Of Eyes and Gut Microbiomes in Nicaraguan Cichlid Fishes – Convergent Diversification at Different Levels of Biological Organization

Of eyes and gut microbiomes in Nicaraguan cichlid fishes – convergent diversification at different levels of biological organization

Dissertation submitted for the degree of Doctor of Natural Sciences

Presented by

Andreas Härer

at the

Faculty of Sciences Department of Biology

Date of the oral examination: 24.09.2018 First supervisor: Prof. Dr. Axel Meyer Second supervisor: Prof. Dr. Cameron Ghalambor

Konstanzer Online-Publikations-System (KOPS) URL: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-72sv5vlomhtp5

Sit down before fact as a little child, be prepared to give up every conceived notion, follow humbly wherever and whatever abysses nature leads, or you will learn nothing. Thomas Henry Huxley

Table of contents

Table of contents ...... iii List of Figures ...... vi List of Tables ...... vi Summary ...... vii Zusammenfassung ...... x General Introduction ...... 1 Chapter I ...... 6 Abstract ...... 6 Introduction ...... 7 Methods ...... 10 Rearing conditions ...... 10 Opsin expression analysis ...... 12 Predicted sensitivities of single and double cones ...... 13 Thyroid hormone signaling ...... 14 Results ...... 14 Ontogenetic changes in cone opsin expression ...... 14 dio2 and dio3 expression as a proxy for Thyroid hormone signaling...... 16 Effects of light environment on cone opsin expression ...... 17 Discussion ...... 18 Chapter II ...... 23 Abstract ...... 23 Impact Summary ...... 24 Introduction ...... 25 Methods ...... 28 Sample collection ...... 28 Library preparation & Illumina Sequencing...... 29 Quality control, transcript assembly and mapping ...... 29 Sequence analyses...... 29 Gene expression analyses ...... 30 Results ...... 31 Discussion ...... 36 Conclusion ...... 39 Chapter III ...... 41 Abstract ...... 41 Introduction ...... 42 iii

Methods ...... 44 Sample collection ...... 44 Trophic analysis ...... 44 Library preparation & Illumina Sequencing...... 44 Sequence processing ...... 45 Gut microbiota analysis ...... 45 Results ...... 46 Diet differentiation among Midas cichlids ...... 46 Gut microbiome differentiation across habitats ...... 47 Gut microbiome differentiation within crater lakes ...... 50 Genetic bases of gut microbiome differentiation ...... 51 Discussion ...... 52 Gut microbiome differentiation across habitats ...... 52 Gut microbiome differentiation within crater lakes ...... 54 Genetic bases of gut microbiome differentiation ...... 55 Chapter IV ...... 57 Abstract ...... 57 Introduction ...... 58 Methods ...... 60 Study area and sample collection ...... 60 DNA analysis ...... 61 Results ...... 61 Geographic distribution ...... 61 Genetic diversity ...... 62 Discussion ...... 64 Habitat alteration and destruction ...... 65 Invasion by non-native species...... 65 Hybridization between genetically distinct populations ...... 67 Political issues concerning the Nicaragua Canal ...... 67 General Discussion ...... 69 References ...... 74 Acknowledgements ...... 88 General acknowledgements ...... 88 Chapter-specific acknowledgements ...... 89 Chapter I ...... 89 Chapter II ...... 89

Chapter III ...... 89 Chapter IV ...... 89 Author contributions ...... 90 Chapter I ...... 90 Chapter II ...... 90 Chapter III ...... 90 Chapter IV ...... 90 Supplementary Material ...... 91 Chapter I ...... 91 Chapter II ...... 100 Chapter III ...... 110 Chapter IV ...... 111

List of Figures

Figure I.1...... 10

Figure I.2...... 11

Figure I.3...... 15

Figure I.4...... 16

Figure I.5...... 17

Figure II.1...... 27

Figure II.2...... 33

Figure II.3...... 34

Figure II.4...... 36

Figure III.1...... 43

Figure III.2...... 47

Figure III.3...... 48

Figure III.4...... 49

Figure III.5...... 51

Figure III.6...... 52

Figure IV.1...... 59

Figure IV.2...... 62

Figure IV.3...... 63

List of Tables

Table IV.1...... 64

Summary

The main topic of this dissertation is to investigate how organisms cope with different environmental conditions. This issue was tackled on different levels of biological organization, by studying the visual system within the life time of single individuals (developmental plasticity, Chapter I) and evolutionary change across multiple generations (Chapter II). Further, the evolution of holobionts, constituted by hosts and their associated gut microbiomes, was investigated (Chapter III). Eventually, the potentially adverse effects of rapid environmental change produced by anthropogenic action are illustrated (Chapter IV).

Chapter I investigates the contributions of ontogeny and phenotypic plasticity in producing phenotypic differences of visual sensitivities in two species of Midas cichlids, Amphilophus citrinellus from great lake Nicaragua and Amphilophus astorquii from crater lake Apoyo. Midas cichlids colonized crater lake Apoyo from great lake Nicaragua and both environments markedly differ in their ambient light conditions, because the light spectrum is enriched in shorter wavelengths in crater lake Apoyo. Accordingly, Midas cichlids from crater lake Apoyo are more sensitive towards light of shorter wavelengths, which is reflected by expression differences of cone opsin genes responsible for color vision. Here, cone opsin expression of these two species was analyzed at several stages of ontogeny (7 days post hatching, 14 days post hatching, 6 months, older than one year) and from different light conditions (blue, cold-white, warm-white, red) using quantitative real-time PCR (qPCR). Even though both species start with a visual phenotype sensitive to short wavelengths during early life and progress towards longer wavelength sensitivity throughout ontogeny, A. astorquii does not do it to the same degree as A. citrinellus. The visual system of adult A. astorquii resembles that of juveniles of A. citrinellus (which, based on knowledge about the colonization history of the lakes, constitutes the inferred ancestral phenotype). This suggests that adaptation to the new environment was mediated by paedomorphosis of the visual system. These observed differences could potentially be produced by varying levels of thyroid hormone (TH) that has been shown to affect cone opsin expression in other species. Here, the effect of TH on Midas cichlids’ cone opsin expression was validated, and it was demonstrated that A. astorquii has lower circulating TH levels than the source population from the great lakes, suggesting this as a potential mechanism involved in the adaptation to the crater lake’s photic environment. Moreover, cone opsin expression is known to be affected by the prevailing light conditions. Hence, exposing fishes of both species to different light treatments revealed adaptive phenotypic plasticity of cone opsin expression during early development. However, at an age of 6 months, only A. astorquii from crater lake Apoyo changed cone opsin expression in response to light treatments, suggesting that phenotypic plasticity has evolved and may have facilitated adaptation to the novel light environment of the crater lake.

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In Chapter II, the question raised is whether the deterministic aspect of adaptive phenotypic evolution favors convergence across seven different cichlid species that colonized the same novel environment. The visual system of cichlids happens to be excellent to tackle this question since there is a good understanding of the phenotypic effects of molecular changes and also of the light conditions in Nicaragua’s aquatic habitats. By analyzing retinal transcriptomes using RNA-Seq, coding sequences of cone opsins as well as expression levels of cone opsins and cyp27c1 - a gene involved in chromophore usage that affects visual sensitivity - were obtained from populations of seven Nicaraguan cichlid species from three distinct environments (river, great lake, crater lake). Overall, species showed convergent phenotypic changes in visual sensitivity which were mainly produced by differential expression of cone opsins rather than by alterations in their coding sequences. This shows that in our study system regulatory changes are more important than structural changes during adaptation to novel light conditions. The observed changes occurred largely in the same direction and adaptively as predicted based on knowledge about the respective light environments. Rearing specimens under common conditions revealed a certain level of phenotypic plasticity, however, cone opsin expression patterns appeared to have a genetic component and most likely evolved after colonizing novel environments. Notably, while phenotypically shifting in the same direction, differences among species were maintained across habitats, suggesting that species’ ecologies are important for determining visual sensitivities. One very interesting point is that the expression patterns of cone opsins that produced these convergent changes varied among species. These results illustrate that convergence on a phenotypic level can be brought about by non-convergent molecular mechanisms.

As a next step, Chapter III explores whether adaptation to novel environments also results in the evolution of holobionts, in this case constituted by Midas cichlids and their associated gut microbiomes. Specifically, this chapter investigates how the environment as well as trophic ecology shape Midas cichlids’ gut microbiomes. Composition of the gut microbiome has been shown to be strongly affected by diet, but also host genetics appears to play an important role. The adaptive radiation of Midas cichlids is very young and, hence, genetic divergence among species is low. However, Midas cichlids living in sympatry within small crater lakes occupy distinct trophic niches. Gut microbiomes of multiple Midas cichlid species were analyzed by sequencing the V4 region of the 16S rRNA gene. This revealed that gut microbiomes differ among species from distinct environments but also among species within crater lakes. Further, divergence of gut microbiomes is correlated with trophic ecology and, notably, two independently evolved limnetic species that occupy similar ecological niches show convergent changes of their gut microbiomes. Since interspecific differences in the composition of gut microbiomes are maintained when fish are reared under common conditions, this provides evidence that host genetics may to some extent affect the composition of gut

viii microbiomes, even under low levels of genetic divergence as in the very young species complex of Midas cichlids.

Chapter IV evaluates the possible effects of a proposed large-scale infrastructure project, the construction of an interoceanic canal, on the freshwater fauna of Nicaragua. If constructed, the Nicaragua Canal will connect two distinct drainage basins and irreversibly change several aquatic ecosystems. Only one third of freshwater fish species are shared among the two drainage basins whereas one third exclusively occurs in either of the two. This emphasizes on the huge threat of biotic homogenization once the two drainage basins will be connected. Further, mitochondrial DNA (cytb) of multiple populations from a diverse set of teleost species across the affected areas was analyzed. For most species, populations from the distinct drainage basins are genetically differentiated whereas within the same drainage basin, genetic differentiation is commonly low. Bringing these differentiated and locally adapted populations into contact through the Nicaragua Canal will most likely cause an extensive loss of genetic diversity. Further, species were detected in areas where they were not known to exist. Taken together, the results of Chapter IV demonstrate that the construction of this interoceanic canal will have adverse effects on Nicaragua’s freshwater biodiversity.

Zusammenfassung

Das Hauptthema dieser Dissertation besteht darin zu untersuchen wie Organismen mit unterschiedlichen Umweltbedingungen zurechtkommen. Dieses Thema wird hierbei auf verschiedenen Ebenen biologischer Organisation angegangen, indem das visuelle System im Laufe des Lebens von einzelnen Individuen (Entwicklungsplastizität, Kapitel I) als auch evolutionäre Veränderungen über mehrere Generationen (Kapitel II) erforscht werden. Zudem wird die Evolution von Holobionten, bestehend aus einem Wirt und dessen mikrobieller Darmflora, untersucht (Kapitel III). Schließlich zeigen wir auf wie abrupte Umweltveränderungen, bedingt durch menschliche Handlungen, negative Auswirkungen auf die Biodiversität haben können. (Kapitel IV).

Kapitel I untersucht die Bedeutung von Ontogenese und phänotypischer Plastizität für phänotypische Unterschiede der visuellen Sensitivität zwischen zwei Arten von Midas Buntbarschen, Amphilophus citrinellus aus dem Nicaraguasee und Amphilophus astorquii aus dem Kratersee Apoyo. Ausgehend vom Nicaraguasee besiedelten Midas Buntbarsche den Kratersee Apoyo. Die zwei Seen unterscheiden sich deutlich in ihren Lichtverhältnissen, da das Lichtspektrum im Kratersee Apoyo an kürzeren Wellenlängen angereichert ist. Daher sind Midas Buntbarsche aus dem Kratersee Apoyo empfindlicher gegenüber Licht kürzerer Wellenlängen, was sich in Unterschieden der Genexpression von Zapfen-Opsinen, die für das Farbsehen verantwortlich sind, widerspiegelt. In dieser Studie wird die Expression von Zapfen-Opsinen dieser beiden Arten in mehreren Stadien der Ontogenese (7 Tage nach Schlüpfen, 14 Tage nach Schlüpfen, 6 Monate, älter als ein Jahr) und unter verschiedenen Lichtbedingungen (blau, kalt-weiß, warm-weiß, rot) mit Hilfe von quantitativer Echtzeit-PCR (qPCR) untersucht. Obwohl beide Arten zunächst einen visuellen Phänotyp haben, der für kürzere Wellenlängen empfindlich ist, und dieser sich während der gesamten Ontogenese zu einer längeren Wellenlängenempfindlichkeit entwickelt, erfolgt dies bei A. astorquii nicht im gleichen Maße wie bei A. citrinellus. Das visuelle System von adulten Fischen der Art A. astorquii ähnelt dem von jugendlichen Fischen von A. citrinellus (wobei es sich, basierend auf unserem Wissen über die Kolonisationsgeschichte der Seen, um den ursprünglichen Phänotypen handelt). Diese Ergebnisse legen nahe, dass die Anpassung an die neue Umgebung durch eine Paedomorphose des visuellen Systems hervorgerufen wurde. Die beobachteten Unterschiede zwischen den Arten könnten möglicherweise durch unterschiedliche Spiegel des Schilddrüsenhormons (TH) hervorgerufen werden, von denen gezeigt wurde, dass sie die Expression von Zapfen-Opsinen bei anderen Arten beeinflussen. Der Effekt von TH auf die Expression von Zapfen-Opsinen wird hier in Midas Buntbarschen validiert. Zudem wird gezeigt, dass A. astorquii einen niedrigeren Spiegel von zirkulierendem TH als die ursprüngliche Population aus dem Nicaraguasee hat, was wiederum darauf hindeutet, dass dies eine Anpassung an die Lichtbedingungen des Kratersees ist. Darüber hinaus ist bekannt, dass die Expression

x von Zapfen-Opsinen von den vorherrschenden Lichtbedingungen beeinflusst wird. Wenn Fische beider Arten daher verschiedenen Lichtbedingungen ausgesetzt werden, zeigen diese adaptive phänotypische Plastizität der Expression von Zapfen-Opsinen während früher Entwicklungsstadien. In einem Alter von 6 Monaten verändert sich hingegen nur noch die Expression von Zapfen-Opsinen in A. astorquii aus dem Kratersee Apoyo als Reaktion auf die unterschiedlichen Lichtbedingungen. Dies deutet darauf hin, dass sich phänotypische Plastizität selbst im Laufe der Evolution verändert hat, möglicherweise um die Anpassung an die neuartige Lichtumgebung des Kratersees zu erleichtern.

In Kapitel II wird die Frage aufgeworfen, ob der deterministische Aspekt von adaptiver phänotypischer Evolution konvergente Veränderungen bei sieben verschiedene Buntbarscharten begünstigt, welche dieselbe neue Umwelt besiedelten. Das visuelle System von Buntbarschen eignet sich hervorragend um diese Frage anzugehen, da ein gutes Verständnis über die phänotypischen Auswirkungen molekularer Veränderungen als auch über die Lichtverhältnisse in den aquatischen Habitaten Nicaraguas vorherrscht. Durch die Analyse von retinalen Transkriptomen mit RNA-Seq werden kodierende Sequenzen von Zapfen-Opsinen sowie Expressionslevel von Zapfen-Opsinen und cyp27c1 - einem Gen, das die Verwendung von Chromophoren reguliert und somit die Sehempfindlichkeit beeinflusst - von Populationen sieben nicaraguanischer Buntbarscharten aus drei verschiedenen Umgebungen (Fluss, Großer See, Kratersee) bestimmt. Insgesamt zeigen die untersuchten Arten konvergente phänotypische Veränderungen in der visuellen Sensitivität, die hauptsächlich durch veränderte Expression von Zapfen-Opsinen, aber nicht durch Veränderungen in ihren codierenden Sequenzen, erzeugt werden. Dies zeigt, dass in den von uns untersuchten Arten regulatorische Veränderungen von größerer Bedeutung für die Anpassung an neue Lichtverhältnisse sind als strukturelle Veränderungen. Die beobachteten Veränderungen treten weitgehend in der gleichen Richtung auf und sind zudem adaptiv, wie es aufgrund unseres Wissens über die jeweiligen Lichtumgebungen vorhergesagt wurde. Die Aufzucht von Fischen unter identischen Bedingungen zeigt ein gewisses Maß an phänotypischer Plastizität, jedoch scheint das Expressionsmuster von Zapfen- Opsinen eine genetische Komponente aufzuweisen und evolvierte höchstwahrscheinlich erst nach der Besiedelung neuer Umgebungen. Während die visuelle Sensitivität phänotypisch konstant in die gleiche Richtung verschoben ist, werden Unterschiede zwischen den Arten in den unterschiedlichen Umgebungen beibehalten, was darauf hindeutet, dass die Ökologie der Arten für die Bestimmung der visuellen Sensitivität wichtig ist. Ein sehr interessanter Punkt ist, dass die Expressionsmuster der Zapfen-Opsine, die diese konvergenten Änderungen erzeugen, zwischen den Arten variieren. Diese Ergebnisse zeigen, dass konvergente Evolution auf einer phänotypischen Ebene durch nicht konvergente molekulare Mechanismen hervorgerufen werden kann.

