity in the continentally-distributed Neotropical cichlids, namely decreasing diversification through time and increasing diversification following the colonization of mainland Central America. On an axis of ram-suction feeding morphology significant decreases in evolutionary rates through time were observed, but also a significant increase upon the colonization of Central America. Similarly, South American cichlids show a pattern of strong morphospace partitioning consistent with an “early burst” of evolution, although Central American cichlids, individually, do not show such a pattern. Early diversification in ecologically-relevant morphological traits in South America occurred concomitantly with a burst of lineage diversification, supportive of a continental adaptive radiation. Central American cichlids show increased functional diversification and overall high lineage diversification compared to contemporary South America cichlids, consistent with ecological release. Decreasing rates of evolution associated with saturation of trophic niches may have been mediated by factors such as morphological adaptations, the composition of Central

American fish communities, lineage specific ecological opportunities or the timing and pattern of the colonization of Central America. Differences in ecological opportunity through time and across geographic regions have contributed to the morphological diversity of Neotropical cichlids.

2.2 Introduction

Ecological opportunity, i.e., the availability of ecological niches within an environment (Schluter

2000; Losos 2010), is likely a key factor underlying patterns of decreasing rates of diversification associated with adaptive radiations (Simpson 1944; Schluter 1996; Gavrilets &

Vose 2005; Gavrilets & Losos 2009; Losos 2010; Mahler et al. 2010; Glor 2010; Slater &

Pennell 2014). The availability of niches is determined by three types of ecological opportunity:

1) ecological – niches are not occupied by other competitive taxa, 2) physical – clades must exist in an environment where niches are available, and 3) evolutionary – clades must possess the pre- adaptations or innovations to allow them to radiate into new niches (Simpson 1953; Glor 2010).

Yoder et al. (2010) define ecological release as the response (in trait variation, population density, niche width, habitat use, diversification rates, etc.) to new ecological opportunity and ecological release may promote adaptive radiation, especially in island communities (Losos &

Queiroz 1997; Parent & Crespi 2009; Bolnick et al. 2010; Roches et al. 2011; Greenberg &

Danner 2013). Classic examples of adaptive radiation include predominantly island-based systems, such as Darwin’s finches (Petren et al. 2005), Greater Antilles Anoles (Butler et al.

2007; Mahler et al. 2010), African Rift Lake cichlids (Seehausen 2006; Takahashi & Koblmüller

2011) and Hawaiian Silverswords (Baldwin 1997). However, it has been postulated that adaptive radiation may frequently operate on a broader scale and have contributed substantially to the diversity of species and biological form (Simpson 1944, 1953; Losos 2010). Improvements in molecular sequencing and phylogenetic inference have spurred numerous advances in detecting the effects of ecological opportunity and release on diversification and adaptive radiation (e.g. e.g Gavrilets & Losos 2009; Losos & Mahler 2010; Glor 2010).

61 While lineage diversification has been more thoroughly studied in the context of ecological opportunity (Rabosky & Lovette 2008a), a recent proliferation of comparative phylogenetic methods for studying trait evolution has enabled a greater focus on the link between decreasing ecological opportunity and decreasing phenotypic diversification (Harmon et al.

2003, 2010; Freckleton & Harvey 2006; Mahler et al. 2010). Patterns of phenotypic evolution may be more resistant to the effects of extinction compared to patterns of lineage diversification,

(Rabosky & Lovette 2008b, 2009; Slater et al. 2010; Derryberry et al. 2011; Schweizer et al.

2014). Early bursts of lineage and phenotypic divergence are generally interpreted as the result of adaptive radiation in the presence of ecological opportunity (Gavrilets & Losos 2009; Slater et al. 2010; Glor 2010). But disagreement exists in the current literature on the commonness of early bursts in morphological evolution (Harmon et al. 2010), and in how the power of certain methods to detect these patterns may influence our interpretation of comparative analyses

(Brown 2014; Slater & Pennell 2014). Testing for the patterns of changing ecological opportunity in the context of morphological evolution, across a variety of spatial scales, ages, trait complexes and environments is therefore an important aspect of increasing our understanding of adaptive radiation as a potential broad scale process.

One utility of island radiation systems is they often allow comparisons between

“replicate” radiations (Losos 2010), illustrating both the relationship between ecological opportunity and diversification rates across independently diverging systems (Mahler et al. 2010) and the extent to which different radiations utilize functionally equivalent adaptations (Losos

1998; Young et al. 2009; Mahler et al. 2013; Grundler et al. 2014). While African Rift Lake cichlids have long been a subject of interest in the study of adaptive diversification (Stiassny

1991; Sturmbauer 1998; Kornfield & Smith 2000; Streelman & Danley 2003; Kocher 2004;

Salzburger et al. 2005; Seehausen 2006; Killen et al. 2007; Cooper et al. 2010), Neotropical

62 cichlids potentially provide an opportunity to examine the influence of ecological opportunity in repeated radiations on a continental scale. Analysis of both lineage and phenotypic diversification has provided evidence for adaptive radiations occurring across at least part of

Cichlinae (López-Fernández et al. 2010, 2013). Additionally, while Neotropical cichlids initially diversified within South America, cichlids later colonized Central America. The invasion of

Central America was a unique occurrence in the evolution of Cichlinae, involving one, or at most two, colonization events probably within a relatively narrow window of evolutionary time

(Hulsey et al. 2010a; Říčan et al. 2013). The colonization of Central America by cichlids may have been associated with ecological release. It has been suggested that a release from competition with South American cichlid lineages, as well as ostariophysan lineages that have been slower to colonize and diversify in Central America (Matamoros et al. 2014), may have influenced lineage and phenotypic diversification in Central American cichlids (Winemiller et al.

1995; Hulsey et al. 2010a; López-Fernández et al. 2010). Comparing the functional evolution of

South and Central American cichlids may help to determine whether independent adaptive radiations have occurred in these regions, and more generally to detect the signal of ecological opportunity and release in the functional evolution of continental radiations. The evolution of

Cichlinae also serves as a useful point of comparison to both African lacustrine radiations, as well as other continental scale radiations (Slater et al. 2010; Claramunt 2010; Derryberry et al.

2011; Schweizer et al. 2014)

The objective of this chapter is to examine patterns of functional evolution consistent with varying ecological opportunity in Neotropical cichlids. Methods for detecting changes in the rate and pattern of phenotypic evolution during cichlid diversification were applied to the multivariate functional morphospace described in Chapter 1 (Arbour & López-Fernández 2014).

Specifically, I used these methods to examine two questions 1) has either radiation shown an

63 evolutionary pattern consistent with decreasing ecological opportunity through time associated with adaptive radiation, and 2) was the colonization of Central America associated with ecological release through increased diversification rates.

2.3 Methods

2.3.1 South and Central American Biogeography

Cichlid biogeography was reconstructed with stochastic character mapping (Huelsenbeck et al.

2003; Springer et al. 2011), using the “make.simmap” function in R package “phytools” (Revell

2012). Stochastic character mapping simulates a set of character state changes along branches under a continuous-time Markov process, based on a set of branch lengths and tip states from a given phylogeny (Huelsenbeck et al. 2003; Bollback 2006). Stochastic character mapping is better able to incorporate uncertainty in the location of transitions between discrete states compared to methods such as maximum likelihood and parsimony (Huelsenbeck et al. 2003;

Bollback 2006). The “make.simmap” function was permitted to estimate an asymmetric transition rate (Q) matrix (“make.simmap” option: model = “ARD”), since colonization of South

America from Central America may have occurred more frequently than vice versa (Hulsey et al.

2010a). Stochastic character histories were simulated across 1000 posterior distribution chronograms (described in Chapter 1), for a total of 1000 character mappings (Collar et al.

2011). Following Hulsey et al. (2010) and Říčan et al. (2013), I grouped the Greater Antilles taxa (Nandopsis haitiensis and Nandopsis tetracanthus) with the South American regime. These taxa have been placed as close relatives of at least two South American genera in recent multilocus molecular phylogenies, and biogeographic analyses suggest that the ancestors of these

64 genera diversified in South America (Hulsey et al. 2010a; López-Fernández et al. 2010, 2013;

Říčan et al. 2013). However, no significant change in the results of the comparative analyses was observed if the Nandopsis species were alternatively considered Central American taxa, and their position does not appear to exert much influence on the analyses described here.

2.3.2 Ecological Opportunity and Evolutionary Rates

A trend of decreasing rates of phenotypic evolution through time has been used as indicative of a pattern of niche-filling evolution (Freckleton & Harvey 2006; Slater et al. 2010; Mahler et al.

2010; Derryberry et al. 2011). The “node height test” (NHT) is a method used to determine whether rates of phenotypic evolution vary through time. The absolute value of standardized independent contrasts can be used as a measure of the rate of morphological evolution at the node over which they were calculated (Felsenstein 1985; McPeek 1995). NHTs are used to determine whether a correlation exists between the absolute value of the standardized independent contrast (an estimate of the rate of evolution) at each node and its “height” from the root (i.e., branch length or age). Decreasing evolutionary rates through time are typically interpreted as a response to decreasing ecological opportunity.

The invasion of Central America provided lineages of Heroini new opportunities for diversification and may have allowed rates of morphological evolution to increase compared to the South American radiation. I modified the NHT to include both a continuous time variable and a discrete variable for geographic region (South America vs. Central America) based on the stochastic character reconstructions for each node (see methods above). I predict that while evolutionary rates should be negatively correlated with time, as a result of decreasing ecological

65 opportunity, rates should be positively correlated with the transition from South America to

Central America, as a result of ecological release (Yoder et al. 2010).

Node Height Tests were carried out using robust regression, as this method is less sensitive to outlier taxa experiencing lineage specific rate/selective constraint variation (Slater &

Pennell 2014). Robust regression identifies and downweights outlier data points and was implemented using the MM-estimation based function “lmrob” from R package “robustbase”

(Rousseeuw et al. 2014). I compared robust linear regression models with and without interaction between the independent variables (time and biogeography) using robust deviance analysis using function “anova.lmrob” in R package “robustbase”, and included an interaction term only where a significant improvement in regression model fit was observed.

I used a simulation-based approach to assess the significance of changes in rate of evolution through time or across geographic regions (Slater & Pennell 2014). The p-values for each of the regression coefficients were calculated by simulating evolutionary histories based on a null, constant-rate model. Maximum likelihood model fitting was carried out for three constant-rate models, Brownian Motion (BM), a single selective peak (OU1) and different selective peaks for South and Central America (OU2). A two-peak model was included as strong divergent selection between multiple peaks may produce very different patterns of diversification from a single selective peak (Astudillo-Clavijo et al. In Review). Models were fit using R functions “fitContinuous” and “OUwie”, and were compared using the Akaike Information

Criterion. Branch lengths were transformed using the R function “rescale.phylo” (Harmon et al.

2008), prior to the calculation of independent contrasts for observed and simulated character histories, to account for selective constraint on PC axes where OU models were preferred (Slater

2013, 2014). Character histories were simulated using function “fastBM” (package “phytools”;

Revell 2012) for the BM or single-peak OU model, or “surfaceSimulate” (package “surface”;

66 Ingram 2014) for the two peak OU model. For the MCC phylogeny and each posterior distribution chronogram, the p-value was determined as the frequency at which simulated character histories produced a regression coefficient that was 1) more negative for the time variable (decreasing ecological opportunity) (Freckleton & Harvey 2006; Slater & Pennell 2014), and 2) more positive for the transition from South to Central America (ecological release from older cichlid lineages or other South American fish families).

2.3.4 Disparity-through-time analyses

Disparity through time analyses (DTT) are used to examine how morphological disparity has been partitioned through the evolutionary history of a clade, compared to an expected distribution based on a null model of morphological evolution. It is most commonly used to test for patterns in which morphospace is strongly divided between early lineages, a pattern which is attributed to decreasing morphological diversification through time. Harmon et al. (2003) found morphological partitioning (through DTT analyses) to be strongly correlated with a similarly calculated measure for lineage diversification, suggesting that such patterns are associated with increasing competition between lineages. Similar to NHTs, DTTs are considered to have more power to detect decreasing morphological diversification (through time) common to adaptive radiations than model-based likelihood methods (Slater & Pennell 2014).

DTT calculates the average morphological disparity for all subclades present at the age of each node in the tree, which is compared to the total disparity of the clade and plotted as a DTT curve (see Fig. 2.1). Differences between a clade’s DTT curve and that expected under particular null models of morphological evolution can be quantified using the morphological disparity index (MDI), which is the area between the observed and simulated median sub-clade disparity

67 curves. Figure 1 illustrates several possible results (associated with varying MDI values) of a

DTT analysis, using BM as a null model. Under a BM model with a constant rate of evolution, variance is expected to increase linearly with time; on average older clades will have higher variance (disparity) and average sub-clade disparity decreases towards the present. If a morphological trait follows a strict Brownian motion pattern of evolution as is illustrated in Fig.

2.1B the MDI value will be zero (when the null distribution is BM), and the relative sub-clade disparity of all sub-clades present at node N (t = 0.5) will be, on average, 50% of the total disparity of the clade. Consistently high DTT curves (positive MDI values) indicate that recent sub-clades are very disparate; in the example sub-clades show 90% of the total disparity at half the age of the root node. Evolution under adaptive constraint, which forces lineages to evolve towards similar trait values, or increasing rates of morphological evolution resulting in young but highly disparate clades, can produce positive MDI values (López-Fernández et al. 2013). DTT curves that show a more rapid decline in relative sub-clade disparity and a negative MDI (Fig.

2.1C), are consistent with a pattern of decreasing morphological diversification through time, with greater morphological differences originating between early lineages than more recent ones

(ex: in Fig. 2.1, the average sub-clade disparity at t = 0.5 is only 10% of the total disparity).

Negative MDI values can be indicative of slowing rates of evolution, consistent with decreasing ecological opportunity during an adaptive radiation (Slater et al. 2010). Negative MDI values can also be driven by strong divergent selection between different adaptive peaks (Astudillo-

Clavijo et al. In Review). It is important to note that while the example in Fig. 2.1 compares the observed DTT curve to that simulated under BM evolution, the expected curve can be simulated under different models of evolution (Slater & Pennell 2014).

Morphological disparity was calculated as the average squared pairwise distance between all members of a subclade (Harmon et al. 2003, 2008; Slater et al. 2010; Slater and Pennell

68 2013). A simulation approach was used to assess the likelihood of an observed MDI occurring under a constant rate process (Slater et al. 2010; Derryberry et al. 2011; Brown 2014; Slater &

Pennell 2014). MDI values and their probability of occurring under evolutionary expectations were calculated for 1000 simulated character histories for the MCC tree and for each of the 1000 posterior distribution trees, based on the best fitting, constant-rate model of morphological evolution. Character histories were simulated as previously described for the NHTs. For each of the chronograms, the area between the observed DTT curve and the DTT curves for each simulated character history was calculated

(Slater et al. 2010; Derryberry et al. 2011; Slater & Pennell 2014). The frequency of simulated

MDI values that were less than the observed value was used as the p-value to test the whether subclades partitioned morphospace more strongly than expected under a constant-rate process (as expected with decreasing ecological opportunity) (Slater et al. 2010; Derryberry et al. 2011;

Brown 2014; Slater & Pennell 2014)..

Following Harmon et al. (2003) the last third of the chronogram was truncated prior to all calculations to account for incomplete taxonomic sampling. Very closely related taxa are expected to be morphologically similar; if taxonomic sampling is incomplete and widely distributed across a phylogeny (as is the case with the present dataset) this can bias recent subclade disparity (Harmon et al. 2003). South American heroins that descended from Central

American lineages (i.e., Australoheros facetus, ‘Cichlasoma’ festae, Heroina isonycterina; Fig.

2.2) were excluded from DTT analyses of South American cichlids due to the inability to account for internal nodes occurring in Central America under the current framework of DTT analyses. However, the inclusion of these taxa on a small random sample of chronograms did not alter the significance of the results for South America.

Fig. 2.1: Disparity-through-time curves (A-C) for a continuous trait evolving across a simulated phylogeny. In A-C, solid lines represent the observed DTT curve, and the dashed line represents the average BM simulated curve, while N denotes the age of a particular node (at t = 0.5) on each of the DTT curves. A) A DTT curve with a positive MDI, resulting from processes such as adaptive constraint or increasing rates of evolution. B) A DTT curve with an MDI of zero, resulting from a constant-rate, random-walk process. C) A DTT curve with a negative MDI, resulting from processes such as an early burst of evolution or strong divergent selection.

2.4 Results

2.4.1 Central and South American Radiations

Stochastic character mapping of Cichlinae biogeography estimated a single colonization of

Central America at the base of the Central American clade (Fig. 2.2, green). This reconstruction also revealed more transitions to South America (ex: ‘Cichlasoma’ festae and Australoheros facetus) than to Central America; of a average 5.58 transitions across 1000 reconstructions, 1.46 were to Central America, while 4.11 were to South America. The node comprising the Greater

Antilles cichlids (genus Nandopsis) and two secondarily South American heroins (Heroina isonycterina and Caquetaia kraussii), was ambiguously reconstructed but with a relatively higher support for a South American origin.

Fig. 2.2: Stochastic character mapping reconstruction of Neotropical cichlid biogeography for the analysis of functional morphological evolution, plotted on the MCC chronogram of 75 species of Cichlinae. Blue indicates South America, while green indicates Central America. Pie graphs indicate the percentage of stochastic maps in which each node was reconstructed as either South or Central American across 1000 total stochastic character reconstructions.

2.4.2 Node Height Tests

Interaction between time and geographic region was not found to be significant (χ2 = 0.0932, p =

0.76) for PC1, and an interaction term was not included in the final model. An interaction between the time and biogeography variables was found to contribute significantly to a multiple regression model for PC2 (χ2 = 8.97, p = 0.00274). Down-weighting in robust regression analysis was associated with very high evolutionary rates (Fig. 2.3, top), possibly associated with lineage-specific adaptive shifts (Slater & Pennell 2014; Arbour & López-Fernández 2014). For example, the contrast between Symphysodon aequifasciatus (Fig. 2.3) and Heros sp. “common” was excluded (robust weight < 0.001) from the NHT on PC1, and Symphysodon is likely evolving towards a novel adaptive peak in Neotropical cichlid functional morphospace (Arbour

& López-Fernández 2014). Down-weighting of evolutionary rates did not show a pattern with time, suggesting that incomplete lineage sampling (which given our broadly distributed data would be associated with more recent nodes) did not bias the evolutionary rates observed or the results of the NHTs.

Node height tests showed a significant decrease in rates of ram-suction morphological evolution (PC1) through time on the MCC chronogram (Table 2.2, Fig. 2.5), consistent with the hypothesis of decreasing ecological opportunity. Furthermore, NHTs showed a significant increase in rates of ram-suction morphological evolution following the invasion of Central

America (Table 2.2, Fig. 2.5 and 2.6), consistent with ecological release from South American cichlids and other South American fish lineages. This increase in rates resulted in an initial rate of diversification in Central America that was similar to that observed in South America (Fig.

2.5A). Under a constant-rate model of evolution (OU1 for PC1), regression coefficients were equally likely to have been positive or negative (Fig. 2.5B and C), and the significance tests of

73 the correlation coefficients were consistent with the results on the MCC chronogram across a majority of chronograms (Table 2.2; Fig. 2.6).

Trends in evolutionary rates through time differed between the South and Central

American radiation in biting/crushing morphology (PC2; Fig. 2.7A and Fig. 2.8C). Based on the

NHT of PC2, rates of evolution varied in the opposite direction of our predictions; rates decreased in South America, although this was followed by a sharp increase in rates within the

Central American radiation (Fig. 2.7A). The apparent trend of increasing rates of evolution through time in Central America appears to have been related to two non-overlapping clusters of rate values, those above a rate estimate of ~2 and those below ~1 (Fig. 2.7A). Of the high rate estimates, 7 occurred between Amphilophine taxa and two occurred within the Herichthyines; one between the two species of Herichthys and one at the divergence of a secondarily South

American species (‘Cichlasoma’ festae; Fig. 2.2).

Table 2.1: Model fitting of null constant rate models of evolution for simulation tests associated with NHT and DTT analyses. Models included Brownian motion (BM), a single selective peak (OU1) and differing adaptive optima between South and Central America (OU2). Values are mean (s.d.), with the exception of rates (σ2) and adaptive constraint (α), which are given as median (bootstrapped s.d.; Efron & Tibshiarni 1986), as they were heavily skewed. The θ term gives the location of adaptive peaks for South America (SA) and Central America (CA) in PC scores. Bolded values give the best supported model.

2 Axis model LogL ΔAIC w σ α θSA θCA

PC1 BM -125.3 0.0892 4.64 (1.88) 3.36 (0.0592) NA NA NA (1.70) (0.0675)

OU1 -121.9 1.23 X 10-4 0.666 1.46 5.97 (0.378) 0.186 NA (2.18) (3.92 X 10-3) (0.0604) (0.0107)

OU2 -121.79 0.245 1.44 2.01 (0.310) 5.92 (0.371) 0.198 -0.160 (2.18) (0.0335) (0.0100)

PC2 BM -93.2 5.31 X 10-3 17.6 (8.644) 1.42 (0.0477) NA NA NA (2.03) (0.0176)

OU1 -84.7 0.291 2.65 - 2.50 (2.43) 3.34 (44.3) NA (3.07) (0.189) (0.0286) 0.0806

OU2 -82.2 0.704 4.17 0.106 (0.293) 4.63 (133) -0.03 -0.718 (4.25) (0.195) (0.163)

Fig. 2.3: Robust regression weights from node height tests for PC1 (left) and PC2 (right). Circle colour represents the weights estimated during regression analyses, with darker colours illustrating stronger down-weighting on specific evolutionary rates (values at nodes). Values near 0 were excluded as outliers.

Fig. 2.4: Weights from robust regression analysis of evolutionary rate on relative age of nodes (time) and biogeography (South America vs. Central America), for PC1 (left) and PC2 (right). Top: robust weight compared to the evolutionary rate calculated for each node on the MCC chronogram. Bottom: robust weight compared to the relative time since root for each node on the MCC chronogram.

Table 2.2: Summary of Node Height Tests of Cichlinae functional morphology for the MCC tree and 1000 posterior distribution chronograms. Time is a continuous variable giving the relative time since root node, while geographic region is a discrete variable (South America = 0, Central America = 1) based on the stochastic character reconstructions. Regression coefficients are given as mean (s.e.). For time and geographic region, p-values were calculated for one-tailed tests of decreasing rates with time and increasing rates in Central America. The interaction term (time: region) p-value was calculated based on a two-tailed test. Bolded values were significant on the MCC tree and the majority of posterior distribution chronograms.

MCC 1000 posterior distribution chronograms

Coefficient p Coefficient Median p (95% Frequency

range) p < 0.05

PC1 time -1.54 0.029 -1.49 (0.711) 0.035 (0.021, 0.727 (0.4305) 0.106)

region 1.07 (0.457) 0.012 1.02 (0.467) 0.011 (0.006, 0.971 0.051)

PC2 time -1.02 0.085 -3.87 (3.44) 0.0905 (0.04, 0.353 (0.761) 0.435)

region -5.16 (1.67) 0.996 -9.17 (7.09) 0.993 (0.959, 0 1.00)

time : region 6.83 (2.34) 0.005 12.1 (9.52) 0.016 (0.004, 0.68 0.63)

Fig. 2.5: Changes in evolutionary rates of ram-suction morphology in South and Central America. Node height tests of PC1 on the MCC chronogram of 75 species of Neotropical cichlid. A) Plot of evolutionary rate estimates (absolute value of standardized independent contrasts) through time for South American (blue, circles) and Central American (green, triangles) taxa, including the regression line (shaded region = 95% CI) from the NHT. An outlier (as determined by robust analysis) in South America is given by an open circle. B and C) Simulated distribution of regression coefficients based on based on the best fit single rate model (Table 2.1: OU1) . Dashed lines show the observed regression coefficient.

Fig. 2.6: Summary of Node Height Tests of PC1 carried out across 1000 posterior distribution chronograms. P-values for robust regression coefficients (A – time, B – region). An interaction between time and geographic region did not contributed significantly to the model for PC1 and was not included in the final analyses. All dashed lines show p = 0.05.

