Phylogenetic and Experimental Evidence for an Evolutionary Precursor to Male Colouration in Poeciliid Fishes and Their Relatives

Home , Alfaro cultratus, Allotoca maculata, Neoheterandria, Neumania papillator, Poecilia parae, Xenophallus umbratilis

Michael Ryan Foisy

A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Ecology and Evolutionary Biology University of Toronto

Phylogenetic and Experimental Evidence for an Evolutionary Precursor to Male Colouration in Poeciliid Fishes and Their Relatives

Michael Ryan Foisy

Master of Science

Department of Ecology and Evolutionary Biology University of Toronto

2017 Abstract In many animals, phenotypic diversity in males originates through sexual selection.

However, the evolutionary origins of female preferences are unclear. The sensory bias hypothesis suggests female preferences can originate as a byproduct of natural selection operating on sensory systems in non-sexual contexts, like foraging. Despite over 25 years of investigation, fundamental tenets of the sensory bias hypothesis remain unresolved. We explicitly test one of these tenets: the historical dependency of male traits on pre-existing biases in females. We marry phylogenetic comparative methods and behavioural experiments to test for this dependency in poeciliids and related fishes. Our results demonstrate that female biases for long-wavelength colours drove the evolution of long-wavelength colouration in males. In addition to demonstrating this dependency, our study suggests (i) behaviour can be phylogenetically conserved, and (ii) natural selection may be the ultimate explanation for a tremendous amount of variation that has classically been attributed to sexual selection.

Acknowledgments

My highest gratitude goes out to my supervisors, Helen Rodd and Luke Mahler, for their limitless patience and enthusiasm. I am thankful for their guidance and for giving me enough line to take this project into greater depths, while still reeling me in when I was in too deep. I am grateful for the opportunity to have travelled so much for my experimental work, which lent to all sorts of interesting experiences including: eating alligator in Texas, hiking in California, and catching amphisbaenids orchid bees in Trinidad. Really, I could not have had better supervisors.

Thank you for everything.

I have many friends and colleagues to thank for helping me along the way. Rebecca

Schalkowski, du warst die erste Freundin, die ich in Toronto gemacht habe. Ich bedanke mich für deine Unterstützung, deine aufrichtige Freundschaft, und für gelegentlich Gespräche auf

Deutsch. I would also like to thank: Augustin Le Bouquin for curious conversations and sharing your aesthetic views of the world; James Boyko for constantly debating science with me, and for not getting upset after it took me five months to realize that your name was not “David”;

Charlotte for chatting to me about bees at a frequency no one else could possibly tolerate; the

ROMulans for sneaking me into the collections and exhibitions; Viviana Astudillo-Clavijo and

Melanie Massey for an incredibly beautiful card; Isabela Borges e Eduardo Gutierrez por sua amizade e apoio, e por me ensinar a fazer comida brasileira; Alex DeSerrano and Mitchel Daniel for teaching me how to work in a fish lab and conduct the behavioural experiments; the Rodd lab and work studies for helping care for the fishes; Phylo club for being a fun phylogenetics family who taught me a lot; Christopher Boccia, Luke Owen Frishkoff and the rest of the Mahler lab for teaching me about macroevolution; Jeremy Beaulieu, for developing these super cool methods, and for responding to the barrage of e-mails I’ve sent you over the past year; Markita Savage and

Cristina Mendoza at the Xiphophorus Genetic Stock Center for their generosity and assistance iii

with the xiphophorine experiments; Gita Kolluru for generosity and guidance with the

Girardinus experiments in California; Charles and Susie Clapsaddle, and their family at Goliad

Farms, for providing an unparalleled opportunity to test my model, for teaching me about the surrounding plants and towns, and for making me carnitas; Arcadio Valdez-Gonzalez for collaborating and testing several species of Goodeidae; Bill Cole for cold beers and good laughs;

Kitty Lam for being an administrative superwoman; Locke Rowe, Heather Proctor, Mike Ryan,

Deborah McLennan, and Aneil Agrawal for insightful discussions and feedback on my work;

Ben Sandkam for measuring disc reflectance and ambient light spectra; Stephen Wright for letting me run simulations on the grandiflora server; Corlett Wood and Julia Kreiner for help with R; David Punzalan and Alex DeSerrano for help with SAS; and lastly, the Toronto Transit

Commission for keeping streetcars (despite their horrible inefficiency), which provided a charming environment for reading and editing papers. We thank NSERC for funding to MRF

(NSERC CGS M), DLM and FHR (NSERC Discovery grants).

Table of Contents

Acknowledgments ...... iii

Table of Contents ...... v

Chapter 1 Background on Sexual Selection, Especially Sensory Bias ...... 1

Background ...... 1

Chapter 2 Phylogenetic and Experimental Evidence for an Evolutionary Precursor to Male Colouration in Poeciliidae and Their Relatives ...... 8

Introduction ...... 8

Methods and Results ...... 11

2.1.1 Phylogenetic test for a precursor underlying male colouration ...... 11

2.1.2 A phylogenetic precursor underlies the evolution of male colouration ...... 12

2.1.3 Experimental tests of the precursor-2 model ...... 13

2.1.4 The precursor is a pre-existing long-wavelength bias in males and females ...... 14

Discussion ...... 16

Conclusion ...... 22

References ...... 27

Appendix A – Supplemental Methods ...... 38

Appendix B – Supplemental Figures ...... 50

Appendix C – Supplemental Tables ...... 61

Chapter 1 Background on Sexual Selection, Especially Sensory Bias

The main body of your thesis begins here. Background

Charles Darwin (1859, 1871) was the first to propose the concept of sexual selection. He drew inspiration from gaudy male traits, like the peacock’s tail, which were too costly to be explicable under traditional Darwinian evolution. So, Darwin intuited sexual selection (1859, pg.

117), and later on (1871), laid out two central tenets of his theory: (i) males may evolve costly traits (i.e., “armaments”, like antlers) to gain access to females, and (ii) females may evolve preferences for particular male traits (i.e., “ornaments”, like brilliant colouration). These two principles were the foundation of most modern work on sexual selection, and provide a robust explanation for much of life’s rich and beautiful phenotypic diversity.

But it wasn’t always this way. While Darwin’s hypothesis that males evolve costly traits to gain access to females was readily accepted, his suggestion that females evolve preferences for particular male traits was passionately rejected by scientific authorities at the time (Wallace,

1895, 1889; Huxley, 1938; reviewed in: Cronin, 1991). This was likely because the notion that female choice could sculpt the direction of male evolution clashed with cultural biases prevalent in 19th-century Victorian society (Cronin, 1991), and because leading biologists were skeptical that females could impose a potent selective force on males (Wallace, 1895, 1889; Huxley,

1938). Ultimately, this rejection set the study of female preferences into a state of torpor, causing the early study of sexual selection to be almost entirely male-centric (Cronin, 1991; Jones &

Ratterman, 2009; Parker & Pizzari, 2015). Despite this bias, the field developed insights about many aspects of reproduction including: parent and sex roles, male-male competition, armament

and ornament evolution, territoriality and mating strategies, and mating system evolution

(Andersson, 1994).

Focus eventually shifted away from males (Bateson, 1983; Andersson, 1994), however, and our understanding of the role of female preferences in evolution by sexual selection improved dramatically during the 20th century (reviewed in: Andersson and Simmons, 2006;

Kokko et al., 2006; Jones & Ratterman, 2009). Ronald Fisher was one of the first to push back against the prevailing skepticism about the role of female mate preferences. He presented playful arguments concerning bad breath and blushed cheeks, as well as pigeons in beauty pageants, to argue that there are in fact traits that females objectively prefer, whether adaptive or not (Fisher,

1915). Since Fisher’s verbal arguments, other formal models for sexual selection via female preferences have been proposed, and a diversity of models now describe the evolution of male traits and female mating preferences, including: the Fisher process (Fisher, 1915, 1930, 1958;

Lande 1981), “good genes” (Fisher, 1930; Hamilton & Zuk, 1982), indicator mechanisms

(Zahavi, 1975;), and sexual conflict (Arnqvist & Rowe, 2005) (reviewed in Andersson, 1994;

Mead & Arnold, 2004; Fuller et al., 2005; Kokko et al., 2006). These models are foundational in the field of sexual selection; broadly, they provide quantitative and theoretical frameworks for understanding the maintenance, exaggeration and co-evolutionary dynamics of female mating preferences and male secondary sexual traits.

Despite these achievements, the field of sexual selection has two key problems that remain difficult to resolve: (i) we do not understand the relative importance of each sexual selection process/model, and (ii) we do not know why female preferences initially originate

(Marler & Ryan, 1997; Payne & Pagel, 2001; Fuller et al., 2005; Arnqvist, 2006; Jones &

Ratterman, 2009). All of the aforementioned classical models explain the evolution of female preferences either in tandem with, or as a response to, some feature of males (future reproductive

potential, direct benefits, sexual arms, et cetera). However, in the 1980s and 1990s, biologists began to incorporate sensory physiology into studies of sexual selection, and a new hypothesis, called the sensory bias hypothesis, emerged to explain the origins of female preferences.

But before the role of sensory physiology was considered in the origins of female preferences, much of the initial progress on this model of sexual selection, as far as I am aware, focused on the evolution of male traits. The first synthesis that considered the role of sensory systems in the evolution of sexual signals was by West-Eberhard (1984). She collected a number of case studies from insects to illustrate that male displays often tapped into female sensory systems that almost certainly evolved under other contexts. For example, in the case of a species of Xylocopa from Colombia, sexual pheromones in males appear to mimic the odours that attract females to flowers. This synthesis led researchers to consider that sexual preferences may evolve as a byproduct of natural selection operating in non-sexual contexts, like foraging. This hypothesis has been termed the “sensory bias hypothesis” (Basolo, 1990), and “sensory exploitation” (Ryan, 1990) and, while there are some inconsistencies in the usage of these two terms, for the purposes of writing this thesis, I will consider them synonymous (but see: Endler

& Basolo, 19981).

In the late 1980s and early 1990s, a series of experiments aimed at testing the sensory bias hypothesis piqued the interests of evolutionary biologists. First, Ryan et al. (1990) and Ryan and Rand (1990) showed that, in two related frog species (the túngara frog, and a close relative), components of the female auditory system (basilar papilla tuning) evolved prior to the some of the low-frequency acoustic signals (“chucks”) made by males. Thus, the male “chuck” evolved after females already had a sensory bias tuned to this low-frequency sound, and could not have

1 The adoption of terminology in sensory bias research was complex and, in early days, sometimes inconsistent. It seems that the primary terms used in this field appeared in the literature in the following order: “sensory drive” (Endler, 1988), “sensory exploitation” (Ryan, 1990), “sensory bias” (Basolo, 1990). 3

played a role in the initial evolution of the female’s preference for the sound. Around the same time, Alexandra Basolo (1990) conducted experiments showing that the sword-like projections on the caudal fins of xiphophorine fishes were preferred by females of related species in which the males lacked swords. The historical inferences made in these studies were later corroborated by phylogenetic reconstruction of ancestral states (Ryan & Rand, 1993; Basolo, 1995), and some of the best neurophysiological studies of sensory bias come from the túngara frog (Ryan, 1998,

Wilczynski et al., 2001). Although now regarded as “classics”, both of these studies were initially subjected to rigorous bouts of scientific criticism (túngara frog, Pomiankowski, 1994;

Sherman & Reeve, 1999; Ron, 2008; swordtails, Meyer, 1997; Rosenthal & Evans, 1998;

Makowicz et al., 2015). The primary criticism of these works focused on the reliability of their phylogenetic inferences. Due to a paucity of female preference data, the use of small numbers of species in the phylogenies, and the application of simple ancestral trait reconstruction methods applied to male traits (and sometimes female preferences), these tests do not distinguish with certainty whether the experiments had unveiled a pre-existing female preference, or whether the male trait had actually evolved first, and then been secondarily lost (Pomiankowski, 1994;

Meyer, 1997; Sherman & Kern Reeve, 1999; Ron, 2008). To date, this problem has yet to be addressed in these systems.

Nevertheless, since these studies, empirical patterns consistent with sensory bias have been observed across several sensory modalities, and in many groups of animals (reviewed in

Ryan and Cummings, 2013). Although the sensory bias hypothesis has been gaining support for nearly three decades, fundamental tenets of the hypothesis remain untested (Fuller et al., 2005;

Andersson & Simmons, 2006; Ryan & Cummings, 2013; but see Cummings et al., 2007; Fuller et al., 2009; Fuller & Noa, 2010). Of course, this is true for virtually all sexual selection models; despite the attention directed towards them, most of them have not been thoroughly tested

(Andersson & Simmons, 2006; Kokko et al., 2006; but see: Houde 1994; Wilkinson & Reillo,

1994; Arnold & Houck, 2016). The sensory bias model has been difficult to examine primarily because of insufficient data on female mate preferences, but also because of the challenges and logistical limitations posed by the genetic and behavioural experiments required to test the model via traditional means (Fuller et al., 2005; Andersson & Simmons, 2006; A. Moehring & H.

Hoekstra, personal communications). Also, we still have little empirical evidence for the ecological bases of sensory biases, although this has been a growing area of interest (Ryan &

Cummings, 2013). Lastly, despite many indirect phylogenetic investigations in which the evolution of male traits (and sometimes female preferences) were modeled, the explicit historical dependency of male traits on pre-existing traits in females predicted by the sensory bias hypothesis has never been rigorously demonstrated. This historical dependency is what I tested in this thesis.

Improvements in knowledge of the tree of life and in the development of more powerful phylogenetic comparative methods provide new opportunities for explicitly testing the historical dependency that is central to the sensory bias hypothesis. In previous sensory bias studies, phylogenetic approaches have not been robust (Ryan & Rand, 1993; Basolo 1995), often incorporating a very small number of species and employing unrealistic character reconstruction methods like maximum parsimony, which performs poorly when applied to traits that are evolutionarily labile. More importantly, it has been argued that the inferences that have been drawn from these approaches can be explained by secondary losses of the male traits

(Pomiankowski, 1994; Meyer, 1998; Fuller et al., 2005); this is because previous investigators have assayed for sensory bias by independently reconstructing the histories of male and female trait evolution and comparing them, rather than explicitly modeling their historical interactions.

Fortunately, recent phylogenetic methods (Marazzi et al., 2012; Beaulieu et al., 2013; Beaulieu

& O’Meara, 2016) can circumvent some of these issues by allowing us to model and test for evolutionary precursors underlying a trait of interest (see Chapter 2). In my work, I apply these recent methods to model and explicitly test for a pre-existing bias in females that underlies and facilitates the evolution of male colouration. More generally, phylogenetic methods provide a promising framework for testing macroevolutionary predictions generated by models of sexual selection. For example, phylogenetic methods can help us understand the origins (e.g., Endler &

Basolo, 1998; Arnqvist, 2006), patterns (e.g., Prum, 1997; Emlen et al., 2005), and ecological drivers (e.g., Ciccotto & Mendelson, 2016) of sexual preferences and/or male traits; the co- evolutionary tango between male traits and female mate preferences (Arnold & Houck, 2016); and the role of sexual selection in diversification (Masta & Maddison, 2002; Seddon et al.,

2013). However, while recent advancements in phylogenetic methods offer unique opportunities for testing such macroevolutionary predictions of sexual selection models, the most powerful tests should occur when phylogenetic insights are complemented with experimental study of living organisms (Weber & Agrawal, 2012).