Als nächstes wird in Kapitel III untersucht, ob Anpassung an neue Umgebungen auch zur Evolution von Holobionten führt, welche in diesem Fall aus Midas Buntbarschen und deren xi mikrobieller Darmflora gebildet werden. Konkret untersucht dieses Kapitel, wie die Umwelt und die trophische Ökologie der Fische die mikrobielle Darmflora von Midas Buntbarschen beeinflussen. Es wurde gezeigt, dass die Zusammensetzung der mikrobiellen Darmflora stark von der Ernährung beeinflusst wird, aber auch der genetische Hintergrund des Wirts scheint eine wichtige Rolle zu spielen. Die adaptive Radiation der Midas Buntbarsche ist sehr jung und daher sind die genetischen Unterschiede zwischen den Arten gering. Midas-Buntbarsche, die sympatrisch in kleinen Kraterseen vorkommen, besetzen innerhalb der Seen unterschiedliche trophische Nischen. Die mikrobielle Darmflora mehrerer Arten von Midas Buntbarschen wird hier durch Sequenzierung der V4 Region des 16S rRNA Gens analysiert. Dies zeigt, dass sich die mikrobielle Darmflora zwischen Arten aus verschiedenen Umgebungen, aber auch zwischen Arten innerhalb desselben Kratersees unterscheidet. Darüber hinaus korreliert die Divergenz der mikrobiellen Darmflora mit der trophischen Ökologie der Fische. Zwei limnetische Fischarten, welche sich unabhängig voneinander entwickelten aber ähnliche ökologische Nischen besetzen, zeigen konvergente Veränderungen ihrer mikrobiellen Darmflora auf. Interspezifische Unterschiede in der Zusammensetzung der mikrobiellen Darmflora bleiben bei der Aufzucht von Fischen unter identischen Bedingungen erhalten. Dies ist ein Beleg dafür, dass der genetische Hintergrund des Wirts die Zusammensetzung der mikrobiellen Darmflora bis zu einem gewissen Grad kontrolliert, auch im jungen Artenkomplex der Midas Buntbarsche, innerhalb dessen nur geringe interspezifische genetische Unterschiede bestehen.

Kapitel IV evaluiert die möglichen Auswirkungen eines geplanten großangelegten Infrastrukturprojekts, des Baus eines interozeanischen Kanals, auf die Süßwasserfauna Nicaraguas. Falls es zum Bau kommt, wird der Nicaragua Kanal zwei getrennte Flusssysteme verbinden und mehrere aquatische Ökosysteme irreversibel verändern. In dieser Studie zeigen wir, dass nur ein Drittel aller Arten von Süßwasserfischen in den beiden Flusssystemen vorkommen, während jeweils ein Drittel ausschließlich in einem der beiden Flusssysteme vorkommt. Dies zeigt die große Gefahr von biotischer Homogenisierung, sobald die beiden Flusssysteme miteinander verbunden werden. Zudem wird mitochondriale DNA (cytb) mehrerer Populationen unterschiedlicher Arten von Teleostei in den betroffenen Gebieten analysiert. Bei den meisten Arten sind Populationen aus den getrennten Flusssystemen genetisch differenziert, während innerhalb eines Flusssystems die genetische Differenzierung im Allgemeinen gering ist. Wenn diese differenzierten und lokal angepassten Populationen durch den Nicaragua Kanal künstlich in Kontakt gebracht werden, wird dies höchstwahrscheinlich zu einem Verlust an genetischer Vielfalt führen. Zusätzlich haben wir Arten in Gebieten entdeckt, in denen sie bisher nicht bekannt waren. Zusammenfassend zeigen unsere Ergebnisse, dass der Bau des interozeanischen Kanals negative Auswirkungen auf die Süßwasser- Biodiversität Nicaraguas haben wird.

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General Introduction

The eye, a fascinating organ that evolved multiple times in diverse groups of animals, has intrigued generations of biologists ever since Charles Darwin. At first sight, Darwin appeared to struggle with the idea that an eye “could have been formed by natural selection” and to him it seemed “absurd in the highest degree” (Darwin 1859). This quote is regularly taken out of context, mainly by opponents of Darwin’s theory of natural selection. However, when one continues to read this passage of the Origin of Species, Darwin argues that a complex and well-adapted organ such as the eye can indeed evolve through a series of small, beneficial changes that are inherited from one generation to the next (Darwin 1859). Of all things, the eye has become a prime model system in evolutionary biology to study a wide range of topics, including adaptation, convergence and phenotypic plasticity. The beauty and elegance of Darwin’s theory lies in its simplicity that complex organs and organisms can evolve solely by the forces of natural selection. Selection acting on variants within populations that are differentially well- adapted to local conditions and the emergence of barriers to gene flow have, over large periods of time, led to the outstanding abundance of biodiversity on our planet today. However, biodiversity is currently at risk on an alarming scale, predominantly, due to direct and indirect effects of human actions (Carroll et al. 2014). In the Anthropocene, it is therefore of highest importance and urgency to better comprehend how organisms cope with environmental change (Zalasiewicz et al. 2011). This will not only help us to preserve biodiversity but also improve our general understanding of the mechanisms that allow adaptive evolution.

When organisms are exposed to unfamiliar environmental conditions, may it be due to environmental change or colonization of novel habitats, it is crucial to approximate local fitness optima to allow for population persistence. One way to achieve this is by phenotypic plasticity, the ability of one genotype to produce more than one phenotype in response to changes in the environment (Pigliucci et al. 2006, Ghalambor et al. 2007). Phenotypic plasticity occurs within the life span of a single organism and, hence, can instantaneously increase chances to prevail under new conditions (Price et al. 2003, Torres-Dowdall et al. 2012). This is of particular importance when populations are established in previously unoccupied geographical settings. Newly established populations commonly consist of only a small number of individuals subsampled from a larger population. This entails drastically reduced population size and genetic diversity, a process called founder effect (Mayr 1942). In populations with low effective population sizes and low genetic variability, efficiency of natural selection is reduced, which impairs genetic adaptation (Charlesworth 2009). In such settings, phenotypic plasticity may aid in population persistence, especially during early stages after the colonization event. However, this holds only true if the direction of phenotypic plasticity is aligned with the direction of selection pressures in the given environment, i.e., if there is adaptive phenotypic plasticity (Ghalambor et al. 2007). In contrast, plasticity can also be non-adaptive which may cause 1 maladapted phenotypes and decrease an individual’s survival chances. However, the roles of adaptive and non-adaptive plasticity in promoting adaptive evolutionary change remain a matter of debate (Ancel 2000, Ghalambor et al. 2007, Paenke et al. 2007, Ghalambor et al. 2015, Fischer et al. 2016). Some argue that adaptive plasticity would shield organisms from natural selection and thereby prevent adaptive evolution whereas others claim that adaptive plasticity allows population persistence under novel environmental conditions, thereby enabling future evolutionary change.

Moreover, phenotypic plasticity itself has a genetic basis and, thus, can evolve. There are two major theories concerning the fate of plasticity and how it can affect phenotypic evolution. The first was proposed by James Mark Baldwin in the late 19th century (Baldwin 1896, Simpson 1953). Baldwin’s theory is based on the assumption that organisms can plastically respond to environmental input. Accordingly, the organisms that can get the closest to fitness optima via plasticity will be the ones that preferentially survive and reproduce and, hence, will be favored by natural selection (Baldwin 1896, Crispo 2007). Based on this, Baldwin concluded that plasticity would act as a driving force of adaptive evolution. Over time, levels of phenotypic plasticity might stay the same or they could even increase if the most plastic individuals are constantly selected. By contrast, in the mid-20th century Conrad Hal Waddington proposed that selection would act to canalize environmentally induced phenotypes so that they become manifested even when the initial stimulus is absent (Waddington 1961). Waddington referred to this process as genetic assimilation (Waddington 1953). As a consequence, levels of phenotypic plasticity would decrease over time and the phenotype assimilated by the genotype.

Another important source of phenotypic variation is development. Modifying developmental trajectories can produce substantial diversity in adult phenotypes (Shapiro et al. 2004, West-Eberhard 2005), particularly, by altering timing and rate of development, a mechanism termed heterochrony (Gould 1977, West-Eberhard 2003). Two major ways by which heterochrony can alter adult phenotypes is either by retaining juvenile traits into later life (paedomorphosis) or by adding extra stages to the end of the ancestral developmental trajectory (peramorphosis) (Kollman 1885, Gould 1977). To study how developmental modifications produce novel phenotypes, one can compare ontogenetic trajectories among closely related species. However, to infer the direction of change, one needs to further know which phenotype is the ancestral one and which is derived. Nicaraguan Midas cichlids provide an outstanding system to investigate the roles of ontogeny and phenotypic plasticity in producing adaptive phenotypes, as shown in Chapter I. If such ontogenetic changes have a genetic basis and are adaptive, development can promote adaptive evolutionary change (Raff and Wray 1989, Klingenberg 1998). But, developmental changes can also be affected by environmental variation, and phenotypic plasticity is thought to be particularly powerful during an organism’s development (West- Eberhard 2005).

A universal concept of evolutionary biology states that gradual changes in the genetic composition of populations over successive generations, brought about by natural selection acting on heritable genetic variation, can lead to adaptive phenotypic evolution. The current technological advances in DNA/RNA sequencing allow us to identify the underlying genetic mechanisms that produce adaptive phenotypes, thereby vastly improving our understanding on how evolution proceeds. One intriguing question is whether regulatory or structural changes are more important in promoting adaptive evolution (Carroll 2005, Hoekstra and Coyne 2007, Wray 2007). Another question is whether the same or different molecular mechanisms underlie adaptive evolution of similar phenotypes in independent populations or species. However, in many cases it remains challenging to conclusively demonstrate that evolutionary change is indeed adaptive, but scenarios in which multiple species independently and repeatedly evolved similar phenotypes strongly suggest that such phenotypes are the result of local adaptation. Such phenotypic convergence (Arendt and Reznick 2008) has been demonstrated in a diverse set of organisms (Reznick et al. 1996, Rundle et al. 2000, Colosimo et al. 2005, Mahler et al. 2013), including cichlid fishes (Elmer et al. 2010, Elmer et al. 2014). Consequently, when there is knowledge about environmental parameters as well as the adaptive value of certain phenotypes, one can, in theory, predict the direction of evolutionary change. This issue of how predictable evolution is, already occupied the famous Stephen Jay Gould almost 30 years ago (Gould 1989). The visual system of cichlids constitutes a system that allows us to investigate these questions, as shown in Chapter II.

In recent years, it has been proposed that not only an individual but rather a holobiont represents a unit of selection (Zilber-Rosenberg and Rosenberg 2008, Gilbert et al. 2012, Singh et al. 2013). A holobiont is defined as a host (commonly a large organism, i.e., animal, plant or fungus) and all its stably associated microbes (Brucker and Bordenstein 2013a). The entity of all microbes is called the microbiome. In vertebrates, microbial communities associated with the gut of their host are of particular importance for several aspects of their host’s well-being (Backhed et al. 2004, Human Microbiome Project 2012, Brucker and Bordenstein 2013a, Guinane and Cotter 2013), including nutrient metabolism and immune functions (Turnbaugh et al. 2006, Turnbaugh et al. 2009b, Lathrop et al. 2011). Still, it remains disputed which factors principally shape the composition of the gut microbiome. Whereas diet appears to be the main determinant (Bolnick et al. 2014, Baldo et al. 2017, Smits et al. 2017, Rothschild et al. 2018), host genetics has also been proposed to play a role (Benson et al. 2010, Spor et al. 2011, Goodrich et al. 2014). Evolutionary history has been shown to be correlated with differences in gut microbiome composition, a theory named phylosymbiosis (Brucker and Bordenstein 2012b, Brooks et al. 2016). However, signals of phylosymbiosis could be confounded by differences in species’ ecologies (e.g., trophic ecology) or environmental factors (e.g., physical and chemical parameters of habitats), hence, the determining factors for gut microbiome divergence often remain in the dark. Cichlid fishes are an outstanding model to investigate gut microbiome

3 diversification because they have a remarkable variability in trophic ecology and low levels of genetic divergence (Muschick et al. 2012, Brawand et al. 2014). A recent study that compared multiple species of African cichlid fishes, demonstrated that gut microbiome divergence is associated with diet across two independent adaptive radiations, emphasizing on the importance of trophic ecology in shaping the gut microbiome (Baldo et al. 2017). Yet, it remains unresolved whether differentiation and specialization of gut microbiomes are cause or consequence of trophic diversification, particularly, since the African cichlid species studied have long divergence times (Baldo et al. 2017). Nicaraguan Midas cichlids also show diversification in trophic ecology (Elmer et al. 2014), but diverged much more recently, most likely less than 2,000 generations ago (Kautt et al. 2016). Since genetic divergence among species is relatively low (Kautt et al. 2016), Midas cichlids prove to be ideal for studying gut microbiome dynamics during early stages of species divergence and for asking whether divergence of trophic ecology or the gut microbiome comes first, a topic visited in Chapter III.

The aquatic landscape of Nicaragua provides an excellent setting to address all of the aforementioned questions. Several rivers, two great lakes and multiple small crater lakes are inhabited by a diverse set of freshwater fishes, and, particularly, cichlid fishes are commonly found in most aquatic environments (Bussing 1976, ERM 2015). Interestingly, several species of cichlids occur in the same aquatic habitats, allowing comparative studies of phenotypic adaptation to the prevalent environmental conditions. The lakes of Nicaragua vary substantially in size (great lakes are larger), depth (great lakes are more shallow), biodiversity (higher in great lakes) as well as turbidity (great lakes are more turbid) (Barluenga and Meyer 2010, Torres-Dowdall et al. 2017b). Further, there is good knowledge about the geological history of the lakes. The two great lakes (Lake Managua and Lake Nicaragua) are located in the Nicaragua Depression and are estimated to have formed approximately less than 1 million years ago (Bussing 1976). The numerous small and isolated crater lakes are the result of volcanic activity and formed in the calderas of volcanoes after they became inactive, starting around 24,000 years ago (Kutterolf et al. 2007). This data on the geological history of the lakes, combined with investigations of demographic histories of different freshwater species allows to infer the colonization history of Nicaraguan lakes. Population genomic studies of different cichlid species revealed that the crater lakes were colonized from the two great lakes (Kautt et al. 2016, Franchini et al. 2017). Concerning colonization of the two great lakes, this must have occurred via rivers since cichlids colonized Central America long before the great lakes formed (Hulsey et al. 2010). One of the most studied groups of Nicaraguan cichlids are the Midas cichlids, a young adaptive radiation with currently thirteen described species (Barluenga and Meyer 2010, Elmer et al. 2014). The phylogeography of Midas cichlids is well-understood and they are now a renowned study system for speciation with gene flow, rapid adaptation to novel environmental conditions as well as phenotypic plasticity in ecologically relevant traits (Barluenga and Meyer 2004, Barluenga et al. 2006, Elmer et al. 2014, Machado- Schiaffino et al. 2014, Machado-Schiaffino et al. 2017, Torres-Dowdall et al. 2017b). Along these lines,

4 the visual system has emerged as an excellent model to study how these fishes adapt to novel light conditions (Torres-Dowdall et al. 2015, Torres-Dowdall et al. 2017b), which is discussed in more detail in Chapter I and Chapter II.

However, cichlid fishes and in fact Nicaragua’s extraordinary freshwater biodiversity in general might be at risk. In 2013, a Chinese consortium, the Hong Kong Nicaragua Canal Development Group (HKND) was granted a concession by the Nicaraguan government to build an interoceanic shipping canal. The so-called Nicaragua Canal is supposed to connect the Caribbean Sea and the Pacific Ocean for shipping traffic (ERM 2014). The plans to build this canal have been criticized repeatedly (Huete- Pérez et al. 2013, Meyer and Huete-Pérez 2014, Huete-Perez et al. 2015) and a panel of Nicaraguan and international scientists pointed out that ecological concerns were not properly taken into account during the decision making process (Huete-Pérez et al. 2016). Such large-scale infrastructure projects pose immense threats to whole ecosystems, as discussed in Chapter IV. These threats include modification and destruction of habitats, biotic homogenization, and introduction of nonnative species (McKinney and Lockwood 1999, Olden et al. 2005, Rahel 2007, Pelicice and Agostinho 2009). Humans are modifying natural environments at ever increasing scopes and speed with adverse effects on biodiversity all over the planet (Carroll et al. 2014). As a result, human-mediated modifications of the environment have become a major driver of evolutionary change (Palumbi 2001, Davies and Davies 2010). Thus, it is crucial to improve our understanding of how organisms cope with rapid alterations of the environment, a topic that represents the main subject throughout this dissertation.

Chapter I

Rapid adaptation to a novel light environment: the importance of ontogeny and phenotypic plasticity in shaping the visual system of Nicaraguan Midas cichlid fish (Amphilophus citrinellus spp.)

Andreas Härer, Julián Torres-Dowdall & Axel Meyer

MOLECULAR ECOLOGY, Volume 26, Issue 20, Pages 5582-5593, 2017

Abstract

Colonization of novel habitats is typically challenging to organisms. In the initial stage after colonization, approximation to fitness optima in the new environment can occur by selection acting on standing genetic variation, modification of developmental patterns or phenotypic plasticity. Midas cichlids have recently colonized crater lake Apoyo from great lake Nicaragua. The photic environment of crater lake Apoyo is shifted towards shorter wavelengths compared to great lake Nicaragua and Midas cichlids from both lakes differ in visual sensitivity. We investigated the contribution of ontogeny and phenotypic plasticity in shaping the visual system of Midas cichlids after colonizing this novel photic environment. To this end, we measured cone opsin expression both during development and after experimental exposure to different light treatments. Midas cichlids from both lakes undergo ontogenetic changes in cone opsin expression but visual sensitivity is consistently shifted towards shorter wavelengths in crater lake fish, which leads to a paedomorphic retention of their visual phenotype. This shift might be mediated by lower levels of thyroid hormone in crater lake Midas cichlids (measured indirectly as dio2 and dio3 gene expression). Exposing fish to different light treatments revealed that cone opsin expression is phenotypically plastic in both species during early development, with short and long wavelength light slowing or accelerating ontogenetic changes, respectively. Notably, this plastic response was maintained into adulthood only in the derived crater lake Midas cichlids. We conclude that the rapid evolution of Midas cichlids’ visual system after colonizing crater lake Apoyo was mediated by a shift in visual sensitivity during ontogeny and was further aided by phenotypic plasticity during development.