Fig. 2.7: Changes in evolutionary rates of biting/crushing morphology in South and Central America. Node height tests of PC2 on the MCC chronogram of 75 species of Neotropical cichlids. A) Plot of evolutionary rate estimates (absolute value of standardized independent contrasts) through time for South American (blue, circles) and Central American (green, triangles) taxa, including the regression line (shaded region = 95% CI) from the NHT. B and C) Simulated distribution of regression coefficients (including an interaction term). Dashed lines show the observed regression coefficient.

Fig. 2.8: Summary of Node Height analysis of PC2 carried out across 1000 posterior distribution chronograms. P-values for robust regression coefficients for (A) time, (B) region, and (C) the interaction between these two variables. All dashed lines show p = 0.05, for a one-tailed test in A-C and a two-tailed test in D.

2.4.3 Disparity-Through-Time Analyses

DTT analysis of PC1 (ram-suction/biting morphology) showed a low average subclade disparity even as early as the divergence of Cichlini + Retroculini from all other Neotropical cichlids (Fig.

2.9, top left; Table 3), indicating early morphological divergence and finer partitioning of morphospace than expected under a constant rate model (OU1). Average subclade disparity was particularly low during the time period corresponding to early divergence of Geophagini +

Chaetobranchini and of Cichlasomatini + Heroini, indicating particularly strong segregation of early lineages in morphological diversity. This significant result was reflected across the overwhelming majority of posterior distribution chronograms (Table 2.3). The negative MDI observed across Cichlinae on PC1 appears to have been largely driven by evolution among the

South American radiation. Across Primary South American taxa (i.e., those not descended from

Central American Heroini), the DTT curve was similarly low (Fig. 2.10, top left), resulting in a signficantly negative MDI (MDIMCC = -0.189, ppp = 0.003) that was supported over the vast majority of chronograms (97.9%, and see Table 3). Comparatively, Central American heroins exhibited a slightly positive MDI (Fig. 2.11, top left) that did not differ significantly from expectations under a null OU model of evolution (Table 2.3). Furthermore, none of the 1000 posterior distribution chronograms generated a significant MDI for Central America on PC1.

DTT analysis of PC2 did not show significantly lower MDI values than expected under a constant rate, two peak model of evolution (OU-P), in Cichlinae or within either the South or

Central American radiations (Fig. 2.9-2.11, bottom left; Table 3). The South American radiation did show a negative MDI, but it was non-significant on the MCC chronogram and across 82.5% of posterior distribution chronograms. In contrast, the DTT curve was close to the median simulated curve early during the diversification of the Central American clade, but later

83 increased substantially (Fig. 2.11, bottom left), resulting overall in a very high MDI, albeit with considerable variability (Table 3, MDI1000 sd).

Table 2.3: Summary of DTT analysis of Cichlinae functional morphology for the MCC tree and 1000 posterior distribution chronograms. The p-value was calculated to test the hypothesis that subclade disparity was lower than expected under constant-rate models of evolution (MDIobs <

MDIsimulated). Bolded values show significant results.

MCC 1000 posterior distribution chronograms

Clade Axis MDI p MDI (s.d.) Median p-value (95% Frequency range) p < 0.05

Cichlinae PC1 -0.191 0.006 -0.182 (0.0271) 0.009 (0.004, 0.0450) 0.980

PC2 -0.0479 0.254 -0.0678 (0.0594) 0.181 (0.0920, 0.681) 0.102

South PC1 -0.189 0.003 -0.176 (0.0280) 0.005 (0.002, 0.042) 0.979 America PC2 -0.0633 0.157 -0.0889 (0.0629) 0.134 (0.062, 0.591) 0.175

Central PC1 0.0976 0.649 0.0721 (0.0666) 0.628 (0.494, 0.867) 0 America PC2 0.313 0.945 0.269 (0.112) 0.944 (0.84, 0.997) 0

Fig. 2.9: Disparity-through-time analysis of PC1 (ram-suction morphology, top row) and PC2 (biting/crushing morphology, bottom row) across 75 species of Cichlinae. Left: DTT plots generated based on the MCC chronogram. Solid, black lines show the observed DTT curve, dashed lines show the median simulated DTT curve and the shaded region shows the 95% range of simulated DTT curves. Lighter region shows the area excluded from MDI calculations to account for incomplete taxonomic sampling. Right: histograms of p-values calculated for MDI values across 1000 posterior distribution chronograms. Values to the left of the dashed vertical line are significant.

Fig. 2.10: Disparity-through-time analysis of PC1 (ram-suction morphology, top row) and PC2 (biting/crushing morphology, bottom row) across 48 species of primary South American cichlids. Left: DTT plots generated based on the MCC chronogram. Solid, black lines show the observed DTT curve, dashed lines show the median simulated DTT curve and the shaded region shows the 95% range of simulated DTT curves. Lighter region shows the area excluded from MDI calculations to account for incomplete taxonomic sampling. Right: histograms of p-values calculated for MDI values across 1000 posterior distribution chronograms. Values to the left of the dashed vertical line are significant.

Fig. 2.11: Disparity-through-time analysis of PC1 (top row, ram-suction morphology) and PC2 (bottom row, biting/crushing morphology) across 21 species of Central American cichlids. Left: DTT plots generated based on the MCC chronogram. Solid, black lines show the observed DTT curve, dashed lines show the median simulated DTT curve and the shaded region shows the 95% range of simulated DTT curves. Lighter region shows the area excluded from MDI calculations to account for incomplete taxonomic sampling. Right: histograms of p-values calculated for MDI values across 1000 posterior distribution chronograms. Values to the left of the dashed vertical line are significant.

2.5 Discussion

2.5.1 Patterns of Neotropical cichlid evolution

Under a model of adaptive radiation, ecological opportunity is a limiting factor on lineage divergence and the evolution of ecologically-relevant phenotypic traits (e.g Simpson 1944, 1953;

Schluter 2000; Losos 2010; Mahler et al. 2010; Glor 2010). Along PC1, an axis primarily characterizing variation in ram-suction feeding morphology, a pattern of morphological evolution varying with ecological opportunity was strongly supported from both DTT and NHT analyses in South America. I also found strong support for a predicted increase in rates of feeding-related morphological evolution following the colonization of Central America.

However, DTT analysis of PC1 in Central America did not find support for pattern of decreasing morphological diversification. Differences in selective optima were found between South and

Central American cichlids on PC2 (characterizing oral jaw muscle size and pharyngeal crushing potential). Rates of evolution on PC2 did not appear to vary with ecological opportunity based on

DTT and NHT analyses. Functional diversification in ram-suction feeding morphology is congruent with an adaptive radiation in South America. However, there is not clear support for an adaptive radiation in Central America. It is possible that trophic niches may not yet be saturated in Central America. Additionally, the timing of Central American diversification or other morphological adaptations could influence patterns of functional diversification (see below for full discussion).

2.5.2 Ecological opportunity in South America

PC1 was previously found to represent a gradient from elongate-bodied fish with fast oral jaw biomechanics (ram-feeders) to tall-bodied fish with high force transmission and suction capability (suction feeders and “biters”; see Chapter 1 discussion). South American cichlids exhibited a strong pattern of decreasing subclade disparity through time in ram-suction morphology (negative MDI on PC1, Table 2.3, Fig. 2.10). Such a pattern indicates that morphospace was partitioned early in the radiation, i.e., that subclades tended to show less overlap in trait values than expected based on a random walk through time. A negative MDI in

South American cichlids on PC1 being driven by decreasing rates of morphological evolution was further supported by the results of the Node Height Tests on PC1, which showed a significant correlation between evolutionary rate (as inferred by standardized independent contrasts) and node age (Table 2.2). Interestingly, evolutionary rates on PC2 (biting/crushing morphology) in South America showed a decreasing pattern through time (Fig. 2.7), and low subclade disparity after a relative time of ~0.2 (Fig. 2.9), but did not show an overall significant pattern of decreasing subclade disparity. It is possible that changes in rates/patterns of diversification lagged in biting and crushing morphology compared to ram-suction traits, however greater sampling would likely be necessary to elucidate such a pattern, nor are most comparative methods capable of detecting evolutionary lags.

Within South America, where the initial diversification of the sub-family Cichlinae occurred, ram-feeding optimized morphospace was dominated by the predatory (largely piscivorous) genus Crenicichla. Crenicichla is also the largest genus of Neotropical cichlids and its diversification may have been benefitted by the early colonization of an adaptive peak in functional morphospace (Astudillo-Clavijo et al. In Review; Arbour & López-Fernández 2013).

89 Another South American taxon occupying this ram-feeding space was the genus Cichla (and see

Chapter 1, Fig. 2.8), the next largest clade of elongate-bodied predators among the cichlids of

South America and belonging to a basal lineage of Cichlinae. Other biomechanically ram- optimized taxa include the planktivorous Chaetobranchus (and presumably Chaetobranchopsis, another large-gaped planktivore with long gill-rakers) that together form the sister group to

Geophagini. Comparatively, morphospace on PC1 associated with generalized feeding strategies was occupied by a dense cluster of cichlasomatins as well as benthic-feeding geophagin lineages, while biomechanical traits more associated with herbivory and detritivory were largely occupied by the comparably younger Heroini (Arbour & López-Fernández 2014). Overall, basal lineages

(Cichlini + Retroculini, Chaetobranchini, Geophagini) and those diversifying early in the history of Cichlinae tended to evolve ram-optimized to moderate ram-suction traits (ex: ram-feeding predators in Crenicicha, substrate-sifters in Geophagus, small bodied benthic pickers in

Biotoecus), while younger lineages were more likely to occupy suction/biting morphospace (ex: deep-bodied South American heroins like Symphysodon, Uaru and Heros). DTT and NHT analyses support the conclusion that this relatively clade-specific distribution of feeding-traits in

South America were unlikely to have occurred by chance.

Across a multilocus phylogeny of Cichlinae, López-Fernández et al. (2010) identified significantly shorter than expected basal branches in Geophagini and Heroini, which were interpreted as evidence of rapid evolution during the early history of Cichlinae. López-Fernández et al. (2013) further found evidence for decreasing rates of lineage diversification, through both

Akaike information criterion support for diversity-dependent models of lineage accumulation

(Rabosky & Lovette 2008b) and significant gamma statistics, which analyze the distribution of internal nodes in a phylogeny (Pybus & Harvey 2000). An analysis of morphological evolution in Cichlinae using traditional linear morphometrics (i.e., not direct performance indicators)

90 showed a significant, negative MDI in Geophagini alone, but not in Cichlinae as a whole (López-

Fernández et al. 2013). Comparably, my data exhibited a signal of decreasing ecological opportunity across all Cichlinae lineages examined (driven by early diversification in South

America). Based on functionally-explicit traits I conclude that other basal lineages (Cichlini,

Retroculini, Chaetobranchini, early Heroini-Cichlasomatini lineages) also contributed to a pattern of decreasing rates of functional evolution and finer partitioning of morphospace through time in Neotropical cichlids, alongside Geophagini (López-Fernández et al. 2005b; Arbour &

López-Fernández 2013). López-Fernández et al. (2013) found support for patterns of decreasing rates of lineage diversification within Cichlinae as a whole and within the largest South

American tribe Geophagini. Analysis of lineage diversification of Cichlinae as a whole in the aforementioned study included similar truncations to the DTT analyses presented here (to account for incomplete taxonomic sampling) and the Cichlinae results were predominantly determined by South American diversification patterns. Comparatively, McMahan et al. (2013) found no evidence for a change in rates of diversification early in the evolution of cichlids.

However, this study made use of MEDUSA (Alfaro et al. 2009c) for the analysis of lineage diversification, a method that tests for point rate shifts occurring at specific locations in a phylogeny, as opposed to broader trends in rates occurring in smaller steps distributed across a phylogeny. Our results suggest that after the initial diversification of Cichlinae in the Neotropics, functional evolution varied with decreasing ecological opportunity in a manner consistent with niche-filling, alongside decreasing lineage diversification, consistent with the predictions of an adaptive radiation model.

91 2.5.3 Ecological release in Central America

Ecological release is associated with increased trait variation resulting from changes in the rate or mode of diversification following the invasion of a new habitat, the extinction of an antagonist or the development of a key innovation (Simpson 1944; Yoder et al. 2010; Roches et al. 2011;

Slater 2013). Analysis of ram-suction morphology (PC1) showed that evolutionary rates increased substantially following the transition to new habitats in Central America (Table 2.2), consistent with a pattern of ecological release. The invasion of Central America was associated with an increase in rate of functional evolution comparable to that observed during the initial diversification of basal South American lineages (Fig. 2.5A), suggesting that the forces driving diversification along this axis are similar between the two radiations. Functional morphology, especially in ram-suction associated traits, overlapped between South and Central American cichlids (Arbour & López-Fernández 2014). Similarly, Winemiller et al. (1995) observed convergence in dietary specialization and ecomorphology among cichlid taxa from South and

Central America, and López-Fernández et al. (2013) found parallels in multivariate ecomorphogical divergence between the Central American heroins and South American species.

Furthermore, McMahan et al. (2013) identified an increase in lineage diversification within

Heroini, possibly associated with increased speciation following ecological release (Yoder et al.

2010). While it has been postulated that ecological release may be an important determinant in island radiations (Simpson 1953; Seehausen 2007; Yoder et al. 2010), our results suggest that, in the Neotropical cichlids, release from competition had a role in facilitating adaptive diversification at a broad geographical scale, contributing to the diversity of forms present in

Central American cichlids.

While the NHT analyses showed a correlation between rates of evolution and time across

Cichlinae, DTT analysis showed a (non-significantly) positive MDI in Central America on PC1.

92 Considering the significant DTT result in South America on PC1, it is possible that the pattern of decreasing rates of evolution as detected by NHT occurred primarily in South America. Rates of evolution on PC1 may have not slowed through time among Central American cichlids due to differences in adaptive processes. For example, changes to selection on other aspects of the feeding apparatus (see discussion of PC2 below) following the invasion of Central America may have permitted a longer period of rapid evolution on PC1, through finer niche partitioning or similar factors. It is also possible that varying competition with other Neotropical fish families may have influenced diversification in South and Central America. While South American cichlids diversified alongside a multitude of ostariophysan clades (Malabarba & Lundberg 2007;

López-Fernández & Albert 2011; Weiss et al. 2012), ostariophysans were slow to colonize and diversify in Central America and never reached the taxonomic (Matamoros et al. 2014) or ecomorphological (Winemiller 1991; Winemiller et al. 1995) diversity of their South American counterparts, perhaps allowing Central American cichlids to diversify for a longer period and into niches not available to South American cichlids.

Alternatively, a true pattern of decreasing rates/decreasing subclade disparity through time may be ambiguous in Central America for a number of reasons. Firstly, because the radiation is younger, rates may not have slowed enough for a strong signal given the level of variation (from selection, sampling, etc.). Slater & Pennell (2014) showed that the power to detect early bursts of phenotypic evolution using DTT, NHT or model fitting methods is low early in a radiation. Secondly, I employed the common practice of using node relative age as a proxy for diversity, under the assumption that diversity generally increases through time, to infer whether rates have declined with ecological opportunity. However, some methods have found improved support for models of changing evolutionary rates with ecological opportunity when using a more explicit measure of the number of competing lineages (Mahler et al. 2010). It is

93 possible that a more complete sampling of Central American cichlids combined with such metrics would increase the power to detect decelerating evolutionary rates. Additionally, most

Neotropical cichlids fossils are known from South America, so age calibration of the Central

American radiation has relied largely on biogeographically-based information (Hulsey et al.

2010a; b; Říčan et al. 2013). If the cichlid colonization of Central America occurred over a longer period of time, in stages, or within more restricted geographic regions, this may have influenced how rates of morphological evolution varied through time. Lastly, the number and timing of Central American cichlid invasions may influence diversification patterns. While our analysis agreed with some previous studies showing a single colonization (Hulsey et al. 2010a),

Říčan et al. (2013) found support for two separate invasions into Central America. Říčan et al.

(2013) used a finer scale reconstruction for ancestral areas and greater taxonomic sampling within Central America, and I suspect the difference from the results observed here was also influenced by the placement of a secondarily South American heroin (Australoheros), which was more basal among the “Central American clade” in the aforementioned study. Our current interpretation of these (PC1) results is that they are insufficient to support an independent adaptive radiation within Central America; however greater taxonomic sampling within this group may clarify this in the future. However, support was found for ecological release from

South American fishes following the invasion of Central America.

2.5.4 Differences in functional evolution between South and

Central America

Evolution on PC2 was not indicative of a pattern of decreasing ecological opportunity in

Neotropical cichlids; however, it did reveal evolutionary differences between the South and

Central American radiations. The second axis of functional morphology (PC2) characterized a gradient in relative oral jaw muscle mass and pharyngeal jaw mass which may indicate differences in the use of biting and crushing during feeding. Along this axis I found support for different selective regimes in South and Central America. The adaptive peak of Central America favored larger oral jaw muscles and larger lower pharyngeal jaws. At least some feeding specializations that may require a greater reliance on biting or crushing, such as detritivory, algae-scraping, molluscivory, etc., are more common within the Central American clade

(Winemiller et al. 1995; Hulsey et al. 2008). Rates of evolution were initially slower in Central

America than South America, after accounting for multiple adaptive peaks (Table 2.3 and 2.5,

Fig. 2.4 and 10). It is possible that either larger oral jaw muscles or larger pharyngeal jaws may impart structural constraints on the feeding apparatus of Central American cichlids, leading to decreased trophic diversification (Hulsey & Hollingsworth Jr 2011). However, opposite to South

American cichlids, Central American taxa may possess increasing rates of evolution on PC2

(with high rates clustered among Amphilophine nodes). DTT analysis also showed a positive

MDI value in Central America, with subclade disparity especially high towards the present. If reflective of an underlying process, this trend may be related to trophic polymorphism and phenotypic plasticity, which have been observed in both oral and pharyngeal traits in a number of Central American species (Meyer 1987, 1990; Wanson et al. 2003; Hulsey 2006; Muschick et al. 2011), especially within amphilophine taxa and Herichthys, both of which also exhibited high

95 estimated rates of evolution on PC2 (see results). Additionally, in at least some Amphilophine taxa (particularly within the genus Amphilophus), the colonization of crater lake systems has been associated with sympatric divergence associated with trophic differentiation (Elmer et al.

2010; Recknagel et al. 2014). Increased ecological opportunity in these lake systems may provide lineage-specific opportunities for rapid diversification among some Central America cichlids.

2.5.5 Conclusions

Ecological opportunity has likely been an important factor in the functional evolution of

Neotropical cichlids, but its influence has varied geographically. I found evidence for an adaptive radiation within the South American cichlids on a multivariate axis of feeding functional morphology related to trade-offs in ram- and suction-feeding behaviours and body shapes. I did not find strong evidence for an independent adaptive radiation in Central America, however an increase in rates of evolution following the colonization of Central America, coupled with similar morphological adaptations between the two radiations (Winemiller et al. 1995;

López-Fernández et al. 2013; Arbour & López-Fernández 2014) and increased lineage diversification rates (McMahan et al. 2013), suggests ecological release from basal cichlid lineages in South America. This contrast between South and Central America parallels some observations of island-based radiations: that adaptive radiation in some island systems is not necessarily a determinant of adaptive radiation in closely related clades in other geographic or ecological contexts (Gillespie 2002; Joyce et al. 2005; Seehausen 2006; Losos 2010). A South

American cichlid adaptive radiation adds to a growing body of evidence for continental adaptive radiations (Slater et al. 2010; Derryberry et al. 2011; Schweizer et al. 2014), although evidence differs from several other continental radiations in that cichlids exhibit decreasing rates of both

96 lineage and phenotypic diversification, whereas other radiations have been found to have decreasing rates in only one.

3. Chapter Three

Morphological diversification in extant and extinct Neotropical cichlids

Jessica Hilary Arbour1

1 Department of Ecology and Evolutionary Biology, University of Toronto, 25 Wilcocks St., Toronto, Ontario M5S 3B2, Canada

3.1 Abstract

Recent years have seen an explosion of phylogenetic comparative methods for the analysis of lineage and phenotypic diversification; however the inclusion of fossil material in such analyses has been limited. I examined eight species of extinct, fossil Neotropical cichlids (subfamily

Cichlinae) in the context of modern Cichlinae morphological diversity and evolution. I used multivariate analyses, a multiple-imputation approach to fossil phylogenetic placement and evolutionary simulations to compare ecomorphological diversity among extant and extinct cichlids. A stepwise AIC approach was used to estimate the adaptive landscape of ecomorphospace in Neotropical cichlids to test whether extinct cichlids evolved under different adaptive constraints than modern cichlids. I found that extinct Neotropical cichlids were as morphologically diverse as modern species, after taking into account sample size and phylogenetic position. Morphospace occupation was similar between extinct and extant cichlids, however ~6% of morphospace related to elongate head shapes has been lost via extinction. The best predictor of the ecomorphology of an important fossil, Gymnogeophagus eocenicus, from the Argentinian Lumbrera formation (~48.6 Ma) was its previously inferred phylogenetic position within an extant genus. Extinct cichlids evolved under similar adaptive constraints to modern cichlids. These results strongly suggest that macroevolutionary processes underlying

Neotropical cichlid evolution have remained relatively stable over tens of millions of years.

Fossils contribute to our understanding of morphological evolution in Neotropical cichlids, and emphasize the need for systematic paleontological sampling in South and Central America.

3.2 Introduction

The fossil record can be a valuable source of information in the study of diversification.

However, the inclusion of fossil material into comparative analyses of extant groups has thus far been limited from both a lineage and phenotypic perspective (Slater et al. 2012). A lack of understanding of extinction rates from fossil data may bias the analysis of lineage diversification

(Rabosky & Lovette 2008b, 2009; Rabosky 2010), and rates of lineage diversification have been shown to vary between extant- and fossil-driven estimates (Quental & Marshall 2010). Recent expansions of comparative methods for quantifying phenotypic evolution have also proven increasingly useful in studying evolutionary factors such as ecological opportunity (Mahler et al.

2010), innovations (Price et al. 2010; Near et al. 2012), adaptive landscapes (Ingram & Mahler

2013; Mahler et al. 2013; Grundler et al. 2014), modularity/integration (Klingenberg &

Marugán-Lobón 2013), among other subjects. As fossil material can improve model inference for continuous trait evolution (Slater et al. 2012), the incorporation of fossil material is an important aspect in the analysis of tempo and mode of morphological diversification. However, the inclusion of fossil taxa in phylogenetic comparative analyses has been complicated by the uncertainty in the evolutionary relationships between fossil and extant species associated with fossil incompleteness and the resulting biases in phylogenetic analyses (Sansom et al. 2010;

Sansom & Wills 2013). The lack of molecular data from fossils also complicates the estimation of branch lengths in molecular phylogenetics and subsequent divergence time analyses.

Recent years have seen an increase in the descriptions of Neotropical cichlid fish fossils, including the oldest known fossils from South America (Malabarba et al. 2006, 2010, 2014;

Malabarba & Malabarba 2008; Perez et al. 2010). Many of these fossils show derived features, and phylogenetic analyses have placed one Eocene-age fossil, †Gymnogeophagus eocenicus, into

100 an extant genus (Malabarba et al. 2010, 2014). While some specimens are incomplete or partially disarticulated (Casciotta & Arratia 1993; Chakrabarty 2007), a number of relatively complete and well preserved fossils exist spanning tens of millions of years of evolutionary history. This expansion of the fossil history of Cichlinae (in addition to African fossils; Murray, 2001) has reinvigorated the debate over the age and divergence history of Cichlidae (Friedman et al. 2013;

López-Fernández et al. 2013; McMahan et al. 2013; Říčan et al. 2013).