Brief summary of MSc research

For my MSc research, I have coupled phylogenetic comparative models with laboratory experiments to explicitly test for the role of pre-existing biases in the evolution of male colouration. I carried out this research on a large clade of poeciliid fishes and their relatives. The

Poeciliidae have become a classic system for the study of sexual selection (Meffe & Snelson,

1989; Houde, 1997; Magurran, 2005). Species in this group, and in closely related groups, range from having highly pigmented males and strong female preferences for male colouration to having drab, plain-looking males and females that do not apparently exhibit colour-based mate choice preferences (Meffe & Snelson, 1989). The evolution of female mate preferences in some members of this group has traditionally been attributed sexual selection (Houde, 1997), and

recent empirical work suggests that sensory bias (Rodd et al., 2002) is a likely mechanism for patterns observed in female guppy mate choice criteria, rather than alternative hypotheses such as good genes/indicator mechanisms (Grether et al., 2004, 2005). If true, this would suggest that natural selection (on sensory systems) plays an important role in shaping colour evolution in this system. Here I evaluate the historical role of sensory bias in this clade by testing the historical dependency of long-wavelength (yellow to red) colouration in males on pre-existing long- wavelength biases in females.

Chapter 2 Phylogenetic and Experimental Evidence for an Evolutionary Precursor to Male Colouration in Poeciliidae and Their Relatives begins here. Introduction

In many clades of animals, males are much more phenotypically diverse than females due to sexual selection by females for exaggerated and often conspicuous male traits (e.g., Darwin

1859, 1871; Andersson, 1994). The evolutionary origins of female preferences, however, are much less clear (Searcy, 1982; Kirkpatrick and Ryan, 1991; Maynard Smith, 1991; Williams

1992; Jones & Ratterman, 2009). According to theory, the processes responsible for the origin of female preferences may differ from those that govern the subsequent exaggeration and maintenance of female preferences and male traits (Arnqvist, 2006). To understand how sexual selection drives so much ornate diversity in nature, we must first understand the origins of female preferences.

According to the sensory bias hypothesis, sexual preferences originate as a byproduct of natural selection operating on sensory systems in non-sexual contexts, such as food detection or predator avoidance (West-Eberhard, 1984; Basolo, 1990, 1995; Ryan & Rand, 1990; Ryan et al.,

1990; Ryan, 1998; Endler & Basolo, 1998). In recent decades, evidence has accumulated for the sensory bias hypothesis (reviewed in: Ryan & Cummings, 2013); however, the potential role of sensory biases in the initial evolution of female preferences is poorly understood, leaving a fundamental component of the sensory bias hypothesis unresolved (Fuller et al., 2005;

Andersson & Simmons, 2006; Arnqvist, 2006; but see: Fuller et al., 2009; Fuller & Noa, 2010).

Testing this unresolved component of the sensory bias hypothesis is crucial because, if true, very

8 9 different mechanisms may ultimately be responsible for much of the diversity traditionally attributed directly to sexual selection.

Despite nearly three decades of investigation, critical features of the sensory bias hypothesis have not been explicitly tested for several reasons. First, the selection experiments necessary to test these features are daunting undertakings (Fuller et al., 2005, but see: Fuller et al., 2009; Fuller & Noa, 2010). Moreover, the historical nature of the hypothesis makes it difficult to explore because (i) paleontological data on female preferences are highly controversial (Knell et al., 2013), and (ii) traditional phylogenetic tests usually cannot be used because female mate preferences are rarely known for all members of large, variable clades (e.g.,

Pagel, 1994; but see: Basolo, 1996). Despite these challenges, there have been a number of indirect tests of the phylogenetic predictions of the sensory bias hypothesis (Basolo 1990, 1995;

Proctor, 1991, 1992; Ryan et al., 1990; Ryan & Rand, 1990; Ryan, 1998; Smith et al., 2004).

While concordant with the patterns predicted by the sensory bias hypothesis (Shaw, 1995; Endler

& Basolo, 1998), these classic tests do not directly test a fundamental assumption of the sensory bias hypothesis, that is: the historical dependency of male traits on pre-existing female biases

(Fuller et al., 2005). As a consequence, it is not clear that the female mating biases both pre- existed, and facilitated, the evolution of male traits in these studies (Pomiankowski, 1994;

Meyer, 1997; Sherman & Kern Reeve, 1999; Ron, 2008).

Recently developed phylogenetic hidden Markov models (pHMMs) provide a powerful approach to explicitly test the dependency of male traits on pre-existing behaviours in females

(Marazzi et al., 2012; Beaulieu et al., 2013). pHMMs employ a hidden Markov model of character evolution within a phylogenetic framework to model the historical evolution of a latent precursor trait that underlies the evolution of a measured trait of interest. They do so by taking into account the phylogenetic distribution of evolutionary transitions (gains and loss rates), such

10 as those predicted for male ornaments by the sensory bias hypothesis. Under the sensory bias hypothesis, biases in females’ sensory systems (and mating preferences) are predicted to precede and facilitate the evolution of male ornaments by sexual selection (Shaw, 1995; Endler &

Basolo, 1998), and if present, are expected to trigger a surfeit of evolutionary transitions in male colour. Therefore, under a pHMM framework, we can explicitly model the dependency of male traits (e.g., long-wavelength colouration (yellow to red)) on an unobserved precursory bias in females (e.g., for long-wavelength colours), using the phylogenetic clustering of gains and losses of male colouration (see Supplemental Methods). By comparing models in which evolution involves a precursor to models in which it does not, we can explicitly test for the phylogenetic signature of an historical sensory bias that is precursory to the evolution of male traits.

Here we apply phylogenetic HMMs to test for an historical dependency (on sensory biases) in the evolution of male colouration in poeciliid fishes and their relatives (Poeciliidae and

Goodeidae, plus several outgroups; for simplicity, we refer to all taxa in the present study as

“poeciliids” even though our sample contains additional taxa). Poeciliids exhibit remarkable diversity in male colouration and pattern, and for decades much of this diversity has been attributed to sexual selection (Meffe & Snelson, 1989; Houde, 1997; Magurran, 2005). However, in guppies and several related species, it has been suggested that female mating preferences for red (Smith et al., 2004; but see: Fuller & Noa, 2010), orange (Rodd et al., 2002), and yellow

(Garcia & Ramirez, 2005) colouration on males may have originated not in a sexual context, but rather via sensory biases that evolved in foraging contexts. Indeed, female mating preferences appear to be concordant with foraging preferences in several fish species, with females exhibiting particularly strong biases towards long-wavelength visible colours (i.e., red, orange, and yellow) that match nutritious and desirable food items (Rodd et al; 2002; Smith et al., 2004;

Garcia & Ramirez, 2005; Bourne & Watson, 2009). Despite these associations, the dependency

11 of male colouration on pre-existing biases in females remains untested. Exploring this tenet of the sensory bias hypothesis is interesting, because if true, it would mean that natural selection is the ultimate source of the patterns of male ornaments that have been largely attributed to sexual selection. Fortunately, phylogenetic relationships have recently been estimated for poeciliids and a large number of related fish species (Pollux et al., 2014; Reznick et al., 2017), making this a powerful and tantalizing group for using pHMMs to explicitly test the sensory bias hypothesis.

Methods and Results *Note: here, I present a summary of the methods. Please see the Supplemental Methods section for additional details.

2.1.1 Phylogenetic test for a precursor underlying male colouration

One of us (MRF) collected male colouration data for 232 species of poeciliids and related species, and we used these data to test for the phylogenetic signature of an historical sensory bias. Male colouration was scored as the presence or absence of long-wavelength visible colour in external ornaments (i.e., red, orange, and/or yellow colouration) using live specimens and colour photographs (Supplemental Tables 1, 2; Supplemental Methods). We then used these data to model the evolution of male colouration data under several phylogenetic models, including (1) a precursor-2 model, in which a single precursor was required for the evolution of male colouration, (2) a null model, in which the precursor assumption was removed, and two more complex models: (3) a two-rate model, in which the precursor path of evolution was optional, and (4) a three-rate model, in which multiple precursors were possible (Figure 1; see

Supplemental Methods). We fit all models using the corHMM function in the corHMM package in R (Beaulieu et al., 2013), and used parametric bootstrapping (Boettiger et al., 2012) to compare pairs of alternative models (specifically, the precursor model [1] was compared to the

12 null model [2] and the two-rate model [3]). For the parametric bootstrapping tests, we simulated

1000 datasets under each of the models being compared using rTraitDISC in the ape software package (Paradis, 2004), and then refit each model to each relevant simulated dataset using corHMM in the corHMM software package (Beaulieu et al., 2013). We then calculated bootstrapped p-values from bootstrapped likelihood ratio tests against the relevant “null” model in the pair (see Supplemental Methods). Bootstrap p-values for the precursor-2 and two-rate models were calculated from bootstrapped likelihood ratio tests against the null model and the precursor model, respectively, following the Monte Carlo procedure presented in Figure 2 of

Boettiger et al. (2012). Due to computational limitations, we did not use bootstrapped likelihood ratio tests to assess the three-rate model (instead, we used p-values calculated from a single likelihood ratio test of the three rate model [4] against the precursor model [2] as a nested null.

2.1.2 A phylogenetic precursor underlies the evolution of male colouration

Our pHMM analyses reveal strong phylogenetic evidence for the presence of a precursor that is necessary but not sufficient for the evolution of long-wavelength colouration in male poeciliids, just as predicted by the sensory bias hypothesis. A model in which male colouration is constrained to occur only after the evolution of a pre-existing bias was strongly favoured over an unconstrained model, in which male colouration could be gained and lost freely (p < 0.001;

Table 1). Support for the precursor-2 model is not simply a product of its greater complexity; the precursor-2 model was also favoured over more complex alternative models, including a single precursor model where evolution via a precursor pathway was optional (two-rate), and a two precursor model where the precursor paths were also optional (three-rate) (Table 1;

Supplemental Methods).

After reconstructing the likeliest ancestral states under the precursor-2 model

(Supplemental Methods), we found that the precursor appears to be nearly ubiquitous across the phylogeny (Figure 2). Estimates of the precursor’s probability in extant taxa, calculated from marginal likelihoods of all possible state combinations at the tips of the phylogeny

(Supplemental Methods), indicate that the precursor-2 model predicts that 86% of extant taxa are more likely to have the precursor than to lack it (i.e., having a >50% probability of possessing the precursor; Supplemental Table 3). The widespread nature of the reconstructed precursor reflects the slow rates of gain and loss estimated for the precursor (q0N→1P =1.28 transitions per million years (T/My); q1P→0N = 1.30 T/My; Figure 1), relative to the high rates of gains and losses of male colouration (q0P→1P = 12.03 T/My; q1P→0P = 5.07 T/My; Figure 1). This tip state reconstruction also identifies a number of species lacking male ornamentation that either should have retained, or should have lost, the precursor (Supplemental Tables 2, 3). Thus, our phylogenetic model makes predictions about candidate, pre-existing sensory biases in extant taxa that we can test experimentally. Therefore, one of us (MRF) and a colleague (Arcadio Valdez-

González) conducted a series of behavioural experiments using such taxa to identify the nature of the precursor trait predicted to underlie male colouration in this system.

2.1.3 Experimental tests of the precursor-2 model

Given empirical work showing associations between long-wavelength male colouration, female mate preferences, and colour preferences in non-mating contexts in several species in our study group (Rodd et al., 2002; Smith et al., 2004; Garcia & Ramirez, 2005; Spence & Smith,

2008; but see: Fuller & Noa, 2010), we asked whether a general sensory bias for long- wavelength colours (red, orange, yellow) in a non-mating context could be the precursor identified by our phylogenetic analyses. Using the tip state estimates of the precursor

14 probabilities in extant taxa (Supplemental Table 2), we selected and tested 14 species that were:

(i) colourful and predicted to have the precursor, or (ii) plain (no red, orange, or yellow colouration) and predicted to have the precursor, or (iii) plain and predicted to lack the precursor

(Figure 2; Supplemental Table 3). These taxa were selected to represent as many independent evolutionary origins as possible, for each colour-and-precursor combination, with independent origins inferred from the maximum likelihood reconstruction of ancestral states. We tested colour preferences using disc “approach and peck” experiments following the protocol of Rodd et al. (2002) (Supplemental Methods). The pecking behaviour of some fish during the experiments suggests that the inferred latent preference for long-wavelength colours may be a foraging preference for those species (Supplemental Table 4); however, because not all fish pecked discs, we focus our analyses on durational data (i.e., the duration of time the fish spent in close proximity to each coloured disc). Peck data are summarized in Supplemental Table 4.

Since some fish might be more responsive to discs that mimic the colour of their food, we photographed laboratory foods next to the coloured discs for comparison (Supplemental

Methods; Supplemental Figure 5). We also measured reflectance spectra for the coloured discs

(Supplemental Methods; Supplemental Figure 6) and the light spectra for the ambient light environments under which some species were tested (Supplemental Methods; Supplemental

Figure 7).

2.1.4 The precursor is a pre-existing long-wavelength bias in males and females

Our experimental tests suggest that the hidden precursor is indeed associated with an underlying bias towards long-wavelength colours (red, and/or orange, and/or yellow). In total,

10/14 experimental tests were concordant with the predictions of the precursor-2 model (Figures

2, 3). First, when a precursor was predicted, a significant female bias towards red, orange, and/or

15 yellow discs was found in 4 of 5 colourful species (+colour, +precursor) (exception: Poecilia mexicana preferred green), and in 4 of 7 plain species (-colour, +precursor) (exceptions:

Ataeniobius toweri, Gambusia affinis, and Skiffia bilineatus all showed no preferences). In contrast, when the precursor was predicted to be absent, female fish showed no bias towards red, orange, or yellow discs in 2 of 2 plain species (-colour, -precursor) (Figure 2; Figure 3). When analyzed as a binary predictor in a two-class ROC analysis (Fawcett, 2006), our phylogenetic precursor-2 model is an accurate and non-random predictor of experimentally assayed female colour biases (auroc = 0.771); this holds true if, from this dataset, we remove (i) the “+colour,

+precursor” tests (auroc = 0.75), or (ii) the “-colour, -precursor” tests (auroc = 0.656), or (iii) the

“-colour, +precursor” tests (auroc = 0.833).

While there is some variability in colour preferences across species (Supplemental Figure

3), on average there is a clear difference between species that are predicted to have a precursor versus those that are predicted to lack a precursor (Figure 3). In fact, for the species predicted to have a precursor, there was not a significant difference between the colourful (+,+) and dull (-,+) classes (Figure 3; rmANOVA colour*class interaction, Pillai’s Trace: F(6,5) = 3.76, p = 0.084); but, in analysis with all 3 experimental groups, both do differ significantly from the class of dull species predicted to lack a precursor (Figure 3; rmANOVA colour*class interaction, Pillai's

Trace: F(12,14) = 3.33, p = 0.018). The estimated probability of possessing the precursor does not significantly predict the strength of female long-wavelength biases, with the latter defined as total average duration (rs = -0.061, p = 0.837) or as maximum average duration (rs = -0.025, p =

0.933). In addition, for the two experimental groups for which a precursor was predicted (the plain fishes, and the colourful fishes), the strength of female biases did not statistically differ, whether defined as total average duration (t = 0.362, df = 11.76, p = 0.724), or maximum average duration (t = 0.063, df = 11.55, p = 0.951). In addition, in a qualitative assessment of the data, we

16 found that male and female preferences were similar in 10/13 species (male data were not available for G. affinis; Supplemental Figures 3, 4). Previously, responses shared between the sexes have been interpreted as additional support for a sensory bias, as such responses are unlikely to be the result of sexual selection, which is almost always sex-specific (Rodd et al.,

2002; Garcias & Ramirez, 2005); however, genetic correlations are an alternative explanation for this pattern. Overall, our experimental tests provide strong validation of our precursor-2 model, and suggest that a pre-existing long-wavelength bias in females was a necessary precursor to the evolution of long-wavelength colouration in males.