Introduction

Adaptation to changing environmental conditions, e.g. after colonization of a novel habitat, is crucial for population persistence and diversification and can result in population differentiation in the absence of gene flow (reviewed in Kawecki and Ebert 2004). Hence, it is crucial to understand the sources of phenotypic variation to further our understanding of how organisms adapt to such new environmental factors. These sources of variation include, amongst others, developmental shifts (e.g., heterochrony) and environmentally induced changes (i.e., phenotypic plasticity) which can play an important role in local adaptation (Price et al. 2003, Torres-Dowdall et al. 2012, Schaum and Collins 2014, Ghalambor et al. 2015). However, the contribution of development and phenotypic plasticity to adaptive evolution has commonly received little attention, but incorporating these could help us to better comprehend how organisms adapt to novel and potentially challenging environments.

Developmental changes during ontogeny obviously represent an important source for phenotypic diversity and possibly evolutionary diversification (West-Eberhard 2003, Shapiro et al. 2004, Tanaka et al. 2009). Adult phenotypes can develop by either of two processes: they are already present in the adult form early in larval or juvenile developmental stages (direct development) or are the result of changes during development (ontogenetic change). By comparing developmental trajectories among closely related species, one can make inferences about shifts in the relative timing and rate of developmental processes, termed heterochrony (Gould 1977, West-Eberhard 2003). Heterochrony has the potential to bring about novel adult phenotypes by maintaining juvenile characteristics into adulthood (paedomorphosis) or by undergoing extended development beyond the previous state (peramorphosis) (Kollman 1885, Gould 1977). Gould argues that variation in rate and timing of developmental processes provides raw material upon which natural selection can act to produce new phenotypic variants. Hence, heterochrony might facilitate evolutionary change (Gould 1977, Raff and Wray 1989, Klingenberg 1998).

Another mechanism by which different phenotypes can arise is phenotypic plasticity, the ability of one genotype to produce more than one phenotype when exposed to different environmental conditions (Pigliucci et al. 2006). The importance of phenotypic plasticity in promoting adaptive evolutionary change is still highly contentious (e.g., Ancel 2000, Price et al. 2003, Paenke et al. 2007, Ghalambor et al. 2015), particularly, after colonization of novel habitats with fitness optima differing from the original habitat. In such scenarios, population sizes are commonly small, restricting a population’s potential to genetically adapt to the new environment due to low levels of standing genetic variation (Nei et al. 1975) and, consequently, low efficiency of natural selection (Charlesworth 2009). Some evolutionary biologists argue that adaptive phenotypic plasticity might allow populations to persist in novel environments and, hence, represents a crucial step during adaptive evolution (Losos et al. 2000, Pigliucci and Murren 2003, Ghalambor et al. 2007). Already in the late 19th century, J. M.

Baldwin proposed a theory concerned with the effects of phenotypic plasticity on evolutionary change (Baldwin 1896), later termed the “Baldwin effect” (Simpson 1953). According to Baldwin, adaptive phenotypic plasticity allows populations to persist and increases survival and reproduction of the most plastic individuals (Crispo 2007). Over time, selection on standing genetic variation within populations could subsequently lead to evolutionary change in the direction of the initial plastic response (Crispo 2007, Schneider and Meyer 2017). During this process, the reaction norm, i.e. the extent of phenotypic change among environments, would either stay the same or even increase due to a selective advantage of the most plastic individuals (Nussey et al. 2005, Schneider and Meyer 2017). In contrast, others have claimed that non-adaptive plasticity, which moves environmentally induced phenotypes away from a local optimum, promotes rapid evolutionary change by increasing strength of selection (Ghalambor et al. 2015).

Cichlid fishes are famous for their phenotypic diversity (e.g., Fryer and Iles 1972, Meyer 1993) and have been investigated also in terms of phenotypic plasticity. Some lineages more than others are phenotypically plastic in their ecologically crucial tooth morphology (Meyer 1987, Schneider et al. 2014), but also hypertrophied lips, that play an essential role both in terms of natural and sexual selection (Machado-Schiaffino et al. 2014, Machado-Schiaffino et al. 2017). Besides, several other traits were shown to be particularly plastic in cichlids (reviewed in Schneider and Meyer 2017). In fact, it has recently been suggested that this degree of plasticity coupled with genetic assimilation might have contributed to the evolutionary success of cichlids (Schneider and Meyer 2017).

Heterochrony and phenotypic plasticity have also been shown to contribute to shaping the visual system of cichlid fishes (Carleton et al. 2008, Hofmann et al. 2010, O'Quin et al. 2011a, Dalton et al. 2015). Cichlids are an interesting group to study visual ecology due to a high variability of the visual system among species (Carleton et al. 2016), which enables studying molecular mechanisms promoting adaptive evolutionary change (Terai et al. 2006, Hofmann and Carleton 2009, Hofmann et al. 2009). In cichlids, the retina is highly organized and is composed of single and double cones responsible for color vision, as well as dim-light sensitive rods (Fernald 1981). Vision is initiated when a photon reaches a visual pigment, which is composed by a light-sensitive chromophore covalently bound to an opsin protein, in the photoreceptors (Wald 1968, Yokoyama 2000, Ebrey and Koutalos 2001). Structural changes and expression differences of genes coding for opsin proteins were shown to be the determinants of most of cichlids’ variation in visual sensitivity. Cichlids possess seven cone opsins absorbing light at different wavelengths of the light spectrum, although they commonly express three of these at the same time. One short wavelength sensitive opsin (sws1, sws2a or sws2b) is expressed in single cones, and two types of longer wavelength sensitive opsins (rh2b, rh2aβ, rh2aα and lws) are expressed in double cones (Carleton and Kocher 2001, Parry et al. 2005). It has been proposed that heterochronic shifts in opsin expression are involved in shaping some of the impressive phenotypic variation observed in adult visual sensitivity among cichlid species (Carleton et al. 2008). 8

Moreover, opsin expression is phenotypically plastic in some Lake Malawi cichlids, with varying degrees of plasticity among species (Hofmann et al. 2010). In most cases the underlying molecular mechanisms shaping the visual system remain elusive; but a few studies have shown that thyroid hormone (TH) signaling regulates cone opsin identity during development (Ng et al. 2001), controls cone opsin expression (Glaschke et al. 2011) and shifts visual sensitivity towards longer wavelengths (Roberts et al. 2006). To summarize, previous work on African cichlids suggests that both heterochrony and phenotypic plasticity of the visual system might have facilitated ecological diversification. However, the evolutionary significance of these two mechanisms in initial periods after colonizing novel habitats remains to be investigated.

The Neotropical Midas cichlids of the Amphilophus cf. citrinellus species complex represent an ideal model system to study the divergence of the visual system in different light environments. Midas cichlids inhabit multiple lakes on the western coast of Nicaragua (e.g., Barlow 1976, Barluenga and Meyer 2004, Barluenga and Meyer 2010). These include ancient great lakes, Lake Managua and Lake Nicaragua, with an age of approximately 500,000 years and numerous, relatively young crater lakes. The oldest of these crater lakes is Lake Apoyo with an estimated age of 23,000 years (Bice 1985). Midas cichlids colonized Lake Apoyo from Lake Nicaragua approximately 1,700 generations ago (Kautt et al. 2016). The light spectrum in the turbid great lakes is red-shifted compared to the clear crater lakes (Fig. I.1, white inset). Recent work has shown that Midas cichlids possess, like African cichlids, seven cone opsins (Torres-Dowdall et al. 2017b). This study further revealed that species from different lakes (Lake Nicaragua and Lake Apoyo) differ in photopigment sensitivity (Torres-Dowdall et al. 2017b). These differences in visual sensitivity can mostly be explained by differential cone opsin expression among species from the two lakes (Fig. I.1). Since these differences are maintained when fish are reared in a common light environment, species-specific cone opsin expression patterns are assumed to be genetically determined (Torres-Dowdall et al. 2017b). However, it is still unknown whether cone opsin expression changes during development and what impact the environment has on shaping the visual system.

Fig. I.1: Midas cichlids from Lake Nicaragua (A. citrinellus) and Lake Apoyo (A. astorquii) differ in cone opsin expression. The photic environment in Lake Nicaragua (Great lake, white inset) is red-shifted compared to Lake Apoyo (Crater lake, white inset). Cone opsin expression and relative irradiance data were obtained from Torres-Dowdall et al. (2017).

Here, we experimentally investigated the contributions of development and phenotypic plasticity to the evolution of visual system divergence among closely related Midas cichlid species. Particularly, we determined whether cone opsin expression changes during ontogeny or develops directly and whether color vision is phenotypically plastic in these species. To this end, we investigated (i) cone opsin expression and thyroid hormone levels during ontogeny of great lake Midas cichlids (Amphilophus citrinellus from Lake Nicaragua) and crater lake Midas cichlids (Amphilophus astorquii from Lake Apoyo), and (ii) determined whether different light treatments affect cone opsin expression in three developmental stages.

Methods

Rearing conditions

Previous results showed differences in cone opsin expression between adult fish (older than one year) of A. citrinellus from Lake Nicaragua (further referred to as great lake Midas cichlids) and A. astorquii from Lake Apoyo (further referred to as crater lake Midas cichlids) (Fig. I.1; Torres-Dowdall et al. 2017b). Here, we investigated gene expression in second-generation laboratory born descendants of wild caught fish during ontogeny and as a response to different light treatments. All fish used in this study (and their parental generation) were reared under standard fluorescence lamp illumination (Fig. S.I.1) at the animal facility of the University of Konstanz, Germany, before they were used for this study. Our first goal was to determine cone opsin expression during ontogeny of the two studied species,

10 therefore, samples were collected at the ages of 7 days, 14 days, 6 months and older than one year (n = 6 for the first three time points and n = 8 for fish older than one year). With the first two time points, we aimed at obtaining information about early stages of development. We chose the 6 months time point because opsin expression in African cichlids reaches the adult phenotype (Carleton et al. 2008, O'Quin et al. 2011a) and many cichlid species are sexually mature at this age. To verify whether Midas cichlids, like African cichlids, already have an adult phenotype at the age of 6 months, we included data recently published on opsin expression profiles of adult Midas cichlids (older than one year; Torres- Dowdall et al. 2017b).

Fig. I.2: To investigate phenotypic plasticity, specimens were randomly assigned to one of four light treatments, each covering a particular part of the light spectrum (A). Fish were either introduced to the light treatment shortly before hatching (7 days and 14 days groups) or at the age of 6 months (B). Light treatments lasted either for 7 days (7 days group) or 14 days (14 days and 6 months group).

Our second goal was to determine whether cone opsin expression is affected by the light environment. For this experiment, cone opsin expression was analyzed at the same three time points as described above: 7 days, 14 days and 6 months. Specimens were randomly divided into four groups and exposed to different light conditions (n = 6/treatment) covering a particular range of the light

11 spectrum (Fig. I.2A). One 6 months old A. astorquii specimen from the blue light treatment represented an extreme outlier and, thus, was excluded from all statistical analyses. Two treatments had broad light spectra simulating natural divergence across habitats (cold-white, warm-white) and two had narrow spectra at the extremes of the visible light spectrum (blue, red; Fig. I.2A). Tanks were illuminated with wavelength-specific LEDs (Cree Inc., Durham, North Carolina; Table S.I.1). Fish were either introduced to the respective light conditions immediately after hatching (for the 7 days and 14 days groups) or at the age of 6 months (for the 6 months group) and treatments lasted either one week (for the 7 days group) or two weeks (for the 14 days and 6 months group) using a 12:12 light:dark cycle (Fig. I.2B). Relative quantum catch was calculated for each cone opsin in the four light treatments (Fig. S.I.2). All specimens were sacrificed by applying an overdose of MS-222 (400 mg/l) and subsequent cutting of the vertebral column (in 6 months old fish). For the 7 days and 14 days groups, whole fish were used; for 6 month old fish, only retinas were dissected and further used for expression analyses. Opsins are predominantly expressed in the retina, hence, extracting RNA from the whole body of larvae will not affect the analysis of cone opsin expression. All tissues were stored in RNAlater (Sigma-Aldrich, St. Louis, Missouri) until RNA extraction.

Opsin expression analysis

RNA was extracted using the commercial RNeasy Mini Kit (Qiagen, Hilden, Germany), RNA quality was checked by visually examining 28S and 18S rRNA bands after agarose gel electrophoresis (1% agarose gel) and the respective concentrations were measured using the Colibri Microvolume Spectrometer (Titertek Berthold, Pforzheim, Germany). 500 ng – 1 µg of total RNA were reverse transcribed with a first-strand cDNA synthesis kit according to the manufacturer’s protocol (GoScriptTM Reverse Transcription System; Promega, Madison, Wisconsin) and diluted to a final concentration of 5 ng/µl. Quantitative Real-Time PCR (qPCR) was performed to quantify proportional expression levels for six cone opsin genes (sws1, sws2b, sws2a, rh2b, rh2aβ and lws). rh2aα is not expressed in adult Midas cichlids (Torres-Dowdall et al. 2017b) and we confirmed this in all fish used in this study. We found that rh2aα expression did not exceed 1% of total rh2a expression in any sample, hence, it was not included in our analyses. After an initial denaturation step (95°C for 2 min), qPCR reactions were run for 40 cycles (95°C for 15 s, 60°C for 1 min) (CFX96TM Real-Time System; Bio-Rad Laboratories, Hercules, California) using specifically designed primers (Table S.I.2) for which amplification efficiencies were previously determined (Table S.I.3). qPCR was followed by a melt curve analysis to test for amplification specificity. The two primers of each pair were designed to be located on adjacent exons to detect possible genomic DNA contamination. Specificity of amplification was further checked by Sanger sequencing of purified PCR products on an ABI 3130xl Genetic Analyzer (Life Technologies, Carlsbad, USA). Expression levels of opsin genes were quantified with three technical replicates and mean threshold cycle (Ct) values were used for further analysis. If reactions did not amplify and, hence, no 12

Ct values could be determined, Ct values for the respective genes were assigned a value of 40 for calculations of proportional cone opsin expression. Although this represents an overestimation of gene expression, opsins with a Ct value of 40 never exceeded 1-10% of total cone opsin expression. The total volume for each reaction was 20 µl consisting of 2 µl cDNA (5 ng/µl), 0.5 µl forward primer (10 µM), 0.5 µl reverse primer (10 µM), 10 µl GoTaq qPCR Master Mix, 2x (GoTaq qPCR Master Mix; Promega,

Madison, Wisconsin) and 7 µl Nuclease-free H2O. Proportional opsin expression was determined for each specimen by calculating the proportion of each cone opsin (Ti) relative to the total cone opsin expression (Tall), but note that RH2Aα expression is not included in this calculation as described above, after Fuller et al. (2004) using the following equation:

퐶푡푖 푇푖 (1/((1 + 퐸푖) )) = 퐶푡 푇푎푙푙 ∑(1/((1 + 퐸푖) 푖))

Ei represents the primer efficiency for primer i and Cti is the critical cycle number for gene i (the proportional expression values of the six cone opsins add up to 1 for each specimen).

Predicted sensitivities of single and double cones

Predicted single and double cone sensitivities for each specimen were calculated according to

Hofmann et al. (2009) using λmax values previously reported for Midas cichlids (Torres-Dowdall et al. 2017b): SWS2B = 425 nm, SWS2A = 456 nm, RH2B = 472 nm, RH2Aβ = 517 nm and LWS = 560 nm.

Because λmax of SWS1 is not known for Midas cichlids, the Nile Tilapia value was used instead (360 nm) (Spady et al. 2006). Predicted visual sensitivities were calculated for each fish by taking into account peaks of maximum light absorption and proportional expression (PE) of each cone opsin using the following equations for single cones 푃퐸(푆푊푆1) ∗ 360 + 푃퐸(푆푊푆2퐵) ∗ 425 + 푃퐸(푆푊푆2퐴) ∗ 456 휆 (SC) = 푚푎푥 푃퐸(푆푊푆1) + 푃퐸(푆푊푆2퐵) + 푃퐸(푆푊푆2퐴) and double cones 푃퐸(푅퐻2퐵) ∗ 472 + 푃퐸(푅퐻2퐴훽) ∗ 517 + 푃퐸(퐿푊푆) ∗ 560 휆 (DC) = 푚푎푥 푃퐸(푅퐻2퐵) + 푃퐸(푅퐻2퐴훽) + 푃퐸(퐿푊푆) By conducting Shapiro-Wilk tests, we found that our data was not normally distributed (Shapiro and Wilk 1965). Hence, to test whether age and species identity (ontogenetic change experiment) or age and light conditions (phenotypic plasticity experiment) affect predicted single and double cone sensitivities, we used a non-parametric equivalent to a two-way ANOVA, the Scheirer-Ray-Hare test (Scheirer et al. 1976). We tested all linear regression models for heteroscedasticity with Breusch-Pagan tests (Breusch and Pagan 1979). Only the model for predicted A. citrinellus single cone sensitivity during ontogeny showed heteroscedasticity (p = 0.02), however diagnostic plots did not reveal any aberrant patterns (Fig. S.I.3). All p values were corrected for multiple comparisons using FDR. Statistical analyses were performed in R (R Core Team 2015).