While fossil material has largely been used as calibrations to estimate divergence times for Cichlidae and its subclades in the context of molecular phylogenetic hypotheses (i.e., similar to acanthomorphs in general; Betancur-R. et al., 2013; Friedman et al., 2013; Near et al., 2013;

Chen et al., 2014), analysis of the evolution of form and function in Neotropical cichlids may be benefitted by the incorporation of fossil material. For example, Slater et al. (2013, 2014) used fossil traits as Bayesian node priors to test for shifts in the rate and mode of caniform carnivoran body size evolution following the Cretaceous-Tertiary (KT) extinction. Fossil ray-finned fish morphology has been used to test the “radiation in stages” model of vertebrate evolution

(Streelman & Danley 2003; Sallan & Friedman 2011). Fossil data has also been used to examine the rapid increase in morphological diversification in birds compared to other theropod dinosaurs

(Benson et al. 2014; Brusatte et al. 2014). Fossil data can improve the inferences of rates and patterns of evolution in extant groups, provide more explicit tests for the role of extinction, and complement our understanding of phenotypic and functional diversity of extant groups by revealing extinct morphologies or transitional phenotypes between extant lineages.

The objective of this study was to examine the effect of extinction on Neotropical cichlid morphological diversity and adaptive landscape dynamics (ex: are there adaptive zones in morphospace unique to extinct cichlids?). I placed several extinct species from relatively complete fossils into an ecologically-relevant morphospace of extant species, and used

101 comparative analyses to test for differences in disparity, morphospace occupation and adaptive landscapes of morphology. Importantly, I performed these analyses while accounting for the uncertainty in our understanding of the evolutionary relationships of Cichlinae fossils in all morphological and evolutionary analyses.

3.3 Methods

3.3.1 Morphometrics

Previous multivariate analyses of functional morphology (Arbour & López-Fernández 2014) and linear morphometrics (López-Fernández et al. 2013) derived from ecomorphological studies

(Winemiller et al. 1995; López-Fernández et al. 2012, 2014) found a primary axis of variation from elongate bodies (with associated ram-feeding functional traits) and deep-bodies (with associated suction-feeding functional traits). These analyses suggest that variation in both sets of traits is being driven by the same trade-off in ecological performance between fast-burst predators feeding on evasive prey, and slower moving, suction-feeders consuming immobile foods (detritus, algae, some benthic invertebrates). I combined these two datasets (López-

Fernández et al. 2013; Arbour & López-Fernández 2014), with the goal of placing extinct

Neotropical cichlids (fossil taxa) into the context of a modern, ecologically-relevant morphospace.

Linear morphometric data were obtained from the data associated with López-Fernández et al. (2013) as well as 10 species not included in the previous study (Appendix 3.1).

Morphometric measurements included: 1) head length, measured from the tip of the closed upper lip to the posterior edge of the operculum; 2) head height, measured vertically through the eye, 3)

102 eye position, measured as the vertical distance between the ventral edge of the head to the centre of the eye; 4) eye diameter, measured as the horizontal distance across the eye; 5) snout length, measured from the closed upper lip to the centre of the eye; 6) body depth, measured vertically at the deepest part of the body; and 7) caudal peduncle depth, measured vertically through the midpoint of the peduncle. I did not include gape width from the López-Fernández et al. (2013) dataset, since gape width was also included in the calculation of suction index (see methods

Chapter 1). Species average values were corrected to body size using phylogenetic size correction (Revell 2009). Residuals of a log-log phylogenetic regression of linear morphometrics on standard length (measured as the distance between the closed upper lip and the posterior margin of the caudal peduncle) were used in subsequent analyses. Functional morphological data were size-corrected as described in Chapter 1, for all size-dependent variables (AM mass, ST mass, CB5 mass and jaw protrusion length).

3.3.2 Phylogenetic Canonical Correlation Analysis

Canonical correlation analysis (CCoA) was applied to the functional morphological and linear morphometric data to 1) verify that there was a significant relationship between the two datasets, and 2) evaluate which variables may be more resistant to the estimation of missing data.

Canonical correlation analysis is used to find the maximum correlation between two multivariate data sets (Thompson 1984; Revell & Harrison 2008; Fan & Konold 2010). Revell & Harrison

(2008) developed a phylogenetically-correlated canonical correlation analysis, using the C matrix (shared branch length matrix), that allows for canonical scores to be plotted in species trait space (as opposed to previous methods relying on independent contrasts and plotting in node space). The R function “phyl.cca” (package phytools) was used to compute a phylogenetic

103 canonical correlation analysis of functional morphology and ecomorphology in 74 species of

Neotropical cichlids. I also used this R function to carry out the χ2 test of Wilk’s λ for the significance of each canonical correlation.

Structural coefficients (the correlation coefficient between each variable and a canonical correlation function; Thompson, 1984) were computed using a phylogenetically-corrected correlation matrix derived using the internal function “phyl.vcv” from “phytools” (Revell 2012).

Commonly, values above 0.316 are used to indicate which structural coefficients contribute significantly to each axis, which corresponds to at least 10% of variation in a given variable being explained by a particular axis (Fan & Konold 2010). The sum of all squared structure coefficients for a variable is the communality coefficient (h2), and describes how much variation is explained by the CCoA.

3.3.3 Fossil Ecomorphology and Phylogenetic Placement

The objective of the following analyses was to place fossil cichlid species into the multivariate space of modern cichlid ecomorphology and biomechanics, and to compare extant and fossil cichlid disparity and evolution. Linear morphometrics (Appendix 3.1) and oral jaw biomechanical coefficients (Appendix 3.2) were collected from eight species of Neotropical cichlids known only from fossil material and ranging in age from ~3.6 to ~48.6 Ma (Table 3,

Appendix 3.1 and 3.2). I used Bayesian Principal Component Analysis to estimate size-corrected missing data, as this method has found to be reliable on a number of datasets, as well as preferable to pairwise deletion and the removal of incomplete specimens or variables in the estimation and analysis of morphospaces (Oba et al. 2003; Brown et al. 2012; Arbour & Brown

2014). I excluded those variables with the most missing data if they were poorly correlated to the

104 combined axes of functional morphology (Table 3.2) or were not critical to the morphospace of extant species (Arbour & Brown 2014 and see Appendix 4.2), to minimize estimation error

(Arbour & Brown 2014). The results of principal component analyses on datasets with and without incomplete variables were compared to ensure their inclusion did not strongly bias the relative position of the fossil taxa in morphospace.

I carried out a phylogenetic principal component analysis (Revell 2009, 2012) on the combined functional and ecomorphological data across 1000 chronograms with fossil taxa included as described below. The phylogenetic PCA used a correlation matrix to account for the different scales that variables were measured at, and phylogenetically-corrected parallel analysis was used to determine the critical number of axes.

I placed fossil species in the phylogenetic tree based on previous research while allowing for uncertainty in their phylogenetic position and age of divergence. I randomized the position of each fossil taxon on the phylogeny of López-Fernández et al. (2010) and López-Fernández et al.

(2013) based on previous phylogenetic or taxonomic assessments and the best-supported age for each fossil (see summary in Table 3.1 and descriptions below). I placed fossil taxa along a chosen branch so that they were more likely to have to have diverged close to their estimated age, i.e., the time of divergence of each fossil lineage along a branch was selected as a function of decreasing probability with time before the age of the fossil. Following the addition of all fossil species, each chronogram was scaled to a total length of 1 to make results of subsequent analyses more directly comparable. Figure 3.1 shows a sample phylogeny with fossil placements

(out of 1000 used in the following analyses).

Three fossil cichlid species, †Gymnogeophagus eocenicus, †Plesioheros chauliodus and

†Proterocara argentina (Malabarba et al. 2014) have been described from outcrops of the

105 Eocene Lumbrera Formation, a geological unit consisting of continental deposits, in northern

Argentina. The fossils come from laminated claystone in the Faja Verde layer of the Lumbrera

Formation, interpreted as a fossil lake bed. Radiometric dating of a crystal-tuff layer stratigraphically 240m above the fossil layer places an absolute minimum age of the fossils at

39.9 Ma (del Papa et al. 2010), however, palaeoclimatic studies have associated the fossiliferous layer with the Early Eocene Climatic Optimum (White et al. 2009; del Papa et al. 2010;

Malabarba et al. 2014), with the cichlid fossil layer age at approximately 48.6 Ma. Additionally, radiometric dating analysis of carbonate nodules from paleosol formation at the discontinuity of the lower and upper Lumbrera (consistent with the top of the Faja Verde) support an age of 47

Ma (± 7 Ma, 2 s.d.), consistent with an age older than ~40 Ma for the fossil layer (DeCelles et al.

2011; Galli et al. 2014).

†Gymnogeophagus eocenicus was placed within the genus Gymnogeophagus based on two synapomorphies: a lack of supraneurals and an anteriorally directed spine on the first dorsal pterygiophore (Malabarba et al. 2010, 2014). I allowed †G. eocenicus to vary within

Gymnogeophagus to account for phylogenetic uncertainty, however †G. eocenicus may be more closely related to the G. gymnogenys subclade than the G. rhabdotus subclade (Malabarba et al.

2010). †Plesioheros chauliodus was placed in Heroini on the basis of dental characteristics, and phylogenetic analyses placed it closer to some South American taxa (Perez et al. 2010), albeit that study did not include Central American heroines. Therefore the phylogenetic placement of

Plesioheros was allowed to vary across South and Central American heroin nodes. In its initial description †Proterocara argentina was placed basal to a clade comprised of Geophagini,

Heroini and Cichlasomatini, based on morphological characters alone. However, a combined molecular and morphological analysis placed †Proterocara argentina with Crenicichla and

106 Teleocichla (Smith et al. 2008). I allowed it to vary between both phylogenetic positions in our analyses.

The extinct genus †Tremembichthys is represented by two Brazilian species.

†Tremembichthys pauloensis is from the Tremembé Formation in the Taubaté basin and has been dated to late Oligocene-early Miocene (Schaeffer 1947; Lima et al. 1985), and †T. garciae is from the Entre-Córregos Formation, dated Eocene-Oligocene (Malabarba 2008). Phylogenetic analysis places this genus within Cichlasomatini, with possible affinities to taxa such as

Cleithracara, Laetacara, Nannacara and Aequidens hoehnei (Malabarba 2008).

Two Miocene cichlid species (†Palaeocichla longirostrum and †‘Aequidens’ saltensis) were described by Bardack (1951) from the Anta Formation near La Yesera Creek in Salta,

Argentina (Cione et al. 1995; Starck & Anzo 2001). Sadly the type specimens for both these species have been lost, however detailed plates still exist and further analysis of †Palaeocichla longirostrum have been carried out (Casciotta & Arratia 1993). †‘Aequidens’ saltensis was described from a complete specimen (see plate in Bardack, 1951) and is unlikely to belong to

Aequidens sensu stricto based on fin spine and vertebral counts, but rather may be a geophagin

(Kullander 1983). Casciotta & Arratia (1993) examined an unidentified geophagin fossil that was morphologically congruent with the description of †‘Aequidens’ saltensis, and found it was comparable to deep-bodied geophagin genera including Geophagus, Acaricthys and

Satanoperca. As the taxonomic status of A. saltensis is uncertain, I allowed it to vary across

Geophagini excluding Crenicichla and Teleocichla, from which it varies substantially in body shape and osteological characters.

The second Salta fossil species, †Palaeocichla longirostrum, was originally placed in

Acaronia (Bardack 1961), however later morphological phylogenies placed it as the sister taxon

107 to Cichla and it was moved to its own genus. Linear morphometrics in this study were measured from the plate of specimen YPF 19664 published by Bardack (1961). Most Palaeocichla specimens are incomplete, including YPF 19664 (Bardack 1961), and standard length (SL) could not be measured. However it is morphologically unique among fossil Neotropical cichlids

(elongate body, long jaws) and therefore body length was estimated from extant data to allow its inclusion. The standard length (SL) of †Palaeocichla longistrum fossil YPF 19664 was estimated based on a stepwise multiple regression (log-log) of SL on all uncorrected morphometric variables from all specimens of extant species (Brown et al. 2012). This multiple regression explained 98.9% of variation in SL and predicted a SL of 142.8 mm for this specimen of Palaeocichla, which was consistent with the reported range of estimated total lengths (60 to

190 mm) from Bardack (1951).

†Macracara prisca (Woodward 1939) is a comparably recent fossil (Dino et al. 2006) described from Brazil. This species has at times been attributed to Geophagus (Casciotta &

Arratia 1993), however no formal phylogenetic or taxonomic analysis has been carried out. Due to this uncertainty, I allowed the placement of †Macracara prisca to vary as described above for

†Aequidens saltensis, another taxa with possible geophagin affinities.

Several Neotropical cichlid fossils were not included in these analyses due to their state of preservation. Casciotta & Arratia (1993) describe specimens from Crenicichla and

Gymnogeophagus, as well as several unidentified geophagins, based on incomplete fossils from

Salta, Argentina. An additional species from a modern genus, the Miocene †Nandopsis woodringi, is based on an incomplete and partially disarticulated fossil from Haiti (Cockerell

1923; Chakrabarty 2006). These fossils may represent ecomorphological variation not captured by our dataset or may be useful in dating molecular phylogenies; however their inclusion in the following analyses was not possible due to their preservation.

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Fig. 3.1: One of 1000 chronograms used in the following analyses, with the locations of fossil taxa (all non-contemporaneous tips) randomly sampled as outlined in Table 3.1 and the methods described above. Fossil taxa are given in red text.

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Table 3.1: Summary of Neotropical fossil cichlids used in ecomorphological analyses. References provided give the approximate ages and phylogenetic analysis of taxonomic description of evolutionary relationships to modern taxa.

Species ~ Fossil Taxonomic placement References Age (Ma)

Bosio et al. 2009; White et al. Gymnogeophagus Within the extant genus 48.6 2009; del Papa et al. 2010; eocenicus Gymnogeophagus Malabarba et al. 2010, 2014

Bardack 1961; Casciotta & Palaeocichla Arratia 1993; Cione et al. 13 Sister to Cichla longirostrum 1995; Starck & Anzo 2001; Perez et al. 2010

Bardack 1961; Casciotta & ‘Aequidens’ Arratia 1993; Cione et al. 13 Possible geophagin saltensis 1995; Starck & Anzo 2001; Perez et al. 2010

Basal cichlasomatin or sister Tremembichthys to a clade including Malabarba 2008; Perez et al. 34 garciae Laetacara, Cleithracara and 2010 Nannacara

Basal cichlasomatin or sister Tremembichthys to a clade including Schaeffer 1947; Lima et al. 23 pauloensis Laetacara, Cleithracara and 1985; Malabarba 2008 Nannacara

Related to Malabarba et al. 2006, 2014; Proterocara Crenicichla/Teleocichla, or 48.6 Smith et al. 2008; Bosio et al. argentina basal to Geophagini + 2009; White et al. 2009 (Heroini + Cichlasomatini)

Bosio et al. 2009; White et al. Plesioheros 48.6 Heroini 2009; Perez et al. 2010; chauliodus Malabarba et al. 2014

Macracara Possible geophagin or Woodward 1939; Dino et al. (“Geophagus”) 3.6 Geophagus 2006; Perez et al. 2010 prisca

110 3.3.4 Fossil disparity and morphospace occupation

To test whether fossil cichlids are representative of modern ecomorphological diversity I examined the disparity and morphospace occupation of fossil cichlid species compared to their extant counterparts. If fossil cichlids exhibit similar morphologies and diversity to modern taxa, this would support previous analyses showing that ecologically-relevant morphological diversity was established early in cichlid evolution in the Neotropics, consistent with an adaptive radiation. I calculated disparity as the average squared pairwise distance using function

“disparity” in the R package “geiger” (Harmon et al. 2008) for the 8 fossil taxa and the 74 modern cichlids. I further calculated the functional disparity of 1000 randomized subsamples of eight of the 74 extant species to determine whether differences between the disparity of modern and fossil taxa could have occurred as a result of the low sample size of fossil taxa (Cooper et al.

2010). I also determined whether the disparity of fossil cichlids could have occurred under random-walk evolution based on their phylogenetic positions. I simulated species PC scores of ecomorphology (Fig. 3.4) across all taxa, with or without selective constraint based on the best fitting model (Brownian Motion, BM – pure random walk, or Ornstein-Uhlenbeck, OU – random walk with selective constraint) for each PC axis (Table 3.4). Model parameters for OU models were fit using VCV methods as the inclusion of fossil taxa produced non-ultrametric trees (Slater

2013, 2014). Disparity of the eight fossil species was calculated from 1000 sets of simulated PC scores per chronogram.

I then tested whether fossil cichlids represented novel regions of morphospace compared to modern taxa. I calculated the amount of morphospace representing only fossil species as a percentage of the total morphospace of all 82 species, using convex hulls to the calculate areas of morphospaces and overlap, using functions “convex.hull” and “area.poly” from the R packages

“tripack” and “gpclib” respectively (Peng et al. 2013; Renka et al. 2013). I tested whether a

111 similar sample size of modern taxa will have the same percentage of novel morphospace, compared to that observed with fossil taxa. I further tested whether the observed percentage overlap in morphospace could have occurred under random-walk evolution, based on the best-fit model (see above).

All disparity and morphospace analyses were calculated over 1000 posterior distribution chronograms. For all tests described above, I calculated the frequency at which the observed value fell within the upper or lower 2.5% percentiles of the simulated or randomized values. The p-value for these tests was calculated as twice the frequency (two-tailed test, alpha = 0.05) of values in this combined set that were either ≥ observed value (if obs was in the lower tail) or ≤ observed value (if obs was in the upper tail).

3.3.5 Gymnogeophagus ecomorphological diversity

The Lumbrera fossils are particularly important to understanding the age and biogeography of cichlids due to their age, 48.6 Ma, state of preservation and taxonomic placement. Of the species currently described from Lumbrera Formation, †Gymnogeophagus eocenicus is particularly important due to its apical placement in Geophagini, within the extant genus Gymnogeophagus, based on two osteological synapomorphies (Malabarba et al. 2010, 2014).

I tested whether †G. eocenicus was more similar to modern Gymnogeophagus than expected by chance based on other modern and fossil taxa. I calculated the similarity between

†G. eocenicus and modern Gymnogeophagus in our dataset (G. balzanii and G. rhabdotus) using morphological disparity as the average squared pairwise distance in PC scores (lower average disparity = increased ecomorphological similarity). This was compared to the disparity generated

112 by calculating the average squared pairwise distance in PC scores between the two modern

Gymnogeophagus species and (individually) each other species examined in this dataset across all chronograms (i.e., incorporating phylogenetic uncertainty). The one-tailed p-value for this test was the frequency of the modern species that produced a disparity equal to or lower than that of

†G. eocenicus. I further tested whether the disparity of all three Gymnogeophagus species could have occurred under random walk evolution, based on 1000 character simulations of PC scores of ecomorphology, under BM or OU evolution based on the best-fitting model of evolution for each axis (Table 3.3). Lastly I tested whether the observed disparity of the three

Gymnogeophagus species was lower than that of the two modern species and a third random species (sampled from the total dataset) under random walk evolution (i.e., could the observed similarity have occurred with a random taxon under random walk).

3.3.6 Cichlid Ecomorphological Adaptive Landscape

I tested whether extinct Neotropical cichlids evolved under similar selective constraints to that observed among modern taxa. I used a stepwise-algorithm, SURFACE to estimate the adaptive landscape of extant and extinct Neotropical cichlids using multi-peak Ornstein-Uhlenbeck (OU) models (also see Chapter 1 methods). SURFACE proceeds in two stages, the forward stage progressively adds OU peaks, and the backwards phase collapses similar adaptive peaks, each until the AIC score is no longer improved (decreased) at each step (Ingram & Mahler 2013;

Mahler et al. 2013).

I carried out SURFACE analyses on a sample of 100 chronograms with fossil taxa. I grouped adaptive peaks across chronograms by their position in morphospace and by their taxonomic composition. I summarized data for any peaks that occurred on at least 5% of

113 chronograms. Following Ingram & Mahler (2013), convergence parameters were summarized from the resulting Hansen models (multi-peak OU processes), and included: the number of peaks

(k), the reduction in landscape complexity in the backwards phase (Δk), the number of unique peaks (k’), the number of convergent peak shifts, i.e., shifts to peaks occupied by other lineages

(c).

3.4 Results

3.4.1 Phylogenetic Canonical Correlation Analysis

Phylogenetic CCoA revealed 3 axes significant across all 1000 posterior distribution chronograms (Table 3.2, χ2 p-value). A fourth axis was significant across only 40% of chronograms and non-significant on the MCC chronogram (p = 0.0753), and was not analyzed further. While most functional and morphological variables were well represented by the CCoA, variation in jaw protrusion and eye diameter were poorly explained across all three axes (h2 =

0.064 and 0.061).

The first CCoA axis was loaded most strongly by suction index (+), body depth (+) and

AM mass (-; Table 3.2). Fish on the negative extreme of this axis possessed elongate bodies

(shallow bodies and heads, more ventrally positioned eyes) with rapid oral jaw kinematics (both low lower jaw MA and high oral jaw KT), evenly occluding jaws and proportionately larger oral jaw muscles (Fig. 3.2, Table 3.2). Taxa with negative CCoA 1 scores included the predatory genera Cichla and Crenicichla, as well as some elongate-bodied dwarf taxa such as Taeniacara and Biotoecus (Fig. 3.2). Fish with positive CCoA 1 scores possessed deep bodies, with strong suction feeding ability, and high force transmission in unevenly occluding oral jaws. Taxa with

114 positive CCoA 1 scores were largely heroins and cichlasomatins, including Symphysodon,

Pterophyllum and Cleithracara (Fig. 3.2).

The second CCoA axis was positively correlated with head length, hyoid KT and ST mass, and negatively correlated with CB5 mass and peduncle depth (Fig. 3.2; Table 3.2). CCoA axes 2 separated taxa with long heads, large ST muscles and the ability to make rapid buccal movements from those with compact heads, thicker caudal peduncles and proportionately greater crushing ability (Fig. 3.2). The third CCoA axis was positively correlated with lower jaw opening MA, AM mass, CB5 mass, eye position, head length, head height and snout length. Axis

3 separated taxa with proportionately more gracile heads with rapid lower jaw opening, from those with robust heads with more dorsally positioned eyes and strong biting and crushing (Fig.

3.3). Interestingly the two deepest bodied taxa, Pterophyllum and Symphysodon, were strongly separated along this axis (Fig. 3.3).

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Fig. 3.2: Phylogenetic canonical correlation analysis (axis 1 and 2) of functional morphology and body shape in 74 species of Neotropical cichlids, summarized across 1000 posterior chronograms. Arrows indicate the direction and magnitude of structural coefficients for each variable across each axis. Structural coefficients were proportionately scaled to improve visualization. Black arrows = ecomorphology, red dashed arrows = functional morphology. Small circles indicate the canonical scores, and are coloured by tribe (see legend above).

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Fig. 3.3: Phylogenetic canonical correlation analysis (axis 1 and 3) of functional morphology and body shape in 74 species of Neotropical cichlids, summarized across 1000 posterior chronograms. Arrows indicate the direction and magnitude of structural coefficients for each variable across each axis. Structural coefficients were proportionately scaled to improve visualization. Black arrows = ecomorphology, red dashed arrows = functional morphology. Small circles indicate the canonical scores, and are coloured by tribe (see legend above).

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Table 3.2: Summary of phylogenetic CCoA of functional morphology and body shape (ecomorphology) in 74 cichlid species across 1000 posterior distribution chronograms. R values are the redundancy coefficients for each set of variables. Values given as mean (s.d.), except for p-values, which are given as the median value. Variable terms are the structure coefficients given as mean (s.d.). Bold structural coefficients indicate r > 0.316, or more than 10% of variation explained by a given axis.