Discussion

Using a combination of both phylogenetic and experimental approaches, we found strong support for the hypothesis that a sensory bias in females underlies the evolution of male colouration in poeciliid fishes and their relatives. Our phylogenetic analyses suggest that a single, hidden precursor facilitated the evolution of long-wavelength (yellow to red) colouration in males, and experimental tests of our precursor model indicate that this precursor is likely a pre-existing female bias towards long-wavelength colours. Our integrative study provides an explicit test of the historical dependency of male ornament evolution on pre-existing biases in females, supporting one of the critical tenets of the sensory bias hypothesis (Endler & Basolo,

1998). Interestingly, we find that while a female bias for long-wavelength colours is widespread and slowly evolving across this species-rich fish clade, the male ornamentation that evolves in the presence of this bias is highly labile. In fact, this female bias is much more widespread, and far more conserved, than indicated in any previous study of sensory bias (reviewed in: Ryan &

Cummings, 2013), and it is possible that a long-wavelength bias may extend deep into the evolutionary history of fishes. This pattern would have been difficult to detect using

17 conventional tests of the sensory bias hypothesis (Endler & Basolo, 1998), and our findings therefore suggest that there may be many cryptic cases of sensory bias in nature.

Alternative sexual selection models for the evolution of conspicuous male colouration are unlikely to explain the patterns we have uncovered in our experimental tests of the precursor-2 model. Although other sexual selection models are consistent with the results of our phylogenetic comparative analyses, they cannot explain the presence of the long-wavelength bias in males, a result fully concordant with a sensory bias that is unassociated with mating (Rodd et al., 2002). It is possible that female biases may arise in sexual contexts; however, the presence of the bias in males suggests that it is the result of natural selection, not sexual selection (Supplemental Figure

3). While it is possible that the presence of a bias in males could result from genetic linkages, such pleiotropy rarely hinders the evolution of pronounced sex differences in other traits in organisms experiencing strong sexual selection (Andersson, 1994).

One of the key components of the sensory bias hypothesis is that it offers an explanation for the origin of female preferences. In addition to sensory biases, Arnqvist (2006) suggests three additional mechanisms for the origins of female preferences: hidden preferences, Fisherian origins, and Darwinian origins. The hidden preferences hypothesis is essentially the sensory bias hypothesis but assumes that selectively neutral biases arise randomly (e.g., through drift), rather than via direct selection; however, we argue that this hypothesis is less likely to explain our results, given the widespread nature of the long-wavelength bias. Next, the Darwinian origin hypothesis is that female preferences originate in response to pre-existing variation in a male trait associated with direct benefits to females (Arnqvist, 2006); we find this model unlikely to explain the high levels of male signal convergence observed across our tree, and we likewise are unaware of any substantial direct benefits that male poeciliids provide to females (nutritious seminal fluid could represent one possibility, but this is not well-studied). Similarly, the

Fisherian origin is essentially the “good genes hypothesis”, whereby female preferences originate in response to indirect benefits associated with pre-existing variation in an indicator of male genetic quality (Arnqvist, 2006). Experiments measuring clade-wide variation in male quality and its relation to variation in female reproductive benefits could be conducted but, if good genes patterns were found in extant taxa, this does not mean that good genes drove the initial evolution of female preferences and male colouration. In fact, in this group of fishes there is empirical evidence to suggest that sensory bias can initiate sexual selection, which can then proceed in accordance with the good genes model (Garcia & Ramirez, 2005). Lastly, because our results suggest that the precursor has been gained and lost multiple times, it seems highly improbable

(though not impossible) that the Fisher process would generate such widespread patterns of convergence in male sexual signals (Prum, 1997). Overall, we argue that the most compelling interpretation of our phylogenetic and experimental results supports the sensory bias hypothesis, and future studies can investigate the axes of natural selection, and the constraints, underlying this pattern.

Future study into the subsequent co-evolutionary processes (e.g., Fisherian, good genes) following the origins by sensory bias are warranted, particularly because we do not see evidence of the bias being exaggerated in species in which the males have evolved colour. That is, our observation that “-colour, +precursor” and “+colour, +precursor” experimental groups did not differ in their responses to the discs suggests that the long-wavelength bias has not become exaggerated in the species with more colourful males. This is interesting because if studies of female preference find exaggeration in certain clades of the tree, this means that although sensory bias explains the origins, other processes (e.g. Fisherian runaway, good genes, sexually antagonistic conflict) are driving subsequent exaggeration of male traits and female preferences.

Our results have interesting implications for the tempo of behavioural evolution. For decades, there has been an overarching view that behaviour is evolutionarily labile in comparison to morphological traits (Mayr 1963, 1982; Bush 1986; Huey and Bennett 1987; Plomin 1990;

Gittleman, 1996; Martins, 1996). In fact, several comparative analyses provide evidence for particularly rapid evolution in behaviour-linked traits (Blomberg et al., 2003; Hertz et al., 2013), and systematists have long mourned the paucity of information about deep homology generally found in behavioural phenotypes (Brooks & McLennan, 1991; but see Greene & Burghardt,

1978). The apparent lability of behavioural traits has been attributed to a variety of causes, including non-genetic (environmental) effects, greater levels of polymorphism, more common disruptive selection, low heritability, and measurement error (West-Eberhard 1987, 1989;

Blomberg et al., 2003), and we do in fact see that for many behavioural traits, there is low phylogenetic signal relative to non-behavioural traits (Blomberg et al., 2003; see also Gittleman et al., 1996). Despite this longstanding view of behaviour being evolutionarily labile, our results suggest a stark contrast, whereby a slowly-evolving and hidden behaviour in females underlies a conspicuous and rapidly-evolving morphological trait in males. Of course, many factors could have contributed to this variation in male colour patterns once long-wavelength colouration arose. For example, predation can select for reduced overall “brightness,” for fewer patches of colour, and for crypsis (Endler, 1980, 1987), and specific patterns can likewise be employed to facilitate shoaling (stripes) or male-male competition (bars) (Price et al. 2008).

The long-wavelength bias in poeciliids and their relatives joins a small but interesting list of relatively stable behavioural phenotypes that have profoundly shaped the natural histories of large groups of organisms over macroevolutionary timescales. For example, a similar pattern has been inferred for the evolutionary history of constriction behaviour in alethinophidian snakes

(Greene & Burghardt, 1978), in which the phylogenetic conservatism of constriction is putatively

20 due to being a “key innovation” that was retained for its value in subduing prey. We think it premature to consider a long-wavelength bias to be a key innovation; however, it is possible that this bias may have been conserved for its value in finding high quality foods (e.g., ones containing carotenoids) (Robinson & Wilson, 1998; Rodd et al., 2002), or its use in detection via attunement to contrast against the higher energy spectra dominating the backgrounds of many aquatic environments (Stevens, 2013; but see Endler, 1992). Investigations into the costs and benefits of the long-wavelength bias in females would be useful, given that theory would lead us to expect costlier traits (like male display) to be more labile than less costly traits (like female preferences) (Wiens, 2001).

In contrast to the conserved nature of female biases, we find that male traits are highly labile, and this may be explained by conserved genetic or biochemical pathways for colouration.

Not much is known about the pigmentation of most poeciliids (Meffe & Snelson, 1989) or most other species in our study (but see: Singh & Nüsslein-Volhard, 2015); however, it is possible that some conserved genetic or biochemical mechanism may underlie the labile evolution and widespread diversification of long-wavelength traits in males. Recent work in birds has shown that, in several bird families, different carotenoid-based colours share a conserved biochemical pathway, in the form of a gene complex called CYPJ219. Changing gene expression in this gene complex causes colouration to change from red to yellow, and vice versa (Mundy et al., 2016;

Lopez et al., 2016). Similarly, work in this area may also help explain the puzzling diversity in the types of long-wavelength traits that have evolved in our system (e.g. long-wavelength spots, stripes, and bands, on the body, dorsal fins, caudal fins, anal fins). This diversity is especially perplexing given the apparent conservatism in the female long-wavelength bias, but recent work in nymphalid butterflies shows that a single homologous gene is responsible for the evolution of

21 many different components of butterfly wing patterns and colouration (Mazo-Vargas et al.,

2017).

The pHMM framework we have applied is unique because we can use contemporary information about the present not just to understand the past, but also to understand more about the present. Unlike traditional trait reconstruction methods in which character values are estimated (or “reconstructed”) for ancestor nodes based on measured values for extant species, phylogenetic relationships, and a simple model of trait evolution, hidden characters inferred under pHMMs are not directly measured for extant species. However, the pHMM model can be used to reconstruct the expected values of hidden characters in extant species, just as is traditionally done for ancestor nodes. Our pHMM analyses revealed a hidden bias amongst a broad swath of living taxa, the widespread nature of which would otherwise not have been detectable without extensive behavioural investigation of many species. This comparative approach has limitations, however, and we note that with pHMMs, any process that differentially affects the rates of evolution among clades, or that biases the ‘clumpiness’ of male colouration transitions across the phylogeny (Marazzi et al., 2012), could affect the inference of a precursor.

For example, ecological factors like predation, if exerting variable selection on male colouration across entire clades, could lead to clustering of gains and losses of male colouration, and generate rate asymmetry across clades.

Here, however, experimentation provides a path forward. Although an unmeasured confounding variable may be responsible for phylogenetic clumping in male colouration, there is no a priori reason to expect such a variable to generate a similar precursory pattern in both colour preferences and male colouration, unless these variables are causally linked. By experimentally testing key species for their colour preferences, guided by the predictions of our model, we were able to corroborate our phylogenetic inferences with an independent line of

22 inquiry, and overcome a principal limitation of the comparative method. So, while modern phylogenetic methods offer unique opportunities for testing historical hypotheses, the most powerful tests will occur when phylogenetic methods are validated by targeted experimentation

(Weber & Agrawal, 2012).

Conclusion

Here we marry comparative and experimental methods to explicitly test the sensory bias hypothesis in poeciliids and related fishes. Our results suggest that (i) a pre-existing and widespread long-wavelength female bias played an important role in the evolution of male colouration, and (ii) natural selection may be the ultimate explanation for the diversity of male colouration in this group, which has classically been largely attributed to sexual selection. In addition, we our approach suggests that there may be many more cryptic cases of sensory bias that are not detectable using alternative approaches. Lastly, this integrative study reveals interesting insights into the evolution and possible phylogenetic conservatism of behaviour, and demonstrates the value of coupling phylogenetic and experimental approaches to address macroevolutionary questions.

Table 1. Summary of models describing the evolution of male colouration. The best fitting model, based on Akaike weights (wi), was the precursor-2 model (shown in bold). This model assumed that a hidden precursor state (e.g., sensory bias) was required for the evolution of male colouration. Bootstrap p-values for the precursor-2 and two-rate models were calculated from bootstrapped likelihood ratio tests against the null model and the precursor model, respectively, following the Monte Carlo procedure presented in Figure 2 of Boettiger et al. (2012). Due to computational limitations, we assessed the three-rate model using p-values calculated from a single likelihood ratio test against the null model. See Supplemental Figure 1 for density plots.

Table 1. Summary of models describing the evolution of male colouration.

model K neg.lnL AICc wi p null (one.rate) 2 -143.97 291.99 0.052 – precursor2 4 -139.04 286.26 0.915 0.001 two.rate 8 -138.93 294.51 0.015 0.811 three.rate 14 -132.11 294.16 0.015 0.180

23 24

Figure 1. Visual models and their parameter estimates for the evolution of male colouration. Circles represent states, and arrows indicate transition rates (arrows are to scale), which describe the instantaneous rates of moving between states. Here we present the null and precursor-2 models. While the precursor is slow to be gained and lost (q0→P =1.28; qP→0 = 1.30), once the precursor does evolve, colouration is gained and lost relatively quickly (q1→p = 12.03; qp→1 = 5.07) [*note that these rates describe the probability of an instantaneous transition per unit time (i.e., per 100MY given our tree)].

(a) null model

(b) precursor-2 model

Figure 2. Ancestral state reconstruction under the precursor-2 model. Pie charts depict the relative likelihood of being in each possible state at a given node, reconstructed from ML estimates under the precursor-2 model. Data at the tips are defined in the legend.

hyp.testconcord? Chirocentrus dorab Dorosoma cepedianum Chanos chanos Ictalurus punctatus Danio rerio Notemigonus crysoleucas Semotilus atromaculatus Esox lucius Oncorhynchus mykiss Aphredoderus sayanus Gadus morhua Zeus faber Gasterosteus aculeatus Takifugu rubripes Tetraodon nigroviridis Monopterus albus Oryzias latipes Rivulus hartii Aplocheilus lineatus Epiplatys annulatus Aphyoplatys duboisi Adamas formosus Aphyosemion bitaeniatum Cubanichthys cubensis Floridichthys carpio Cyprinodon variegatus Jordanella floridae Fundulus cingulatus Fundulus lineolatus Lucania parvae Lucania goodei Profundulus punctatus Empetrichthys latos Crenichthys nevadae Crenichthys baileyi Characodon audax Characodon lateralis Allodontichthys zonistius Allodontichthys polylepis Allodontichthys tamazulae Allodontichthys hubbsi Xenotaenia resolanae Ilyodon furcidens Ilyodon xantusi Ilyodon whitei Goodea atripinnis Goodea gracilis Xenotoca eiseni Xenotoca melanosoma Xenoophorus captiva Zoogoneticus tequila Zoogoneticus quitzeoensis Ameca splendens Xenotoca variatus Alloophorus robustus Chapalichthys encaustus Chapalichthys pardalis Ataeniobius toweri Neotoca bilineata Girardinichthys viviparus Girardinichthys multiradiatus Skiffia bilineatus Skiffia lermae Skiffia multipunctata Skiffia francesae Hubbsina turneri Allotoca regalis Allotoca maculata Allotoca goslinei Allotoca dugesii Allotoca zacapuensis Allotoca catarinae Allotoca meeki Allotoca diazi Valencia hispanica Aplocheilichthys spilauchen Aplocheilichthys normani Fluviphylax pygmaeus Oxyzygonectes dovii Anableps anableps Anableps dovii Jenynsia lineata Jenynsia multidentata Xenodexia ctenolepis Tomeurus gracilis Phalloptychus januarius Phalloceros caudimaculatus Cnesterodon hypselurus Cnesterodon decemmaculatus Cnesterodon septentrionalis Micropoecilia wingei Micropoecilia obscura Micropoecilia reticulata Micropoecilia picta Micropoecilia parae Micropoecilia bifurca Poecilia vivipara Poecilia caucana Poecilia latipinna Poecilia velifera MP737 Poecilia latipunctata Poecilia petenensis Campeche Poecilia chica Poecilia butleri MR04 Poecilia orri Poecilia salvatoris Poecilia gilli Poecilia mexicana mex Poecilia catemaconis Poecilia sphenops Poecilia sulphuraria Pamphorichthys hollandi Limia heterandria Limia melanogaster Limia zonata Limia caymanensis Limia vittata Limia dominicensis Limia nigrofasciata Limia garnieri Limia sulfurophila Limia perugiae Limia tridens Quintana atrizona Dactylophallus denticulatus Dactylophallus ramsdeni Glaridichthys uninotatus Glaridichthys falcatus Girardinus creolus Girardinus metallicus Girardinus microdactylus Pseudopoecilia festae Heterandria formosa Xenophallus umbratilis Alfaro cultratus Alfaro hubberi Priapichthys annectens Brachyrhaphis cascajalensis Brachyrhaphis parismina Phallichthys tico Phallichthys quadripunctatus Phallichthys pittieri Phallichthys amates Brachyrhaphis hartwegi Brachyrhaphis holdridgei Brachyrhaphis rhabdophora Brachyrhaphis roseni Brachyrhaphis terrabensis Neoheterandria elegans Neoheterandria tridentiger Poeciliopsis paucimaculata Poeciliopsis elongata Poeciliopsis retropinna Poeciliopsis balsas Poeciliopsis viriosa Poeciliopsis monacha Poeciliopsis infans Poeciliopsis prolifica Poeciliopsis lucida Poeciliopsis occidentalis Poeciliopsis baenschi Poeciliopsis fasciata Poeciliopsis latidens Poeciliopsis turrubarensis Poeciliopsis scarlii Poeciliopsis turneri Poeciliopsis presidionis Poeciliopsis pleurospilus Poeciliopsis gracilis Poeciliopsis catemaco Poeciliopsis hnilickai Scolichthys iota Scolichthys greenwayi Carlhubbsia kidderi Carlhubbsia stuarti Priapella intermedia Priapella olmecae Priapella compressa Heterandria jonesi Heterandria bimaculata Xiphophorus pygmaeus Xiphophorus nezahualcoyotl Xiphophorus malinche Xiphophorus montezumae Xiphophorus cortezi Xiphophorus continens Xiphophorus birchmanni Xiphophorus multilineatus Xiphophorus nigrensis Xiphophorus monticolus Xiphophorus clemenciae Xiphophorus hellerii Xiphophorus alvarezi Xiphophorus mayae Xiphophorus signum Xiphophorus andersi Xiphophorus maculatus Xiphophorus milleri Xiphophorus evelynae Xiphophorus variatus Xiphophorus xiphidium Xiphophorus meyeri Xiphophorus gordoni Xiphophorus couchianus Belonesox belizanus Heterophallus rachovii Heterophallus milleri Gambusia luma Gambusia sexradiata Gambusia eurystoma Gambusia heterochir Gambusia geiseri Gambusia holbrooki Gambusia affinis Gambusia hispaniolae Gambusia wrayi Gambusia melapleura Gambusia nicaraguensis Gambusia hubbsi Gambusia manni Gambusia yucatana Gambusia puncticulata Gambusia caymanensis Gambusia oligosticta Gambusia zarskei Gambusia punctata Gambusia rhizophorae Gambusia atrora Gambusia marshi Gambusia panuco Gambusia vittata Gambusia hurtadoi

Figure 3. Summary of disc colour preference experiments in females. Split scatter plots illustrate a long-wavelength bias, on average, in the experimental groups where the precursor was predicted (“+precursor”). Points denote standardized mean total duration for each species, dotted lines represent the mean across each hypothesis test group (dotted lines), and grey zones indicate

95% confidence intervals around that mean.