Thyroid hormone signaling

Current methods to determine thyroid hormone (TH) levels in fish require small but significant amounts of blood serum (Noyes et al. 2014), which cannot be obtained from cichlid larvae (14 days old larvae are approximately 7 mm long and weigh 2 mg). Thus, we had to rely on proxies for TH levels. Two genes have been commonly used for this purpose, the iodothyronine deiodinase type 2 (dio2) and type 3 (dio3). Dio2 and Dio3 enzymes are involved in regulation of TH signaling by transforming the inactive prohormone thyroxine (T4) into the active form triiodothyronine (T3) and by catabolizing T3, respectively (Bianco and Larsen 2005, Jarque and Pina 2014). More importantly, TH is known to downregulate dio2 and upregulate dio3 gene expression; this has been shown in multiple teleost species (Garcia et al. 2004, Johnson and Lema 2011, Marlatt et al. 2012) as well as other vertebrates (reviewed in Gereben et al. 2008, Glaschke et al. 2011). To validate this in our study system, we experimentally exposed one week old Midas cichlid larvae to thyroxine (T4; Sigma-Aldrich, St. Louis, MO) for a time period of two weeks. T4 was added directly to the water to a final concentration of 300 µg/l. T4 treatment did significantly downregulate expression of dio2 (Fig. S.I.4; Wilcoxon rank-sum test, p = 0.008) and upregulate dio3 (p = 0.002). Thus, we conclude that dio2 and dio3 are valid proxies for TH. Hence, we measured gene expression of dio2 and dio3 for specimens in the ontogenetic change experiment (i.e., a broad light spectrum environment) to determine if TH levels can explain some of the variation observed between the studied Midas cichlid species. Expression levels were normalized with the geometric mean of two housekeeping genes (HKGs), gapdh2 and imp2. Stability of HKGs was confirmed by calculating the Pearson Correlation Coefficient in R (R Core Team 2015). Across all samples, we find a strong and significant correlation (r = 0.827, p < 0.001) between gene expression of gapdh2 and imp2. Normalization was done using the following equation:

2퐶푡퐻퐾퐺 푅푄푖 = 2퐶푡푖 where CtHKG is the geometric mean of two housekeeping genes, Cti is the critical cycle number for gene i. As we were interested in comparing relative expression of the two deiodinase genes in the two Midas cichlid species at the three different ages, we report (for each age group separately) dio2 and dio3 expression levels relative to that seen in great lake Midas cichlids, the ancestral population. Hence, crater lake Midas cichlid dio2 and dio3 expression values can be regarded as fold change compared to great lake Midas cichlids. Relative expression was compared between species for each age group using Wilcoxon rank-sum tests. All p values were corrected for multiple comparisons using FDR.

Results

Ontogenetic changes in cone opsin expression

To determine the contribution of ontogenetic changes to the divergence in visual sensitivity observed in Nicaraguan Midas cichlids, we analyzed predicted visual sensitivities and cone opsin expression

14 during ontogeny of great lake Midas cichlids (A. citrinellus) and crater lake Midas cichlids (A. astorquii). Both species underwent ontogenetic changes in predicted visual sensitivities, shifting from short wavelengths to longer wavelengths, both in single cones and double cones (Fig. I.3A and B).

Fig. I.3: Predicted sensitivity (A & B) and proportional expression of single and double cone (C & D) opsins during ontogeny in great lake Midas cichlids and crater lake Midas cichlids. The effects of species identity and age were tested with Scheirer-Ray-Hare tests and significance values (FDR corrected) are indicated. Proportional expression values for each cone opsin are relative to overall cone opsin expression, i.e. expression values sum up to 1 for each individual (across C & D).

Nonetheless, throughout ontogeny, great lake Midas cichlids (Fig. I.3A and B, black bars) were sensitive towards longer wavelengths compared to crater lake Midas cichlids. No differences in the rate of progression were found among species (age x species interaction was non-significant in all cases). The ontogenetic changes in predicted sensitivities were due to differences in the set of opsins expressed at different ages (Fig. I.3C and D). In single cones, expression of the UV sensitive sws1 and the blue sensitive sws2a decreased and increased with age, respectively (Fig. I.3C). However, sws1 expression seemed to be higher and sws2a expression lower in crater lake Midas cichlids compared to great lake Midas cichlids. The violet sensitive sws2b was only expressed at early stages (7 and 14 days) in great lake Midas cichlids but expression was highest at 6 months in crater lake Midas cichlids and decreased thereafter. In contrast to great lake Midas cichlids, both sws2a and sws2b were expressed in crater lake Midas cichlids older than one year.

In double cones, expression of the blue-green sensitive rh2b was restricted to younger ages and decreased with time in both species. Expression levels appeared to be overall higher in crater lake Midas cichlids and at the age of 6 months, it was only expressed in this species (Fig. I.3D). Expression of the red sensitive lws increased with age in both species and expression was seemingly higher in great lake Midas cichlids. The green sensitive rh2aβ had highest expression levels in early ages and decreased afterwards. Expression of rh2aβ was apparently higher in crater lake Midas cichlids and these differences were most pronounced in fish older than one year (mean proportional expression of 10% in great lake Midas cichlids and 47% in crater lake Midas cichlids).

Fig. I.4: Relative expression of dio2 and dio3, two genes involved in the thyroid hormone signaling pathway. Expression data was normalized with the geometric mean of two housekeeping genes. Expression levels are reported relative to the mean of great lake Midas cichlids, the ancestral population, for each time point, i.e. crater lake Midas cichlid expression values are fold change compared to great lake Midas cichlids. Normalized expression values were compared between species at each time point (Wilcoxon rank sum test, ** P < 0.01, FDR corrected). dio2 and dio3 expression as a proxy for Thyroid hormone signaling

We measured dio2 and dio3 gene expression during ontogeny as proxies for thyroid hormone (TH) levels as transcription of these genes is down- and upregulated by TH, respectively (Fig. S.I.4). In great lake Midas cichlids, expression of dio2 (negatively regulated by TH) was significantly lower compared to crater lake Midas cichlids in 7 days (Wilcoxon rank-sum test, p = 0.007) and 14 days old fish (p = 0.007) (Fig. I.4). In contrast, dio3 (positively regulated by TH) showed higher expression in great lake Midas cichlids at 7 days (p = 0.006) and 14 days (p = 0.006) compared to crater lake Midas cichlids. In retinas of 6 months old fish, expression of dio2 and dio3 showed the same pattern as at earlier ages, however, interspecific expression differences were not statistically significant (Fig. I.4). Taken together, these results suggest that TH levels are higher in great lake Midas cichlids than in crater lake Midas cichlids during early stages of development.

Effects of light environment on cone opsin expression

Fig. I.5: Plasticity in the predicted visual sensitivity for single (top row) and double cone opsins (bottom row) of great lake Midas cichlids (left column) and crater lake Midas cichlids (right column). The effects of light treatment and age were tested with Scheirer-Ray-Hare tests and significance values (FDR corrected) are indicated.

We determined the effects of an environmental cue (spectrum of ambient light) on the visual system of great lake Midas cichlids (from turbid Lake Nicaragua) and crater lake Midas cichlids (from clear Lake Apoyo) by analyzing predicted visual sensitivities under different light conditions (Fig. I.5). For both species, light treatment and age had highly significant effects on predicted sensitivities (Fig. I.5). Light treatments did affect the ontogenetic progression from short to long wavelength sensitivity. In the blue treatment, this progression was slowed down whereas in the red treatment it was accelerated. In great lake Midas cichlids, we also found a significant interaction between light treatment and age. This can be explained by the fact that light treatments did induce strong changes in predicted sensitivities during early stages of development but not at 6 months (Fig. I.5, left column). At this age, predicted sensitivities were much less variable in great lake Midas cichlids compared to earlier stages. In contrast, predicted sensitivities were still affected in crater lake Midas cichlids at the age of 6 months; single cone sensitivities were altered by the red treatment (Fig. I.5, right column).

Additionally, we determined proportional cone opsin expression in different light treatments (Fig. S.I.6). Both in single and double cones, the opsins most sensitive to the shortest wavelengths (sws1 and rh2b, respectively) appeared to show elevated expression in short wavelength treatments, but lower levels in the long wavelength treatments. The opposite was true for cone opsins most sensitive to the longest wavelengths (sws2a and lws in singles and double cones, respectively). Besides, expression of sws1, sws2a, rh2b and lws seemingly changed with age in both species and additionally, sws2b changed in crater lake Midas cichlids (Fig. S.I.6).

Discussion

Midas cichlids inhabit multiple lakes in Nicaragua and have been established as a model system to study early stages of divergence and sympatric speciation (Klingenberg et al. 2003, Barluenga et al. 2006, Elmer et al. 2010, Elmer et al. 2014). Midas cichlids colonized crater lake Apoyo from great lake Nicaragua around 1,700 generations ago (Kautt et al. 2016). These two lakes differ strongly in their photic environment with the irradiance spectrum in Lake Apoyo shifted towards shorter wavelengths compared to Lake Nicaragua (inset in Fig. I.1; Torres-Dowdall et al. 2017b). Recent work suggests that adult Midas cichlids from the two lakes differ in cone opsin expression patterns, consistent with differences in the photic environment (Fig. I.1). These interspecific differences are maintained when fish are reared in a common light environment, arguing for a genetic basis for the observed phenotypic divergence (Torres-Dowdall et al. 2017b). Due to their known phylogenetic history and very recent divergence among species, Midas cichlids represent an ideal system to study the role of ambient light in shaping the visual system at a very early stage of population divergence and the effects of development in producing interspecific differences. Since Midas cichlids colonized Lake Apoyo from Lake Nicaragua, visual sensitivity in great lake Midas cichlids represents the ancestral state and crater lake Midas cichlids have a derived phenotype.

In this study, we show that great lake Midas cichlids and crater lake Midas cichlids undergo an ontogenetic change in cone opsin expression from short to long wavelength sensitivity. Many species of African cichlids have a direct development of cone opsin expression, especially those inhabiting the African great lakes (Lake Malawi and Lake Victoria; Carleton et al. 2016). However, riverine lineages of cichlids commonly show ontogenetic changes in cone opsin expression similar to that observed in Midas cichlids (Carleton et al. 2008, Carleton et al. 2016). The developmental progression in visual sensitivity might be an adaptation to shifts in food sources. These fish commonly have a UV-sensitive phenotype in larvae but UV sensitivity is lost later in life. Larvae typically feed on zooplankton and the presence of UV sensitive cones improves foraging performance in zebrafish and rainbow trout larvae (Novales Flamarique 2013, 2016). Midas cichlid skin reflects UV light (Torres-Dowdall et al. 2017a) and since larvae feed on the mucus of their parents’ skin, UV sensitivity might enhance the ability to find their parents and thereby improve feeding success. The ontogenetic change observed in lacustrine

Neotropical Midas cichlids has also, as mentioned above, been described in few species of riverine African cichlids whereas the majority of cichlid species from the African great lakes show direct development. Hence, our results illustrate that the ontogenetic change in cone opsin expression might be a more common phenotype and is not restricted to riverine species.

Developmental shifts might represent a source of phenotypic variation in adult cone opsin expression profiles (Spady et al. 2006, Carleton et al. 2008, Shand et al. 2008, O'Quin et al. 2011a, Matsumoto and Ishibashi 2016). Particularly, by comparing closely related species insights into developmental changes of the visual system can be obtained. Predicted visual sensitivities in recently diverged Midas cichlids are consistently shifted towards shorter wavelengths during development of the derived crater lake species compared to the ancestral species. However, the rate of ontogenetic change is similar in both species, hence, providing no evidence for a heterochronic shift, as defined by a change in relative rate of developmental progression (Rice 1997). Nonetheless, the progression of cone opsin expression towards long wavelength sensitivity is terminated early in crater lake Midas cichlids and, hence, adults of this species paedomorphically resemble juveniles of great lake Midas cichlids regarding their visual phenotype (compare opsin expression profiles of 6 months old great lake Midas cichlids and crater lake Midas cichlids older than one year in Fig. I.3). Apparently, after colonizing crater lake Apoyo, Midas cichlids shifted their visual sensitivity throughout ontogeny leading to a paedomorphic adult phenotype that is more sensitive to shorter wavelengths. This shift is most likely adaptive since the light environment in crater lake Apoyo is blue-shifted compared to great lake Nicaragua (Torres-Dowdall et al. 2017b). Interestingly, the observed differences in visual sensitivities of Midas cichlids evolved within less than 1,700 generations (Kautt et al. 2016), suggesting that the visual system of Midas cichlids can rapidly adapt to novel light environments by altering the developmental progression from short to longer wavelength sensitivity. A similar pattern, where adults show a paedomorphic phenotype, can be found in some cichlids from the African great lakes. In these lakes, some species do not undergo the ancestral developmental progression from short to long wavelength sensitivity, but retain either a short or medium wavelength sensitive visual system, emphasizing that ontogenetic changes might be a common mechanism underlying phenotypic divergence in both African and Neotropical cichlid fishes (Carleton et al. 2016).

Yet, the molecular mechanisms underlying this shift in cone opsin expression remain to be investigated, but thyroid hormone (TH) has been shown to shift visual sensitivity from short to long wavelengths in multiple species. TH signaling affects cone opsin identity during development of the model systems mice and zebrafish, but also other fish species such as rainbow trout and coho salmon (Ng et al. 2001, Cheng et al. 2009, Suliman and Flamarique 2014) and adult cone opsin expression in mice (Glaschke et al. 2011). The active form of the hormone, triiodothyronine (T3) is synthesized from the prohormone thyroxine (T4) by the enzyme iodothyronine deiodinase type 2 (Dio2), whereas the enzyme iodothyronine deiodinase type 3 (Dio3) catabolizes T3 (Bianco and Larsen 2005, Jarque and 19

Pina 2014). Knockdown of deiodinases in zebrafish causes morphological defects of the eye and disrupted visual function (Houbrechts et al. 2016). Dio3 deficient mice have degenerated cone cells, but deletion of Dio2 in this strain recovered a normal phenotype (Ng et al. 2017). These results emphasize the crucial role of TH and its regulation via deiodinases in eye development. Moreover, expression of the two deiodinases is controlled by TH via feedback-loops and increased levels of TH downregulate dio2 and upregulate dio3 gene transcription in Midas cichlids (Fig. S.I.4), other fishes (Garcia et al. 2004, Johnson and Lema 2011, Marlatt et al. 2012) and terrestrial vertebrates (Gereben et al. 2008, Glaschke et al. 2011). In developing mouse retinas, thyroid hormone inhibits expression of short wavelength sensitive cone opsins and activates expression of medium wavelength sensitive cone opsins, thereby causing a shift in visual sensitivity towards longer wavelengths (Roberts et al. 2006). We validated this in Midas cichlids, where treatment with TH increased relative expression of the cone opsins most sensitive to the longest wavelengths in single cones (sws2a) and double cones (lws; Fig. S.I.4). We hypothesized that the observed differences between great lake Midas cichlids and crater lake Midas cichlids might be caused by changes in TH levels during development. To this end, we used two deiodinase genes (dio2 and dio3) as proxies for TH levels (see Methods and Fig. S.I.4) and found that dio2 expression is significantly higher in developing crater lake Midas cichlids, while dio3 is significantly lower compared to great lake Midas cichlids (Fig. I.4). Moreover, there is some evidence that expression of dio2 (indicative of high TH levels) is positively correlated with a short wavelength sensitive cone opsin in single cones (sws2b), and expression of dio3 is positively correlated with more long wavelength sensitive cone opsins in single (sws2a) and double cones (rh2aβ and lws; Fig. S.I.5), which are commonly expressed in adult fish. These results suggest that circulating TH levels are lower in developing crater lake Midas cichlids compared to great lake Midas cichlids and that TH levels might affect cone opsin expression. The lower TH levels we inferred during development of crater lake Midas cichlids might have promoted the retention of short wavelength shifted sensitivity. This, in turn, could have contributed to the paedomorphic phenotype in adult crater lake Midas cichlids (Fig. I.3). However, further experiments are needed to validate these hypotheses.

We observed that the light environment affects the visual system of developing Midas cichlids. These differences were most pronounced when fish were exposed to light conditions at the extremes of the visible spectrum (Fig. I.5), but also broad light conditions resembling variation in the natural environment induced changes in cone opsin expression. Short wavelength shifted light caused a retention of the larval phenotype and a delay of the progression from short to long wavelength sensitivity (Fig. I.5), resulting in a paedomorphic visual phenotype (Gould 1977, Alberch et al. 1979, Rice 1997). In contrast, exposure to long wavelength shifted light caused a faster progression of cone opsin expression due to acceleration. Since in all cases, rate and timing of cone opsin expression are changed during development, the ambient light environment induced plastic heterochronic shifts in cone opsin expression (Gould 1977).

These plastic shifts in visual sensitivity as a response to different light treatments occurred in the direction we expected, i.e. fish in the blue and red light treatments expressed more short and long wavelength sensitive opsins, respectively. The shift in the blue light treatment resembles what has been observed in adult crater lake Midas cichlids with their short wavelength shifted phenotype (Torres-Dowdall et al. 2017b), supporting the idea that Midas cichlids are able to adaptively adjust their visual system to the prevalent light environment. The ability to change cone opsin expression patterns in a novel light environment might have facilitated colonization of crater lake Apoyo, emphasizing the importance of adaptive phenotypic plasticity.