Structure Coefficients Total Communality Variables Axis 1 Axis 2 Axis 3 Coefficients

Canonical 0.808 (0.00713) 0.697 (0.0164) 0.610 (0.0135) NA Correlation

χ2 p-value <0.0001 <0.0001 0.00266 NA

Functional Morphology:

Jaw protrusion -0.113 (0.0384) 0.215 (0.0776) 0.0692 (0.0833) 0.064

AM mass -0.638 (0.0254) 0.057 (0.0814) 0.543 (0.0437) 0.705

ST mass 0.148 (0.0594) 0.624 (0.0472) 0.294 (0.0724) 0.498

CB5 mass -0.297 (0.0608) -0.403 (0.0782) 0.462 (0.0911) 0.465

Lower jaw MA 0.609 (0.021) -0.229 (0.0572) 0.1 (0.0616) 0.433 (closing)

Lower jaw MA 0.483 (0.0308) -0.197 (0.0888) 0.586 (0.0742) 0.615 (opening)

Quadrate offset 0.547 (0.0306) -0.22 (0.057) -0.246 (0.0863) 0.408

Hyoid KT -0.22 (0.0578) 0.618 (0.0499) 0.0354 (0.0854) 0.432

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Oral jaw KT -0.383 (0.0344) -0.0436 (0.0521) -0.167 (0.0358) 0.176

Suction Index 0.744 (0.0232) 0.125 (0.0769) -0.135 (0.0633) 0.588

Ecomorphology:

Head Length -0.02 (0.0646) 0.539 (0.0799) 0.706 (0.0945) 0.789

Head Height 0.8 (0.0247) -0.277 (0.0866) 0.4 (0.0683) 0.877

Eye Position 0.607 (0.0305) -0.246 (0.0939) 0.7 (0.0605) 0.919

Eye Diameter 0.193 (0.0238) -0.041 (0.0533) 0.15 (0.0687) 0.061

Snout Length 0.256 (0.0419) -0.048 (0.0925) 0.66 (0.0528) 0.503

Body Depth 0.897 (0.0122) 0.047 (0.0709) 0.132 (0.043) 0.824

Peduncle Depth 0.463 (0.0375) -0.329 (0.0699) 0.155 (0.0812) 0.347

3.4.2 Cichlid Ecomorphospace

The phylogenetic principal component analysis of morphology and feeding biomechanics resulted in two critical axes explaining 35.6% and 17.8% of morphological variation respectively. PC1 characterized a transition between elongate bodied fish with fast oral jaw movement (low lower jaw MA and high oral jaw KT; Table 3.3) and tall-bodied fish, with tall heads and dorsally positioned eyes, as well as efficient force transmission and strong suction ability. Elongate-bodied taxa on PC1 comprised primarily predatory taxa (ex: Crenicichla,

Cichla and Petenia) and extremely small-bodied “dwarf” cichlids such as Biotoecus, Taeniacara and Dicrossus. Tall-bodied taxa on PC1 included substrate-sifters (Geophagus,

Gymnogeophagus and Satanoperca), detritivores/algivores (Hypsophrys, Symphysodon,

119 Paraneetroplus), invertebrate pickers (Archocentrus centrarchus) and generalists. PC2 varied primarily in terms of head shape, from taxa with compact heads, unevenly occluding jaws and strong suction ability compared to long headed taxa with proportionately large oral jaw muscles, more evenly occluding jaws and poor suction ability (Table 3.3, Fig. 3.4). Suction ability was maximized for those taxa with high PC1 and PC2 scores, such as Symphysodon and Cleithracara

(Fig. 3.4), while ram-feeding characteristics (low MA and high KT) were maximized for taxa with low PC1 and PC2 scores (Fig. 3.4, Table 3.3). Sediment-sifters (Geophagus,

Gymnogeophagus, Satanoperca, Acarichthys, Biotodoma, Mikrogeophagus, Thorichthys, and

Astatheros robertsoni) were relatively more common among high PC1 and low PC2 space; ie., possessed moderate suction ability but long heads and proportionately larger buccal cavities (Fig.

3.4), although small-bodied sifters (Biotodoma and Mikrogeophagus) did possess relatively compact heads (Fig 3.4). This region of morphospace was also not exclusive to sifters.

Fossil taxa fell well within the range of extant morphological variation (elongate ram- feeders vs. disk-shaped suction feeders) on PC1 (Fig. 3.4); however three taxa, †Tremembichthys garciae, †Tremembichthys pauloensis and in particular † ‘Aequidens’ saltensis, showed elongate heads (low PC2). These three fossil taxa were most similar in head morphology to Astatheros robertsoni, a heroin sediment-sifter, among the extant taxa examined (Fig. 3.4). The

Tremembichthys species in particular possessed relatively narrow jaws compared to most deep or moderately deep-bodied taxa (Appendix 3.2, quadrate offset). Most fossil taxa were relatively deep-bodied, with the exception of †Palaeocichla longirostrum, which was somewhat elongate, similar to its presumed closest relatives, Cichla and Retroculus, as well as some Central

American predatory taxa, including Parachromis and Petenia. It was also similar to modern

Cichla, Crenicichla, Caquetaia, Petenia and Parachromis in terms of its low lower jaw mechanical advantages (Appendix 3.2) and poor (estimated) suction ability (mean SI = 0.0515 ±

120 0.0119 s.d.). †Plesioheros chauliodus possessed lower jaw characteristics (high MAs and high quadrate offset) common to deep-bodied and compact-headed South and Central American heroins (ex: Heros, Symphysodon, Paraneetroplus and Herotilapia), but was somewhat more generalized in ecomorphology by comparison (Fig. 3.4). †Plesioheros chauliodus was estimated to be the strongest suction feeder of the fossil species examined, and all other fossil species except †Palaeocichla were estimated to have moderate or low-moderate suction feeding ability

(SI ~0.1 to 0.3), similar to most extant Neotropical cichlids. †Proterocara argentina exhibited very generalized ecomorphology, while †Gymnogeophagus eocenicus and †Macracara prisca possessed moderately deeper bodies and longer heads (Fig. 3.4).

(Next Page) Fig. 3.4: Phylogenetic principal component scores of ecomorphology in 74 species of modern Neotropical cichlids, and 8 species of extinct, fossil Neotropical cichlids. PC scores were averaged over 1000 posterior distribution chronograms scaled to a length of 1. Fossil taxa are designated with †. Colours correspond to the tribes given in the legend.

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Table 3.3: Mean loading factors from phylogenetic principal component analysis of ecomorphology and functional morphology of 82 species of extant and extinct Neotropical cichlids. Results were summarized over 1000 posterior distribution chronograms. Bold values give the highest loadings on each axis. ST, CB5, Jaw Protrusion and Hyoid KT were excluded due to a high percentage of missing values and weak correlation with other elements of ecomorphology

Variable PC1 PC2 Head Length 0.366 -0.686 Head Height 0.887 -0.123 Eye Position 0.847 -0.310 Eye Diameter 0.223 0.083 Snout Length 0.435 -0.475 Body Depth 0.882 0.087 Peduncle Depth 0.641 -0.013 AM Mass -0.400 -0.529 Lower Jaw Closing 0.586 0.416 MA Lower Jaw Opening 0.590 -0.196 MA Quadrate Offset 0.400 0.688 Oral jaws KT -0.395 0.382 Suction Index 0.575 0.521 % variation 35.6 17.8 explained

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3.4.3 Fossil Disparity and Morphospace Occupation

Fossil taxa were, on average, less morphologically disparate than modern taxa (Fig. 3.5 left, blue vs. red). However, a random subsample of eight modern species produced a wide range of disparities (Fig. 3.5 left, green). Fossil disparity did not differ significantly from the disparity of these randomized taxa (Fig. 3.5 right, green). Additionally, I simulated PC scores of ecomorphology under a random-walk evolutionary process (including selective constraint under an OU model for PC2, see Table 3.4). The variation in fossil disparities possible under a random- walk process was very high (Fig. 3.5 left, yellow). Again, the observed fossil disparity was not significantly different from values simulated based on their phylogenetic positions (Fig. 3.5 right, yellow). Therefore the low disparity of fossil cichlids could have been an artifact of low sample size, and is not likely to reflect a change in evolutionary processes.

Table 3.4: Summary of model fitting parameters for BM and OU evolution on PC1 and PC2 of ecomorphology. Values σ2 and α give the rate of evolution and the strength of the selective parameter respectively. Values are given as mean (s.d.).

Axis PC1 PC2 Model BM OU BM OU Lik -146.4 (7.57) -145.8 (6.86) -118.4 (8.73) -111.1 (6.29) AIC 296.9 (15.13) 297.9 (13.73) 241 (17.46) 228.4 (12.58) ΔAIC 0.56 (2.14) 1.61 (0.8) 12.6 (8.15) 0 (0.1) w 0.65 (0.19) 0.35 (0.19) 0.03 (0.07) 0.97 (0.07) σ2 4.58 (0.95) 5.19 (1.98) 2.33 (0.56) 5.2 (1.03) α - 0.24 (0.36) - 2.52 (0.37)

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Fig. 3.5: Analysis of morphological disparity in PC scores of ecomorphology in fossil and extant species of Neotropical cichlids. Left: Disparity was calculated for 74 extant cichlids species (extant, red) and 8 fossil species (fossil, blue), across 1000 chronograms. Ecomorphological disparity was also calculated for 1000 sub-samples per chronogram of 8 extant species (randomized, green), and 1000 simulated character histories per chronogram of 8 fossil species (simulated, yellow), across 1000 chronograms. Right: distribution of p-values generated from each chronogram by comparing randomized and simulated species values against the observed fossil disparity for each chronogram. Dashed line shows a p-value of 0.05. Random extant taxa and simulated fossil taxa did not exhibit significantly different disparity from the observed fossils.

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I characterized morphospace occupation of modern and extinct fossil cichlids using convex hulls

(Fig. 3.6). The area of the convex hull surrounding all fossil PC scores that did not overlap with the convex hull of modern species could represent morphological space lost via extinction. The median proportion of morphospace (area) occupied only by fossil taxa was 0.0637 (Fig. 3.7A).

Random samples of eight modern taxa did not typically generate novel regions of morphospace compared to all other taxa (Fig. 3.7, B), and these regions were significantly smaller than that observed in the fossil cichlids across the majority of trees (Fig. 3.7 right; 63.0% of chronograms with p < 0.05). Similarly, simulating PC scores of ecomorphology under random-walk evolution for the eight fossil taxa did not generally produce novel regions of morphospace compared to all other taxa (Fig. 3.7 C), and these regions were smaller than that observed in the actual fossils

(Fig. 3.7 right, 66.8% chronograms with p < 0.05). Therefore, a small but significant region of ecomorphospace, associated with elongate heads, is not represented by modern Neotropical cichlid species, and this cannot be explained by the low sample size of fossil cichlids.

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Fig. 3.6: Convex hulls for extant and extinct, fossil Neotropical cichlid PC scores. PC scores shown were summarized across 1000 posterior distribution chronograms. The light blue zone shows morphospace not represented by the 74 extant taxa examined.

Fig. 3.7: Analysis of morphospace occupation in fossil and extant species of Neotropical cichlids. Proportion of combined morphospace unique to A) 8 extinct, fossil taxa summarized across 1000 posterior distribution chronograms (fossil, red), B) 1000 sub-samples per chronogram of 8 extant species (random, blue), and C) 1000 simulated character histories per chronogram of 8 fossil species (simulated, green), across 1000 chronograms. D distribution of p- values generated from each chronogram by comparing the observed proportion of novel morphospace to that generated by the randomized and simulated species values across 1000 chronograms. Values to the left of the dashed line were significant (p < 0.05).

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3.4.4 Gymnogeophagus diversity

I found high ecomorphological similarity of †Gymnogeophagus eocenicus to the two extant

Gymnogeophagus species in our dataset (Fig. 3.4). The disparity of all Gymnogeophagus species was lower than expected based on other taxa and after incorporating phylogenetic uncertainty

(median p = 0.0127, p < 0.05 on 95.1% of chronograms, Fig. 3.8 right). Simulating the PC scores of Gymnogeophagus species under random-walk evolution resulted in an ecomorphological similarity (low disparity, Fig. 3.8) not significantly different from the observed values across most chronograms (median p = 0.0550; p < 0.05 on 39.4% of chronograms; Fig. 3.8 “simulated

1”). However, disparity was significantly higher than that observed among Gymnogeophagus when †G. eocenicus data was simulated based on other phylogenetic positions (median p =

0.019; p < 0.05 on 99.9% of chronograms; Fig. 3.8 “simulated 2”). The ecomorphological similarity among these three species was therefore unlikely to occur under random-walk evolution if †G. eocenicus did not actually belong to Gymnogeophagus. Nor was exceptional convergence necessary to explain their ecomorphological similarity, only the approximate phylogenetic position inferred by Malabarba et al. (2010).

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Fig. 3.8: Ecomorphological disparity of Gymnogeophagus. Left: Ecormorphological disparity of Gymnogeophagus rhabdotus, G. balzani and either a) †Gymnogeophagus eocenicus, b) a random non-Gymnogeophagus species, c) simulated ecomorphology for †G. eocenicus (simulated 1), or d) simulated ecomorphology of taxa at other phylogenetic positions (simulated 2), over 1000 chronograms. Right: Significance of the test of whether conditions b-c produced disparities as low as that observed in condition “a” described above. Values to the left of the dashed line were significant.

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3.4.5 Ecomorphospace Adaptive Landscape Analysis

Adaptive landscapes of ecomorphology in Cichlinae were used to test whether fossil cichlids tended to evolve under similar adaptive constraints to those observed in modern species.

Adaptive landscape analyses incorporating uncertainty in branch lengths and the phylogenetic position of fossil taxa produced between 4 and 8 unique adaptive peaks (Table 3.5) on each of

100 chronograms (each with an independent set of fossil placements). The number and proportion of convergent shifts was variable across phylogenies (Table 3.5). Figure 3.9 illustrates all adaptive peaks that were estimated on at least 5% of chronograms. Peaks 1, 4 and 6 were present across all 100 sampled chronograms, while peaks 2 and 3 were present in the vast majority (96 and 97% respectively). Peaks 5 and 7 were less likely to be estimated on a given chronogram, with peak 5 occurring on 46% of chronograms, and peak 7 occurring on 37% of chronograms.

Peaks 1 and 3 represented predominantly piscivorous or predatory taxa. Peak 1 was the most strongly ram-optimized, with highly elongate bodies and rapid oral jaw kinematics (Fig.

3.9), and always included the monophyletic Crenicichla-Teleocichla. Across half of the chrongrams, peak 1 included the Central American piscivore Petenia splendida. Peak 3 included predatory genera from Cichla and Central American Heroini, and the sediment-sifting

Retroculus. Peak 2 was represented exclusively by “dwarf” geophagin cichlids in the genera

Taeniacara, Dicrossus, Crenicara, Biotoecus and sometimes Apistogramma. Peak 4 was largely estimated as the ancestral peak of Cichlinae, and included taxa from five tribes (Geophagini,

Heroini, Cichlasomatini, Chaetobranchini and Astronotini). Peak 4 included mostly moderately deep bodied taxa (with a few exceptions like Theraps irregularis) but showed extensive variability in head shape (Fig. 3.9). The majority of sediment-sifting species occurred on lower

130 PC2 values around this peak. Peaks 5 and 7 represented deep-bodied suction feeders, predominantly from the South American heroins, but also one cichlasomatin and one Central

American heroin.

Peak 7 (Fig. 3.9, dark blue in pie charts) was not illustrated due to the high variability in its estimated position. Peak 7 was most typically associated with †“Aequidens” saltensis, and in a few cases also represented †Macracara prisca and all three Gymnogeophagus species. Since the phylogenetic position of †“A.” saltensis within Cichlinae is poorly understood, it was allowed to vary substantially across the phylogeny (see methods). When placed with very recently diverged, small-bodied lineages occupying peak 2 (Fig. 3.9), the estimated position of peak 7 was often extreme (PC scores >50 on both axes), likely due to the short branches and relative position of peak 2. When placed with lineages evolving towards peak 4 (ex: grouped with Gymnogeophagus), peak 7 was reconstructed within the morphospace pictured in fig. 3.9.

Overall, fossil taxa were found to be evolving towards adaptive peaks represented by modern species. Most fossil taxa were found evolving towards the relatively generalized peak 4, along with the vast majority of modern Cichlinae species. †Palaeocichla longirostrum was consistently reconstructed (97% of chronograms) as evolving towards the same adaptive peak as

Cichla (Fig. 3.9, peak 3). †“Aequidens” saltensis was the only fossil to evolve towards a novel adaptive peak on a substantial number of chronograms. However, †“A.” saltensis evolved towards the main adaptive peak (peak 4) across two thirds of chronograms. The adaptive constraints on extinct cichlid species were therefore recovered as similar to those of many modern Neotropical cichlids.

131

Fig. 3.9: Adaptive landscape of ecomorphology in modern and extinct Neotropical cichlids. Pie charts illustrate the proportion of 100 chronograms in which taxa were assigned to a particular peak. All peaks illustrated were estimated on at least 5% of chronograms, with black wedges corresponding to other peaks. Peak 7 (dark blue, see “†Aeq sal”) was not illustrated in PC space, see results for explanation. Fossil taxa are shown in red text. See Chapter 4 for discussion of feeding categories.

132

Table 3.5: Summary of adaptive peak shifts and convergence in Cichlinae functional morphology. k = adaptive peaks, k’ = unique adaptive peaks, Δk = reduction in landscape complexity with convergence (k-k’), c = number of shifts to convergent peaks, k’conv = number of peaks reached by independent lineages, c/k = convergent shifts proportionate to the number of adaptive peaks. Values are summarized from 100 chronograms each with an independent placement of fossil taxa.

Variable Median (Range)

k 11 (6, 15)

k' 6 (4, 8)

Δk 5 (1, 8)

c 9 (2, 12)

c/k 0.782 (0.333, 0.917)

k' 4 (1, 5)

3.5 Discussion

3.5.1 Cichlid Functional Morphology and Body Shape

Both feeding and body shape characteristics in Neotropical cichlids appear to be have been historically constrained by similar selective factors. There was a significant correlation between feeding functional morphology and linear morphometrics describing body shape that reflected

133 axes of variation identified in previous, independent morphological analyses. In particular, the first axis reflected the previously observed and ecologically significant trade-off between elongate taxa with rapid feeding kinematics and deep-bodied taxa, with slower feeding kinematics but improved suction feeding capacity (López-Fernández et al. 2013; Arbour &

López-Fernández 2014). I therefore felt it was appropriate to combine these datasets to analyze ecomorphospace in the context of fossil cichlids.

3.5.2 Fossil ecomorphology

Well-preserved fossil Neotropical cichlids were largely grouped in a region of morphospace representing moderate suction-feeders consuming predominantly benthic and epibenthic invertebrates, including a large number of sediment-sifting taxa from geophagini and heroini

(Fig. 3.4, †Macracara prisca, both species of †Tremembichthys, †‘Aequidens’ saltensis and

†Gymnogeophagus eocenicus). †Plesioheros chauliodus was potentially a stronger suction feeder (from estimates of its suction index) and exhibited morphological characteristics similar to some heroin and cichlasomatin taxa that consume larger percentages of vegetation and detritus

(Fig. 3.4 and 3.9, Heros sp. “common”, Herichthys carpintis and Mesonauta insignis).

†Palaeocichla longirostrum was most similar to modern piscivorous/predatory taxa, such as those in Cichla, Parachromis and Petenia (Fig. 3.4 and 3.9), consistent with its elongate form and long jaws/large mouth (Bardack 1961; Casciotta & Arratia 1993). Extinct cichlids were also as morphologically diverse as similarly sized sub-samples of modern taxa. Reconstruction of morphological diversity of fossil taxa under random-walk evolution and accounting for estimated phylogenetic position resulted in values that overlapped with that observed in the actual fossils.

However, the variation of disparity of small samples of cichlids was high.

134 While ram—suction/elongate—deep-bodied morphospace on PC1 overlapped between extinct and extant lineages, unique head morphologies were observed among fossil cichlids.

Tremembichthys species and particularly †“Aequidens” saltensis demonstrated proportionately longer heads (and likely correspondingly large AM muscles, Table 3.3) than modern cichlids across most chronograms. However, the morphologies of these three fossil species may not be unusual given the large variation in head shape associated with peak 4, to which they were largely reconstructed as evolving towards (Fig. 3.9). In general, these long-headed morphologies corresponded to ~6% of Neotropical cichlid ecomorphospace that has been lost to extinction

(Fig. 3.6 and 3.7); however this may decrease somewhat with increased sampling of modern lineages.

3.5.3 Fossil Cichlids and Adaptive Landscape Dynamics

Extinct Neotropical cichlids were generally found to be evolving towards two adaptive peaks, with most species evolving towards the ancestral adaptive peak (Fig. 3.9, peak 4), along with most Cichlinae lineages. However, †Palaeocichla longirostrum was consistently found to be evolving towards an adaptive peak including the South American Cichla, and was sometimes convergent with Central American Parachromis (Fig. 3.9). Several adaptive peaks found among extant Neotropical cichlids were not observed among extinct lineages. While this is likely largely a result of the small sample size of fossil taxa, and given that most fossils were found in heavily populated regions of generalized morphospace, there are other factors which may influence the distribution of fossils in ecomorphospace. Peak 1 (predatory species within

Crenicichla/Teleocichla and sometimes Petenia) would likely be represented by at least Miocene lineages, however the only Crenicichla fossil is very incomplete and could not be included in

135 these analyses (Casciotta & Arratia 1993). The lack of extinct lineages inhabiting peak 2 (Fig.

3.9) may relate to the small body size of taxa evolving under this selective regime, and a resulting bias in preservation (Cooper et al. 2006; Valentine et al. 2006; Brown et al. 2013).

These relatively elongate, “dwarf” taxa also share a reduction in skeletal elements, likely associated with miniaturization (López-Fernández et al. 2005a), that may also reduce the likelihood of preservation. At least one adaptive peak occupied by modern cichlids (and including most lineages examined) represents selective constraints that have been acting on cichlid ecomorphology for at least ~49Ma. Additionally, extinction has not substantially altered the adaptive landscape of Neotropical cichlids based on the taxa examined here; only one adaptive peak was estimated as unique to extinct taxa, and it occurred on only a small portion of sampled chronograms.

3.5.4 Morphological significance of †Gymnogeophagus eocenicus

†Gymnogeophagus eocenicus showed significant ecomorphological similarities to two extant

Gymnogeophagus species, in traits expressly unrelated to the synapomorphies used to place it within its genus. Such similarity to modern ecomorphs was recovered when simulating under the presumed position of †G. eocenicus (within modern Gymnogeophagus; Malabarba et al., 2010,

2014; López-Fernández et al., 2013; McMahan et al., 2013) but could not be recovered when simulating in other phylogenetic positions. While these results do not represent an independent phylogenetic analysis of the position of †G. eocenicus within Cichlinae/Geophagini, they are fully in support of previous assessments of this species belonging to the extant genus

Gymnogeophagus. Since this species also represents one of the oldest fossil cichlid species, it would support assertions from previous macroevolutionary studies that morphological diversity

136 at least within Geophagini (López-Fernández et al. 2012, 2013) and likely within other South

American lineages (see Chapter 2), has been maintained for tens of millions of years.

3.5.5 Phylogenetic and fossil uncertainties

Variable estimates of the age of Cichlidae and the age of divergence of the African and

American radiations exist in the current literature (Betancur-R. et al. 2013; Friedman et al. 2013;

López-Fernández et al. 2013; Near et al. 2013; McMahan et al. 2013; Chen et al. 2014). Sauquet et al. (2012) demonstrated that even with a well-established fossil record, substantial variation may exist in divergence time estimates. However, Chen et al. (2014) noted that several recent age estimates for cichlids (alone or as part of larger acanthomorph studies) were made without reference to any of the South American or African Eocene-age fossils, including an age estimate for Cichlinae by Friedman et al. (2013) that is significantly and considerably younger than the

Lumbrera Neotropical cichlid fossils (Malabarba et al. 2006, 2010, 2014; Bosio et al. 2009;

White et al. 2009; Perez et al. 2010; del Papa et al. 2010). Nevertheless, the age of Neotropical cichlids is likely to vary from that used in this study with, for instance, expanded molecular

(Ilves & López-Fernández 2014) and palaeontological data. Updating and expanding the morphological and macroevolutionary analyses of fossil Cichlinae will be important as improved cichlid phylogenies become available.