− colour, − precursor 3

−1

−2

− colour, + precursor 3

2 colour R O 1 Y G 0 B P −1 K

standardized total duration standardized W

−2

+ colour, + precursor 3

−1

−2

R O Y G B P K W colour

References

Andersson, M.B., 1994. Sexual selection. Princeton University Press, Princeton.

Andersson, M. and Simmons, L.W., 2006. Sexual selection and mate choice. Trends in ecology

& evolution, 21(6), pp.296-302.

Arnold, S.J. and Houck, L.D., 2016. Can the Fisher-Lande Process Account for Birds of Paradise

and Other Sexual Radiations? The American Naturalist,187(6), pp.000-000.

Arnqvist, G., 2006. Sensory exploitation and sexual conflict. Philosophical Transactions of the

Royal Society B: Biological Sciences, 361(1466), pp.375-386.

Arnqvist, G. and Rowe, L., 2005. Sexual conﬂict. Princeton University Press.

Ballings, M. and Van den Poel, D., 2013. AUC: Threshold independent performance measures

for probabilistic classifiers. R package version 0.3. 0.

Basolo, A.L., 1990. Female preference predates the evolution of the sword in swordtail

fish. Science, 250(4982), pp.808-810.

Basolo, A.L., 1995. Phylogenetic evidence for the role of a pre-existing bias in sexual

selection. Proceedings of the Royal Society of London B: Biological Sciences, 259(1356),

pp.307-311.

Bateson, P.P.G., 1983. Mate choice. Cambridge University Press.

Beaulieu, J.M., Oliver, J.C., O'Meara, B. and Beaulieu, M.J., 2017. Package ‘corHMM’.

Beaulieu, J.M. and O’Meara, B.C., 2016. Detecting hidden diversification shifts in models of

trait-dependent speciation and extinction. Systematic biology, 65(4), pp.583-601.

Beaulieu, J.M., O'Meara, B.C. and Donoghue, M.J., 2013. Identifying hidden rate changes in the

evolution of a binary morphological character: the evolution of plant habit in campanulid

angiosperms. Systematic biology,62(5), pp.725-737.

Blomberg, S.P., Garland Jr, T. and Ives, A.R., 2003. Testing for phylogenetic signal in

comparative data: behavioral traits are more labile. Evolution, 57(4), pp.717-745.

Boettiger, C., Coop, G. and Ralph, P., 2012. Is your phylogeny informative? Measuring the

power of comparative methods. Evolution, 66(7), pp.2240-2251.

Bourne G. R., Watson L. C., 2009 Receiver-bias implicated in the nonsexual origin of female

mate choice in the pentamorhic fish Poecilia parae Eigenmann, 1894. AACL Bioflux

2(3):299-317.

Bush, G. L. 1986. Evolutionary behavior genetics. M. D. Huettel, ed. Evolutionary genetics of

invertebrate behavior, progress and prospects. Plenum Press, New York.

Brooks, D.R. and McLennan, D.A., 1991. Phylogeny, ecology, and behavior: a research

program in comparative biology. University of Chicago press.

Ciccotto, P.J. and Mendelson, T.C., 2016. The ecological drivers of nuptial color evolution in

darters (Percidae: Etheostomatinae). Evolution, 70(4), pp.745-756.

Cronin, H. 1991 The ant and the peacock. Cambridge University Press.

Cummings, M.E., 2007. Sensory trade-offs predict signal divergence in

surfperch. Evolution, 61(3), pp.530-545.

Darwin, C. and Bynum, W.F., 2009. The origin of species by means of natural selection: or, the

preservation of favored races in the struggle for life (pp. 441-764). AL Burt.

Darwin C. 1871. The Descent of Man and Selection in Relation to Sex. London: Murray

Emlen, D.J., Marangelo, J., Ball, B. and Cunningham, C.W., 2005. Diversity in the weapons of

sexual selection: horn evolution in the beetle genus Onthophagus (Coleoptera:

Scarabaeidae). Evolution, 59(5), pp.1060-1084.

Endler, J.A., 1980. Natural selection on color patterns in Poecilia reticulata. Evolution, 34(1),

pp.76-91.

Endler, J.A., 1991. Variation in the appearance of guppy color patterns to guppies and their

predators under different visual conditions. Vision research, 31(3), pp.587-608.

Endler, J.A., 1988. Natural selection. University of Queensland.

Endler, J.A. and Basolo, A.L., 1998. Sensory ecology, receiver biases and sexual

selection. Trends in ecology & evolution, 13(10), pp.415-420.

Fawcett, T., 2006. An introduction to ROC analysis. Pattern recognition letters, 27(8), pp.861-

874.

Fisher, R. A. 1915 The evolution of sexual preference. Eugen. Rev. 7, 184–191.

Fisher, R. A. (1930) 1958. The genetical theory of natural selection. Clarendon Press.

Fuller, R.C., Houle, D. and Travis, J., 2005. Sensory bias as an explanation for the evolution of

mate preferences. The American Naturalist, 166(4), pp.437-446.

Fuller, R.C., 2009. A test of the critical assumption of the sensory bias model for the evolution of

female mating preference using neural networks. Evolution, 63(7), pp.1697-1711.

Fuller, R.C. and Noa, L.A., 2010. Female mating preferences, lighting environment, and a test of

the sensory bias hypothesis in the bluefin killifish. Animal Behaviour, 80(1), pp.23-35.

Garcia, C.M. and Ramirez, E., 2005. Evidence that sensory traps can evolve into honest

signals. Nature, 434(7032), p.501.

Gittleman, J.L., Anderson, C.G., Kot, M. and Luh, H.K., 1996. Phylogenetic lability and rates of

evolution: a comparison of behavioral, morphological and life history traits. Phylogenies

and the comparative method in animal behavior, pp.166-205.

Greene, H.W. and Burghardt, G.M., 1978. Behavior and phylogeny: constriction in ancient and

modern snakes. Science, 200(4337), pp.74-77.

Grether, G.F., 2000. Carotenoid limitation and mate preference evolution: a test of the indicator

hypothesis in guppies (Poecilia reticulata). Evolution, 54(5), pp.1712-1724.

Grether, G.F., Kasahara, S., Kolluru, G.R. and Cooper, E.L., 2004. Sex–specific effects of

carotenoid intake on the immunological response to allografts in guppies (Poecilia

reticulata). Proceedings of the Royal Society of London B: Biological Sciences, 271(1534),

pp.45-49.

Grether, G.F., Kolluru, G.R., Rodd, F.H., De la Cerda, J. and Shimazaki, K., 2005. Carotenoid

availability affects the development of a colour-based mate preference and the sensory bias

to which it is genetically linked. Proceedings of the Royal Society of London B: Biological

Sciences, 272(1577), pp.2181-2188.

Hamilton, W.D. and Zuk, M., 1982. Heritable true fitness and bright birds: a role for

parasites?. Science, 218(4570), pp.384-387.

Hertz, P.E., Arima, Y., Harrison, A., Huey, R.B., Losos, J.B. and Glor, R.E., 2013.

Asynchronous evolution of physiology and morphology in Anolis lizards. Evolution, 67(7),

pp.2101-2113.

Houde, A.E., 1994. Effect of artificial selection on male colour patterns on mating preference of

female guppies. Proceedings of the Royal Society of London B: Biological

Sciences, 256(1346), pp.125-130.

Houde, A.E., 1997. Sex, color, and mate choice in guppies. Princeton University Press.

Huxley, J.S., 1938. Darwin's theory of sexual selection and the data subsumed by it, in the light

of recent research. The American Naturalist, 72(742), pp.416-433.

Kirkpatrick, M. and Ryan, M.J., 1991. The evolution of mating preferences and the paradox of

the lek. Nature, 350(6313), p.33.

Kokko, H., Jennions, M. D. & Brooks, R. 2006. Unifying and testing models of sexual selection.

Annu. Rev. Ecol. Evol. Syst. 37, 43–66.

Huey, R. B., and A. F. Bennett. 1987. Phylogenetic studies of coadaptation: preferred

temperatures versus optimal performance temperatures of lizards. Evolution 41:1098–1115.

Jones, A.G. and Ratterman, N.L., 2009. Mate choice and sexual selection: what have we learned

since Darwin? Proceedings of the National Academy of Sciences, 106(Supplement 1),

pp.10001-10008.

Knell, R.J., Naish, D., Tomkins, J.L. and Hone, D.W., 2013. Sexual selection in prehistoric

animals: detection and implications. Trends in ecology & evolution, 28(1), pp.38-47.

Kornerup, A. and Wanscher, J.H., 1978. Methuen handbook of colour (3rd) Methuen.

Lande, R., 1981. Models of speciation by sexual selection on polygenic traits. Proceedings of the

National Academy of Sciences, 78(6), pp.3721-3725.

Lewis, P.O., 2001. A likelihood approach to estimating phylogeny from discrete morphological

character data. Systematic biology, 50(6), pp.913-925.

Lopes, R.J., Johnson, J.D., Toomey, M.B., Ferreira, M.S., Araujo, P.M., Melo-Ferreira, J.,

Andersson, L., Hill, G.E., Corbo, J.C. and Carneiro, M., 2016. Genetic basis for red

coloration in birds. Current Biology, 26(11), pp.1427-1434.

Maddison, W.P., Midford, P.E. and Otto, S.P., 2007. Estimating a binary character's effect on

speciation and extinction. Systematic biology, 56(5), pp.701-710.

Magurran, A.E., 2005. Evolutionary ecology: the Trinidadian guppy. Oxford University Press on

Demand.

Makowicz, A.M., Tanner, J.C., Dumas, E., Siler, C.D. and Schlupp, I., 2015. Pre-existing biases

for swords in mollies (Poecilia). Behavioral Ecology, 27(1), pp.175-184.

Marazzi, B., Ané, C., Simon, M.F., Delgado-Salinas, A., Luckow, M. and Sanderson, M.J., 2012.

Locating evolutionary precursors on a phylogenetic tree. Evolution, 66(12), pp.3918-3930.

Marler, C.A. and Ryan, M.J., 1997. Origin and maintenance of a female mating

preference. Evolution, 51(4), pp.1244-1248.

Martins, E.P. ed., 1996. Phylogenies and the comparative method in animal behavior. Oxford

University Press.

Masta, S.E. and Maddison, W.P., 2002. Sexual selection driving diversification in jumping

spiders. Proceedings of the National Academy of Sciences, 99(7), pp.4442-4447.

Mayr, E. 1963. Animal species and evolution. The Belknap Press of Harvard University Press.

Mayr, E. 1982. The growth of biological thought: diversity, evolution, and inheritance. Belknap

Press, Cambridge, MA.

Mazo-Vargas, A., Concha, C., Livraghi, L., Massardo, D., Wallbank, R.W., Zhang, L., Papador,

J.D., Martinez-Najera, D., Jiggins, C.D., Kronforst, M.R. and Breuker, C.J., 2017.

Macroevolutionary shifts of WntA function potentiate butterfly wing-pattern

diversity. Proceedings of the National Academy of Sciences, p.201708149.

Mead, L.S. and Arnold, S.J., 2004. Quantitative genetic models of sexual selection. Trends in

Ecology & Evolution, 19(5), pp.264-271.

Meffe, G.K. and Snelson, F.F., 1989. Ecology and evolution of livebearing fishes (Poeciliidae).

Prentice Hall.

Meyer, A., 1997. The evolution of sexually selected traits in male swordtail fishes (Xiphophorus:

Poeciliidae). Heredity, 79(3), pp.329-337.

Mundy, N.I., Stapley, J., Bennison, C., Tucker, R., Twyman, H., Kim, K.W., Burke, T., Birkhead,

T.R., Andersson, S. and Slate, J., 2016. Red carotenoid coloration in the zebra finch is

controlled by a cytochrome P450 gene cluster. Current Biology , 26(11), pp.1435-1440.

Pagel, M., 1994. Detecting correlated evolution on phylogenies: a general method for the

comparative analysis of discrete characters. Proceedings of the Royal Society of London B:

Biological Sciences, 255(1342), pp.37-45.

Paradis, E., Claude, J. and Strimmer, K., 2004. APE: analyses of phylogenetics and evolution in

R language. Bioinformatics, 20(2), pp.289-290.

Parker, G.A. and Pizzari, T., 2015. Sexual selection: the logical imperative. In Current

perspectives on sexual selection (pp. 119-163). Springer Netherlands.

Payne, R.J. and Pagel, M., 2001. Inferring the origins of state-dependent courtship traits. The

American Naturalist, 157(1), pp.42-50.

Plomin, R. 1990. The role of inheritance in behavior. Science 248: 183–188.

Pomiankowski, A. 1994. News and views. Nature, 368, 494-495.

Price, A.C., Weadick, C.J., Shim, J., and F.H. Rodd. 2008. Pigments, patterns and fish behavior.

Zebrafish 5: 297-307.

Proctor, H.C., 1991. Courtship in the water mite Neumania papillator: males capitalize on

female adaptations for predation. Animal Behaviour, 42(4), pp.589-598.

Proctor, H.C., 1992. Sensory exploitation and the evolution of male mating behaviour: a cladistic

test using water mites (Acari: Parasitengona). Animal Behaviour, 44(4), pp.745-752.

Prum, R.O., 1997. Phylogenetic tests of alternative intersexual selection mechanisms: trait

macroevolution in a polygynous clade (Aves: Pipridae).American Naturalist, pp.668-692.

Pollux, B.J.A., Meredith, R.W., Springer, M.S., Garland, T. and Reznick, D.N., 2014. The

evolution of the placenta drives a shift in sexual selection in livebearing

fish. Nature, 513(7517), pp.233-236.