The effects of phenotypic plasticity in evolution are a matter of debate (e.g., Ancel 2000, Price et al. 2003, Paenke et al. 2007, Ghalambor et al. 2015). Some claim that adaptive phenotypic plasticity might facilitate population persistence by moving organisms closer to a new adaptive peak (Robinson and Dukas 1999, Amarillo-Suarez and Fox 2006), thereby acting as a driving force for further evolutionary change (Baldwin 1896, Crispo 2007). Others have stated that non-adaptive plasticity promotes evolutionary change by moving environmentally induced phenotypes further away from adaptive peaks, thereby increasing strength of selection (Ghalambor et al. 2015). In this context, we argue that in Midas cichlids, adaptive phenotypic plasticity of the visual system has facilitated evolutionary change that led to species divergence after colonization of a novel light environment. Accordingly, high levels of phenotypic plasticity might have been advantageous in the early stages after colonizing crater lake Apoyo and enabled Midas cichlids to slow down the ontogenetic change of cone opsin expression leading to a shorter wavelength sensitive adult phenotype. Subsequently, selection on standing genetic variation could have led to genetic assimilation of the initially environmentally induced plastic response (Crispo 2007, Ghalambor et al. 2007), causing the interspecific differences in adult Midas cichlids (Torres-Dowdall et al. 2017b). Notably, after colonization of crater lake Apoyo, Midas cichlids formed a small adaptive radiation and five distinct genetic clusters have been reported, most probably referring to different species (Kautt et al. 2016). It has recently been proposed that high levels of phenotypic plasticity are correlated with evolutionary diversification of cichlid fishes, according to the flexible stem model (Schneider and Meyer 2017). The contribution of Midas cichlids’ plastic visual system to the adaptive radiation in crater lake Apoyo remains to be assessed.

Interestingly, we found that the ancestral great lake Midas cichlids have a reduced plastic response at the juvenile/adult stage (> 6 months) compared to earlier stages of development. In fact, the light treatments did not elicit any effect in the expression of opsin genes at this stage. However, when analyzing 6 months old derived Midas cichlids from crater lake Apoyo, we found that at this age, specimens retained the ability to plastically respond to the light environment (Fig. I.5, Fig. S.I.6). Thus, plasticity itself appears to have evolved after the colonization of crater lake Apoyo with its blue-shifted light environment. Although it will be necessary to test plasticity under different light conditions in adult fish, there is some evidence that plasticity might be retained also in adults as there are 21 differences in the expression levels of cone opsin genes between laboratory reared and wild caught fish. These differences tend to be higher in the derived crater lake populations than in the ancestral great lake Midas cichlids (Torres-Dowdall et al. 2017b). This pattern of increased plasticity in derived populations after the colonization of a new environment, as we see in crater lake Midas cichlids, is consistent with the “Baldwin effect” (Baldwin 1896, Crispo 2007, Torres-Dowdall et al. 2012); but inconsistent with models predicting genetic assimilation (e.g., Lande 2009). The retention of plasticity into juvenile/adult stages in the crater lake Midas cichlids is in agreement with our interpretation of these fish having a paedomorphic visual system compared to the source population from the turbid great lake. Thus, increased selection for a short wavelength sensitive phenotype in the clear water crater lake might have resulted in a correlated increase of plasticity. Alternatively, plasticity itself could have been selected for, if the crater lakes have a more variable light environment. This is a possible explanation given that light environment is expected to greatly vary with depth in the deep crater lakes. Nonetheless, this hypothesis remains to be tested.

To summarize, Midas cichlids tuned their visual system after colonizing crater lake Apoyo, which represents a novel light environment, by altering cone opsin expression compared to the ancestral great lake Midas cichlids. Our results show that the interspecific differences in adult cone opsin expression patterns are brought about by shifting visual sensitivity towards shorter wavelengths throughout development, possibly mediated by thyroid hormone signaling. Moreover, cone opsin expression is plastic in early development of Midas cichlids and changes according to the ambient light. Hence, phenotypic plasticity can be considered adaptive and might have, via interaction with development, facilitated colonization, population persistence and subsequently speciation in the novel environment of crater lake Apoyo. Remarkably, these changes evolved in a very short time frame of less than 1,700 generations, representing a case of exceptionally rapid adaptive evolution that was possibly aided by developmental phenotypic plasticity.

Chapter II

Convergent phenotypic evolution of the visual system via different molecular routes: how Neotropical cichlid fishes adapt to novel light environments

Andreas Härer, Axel Meyer & Julián Torres-Dowdall

Currently under review in EVOLUTION LETTERS

Abstract

How predictable is evolution? This remains a fundamental but contested issue in evolutionary biology. When independent lineages colonize the same environment, we are presented with a natural experiment that allows us to ask if genetic and ecological differences promote species-specific evolutionary outcomes or whether species phenotypically evolve in a convergent manner in response to shared selection pressures. If so, are the molecular mechanisms underlying phenotypic convergence the same? In Nicaragua, seven species of cichlid fishes concurrently colonized two novel photic environments. Hence, their visual system represents a compelling model to address these questions, particularly since the adaptive value of phenotypic changes is well-understood. By analyzing retinal transcriptomes, we found that differential expression of genes responsible for color vision (cone opsins and cyp27c1) produced rapid and mostly convergent changes of visual sensitivities. Notably, the differentially expressed genes that produced this convergence varied among species. Our findings further show that even when species differ substantially in ecology and genetic variation, adaptive phenotypes evolved deterministically in response to similar selective forces of the visual environment. However, similar phenotypic outcomes were produced by expression changes of different genes, illustrating non-convergence of molecular mechanisms.

Impact Summary

Almost 30 years ago, the famous paleontologist and evolutionary biologist Stephen J. Gould argued that contingency plays a dominant role in evolution and proposed that if we were to replay the tape of life, the world would look quite different and most likely lack humans. Others, such as Simon Conway Morris, have argued that evolution is deterministic and that human-like, intelligent beings are unavoidable evolutionary outcomes. This debate remains largely unsolved and while Gould’s view might hold true over long time scales, evidence is accumulating that over short time scales evolutionary change can actually be deterministic, in particular when natural selection is strong. Natural experiments, in which ecologically differentiated species colonized similar or, ideally, the same novel environment provide valuable insights into the roles of determinism and contingency in phenotypic evolution. In this study, we took advantage of such a natural experiment to ask whether multiple species show similar phenotypic change in their visual system in response to shared selection due to the concurrent colonization of new light environments. We focused particularly on the visual system since these environments differ dramatically in their light conditions and because there is a good understanding of the physics and chemistry of vision. This, in turn, enabled us to link molecular changes to phenotypic peculiarities. In our study species, expression changes of genes responsible for color vision caused adaptive and mostly convergent shifts in predicted visual sensitivities. However, the set of differentially expressed genes that caused these shifts varied among lineages. At the time scale observed in our system, phenotypic evolution appears to be deterministic, but we want to emphasize the importance of contingency in the underlying gene expression patterns. Thus, we conclude that both contingency and determinism are important factors in evolution and largely depend on the level of biological organization.

Introduction

What are the relative contributions of deterministic (natural selection) and stochastic (e.g., random mutations, genetic drift or environmental fluctuations) factors during evolution? Or, phrased differently, how predictable is evolution? This major question has been addressed at different levels of biological organization since convergent evolution was recognized to be omnipresent in all evolutionary lineages. Stephen J. Gould asked this in his famous thought experiment of “replaying life’s tape” (Gould 1989). He concluded that stochastic factors were predominant and would preclude predictability of evolutionary change (Gould 1989). Gould’s question remains unsolved and is as vexing and current today as it was almost 30 years ago (Conway Morris 2003, Losos 2017). Although it is merely theoretical, a simplified form could be addressed by assessing predictability of evolutionary change across different temporal and phylogenetic scales (Orgogozo 2015). Specifically, one could ask (i) how predictable phenotypic change is when ecologically differentiated lineages are exposed to similar environmental conditions, (ii) and more precisely, whether there is convergence among such lineages with respect to both direction and magnitude of change, and (iii) if the same molecular mechanisms, i.e., structural or regulatory changes of the same genes, underlie convergent changes.

Certain aspects of evolution are stochastic, including random mutations, genetic drift and environmental fluctuations. Yet, natural selection is deterministic and allows predicting evolutionary change when selective agents are known. Repeated evolution of similar phenotypic traits in independent lineages, termed convergence (sensu Arendt and Reznick 2008), provides strong evidence for natural selection. Examples of convergence are the repeated loss of armor plates in threespine sticklebacks (Colosimo et al. 2005), changes in body shape in Anolis lizards (Mahler et al. 2013), threespine sticklebacks (Schluter and McPhail 1992, Rundle et al. 2000) and cichlid fishes (Meyer 1990a, Elmer et al. 2010, Elmer et al. 2014) and life history evolution in guppies (Reznick and Endler 1982, Reznick et al. 1996). These cases, in which populations of the same, or closely related species, independently colonized similar environments and predictably and repeatedly diverged from the ancestral state have provided valuable insights into how adaptive evolution proceeds. However, hidden environmental heterogeneity might confine the extent of convergence (Fitzpatrick et al. 2014, Stuart et al. 2017). A compelling, but less frequently applied approach is to study convergent evolution of certain phenotypic traits in lineages that concurrently colonized the same environment (Rosenblum 2006, Rosenblum et al. 2017).

Still, identifying the molecular mechanisms underlying convergent phenotypic changes proves challenging, particularly since the phenotypic effects of molecular changes are rarely understood in non-genetic model systems. Further, adaptive evolution can result from non-synonymous substitutions in coding regions of genes (Hoekstra and Coyne 2007) or by changes in regulatory regions that modify gene expression patterns (Carroll 2005, Wray 2007), which might differ case by case. Since

25 regulation is more modular in its organization, mutations are less likely to have negative pleiotropic effects (Stern and Orgogozo 2008). Therefore, regulatory regions might harbor more standing genetic variation and selection on this (previously neutral) variation is thought to promote rapid adaptation (Stone and Wray 2001, Innan and Kim 2004, Barrett and Schluter 2008, Leder et al. 2015). This, in turn, predicts that during early stages of divergence, most phenotypic differences among lineages will be produced by regulatory rather than structural changes (Ghalambor et al. 2015, Leder et al. 2015). In the longer term, mutations altering protein structures could occur and get fixed, thus, both structural and regulatory differences are expected during later stages of divergence. In order to further comprehend these general evolutionary processes, we need to identify the mechanisms producing adaptive phenotypic changes.

The visual system represents a fascinating model for studying convergent adaptive evolution since (i) it is highly variable, (ii) the phenotypic effects of molecular changes are well-understood (reviewed in Bowmaker 2008), (iii) there is a good understanding of the adaptive value of visual phenotypes under certain environmental conditions, and (iv) – as in the case of the natural experiment in Nicaraguan lakes where multiple species have concurrently colonized novel photic environments – one can test how deterministic evolution is. Among vertebrates, cichlid fishes show a remarkably high visual system diversity (Carleton et al. 2016) and molecular mechanisms facilitating adaptive evolution have been studied extensively (Terai et al. 2006, Hofmann and Carleton 2009, Hofmann et al. 2009, Schulte et al. 2014, Torres-Dowdall et al. 2015, Hauser et al. 2017, Torres-Dowdall et al. 2017b). Color vision is particularly variable in cichlids and is mediated by visual pigments located in photoreceptor cells (cones) of the retina, which are composed of a light-absorbing chromophore that is covalently bound to a transmembrane opsin protein (Wald 1968, Yokoyama 2000, Ebrey and Koutalos 2001). Cichlids have two types of cone cells, single and double cones, which are characterized by expression of short (sws1, sws2b, sws2a) and medium to long wavelength sensitive cone opsins (rh2b, rh2aβ, rh2aα, lws), respectively (Fernald 1981, Carleton and Kocher 2001, Hofmann et al. 2009). Shifts in visual sensitivity are mainly produced by structural changes or differential expression of opsins (Terai et al. 2006, Hofmann et al. 2009, O'Quin et al. 2010, Torres-Dowdall et al. 2017b). Additionally, many aquatic vertebrates change visual sensitivity by differential use of vitamin A1 and A2 derived chromophores, which is mediated by the enzyme cyp27c1, a member of the cytochrome P450 family (Enright et al. 2015). In Nicaraguan Midas cichlids, expression of cyp27c1 is correlated with A1 and A2 based chromophore usage (Torres-Dowdall et al. 2017b). This high variability enabled cichlids to tune their visual system to a wide range of light conditions (reviewed in Carleton et al. 2016).

Several Neotropical cichlid species from Nicaragua have concurrently colonized novel photic environments (Fig. II.1) and, hence, prove ideal for studying convergent evolution of the visual system. These fishes inhabit the same environments; many rivers, two old great lakes (Lakes Nicaragua and Managua) and a number of crater lakes (Villa 1976), but vary substantially in size, habitat preference, 26 trophic level, and coloration (Table S.II.1). The two great lakes are located in the Nicaragua Depression and geological data suggests that the lake basin formed less than 1 Mya (Bussing 1976). Presumably shortly after their formation (approximately 500,000 years ago; Bussing 1976, Elmer et al. 2010), cichlids colonized these lakes from adjacent rivers (most likely including Río San Juan and Río Punta Gorda; Fig. II.1).

Fig. II.1: Map of Nicaragua showing all sampling locations including two rivers (Punta Gorda & San Juan), two great lakes (Nicaragua & Managua) and one crater lake (Xiloá). The great lakes were colonized from riverine environments around 500,000 years ago and crater lake Xiloá was colonized from great lake Managua, most likely less than 2,000 years ago (Kautt et al. 2016, Franchini et al. 2017). Rivers and great lakes are characterized by shallow and turbid water, crater lakes have much clearer, deeper water, which causes shifts in the ambient photic environment. Absolute irradiance measurements of downwelling light are shown for a depth of one meter. In the crater lake, overall levels of light are higher but are also more shifted towards shorter wavelengths. Light spectra of the great lake and river are very similar from 300 to 580 nm. At longer wavelengths, more light is present in the river compared to the great lake leading to a longer wavelength shifted light environment in the river.

More recently and in some cases less than 2,000 generations ago, cichlids further colonized numerous, very young (1,200 to 22,000 years; Kutterolf et al. 2007, Pardo et al. 2008) crater lakes from the two great lakes (Elmer et al. 2010, Kautt et al. 2016, Franchini et al. 2017). These environments differ considerably in their light conditions (Torres-Dowdall et al. 2017b); the rivers are shallow and turbidity varies strongly during the year associated with seasonality in precipitation, the great lakes are shallow and constantly turbid, whereas the crater lakes are considerably deeper and the water is

27 much clearer (Cole 1976, Elmer et al. 2010). As a result, the crater lake photic environment is shifted towards shorter wavelengths compared to the turbid great lakes (Fig. II.1). Accordingly, Midas cichlids (Amphilophus cf. citrinellus) adaptively shifted visual sensitivity towards absorbing light at shorter wavelengths in crater lakes (Torres-Dowdall et al. 2017b). These changes in cone opsin expression are largely genetically determined (Torres-Dowdall et al. 2017b), but phenotypic plasticity could also contribute to the observed differences among photic environments (Härer et al. 2017).

Since several ecologically distinct cichlid species followed the same colonization route, this natural experiment could help to understand how predictable evolution of the visual system is in response to shared environmental conditions and to what extent it is affected by ecological differences among species. Based on our knowledge on photic environments and on Midas cichlids’ visual system (Torres-Dowdall et al. 2017b), we predicted that cichlids changed their visual sensitivities after colonizing novel photic environments. However, unique demographic histories and differences in standing genetic variation of the source populations impede making reliable predictions concerning convergence of the underlying molecular mechanisms. Still, we further predicted that rapid adaptation occurred predominantly via regulatory changes. To this end, we analyzed retinal transcriptomes from three populations of seven cichlid species using high-throughput RNA sequencing (RNA-Seq) and further analyzed coding sequences to explore structural and regulatory variation.

Methods

Sample collection

We investigated the visual system of the following Neotropical cichlids from Nicaragua: Amatitlania siquia, Archocentrus centrarchus, Astatheros rostratus, Hyposphrys nematopus, Hyposphrys nicaraguensis and Parachromis manguensis. Recently, quantitative Real-Time PCR (qPCR) and in situ hybridization analyses showed that crater lake Midas cichlids (Amphilophus cf. citrinellus) have a short wavelength shifted visual system compared to one source population from the great lakes (Torres- Dowdall et al. 2017b). Thus, we included A. sagittae from Lake Xiloá, A. astorquii from Lake Apoyo, and A. citrinellus from Río San Juan and Lake Managua to validate that different molecular techniques yield similar results (Fig. S.II.1). Generally, 5-6 specimens per species and sampling location (except for A. centrarchus from the Great lake with n=3) were obtained from turbid rivers (Río San Juan and Río Punta Gorda), turbid great lakes (Lakes Managua and Nicaragua) and clear crater lakes (Lake Xiloá for all species and Lake Apoyo for A. astorquii; Fig. II.1, Table S.II.2). Many cichlids undergo ontogenetic changes in opsin expression (Carleton et al. 2008, Härer et al. 2017), hence, only adult fish were collected. Fish were caught using gill nets at depths between 0 to 5 meters. Nets were regularly checked and alive specimens were removed from the nets and immediately sacrificed by applying an overdose of MS-222 and subsequent cutting of the vertebral column. Retinae were dissected and stored in RNAlater (Sigma-Aldrich, St. Louis, Missouri) until RNA extraction. To reduce diurnal variation 28 in opsin expression, retinas were dissected only in bright daylight conditions (between 10 am and 2 pm). All samples were collected during field expeditions in 2013 and 2015 (under MARENA permits DGPN/DB-IC-004-2013 & DGPN/DB-IC-015-2015). Further, absolute irradiance was measured for each environment at a depth of 1 meter, using an Ocean Optics FLAME-S-XR1-ES spectrometer and a cosine corrector. Conversion from watts to photons is based on calculations from Johnsen (2011).