Additionally, the number of fossil Cichlinae available for analysis remains low. Those fossils not included in the analyses presented here represented incomplete specimens from unidentified geophagin species (Casciotta & Arratia 1993; Chakrabarty 2007; Malabarba et al.

2014). Our current understanding of fossil cichlids is also biased towards mainland South

American taxa, with one crushed fossil Nandopsis (Heroini) from Haiti and no Central American

137 specimens. Systematic palaeontological sampling of Neotropical fossil lake and river beds potentially containing cichlids is also necessary to fully document the diversity of Neotropical cichlid fossils. The Lumbrera Formation in Argentina, despite bearing arguably the most important cichlid fossils due to their Eocene age (Malabarba et al. 2014), has never been rigorously sampled by palaeontologists, with existing fossil specimens having been acquired through geological surveys (Malabarba pers. comm.) The fossil species most morphologically disparate from modern cichlids, and the only one found to be evolving under unique selective constraints on a considerable number of trees (1/3), was †“Aequidens” saltensis. Unfortunately the holotype of this species has probably been lost (Bardack 1961; Casciotta & Arratia 1993).

Further surveys of the Anta Formation at the La Yesera Creek and associated localities (Salta,

Argentina) fossil icthyofauna may therefore be particularly important to understanding historical morphospace and the role of extinction in Cichlinae macroevolution.

3.5.6 Conclusions

Extinct Neotropical cichlids species show ecomorphological characteristics similar to those of modern piscivores, benthic/epibenthic invertivores and algae/detritus feeders. Corrected to account for sample size, the disparity of extinct Neotropical cichlids was similar to that of their modern day counterparts, and the selective constraints on extinct Neotropical cichlids were reflective of the majority of extant species examined here. However, a small but significant portion of cichlid morphospace may have been lost to extinction. The ecomorphology of

†Gymnogeophagus eocenicus, one of the oldest cichlid fossil species, was best explained by its relationship to modern Gymnogeophagus species as suggested by previous phylogenetic analyses. The analysis of fossil Neotropical cichlids would suggest that macroevolutionary

138 studies of this group, especially those with a focus on disparity, have probably not been significantly biased by extinction and the lack of incorporation of fossil material. I hope that the disparity and macroevolutionary analyses presented here highlight the need for greater sampling and study of Neotropical cichlid fossils, as they are likely to contribute significantly to our understanding of cichlid and acanthomorph divergence, as well as biogeography and morphological evolution of one of the largest fish families.

139

3.6 Appendices

Appendix 3.1: Linear morphometric values for 82 species of Neotropical cichlids (N = number

of specimens per species). Mean values presented for each species, † signified extinct species.

Eye depth

Species N SL Snout

Length

Peduncle

Diameter

Body depth

Head Height Eye Position Head Length

†'Aequidens' saltensis 1 107 46.9 36.4 25.4 7.4 20.5 45.8 14.1

†Gymnogeophagus 2 52.7 20.4 16.1 11.8 5.6 8.9 20.9 10.1 eocenicus

†Macracara (Geophagus) 1 140.7 53.7 47.4 34.5 10.3 29.9 57.8 18.1 prisca

†Palaeocichla longirostrum 1 142.8 50.3 31.8 21.2 8.6 21.9 44.2 NA

†Plesioheros chauliodus 1 53.1 18.9 18.1 12.1 5.1 7.9 26.2 NA

†Proterocara argentina 1 51.9 18.2 15.7 9.1 4.7 6.2 21.4 9.2

†Tremembichthys 1 145.6 51.9 47.9 30.3 10.6 28.7 63.8 23 pauloensis

†Trememichthys garciae 1 90.6 40.2 25.2 19.9 8.7 19.3 42.9 17.7

Acarichthys heckelii 5 73 23.7 23.2 16.9 7.9 13.6 32.2 10.1

Acaronia nassa 5 101 39 31.1 21.9 13.8 16.9 47.3 19.1

Aequidens tetramerus 5 100.7 33.6 31.2 19.4 11.5 15.5 45.3 17.1

Amatitlania siquia 5 53.6 18.5 16.6 12.5 6.2 8.9 23.8 8.3

Amphilophus citrinellus 5 113.7 39.4 34.2 21.2 11.4 18.2 57.7 17.7

140

Andinoacara pulcher 5 82.4 27.8 25.1 16.8 8.9 12.4 36.9 15.2

Andinoacara rivulatus 5 91.9 32.2 26 18 8.3 15.6 37.6 14.9

Apistogramma hoignei 4 29.6 10.3 6.5 4.3 2.6 4 10.8 4.4

Archocentrus centrarchus 5 71.1 23 19.9 10.4 8 10.1 38.2 11.4

Astatheros octofasciatus 3 129.3 42.2 34.5 24.1 10.5 18 49.2 19.4

Astatheros robertsoni 7 121.1 45.3 37.8 25.9 11.4 23.8 55.4 18.7

Astronotus ocellatus 4 208 74.5 57.2 36.9 16.3 31.9 97.5 38.7

Australoheros facetus 2 125.8 41.6 42.5 26.4 11 18.2 64.5 24.5

Biotodoma wavrini 5 96.5 29 29.8 19.6 9.2 16.3 41.6 13.2

Biotoecus dicentrarchus 5 32.4 9.6 5.7 3.8 2.7 4.3 7.2 2.8

Bujurquina megalospilus 4 79.2 29.9 24 17.1 8.8 12.6 34.3 13.9

Caquetaia kraussii 5 140.1 50.1 37.9 25.2 13.3 21.6 65.2 22.3

Chaetobranchus flavescens 9 130.3 48 40.4 25.7 14.2 21.8 62.5 23.6

Cichla ocellaris 3 221 71 45.9 34.4 15.1 30.6 66.3 24.5

Cichla temensis 3 223.3 71.3 39.1 28.7 12.6 34.4 57.1 28.8

Cichlasoma bimaculatum 4 79 29.1 25.2 16.9 7.9 12.8 37 14.1

'Cichlasoma' festae 3 180.3 62.4 57.5 39.5 11.1 29.7 81.4 25.7

'Cichlasoma' salvini 5 83.4 30.7 22.7 15.6 9.1 14.5 35.2 12.5

'Cichlasoma' urophthalmus 8 135.2 46.3 39.5 26.5 12.6 21.1 60.2 21.4

Cleithracara maronii 3 55.2 17.2 18.8 11.3 6.2 6.9 29.8 11.4

141

Crenicara punctulatum 5 50.3 14.5 11.8 7.8 5.7 6.6 18.5 7

Crenicichla multispinosa 5 210.1 59.5 22.6 17.1 12.2 26 35.4 22.7

Crenicichla saxatilis 3 145.7 50 20.7 14.6 10.2 19.8 33.2 17.3

Crenicichla sp. "Orinoco- 5 213 64 27.1 19.8 11.3 30.8 46.2 27.5 lugubris"

Crenicichla sp. "Orinoco- 5 48.9 14.7 5.2 3.2 3.8 6.3 7.6 4.3 wallacii"

Cryptoheros chetumalensis 5 70.7 21.6 20.3 12.8 7.2 10.9 34.1 10.6

'Cryptoheros' sajica 7 53.3 17.2 15.6 9.8 5.5 7.7 25.9 8.4

Dicrossus maculatus 2 34.5 10.4 6.9 4.5 4.3 4.9 8.5 4

Geophagus abalios 5 170.6 54.8 57 38.5 13.6 33.6 71.8 24.5

'Geophagus' brasiliensis 5 124.8 41.9 40.3 27.2 11.1 22.7 56.2 18.8

Geophagus dicrozoster 5 178 55.5 55.2 36.5 13.7 35.2 70.5 23.9

'Geophagus' steindachneri 3 89.7 31.3 28.2 19.3 8.3 17.7 37.1 13.8

Guianacara dacrya 4 84.4 28 32 22.2 9.6 17 40 13.4

Gymnogeophagus balzanii 6 84.9 30.4 34.7 22.3 9.1 17.1 43.3 15.8

Gymnogeophagus rhabdotus 5 76.8 28.6 25.6 17.7 8 15 35.2 12.7

Herichthys carpintis 3 150.2 47.8 56.7 32.7 12.7 22.5 75.8 26.3

Herichthys cyanoguttatus 5 125.7 40.6 43.1 26.1 10.8 18.4 64.4 21.9

Heroina isonycterina 4 80.4 28.8 28.5 17.6 8.3 14.5 42 12.9

Heros sp. "common" 5 100.8 35 39.1 23.9 11.1 19.3 56.8 18.4

142

Herotilapia multispinosa 6 77.8 25.4 23.5 13.9 7.3 10.7 35 13.2

Hoplarchus psittacus 5 185.4 66.3 65.5 41.8 15.5 36.8 85.1 29.2

Hypselecara coryphaenoides 5 107.2 36.3 35 20.7 11.3 18.8 49.6 19.2

Hypsophrys nicaraguensis 3 95.9 28.5 22.7 16.4 9.6 13.7 35.7 11.1

Krobia guianensis 4 131 44.5 43.9 28.4 11.2 21.7 57.6 22.1

Laetacara dorsigera 2 45.2 14.8 12.8 7.8 5.6 5.9 18.5 9.2

Mazarunia charadrica 5 74.9 24.4 20.8 14.5 6.4 13.8 27.9 11.2

Mazarunia mazarunii 7 59.5 18.4 14.7 9.2 6.2 9 23.5 8.2

Mesonauta insignis 2 82.5 28.3 24.9 16 9.3 15.9 42.8 16.6

Mikrogeophagus 5 42.1 14.1 11.4 7.8 6 6.2 17.1 6.1 altispinosus

Mikrogeophagus ramirezi 5 31.2 10 7.5 4.5 3 4.2 13 4.6

Nandopsis haitiensis 8 103.3 38.5 29.9 20.4 9.8 17.7 43 14.5

Nandopsis tetracanthus 5 143.1 50.4 39.3 27.1 12.8 21.3 67.9 25.5

Nannacara anomala 1 36.3 11.6 8.9 6.2 4.1 3.8 13.4 5.5

Parachromis friedrichsthalii 5 144.4 53.6 37 27.8 13.8 23.9 54 20.4

Parachromis managuense 4 133.9 50.5 31.5 23.9 11.9 20.2 50.3 18.4

Paraneetroplus guttulatus 8 140.1 44 40.7 26.5 10.9 23.1 54.7 18.2

Paraneetroplus synspilus 5 104.8 32.1 31.2 19.1 9.8 15.8 49.3 16.1

Petenia splendida 6 207 72.3 49.6 35.7 15.8 34.1 68.1 23.7

Pterophyllum scalare 5 66.7 24.4 26.4 14.4 8.7 12.8 52.9 13.7

143

Retroculus lapidifer 3 111 38.5 27.7 19.7 10.2 22.5 38.5 15.4

Satanoperca daemon 5 157.6 54.4 45.7 31.9 12.4 35.8 61.3 23.6

Satanoperca jurupari 6 154.1 54.7 49.4 35.8 14 35.2 64.2 23.1

Symphysodon aequifasciatus 4 58.2 20.2 22.6 14.4 7.5 10.2 44.2 8.6

Taeniacara candidi 3 35 9.9 6 4.3 3.8 4.1 8.5 4.9

Teleocichla preta 4 106.5 31.3 19.2 13.1 6.6 17.5 20.8 13.4

Theraps intermedia 10 135.1 41.7 39.2 24.7 11.8 20 56 18.2

Theraps irregularis 6 116.4 34.7 25.6 18.4 10.5 17.2 36 13.8

Thorichthys meeki 5 109.4 38.8 36.2 25.6 10.7 21.2 50.3 15.8

Uaru amphiacanthoides 3 100.1 30.7 42.5 26.3 11.1 16 59.9 17.2

144

Appendix 3.2: Biomechanical coefficients from eight extinct species of cichlid.

Species N Lower Jaw Lower Jaw Quadrate Oral Closing MA Opening MA offset KT

†Gymnogeophagus eocenicus 2 0.262 0.301 0.363 1.08

†Proterocara argentina 1 0.216 0.302 0.336 NA

†Plesioheros chauliodus 1 0.245 0.283 0.519 NA

†Tremembichthys pauloensis 1 0.241 0.347 0.196 NA

†Trememichthys garciae 1 0.312 0.280 0.264 NA

†'Aequidens' saltensis 1 NA 0.337 NA NA

†Macracara (Geophagus) prisca 1 NA 0.278 0.433 NA

†Palaeocichla longirostrum 1 0.196 0.202 NA 0.816

145

4. Chapter Four

Feeding Ecology and Functional Diversification in

Neotropical cichlids

Jessica Hilary Arbour1

1 Department of Ecology and Evolutionary Biology, University of Toronto, 25 Wilcocks St.,

Toronto, Ontario M5S 3B2, Canada

146 4.1 Abstract

Adaptation to ecological strategies may impact the rate and pattern of morphological evolution.

However, the relationship between ecological and morphological diversity across clades may be complicated by factors such as physiological or phylogenetic constraints, biomechanical trade- offs and “many-to-one mapping”, among other factors. I examined the relationship between feeding ecology and functional diversity across Neotropical cichlids. Volumetric stomach content data was compiled from the literature and compared with 10 biomechanical and morphological variables related to feeding function for 41 species of Neotropical cichlid. Model fitting was used to compare evolutionary rates and selective constraint on functional diversification from different feeding guilds and varying dietary specialization. Neotropical cichlid feeding ecology varied primarily along axes of fish/macroinvertebrates, small benthic invertebrates and vegetation/detritus. I found significant correlations between dietary composition and feeding functional morphology, both with and without phylogenetic correction.

Feeding roles were associated with a trade-off in rates of functional diversification along two axes of variation. Feeding specialization was shown to influence rates of functional diversification, with higher rates of evolution in ram-suction morphology among specialists producing more extreme feeding traits. Functional evolution likely reinforces trade-offs between benthic and pelagic foragers, contributing to the partitioning of morphospace in the radiation of cichlids. The high evolutionary rate of specialists may contribute to “early bursts” of morphological diversification during adaptive radiations. Functional diversity is strongly tied to feeding ecology across a broadly distributed clade of fish.

147 4.2 Introduction

A relationship between the diversity of ecology and the diversity of form and function underlies many modern macroevolutionary studies of ecological opportunity and adaptive diversification.

How do ecological strategies shape selection on morphological traits? What are the consequences of specialization on the types of shapes and forms that can be achieved by a species? Adaptation to particular ecological strategies has been shown in some clades to influence factors such as rate and convergence of morphological diversification (Collar et al.

2005, 2009; Dumont et al. 2012; Davis et al. 2014; López-Fernández et al. 2014). Additionally, a number of studies have linked ecological variation to phenotypic trait axes associated with

“early burst” patterns of diversification (Slater et al. 2010; Mahler et al. 2010). However, even in classic systems of adaptive diversification, such as African cichlids, discordance between morphological and ecological diversity has been observed (ex: morphologically-specialized taxa may be ecological generalists; Binning et al. 2009). Grundler et al. (2014) found contrasting patterns of convergence in trophic morphology associated with divergence in trophic ecology in continental radiations of snakes. The relationship between morphological adaptation and ecology is further complicated by traits with “many-to-one mapping” in which multiple phenotypes may result in the same emergent functional properties (Alfaro et al. 2005; Wainwright et al. 2005;

Parnell et al. 2008). Other aspects of trait evolution, such as physiological constraints (Hulsey et al. 2007; Hulsey & Hollingsworth Jr 2011) and biomechanical trade-offs (Hulsey & Wainwright

2002; Camp et al. 2009; Holzman et al. 2012), may limit ecological diversity or the correlation between phenotype and ecology.

Interpreting evolutionary patterns in morphology under an adaptive framework requires an understanding of the link between phenotype and ecology (Schluter 2000; Glor 2010). The evolution of functional traits are useful from this perspective, as functional morphology is

148 thought to more directly link morphological adaptation to ecology through performance capability (Wainwright 2007). Many biomechanical indices in fish have previously been demonstrated to correlate with behaviour or performance (Wainwright & Richard 1995; Carroll et al. 2004). However, it is important to examine the relationship between function and ecology as different clades or communities may utilize resources in different ways, and this may impact how traits diversify (Winemiller et al. 1995).

Variation in feeding and trophic ecology is extensive among cichlids, with species from

Africa and the Neotropics demonstrating an array of specializations (e.g. Sturmbauer et al. 1992;

Norton & Brainerd 1993; Winemiller et al. 1995; Seehausen 2006; Takahashi et al. 2007;

Burress et al. 2013; Montaña & Winemiller 2013; López-Fernández et al. 2014). Cichlid feeding ecology and ecomorphology has been studied in relation to subjects such as convergence with other fish radiations (ex: centrarchids, Montaña & Winemiller 2013), morphological and behavioural specialization (López-Fernández et al. 2012, 2014), phenotypic plasticity (e.g.

Meyer 1987; Wimberger 1991; Muschick et al. 2011; Gunter & Meyer 2014), trophic polymorphisms (e.g. Kornfield & Taylor 1983; Meyer 1990; Wanson et al. 2003) and speciation

(Sturmbauer 1998; Salzburger et al. 2005; Gavrilets et al. 2007; Genner et al. 2007a; Elmer et al.

2010). Recently, Montaña & Winemiller (2013) compared South American cichlids and North

American centrarchids, finding convergence in morphological diversity as well as in the relationship between diet and morphology. Burress et al. (2013) linked morphological specialization to novel trophic strategies within a flock of Crenicichla species. Trait correlations and convergence has been observed in trophic morphology relating to benthivory or benthic sifting, largely within geophagin cichlids (López-Fernández et al. 2012, 2014). Pharyngeal jaw adaptations have been linked to the evolution of molluscivory in heroins (Hulsey 2006; Hulsey et al. 2008) and resource polymorphisms in crater lake Amphilophus radiations (Barluenga et al.

149 2006; Gavrilets et al. 2007; Muschick et al. 2011). The contribution of jaw protrusion and oral jaw functional traits has been linked to ram-feeding performance and the consumption of evasive prey, especially within heroins, (Wainwright et al. 2001; Waltzek & Wainwright 2003; Hulsey &

Garcia De Leon 2005; Higham et al. 2007). Many analyses of Neotropical cichlid ecomorphology are geographically (ex: only South American), or phylogenetically restricted (ex: specific tribes or species groups), and the relationship between functional diversification and feeding ecology across the diversity of Cichlinae has not been addressed.

Cichlid functional morphology appears to evolve under complex adaptive constraints

(Arbour & López-Fernández 2014) and diversification of functional traits appears to vary with time and biogeography (Chapter 2). The objective of this chapter is to test for a relationship between feeding ecology and functional morphology across a broad diversity of cichlids. To accomplish this I examine variation in diet composition and test for significant relationships between diet composition and functional morphology. I also use a model fitting approach to test whether the consumption of particular resources is linked to functional adaptation and diversification. Lastly, I use a model fitting approach to examine the consequences of feeding specialization on functional diversification (i.e., do specialists tend to show similar adaptations or increased/decreased rates of evolution).

4.3 Methods

4.3.1 Neotropical cichlid feeding ecology

Volumetric stomach content data was obtained from previous studies of Neotropical cichlids

(Winemiller 1991; Arcifa & Meschiatti 1993; Winemiller et al. 1995; Meschiatti & Arcifa 2002;

150 Moreira & Zuanon 2002; de Moraes & Barbola 2004; Gonz & Vispo 2004; Pease 2010;

Cochran-Biederman & Winemiller 2010; López-Fernández et al. 2012; Montaña & Winemiller

2013), and from unpublished datasets by the authors. Diet data was obtained from 4572 adult specimens representing 41 species (see Appendix 4.1 for species information and corresponding references). Items from stomach content analysis were grouped into 12 major categories that have been previously used in other studies of Neotropical cichlid feeding ecology (Winemiller et al. 1995; Pease 2010; López-Fernández et al. 2012; Montaña & Winemiller 2013) and the mean volumetric contribution (as proportion of total volume) was determined per category per species

(Appendix 4.1). The 12 major categories were as follows:

 Fish (including bones, fins and flesh)

 Macrocrustacea (decapods, especially palaemonid shrimp)

 Microcrustacea (amphipods, branchipods, cladocerans, copepods, isopods and

ostracods)

 Meiofauna/Microfauna (small benthic/epibenthic invertebrates, including mites,

nematodes, annelids, rotifers, bryozoans, tardigrades, protozoans, sponges, protozoans,

invertebrate eggs and horsehair worms)

 Mollusks (bivalves and gastropods)

 Aquatic insects (larvae and aquatic adults)

 Terrestrial Invertebrates (terrestrial insects and arachnids)

 Terrestrial vegetation (plants, fruits, seeds and flowers)

 Aquatic vegetation (filamentous algae, diatoms, aquatic plants)

 Vegetative Detritus (leaf litter, woody debris, fine and coarse organic detritus)

 Animal Detritus (scales and arthropod fragments)

 Sand

151

Variation in Neotropical cichlid dietary data was analyzed using non-metric multidimensional scaling (NMDS), which ordinates data based on a dissimilarity (i.e., distance) matrix. NMDS iterates the position of samples (species) on a pre-determined number of axes to maximize the rank-order correlation between the distance matrix and the distances in the ordination space (Faith et al. 1987; Minchin 1987; Oksanen et al. 2014). As the number of axes in NMDS is pre-determined, I varied the number of axes from 1 to 12 (number of possible axes given the number of diet categories), and selected a number of axes after which improvements in fit (between NMDS scores and the distance matrix) were minimal. NMDS was carried out using a Bray Curtis index (Bray & Curtis 1957; Faith et al. 1987) on arcsine-squareroot transformed data (Ahrens et al. 1990; Montaña & Winemiller 2013) using the “metaMDS” function in the R package “vegan” (Oksanen et al. 2014). As NMDS axes are arbitrary (the first axis may not represent the largest axis of variation), the resulting NMDS scores were rotated using a principal component analysis (Oksanen et al. 2014).

4.3.2 Relationships between Functional Morphology and Diet

Canonical (a.k.a. constrained) correspondence analysis (CCA) was used to examine the relationships between functional morphology and diet in the 41 species of Neotropical cichlids examined. Canonical correspondence analysis applies a correspondence analysis to a data matrix that has been subject to weighted linear regression on a set of constraining variables, to identify significant correlations between two multivariate datasets (Ter Braak 1986; Legendre &

Legendre 2012). CCA was carried out on arcsine square-root transformed mean proportional

(volumetric) stomach content data constrained by the 10 functional morphological variables

152 described in Chapter 1 (Arbour & López-Fernández 2014), using the R function “cca” from the package “vegan” (Oksanen et al. 2014). All size dependent variables were phylogenetically size- corrected using a log-log regression on cube-root body mass, as described in Chapter 1, prior to

CCA. Permutation tests were used to assess the significance of each CCA axis using function

“permutest” in R package “vegan” (Legendre & Legendre 2012; Legendre et al. 2011; Oksanen et al. 2014).

To test for the effect of phylogenetic relatedness on the relationship between diet and morphology, I also carried out CCAs on standardized independent contrasts of proportional dietary data and functional morphology (Felsenstein 1985; López-Fernández et al. 2012).

Dietary contrasts were transposed by the absolute value of the minimum independent contrast

(CCA as implemented in function “cca” in vegan will not permit negative values). PROTEST was applied to the uncorrected and phylogenetically-corrected CCA diet and morphology vectors to determine whether phylogenetic relatedness significantly influenced relationships between diet and morphology. PROTEST (using function “protest” from R package “vegan”) examines the correlation between two multivariate datasets after procrustes rotation and scaling, and applies a randomization test to determine the significance of the correlation (Peres-Neto &

Jackson 2001). CCA from phylogenetically corrected and uncorrected data were compared across 1000 posterior distribution chronograms.