Reznick, D.N., Furness, A.I., Meredith, R.W. and Springer, M.S., 2017. The origin and

biogeographic diversification of fishes in the family Poeciliidae. PloS one, 12(3),

p.e0172546.

Robinson, B.W. and Wilson, D.S., 1998. Optimal foraging, specialization, and a solution to

Liem's paradox. The American Naturalist, 151(3), pp.223-235.

Rodd, F.H., Hughes, K.A., Grether, G.F. and Baril, C.T., 2002. A possible non-sexual origin of

mate preference: are male guppies mimicking fruit? Proceedings of the Royal Society of

London B: Biological Sciences, 269 (1490), pp.475-481.

Rodríguez, R.L., Boughman, J.W., Gray, D.A., Hebets, E.A., Höbel, G. and Symes, L.B., 2013.

Diversification under sexual selection: the relative roles of mate preference strength and

the degree of divergence in mate preferences. Ecology letters, 16(8), pp.964-974.

Ron, S.R., 2008. The evolution of female mate choice for complex calls in túngara frogs. Animal

Behaviour, 76(6), pp.1783-1794.

Rosenthal, G.G. and Evans, C.S., 1998. Female preference for swords in Xiphophorus helleri

reflects a bias for large apparent size. Proceedings of the National Academy of

Sciences, 95(8), pp.4431-4436.

Ryan, M.J. (1990) Sexual selection, sensory systems and sensory exploitation, Oxf. Surv. Evol.

Biol. 7, 157–195

Ryan, M.J., Fox, J.H., Wilczynski, W. and Rand, A.S., 1990. Sexual selection for sensory

exploitation in the frog Physalaemus pustulosus. Nature, 343(6253), pp.66-67.

Ryan, M.J. and Rand, A.S., 1990. The sensory basis of sexual selection for complex calls in the

túngara frog, Physalaemus pustulosus (sexual selection for sensory

exploitation). Evolution, 44(2), pp.305-314.

Ryan, M.J. and Rand, A.S., 1993. Sexual selection and signal evolution: the ghost of biases

past. Philosophical Transactions: Biological Sciences, pp.187-195.

Ryan, M.J., 1998. Sexual selection, receiver biases, and the evolution of sex

differences. Science, 281(5385), pp.1999-2003.

Ryan, M.J. and Cummings, M.E., 2013. Perceptual biases and mate choice. Annual Review of

Ecology, Evolution, and Systematics, 44, pp.437-459.

Searcy, W.A., 1982. The evolutionary effects of mate selection. Annual Review of Ecology and

Systematics, 13(1), pp.57-85.

Seddon, N., Botero, C.A., Tobias, J.A., Dunn, P.O., MacGregor, H.E., Rubenstein, D.R., Uy,

J.A.C., Weir, J.T., Whittingham, L.A. and Safran, R.J., 2013. Sexual selection accelerates

signal evolution during speciation in birds. Proceedings of the Royal Society of London B:

Biological Sciences, 280(1766), p.20131065.

Shaw, K., 1995. Phylogenetic tests of the sensory exploitation model of sexual selection. Trends

in Ecology & Evolution, 10(3), pp.117-120.

Sherman, P.W. and Reeve, H.K., 1999. Sexual selection and sensory exploitation. Science,

283(5405), pp.1083-1083.

Singh, A.P. and Nüsslein-Volhard, C., 2015. Zebrafish stripes as a model for vertebrate colour

pattern formation. Current Biology, 25(2), pp. R81-R92.

Smith, C., Barber, I., Wootton, R.J. and Chittka, L., 2004. A receiver bias in the origin of three-

spined stickleback mate choice. Proceedings of the Royal Society of London, Series B:

Biological Sciences, 271(1542), pp.949-955.

Smith, J.M., 1991. Theories of sexual selection. Trends in Ecology & Evolution, 6(5), pp.146-

151.

Spence, R. and Smith, C., 2008. Innate and learned colour preference in the zebrafish, Danio

rerio. Ethology, 114(6), pp.582-588.

Stevens, M., 2013. Sensory ecology, behaviour, and evolution. OUP Oxford.

Symes, L.B. and Price, T.D., 2015. Sexual Stimulation and Sexual Selection. The American

Naturalist, 185(4), pp.iii-iv.

Wallace, A. R. 1895 Natural selection and tropical nature, 2nd edn. New York, NY: Macmillan

and Co.

Wallace, A. R. 1889. Darwinism: An Exposition of the Theory of Natural Selection with Some of

its Applications. London: MacMillan & Co.

Weber, M.G. and Agrawal, A.A., 2012. Phylogeny, ecology, and the coupling of comparative

and experimental approaches. Trends in ecology & evolution, 27(7), pp.394-403.

West-Eberhard, M.J., 1984. Sexual selection, competitive communication and species-specific

signals in insects. Insect communication, pp.283-324.

West-Eberhard, M.J., 1987. Flexible strategy and social evolution. Animal societies: theories and

facts, pp.35-51.

West-Eberhard, M.J., 1989. Phenotypic plasticity and the origins of diversity. Annual review of

Ecology and Systematics, 20(1), pp.249-278.

Wiens, J.J., 2001. Widespread loss of sexually selected traits: how the peacock lost its

spots. Trends in Ecology & Evolution, 16(9), pp.517-523.

Wilczynski, W., Rand, A.S. and Ryan, M.J., 2001. Evolution of calls and auditory tuning in the

Physalaemus pustulosus species group. Brain, Behavior and Evolution, 58(3), pp.137-151.

Wilkinson, G.S. and Reillo, P.R., 1994. Female choice response to artificial selection on an

exaggerated male trait in a stalk-eyed fly. Proceedings of the Royal Society of London B:

Biological Sciences, 255(1342), pp.1-6.

Williams, G. C. 1992. Natural selection. Oxford University Press.

Wischnath, L., 1993. Atlas of livebearers of the world. TFH Publications; National Book

Network.

Zahavi, A., 1975. Mate selection—a selection for a handicap. Journal of theoretical

Biology, 53(1), pp.205-214.

Appendix A – Supplemental Methods

Male colouration data. One of us (MRF) scored male colouration for 232 species of poeciliid fishes and their relatives, collecting data from published photographs and live specimens (museum specimens were not used, as pigmented colouration can be quickly lost from preserved specimens). Male colouration was coded as a binary character, based on the presence

(1) or absence (0) of red, and/or orange, and/or yellow ornamentation. We delimited colours by qualitative hue, chroma (intensity), and value (tone) (Supplementary Table 1), while simultaneously matching areas of male colouration to colour standards (Kornerup & Wanscher,

1978) against a grey standard background (ViewCatcher, product #CW7002). For example, males were scored as having an area of orange if one or more patches of colour matched all of the criteria listed in the top row of Supplementary Table 1. For traits for which there was uncertainty, a second naïve observer was asked to score the male, and their decision was used as the final say. Male colouration data and references can be found in Supplementary Table 2.

Phylogenetic modelling. To model the evolution of male colouration with an underlying sensory bias, we fit a phylogenetic hidden Markov model (pHMM) (Beaulieu et al., 2013). With pHMMs, we can represent a putative pre-existing sensory bias as a “hidden” (i.e., unobserved) character or state that influences the rates of gain and loss of male colouration in a complex model of Markovian trait evolution. Our sensory bias model, which we refer to as the “precursor-

2 model”, was inspired by the precursor-2 model of Marazzi et al. (2012); this model allows gain and loss rates to differ. This rate asymmetry was maintained across all models, for consistency with empirical inferences about ornament and preference evolution (Wiens, 2001).

Precursor-2 model. The likelihood of our precursor-2 model is defined as being proportional to the probability of the data given our tree and a model of evolution, Q, for which

39 we defined a continuous-time Markov process represented by the following 3 x 3 state transition matrix:

0N − �→ 0 � = 0P �→ − �→ (1) 1P 0 �→ − where q0N→0P and q0P→0N are the asymmetric transition rates governing the gain and loss of the hidden precursor trait (e.g., a pre-existing colour bias, represented by states “N” and “P” for “no precursor” and “precursor” respectively), and q0P→1P and q1P→0P are the asymmetric transition rates describing the gain and loss of male colouration (states “0” and “1” for absence and presence of colour, respectively) once the precursor has evolved. The precursor-2 model mimics the sensory bias process because (i) the precursor (i.e., a pre-existing sensory bias) is assumed to affect the probability of male colouration evolving, (ii) the gain of long-wavelength (yellow to red) male colouration is not possible without the prior gain of a precursor trait, and (iii) the likelihood of this precursor-2 model should reflect the degree of phylogenetic clustering and nestedness of the gains or losses of male colouration across the phylogeny (Shaw, 1995; Endler

& Basolo, 1998). Note that transitions involving simultaneous changes in both the precursor and the colour trait (e.g., 0N→1P) are set to zero to force such transitions to first pass through the precursor (i.e., 0N→ 0P→1P). This is because the sensory bias hypothesis predicts that a pre- existing bias is a necessary precursor for the evolution of male displays of interest (Fuller et al.,

2005). This also simplifies the model to a more tractable number of parameters, and ensures that the model does not misattribute transitions when both state (i.e., colour) and rate (which is a function of the precursor) change over time (Pagel, 1994).

Null model. Next, we fit a likelihood-based, time-homogeneous null model in which male colouration evolves freely, without the influence of a precursor (the mk-2 model of Lewis,

2001). The null model, Q, is represented by the following 2 x 2 matrix:

0� − �→ � = (2) 1� �→ − where q0N→1N is the instantaneous rate of the gain of male colouration, and q1N→0N is the instantaneous rate of the loss of male colouration. Note that our null model allows for asymmetry in the rates of gain and loss of male colouration. In addition, it is sometimes referred to as a one- rate model (Beaulieu et al., 2013) because there is only one rate class (compare to the two-rate

[3] and three-rate [4] models below). We include “N” (“no precursor”) in the definition of our terms to emphasize that there is no precursor in our null model.

Two-rate HRM. Next we fit a likelihood-based, time-homogeneous null model in which male colouration can evolve via a precursor but is not required to do so, using the two-rate HRM of Beaulieu et al. (2013). This model may fit well if the presence of a sensory bias increases the probability that an ornament is gained, but is not strictly required for the ornament to evolve. In our case, we would expect rates of ornament loss and gain to be different in the presence of a pre-existing sensory bias compared to lineages in which it is absent. The two-rate model, Q, is represented by the following 4 x 4 transition matrix:

ON − �→ �→ 0 1N � − 0 � � = → → (3) 0P �→ 0 − �→ 1P 0 �→ �→ − where q0N→1N and q1N→0N are the asymmetric transition rates governing the gain and loss of the male colouration within the slower rate class “N” (corresponding to colour gain/loss in the absence of a precursor trait), q0P→1P and q1P→0P are the asymmetric transition rates describing the gain and loss of male colouration within the faster rate class “P” (corresponding to gain/loss

in the presence of a precursor trait), and q0N→0P , q0P→0N , q1N→1P , and q1P→1N are the asymmetric transition rates describing transitions between the slow and fast rate classes (i.e., the gain and loss rates of the hidden precursor trait). Transitions involving simultaneous changes in both the precursor trait and male colour (e.g., 0N→1P) are set to zero to force such complex transitions to occur in a stepwise fashion (e.g., 0N→ 0P→1P, or 0N→1N→1P). This two-rate

HRM models two alternative paths for the evolution of male colouration: one with a precursor, and one without a precursor.

Three-rate HRM. Using the three-rate HRM of Beaulieu et al. (2013), we also fit a likelihood-based, time-homogeneous null model in which male colouration can evolve with up to two precursors, but is not required to do so. We fit this model to test whether a precursory role of a pre-existing sensory bias might be distinguishable from a precursory role of a female mating preference (Endler & Basolo, 1998), or more generally, to test for multiple precursors underlying the evolution of male colouration. This would make biological sense if male colouration is slower to evolve via sensory exploitation (i.e., when a female mating preference is absent), and faster to evolve via sensory bias (i.e., when a female mating preference is involved) (Endler,

1998). The three-rate model, Q, is represented by the following 6 x 6 transition matrix:

0N − �→ �→ 0 0 0 1N �→ − 0 �→ 0 0 0P1 � 0 − � � 0 � = → → → (4) 1P1 0 �→ �→ − 0 �→ 0P2 0 0 �→ 0 − �→ 1P2 0 0 0 �→ �→ − where q0N→1N and q1N→0N are the asymmetric transition rates governing the gain and loss of the male colouration in the slowest rate class “N” (corresponding to colour gain/loss in the absence of either precursor trait), q0P1→1P1 and q1P1→0P1 are the asymmetric transition rates describing the gain and loss of male colouration within the next fastest rate class “P1” (corresponding to colour

42 gain/loss in the presence of a precursor trait, in this case assumed to be a pre-existing sensory bias), q0P2→1P2 and q1P2→0P2 are the asymmetric transition rates describing the gain and loss of male colouration within the fastest rate class “P2” (corresponding to colour gain/loss in the presence of a second precursor trait, in this case assumed to be a pre-existing female mate preference), and q0N→0P1 , q0P1→0N , q1N→1P1 , q1P1→1N , q0P1→0P2 , q0P2→0P1 , q1P1→1P2 , and q1P2→1P1 are the asymmetric transition rates describing transitions between the slow, faster, and fastest rate classes (i.e., the gain and loss rates of the two hidden precursor traits). As in the other models, simultaneous character transitions are not permitted under this model. Note that the state transition pathway describing the evolution of male colouration by sensory bias (pre-existing bias → female mate preference → male trait) is 0N→0P1→0P2→1P2. In contrast, the pathway describing the evolution of male colouration by sensory exploitation (pre-existing bias → male trait) is 0N→0P1→1P1.

Model tests. To test the role of a precursor in the evolution of male colouration, we compared the precursor-2 model (1) to the precursor-less null model (2). We also compared the precursor-2 model (1) to the two-rate model (3) and three-rate model (4) to test more complex hypotheses about non-obligate precursors and multiple precursors. We fit all models with maximum likelihood using the corHMM function in the corHMM package in R (Beaulieu et al.,

2013), and compared them using the small-sample-size corrected Akaike information criterion

(AICc). Because phylogenetically structured data may confound robust model comparison using information criteria, we also compared models using parametric bootstrapping (Boettiger et al.,

2012). We simulated 1000 datasets under each model using rTraitDISC in the ape software package (Paradis, 2004), and refit all models to each simulated dataset using corHMM in the corHMM software package (Beaulieu et al., 2013). Bootstrap p-values for the precursor-2 and

43 two-rate models were calculated from bootstrapped likelihood ratio tests against the null model and the precursor model, respectively, following the Monte Carlo procedure summarized in

Figure 2 of Boettiger et al. (2012). Due to computational limitations, we did not use bootstrapped likelihood ratio tests to assess the three-rate model; instead we used p-values calculated from a single likelihood ratio test against the precursor-2 model (as a nested null model).

A precursor model should outperform a simple null model of Markovian trait evolution when gains and losses of the trait of interest exhibit phylogenetic clustering, with the remainder of the phylogeny being comparatively uniform in lacking (or possessing) the trait (see: Figure 3 in Marazzi et al., 2012). A precursor will not be detectable using the comparative method, however, if it is uniformly present throughout the study group (i.e., if patterns of precursor- dependent trait gain and loss are relatively homogenous across the tree). Such a pattern would be sufficiently explicable by our simpler null mk-2 model, even if the precursor is actually present.

Likewise, if the evolution of a precursor is immediately and irreversibly followed by the evolution of a trait that depends on it (e.g., if male colour evolves immediately and is never lost following the evolution of a sensory bias), there will not be evidence for the precursor in comparative data. In this manner, comparative tests for evolutionary precursors are conservative: precursor models will only be supported when a precursor is necessary, but not sufficient, for the evolution of a trait of interest. Lastly, if there is substantial and consistent rate variation across clades, then a more complex model of rate variation where precursors are optional (e.g., our two- rate model), would better explain the male data.