Library preparation & Illumina Sequencing

RNA was extracted using the commercial RNeasy Mini Kit (Qiagen, Hilden, Germany) and concentrations were measured on a Qubit Fluorometer (Thermo Fisher Scientific, Waltham, Massachusetts). RNA integrity was determined (Agilent 2100 Bioanalyzer; Agilent Technologies, Santa Clara, California), all samples had RIN values above 6. Libraries were generated using the TruSeq Stranded mRNA HT sample preparation kit (Illumina, San Diego, California) according to the manufacturer’s protocol. The final libraries were amplified using 15 PCR cycles, quantified on a Qubit Fluorometer and quality was assessed (Agilent 2100 Bioanalyzer). All samples were individually labeled with unique barcodes and 30 ng of each sample were pooled and paired-end sequenced in two lanes of the Illumina flow cell (2 x 150 bp) on an Illumina HiSeq2500 platform at TUCF Genomics (Tufts University, Medford, Massachusetts).

Quality control, transcript assembly and mapping

A total of 246,322,577 reads were collected (median: 1,410,687 reads per individual; Table S.II.3). Illumina adapters were removed and reads were trimmed with Trimmomatic v0.36 (Bolger et al. 2014). For opsin expression analyses, trimmed reads were mapped against 50 bp of the 5’ UTR and the first 150 bp of the CDS for all cone opsins of each species with bowtie 2.3.0 (Langmead and Salzberg 2012). We chose this approach instead of mapping against the full CDS because rh2aα and rh2aβ underwent gene conversion (Torres-Dowdall et al. 2017b). Only the 5’ UTR and the first exon show sequence variation whereas the rest of the CDS is identical, hence, reads could not be unambiguously assigned to either of the two paralogs for exons 2-5. To analyze cyp27c1 gene expression, reads were mapped against the full CDS from A. citrinellus (Torres-Dowdall et al. 2017b) using TopHat2 v2.1.1 (Kim et al. 2013). Bam files were converted into sorted sam files with samtools v1.43 (Li et al. 2009) and count tables were created with HTSeq v0.6.1 (Anders et al. 2015).

Sequence analyses

Complete CDS of opsin genes consistently expressed at high levels (sws2a, rh2aβ and lws) were obtained by mapping reads against A. citrinellus reference sequences (Torres-Dowdall et al. 2017b) with CLC Genomics Workbench 8 (Qiagen, Hilden, Germany). Consensus sequences were generated with a minimum coverage of 10x per locus and the noise threshold was set to 0.35 (i.e., heterozygotes were only scored if the minor allele frequency was > 35%). Nucleotide sequences were aligned using SeaView v4 (Gouy et al. 2010) and translated into amino acid sequences to score non-synonymous 29 substitutions. Only non-synonymous substitutions that were variable within species are shown in Table S.II.4, all non-synonymous substitutions among species are shown in Tables S.II.5-S.II.7. To investigate sites under positive selection, random site models in PAML were used (Yang 2007). We tested for variation in ω (i.e., dN/dS) across sites (M3/M0) and for the presence of positively selected sites (M1/M2) which were identified with Bayes’ Empirical Bayes in PAML (Table S.II.8; Yang 2007).

Gene expression analyses

Gene expression analyses were performed in R v3.2.3 (R Core Team 2015). Cone opsin expression was calculated as the proportion of each cone opsin relative to the overall cone opsin expression using the following equation:

푅푒푎푑 푐표푢푛푡푖 푃푟표푝표푟푡푖표푛푎푙 푒푥푝푟푒푠푠푖표푛 (푃퐸)푖 = 푅푒푎푑 푐표푢푛푡 ∑ 푎푙푙 푐표푛푒 표푠푖푛푠

Read counti represents number of reads for a particular cone opsin. We further calculated predicted sensitivity indices, which provide information on visual sensitivity by integrating the peak of maximum absorption (λmax) and proportional expression for each opsin. λmax values are based on Midas cichlids (sws2b, sws2a, rh2b, rh2aβ, rh2aα & lws; Torres-Dowdall et al. 2017b) and Nile Tilapia (sws1; Spady et al. 2006):

푋푗 = 푃퐸푠푤푠1 ∗ 360푛푚 + 푃퐸푠푤푠2푏 ∗ 425푛푚 + 푃퐸푠푤푠2푎 ∗ 456푛푚 + 푃퐸푟ℎ2푏 ∗ 472 + 푃퐸푟ℎ2푎훽 ∗ 517푛푚

+ 푃퐸푟ℎ2푎훼 ∗ 527푛푚 + 푃퐸푙푤푠 ∗ 560푛푚

PE is the proportional expression of each cone opsin in specimen j. This equation is adapted from Carleton et al. (2016), but was modified to incorporate single and double cone opsin expression in one index. Relative expression of cyp27c1 was measured either as number of reads mapped to the cyp27c1 coding sequence per one million reads (Fig. II.2) or relative to the highest expression value of cyp27c1 within each species to reduce among-species variation in overall expression levels to better illustrate convergence (Fig. II.3, Fig. II.4).

Regarding cone opsin expression, the photic environment could affect visual sensitivity in the short, medium and long parts of the light spectrum, hence, we hypothesized that (i) total opsin expression comprised by single cone opsins, (ii) proportions of the green-sensitive rh2aβ and rh2aα, in case that both paralogs of rh2a were expressed (as shown in A. ocellatus; Escobar-Camacho et al. 2017), and (iii) proportional expression of the red-sensitive lws (as seen in Torres-Dowdall et al. 2017b) would be affected. Accordingly, we tested for effects of species, photic environment and their interaction for these different aspects of cone opsin expression (single cone opsin expression, rh2aβ/rh2aα ratio, lws expression), as well as for cone opsin expression indices and cyp27c1 expression. For this, we used Scheirer-Ray-Hare tests, a non-parametric equivalent to two-way ANOVA (Scheirer et al. 1976). Further, we tested the above mentioned three a priori hypotheses separately for each species using non-parametric Kruskal-Wallis tests.

Following Stuart et al. (2017), we tested for parallelism of color vision across species associated with colonization of novel photic environments. First, we produced two dimensional vectors for each species, connecting mean values of populations (sample sizes are shown in Table S.II.2) from the three environments by taking into account cone opsin expression (predicted sensitivity index) and cyp27c1 gene expression (Fig. S.II.2). We calculated pairwise differences between mean angles and lengths of vectors among all species for both colonization events (colonization 1: river-great lake, colonization 2: great lake-crater lake) separately. In total, this added up to 21 pairwise tests and next, we calculated the sums of all pairwise differences. Note that we tested for parallelism for each colonization event separately but also for both colonization events combined. For the latter, we added the sums of all pairwise differences from both colonization events. We then randomized species identity within each environment and created 999 data sets with vectors connecting one individual from the ancestral environment (river or great lake in colonization 1 or 2, respectively) and the derived environment (great lake or crater lake in colonization 1 or 2, respectively). Within each randomized data set, we calculated differences between angles and lengths as described above. In total, we performed a total of 21 pairwise comparisons to obtain the same number of comparisons as in our original data set. We added all sums of pairwise differences within each data set and determined in how many cases the sums in the permuted data sets were smaller compared to our actual data. Statistical significance was determined at the 0.05 level.

Results

The aquatic landscape of Nicaragua represents an intriguing setting, where seven ecologically differentiated species of cichlid fishes have concurrently colonized two novel photic environments (Fig. II.1, Table S.II.1). Color vision is particularly variable in cichlids and is mediated by visual pigments located in photoreceptor cells of the retina, which are composed of a light-absorbing chromophore that is covalently bound to a transmembrane opsin protein (Wald 1968, Yokoyama 2000, Ebrey and Koutalos 2001). In aquatic vertebrates, visual sensitivity is mainly determined by three molecular mechanisms: chromophore usage (indirectly measured as cyp27c1 gene expression, see Methods for more details), as well as structural changes or differential expression of opsin genes (Terai et al. 2006, Hofmann et al. 2009, O'Quin et al. 2010, Torres-Dowdall et al. 2017b). Our analyses identified divergence in these three mechanisms among populations of the same species (sample sizes are shown in Table S.II.2) for all seven species (Fig. II.2).

We obtained complete CDS of sws2a, rh2aβ and lws from all species since these genes were consistently expressed at high levels (Fig. II.2B). Overall, we found ten (sws2a), seven (rh2aβ) and eighteen (lws) non-synonymous substitutions among species (Tables S.II.5-S.II.7). Out of those, only two (rh2aβ) and six (lws) varied among populations within species, but none in sws2a (Fig. II.2A, Table S.II.4). As we were interested in changes associated with the colonization of novel photic

31 environments, we focused on amino acid substitutions that varied among populations of the same species. All, but one, of these non-synonymous substitutions were located in transmembrane regions that are important for functional dynamics and spectral tuning of photopigments (Asenjo et al. 1994, Hunt et al. 2001, Carleton et al. 2005, Seehausen et al. 2008, Hofmann et al. 2009). The number of variable amino acid residues differed strongly among species (Fig. II.2A, Table S.II.4). Analyses of molecular evolution suggest that of those residues varying among populations, one (RH2Aβ) and three (LWS) were under positive selection (Table S.II.4, grey boxes); but in total, nine residues were found to be under selection in LWS (Table S.II.8). Two of these LWS residues were shared among A. siquia and H. nematopus (S40A and L45F; Table S.II.4). However, these showed fixed differences between river and both crater and great lakes in A. siquia (Fig. II.2A), but in H. nematopus these were polymorphic in the great lake and fixed for the same allele in river and crater lake. Hence, there was no convergence in any of the species after colonizing novel photic environments (Fig. II.2A).

Fig. II.2: Structural changes of cone opsin genes and regulatory changes of cone opsins and cyp27c1 associated with colonization of novel photic environments. (A) Amino acid sites variable within species are shown (numbers according to bovine RH1) for LWS (red) and RH2Aβ (green), but none were found for SWS2A (blue). Three of the variable LWS sites and one RH2Aβ site are under positive selection (bold). Phylogenetic relationships among species are based on Lopez-Fernandez et al. (2010). Note that the relationships among populations within each species are inferred based on our knowledge on the colonization history of Nicaraguan lakes. (B) Species shift visual sensitivity among environments by differential expression of varying genes, including changes in proportional expression of single cone opsins (1), ratio between shorter and longer green-sensitive opsins (rh2aβ and rha2aα; 2), proportional expression of the red-sensitive lws (3) and cyp27c1 expression (4). Asterisks show significant expression changes among all three populations (Kruskal-Wallis tests, * P < 0.05, ** P < 0.01, FDR corrected).

Since there were no convergent changes in structural variation associated with photic environments, we focused on the other main axes by which visual sensitivity can be altered (opsin expression and chromophore usage). Most specimens, independent of habitat of origin, expressed a long wavelength sensitive cone opsin subset (sws2a, rh2aβ/α and lws; sensu Carleton et al. 2016; Fig. II.2B). This expression pattern is likely well-suited to the predominantly long wavelength shifted light conditions of turbid large rivers, like the San Juan river, of the Neotropics (Escobar-Camacho et al. 2017). The only exception to this pattern was the population of A. siquia from the crater lake, that expressed the violet-sensitive sws2b instead of the blue-sensitive sws2a in single cones and the blue- green sensitive rh2b in double cones (Fig. II.2B). By using quantitative Real-Time PCR, we recently found that similar changes occurred in Midas cichlids from another crater lake (Torres-Dowdall et al. 2017b), and here we confirmed these results using RNA-Seq (Fig. S.II.3). These two species were the only ones that expressed a different subset of opsin genes as adults, which changed predicted visual sensitivity towards the short wavelength shifted photic environment of the crater lake (Fig. II.3).

Fig. II.3: Predicted sensitivity index (A) and relative cyp27c1 expression (B) changes for both colonization events. Predicted sensitivity index is affected by environment (P < 0.001) and by species identity (P = 0.026) showing convergent changes among species. Differences among species are maintained (based on Spearman's rank correlation coefficient) after colonizing crater lake (ρ = 0.786, P = 0.036) but only suggested after colonizing the great lake (ρ = 0.679, P = 0.094). For expression of cyp27c1, only the species by environment interaction term was significant (P = 0.03), suggesting that there is no convergence among species. Along the same line, we could not detect evidence for maintenance of ranks after colonizing novel environments.

Most species shifted gene expression of cone opsins towards absorbing light at shorter wavelengths following the two colonization events, particularly in the clear water crater lake, but the

34 exact differentially expressed genes were not necessarily shared among all species. We specifically tested whether species changed expression of short (total expression of single cone opsins), medium (rh2aβ/rh2aα ratio) or long (lws expression) wavelength sensitive opsins. Across all species, proportional expression of short wavelength sensitive opsins commonly increased after colonization of the crater lake and varied among environments in a way that depended on species identity (Scheirer-Ray-Hare test, species x environment interaction term P < 0.001). This significant interaction can most likely be explained by deviating expression patterns in A. centrarchus, which did not change expression, and in A. siquia, which decreased the overall proportional single cone opsin expression in the crater lake, but switched from expressing sws2a to sws2b (Fig. II.2B). Expression ratio of the green- sensitive rh2a paralogs differed significantly among environments (P < 0.001) and species (P = 0.014). However, only A. centrarchus, A. siquia, H. nicaraguensis and A. rostratus showed substantial expression of both rh2a paralogs. For these four species, rh2aβ/rh2aα ratios changed significantly and gradually increased expression of the shorter wavelength sensitive rh2aβ (proportional to overall rh2a expression) from rivers to great lake to crater lake (Fig. II.2B, Fig. S.II.4). Expression of the red-sensitive lws differed among environment (P < 0.001) and species (P = 0.011) and all species, except for P. managuensis, changed lws expression, which was consistently lower in the crater lake (Fig. II.2B). Rearing fish under common laboratory conditions revealed certain levels of phenotypic plasticity, but overall differences in cone opsin expression patterns appeared to be genetically determined (Fig. S.II.5, Fig. S.II.6). The variation in cyp27c1 gene expression (i.e., chromophore usage) was affected by the environment but dependent upon species identity (interaction term P = 0.03; Fig. II.3). In general, expression of cyp27c1 was lower in rivers compared to the turbid great lake (except for A. cf. citrinellus) and higher in the turbid great lake compared to the clear crater lake (except for A. rostratus; Fig. II.2B). This suggests changes in A1 to A2 ratios, with higher A2 usage in the great lake compared to both the crater lake and riverine populations, resulting in sensitivities of the individual visual pigments shifted toward longer wavelength in the former compared to the latter environments.

To integrate overall opsin gene expression and to predict shifts in visual sensitivity, we calculated predicted sensitivity indices (see Methods), which were significantly affected by photic environment (P < 0.001) and by species identity (P = 0.026), but not by their interaction (Fig. II.3A). As stated above, for expression of cyp27c1, only the species x environment interaction was significant (Fig. II.3B). To determine levels of convergent change in color vision, we integrated cone opsin expression (predicted sensitivity index) and cyp27c1 expression. We performed vector analyses and specifically tested for parallelism across species in direction (vector angles, γ) and magnitude (vector lengths, ΔL) of phenotypic change (Fig. II.4). When considering both colonization events combined, we detected parallelism in direction (γ, P = 0.037) but not in magnitude (ΔL) of phenotypic change among all species (Fig. II.4B). When investigating each colonization event separately, parallel changes were only found for the direction of change during colonization of the crater lake (P = 0.041). Notably, for each

35 colonization event, only six species appeared to show parallelism and one species (A. cf. citrinellus for colonization 1 and A. rostratus for colonization 2) deviated from this general pattern (Fig. II.3A). Within the same environment, species varied substantially, particularly in predicted sensitivity and these differences and the rank order of the species are maintained after colonizing crater lake (Spearman's rank correlation coefficient; ρ = 0.786, P = 0.036) but only suggested after colonizing the great lake (ρ = 0.679, P = 0.094) across all three environments (Fig. II.3A).

Fig. II.4: Most species show parallel changes in cone opsin expression (predicted sensitivity index) and relative cyp27c1 expression across environments. Briefly, the predicted sensitivity index takes into account proportional expression and peaks of maximum absorption (see Methods for more details on calculation) and provides information on predicted visual sensitivity. cyp27c1 expression is indicated relative to the highest value for each species. Lower values on both axes represent visual sensitivities at shorter wavelengths. For each species, vectors connect mean values for populations from three environments (sample sizes are indicated in Table S.II.2). Distances between direction and length of vectors were measured for both colonization events separately and combined. Actual data was compared to a random set of 999 permutations to obtain significance values. Significant parallelism in the direction of change was detected for colonization 2 (P = 0.041) but also when both colonization events were combined (P = 0.037).

Discussion

Convergent evolution is recognized as compelling evidence for natural selection and has been observed in many traits from a diverse set of organisms (Reznick et al. 1996, Rundle et al. 2000, Mahler et al. 2013, Rosenblum et al. 2017), including cichlids (Barluenga and Meyer 2004, Elmer et al. 2010). However, the exact environmental parameters promoting evolutionary change and the adaptive value of phenotypes, as well as the underlying molecular mechanisms, often remain elusive (Barrett and Hoekstra 2011, Orgogozo 2015). The vertebrate visual system overcomes some of these uncertainties since there is a good understanding of how molecular changes in coding regions as well as modified

36 expression patterns of opsin genes affect visual phenotypes and how phenotypic differences are shaped by the photic environment (Yokoyama 2000, Bowmaker 2008, Hofmann et al. 2009, Ryan and Cummings 2013, Cronin et al. 2014, Enright et al. 2015, Marshall et al. 2015, Carleton et al. 2016). The natural experiment in Nicaraguan lakes where several cichlid species concurrently colonized the same two novel photic environments allows us to test whether these species show a similar evolutionary response due to shared selection pressures in novel environments.