4.3.3 Functional Diversification and Feeding Roles

I tested whether variation in feeding ecology in Neotropical cichlids was associated with functional adaptations or changes in the rate of diversification of functional traits. Based on the ordination of dietary data (particularly Fig. 4.1 and Fig. 4.2) and on previous analyses of cichlid feeding behaviour (Winemiller et al. 1995; Cochran-Biederman & Winemiller 2010; López-

153 Fernández et al. 2012; Montaña & Winemiller 2013), I established four categories of feeding ecology. Predators were defined as those taxa consuming a combined total of >50% (Collar et al. 2009) fish (mean = 64.4%), macrocrustacea (mean = 16.2%) and terrestrial arthropods (mean

= 6.74%). These included generally large-bodied taxa (ex: Cichla, Parachromis and Petenia) consuming evasive prey at a variety of depths. Herbivores (including vegetative detritivores) were those taxa which consumed a total of >50% aquatic vegetation (25.7%), terrestrial vegetation (15.3%) and vegetative detritus (32.5%). Micro-invertivores consumed a combined total of >50% microcrustacea (14.6%), meiofauna (9.68%) and aquatic insects (52.3%). This group preyed largely upon benthic and epibenthic invertebrates and included a number of

“dwarf” taxa (Apistogramma, Biotoecus, Dicrossus, Mikrogeophagus, Laetacara, Biotodoma and a small-bodied Crenicichla). Those that did not fall into the above categories were considered omnivores, with the most frequent food items being aquatic insects (22.7%), vegetative detritus (18.9%), animal detritus (14.8%, largely scales), fish (10.4%) and mollusks

(9.95%). While feeding ecology in cichlids is far more complex than the four groups listed here, this provides an objective and discrete categorization of cichlid feeding ecology that is consistent with observed variation in the 41 species examined (Fig. 4.1), largely reflects previous assessments of feeding ecology, represents a small enough set of groups for effective model fitting and results in no groups with a very small sample size.

154 Table 4.1: Taxa assigned to each of four feeding categories for the purpose of fitting evolutionary models to functional morphology.

Herbivores (including Predators Micro-invertivores Omnivores Vegetative Detritivores) Apistogramma Acaronia nassa Amatitlania siquia Aequidens tetramerus hoignei Chaetobranchus Archocentrus Amphilophus Caquetaia krausii flavescens centrarchus citrinellus Biotoecus Cichla ocellaris Cichlasoma bimaculatum Andinoacara pulcher dicentrarchus Cryptoheros Cichla temensis Biotodoma wavrini Astronotus ocellatus chetumalensis ‘Cichlasoma’ Crenicichla sp. Geophagus dicrozoster Astatheros robertsoni uropthalmus “Orinoco-wallacii” Crenicichla sp. ‘Geophagus’ Dicrossus maculatus ‘Cichlasoma’ salvini “Orinoco-lugubris” steindachneri Parachromis ‘Geophagus’ Herichthys carpintis Geophagus abalios friedrichsthalii brasiliensis

Petenia splendida Herotilapia multispinosa Laetacara dorsigera Heros sp.” common”

Mikrogeophagus Paraneetroplus synspilus Hoplarchus psittacus ramirezi Hypselecara Theraps intermedia Retroculus lapidifer coryphaenoides

Satanoperca daemon

Satanoperca jurupari

Thorichthys meeki

155 Maximum likelihood model fitting was used to fit a series of models differing in adaptive constraints and evolutionary rates (for each feeding role defined above) on the PC scores of functional morphology (see Chapter 1 and Arbour & López-Fernández, 2014), using the R function “OUwie” (Beaulieu & O’Meara 2013). Feeding roles were reconstructed on the phylogeny for model fitting using stochastic character mapping (Huelsenbeck et al. 2003;

Bollback 2006), using function “make.simmap” in R package “phytools” (Revell 2012), over each of 1000 chronograms. Null models of evolution included a single rate Brownian Motion

(BM) model of character evolution (random-walk evolution), defined by the evolutionary rate parameter (σ2), and a single rate Ornstein-Uhlenbeck (OU) model (random-walk towards a selective peak), defined by both the rate parameter and a parameter for selective constraint (α).

Based on the four feeding groups, I fitted models that allowed for varying rates (V), varying adaptive peaks (M) or both (MV) between feeding roles (ex: OU-M fits a model with four adaptive peaks with the same rate of evolution). I also fit multi-peak/rate OU and BM models (V,

M and MV) that grouped feeding roles together resulting in 3 peaks/rates or 2 peaks/rates. For computational simplicity (as all possible combinations would require 45 models per axis), 2 or 3 peak/rate models (ex: omnivores vs. all other roles) were selected based on the similarity between adaptive peaks and rates in the 4 group models. Based on the MCC tree and a sample of the 1000 chronograms (~20), models not included in Tables 4.4 to 4.9 produced a poor fit

(ΔAICc > 5).

Evolutionary models were compared using sample-size corrected Akaike Information

Criteria following Burnham and Anderson (2002). I calculated the ΔAIC and Akaike weight of evidence (w) for each model over the 1000 trees in the posterior distribution. Preferred models of evolution were those with a ΔAIC of < 2 (Burnham & Anderson 2002). The 1000 posterior distribution chronograms were scaled to relative time (total tree length of 1) prior to model

156 fitting, to make the results more directly comparable.

4.3.4 Functional diversification and specialization

I tested whether feeding specialization impacted the diversification of Neotropical cichlid functional morphology. I calculated an index of feeding specialization (FS) based on Levin’s index of niche breadth (Krebs 1999), following Belmaker et al. (2012). In the equation below, p is the proportional volume food category i (n = total food categories), where 0 represents a perfect generalist, feeding equally on all resources, and 1 represents taxa completely specialized on a single resource.

Based on the variation in FS index values among the 41 species examined, I established two classes of relative specialization (low vs. high) based on a cutoff of FS = 0.8, which was the mean FS value, the approximate ancestral value for the species examined here (Fig. 4.7) and resulted in fairly even sample sizes per group. I then used the model fitting approach described above for feeding roles to test whether relative specialists vs. generalists differed in functional adaptations or rate of diversification. Specialization classes were reconstructed on the phylogeny using stochastic character mapping, using the R function “make.simmap” (and see Chapter 2).

Using maximum likelihood model fitting in the R function “OUwie” (Beaulieu & O’Meara

2013) I fit BM and OU models differing in the number of adaptive peaks (0, 1 or 2) and the number of evolutionary rates (1 or 2) for the low and high specialization classes. Models were

157 compared using sample-size corrected Akaike information criterion (Burnham & Anderson

2002).

4.4 Results

4.4.1 Neotropical Cichlid Diet Variation

On average, the most important food items for the 41 cichlid species examined were aquatic insects (23.0%), fish (16.9%) and vegetative detritus (15.5%). NMDS of Neotropical cichlid dietary data found that stress was minimized (0.11) with three axes (subsequent axes did not improve the fit between the NMDS scores and diet data). The first NMDS axis most strongly separated piscivorous taxa and taxa feeding on a mixture of fish and large/evasive invertebrates

(macrocrustacea and terrestrial arthropods), from all others (Fig. 4.1). A second axis separated taxa consuming vegetative matter, including vegetative detritus, such as Herichthys, Herotilapia and Paraneetroplus (Fig. 4.1 and Fig. 4.2 group B) from taxa consuming small benthic invertebrates, particularly meiofauna, aquatic insects and microcrustacea (Fig. 4.1 and Fig. 4.2 group C). Herbivorous taxa were largely Central American heroins, particularly those feeding primarily on aquatic vegetation and vegetative detritus (Fig. 4.2 pie charts and Appendix 4.1), while South American herbivores consumed comparably greater proportions of terrestrial plants

(ex: ‘Geophagus’ steindachneri consumed ~40% seeds/fruit (Appendix 4.1). Invertebrate feeders included small-bodied, elongate taxa (ex: Dicrossus and Crenicichla sp. “Orinoco-wallacii”) and larger, broader-bodied substrate-sifters (ex: Retroculus, Thorichthys and Satanoperca). The third

NMDS axis represented a gradient between largely heroin taxa feeding on mollusks and macrocrustacea, to substrate-sifters and benthic “gleaners” (Montaña & Winemiller 2013), such as Heros sp. “common”, feeding on microfauna/meiofauna, animal detritus, and that ingested a

(comparably) large proportion of sand (Appendix 4.1).

158

Fig. 4.1: Non-metric multidimensional scaling of Neotropical cichlid mean proportional dietary composition. Point colours indicate Neotropical cichlid tribes. Species name abbreviations are defined in appendix 4.1.

159

Fig. 4.2: Dendrogram of dietary data from 41 species of Neotropical cichlid (complete linkage analysis of Bray-Curtis dissimilarity). Pies next to taxa names show the mean proportion of 12 diet categories (shown in legend). Barplots indicate specialization index.

160 4.4.2 Diet and Functional Morphology

Canonical correspondence analysis of functional morphology and diet revealed a significant correlation between diet and morphology in Neotropical cichlids on three CCA axes that explained nearly one third of dietary variation (Table 4.2). The negative extreme of the first axis was most strongly determined by the consumption of fish, which was associated with fast oral jaw biomechanics and a rapidly expandable buccal cavity (high hyoid KT, low lower jaw MAs;

Fig. 4.3). This compared with the positive extreme of axis 1 which was primarily determined by consumption of aquatic vegetation, and to a lesser extent small-invertebrates and detritus, food items which were associated with high suction capability (high suction index), strong lower jaw movements (high lower jaw MAs) and unevenly occluding jaws (high QO). Negative scores on axis 2 associated large and hard invertebrates (macrocrustacea, mollusks and terrestrial arthropods) with large oral jaw muscles (especially ST) and high pharyngeal crushing potential

(high CB5 mass; Fig. 4.3). Small oral muscle and pharyngeal jaw masses on axis 2 were associated with the consumption of benthic/epibenthic items including terrestrial plants

(primarily fruit and seeds), meiofauna and sand (Fig. 4.3). The third axis associated the consumption of small invertebrates (meiofauna, aquatic insects and microcrustacea) with mobile oral jaws (high oral KT) and fast lower jaw opening. Expansion of the buccal cavity and strong lower jaw opening was associated with the consumption of plants (terrestrial and aquatic) and mollusks on axis 3 (Fig. 4.3).

Accounting for the influence of phylogenetic relatedness using standardized phylogenetic independent contrasts still resulted in significant correlations between diet and morphology, in fact improving the proportion of diet variation explained by the analyses (Table 4.2). The first axis was still primarily driven by the consumption of fish and efficient transmission of movement in the oral jaws (high oral jaw KT, low lower jaw MAs), poor suction (low SI) and

161 evenly occluding jaws (low QO), rapid expansion of the buccal cavity and small pharyngeal jaws. The second axis separated suction feeding traits associated with the consumption of vegetation and small invertebrates from taxa with biting and crushing traits consuming large and hard invertebrates (Fig.4.4). The third axis again associated the strength and expansion of the oral jaws (ST mass and high oral KT), as well as higher pharyngeal crushing potential (CB5 mass), with the consumption of small invertebrates, in this case aquatic insects in particular (Fig.

4.4). Strong, unevenly occluding oral jaws (high lower jaw MA and QO) and mobile buccal cavities (high hyoid KT) were associated with the consumption of detritus and vegetation, but not mollusks, on the third phylogenetically corrected axis (Fig. 4.4).

Table 4.2: Summary of CCA of diet and functional morphology in 41 species of Neotropical cichlid, with and without phylogenetic correction, across 1000 posterior distribution chronograms. Values are given as mean (s.d.), except p-values, which give the maximum across 1000 posterior distribution chronograms.

CCA uncorrected CCA corrected Axis 1 Axis 2 Axis 3 Axis 1 Axis 2 Axis 3 Max p-value 0.001 0.007 0.021 0.002 0.009 0.008 Proportion of 0.192 (7.35 0.0615 (2.70 0.0529 (4.07 0.240 0.102 0.0723 -4 -4 -4 Variance X 10 ) X 10 ) X 10 ) (0.0268) (0.0125) (0.00529) Explained Cumulative 0.192 (7.35 0.253 0.306 0.240 0.341 0.414 -4 -4 -4 Variance X 10 ) (6.77X10 ) (5.49X10 ) (0.0268) (0.0285) (0.0261) Explained Diet-Morphology 0.818 0.725 0.641 0.879 0.732 0.730 Pearson (0.00125) (0.00525) (0.00582) (0.0301) (0.0180) (0.0167) correlation

162

Fig. 4.3: Mean coefficients and scores from canonical correspondence analysis of dietary composition (red, +) and functional morphology (blue, arrows) in 41 species of Neotropical cichlid (points) across 1000 posterior distribution chronograms. Functional abbreviations: AM – adductor mandibulae mass, ST – sternohyoideus mass, CB5 – fifth ceratobranchial mass, MA – mechanical advantage, KT – kinematic transmission coefficient, QO – quadrate offset, SI – suction index.

163

Fig. 4.4: Mean coefficients from canonical correspondence analysis of standardized independent contrasts of diet (red) and functional morphology (blue, arrows) in 41 species of Neotropical cichlid across 1000 posterior distribution chronograms. Functional abbreviations: AM – adductor mandibulae mass, ST – sternohyoideus mass, CB5 – fifth ceratobranchial mass, MA – mechanical advantage, KT – kinematic transmission coefficient, QO – quadrate offset, SI – suction index.

164

PROTEST analyses supported the similarity between the diet-morphology relationships in the phylogenetically corrected and uncorrected data. Significant correlations were found between the diet coefficients (1000 chronograms, maximum p = 0.005) and the functional morphological coefficients (1000 chronograms, maximum p = 0.01) from both sets of CCA. The largest differences in diet coefficients between the standard and phylogenetically corrected CCAs were the relative contribution of aquatic vegetation to axis 1 and 2 (Fig. 4.5A) and the relative contributions of aquatic insects and meiofauna to axis 3 (Fig. 4.5B). The most significant differences in functional morphology coefficients before and after phylogenetic correction were the contribution of jaw protrusion to the first axis (Fig. C) and the relative contributions of oral jaw muscle mass and pharyngeal jaw mass on axes 2 and 3 (Fig. 4.5C and D). However, most

CCA coefficients maintained similar directionality and scale (proportionately) after phylogenetic correction. Overall, phylogenetic relatedness did not drive, nor significantly alter, the observed correlations between diet and morphology.

165

Fig. 4.5: Procrustes rotation of mean standard and mean phylogenetically-corrected CCA diet (left) and functional morphology (right) coefficients (from 1000 posterior distribution chronograms). Points show the position of phylogenetically-corrected CCA coefficients, with arrows showing their deviation from the uncorrected coefficients. Solid lines show the optimal procrustes rotation of corrected coefficients onto the standard coefficients. See Fig. 4.3-4.4 for functional trait abbreviations.

166

4.4.3 Functional diversification and feeding roles

Similar to phylogenetic PCA of the full 75 species in Chapter 1 (Arbour & López-Fernández

2014), two critical axes of functional morphology were found across the 41 species examined here. The variables with high loading factor coefficients were also nearly identical to that observed in Chapter 1. The first axis (PC1; Fig. 4.6 and Table 4.3) represented a gradient between efficient velocity transmission, evenly occluding jaws and poor suction ability (ram- feeders) and those with efficient force transmission, unevenly occluding jaws and high suction capability (suction-feeders/biters). Low PC1 scores (ram-feeding) were associated with elongate- bodied taxa, while suction-feeding and biting morphology was associated with deeper bodied fish (Fig. 4.6). The second axis represented primarily increasing biting muscle size (AM mass) and crushing potential (CB5 mass).

Along an axis of ram-suction morphology (PC1) the best supported model (lowest ΔAIC, most frequently chosen as the best model and least frequently disfavoured; Table 4.4 model OU-

MVHIO-P), included both separate adaptive peaks and evolutionary rates for predators compared to omnivores, herbivores and microinvertivores. The slowest rates of functional evolution were observed among predatory taxa; under the best supported model (OU-MVHIO-P; Table 4.5) predators evolved, on average, at only 23% (s.d. = 13%) of the rate of other feeding roles (Fig.

4.6). Under the best supported models, predatory taxa were found to be evolving towards a peak characterized by high lower jaw velocity transmission (lower jaw MAs) and poor suction capability (i.e., ram-feeding), while other feeding roles had comparatively improved suction feeding (high force transmission, higher suction index; Table 4.6).

167 Two other models with support across a large frequency of chronograms both featured three adaptive peaks, always with a separate, ram-feeding adaptive peak for predators. One of these models (OU-MVHI-O-P; Table 4.4), found that in addition to the unique ram-feeding peak and slow rate of evolution among predators, herbivores/microinvertivores were evolving towards a more suction-feeding optimized peak and at a faster rate than omnivores (Table 4.5 and 4.6). A third model with high support included three adaptive peaks (OU-M(H-OI-P); Table 4.5) combining omnivores and microinvertivores at a moderate ram-suction feeding peak, but found no differences in rates of evolution among feeding roles. Across all 1000 chronograms, the cumulative weight of evidence for multiple adaptive peaks was 0.99, and for a multi-rate model was 0.60. Models with no adaptive constraint were always poorly supported (Table 4.4: BM1 and BM-V models).

Table 4.3 Mean loading factor coefficients from phylogenetic principal component analyses of functional morphology in 41 species of Neotropical cichlid across 1000 posterior distribution chronograms.

Variable PC1 PC2 Maximum Oral Jaw -0.445 0.264 Protrusion AM Mass -0.527 -0.677 ST Mass -0.255 -0.513 CB5 Mass -0.235 -0.762 Lower Jaw Closing 0.859 -0.184 MA Lower Jaw Opening 0.510 -0.485 MA Quadrate Offset 0.860 -0.0382 Hyoid KT -0.531 0.395 Oral Jaw KT -0.112 -0.411 Suction Index 0.890 0.0697 Variation Explained 34.5% 20.1%

168

Fig. 4.6: Phylogenetic principal component scores of functional morphology for 41 species of Neotropical cichlid summarized across 1000 posterior distribution chronograms. Ellipses are centered at the adaptive peaks from the best fitting models for each axis and are scaled along each axis proportional to their associated rate of evolution. Points and ellipses are coloured by feeding role (red – predators, green – herbivores/detritivores, blue – microinvertivores, brown – omnivores). Bolded terms indicate the variables with the highest loading factors on each axis.

169

Table 4.4: Summary of BM-OU model fitting results for PC1 (ram-suction morphology) across 1000 chronograms. M models feature multiple adaptive peaks, V models feature multiple evolutionary rates, and MV models include both. N is the number of selective regimes model, groups displays the feeding roles associated with each regime: H – herbivores, P – predators, I – microinvertivores, O – omnivores. Lik = loglikelihood, k = number of parameters in each model, w = Akaike weight of evidence. “Freq. best” gives the frequency across 1000 chronograms in which ΔAIC = 0 (best supported model), while “freq. poor” gives the frequency for which ΔAIC > 2 (i.e., the model had poor support). All model fitting parameters are given as mean (s.d.), except ΔAIC which is median (95% range). Standard deviation includes both uncertainty in the phylogeny and in the ancestral reconstruction of feeding roles. Sorted by increasing ΔAICc.

Model N Groups k Lik ΔAICc w Freq. best Freq. poor OU-MV 2 HIO-P 8 -56.54 (1.65) 0.42 (0, 7.86) 0.23 (0.13) 0.4 0.23 OU-MV 3 HI-O-P 9 -54.02 (1.95) 1.00 (0, 10.11) 0.2 (0.17) 0.21 0.34 OU-M 3 H-IO-P 6 -56.82 (1.89) 1.11 (0, 9.95) 0.2 (0.15) 0.28 0.32 OU-MV 3 H-IO-P 9 -54.51 (1.89) 2.18 (0, 11.47) 0.12 (0.1) 0.03 0.55 OU-M 2 HIO-P 5 -59.17 (1.73) 3.65 (0.18, 10.59) 0.07 (0.07) 0.02 0.88 OU-M 4 P-H-I-O 7 -56.8 (1.9) 4.09 (0.33, 12.04) 0.06 (0.07) 0.02 0.9 OU-M 3 HI-O-P 6 -58.48 (1.85) 4.79 (1.17, 12.94) 0.04 (0.06) 0.01 0.95 OU-MV 4 P-H-I-O 10 -52.72 (2.01) 5.19 (0.5, 14.05) 0.04 (0.09) 0.02 0.95 OU-M 2 H-PIO 5 -61.61 (1.72) 8.42 (5.17, 15.72) 0.01 (0.02) 0 1 OU-M 3 H-I-PO 6 -60.65 (1.79) 9.19 (5.16, 16.45) 0.01 (0.02) 0 1 OU-M 2 HI-OP 5 -62 (1.69) 9.29 (4.8, 16.73) 0.01 (0.02) 0 0.99 OU-MV 2 HI-OP 8 -60.92 (1.64) 9.75 (5.16, 16.92) 0 (0.01) 0 1 OU-MV 2 H-PIO 8 -61.58 (1.71) 11 (7.65, 18.24) 0 (0) 0 1 OU-MV 3 H-I-PO 9 -59.68 (1.8) 12.99 (8.32, 20.17) 0 (0.01) 0 1 OU1 1 4 -65.51 (1.53) 13.7 (11.87, 20.55) 0 (0) 0 1

BM-V 3 H-IO-P 4 -67.57 (2.35) 20.59 (11.35, 30.16) 0 (0) 0 1 BM-V 4 P-H-I-O 5 -66.69 (3.06) 22.37 (8.26, 31.64) 0 (0.03) 0 0.99 BM-V 3 HI-O-P 4 -68.6 (2.55) 23.31 (11.46, 32.47) 0 (0.01) 0 1 BM-V 2 HIO-P 3 -70.23 (1.75) 23.63 (16.39, 32.08) 0 (0) 0 1 BM1 1 2 -71.56 (0.93) 23.68 (18.71, 31.83) 0 (0) 0 1

BM-V 2 H-PIO 3 -70.6 (1.35) 24.37 (16.87, 32.55) 0 (0) 0 1 BM-V 3 H-I-PO 4 -69.57 (1.57) 24.7 (17.81, 32.48) 0 (0.01) 0 1 BM-V 2 HI-OP 3 -70.92 (1.2) 24.88 (18.84, 32.41) 0 (0) 0 1

170 Table 4.5: Summary of evolutionary rates (σ2) from BM-OU model fitting on PC1 (ram-suction morphology) for each feeding regime across 1000 chronograms. M models feature multiple adaptive peaks, V models feature multiple evolutionary rates, and MV models include both. N is the number of selective regimes model, groups displays the feeding roles associated with each regime: H – herbivores, P – predators, I – microinvertivores, O – omnivores. All model fitting parameters are given as mean (s.d.). Standard deviation includes both uncertainty in the phylogeny and in the ancestral reconstruction of feeding roles. Sorted by increasing ΔAICc.