Ancestral and tip state reconstructions. The likeliest states at all ancestor nodes were reconstructed from the marginal probabilities estimated under the precursor-2 model. The marginal probability of state i for a given node is the overall likelihood of the tree and data when

44 the state at that same node is fixed to be in state i, divided by the sum of the individual likelihoods with the node fixed in each possible state. We fixed the root to be in state zero (i.e., no precursor and no red/orange/yellow colouration on males), as this is the order assumed in the sensory bias model; however, relaxing this assumption had very little impact on the reconstructions (Supplemental Figure 2).

The pHMM framework is unique because we can use information about the present, not just to understand the past, but also to understand more about the present. Unlike traditional phylogenetic models of ancestral character estimation, in which observable characters are measured for all extant species and estimated (or “reconstructed”) for ancestor nodes, hidden characters inferred under pHMMs are not directly measured for extant species, but can be estimated at the tips under the model. Therefore, pHMMs can be used to reconstruct the expected values of hidden characters in extant species, just as is traditionally done for ancestors. To estimate the probability of the hidden precursor state for each observed tip, we extended the marginal reconstruction algorithm (that we applied to internal nodes) to each tip in the phylogeny

(Beaulieu & O’Meara, in prep.). These tip state reconstructions represent three predictions (“- colour, - precursor”, “-colour, +precursor”, and “+colour, +precursor”) under the fitted model, and these predictions can be tested through complementary experiments in extant species.

Ancestral and tip state estimates were obtained using the corHMM function in the corHMM package in R (Beaulieu et al., 2013).

Colour bias experiments. Following the protocol of Rodd et al. (2002), we filled an aquarium (91.44 x 45.72 x 38.1 cm l*w*h), to a depth of 24cm using a 50:50 mix of stock tank and conditioned dechlorinated water, and covered the bottom of the tank with natural-coloured gravel. The right, left, and back sides of the tank were covered with beige paper, and the tank

45 was illuminated with two 60 watt incandescent bulbs and one full spectrum UV fluorescent bulb, all suspended roughly 60 cm from the water’s surface. For the Texas and California experiments, we used a full spectrum UV lamp (Verilux Original Natural Spectrum Lamp, VD01AA1). For the Toronto experiments we used a Duralux full-spectrum fluorescent bulb. Only these lights were illuminated during the trial, and all other lights in the room were turned off. We made coloured discs by painting a thick layer of Liquitex acrylic paint on an acetate sheet, and then, once dry, using a 1.3-cm diameter circular leather punch tool to cut them out. The discs were then glued onto microscope cover slips using aquarium-safe silicone, acetate side up. The

Liquitex acrylic pigments we used are: red (Naphthol Crimson, Munsell Hue [MH] = 6.0R), orange (Indo Orange Red, MH = 9.5R), yellow (Cadmium Yellow Medium Hue, MH = 1.3Y), white (Titanium White, MH = white), green (Light Green, MH = 1.2G), blue (Ultramarine Blue,

MH = 8.3BP), black (Ivory Black, MH = black), and brown (Raw Sienna, MH = 4.98). We established proximity zones around each coloured disc by burying a clear plastic lid of a food- safe container (~16 cm in diameter) in the gravel of the observation tank, and then placing a disc in the centre of each buried lid/zone. Only the rim of the dish was exposed, providing an inconspicuous boundary around each coloured disc. The zones were staggered, evenly spaced in the observation tank, approximately 6-8 cm apart, and approximately 3 cm from any nearby walls of the tank. A straight line was drawn on the front of the tank, 12 cm above the gravel, to delimit the upper boundary of a cylindrical zone around each coloured disc. All observations were conducted with this line at eye level by MRF for all species except S. bilineatus and A. toweri (observed by AVG).

At the beginning of each test, all eight coloured discs were randomly assigned a position in the observation tank using a random number generator. An individual fish was then placed in

46 a small (approx. 15.24 cm diameter x 7.62 cm depth), clear plastic cup, half filled with water taken from the main testing tank. The cup was floated on the surface of the water in the test tank for a 5-minute acclimation period. The fish was then gently released into the tank and observed for 7 minutes, or, if there were no zone entries, the trial was terminated after 4 minutes. Observer

MRF wore the same black sweater for every trial, and the other observers wore similar dark clothing. We used JWatcher to record (i) the number of pecks to each disc, and (ii) the duration of time spent in the cylindrical zone around each disc. The only notable deviation in our protocol from that of Rodd et al. (2002) was the use of brown discs, rather than purple (see Supplemental

Figure 5 for photos of laboratory food placed next to the discs). Individuals that did not enter any zone were excluded from analyses. On average, we collected data for 21.1 ± 8.3 females (range:

12-46), and 13.8 ± 3.6 males (range: 0-20) per species. Experimental results for are summarized for each species in Supplemental Figure 3.

Disc reflectance and ambient light spectra. We recorded reflectance spectra on each of the coloured discs using an Ocean Optics USB4000 spectrometer. We took 5 readings, and present the averaged % reflectance for each coloured disc (Supplemental Figure 6). For P. gracilis only, we also measured the light spectra under which experiments were conducted using an Ocean Optics Jaz spectrophotometer running the JazIrrad program. The instrument was fitted with a cosine-corrector (meaning it measures all light from a 180º field of view). Measures start at 350 nm, which is the shortest relevant wavelength for guppies, and shortest calibration available (Ben Sandkam, personal communication). First, we calibrated the spectrophotometer to ambient light, and then to no light. Next we measured spectra under the experimental lighting environments, with a full spectrum UV lamp (Verilux Original Natural Spectrum Lamp,

VD01AA1), or with Duralux full-spectrum fluorescent bulb (Toronto experiments). We took five

47 measurements with the spectrophotometer probe underwater and facing up, and present the averaged absolute irradiance in uW/cm2/nm per each lighting environment (Supplemental Figure

7).

Statistical analyses of experimental results. The pecking behaviour of some fish during the experiments suggests that the inferred latent preference for long-wavelength colours in those species may be a foraging preference (Supplemental Table 4). However, because not all fish pecked discs, we only analyzed data for the duration spent in the zones; peck data are available in Supplemental Table 4. The analyses that follow were conducted on duration data for females.

First, to test for general colour preference differences among the three classes of precursor model predictions that we tested experimentally (i.e., “-colour, - precursor”; “-colour,

+ precursor”; “+colour, + precursor”), using JMP, we performed a repeated-measures ANOVA using the standardized total duration spent in the zones for each coloured disc as the repeated measure. For this analysis, we took the species average duration for each coloured disc, and then standardized those values against the respective species’ mean and standard deviation as z- scores. We calculated the total duration for each colour and standardized the values within each species to account for differences in activity levels. Note that to avoid issues associated with the interdependence of these data, white was dropped from the analyses. We used experimental class as the predictor variable, and Pillai’s trace as our test statistic (Pillai, 1955). Then, to ask whether fishes’ colour preference behaviour differed between fish that are all predicted to have the precursor but that are plain or colourful, we ran the same repeated-measures ANOVA, omitting the “–colour, -precursor” class (i.e., only using colour preference data for the “-colour, + precursor” and “+colour, + precursor” classes). Lastly, to evaluate for differences in female bias strength between “+colour, + precursor” versus “-colour, +precursor” species, we conducted

Welch two-sample t-tests on the female preference strength, where preference strength was defined as total average durations (red+orange+yellow), or as maximum average duration (i.e., the highest value out of average durations spent in red, orange, and yellow zones). Separate analyses were carried out for each definition of preference strength.

To ask about the specific colour preferences of each species, we also analyzed the responses to the colour discs for each species independently. To do so, we conducted an independent repeated-measures ANOVA for each species, with disc colour as the repeated measure and average total duration visiting each colour zone as the predictor variable. The analysis included pairwise comparisons of all colours (n=28), so we conducted post-hoc Tukey tests using a Tukey-Kramer method of multiple test correction. Based on the Tukey-Kramer adjusted p-values, for each species, we assigned significance levels to the responsiveness to each disc. These analyses were carried out in SAS, version 9.2 of the SAS System for Windows 7 (64- bit). Copyright Ó 2009 SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc., Cary, NC, USA.

To evaluate how well the precursor-2 model predicted female disc colour preferences (or lack thereof), we first binarized female experimental responses based on the outputs from the repeated-measures ANOVA with Tukey-Kramer analyses (previous paragraph). Each species was classified as either showing a significant preference for one or more long wave-length colours or not, and then binned into one of two classes: (i) concordant, if a significant bias (or lack thereof) towards long-wavelength (i.e., orange, red and/or yellow) coloured discs was correctly predicted in females, or (ii) discordant, if a long-wavelength bias (or lack thereof) was incorrectly predicted in females. Using these binarized responses, we performed a two-class

ROC analysis (Fawcett, 2006) using the AUC package in R (Ballings & Van den Poel, 2013). A

49 value of 0.5 indicates a random predictor, and a value of 1 indicates a near-perfect accuracy predictor.

We were also interested in testing for an association between the precursor probabilities estimated at the tips, and species’ strength of colour preference (defined as above, as either (i) the average total duration spent across red, orange, and yellow zones, or as (ii) the maximum average time spent in a red, orange, or yellow zone). We used Spearman’s rho due to the non- linear and non-normal distributions of the aforementioned variables. These analyses were performed using the stats package that is baked into R.

Appendix B – Supplemental Figures

Supplemental Figure 1. Output from parametric bootstrap model comparison (1000 replicates).

For each model comparison, the grey density plot depicts the distribution of likelihood ratio test statistics (LRs) for data simulated under the null model, while the red density plot depicts the distribution of LRs simulated under the alternative model. Solid and dashed lines indicate the critical (95%) and observed test statistics for the bootstrap significance test, respectively. The top panel compares the precursor-2 and null models; the bottom panel compares the two-rate and precursor-2 models (with the latter treated here as a nested null model). 0.6 null precursor 0.5 0.4 0.3 density 0.2 0.1 0.0

−5 0 5 10 15

LRs 0.6 precursor two.rate 0.5 0.4 0.3 density 0.2 0.1 0.0

−5 0 5 10 15 LRs

Supplemental Figure 2. Ancestral state reconstruction under the precursor-2 model when we relax the assumption that the ancestor (i.e., the root) lacks the precursor. In this reconstruction, we assume that all states are equally likely at the root. Changing the root state assumption does precursor2 not qualitatively alter our reconstruction of the evolution of the precursor or male colouration.

Chirocentrus dorab Dorosoma cepedianum Chanos chanos Ictalurus punctatus Danio rerio Notemigonus crysoleucas Semotilus atromaculatus Esox lucius Oncorhynchus mykiss Aphredoderus sayanus Gadus morhua Zeus faber Gasterosteus aculeatus Takifugu rubripes Tetraodon nigroviridis Monopterus albus Oryzias latipes Rivulus hartii Aplocheilus lineatus Epiplatys annulatus Aphyoplatys duboisi Adamas formosus Aphyosemion bitaeniatum Cubanichthys cubensis Floridichthys carpio Cyprinodon variegatus Jordanella floridae Fundulus cingulatus Fundulus lineolatus Lucania parvae Lucania goodei Profundulus punctatus Empetrichthys latos Crenichthys nevadae Crenichthys baileyi Characodon audax Characodon lateralis Allodontichthys zonistius Allodontichthys polylepis Allodontichthys tamazulae Allodontichthys hubbsi Xenotaenia resolanae Ilyodon furcidens Ilyodon xantusi Ilyodon whitei Goodea atripinnis Goodea gracilis Xenotoca eiseni Xenotoca melanosoma Xenoophorus captiva Zoogoneticus tequila Zoogoneticus quitzeoensis Ameca splendens Xenotoca variatus Alloophorus robustus Chapalichthys encaustus Chapalichthys pardalis Ataeniobius toweri Neotoca bilineata Girardinichthys viviparus Girardinichthys multiradiatus Skiffia bilineatus Skiffia lermae Skiffia multipunctata Skiffia francesae Hubbsina turneri Allotoca regalis Allotoca maculata Allotoca goslinei Allotoca dugesii Allotoca zacapuensis Allotoca catarinae Allotoca meeki Allotoca diazi Valencia hispanica Aplocheilichthys spilauchen Aplocheilichthys normani Fluviphylax pygmaeus Oxyzygonectes dovii Anableps anableps Anableps dovii Jenynsia lineata Jenynsia multidentata Xenodexia ctenolepis Tomeurus gracilis Phalloptychus januarius Phalloceros caudimaculatus Cnesterodon hypselurus Cnesterodon decemmaculatus Cnesterodon septentrionalis Micropoecilia wingei Micropoecilia obscura Micropoecilia reticulata Micropoecilia picta Micropoecilia parae Micropoecilia bifurca Poecilia vivipara Poecilia caucana Poecilia latipinna Poecilia velifera MP737 Poecilia latipunctata Poecilia petenensis Campeche Poecilia chica Poecilia butleri MR04 Poecilia orri Poecilia salvatoris Poecilia gilli Poecilia mexicana mex Poecilia catemaconis Poecilia sphenops Poecilia sulphuraria Pamphorichthys hollandi Limia heterandria Limia melanogaster Limia zonata Limia caymanensis Limia vittata Limia dominicensis Limia nigrofasciata Limia garnieri Limia sulfurophila Limia perugiae Limia tridens Quintana atrizona Dactylophallus denticulatus Dactylophallus ramsdeni Glaridichthys uninotatus Glaridichthys falcatus Girardinus creolus Girardinus metallicus Girardinus microdactylus Pseudopoecilia festae Heterandria formosa Xenophallus umbratilis Alfaro cultratus Alfaro hubberi Priapichthys annectens Brachyrhaphis cascajalensis Brachyrhaphis parismina Phallichthys tico Phallichthys quadripunctatus Phallichthys pittieri Phallichthys amates Brachyrhaphis hartwegi Brachyrhaphis holdridgei Brachyrhaphis rhabdophora Brachyrhaphis roseni Brachyrhaphis terrabensis Neoheterandria elegans Neoheterandria tridentiger Poeciliopsis paucimaculata Poeciliopsis elongata Poeciliopsis retropinna Poeciliopsis balsas Poeciliopsis viriosa Poeciliopsis monacha Poeciliopsis infans Poeciliopsis prolifica Poeciliopsis lucida Poeciliopsis occidentalis Poeciliopsis baenschi Poeciliopsis fasciata Poeciliopsis latidens Poeciliopsis turrubarensis Poeciliopsis scarlii Poeciliopsis turneri Poeciliopsis presidionis Poeciliopsis pleurospilus Poeciliopsis gracilis Poeciliopsis catemaco Poeciliopsis hnilickai Scolichthys iota Scolichthys greenwayi Carlhubbsia kidderi Carlhubbsia stuarti Priapella intermedia Priapella olmecae Priapella compressa Heterandria jonesi Heterandria bimaculata Xiphophorus pygmaeus Xiphophorus nezahualcoyotl Xiphophorus malinche Xiphophorus montezumae Xiphophorus cortezi Xiphophorus continens Xiphophorus birchmanni Xiphophorus multilineatus Xiphophorus nigrensis Xiphophorus monticolus Xiphophorus clemenciae Xiphophorus hellerii Xiphophorus alvarezi Xiphophorus mayae Xiphophorus signum Xiphophorus andersi Xiphophorus maculatus Xiphophorus milleri Xiphophorus evelynae Xiphophorus variatus Xiphophorus xiphidium Xiphophorus meyeri Xiphophorus gordoni Xiphophorus couchianus Belonesox belizanus Heterophallus rachovii Heterophallus milleri Gambusia luma Gambusia sexradiata Gambusia eurystoma Gambusia heterochir Gambusia geiseri Gambusia holbrooki Gambusia affinis Gambusia hispaniolae Gambusia wrayi Gambusia melapleura Gambusia nicaraguensis Gambusia hubbsi Gambusia manni Gambusia yucatana Gambusia puncticulata Gambusia caymanensis Gambusia oligosticta Gambusia zarskei Gambusia punctata Gambusia rhizophorae Gambusia atrora Gambusia marshi Gambusia panuco Gambusia vittata Gambusia hurtadoi

Supplemental Figure 3. Average total male and female durations spent in coloured disc zones. Asterisks indicate significance levels, determined by repeated-measures ANOVA with Tukey- Kramer method of multiple test correction (*indicates p≤0.5; ** indicates p≤0.01; *** indicates p≤0.001). For each species (a-n), female values are on the upper panel and male values are on the bottom panel. Parentheses indicate the hypothesis test (+/- colour, +/- precursor). Male data were not analyzed for significance. Data are not normally distributed, so treat with caution.