Over short time scales, e.g., after the recent colonization of the crater lake, only 1,300 to 1,700 generations ago for A. cf. citrinellus and A. centrarchus (Kautt et al. 2016, Franchini et al. 2017), selection is assumed to have acted predominantly on standing genetic variation rather than on de novo mutations (Innan and Kim 2004, Barrett and Schluter 2008). Assuming that regulatory regions are under fewer constraints than coding regions (Stern and Orgogozo 2008, Ghalambor et al. 2015), we would expect more variation in gene expression. Hence, especially after crater lake Xiloá was colonized, most adaptive changes were expected to be regulatory. Indeed, we found only few structural changes across localities and none that were convergent among species (Fig. II.2A). Note that the potential phenotypic effects of these substitutions (i.e., changes in peaks of maximum absorption) were not evaluated in this study but are a matter of future research. Expression of cone opsins (functional under bright light and responsible for color discrimination) and cyp27c1 (conversion of vitamin A1 to A2 derived chromophore; Enright et al. 2015) substantially varied among populations in all species and often changed in the same direction (Fig. II.3, Fig. II.4). After colonizing the crater lake from the great lake, changes occurred in the direction predicted based on environmental differences and knowledge on the visual system of Midas cichlids (Fig. II.1; Torres-Dowdall et al. 2017b). For the colonization of great lakes from rivers, cone opsin expression differed in a way that resulted in predicted visual sensitivities slightly shifted towards shorter wavelengths which fits the differences in light conditions (compare Fig. II.1 to Fig. II.3A). However, changes in cyp27c1 expression suggest that sensitivity of the resultant visual pigments would be shifted towards longer wavelengths in the great lake compared to the riverine populations (Fig. II.3B). Nonetheless, the changes in cone opsin and cyp27c1 expression occurred in the same direction in all species (but A. cf. citrinellus), hence, we interpret these to be adaptations to the light conditions of the great lake (Fig. II.3, Fig. II.4). Microspectrophotometry will be required to determine the overall effects of changes in opsin and cyp27c1 expression on visual sensitivity.

Recent studies have shown that phenotypic convergence can be mediated by the same (Zhen et al. 2012, Projecto-Garcia et al. 2013) or different (Natarajan et al. 2016, Castiglione et al. 2018) molecular mechanisms. In our study, convergent changes in predicted visual sensitivity were produced by differential expression of varying sets of genes, which we refer to as different molecular mechanisms, among species; i.e. adaptation to new environments occurred by changing expression of single cone opsins, the two green-sensitive rh2a genes and/or the red-sensitive lws, as well as cyp27c1 37

(Fig. II.2B). These differing mechanisms could be explained by the independent demographic histories and, hence, differences in standing genetic variation, as seen in some of our study species (Elmer et al. 2013, Franchini et al. 2017). On the contrary, shared genetic variation could also promote convergence among closely related species by constraining phenotypic evolution (Haldane 1932, Futuyma et al. 1995, Schluter 1996). However, we argue that in our system, convergent evolution is mainly driven by shared selection pressures rather than genetic constraints since species have long divergence times (5 to 15 million years (my) based on Hulsey et al. 2010, 13 to 55 my based on Lopez-Fernandez et al. 2013) and expression patterns varied within monophyletic groups but were shared among more distantly related species (e.g., rh2aβ to rh2aα ratio; Fig. II.2B, Fig. S.II.4).

The largest shift in cone opsin expression, resulting in the shortest wavelength sensitive visual phenotype, was observed in A. siquia from crater lake Xiloá (Fig. II.3). This population deviated from the common subset of expressed cone opsins (sws2a, rh2aβ/α, lws) and additionally expressed opsins sensitive to shorter wavelengths, sws2b and rh2b, in single and double cones, respectively (Fig. II.2B). Similar results were found in Midas cichlids from another crater lake (Torres-Dowdall et al. 2017b). This pattern of expressing more than three cone opsins at the same time differs from what is commonly observed in African cichlids (reviewed in Carleton et al. 2016), but see (Sabbah et al. 2010), suggesting that adaptive evolution of the visual system might be produced by different mechanisms in these cichlid lineages. Crater lake populations of A. siquia and A. cf. citrinellus not only changed the proportions of cone opsins, but also expressed a different subset of cone opsins as adults compared to the other cichlid species investigated in this study. This nicely illustrates that convergent phenotypic evolution occurred via different routes, as also suggested for adaptive evolution of the dim-light sensitive rhodopsin (Castiglione et al. 2018). However, at this point, we are still lacking knowledge about the underlying molecular bases for the observed gene expression differences that produced the convergent changes in visual sensitivity. However, QTL analyses in African cichlids have shown that opsin expression is regulated largely independently (Carleton et al. 2010, O'Quin et al. 2011b, Nandamuri et al. 2017a). Based on these results, we conclude that convergent evolution of visual sensitivity in our system is brought about by different molecular mechanisms (i.e., differential expression of varying sets of genes), which might further have distinct genetic bases, a hypothesis that is currently under investigation.

The expression changes seen in all species across localities most likely represent adaptations to the respective photic environments but the question remains why differences among species are maintained within each environment. Many factors are associated with the great diversity of cone opsin expression seen in African cichlids, among which species-specific ecology appears to play a very important role (Hofmann and Carleton 2009, O'Quin et al. 2010). The Nicaraguan cichlids investigated in our study differ considerably in body size, trophic level, habitat use, coloration (Table S.II.1), and in population size and genetic diversity (Elmer et al. 2013, Franchini et al. 2017). Still, almost all species 38 evolved patterns of gene expression resulting in similar changes of predicted visual sensitivity, providing evidence for common selection pressures acting on the visual system. This convergence suggests that the overall photic environment shapes visual sensitivity, which is in line with results from other organisms (Cronin et al. 2014, Marshall et al. 2015). Yet, within each environment we found that cone opsin expression differed remarkably among species and these differences were maintained across environments (Fig. II.3A). For instance, H. nematopus and P. managuensis, two species with extremely different ecologies (Table S.II.1), changed visual phenotypes in parallel across river, great lake and crater lake in a way that maintained interspecific differences in each of these environments (Fig. II.4A). Notably, even though both species shifted their vision to absorb more short wavelength light in the crater lake compared to the river, the riverine population of H. nematopus still has a shorter wavelength shifted sensitivity than P. managuensis from the crater lake. Taken together, these results strongly suggest that overall photic environment and ecological characteristics of the species both influence the evolution of the visual system, which is in agreement with existing knowledge on adaptive evolution of the visual system (Cronin et al. 2014).

The observed phenotypic differences within species across environments could potentially be produced by phenotypic plasticity since the visual system is plastic in some species of African and Neotropical cichlids, particularly during early development (Hofmann et al. 2010, Dalton et al. 2015, Härer et al. 2017, Nandamuri et al. 2017b). However, opsin gene expression has a strong genetic basis in Midas cichlids as differences in natural populations are maintained under common garden conditions (Torres-Dowdall et al. 2017b). To better understand the role of phenotypic plasticity in producing the patterns of gene expression observed in wild-caught fish, we raised specimens of a subset of our study species (A. cf. citrinellus and A. centrarchus from the great lake, and A. siquia and H. nematopus from the crater lake) in a common laboratory environment. Although we observed a certain level of phenotypic plasticity, the different patterns of cone opsin expression observed in the wild were largely maintained under common rearing conditions (e.g. expression of sws2b and rh2b in laboratory-reared A. siquia; Fig. S.II.5). Moreover, specimens from the same habitat of origin clustered together and were more similar to each other compared to specimens from the other environment, independent of rearing condition (Fig. S.II.6). Taken together, these results suggest that phenotypic plasticity might have contributed to some of the observed variability, but current evidence suggests that visual system divergence has a strong genetic component and evolved after colonizing Nicaraguan lakes.

Conclusion

Coming back to Gould’s thought experiment of “replaying life’s tape” (Gould 1989), how predictable is evolution after all? Gould argued that contingency would be dominant, which might hold true across long time scales, but at short time scales evidence is accumulating that evolutionary change can indeed

39 be predictable, particularly when natural selection is strong and there is reasonable knowledge on the nature of this selection (Reznick et al. 1996, Rosenblum 2006, Elmer et al. 2014, Hendry 2017, Losos 2017, Nosil et al. 2018). The visual system of Nicaraguan cichlids represents an extraordinary model for studying adaptive evolution as lakes differ markedly in their photic environments and we have a very good understanding of the colonization history of these different lakes. Here, we present a natural system where after sequentially colonizing two novel photic environments, changes in expression levels of different genes affecting the visual system resulted in rapid and convergent changes of predicted visual sensitivity of seven cichlid species. Albeit these convergent changes, cone opsin expression differences among species appeared to be maintained after colonizing new environments, emphasizing on the important role of species’ ecology in shaping visual systems. In sum, based on our results and knowledge on the visual system of cichlids, we argue that these changes (i) are, at least partially, genetically determined, (ii) represent an example of convergent phenotypic adaptation to the prevalent photic environments and (iii) have different underlying molecular mechanisms (i.e., they were produced by expression changes of different genes that constitute visual pigments).

Chapter III

The effects of host genetics, environment and trophic ecology on the gut microbiome of the young adaptive radiation of Midas cichlids

Andreas Härer, Julián Torres-Dowdall, Sina Rometsch, Gonzalo Machado-Schiaffino & Axel Meyer

Manuscript currently in preparation

Abstract

Advances in culture-independent sequencing techniques have tremendously improved the understanding of microbial communities, particularly of the gut from numerous vertebrate species. Now, it is well known that a diverse set of factors influence the composition of gut microbiomes and, in particular, diet and host genetics. However, the relative contributions of these factors remain a matter of debate. Further, it is commonly not clear whether gut microbiome composition promotes or is the consequence of trophic diversification. The young adaptive radiation of Nicaraguan Midas cichlids represents an excellent system to tackle these questions since divergence times are short and species occurring in sympatry within small crater lakes show strong divergence in trophic ecology. To this end, we investigated gut microbiomes of multiple species of Midas cichlids by sequencing the V4 region of the 16S rRNA gene. Here, we show that bacterial community composition of Midas cichlids’ guts differ among species from distinct environments and, more interestingly, also among trophically divergent species within one crater lake but not within the other. These results suggest that trophic divergence occurs first and entails subsequent changes of the gut microbiome. However, we further found a strong correlation between pairwise distances of diet and bacterial community composition among sympatric species, which indicates changes in diet and gut microbiome might rather co-occur. Notably, there were convergent changes in bacterial community composition in limnetic species that independently evolved in two crater lakes, emphasizing on the importance of the gut microbiome in adaptation to novel food sources. Moreover, interspecific gut microbiome differences were maintained when fish were reared in a common environment, indicating that these differences are, at least to some extent, determined by host genetics.

Introduction

The importance of microorganisms to diverse aspects of their hosts’ life has been demonstrated for a wide range of animals, from insects to mammals (Backhed et al. 2004, Human Microbiome Project 2012, Brucker and Bordenstein 2013b). The gut microbiome is a complex and dynamic community that is crucial for the host’s health, and particularly, gut bacteria are fundamental for many physiological processes, such as regulation of the immune system (Lathrop et al. 2011) as well as nutrient metabolism (Turnbaugh et al. 2006). Besides, the role of microbes in animal evolution has become increasingly appreciated (Fraune and Bosch 2010); for instance, by affecting mate choice (Sharon et al. 2013) or restricting gene flow among closely related species (Brucker and Bordenstein 2013b). Along these lines, levels of gut microbiota divergence appear to be correlated with host phylogeny and genetic divergence, a concept that has been termed phylosymbiosis (Brucker and Bordenstein 2012a, Brooks et al. 2016). Phylosymbiosis is supported by findings that host genetics, together with environmental effects such as diet, is an important factor in shaping and maintaining gut microbiome composition (Benson et al. 2010, Spor et al. 2011, Goodrich et al. 2014). To overcome the confounding effect of host phylogeny and specifically ask whether species’ ecology promotes gut microbiome differentiation and how rapidly such changes occur, one can study recently diverged species with low levels of genetic divergence. Such systems are of particular interest since it is commonly not known whether gut microbiome differentiation is cause or consequence of changes in diet. Adaptive radiations of cichlid fishes provide a promising system to tackle these questions since overall, divergence times and genetic differentiation among species are relatively low but ecologically, cichlids are highly variable, particularly in regard to trophic ecology (Elmer et al. 2010, Muschick et al. 2012, Brawand et al. 2014, Kautt et al. 2016).

Recent work on African cichlids from two adaptive radiations (Barombi Mbo and Tanganyika) revealed that in both cases gut microbiotas convergently diverged with host diet, showing coevolution of diet and microbiome composition (Baldo et al. 2017). Yet, studies on other groups of fishes did not find evidence for convergent gut microbiome changes (Sullam et al. 2015, Sevellec et al. 2018). Whereas the cichlid radiations of Barombi Mbo (0.5 - 1 myr) and Tanganyika (9 - 12 myr) are rather old, Nicaraguan Midas cichlids (Amphilophus cf. citrinellus) have radiated much more recently, most likely within less than 1,700 generations (Kautt et al. 2016). At present, there are 13 described Midas cichlid species (Barluenga and Meyer 2010, Elmer et al. 2010, Recknagel et al. 2013, Kautt et al. 2016) that inhabit several rivers (e.g., Río San Juan), two ancient great lakes (Lakes Managua and Nicaragua) and numerous young crater lakes (Fig. III.1; Barluenga and Meyer 2010). This distribution is the result of two sequential colonization events during which riverine populations first colonized the great lakes approximately 500,000 years ago (Bussing 1976, Elmer et al. 2010) and subsequently, populations from the great lakes colonized multiple crater lakes that formed in the calderas of volcanoes after they

42 became inactive (all crater lakes are younger than 23,000 years) (Kutterolf et al. 2007). The colonization of crater lakes Apoyo (colonized from Lake Nicaragua) and Xiloá (colonized from Lake Managua) are estimated to have occurred as recently as 1,700 and 1,300 generations ago, respectively (Kautt et al. 2016, Franchini et al. 2017). Within these crater lakes, multiple species evolved in sympatry during this extremely short time span, so that 10 of the currently 13 described species live in Apoyo and Xiloá (Barluenga et al. 2006, Kautt et al. 2016). Notably, one elongated, limnetic species independently evolved in both crater lakes. These limnetic species vary in body shape from several deep-bodied, benthic species and, hence, have been characterized as distinct ecomorphs (Fig. III.1). Benthic and limnetic species differ in feeding habit with limnetic species occupying a higher trophic level (Elmer et al. 2014), but also differentiation of the gut microbiome has been proposed for a benthic-limnetic species pair from Lake Apoyo (Franchini et al. 2014). Taken together, the very young adaptive radiation of Nicaraguan Midas cichlids represents an excellent system to study dynamics of the gut microbiome during early stages of ecological divergence.

Fig. III.1: Map of Nicaragua showing the current distribution of Midas cichlids in Nicaragua, which is the result of two sequential colonization events. In this study, we included A. citrinellus populations from the San Juan River and the two great lakes (Managua and Nicaragua), as well as multiple endemic species from two young crater lakes (Apoyo and Xiloá). Within each crater lake, several deep-bodied, benthic and one elongated, limnetic species (A. sagittae in Xiloá and A. zaliosus in Apoyo) can be found.

In this study, we investigated trophic ecology (based on stable isotope analyses) as well as gut microbiome composition of multiple species of Midas cichlids (Fig. III.1), including A. citrinellus from Río San Juan and the two great lakes Managua and Nicaragua, as well as species from the crater lakes Apoyo (A. astorquii, A. chancho, A. globosus and A. zaliosus) and Xiloá (A. amarillo, A. xiloaensis and A.

43 sagittae). We examined whether gut microbiome composition is correlated with trophic ecology or host genetics by specifically asking the following questions. First, we asked whether across environments (river, great lakes, crater lakes), Midas cichlids diverged in trophic ecology or gut microbiome composition and whether these two measures are correlated? Next, we asked the same questions but among sympatric species within each of the two young crater lakes. The occurrence of multiple recently diverged species of Midas cichlids, that show differences in trophic ecology, in each of these crater lakes (Elmer et al. 2014), allowed us to ask if diet or gut microbiome composition changes first. Moreover, as in both crater lakes one limnetic species independently evolved, we can ask whether the gut microbiomes of these two species changed convergently. Finally, by rearing Midas cichlids together with three other species of Nicaraguan cichlids under common conditions, we asked whether gut microbiome composition is affected by host genetics.

Methods

Sample collection

Wild-caught specimens of the Amphilophus cf. citrinellus species complex were collected during field trips to Nicaragua in 2014 and 2015 (under MARENA permits DGPN/DB-IC-011-2014 & DGPN/DB-IC- 015-2015). In each of the two crater lakes, we caught one elongated, limnetic species (A. sagittae in Xiloá and A. zaliosus in Apoyo), as well as several deep-bodied, benthic species (Fig. III.1). Laboratory- reared fish of A. cf. citrinellus as well as three other Neotropical cichlids (Arochcentrus centrarchus, Amatitlania siquia and Hypsophrys nematopus) were born and reared in the animal facility of the University of Konstanz. Prior to sampling, all laboratory-reared specimens were kept in the same tank and were fed the same food items for 2 weeks to reduce environmental and dietary effects on the gut microbiota. All specimens from the wild and from the laboratory were sacrificed by applying an overdose of MS-222 and subsequent cutting of the vertebral column. Then, whole guts were dissected, cleaned and stored in absolute EtOH until DNA extraction.

Trophic analysis

Stable isotope ratios of carbon (δ13C) and nitrogen (δ15N) were determined based on muscle tissue. Stable isotope analysis was performed at the Limnological Institute at the University of Konstanz. Samples were analyzed by gas chromatography combustion isotope ratio mass spectrometry (GC-C- IRMS).