2 2 2 2 Model N Groups σ P σ H σ I σ O OU-MV 2 HIO-P 139.36 (77.28) 636.9 (331.43) 636.9 (331.43) 636.9 (331.43) OU-MV 3 HI-O-P 113.52 (84.91) 622.43 (392.42) 622.43 (392.42) 224.97 (148.81) OU-M 3 H-IO-P 383.26 (281.26) 383.26 (281.26) 383.26 (281.26) 383.26 (281.26) OU-MV 3 H-IO-P 121.16 (90.25) 510.4 (342.34) 458.17 (305.33) 458.17 (305.33) OU-M 2 HIO-P 437.66 (326.71) 437.66 (326.71) 437.66 (326.71) 437.66 (326.71) OU-M 4 P-H-I-O 384.52 (285.4) 384.52 (285.4) 384.52 (285.4) 384.52 (285.4) OU-M 3 HI-O-P 414.02 (315.18) 414.02 (315.18) 414.02 (315.18) 414.02 (315.18) OU-MV 4 P-H-I-O 105.12 (77.7) 443.78 (300.07) 607.82 (393.05) 209.06 (141.57) OU-M 2 H-PIO 454.61 (374.87) 454.61 (374.87) 454.61 (374.87) 454.61 (374.87) OU-M 3 H-I-PO 407.45 (350.33) 407.45 (350.33) 407.45 (350.33) 407.45 (350.33) OU-M 2 HI-OP 484.64 (375.42) 484.64 (375.42) 484.64 (375.42) 484.64 (375.42) OU-MV 2 HI-OP 280.8 (213.82) 527.18 (394.25) 527.18 (394.25) 280.8 (213.82) OU-MV 2 H-PIO 452.21 (373.32) 448.58 (372.13) 452.21 (373.32) 452.21 (373.32) OU-MV 3 H-I-PO 260.92 (215.18) 367.53 (305.83) 513.23 (413.18) 260.92 (215.18) OU1 1 75.53 (188.8) 75.53 (188.8) 75.53 (188.8) 75.53 (188.8)

BM-V 3 H-IO-P 6.04 (2.53) 5.35 (2.4) 1.28 (0.8) 1.28 (0.8) BM-V 4 P-H-I-O 5.86 (2.6) 5.61 (2.89) 1.57 (1.23) 1.28 (1.43) BM-V 3 HI-O-P 6.06 (2.8) 3.58 (1.84) 3.58 (1.84) 1.64 (1.59) BM-V 2 HIO-P 6.92 (2.91) 2.74 (0.51) 2.74 (0.51) 2.74 (0.51) BM1 1 3.37 (0.09) 3.37 (0.09) 3.37 (0.09) 3.37 (0.09)

BM-V 2 H-PIO 2.81 (0.66) 5.81 (3.08) 2.81 (0.66) 2.81 (0.66) BM-V 3 H-I-PO 3.64 (1.08) 4.9 (2.7) 1.44 (1.04) 3.64 (1.08) BM-V 2 HI-OP 3.69 (0.98) 3.11 (1.41) 3.11 (1.41) 3.69 (0.98)

171 Table 4.6: Summary of adaptive peaks for feeding regimes from BM-OU model fitting on PC1 (ram-suction morphology) across 1000 chronograms. M models feature multiple adaptive peaks, V models feature multiple evolutionary rates, and MV models include both. The parameters α and θ are the selective constraint parameter and the location of the adaptive peaks for each feeding category (H – herbivores, P – predators, I – microinvertivores, O – omnivores) in PC2 score space respectively. All model fitting parameters are given as mean (s.d.). Standard deviation includes both uncertainty in the phylogeny and in the ancestral reconstruction of feeding roles. Models are listed in order of increasing ΔAICc.

Model N Groups α θP θH θI θO OU-MV 2 HIO-P 255.22 (130.16) -1.00 (0.2) 0.6 (0.19) 0.6 (0.19) 0.6 (0.19) OU-MV 3 HI-O-P 198.83 (127.38) -1.00 (0.23) 0.8 (0.29) 0.8 (0.29) 0.31 (0.22) OU-M 3 H-IO-P 203.32 (146.71) -1.03 (0.36) 1.21 (0.32) 0.34 (0.21) 0.34 (0.21) OU-MV 3 H-IO-P 215.01 (140.31) -1.01 (0.21) 1.2 (0.35) 0.34 (0.22) 0.34 (0.22) OU-M 2 HIO-P 206.78 (152.39) -1.06 (0.39) 0.6 (0.18) 0.6 (0.18) 0.6 (0.18) OU-M 4 P-H-I-O 204.88 (149.98) -1.04 (0.37) 1.21 (0.33) 0.39 (0.32) 0.3 (0.29) OU-M 3 HI-O-P 203.29 (152.89) -1.03 (0.38) 0.8 (0.24) 0.8 (0.29) 0.31 (0.3) OU-MV 4 P-H-I-O 186.49 (124.31) -1.02 (0.23) 1.2 (0.36) 0.39 (0.42) 0.3 (0.22) OU-M 2 H-PIO 190.64 (155.07) -0.02 (0.2) 1.23 (0.36) -0.02 (0.2) -0.02 (0.2) OU-M 3 H-I-PO 178.84 (151.72) -0.22 (0.25) 1.24 (0.36) 0.37 (0.36) -0.22 (0.25) OU-M 2 HI-OP 199.13 (152.29) -0.21 (0.25) 0.81 (0.26) 0.81 (0.26) -0.21 (0.25) OU-MV 2 HI-OP 165.64 (123.44) -0.21 (0.21) 0.82 (0.29) 0.82 (0.29) -0.21 (0.21) OU-MV 2 H-PIO 189.52 (154.56) -0.02 (0.2) 1.23 (0.36) -0.02 (0.2) -0.02 (0.2) OU-MV 3 H-I-PO 154.67 (125.96) -0.22 (0.21) 1.24 (0.37) 0.37 (0.43) -0.22 (0.21) OU1 1 28.33 (73.44) 0.29 (0.2) 0.29 (0.2) 0.29 (0.2) 0.29 (0.2)

172

Only one model was supported (Table 4.7, ΔAIC < 2) across a majority of chronograms for PC2 (61% of chronograms; OU-MVP-HIO), an axis representing variation in oral jaw muscle size and pharyngeal jaw mass (i.e., biting and crushing morphology). The model with the most support was the same multi-rate and multi-peak model as was supported on PC1, with a separate rate and adaptive optimum for predators (Table 4.8 and 4.9). However, in contrast to PC1, predators were the fastest evolving feeding category. On average, omnivores, microinvertivores and herbivores evolved at only 35% (s.d. = 10%) of the rate of predators in biting/crushing morphology (Table 4.8). Predators evolved towards an adaptive optimum that was somewhat better optimized for proportionately smaller oral jaw muscles and pharyngeal jaw size (Table

4.9), however these taxa showed high variability in PC2 scores and extensive overlap with other feeding categories (Fig. 4.6).

173

Table 4.7: Summary of BM-OU model fitting results for PC2 (biting/crushing morphology) across 1000 chronograms. M models feature multiple adaptive peaks, V models feature multiple evolutionary rates, and MV models include both. N is the number of selective regimes, groups displays the feeding roles associated with each regime: H – herbivore/detritivore, P – predators, I – microinvertivores, O – omnivores. Lik = loglikelihood, k = number of parameters in each model, w = Akaike weight of evidence. “Freq. best” gives the frequency across 1000 chronograms in which ΔAIC = 0 (best supported model), while “freq. poor” gives the frequency for which ΔAIC > 2 (i.e., the model had poor support). All model fitting parameters are given as mean (s.d.), except ΔAIC which is median (95% range). Standard deviation includes both uncertainty in the phylogeny and in the ancestral reconstruction of feeding roles. Sorted by increasing ΔAICc.

Model N Groups k Lik ΔAICc w Freq. best Freq. poor OU-MV 2 P-HIO 8 -52.02 (1.77) 1.45 (0, 9.95) 0.15 (0.12) 0.20 0.39 OU-M 2 P-HIO 5 -53.66 (1.94) 2.18 (0, 10.54) 0.12 (0.12) 0.14 0.55 OU1 1 4 -54.96 (1.9) 2.34 (0, 10.89) 0.1 (0.08) 0.19 0.55

OU-M 2 OI-HP 5 -54.3 (1.94) 3.46 (0, 12) 0.06 (0.06) 0.03 0.75 OU-M 2 O-PIH 5 -54.65 (1.94) 4.16 (0.8, 12.95) 0.04 (0.04) 0.01 0.87 OU-M 3 OI-H-P 6 -53.39 (2.09) 4.42 (0, 13.53) 0.06 (0.1) 0.06 0.83 OU-MV 2 OI-HP 8 -53.58 (2.01) 4.65 (0.79, 13.51) 0.04 (0.05) 0.01 0.88 OU-MV 2 O-PIH 8 -53.49 (2.32) 4.7 (0, 12.95) 0.06 (0.13) 0.05 0.86 OU-M 3 HI-O-P 6 -53.67 (2.03) 4.9 (0, 13.58) 0.05 (0.08) 0.03 0.88 OU-M 3 PH-O-I 6 -53.98 (1.91) 5.39 (1.06, 14.75) 0.03 (0.04) 0.02 0.94 OU-MV 3 OI-H-P 9 -51.01 (2.16) 5.63 (0, 14.24) 0.05 (0.11) 0.06 0.86 OU-MV 3 HI-O-P 9 -51.45 (2.07) 6.39 (0.06, 14.79) 0.03 (0.09) 0.02 0.94 BM-V 2 P-HIO 3 -57.27 (1.76) 6.87 (0.4, 18.9) 0.03 (0.05) 0.02 0.93 OU-M 4 P-H-I-O 7 -53.19 (2.11) 6.92 (0.04, 16) 0.03 (0.07) 0.03 0.94 BM-V 3 HI-O-P 4 -56.75 (2.15) 8.72 (0, 19.21) 0.03 (0.08) 0.04 0.92 OU-MV 3 PH-O-I 9 -52.59 (2.3) 8.74 (0.14, 18.12) 0.02 (0.09) 0.02 0.96 BM-V 3 OI-H-P 4 -56.82 (1.98) 8.78 (0.07, 19.53) 0.02 (0.06) 0.02 0.95 BM-V 4 P-H-I-O 5 -55.84 (2.16) 9.69 (0.66, 20.14) 0.02 (0.06) 0.02 0.95 BM-V 2 O-PIH 3 -58.78 (2.08) 10.6 (0.98, 19.51) 0.02 (0.06) 0.02 0.96 OU-MV 4 P-H-I-O 10 -50.19 (2.36) 10.86 (1.09, 19.53) 0.02 (0.08) 0.02 0.96 BM1 1 2 -60.43 (0.93) 11.15 (5.27, 19.66) 0 (0) 0 1.00

BM-V 2 OI-HP 3 -59.69 (1.36) 12.09 (5.66, 20.8) 0 (0.01) 0 1.00 BM-V 3 PH-O-I 4 -58.35 (1.99) 12.26 (1.38, 21.12) 0.01 (0.07) 0.02 0.97

174 Table 4.8: Summary of evolutionary rates (σ2) from BM-OU model fitting on PC2 (biting/crushing morphology) for each feeding regime across 1000 chronograms. M models feature multiple adaptive peaks, V models feature multiple evolutionary rates, and MV models include both. N is the number of selective regimes model, groups displays the feeding roles associated with each regime: H – herbivores, P – predators, I – microinvertivores, O – omnivores. All model fitting parameters are given as mean (s.d.). Standard deviation includes both uncertainty in the phylogeny and in the ancestral reconstruction of feeding roles. Sorted by increasing ΔAICc.

2 2 2 2 Model N Groups σ P σ H σ I σ O OU-MV 2 P-HIO 27.46 (72.36) 10.36 (33.47) 10.36 (33.47) 10.36 (33.47) OU-M 2 P-HIO 13.73 (52.93) 13.73 (52.93) 13.73 (52.93) 13.73 (52.93) OU1 1 27.45 (96.97) 27.45 (96.97) 27.45 (96.97) 27.45 (96.97)

OU-M 2 OI-HP 21.77 (81.93) 21.77 (81.93) 21.77 (81.93) 21.77 (81.93) OU-M 2 O-PIH 17.01 (68.07) 17.01 (68.07) 17.01 (68.07) 17.01 (68.07) OU-M 3 OI-H-P 12.14 (45.54) 12.14 (45.54) 12.14 (45.54) 12.14 (45.54) OU-MV 2 OI-HP 35.9 (107.48) 35.9 (107.48) 20.51 (65.86) 20.51 (65.86) OU-MV 2 O-PIH 19.14 (71.23) 19.14 (71.23) 19.14 (71.23) 9.04 (35.61) OU-M 3 HI-O-P 11.73 (42.48) 11.73 (42.48) 11.73 (42.48) 11.73 (42.48) OU-M 3 PH-O-I 15.66 (61.17) 15.66 (61.17) 15.66 (61.17) 15.66 (61.17) OU-MV 3 OI-H-P 39.41 (115.62) 13.32 (46.29) 14.34 (44.9) 14.34 (44.9) OU-MV 3 HI-O-P 35.3 (100.52) 14.52 (45.79) 14.52 (45.79) 10.23 (30.62) BM-V 2 P-HIO 5.62 (1.85) 1.28 (0.18) 1.28 (0.18) 1.28 (0.18) OU-M 4 P-H-I-O 12.8 (52.77) 12.8 (52.77) 12.8 (52.77) 12.8 (52.77) BM-V 3 HI-O-P 4.71 (1.72) 1.2 (0.64) 1.2 (0.64) 1.36 (0.88) OU-MV 3 PH-O-I 25.2 (78.76) 25.2 (78.76) 17.13 (58.94) 11.19 (36.32) BM-V 3 OI-H-P 4.67 (1.65) 0.8 (0.57) 1.57 (0.47) 1.57 (0.47) BM-V 4 P-H-I-O 4.81 (1.6) 0.91 (0.76) 1.63 (1.34) 1.51 (0.96) BM-V 2 O-PIH 2.44 (0.46) 2.44 (0.46) 2.44 (0.46) 0.98 (0.65) OU-MV 4 P-H-I-O 26.67 (93.83) 9.14 (39.56) 12.61 (51.91) 8.27 (30.99) BM1 1 1.96 (0.06) 1.96 (0.06) 1.96 (0.06) 1.96 (0.06)

BM-V 2 OI-HP 2.45 (0.53) 2.45 (0.53) 1.54 (0.44) 1.54 (0.44) BM-V 3 PH-O-I 2.67 (0.64) 2.67 (0.64) 1.74 (1.66) 1.34 (0.96)

175 Table 4.9: Summary of adaptive peaks for feeding regimes from BM-OU model fitting on PC2 (biting/crushing morphology) across 1000 chronograms. M models feature multiple adaptive peaks, V models feature multiple evolutionary rates, and MV models include both. The parameters α and θ are the selective constraint parameter and the location of the adaptive peaks for each feeding category (H – herbivores, P – predators, I – microinvertivores, O – omnivores) in PC2 score space respectively. All model fitting parameters are given as mean (s.d.). Standard deviation includes both uncertainty in the phylogeny and in the ancestral reconstruction of feeding roles. Models are listed in order of increasing ΔAICc.

Model N Groups α θP θH θI θO OU-MV 2 P-HIO 8.7 (28.93) 0.47 (0.75) -0.29 (0.17) -0.29 (0.17) -0.29 (0.17) OU-M 2 P-HIO 9.06 (37.53) 0.57 (0.54) -0.28 (0.19) -0.28 (0.19) -0.28 (0.19) OU1 1 18.29 (67.63) -0.16 (0.18) -0.16 (0.18) -0.16 (0.18) -0.16 (0.18)

OU-M 2 OI-HP 14.61 (57.6) 0.09 (0.33) 0.09 (0.33) -0.33 (0.27) -0.33 (0.27) OU-M 2 O-PIH 11.23 (48.12) -0.09 (0.25) -0.09 (0.25) -0.09 (0.25) -0.27 (0.39) OU-M 3 OI-H-P 8.13 (33.46) 0.41 (0.54) -0.03 (0.46) -0.37 (0.27) -0.37 (0.27) OU-MV 2 OI-HP 18.18 (59.04) 0.03 (0.33) 0.03 (0.33) -0.37 (0.22) -0.37 (0.22) OU-MV 2 O-PIH 10.45 (42.39) -0.1 (0.27) -0.1 (0.27) -0.1 (0.27) -0.25 (0.3) OU-M 3 HI-O-P 7.75 (31.29) 0.36 (0.51) -0.24 (0.3) -0.24 (0.3) -0.32 (0.39) OU-M 3 PH-O-I 10.47 (43.7) 0.11 (0.35) 0.11 (0.35) -0.17 (0.42) -0.46 (0.42) OU-MV 3 OI-H-P 12.4 (40.64) 0.39 (0.72) -0.02 (0.35) -0.37 (0.24) -0.37 (0.24) OU-MV 3 HI-O-P 11.01 (34.48) 0.31 (0.69) -0.25 (0.27) -0.25 (0.27) -0.33 (0.32) OU-M 4 P-H-I-O 8.58 (37.58) 0.42 (0.55) 0.01 (0.48) -0.48 (0.42) -0.26 (0.4) OU-MV 3 PH-O-I 12.4 (42.71) 0.09 (0.37) 0.09 (0.37) -0.19 (0.33) -0.48 (0.39) OU-MV 4 P-H-I-O 8.66 (35.97) 0.43 (0.79) 0.05 (0.39) -0.5 (0.42) -0.24 (0.35)

176

4.4.4 Evolutionary consequences of specialization

In general, Neotropical cichlids were found to be moderately to strongly specialized feeders, however feeding diversity still varied substantially across taxa (FS ~0.6 to 1.0, Fig. 4.2 and 4.7).

Trends towards high or low specialization appear to have been consistent across lineages of

South American taxa (Fig. 4.7, black branches), while the colonization of Central America appears to have been associated with an increase in the diversification of feeding specialization

(Fig. 4.7, red branches). Specialized and comparatively generalized taxa were phylogenetically diverse (Fig. 4.7, text colours). The most specialized feeders were piscivores (both Cichla species, Crenicichla sp. “Orinoco-lugubris” and Petenia splendida) and aquatic insectivores

(Retroculus lapidifer and Crenicichla sp. “Orinoco-wallacii”), and were typically elongate, ram- feeding taxa (Fig. 4.7 and 4.8). The least specialized feeders included substrate sifters

(Astatheros robertsoni and Geophagus abalios) and omnivorous detritivore-invertivores

(Cichlasoma salvini, Aequidens tetramerus and Heros sp. “common”). All “omnivores” (Table

4.1) were classified as low specialization feeders, with the exception of Amphilophus citrinellus, a mollusk specialist (52% by volume) according to our data (unfortunately there were insufficient molluscivores in our data for a separate feeding category). The low specialization category included at least one representative from each feeding regime (piscivore- macroinvertivore, microinvertivore, herbivore-detritivore, omnivore) described in the preceding section.

The best fitting model of ram-suction morphology with respect to feeding specialization was a multi-peak, multi-rate model, which possessed the highest support over the vast majority of chronograms and was rarely poorly supported (Table 4.10, PC1 OU-MV). Taxa in the high specialization category were found to be evolving faster than their less specialized counterparts

177 (Table 4.11, PC1 OU-MV); more generalized-feeders evolved, on average, at 28.3% of the rate of more specialized-feeders in our dataset. Highly-specialized feeders were more common among the extreme values of ram-suction feeding compared to less specialized feeders (Fig. 4.8).

Concomitantly, highly specialized taxa were on average more disparate in ram-suction morphology than their less specialized counterparts (4.82 vs. 1.47 in average squared pairwise distances).

While a multi-peak model was preferred, the location of the adaptive peaks were virtually indistinguishable on the scale of PC1 scores (-0.33 vs. -0.24 on an axis ranging from -2.4 to 3.2) and given the variability associated with the estimated θs (Table 4.11), resulting in the distributions of both groups overlapping considerably (Fig. 4.8). A model including one peak and multiple-rates is not included in the “OUwie” package (Beaulieu & O’Meara 2013), or similar model fitting program. A one-peak, two rate solution may better fit the data, especially considering the relatively strong support of the single adaptive peak compared to the single-rate mutli-peak model (Table 4.10; OU1 vs. OU-M).

Biting-crushing morphology (PC2) showed no differences in adaptation or diversification between feeding specialization levels (Table 4.10, PC2 OU1). High and low specialized feeders were similarly disparate in PC2 scores (1.88 vs. 1.73 average squared pairwise distances, respectively). Therefore, the most important difference between the high and low categories of feeding specialization appears to be the rate of diversification in ram-suction feeding.

178

Fig. 4.7: Phenogram of feeding specialization in 41 species of Neotropical cichlids based on the MCC chronogram. Branches connect observed values to estimated ancestral values. Red branches denote the “Central American” subclade of Heroini. Species names are coloured by tribe: dark blue = Geophagini, green = Heroini, orange = Cichlasomatini, yellow = Cichlini, light blue = Chaetobranchini, purple = Retroculini and black = Astronotini. Pie charts illustrate the relative variation in dietary categories of (from the top) Petenia splendida, Herichthys carpintis, Aequidens tetramerus and Astatheros robertsoni (dietary category colours are as given in Fig. 4.2).

179

Fig. 4.8: Feeding specialization in functional morphospace of 41 Neotropical cichlid species. Principal component scores are summarized over 1000 posterior distribution chronograms. Point size is scaled by the value of the feeding specialization index (see upper right). Colours denote the specialization classes (black = low, FS <0.8; red = high, FS > 0.8) used in model fitting of functional diversification.

180

Table 4.10: Summary of BM-OU model fitting results for functional evolution, based on feeding specialization (high vs. low) across 1000 chronograms. M models feature multiple adaptive peaks, V models feature multiple evolutionary rates, and MV models include both. k = number of parameters in each model, w = Akaike weight of evidence. “Freq. best” gives the frequency across 1000 chronograms in which ΔAIC = 0 (best supported model), while “freq. poor” gives the frequency for which ΔAIC > 2 (i.e., the model had poor support). All model fitting parameters are given as mean (s.d.), except ΔAIC which is median (95% range). Standard deviation includes both uncertainty in the phylogeny and variation in the ancestral reconstruction of feeding specialization. Bolded lines give the best supported model.

Axis Model k Lik ΔAICc w Freq. Best Freq. Poor PC1 BM1 2 -71.56 (0.93) 11.59 (6.85, 20.05) 0 (0) 0 1 OU1 4 -65.51 (1.53) 1.7 (0, 9.31) 0.25 (0.11) 0.04 0.38

BM-V 3 -68.26 (2.78) 7.94 (0, 15.58) 0.09 (0.18) 0.078 0.87

OU-M 5 -65.33 (1.55) 3.93 (1.49, 11.45) 0.09 (0.06) 0.004 0.95

OUM-V 6 -61.96 (1.78) 0 (0, 2.92) 0.57 (0.16) 0.878 0.04

PC2 BM1 2 -60.43 (0.93) 8.59 (2.54, 16.71) 0.02 (0.03) 0 0.99 OU1 4 -54.96 (1.9) 0 (0, 2.63) 0.52 (0.16) 0.825 0.04

BM-V 3 -58.75 (1.7) 7.84 (0.55, 15.65) 0.04 (0.08) 0.017 0.94

OU-M 5 -54.27 (2.01) 1.42 (0, 2.46) 0.3 (0.11) 0.149 0.33

OU-MV 6 -53.98 (1.98) 3.43 (0.82, 4.99) 0.11 (0.06) 0.009 0.88

181

Table 4.11: Summary of adaptive peaks from BM-OU model fitting of functional evolution for the high (H) and low (L) feeding specialization groups across 1000 chronograms. M models feature multiple adaptive peaks, V models feature multiple evolutionary rates, and MV models include both. The parameters α, θ and σ2 are the selective constraint, the location of the adaptive peaks and rate of evolution for each specialization category in PC score space respectively. Values are given as mean (s.d.) and include uncertainty in the phylogeny and the reconstruction of feeding specialization. Bolded lines give the best supported model.

2 2 Axis Model σ H σ L α θH θL PC1 BM1 3.37 (0.09) 3.37 (0.09) - - - OU1 75.53 (188.8) 75.53 (188.8) 28.33 (73.44) 0.29 (0.2) 0.29 (0.2) BMV 5.83 (1.69) 1.37 (0.97) - - - OUM 87.73 (221.04) 87.73 (221.04) 32 (81.38) 0.31 (0.33) 0.26 (0.29) OUMV 180.55 (266.81) 56.38 (87.94) 41.21 (63.62) 0.33 (0.38) 0.24 (0.19) PC2 BM1 1.96 (0.06) 1.96 (0.06) - - - OU1 27.45 (96.97) 27.45 (96.97) 18.29 (67.63) -0.16 (0.18) -0.16 (0.18) BMV 2.91 (0.7) 1.19 (0.34) - - - OUM 26.68 (92.41) 26.68 (92.41) 18.32 (66.02) 0.1 (0.32) -0.33 (0.27) OUMV 25.17 (83.83) 25.74 (93.16) 17.48 (63.44) 0.12 (0.35) -0.32 (0.26)

182

4.5 Discussion

4.5.1 Diet and Function

Dietary composition among Neotropical cichlids varied primarily along axes of fish and large, evasive invertebrates, vegetation and detritus, as well as small benthic and epibenthic invertebrates (Fig. 4.1 and Fig. 4.2). Functional morphology was significantly correlated with diet composition, including after correcting for the influence of phylogenetic relatedness (Fig.