(a) Ameca splendens (+,+) (b) Ataeniobius toweri (-,+)

(e) Girardinus metallicus (-,-) (f) Limia perugiae (+,+)

(g) Limia vitatta (+,+) (h) Oryzias latipes (-,+)

(i) Poecilia mexicana (+,+) (j) Poeciliopsis gracilis (-,-)

(k) Skiffia bilineatus (-,+) (l) Xiphophorus andersi (-,+)

(m) Xiphophorus couchianus (-,+) (n) Xiphophorus mayae (+,+)

Supplemental Figure 4. Summary of disc colour preference experiments in males. Split scatter plots suggest a long-wavelength bias, on average, in the experimental groups where the precursor was predicted (“+precursor”). Points denote standardized mean total duration for each species, dotted lines represent the mean across each hypothesis test group (dotted lines), and grey zones indicate 95% confidence intervals around that mean.

− colour, − precursor

−1

− colour, + precursor

2 colour R O 1 Y G B 0 P K

standardized total duration standardized W −1

+ colour, + precursor

−1

R O Y G B P K W colour

Supplemental Figure 5. Photographs of laboratory diets and the coloured discs for comparison.

Fish were tested in five locations: (a) Rodd lab, University of Toronto, ON, Canada, (b)

Xiphophorus Genetic Stock Center, TX, USA, (c) Goliad Farms, TX, USA, (d) Kolluru lab,

California Polytechnic, CA USA, and (e) González lab, UANL, Monterrey, NL, Mexico.

(a) Poeciliopsis gracilis (photos by Michael Foisy)

brine shrimp mixed flake veggie flake

(b) Oryzias latipes (not fed liver), Xiphophorus andersi, Xiphophorus couchianus (photos by Michael Foisy)

brine shrimp flake food liver paste

(c) Ameca splendens, Gambusia affinis, Gambusia punctata, Limia perugiae, Limia vitatta, Poeciliopsis mexicana, Xiphophorus mayae (photo by Michael Foisy)

dog chow

(d) Girardinus metallicus (photo by Dr. Gita Kolluru)

mixed flake

(e) Ataeniobius toweri and Skiffia bilineatus (photos by Dr. Arcadio Valdes González)

mixed flake veggie mix food shrimp meal food

meat mix food trout starter feed

Supplemental Figure 6. Disc reflectance spectra for coloured discs (see Supplemental Methods).

Disc reflectance spectra

100

long.disc$colour red orange yellow

50 green blue brown black Average % Reflectance Average white

300 400 500 600 700 Wavelength (nm)

Supplemental Figure 7. Ambient light spectra in the observation tank. Poeciliopsis gracilis, was tested under a Duro-test full spectrum lamp (black), all other species, except Skiffia bilineatus and Ataenobius toweri, were tested under a Verilux full-spectrum lamp (purple).

Ambient light spectra

1.0

long.ambient$env with UV without UV

0.5 Average Absolute Irradiance (uW/cm^2/nm) Absolute Irradiance Average

0.0

400 500 600 700 800 Wavelength (nm)

Appendix C – Supplemental Tables

Supplementary Table 1. Definitions of red, orange, and yellow colouration on male fishes, based on Kornerup and Wanscher (1978). Number and letter codes refer to particular cells on colour plates in Kornerup and Wanscher (1978).

COLOUR HUE INTENSITY TONE (CHROMA) (VALUE)

RED TRUE RED: 10A8

• plate 8 (orange-red) Rows 4-8 Columns A-C • plates 9, 10, 11 (pure) • plate 12 (bluish-red)

ORANGE TRUE ORANGE: 6A8

• plates 5, 6 (pure) Rows 4-8 Columns A-C • plate 7 (reddish-orange)

YELLOW TRUE YELLOW: 2A8

• plate 1 (greenish-yellow) Rows 4-8 Columns A-C • plates 2, 3 (pure) • plate 4 (orange-yellow)

Supplementary Table 2. Binarized scores for red, orange, and yellow colouration of males, collected from published photographs and live specimens (when possible) of wild males. Species with at least one of red and/or orange and/or yellow were classified as having long-wavelength colouration. Species indicated in bold were tested in the disc observation test.

long-wavelength Genus_species red orange yellow references colouration Adamas_formosus 1 0 0 1 3 Alfaro_cultratus 0 0 1 1 1 Alfaro_hubberi 0 0 0 0 1 Allodontichthys_hubbsi 0 0 0 0 1; 4 Allodontichthys_polylepis 0 0 0 0 1; 4 Allodontichthys_tamazulae 0 0 0 0 1; 4 Allodontichthys_zonistius 0 0 1 1 1; 4 Alloophorus_robustus 0 0 1 1 1; 4 Allotoca_catarinae 0 0 0 0 1 Allotoca_diazi 0 0 0 0 1 Allotoca_dugesii 0 1 1 1 1; 4 Allotoca_goslinei 0 0 1 1 1; 2; 4 Allotoca_maculata 0 0 0 0 1; 4 Allotoca_meeki 0 0 0 0 1; 4 Allotoca_regalis 0 0 0 0 1; 4 Allotoca_zacapuensis 0 1 1 1 1; 4 Ameca_splendens 0 0 1 1 1; 4; 24 Anableps_anableps 0 0 0 0 1; 25 Anableps_dovii 0 0 0 0 1; 2; 4 Aphredoderus_sayanus 0 0 0 0 2 Aphyoplatys_duboisi 1 0 0 1 2 Aphyosemion_bitaeniatum 1 1 1 1 2 Aplocheilichthys_normani 0 0 0 0 3 Aplocheilichthys_spilauchen 0 0 0 0 3 Aplocheilus_lineatus 1 0 1 1 1; 2, 4 Ataeniobius_toweri 0 0 0 0 1, 4 Belonesox_belizanus 0 0 1 1 1 Brachyrhaphis_cascajalensis 0 0 1 1 1; 2; 9 Brachyrhaphis_hartwegi 0 0 1 1 1 Brachyrhaphis_holdridgei 0 1 1 1 1; 10 Brachyrhaphis_parismina 0 0 0 0 1

Brachyrhaphis_rhabdophora 0 1 1 1 1; 2 Brachyrhaphis_roseni 0 1 1 1 1 Brachyrhaphis_terrabensis 0 0 1 1 1 Carlhubbsia_kidderi 0 1 1 1 1; 2; 20 Carlhubbsia_stuarti 0 0 1 1 1; 2 Chanos_chanos 0 0 0 0 9 Chapalichthys_encaustus 0 0 1 1 1; 4 Chapalichthys_pardalis 0 0 1 1 1; 4 Characodon_audax 1 1 1 1 1; 4 Characodon_lateralis 1 0 1 1 1; 4 Chirocentrus_dorab 0 0 0 0 2 Cnesterodon_decemmaculatus 0 0 1 1 1; 2; 12 Cnesterodon_hypselurus 0 0 0 0 11 Cnesterodon_septentrionalis 0 0 0 0 12 Crenichthys_baileyi 0 1 0 1 1; 2; 4 Crenichthys_nevadae 0 0 0 0 1; 4 Cubanichthys_cubensis 0 1 0 1 9 Cyprinodon_variegatus 0 0 0 0 1; 2; 9 Dactylophallus_denticulatus 0 0 0 0 2 Dactylophallus_ramsdeni 0 0 0 0 2 Danio_rerio 0 0 1 1 2 Dorosoma_cepedianum 0 0 0 0 2; 9 Empetrichthys_latos 0 0 1 1 1; 4 Epiplatys_annulatus 1 0 0 1 1; 2 Esox_lucius 1 0 1 1 2 Floridichthys_carpio 0 0 1 1 1; 2 Fluviphylax_pygmaeus 0 0 0 0 1; 3 Fundulus_cingulatus 1 0 0 1 1; 2 Fundulus_lineolatus 0 0 1 1 1; 2 Gadus_morhua 0 0 0 0 2 Gambusia_affinis 0 0 0 0 1; 9; 24 Gambusia_atrora 0 0 1 1 1 Gambusia_caymanensis 0 1 0 1 1; 21 Gambusia_eurystoma 0 1 0 1 1; 20 Gambusia_geiseri 0 0 0 0 1; 2 Gambusia_heterochir 0 0 0 0 1; 21 Gambusia_hispaniolae 0 1 0 1 1 Gambusia_holbrooki 0 0 0 0 1; 2 Gambusia_hubbsi 0 1 1 1 1; 6, 7, 8 Gambusia_hurtadoi 0 0 0 0 1; 2

Gambusia_luma 0 0 0 0 1; 2; 9 Gambusia_manni 0 1 0 1 1; 9 Gambusia_marshi 0 0 0 0 1 Gambusia_melapleura 0 0 1 1 1; 2 Gambusia_nicaraguensis 0 0 0 0 1 Gambusia_oligosticta 0 1 0 1 1; 21 Gambusia_panuco 0 0 0 0 1 Gambusia_punctata 0 0 0 0 1; 2; 24 Gambusia_puncticulata 0 0 1 1 1; 2; 9 Gambusia_rhizophorae 0 1 0 1 1; 2; 9 Gambusia_sexradiata 0 0 0 0 1; 2 Gambusia_vittata 0 0 1 1 1; 21 Gambusia_wrayi 0 0 0 0 1; 9 Gambusia_yucatana 0 0 0 0 1; 2; 9 Gambusia_zarskei 0 0 0 0 1; 13 Gasterosteus_aculeatus 1 0 0 1 1; 2 Girardinichthys_multiradiatus 0 0 1 1 1; 4 Girardinichthys_viviparus 0 0 0 0 1; 4 Girardinus_creolus 0 0 0 0 1; 2 Girardinus_metallicus 0 0 0 0 1; 2 Girardinus_microdactylus 0 0 0 0 1 Glaridichthys_falcatus 0 0 0 0 1; 2 Glaridichthys_uninotatus 0 0 0 0 1 Goodea_atripinnis 0 0 1 1 1; 4 Goodea_gracilis 0 0 1 1 4; 24 Heterandria_bimaculata 0 1 1 1 1; 2 Heterandria_formosa 0 0 1 1 1; 2 Heterandria_jonesi 0 1 1 1 1 Heterophallus_milleri 0 0 1 1 1; 5 Heterophallus_rachovii 0 0 0 0 1 Hubbsina_turneri 0 0 0 0 1; 2; 4 Ictalurus_punctatus 0 0 0 0 4 Ilyodon_furcidens 0 1 1 1 1; 4 Ilyodon_whitei 0 0 1 1 1; 4 Ilyodon_xantusi 0 0 1 1 1; 4 Jenynsia_lineata 0 0 0 0 1 Jenynsia_multidentata 0 0 0 0 2 Jordanella_floridae 0 1 0 1 1; 2 Limia_caymanensis 1 1 1 1 1; 2 Limia_dominicensis 0 1 0 1 1

Limia_garnieri 0 0 1 1 1; 24 Limia_heterandria 0 0 0 0 1; 2 Limia_melanogaster 0 0 1 1 1; 2 Limia_nigrofasciata 0 0 1 1 1; 2; 24 Limia_perugiae 0 0 1 1 1; 2; 24 Limia_sulfurophila 0 0 0 0 1; 2 Limia_tridens 0 1 1 1 1; 2 Limia_vittata 0 1 1 1 1; 24 Limia_zonata 0 1 1 1 1; 22 Lucania_goodei 1 1 1 1 1; 2 Lucania_parvae 0 1 0 1 1; 9 Micropoecilia_bifurca 1 0 1 1 1 Micropoecilia_obscura 1 1 1 1 1; 15 Micropoecilia_parae 1 0 1 1 1; 16; 25 Micropoecilia_picta 0 1 1 1 1 Micropoecilia_reticulata 1 1 1 1 1; 25 Micropoecilia_wingei 1 1 1 1 17; 24 Monopterus_albus 0 0 0 0 1; 2 Neoheterandria_elegans 0 1 1 1 1 Neoheterandria_tridentiger 0 0 0 0 1 Neotoca_bilineata 0 0 0 0 1; 4 Notemigonus_crysoleucas 0 0 1 1 2 Oncorhynchus_mykiss 1 0 0 1 2 Oryzias_latipes 0 0 0 0 1; 2; 23 Oxyzygonectes_dovii 0 1 1 1 9 Pamphorichthys_hollandi 0 1 0 1 2 Phallichthys_amates 0 0 1 1 1 Phallichthys_pittieri 0 0 1 1 1 Phallichthys_quadripunctatus 0 0 0 0 1; 2 Phallichthys_tico 0 0 1 1 1; 2 Phalloceros_caudimaculatus 0 0 1 1 1 Phalloptychus_januarius 0 0 0 0 1; 2 Poecilia_butleri_MR04 0 1 1 1 1 Poecilia_catemaconis 0 1 1 1 1; 2 Poecilia_caucana 0 0 1 1 1; 2 Poecilia_chica 0 1 0 1 1; 10

Poecilia_gilli 1 1 1 1 2, 9; 24 Poecilia_latipinna 0 1 0 1 1; 20 Poecilia_latipunctata 0 0 1 1 1; 2 Poecilia_mexicana_mex 0 1 1 1 1; 18; 24

Poecilia_orri 0 1 0 1 2, 9 Poecilia_petenensis_Campeche 0 1 0 1 1; 9; 24 Poecilia_salvatoris 1 0 0 1 2 Poecilia_sphenops 0 1 0 1 1; 9; 2 Poecilia_sulphuraria 0 1 0 1 1; 20 Poecilia_velifera_MP737 1 1 0 1 1; 9 Poecilia_vivipara 0 0 1 1 1; 2; 9 Poeciliopsis_baenschi 0 0 0 0 1 Poeciliopsis_balsas 0 0 0 0 1; 2 Poeciliopsis_catemaco 0 0 0 0 1 Poeciliopsis_elongata 0 0 0 0 1; 2 Poeciliopsis_fasciata 0 0 0 0 1; 2 Poeciliopsis_gracilis 0 0 0 0 1; 2; 24 Poeciliopsis_hnilickai 0 0 0 0 1 Poeciliopsis_infans 0 1 0 1 1 Poeciliopsis_latidens 0 0 0 0 1 Poeciliopsis_lucida 0 0 0 0 1 Poeciliopsis_monacha 0 0 0 0 1 Poeciliopsis_occidentalis 0 0 0 0 1; 2 Poeciliopsis_paucimaculata 0 0 0 0 1; 2 Poeciliopsis_pleurospilus 0 0 0 0 1 Poeciliopsis_presidionis 0 0 0 0 1 Poeciliopsis_prolifica 0 0 0 0 1 Poeciliopsis_retropinna 0 0 0 0 1; 2 Poeciliopsis_scarlii 0 0 0 0 1 Poeciliopsis_turneri 0 0 0 0 1 Poeciliopsis_turrubarensis 0 0 0 0 1; 2 Poeciliopsis_viriosa 0 0 0 0 1 Priapella_compressa 0 0 1 1 1; 2 Priapella_intermedia 0 0 1 1 1; 2 Priapella_olmecae 0 1 1 1 1; 2 Priapichthys_annectens 0 1 1 1 1; 2; 20 Profundulus_punctatus 0 1 0 1 1; 2 Pseudopoecilia_festae 0 1 0 1 1; 2 Quintana_atrizona 0 1 1 1 1; 2 Rivulus_hartii 0 1 0 1 1; 2; 25 Scolichthys_greenwayi 0 0 0 0 1; 2 Scolichthys_iota 0 0 0 0 1 Semotilus_atromaculatus 0 1 1 1 2 Skiffia_bilineatus 0 0 0 0 1

Skiffia_francesae 0 0 1 1 1; 4 Skiffia_lermae 0 0 1 1 1; 4 Skiffia_multipunctata 0 0 1 1 1; 4 Takifugu_rubripes 0 0 1 1 1; 2 Tetraodon_nigroviridis 0 0 1 1 1; 2 Tomeurus_gracilis 0 0 0 0 1; 2 Valencia_hispanica 0 0 1 1 2 Xenodexia_ctenolepis 0 0 0 0 1 Xenoophorus_captiva 0 0 0 0 1; 4

Xenophallus_umbratilis 0 0 1 1 2; 20 Xenotaenia_resolanae 0 0 0 0 1 Xenotoca_eiseni 0 1 0 1 1; 2 Xenotoca_melanosoma 0 0 0 0 1 Xenotoca_variatus 0 0 1 1 1 Xiphophorus_alvarezi 1 0 1 1 1; 2; 23 Xiphophorus_andersi 0 0 0 0 1; 23 Xiphophorus_birchmanni 0 0 1 1 1; 23 Xiphophorus_clemenciae 1 0 1 1 1; 2; 23 Xiphophorus_continens 0 0 0 0 1; 23 Xiphophorus_cortezi 0 0 1 1 1; 2; 23 Xiphophorus_couchianus 0 0 0 0 1; 23 Xiphophorus_evelynae 0 1 1 1 1; 2; 23 Xiphophorus_gordoni 0 0 0 0 1; 23 Xiphophorus_hellerii 1 0 1 1 1; 2; 23 Xiphophorus_maculatus 1 0 1 1 1; 2; 23 Xiphophorus_malinche 0 0 1 1 1; 23 Xiphophorus_mayae 1 0 1 1 1; 2; 24 Xiphophorus_meyeri 0 0 0 0 1; 23 Xiphophorus_milleri 0 0 0 0 1; 23 Xiphophorus_montezumae 0 0 0 0 1; 23 Xiphophorus_monticolus 0 1 1 1 23 Xiphophorus_multilineatus 0 0 1 1 1; 23 Xiphophorus_nezahualcoyotl 0 0 1 1 1; 23; 24 Xiphophorus_nigrensis 0 0 1 1 1; 23 Xiphophorus_pygmaeus 0 0 0 0 1; 23 Xiphophorus_signum 0 0 1 1 1; 23 Xiphophorus_variatus 0 1 1 1 1, 2; 23 Xiphophorus_xiphidium 0 0 0 0 1; 23 Zeus_faber 0 0 1 1 2 Zoogoneticus_quitzeoensis 0 1 1 1 1, 4

Zoogoneticus_tequila 1 1 0 1 1, 4 Wischnath, 1993 = 1; http://www.fishbase.ca/ (all mirrors) = 2; Axelrod, 1995 = 3; Riesch et al. 2011 = 5; Riesch et al., 2013 = 6; Martin et al., 2013 = 7; Araújo et al., 2014 = 8; http://biogeodb.stri.si.edu/ = 9; http://old.britishlivebearerassociation.co.uk/Database/ = 10; Lucinda & Garavello, 2001 = 11; Rosa & Costa, 1993 = 12; Meyer et al., 2010 = 13; Langerhans & Makowicz, 2009 = 14; Schories et al., 2009 = 15; Hurtado-Gonzales & Uy, 2010 = 16; Schories et al., 2009 = 17; Plath et al., 2008 = 18; Kato et al., 2005 = 19; https://www.inaturalist.org/ = 20; http://gambusia.zo.ncsu.edu = 21; http://www.ou.edu/schlupp/htm/pics.htm = 22; live specimens at Xiphophorus Genetic Stock Center = 23; live specimens at Goliad Fish Farms = 24; live specimens in Trinidad = 25

Supplementary Table 3. Estimated probability of observing the precursor in extant species. Species tested experimentally are bolded.

Genus_species Probability of having the precursor Adamas_formosus 1.00 Alfaro_cultratus 1.00 Alfaro_hubberi 0.84 Allodontichthys_hubbsi 0.94 Allodontichthys_polylepis 0.95 Allodontichthys_tamazulae 0.94 Allodontichthys_zonistius 1.00 Alloophorus_robustus 1.00 Allotoca_catarinae 0.97 Allotoca_diazi 0.97 Allotoca_dugesii 1.00 Allotoca_goslinei 1.00 Allotoca_maculata 0.90 Allotoca_meeki 0.97 Allotoca_regalis 0.86 Allotoca_zacapuensis 1.00 Ameca_splendens 1.00 Anableps_anableps 0.56 Anableps_dovii 0.56 Aphredoderus_sayanus 0.44 Aphyoplatys_duboisi 1.00 Aphyosemion_bitaeniatum 1.00 Aplocheilichthys_normani 0.54 Aplocheilichthys_spilauchen 0.54 Aplocheilus_lineatus 1.00 Ataeniobius_toweri 0.83 Belonesox_belizanus 1.00 Brachyrhaphis_cascajalensis 1.00 Brachyrhaphis_hartwegi 1.00 Brachyrhaphis_holdridgei 1.00 Brachyrhaphis_parismina 0.99 Brachyrhaphis_rhabdophora 1.00 Brachyrhaphis_roseni 1.00 Brachyrhaphis_terrabensis 1.00 Carlhubbsia_kidderi 1.00

Carlhubbsia_stuarti 1.00 Chanos_chanos 0.43 Chapalichthys_encaustus 1.00 Chapalichthys_pardalis 1.00 Characodon_audax 1.00 Characodon_lateralis 1.00 Chirocentrus_dorab 0.41 Cnesterodon_decemmaculatus 1.00 Cnesterodon_hypselurus 0.83 Cnesterodon_septentrionalis 0.86 Crenichthys_baileyi 1.00 Crenichthys_nevadae 0.93 Cubanichthys_cubensis 1.00 Cyprinodon_variegatus 0.71 Dactylophallus_denticulatus 0.19 Dactylophallus_ramsdeni 0.19 Danio_rerio 1.00 Dorosoma_cepedianum 0.41 Empetrichthys_latos 1.00 Epiplatys_annulatus 1.00 Esox_lucius 1.00 Floridichthys_carpio 1.00 Fluviphylax_pygmaeus 0.60 Fundulus_cingulatus 1.00 Fundulus_lineolatus 1.00 Gadus_morhua 0.44 Gambusia_affinis 0.66 Gambusia_atrora 1.00 Gambusia_caymanensis 1.00 Gambusia_eurystoma 1.00 Gambusia_geiseri 0.66 Gambusia_heterochir 0.66 Gambusia_hispaniolae 1.00 Gambusia_holbrooki 0.66 Gambusia_hubbsi 1.00 Gambusia_hurtadoi 0.94 Gambusia_luma 0.81 Gambusia_manni 1.00 Gambusia_marshi 0.86 Gambusia_melapleura 1.00

Gambusia_nicaraguensis 0.93 Gambusia_oligosticta 1.00 Gambusia_panuco 0.86 Gambusia_punctata 0.94 Gambusia_puncticulata 1.00 Gambusia_rhizophorae 1.00 Gambusia_sexradiata 0.98 Gambusia_vittata 1.00 Gambusia_wrayi 0.96 Gambusia_yucatana 0.96 Gambusia_zarskei 0.86 Gasterosteus_aculeatus 1.00 Girardinichthys_multiradiatus 1.00 Girardinichthys_viviparus 0.91 Girardinus_creolus 0.12 Girardinus_metallicus 0.11 Girardinus_microdactylus 0.11 Glaridichthys_falcatus 0.11 Glaridichthys_uninotatus 0.11 Goodea_atripinnis 1.00 Goodea_gracilis 1.00 Heterandria_bimaculata 1.00 Heterandria_formosa 1.00 Heterandria_jonesi 1.00 Heterophallus_milleri 1.00 Heterophallus_rachovii 0.95 Hubbsina_turneri 0.84 Ictalurus_punctatus 0.43 Ilyodon_furcidens 1.00 Ilyodon_whitei 1.00 Ilyodon_xantusi 1.00 Jenynsia_lineata 0.53 Jenynsia_multidentata 0.53 Jordanella_floridae 1.00 Limia_caymanensis 1.00 Limia_dominicensis 1.00 Limia_garnieri 1.00 Limia_heterandria 0.80 Limia_melanogaster 1.00 Limia_nigrofasciata 1.00

Limia_perugiae 1.00 Limia_sulfurophila 1.00 Limia_tridens 1.00 Limia_vittata 1.00 Limia_zonata 1.00 Lucania_goodei 1.00 Lucania_parvae 1.00 Micropoecilia_bifurca 1.00 Micropoecilia_obscura 1.00 Micropoecilia_parae 1.00 Micropoecilia_picta 1.00 Micropoecilia_reticulata 1.00 Micropoecilia_wingei 1.00 Monopterus_albus 0.54 Neoheterandria_elegans 1.00 Neoheterandria_tridentiger 0.64 Neotoca_bilineata 0.87 Notemigonus_crysoleucas 1.00 Oncorhynchus_mykiss 1.00 Oryzias_latipes 0.57 Oxyzygonectes_dovii 1.00 Pamphorichthys_hollandi 1.00 Phallichthys_amates 1.00 Phallichthys_pittieri 1.00 Phallichthys_quadripunctatus 0.80 Phallichthys_tico 1.00 Phalloceros_caudimaculatus 1.00 Phalloptychus_januarius 0.66 Poecilia_butleri_MR04 1.00 Poecilia_catemaconis 1.00 Poecilia_caucana 1.00 Poecilia_chica 1.00 Poecilia_gilli 1.00 Poecilia_latipinna 1.00 Poecilia_latipunctata 1.00 Poecilia_mexicana_mex 1.00 Poecilia_orri 1.00 Poecilia_petenensis_Campeche 1.00 Poecilia_salvatoris 1.00 Poecilia_sphenops 1.00

Poecilia_sulphuraria 1.00 Poecilia_velifera_MP737 1.00 Poecilia_vivipara 1.00 Poeciliopsis_baenschi 0.08 Poeciliopsis_balsas 0.11 Poeciliopsis_catemaco 0.05 Poeciliopsis_elongata 0.23 Poeciliopsis_fasciata 0.07 Poeciliopsis_gracilis 0.05 Poeciliopsis_hnilickai 0.05 Poeciliopsis_infans 1.00 Poeciliopsis_latidens 0.07 Poeciliopsis_lucida 0.37 Poeciliopsis_monacha 0.10 Poeciliopsis_occidentalis 0.37 Poeciliopsis_paucimaculata 0.24 Poeciliopsis_pleurospilus 0.05 Poeciliopsis_presidionis 0.07 Poeciliopsis_prolifica 0.37 Poeciliopsis_retropinna 0.23 Poeciliopsis_scarlii 0.07 Poeciliopsis_turneri 0.07 Poeciliopsis_turrubarensis 0.07 Poeciliopsis_viriosa 0.10 Priapella_compressa 1.00 Priapella_intermedia 1.00 Priapella_olmecae 1.00 Priapichthys_annectens 1.00 Profundulus_punctatus 1.00 Pseudopoecilia_festae 1.00 Quintana_atrizona 1.00 Rivulus_hartii 1.00 Scolichthys_greenwayi 0.66 Scolichthys_iota 0.66 Semotilus_atromaculatus 1.00 Skiffia_bilineatus 0.88 Skiffia_francesae 1.00 Skiffia_lermae 1.00 Skiffia_multipunctata 1.00 Takifugu_rubripes 1.00

Tetraodon_nigroviridis 1.00 Tomeurus_gracilis 0.64 Valencia_hispanica 1.00 Xenodexia_ctenolepis 0.62 Xenoophorus_captiva 0.91 Xenophallus_umbratilis 1.00 Xenotaenia_resolanae 0.90 Xenotoca_eiseni 1.00 Xenotoca_melanosoma 0.95 Xenotoca_variatus 1.00 Xiphophorus_alvarezi 1.00 Xiphophorus_andersi 0.88 Xiphophorus_birchmanni 1.00 Xiphophorus_clemenciae 1.00 Xiphophorus_continens 1.00 Xiphophorus_cortezi 1.00 Xiphophorus_couchianus 0.88 Xiphophorus_evelynae 1.00 Xiphophorus_gordoni 0.88 Xiphophorus_hellerii 1.00 Xiphophorus_maculatus 1.00 Xiphophorus_malinche 1.00 Xiphophorus_mayae 1.00 Xiphophorus_meyeri 0.88 Xiphophorus_milleri 0.90 Xiphophorus_montezumae 0.98 Xiphophorus_monticolus 1.00 Xiphophorus_multilineatus 1.00 Xiphophorus_nezahualcoyotl 1.00 Xiphophorus_nigrensis 1.00 Xiphophorus_pygmaeus 0.92 Xiphophorus_signum 1.00 Xiphophorus_variatus 1.00 Xiphophorus_xiphidium 0.90 Zeus_faber 1.00 Zoogoneticus_quitzeoensis 1.00 Zoogoneticus_tequila 1.00

Supplementary Table 4. Disc peck data per species for each coloured disc. Total numbers of pecks are reported for females (F) and males (M). Because of the small number of pecks, these data were not used to interpret any behavioural (e.g., foraging) responses to the coloured discs.

Genus_species sex red orange yellow green blue brown black white Ameca splendens F 0 0 2 0 0 0 0 0 Ameca splendens M 0 0 2 0 0 0 0 0 Ataeniobius toweri F 0 0 0 0 0 0 0 0 Ataeniobius toweri M 0 0 0 0 0 0 0 0 Gambusia affinis1 F 0 0 0 0 0 0 0 0 Gambusia punctata F 0 0 3 0 0 0 0 0 Gambusia punctata M 0 4 4 0 0 0 0 0 Girardinus metallicus F 0 2 0 1 0 0 0 0 Girardinus metallicus M 0 0 0 1 0 2 0 0 Limia perugiae F 0 0 2 3 0 1 1 0 Limia perugiae M 1 0 2 4 0 1 0 0 Limia vittata F 1 6 2 0 0 0 0 0 Limia vittata M 0 1 1 0 0 0 0 0 Oryzias latipes F 0 0 0 0 0 0 0 0 Oryzias latipes M 5 0 0 0 0 0 0 0 Poecilia mexicana F 0 0 0 0 0 0 0 0 Poecilia mexicana M 0 0 0 2 0 0 0 0 Poeciliopsis gracilis F 0 0 0 0 0 0 0 0 Poeciliopsis gracilis M 0 0 0 12 0 0 2 0 Skiffia bilineata F 0 0 0 0 0 0 0 0 Skiffia bilineata M 0 0 0 0 0 0 0 0 Xiphophorus andersi F 0 2 1 0 0 0 0 0 Xiphophorus andersi M 0 0 0 0 0 0 0 0 Xiphophorus couchianus F 1 0 1 1 0 3 0 0 Xiphophorus couchianus M 1 0 0 0 0 0 0 0 Xiphophorus mayae F 5 3 6 2 0 0 1 0 Xiphophorus mayae M 2 3 1 0 0 0 0 0 1male data were not collected for Gambusia affinis because there were not enough individuals