4.3 and 4.4, Table 4.2). Canonical correspondence analysis, with and without phylogenetic correction, associated the consumption of fish with velocity-oriented traits and expansion of the buccal cavity, the consumption of vegetation and detritus with suction-feeding and strong, shearing bites, the consumption of hard or large invertebrates (macrocrustacea and mollusks) with force production and crushing, and the consumption of small benthic/epibenthic invertebrates (aquatic insects, meiofauna, etc.) with the mobility of the oral jaws and sometimes force production during jaw opening (ST mass). Feeding ecology strongly contributes to the patterns of variation in feeding functional morphology; correlations between morphology and diet were similar to the major axes of variation in functional morphology (PCs), especially along the first two CCA axes (1 – lower jaw MAs, suction index, quadrate offset, protrusion and hyoid

KT; 2 – AM mass, ST mass and CB5 mass; Fig. 4.3 and 4.6 and Table 4.3).

While canonical correspondence analysis revealed significant relationships between diet and morphology in the Neotropical cichlid species examined, a substantial amount of variation in diet was not explained by functional morphology. While this may partly relate to the number of species examined, or the number of specimens available for some species, other factors may contribute to this variability. Species may possess adaptations for resources that do not comprise

183 a large portion of their diet, or are seasonally variable (Binning et al. 2009; Collar et al. 2009), however in our data functional extremes tended to be associated with dietary specialization (Fig.

4.7). It is also likely that feeding adaptations not captured in our dataset are critical to the consumption of certain food types, such as dental, pharyngeal, digestive or behavioural characteristics (Winemiller et al. 1995; Hulsey 2006; Martin & Wainwright 2011; López-

Fernández et al. 2014). Additionally, intra-specific variation in feeding and morphological adaptations, such as through resource polymorphisms, phenotypic plasticity or individual specialization, may influence the relationship between ecology and phenotype (Meyer 1987,

1990; Bolnick et al. 2003; Wanson et al. 2003; Muschick et al. 2011; Kusche et al. 2014).

However, despite these potential sources of variation, the relationship between functional morphology and dietary composition was significant, and therefore patterns of functional morphological evolution can be informative of variation in the ecology of cichlid species through time.

4.5.2 Functional Diversification and Feeding

Model fitting consistently identified different functional adaptations and rates of diversification for “predators” (taxa consuming primarily fish) compared to taxa consuming largely benthic materials (including detritus, vegetation, small invertebrates and mollusks) (Fig. 4.6 and Table

4.4 and 4.7). The largely piscivorous, predatory taxa were associated with ram-feeding optimized traits on PC1 (velocity-efficient MAs, evenly occluding jaws, low suction ability), generally elongate bodies, as well as slower rates of ram-suction evolution. This suite of morphological traits is consistent with the pursuit of active prey, requiring the ability to accelerate both the body and mouth parts rapidly to engulf prey (Norton & Brainerd 1993; Wainwright et al. 2001;

Waltzek & Wainwright 2003). Herbivores, microinvertivores and omnivores varied considerably in ram-suction morphology; while largely more suction-feeding adapted than predators, some

184 invertivorous (ex: the dwarf Crenicichla and Dicrossus) and omnivorous taxa (‘Cichlasoma’ salvini) were similarly ram-feeding adapted to large piscivores in Cichla, Crenicichla and

Petenia (Fig. 4.6).

The restricted evolution in ram-suction feeding among ram-feeding predators appears to be partially compensated by rapid evolution of biting and crushing morphology compared to herbivores, omnivores and micro-invertivores (Fig. 4.6). While adaptive optima differed between two groups, biting/crushing morphology overlapped between the two adaptive regimes. The relatively tall and short heads of suction-feeding herbivores and invertivores may place physiological constraints on oral jaw muscle size and pharyngeal jaw size, compared to the proportionately longer heads of predatory taxa (Fig. 4.6, and Chapter 3). It appears that in general, pelagic foraging and benthic foraging may incur an adaptive trade-off that alters the primary axis of functional diversification. This is consistent with previous studies of diet and morphology in geophagin and benthivorous, sediment-sifting cichlids (López-Fernández et al.

2012, 2014). For example, López-Fernández et al. (2012) observed a trade-off in the consumption of benthic invertebrates and fish in South American cichlids. Such a trade-off may reinforce morphospace partitioning among lineages and transitions between adaptive regions in morphospace (Chapter 1 and Chapter 2). For example, a larger number of shifts occurred between adaptive peaks in suction-optimized morphospace compared to ram-optimized space based on an adaptive landscape with no a priori ecological hypotheses (Chapter 1), a pattern which may have been facilitated by increased diversification of invertivorous and herbivorous/detritivorous taxa.

Montaña and Winemiller (2013) had previously observed convergence in morphological traits between South American cichlids and North American centrarchids. Here I demonstrate that Neotropical cichlids and North American centrarchids have also converged in

185 macroevolutionary patterns in ram-suction feeding, namely slower rates of evolution among piscivores/macroinvertivores (Fig. 4.6 and Table 4.5). Collar et al. (2009) observed that on an axis of ram-suction morphology, moderate to strongly piscivorous centrarchids (>5% and >50% consumption of fish respectively) experienced slower rates of evolution than other taxa. A number of omnivores in our dataset however were also moderately piscivorous (up to ~20% fish by volume: Satanoperca jurupari, Astronotus ocellatus, ‘Cichlasoma’ salvini, Hypselecara coryphaenoides, Appendix 4.1), while fish consumption among micro-invertivores and herbivores/detritivores was very low. For multi-rate models on PC1, the most diversified diets

(i.e., omnivores) evolved slowly, second only to pelagic predators (Table 4.5). The consumption of fish may overall be the most important limiting factor (from a dietary perspective) on the rate of ram-suction evolution (Collar et al. 2009).

4.5.3 Consequences of Dietary Specialization

Greater dietary specialization was associated with higher rates of ram-suction evolution. The traits associated with this axis of variation are linked to variation in body shape (Chapter 3), likely influencing habitat use, and with trade-offs between pelagic and benthic foraging. Dietary specialists were more likely to have colonized the extremes of Neotropical cichlid ram-suction morphospace, resulting in higher functional disparity. This trade-off in dietary vs. functional diversity likely impacts other aspects of cichlid ecology; trade-offs in ram-suction feeding may alter patterns of body shape evolution and habitat use. Future analyses could compare patterns of diversification in habitat niche breadth among feeding specialist and generalist cichlids to test this hypothesis (Litsios et al. 2014).

186 Martin and Wainwright (2011) similarly found increased rates of diversification in feeding morphology in two ecologically diverse radiations of pupfish, and suggested that trophic novelty is a driving factor in adaptive radiation. It has been postulated that under an adaptive radiation, ecological specialists arise from generalist ancestors, although this pattern may not be supported in all cases (Losos & Queiroz 1997; Glor 2010). An increase in rates of phenotypic evolution associated with the emergence of specialist lineages would be consistent with the

“early bursts” of diversification characteristic of adaptive radiation and similar processes.

Variation in dietary specialization was similar between the South and Central American cichlids sampled (Fig. 4.7). The invasion of Central America was associated with an increase in rates of functional diversification (Chapter 2), and appears to have been associated with an increase in the diversification of feeding specialization (Fig. 4.7, red branches). Winemiller et al.

(1995) also noted a pattern of increased niche diversification among Central American taxa.

While South American heroins were comparatively generalized (however I could not obtain equivalent dietary data for Symphysodon, an algae/periphyton feeder (Crampton 2008) and likely the most extreme suction-feeder in our data), Central American heroins included a number of specialized forms (Fig 4.8). The occurrence of new strongly specialized feeders is likely associated with renewed ecological opportunity in Central America, and concomitant with increased diversification in ram-suction morphology (Chapter 2).

4.5.4 Conclusions

Neotropical cichlids are often described as a clade of interest for (among other factors) their diversity in form and ecology (e.g. Winemiller et al. 1995; López-Fernández et al. 2010, 2012,

2013). Here I illustrate the relationship between ecological variation in feeding and functional

187 morphology as well as functional diversification. Adaptation to particular dietary resources has consequences on the rate of functional evolution, likely resulting in a bentho-pelagic trade-off in patterns of functional diversification. Dietary-specialization is linked to increased diversification of ram-suction morphology and promotes functional novelty. The relationship between feeding and functional variation is dominated by trade-offs occurring along different axes of variation

(ex: functional diversity vs. niche breadth). Such trade-offs are likely to reinforce the partitioning of morphospace in the radiation of cichlids.

4.6 Appendices

188

Appendix 4.1: Mean volumetric proportional contribution of 12 prey categories to the stomach contents of 41 species of cichlid. N gives the number of specimens used in stomach content analysis and the original studies from which dietary data was obtained are

given as footnotes (see reference #). See methods for description of the composition of each food category

Reference Figure

Species N Fish

# code Sand

plants

detritus

Aquatic Aquatic

Mollusks

vegetation

Vegetative Vegetative

Meiofau

Terrestrial Terrestrial Terrestrial

arthropods

Aquatic insects Aquatic

Microcrustacea

Animal detritus Animal Macrocrustacea

Acaronia nassa 44 1 Acr nas 0.503 0.001 0 0.169 0 0.006 0 0.001 0.196 0.040 0.051 0.032

Aequidens 18 1 Aeq tet 0.075 0.099 0.002 0 0 0.001 0 0 0.386 0.195 0.227 0.016 tetramerus Amatitlania 187 2 Ama siq 0 0.149 0.241 0.001 0.060 0.015 0.003 0.055 0.065 0.027 0.373 0.010 siquia Amphilophus 93 2 Amp cit 0.092 0 0.032 0.165 0.001 0 0.001 0.520 0.123 0.004 0.051 0.012 citrenellus Andinoacara 677 2 And pul 0.012 0.203 0.102 0.023 0.008 0.024 0.009 0.125 0.328 0.058 0.084 0.024 pulcher Apistogramma 210 1, 2 Api hoi 0 0.026 0.219 0.007 0.293 0.039 0.001 0.029 0.327 0.001 0.057 0.002 hoignei Archocentrus 182 2 Arc cen 0.007 0.079 0.175 0 0.175 0.003 0 0.001 0.519 0.002 0.037 0.002 centrarchus Astronotus 99 2 Asr oce 0.219 0 0.028 0.019 0.015 0.009 0 0.084 0.317 0.253 0.010 0.047 ocellatus Astatheros 58 4 Ast rob 0 0 0.044 0 0.231 0.005 0.129 0.105 0.082 0 0.250 0.153 robertsoni Biotoecus 71 1, 3 Bio dic 0 0.004 0.002 0 0.248 0.036 0.121 0 0.426 0.003 0.155 0.012 dicentrarchus

189

tus

Species N Reference code

Fish

Sand

plants

detritus

Aquatic Aquatic

Mollusks

Terrestrial Terrestrial vegetation Terrestrial

arthropods

Meiofauna

Vegetative Vegetative

Aquatic insects Aquatic

Microcrustacea

Animal detri Animal Macrocrustacea

Biotodoma 79 1,3 Bio wav 0.006 0.001 0.001 0 0.001 0.111 0.011 0 0.601 0 0.026 0.242 wavrini Caquetaia 370 2 Caq kra 0.463 0.009 0.014 0.056 0.023 0.003 0 0.020 0.242 0.159 0.006 0.005 krausii Chaetobranchus 10 10 Cha fla 0.222 0.296 0 0 0.233 0 0 0 0 0 0.223 0 flavescens Cichlasoma 24 2 Cic bim 0.019 0.433 0.093 0.001 0.022 0.002 0 0.164 0.169 0.002 0.077 0.018 bimaculatum Cichla ocellaris 143 8, 9 Cic oce 0.958 0 0 0 0 0 0 0 0.042 0 0 0 Cichla temensis 271 1 Cic tem 0.106 0 0.101 0.074 0.026 0 0.012 0.010 0.148 0.006 0.269 0.249 ‘Cichlasoma' 51 4, 5 Cic sal 0.991 0 0 0.001 0 0 0 0 0 0 0.008 0 salvini ‘Cichlasoma’ 72 4 Cic uro 0.383 0.007 0.019 0.558 0 0 0 0 0.001 0 0.025 0.007 urophthlamus Crenicichla o- 325 1, 3 Cre lug 0.751 0.011 0 0.107 0 0.003 0 0 0.021 0.075 0.005 0.028 lugubris Crenicichla o- 148 1,3 Cre wal 0.075 0 0 0.032 0.003 0.005 0.001 0 0.834 0.004 0.010 0.037 wallaci Cryptoheros 67 5 Cry che 0 0 0.565 0 0.024 0.003 0.154 0 0.050 0 0.147 0.058 chetumalensis Dicrossus 10 3 Dic mac 0.055 0.007 0 0 0 0.558 0 0 0.240 0 0 0.141 filamentosus Geophagus 90 1, 3 Geo aba 0.116 0.252 0.010 0.009 0.004 0.018 0.072 0 0.266 0.001 0.174 0.077 abalios

190

cea

Species N Reference code

Fish

Sand

detritus

Aquatic Aquatic

Mollusks

vegetation

Terrestrial Terrestrial

arthropods

Meiofauna Vegetative

Aquatic insects Aquatic

Microcrusta

Animal detritus Animal

Macrocrustacea Terrestrial plants Terrestrial

‘Geophagus' 83 7 Geo bra 0 0.008 0.039 0 0.025 0.059 0.039 0.154 0.445 0.017 0.127 0.087 brasiliensis Geophagus 65 1, 3 Geo dic 0.046 0.104 0.005 0.049 0.024 0.028 0.097 0 0.024 0.003 0.426 0.192 dicrozoster Geophagus 2 3 Geo ste 0 0.386 0.096 0 0 0 0 0 0.195 0 0.116 0.207 steindachneri Herichthys 23 4 Hei car 0 0 0.540 0 0 0 0 0 0.019 0 0.351 0.091 carpintis Herotilapia 22 2 Heo mul 0 0.160 0.441 0 0.002 0.003 0.001 0 0.001 0 0.389 0.003 multispinosa Heros sp. 74 1 Her com 0.125 0.006 0.011 0.001 0.001 0.064 0.184 0 0.054 0.013 0.387 0.155 common Hoplarchus 36 1, 3 Hop psi 0.076 0 0 0 0 0.003 0 0 0.327 0.001 0.266 0.326 psittacus Hypselecara 87 1 Hyp cor 0.215 0.013 0.003 0.012 0.002 0.047 0.021 0 0.152 0.028 0.099 0.406 coryphaenoides Laetacara 68 9 Lae dor 0.003 0 0 0 0.446 0.040 0.005 0.027 0.348 0 0.131 0 dorsigera Mikrogeophagus 28 3 Mik ram 0.011 0.015 0 0 0.228 0.097 0 0 0.605 0.005 0.038 0 ramirezi Paraneetroplus 42 4 Paa syn 0.001 0 0.321 0.087 0 0.001 0 0.082 0.002 0 0.503 0.005 synspilus Parachromis 425 2 Par fri 0.110 0.012 0.006 0.401 0.008 0 0 0.027 0.142 0.264 0.012 0.016 friedrichsthali

191

plants

Species N Reference code

Fish

Sand

Mollusks

Terrestrial Terrestrial

arthropods

Meiofauna

Aquatic insects Aquatic

Microcrustacea

Animal detritus Animal

Macrocrustacea

Terrestrial Terrestrial

Aquatic vegetation Aquatic detritus Vegetative

Petenia 35 4, 5 Pet spl 0.993 0 0 0.007 0 0 0 0 0 0 0 0 splendida Retroculus 90 6 Ret lap 0.003 0 0 0.029 0.044 0.020 0 0.002 0.879 0.007 0.010 0.008 lapidifer Satanoperca 82 1, 3 Sat dae 0.020 0.001 0.001 0 0.009 0.067 0.079 0 0.300 0.005 0.097 0.420 daemon Satanoperca 41 1 Sat jur 0.293 0.001 0.001 0 0.049 0.032 0 0 0.177 0.014 0.422 0.011 jurupari Theraps 4 4 The int 0 0 0.266 0 0 0.008 0 0 0.086 0 0.641 0 intermedia Thorichthys 66 4, 5 Tho mee 0 0 0.001 0 0.029 0.083 0.048 0.451 0.293 0 0.067 0.027 meeki

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related to environmental factors at multiple scales. Doctoral Thesis, Texas A&M

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ecomorphology and diets of cichlids in the Bladen River, Belize. Environmental Biology

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193 C. General Conclusions

C.1 Summary

Diversity, whether morphological, ecological or species richness, is unevenly distributed across the tree of life. Groups of species may vary in morphological diversity as a result of neutral processes; for example, older clades are expected to exhibit greater morphological variability under a random-walk process. However, morphological disparity may also be linked to adaptation to local ecological or environmental conditions. While island-based adaptive radiations have provided a fruitful area of research to examine the relationship between ecology and adaptive phenotypic diversification, recent years have seen an expanse into more broadly distributed systems (e.g. Slater et al. 2010; Derryberry et al. 2011; López-Fernández et al. 2013;

Slater 2013; Davis et al. 2014; Grundler et al. 2014; Bravo et al. 2014).

In this dissertation, I have presented multiple lines of evidence for the adaptive evolution of functional morphology across a widely distributed radiation of freshwater fishes, the

Neotropical cichlids. Cichlinae functional morphology varied primarily along an axis of ram- suction feeding, reflective of patterns in other radiations of fish and in some cases representing similar functional underpinnings (Chapter 1). I found that functional diversity has been shaped by selective constraint (Chapter 1), and by adaptation to particular feeding strategies (Chapter 4).

Fossil cichlids have demonstrated that while extinction has resulted in the loss of some attributes, adaptive landscapes in morphology have been stable for tens of millions of years (Chapter 3). I also found that morphological diversity in Cichlinae has been enhanced by the evolution of specialized feeders (Chapter 4). Moreover, the distribution of morphological diversity among

Cichlinae lineages (older vs. younger, South American heroins vs. Central American heroins, etc.) appears to have been influenced by competition and the availability of ecological niches

194 (Chapter 2). Overall, morphological disparity in performance-linked traits in Cichlinae, and its distribution among cichlid lineages, has been strongly influenced by selection, adaptation and ecological opportunity.

Schluter (2000) identified four characteristics of adaptive radiation, which have been further expanded on by Gavrilets & Losos (2009) and Glor (2010), although many other authors have debated the most important and diagnostic features of adaptive radiation. These four criteria include 1) monophyly, 2) rapid diversification, typically identified by “early bursts” in lineage diversification and phenotypic evolution, and “adaptation” (sensu Glor, 2010) demonstrated by

3) trait-environment correlations and 4) trait-utility. The monophyly of Neotropical cichlids has been demonstrated repeatedly by modern molecular phylogenies (Smith et al. 2008; López-

Fernández et al. 2010, 2013; McMahan et al. 2013). Rapid lineage diversification during the early evolution of Cichlinae was demonstrated by López-Fernández et al. (2013), and patterns of decreasing morphological diversification have been strongly demonstrated within the South

American assemblage (Chapter 2). Slater and Pennell (2014) emphasize that changes in patterns of diversification due to lineage specific ecological opportunity does not invalidate a pattern of adaptive radiation for the clade overall. For example, Slater et al. (2010) found a pattern of decreasing morphological diversification in cetacean body size only after accounting for two phylogenetic nodes with high evolutionary rates. These high rates were attributed to the extinction of predatory sperm whale lineages opening an “adaptive zone”. I also found correlations between functional morphology and diet (“trait-environment correlations”), even after accounting for the effect of evolutionary relatedness (Chapter 4), in traits with inherent impacts on performance capability (i.e., “trait utility”). To the extent that adaptive radiations may be defined by the above factors, I find support for a continental adaptive radiation in South

195 American cichlids, with the possibility of a recent burst of diversification in Central America mediated by ecological release from South American cichlids and ostariophysans.

C.2 Future Directions

This dissertation has focused on functional traits related to feeding performance. Examining the relationship between functional morphology and other ecological axes may further delineate factors driving the disparity of Cichlinae, and other diverse or broadly distributed clades. For example, under the vertebrate evolutionary radiation model (“radiation in stages”), phenotypic diversification occurs first along axes of habitat variation (ex: sand and rock forms of African cichlids), followed by diversification in feeding traits (ex: feeding guilds within sand and rock

African cichlids), and lastly in traits related to communication and behaviour (Streelman &

Danley 2003). For example, López-Fernández et al. (2013) found a difference in the mode of evolution between presumed habitat-related and feeding-related ecomorphology, and suggested that the “radiation in stages” pattern may result from varying selective constraint rather than differences in the timing of trait evolution. Initial analyses of swimming functional morphology

(correlated with habitat characteristics) have revealed strong divergent selection driving an early burst signal in phenotypic evolution (Astudillo-Clavijo et al. In Review). Comparisons between the timing of evolution in feeding and swimming morphology would represent a unique test of the “radiation in stages” hypothesis. Selective constraint on body shape and functional morphology are also likely mediated by multiple ecological factors, which may influence trade- offs along ecological axes. For example, are cichlid feeding-specialists more likely to be habitat specialists or generalists? Body shape was well correlated with a suite of largely cranial

196 functional characteristics across Cichlinae (Chapter 3); to what extent might the relationship between feeding and habitat specialization be modulated by shared axes of morphological diversification? Morphological diversification related to trophic ecology and habitat use may also be constrained by shared evolutionary modules (Cooper et al. 2010; Klingenberg &

Marugán-Lobón 2013).

This dissertation, as well as some previous analyses of cichlid morphological diversification (López-Fernández et al. 2013), focused on broad patterns occurring across

Cichlinae lineages, primarily at the genus level and above. This was partially a limitation of 1) the available phylogenetic sampling, which even among the most comprehensive studies reflect perhaps one third of Neotropical cichlid species (e.g. Smith et al. 2008; Musilová et al. 2009;

López-Fernández et al. 2010, 2013; McMahan et al. 2013; Říčan et al. 2013), but also 2) the destructive nature of data collection in comparative functional morphology combined with the limited collections-based resources for some species. However, processes occurring within genera have likely contributed to the distribution of species and morphological diversity. For example, Geophagini is dominated by two genera (Crenicichla and Apistogramma, each > 90 species) that possess both dwarfism and sexual dichromatism (to varying degrees). These genera may in particular be important to the analysis of radiation “stages”; i.e., species-richness in cichlids may be enhanced by evolution of “communication” traits following trophic diversification (Streelman & Danley 2003). Resource partitioning and associated adaptations to trophic morphology may have also contributed to species richness in Crenicichla species flocks

(Burress et al. 2013). Additionally, pairs of Geophagus species occur in sympatry within several

South American river systems (ex: Geophagus dicrozoster and Geophagus abalios), and often feature species-pairs exhibiting similar distinguishing characteristics in body shape and colouration (López-Fernández, Pers. Comm.). Ecological character displacement may have

197 contributed to the diversity of this genus. Linking micro- and macro-evolutionary processes contributing to ecological and morphological diversity will help to provide a more complete explanation of the diversity of cichlids. However, considerable expansion of species-level molecular phylogenies will be required to advance research on such subjects.

Are patterns of morphological evolution generalized among fishes from similar ecological assemblages and experiencing similar environmental conditions? Loricariids

(“suckermouth catfish” or “plecos”) are more species-rich than Neotropical cichlids and vary dramatically in body shape, although they are less variable in trophic ecology (primarily wood- eaters, algivore/detritivores and insectivores) than Neotropical cichlids (Lujan et al. 2012). Jaw biomechanics and morphology have been correlated with trophic ecology in comparative studies of Loricariids (Lujan et al. 2011; Lujan & Armbruster 2012; Lefebvre 2014), and the evolution of such traits may provide a valuable comparison to the patterns of Neotropical cichlid evolution.

Similar to Neotropical cichlids, poeciliids (guppies and allies) may have dispersed to mainland

Central America early compared to ostariophysan lineages (Matamoros et al. 2014). It is possible that morphological diversification of poecillids was also influenced by ecological release as was observed in Cichlinae (Chapter 2). Given the vast diversity of freshwater fishes in the Neotropics

(~7000 species; Reis et al. 2003; Albert & Reis 2011), combining our knowledge of the functional diversification of Cichlinae with that of other Neotropical fish families will represent a considerable contribution to our understanding of vertebrate diversity.

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