Library preparation & Illumina Sequencing

DNA was extracted from approximately 50-100 mg of tissue from the medial gut section using the commercial QIAamp DNA Stool Mini Kit according to the manufacturer’s protocol (Qiagen, Hilden, Germany) and concentrations were measured on a Qubit v2.0 Fluorometer (Thermo Fisher Scientific, Waltham, Massachusetts). We performed two sequential PCRs and after each the amplified product

44 was purified with HighPrepTM PCR beads (MagBio Genomics, Gaithersburg, Maryland). For the first PCR, we used the 515F and 806R primers with a universal 5’ tail, as indicated in the Illumina Nextera library preparation protocol, for DNA amplification of the bacterial V4 region of the 16S ribosomal RNA (292 bp). Briefly, 50 ng DNA were used as template for the first PCR (2min at 98°C, 10 amplification cycles consisting of 15s at 98°C, 20s at 55°C and 20s at 72°C and a final elongation at 72°C for 2 minutes). The purified PCR amplicons were the template for the second PCR (2min at 98°C, 20 amplification cycles consisting of 15s at 98°C, 20s at 67°C and 20s at 72°C followed by a final elongation at 72°C for 2 min) using primers including sequencing barcodes as well as the Illumina adapter sequences. Both PCRs were performed in 25 µl reaction volumes, amplifying with the Q5 High-Fidelity polymerase 2x Master Mix (New England Biolabs, Ipswich, MA). After purification, DNA concentrations were measured and specificity of amplification was checked for all samples using gel electrophoresis. All samples were pooled in an equimolar manner and size selection was performed on a Pippin Prep device (Sage Science, Beverly, MA). The quality of the pooled library was assessed using a Bioanalyzer 2100 (Agilent Technologies, Waldbronn, Germany) and the library was paired-end sequenced in one lane of the Illumina flow cell (2x250 bp) on an Illumnia HiSeq 2500 platform at TUCF Genomics (Tufts University, Medford, Massachusetts).

Sequence processing

Overall, we obtained a total of 62,728,287 raw sequencing reads (median: 238,073 reads) that could be unambiguously assigned to a specific sample. Illumina adapters were removed, reads were trimmed with Trimmomatic v0.36 (Bolger et al. 2014) and paired-end reads were joined using fastq-join with a minimum overlap of 50 bp (Aronesty 2011). Joined reads were filtered and only reads with a total length between 282 and 302 bp were kept to obtain the complete V4 region but still account for variation in sequence length among bacterial taxa. Since most diversity metrics are sensitive to differences in numbers of reads per sample, we chose 25,000 sequencing reads, the approximate number of reads for the sample with the lowest depth, as our sampling depth for all analyses. To determine whether this sampling depth is appropriate to capture a large proportion of the microbial diversity for each sample (bacterial species richness measured as Faith’s phylogenetic diversity), we rarefied our data (Fig 2). This analysis confirmed that a large proportion of the microbial diversity is already captured with a relatively low sequencing depth of less than 20,000 reads/sample (Fig. S.III.2).

Gut microbiota analysis

Operational taxonomic units (OTUs) were assigned with an open reference picking approach using the uclust picking method (Edgar 2010) of the open-source bioinformatics pipeline Quantitative Insights Into Microbial Ecology (QIIME; Caporaso et al. 2010). OTUs were picked based on a 97% similarity threshold and taxonomy was assigned using the Greengenes reference database v13_8 (McDonald et al. 2012). OTUs with abundances lower than 0.001% of overall reads were filtered from the resulting

45 biom table. Due to variation in sequencing depth among samples, we rarified our sequencing data to 25,000 reads per specimen. A phylogenetic tree of bacterial taxa was produced with FastTree 2.1.3 (Price et al. 2010). Bacterial species richness (Faith’s phylogenetic diversity; Faith 1992) and bacterial community composition (unweighted UniFrac; Lozupone and Knight 2005, Lozupone et al. 2011) were calculated in QIIME. Levels and variances of bacterial species richness and PCoA scores of bacterial community composition were compared among multiple species with Kruskal-Wallis tests and Fligner- Killeen tests of homogeneity of variances, respectively, as implemented in the R stats package (Conover et al. 1981). Non-parametric Wilcoxon rank-sum tests were used for pairwise comparisons of bacterial species richness and PCoA scores of bacterial community composition between species (Wilcoxon 1945). Pairwise comparisons were only performed according to colonization history, i.e. the riverine population was compared to the great lake populations, and populations from the great lakes Nicaragua and Managua were only compared to crater lake species from Apoyo and Xiloá, respectively. Correlations between stable isotope data (carbon or nitrogen) and gut microbiomes (bacterial species richness or PCoA scores of bacterial community composition) were tested using the non-parametric Spearman's rank correlation coefficient as data was not normally distributed.

Functional metagenomes were predicted based on 16S rRNA sequencing data with PICRUSt (Langille et al. 2013), using the Galaxy platform (http://huttenhower.sph.harvard.edu/galaxy/). To test for differences among species, ectomorphs or locations, both in terms of taxonomic and functional diversity, we applied Permutational Multivariate Analysis of Variance Distance Matrices (PERMANOVA; Anderson 2001), using the adonis function of the R vegan package. Mean pairwise distances of taxonomic bacterial community composition, Δ(unweighted UniFrac), and trophic level, Δ(δ15N), were calculated among all species within each of the crater lakes. Correlation between these two measures among species within each of the two crater lakes was tested using Pearson's product-moment correlation as data had a normal distribution based on Shapiro-Wilk test results. All P values were corrected for multiple comparisons using FDR. Statistical analyses were performed in R v3.2.3 (R Core Team 2015).

Results

Diet differentiation among Midas cichlids

To obtain information on trophic ecology of all study species, we measured stable isotope ratios of carbon (δ13C) and nitrogen (δ15N). Overall, populations from different aquatic environments significantly differed in δ13C (Kruskal-Wallis test, P < 0.001) and in δ15N (P < 0.001; Fig. III.2) indicating that after Midas cichlids colonized novel environments, the main source of carbon and nitrogen changed, which, in turn, might have affected their gut microbiomes.

Fig. III.2: Stable isotope analysis of carbon (δ13C) and nitrogen (δ15N) based on muscle tissue of all Midas cichlid species included in this study. δ13C and δ15N values vary significantly among environments, but also within each of the crater lakes substantial variation can be observed among species. Within each of the crater lake, there is further variation in δ15N, which indicates differences in trophic levels among species.

Moreover, δ13C differed among species in crater lakes Xiloá (P = 0.001) and Apoyo (P < 0.001). Likewise, trophic levels (δ15N) varied among species in crater lakes Xiloá (P < 0.001) and Apoyo (P < 0.001). These results clearly illustrate that Midas cichlids from different aquatic environments, but also sympatric species within the two crater lakes, feed on different carbon sources. Nitrogen signatures showed that Midas cichlid species within each of the crater lakes also occupy distinctive trophic levels (Fig. III.2). Further, as δ15N values were higher in crater lake Xiloá compared to crater lake Apoyo and δ15N values generally increase with each level of the trophic chain, this suggests that trophic chains might be longer in Xiloá. Hence, we only made inferences about differences in trophic ecology among species within the same environment. Based on these results, we investigated whether gut microbiome divergence is correlated with the observed differences in trophic ecology.

Gut microbiome differentiation across habitats

The gut microbiomes of Midas cichlids were largely composed of eight phyla that together accounted for 98.7% of all sequencing reads (species means varied from 98 - 99.8%; Fig. S.III.1). Proteobacteria (30.8 - 68.6%) and Firmicutes (5.1 - 43.3%) were the most abundant phyla in all species; except for A. citrinellus from Lake Managua where Fusobacteria (19.1%) were more copious than Firmicutes (5.1%). Albeit the major bacterial phyla were essentially similar (Fig. S.III.1), we tested whether bacterial species richness within single specimens (Faith’s phylogenetic diversity) varied among populations/species according to Midas cichlids’ colonization history. Specifically, we compared the riverine population to the great lake populations and, further, the great lake Nicaragua population to crater lake Apoyo populations and the great lake Managua population to the crater lake Xiloá populations (Fig. III.1). A. citrinellus populations from Río San Juan and from great lake Nicaragua showed similar levels of bacterial species richness, whereas the great lake Managua population had a

47 significantly reduced bacterial species richness compared to the Río San Juan population (Wilcoxon rank-sum test, P < 0.001; Fig. III.3A).

Fig. III.3: Bacterial species richness (measured as Faith’s phylogenetic diversity), shows moderate levels of variation among species; only A. citrinellus from Lake Managua shows remarkably reduced bacterial species richness (A). Nitrogen stable isotope signature (δ15N) is negatively correlated (Spearman's rank correlation coefficient) with bacterial species richness (Faith’s phylogenetic diversity), suggesting that individuals feeding on higher trophic levels have a reduced gut microbiome diversity (B). Sampling locations are indicated on top: San Juan river (SJ), great lake Nicaragua (NI), great lake Managua (MA), crater lake Xiloá (XI) and crater lake Apoyo (AP).

None of the crater lake Apoyo species showed elevated levels, except for A. globosus (P = 0.005), or higher variances of bacterial species richness compared to the source population from Lake Nicaragua. For all species from crater lake Xiloá (which was colonized from Lake Managua) bacterial species richness was higher (P < 0.001 for all species) and more variable (P < 0.05 for all species) than the Lake Managua source population. Since there was significant variation in bacterial species richness among some of the populations from distinct environments, we tested for associated differences in stable isotope signatures. When including specimens from all locations bacterial species richness was negatively correlated with δ15N (P = 0.0246) and positively correlated with δ13C (0.0013), indicating that gut microbiome diversity is affected by the source of nitrogen (Fig. III.3B) and carbon intake. Within each of the crater lakes, there was no statistically significant correlation between nor δ13C and bacterial species richness, but only a suggestive negative correlation between δ15N and bacterial species richness in crater lake Apoyo (P = 0.0809). Overall, these results demonstrate that bacterial

48 species richness was rather similar in most populations (except for A. citrinellus from Lake Managua), but that it is generally affected by diet.

Fig. III.4: Principal component analysis of wild-caught Midas cichlids’ gut bacterial community composition (measured as unweighted UniFrac). Specimens clustered by location (river, great lakes, crater lakes) along PCoA2 and PCoA3. We could neither detect differences among crater lakes nor among species within each crater lake. Along PCoA1, intraspecific gut microbiota variance was higher in some crater lake species compared to A. citrinellus from the great lakes and San Juan river.

Next, changes in bacterial community composition among populations from distinct environments were measured using distance matrices (based on Unweighted UniFrac) and were visualized with principal coordinate analysis. Gut bacterial communities of Midas cichlids from different environments were taxonomically divergent (adonis, P = 0.001) and this divergence was mainly explained by variation along PCoA2 and PCoA3 (Fig. III.4). A. citrinellus from Río San Juan was distinct from all other populations mainly along PCoA2; and the two A. citrinellus populations from the great lakes were closer to, but still differentiated from the crater lake species along PCoA2 and PCoA3, nicely mirroring Midas cichlids’ colonization history (Fig. III.1). Populations from both crater lakes mainly clustered together and were largely separated in the same direction from the great lake populations (Fig. III.4). PCoA1 mainly depicted intraspecific variation and some species (A. amarillo from crater lake Xiloá, A. astorquii, A. chancho and A. zaliosus from crater lake Apoyo) apparently showed an increased variance of bacterial community composition compared to A. citrinellus from Río San Juan and the great lakes (Fig. III.4), but none of these differences were statistically significant after correction for multiple comparisons (FDR). Variation along each of the first three axes was significantly

49 correlated with δ13C scores (Spearman's rank correlation coefficient; P < 0.01 for all three axes) as well as with δ15N scores (PCoA1: P < 0.01, PCoA2: P = 0.0238, PCoA3: P < 0.01); all P values were corrected for multiple comparisons (FDR). These results nicely illustrate that diet, measured as carbon and nitrogen isotopic signatures, is a reliable indicator for predicting gut microbiome divergence in Nicaraguan Midas cichlids.

Gut microbiome differentiation within crater lakes

Each of the two crater lakes is inhabited by several ecologically divergent species of Midas cichlids that also significantly differ in diet (Fig. III.2). Yet, within crater lakes Apoyo and Xiloá, levels or variances of bacterial species richness (Faith’s phylogenetic diversity) did not differ among any species of the respective lake. Since there were no differences in bacterial species richness, we further tested whether bacterial community composition (Unweighted UniFrac) varies taxonomically between ecomorphs (benthic vs. limnetic, see Methods and Fig. III.1 for further details) but also among species within each of the crater lakes. In crater lake Apoyo, we detected significant differentiation of bacterial community composition among ecomorphs (P = 0.001) and species (adonis, P = 0.001), but not in crater lake Xiloá. When comparing Midas cichlids from both crater lakes combined, differences in bacterial community composition varied among species from the two crater lakes (P = 0.001), but ecomorph also significantly affected bacterial community composition in both lakes (P = 0.042) and these differences occurred in the same direction (non-significant interaction). Hence, limnetic species in both crater lakes convergently changed composition of their gut bacterial community.

From the taxonomic composition of gut microbiomes, one can infer the functional metagenomes based on KEGG orthology (Langille et al. 2013). When comparing Midas cichlids’ functional composition of metagenomes within each of the two crater lakes, we found significant differentiation among ecomorphs (P = 0.015) and species (adonis, P = 0.001) in Apoyo, but only among species in Xiloá (P = 0.034). In sum, these results suggest that in Midas cichlids from the crater lakes diet specialization occurred first, followed by functional divergence and later by taxonomic divergence of the gut microbiome (at least putatively, as there was no taxonomic divergence observed in crater lake Xiloá yet).

Fig. III.5: Mean pairwise distances of taxonomic gut microbiome composition, Δ(Unweighted Unifrac), and trophic level, Δ(δ15N), among all species within crater lakes Apoyo (triangle symbols) and Xiloá (square symbols). In the crater lakes, interspecific gut microbiome differentiation is positively correlated with divergence in trophic level (based on Pearson's product-moment correlation).

Eventually, we calculated pairwise distances of feeding ecology (measured as pairwise distances of mean δ15N scores) and bacterial community composition (measured as Unweighted UniFrac) among all species within each crater lake (Fig. III.5). These comparisons revealed a strong positive correlation of these two measures, thereby clearly demonstrating that trophic divergence is closely associated with gut microbiome differentiation.

Genetic bases of gut microbiome differentiation

To test for contributions of host genetics to the observed differences in bacterial community composition among Midas cichlids, we reared specimens of five species (A. citrinellus from great lake Nicaragua, A. amarillo and A. sagittae from crater lake Xiloá, A. astorquii and A. zaliosus from crater lake Apoyo) under common conditions. Taxonomic bacterial community composition of the gut was explained by species identity (adonis, P < 0.001), but not by rearing condition or their interaction. Hence, laboratory-reared specimens did not differ significantly from wild-caught fish, suggesting that gut microbiome composition is, to some extent, genetically determined in Midas cichlids. Moreover, as seen for wild-caught fish, variance of bacterial community composition (along PCoA1 and PCoA2) appeared to be higher in laboratory-reared crater lake species (but not statistically significant), particularly in A. xiloaensis and A. sagittae from Lake Xiloá, compared to A. citrinellus from Lake Nicaragua (Fig. III.6).

Fig. III.6: Midas cichlids are clearly distinguishable from other Neotropical cichlid species along PCoA1 and PCoA2 (Unweighted UniFrac measures). The A. citrinellus population from Lake Nicaragua appears to have a lower (although statistically non- significant) intraspecific gut microbiome variance compared to crater lake Midas cichlids, but also compared to other Neotropical cichlids.

By rearing three more cichlid species, that coexist with Midas cichlids in several natural environments of Nicaragua, under the same common conditions, we found that gut bacterial community composition of Midas cichlids was significantly differentiated from these three other Neotropical cichlid species (adonis, P = 0.001; Fig. III.6). In sum, host genetics apparently affects bacterial community composition of the gut, even in the recently diverged Midas cichlid species complex but also across larger a phylogenetic scale.

Discussion

By investigating eight species from the young adaptive radiation of Midas cichlids (Fig. III.1; including three populations of A. citrinellus from different environments), we found divergence in trophic ecology, both among populations from distinct environments but also among sympatric species within each of two crater lakes (Fig. III.2). Based on these results, the main objective of this study was to determine whether these differences in trophic ecology are correlated with Midas cichlids’ gut microbiomes.

Gut microbiome differentiation across habitats

Numerous studies on a diverse set of vertebrate species have convincingly demonstrated that gut microbiome composition is affect by diet (Bolnick et al. 2014, Baldo et al. 2017, Rothschild et al. 2018), and a recent study by Smits et al. on human gut microbiomes emphasized on its very dynamic nature (2017). In Midas cichlids, variation in diet among environments was based on carbon and nitrogen isotopic signatures, illustrating that primary sources of carbon and nitrogen differ among river, great

52 lakes and crater lakes. The variation in carbon isotopic signatures also nicely reflected the colonization history of Midas cichlids, with the riverine population being more similar to the great lake populations and those, in turn, being more similar to the crater lake populations. Since δ13C and δ15N values were significantly correlated with bacterial community composition, dietary intake (reflected by carbon and nitrogen isotopic signatures) appears to be involved in determining gut microbiome divergence among Midas cichlids.

A commonly observed pattern in a wide range of animals is that species feeding on lower trophic levels show higher gut microbiome diversity (i.e., bacterial species richness) compared to species feeding on higher trophic levels (De Filippo et al. 2010, Kohl and Dearing 2012, Baldo et al. 2017). When comparing across all habitats, we also detected such a correlation in our study system based on nitrogen isotopic signature (Fig. III.3B). However, among species within each of the crater lakes we could only detect suggestive evidence for a negative correlation in crater lake Apoyo. One interesting observation is that nitrogen isotopic signatures are constantly higher in crater lake Xiloá compared to crater lake Apoyo. Such a pattern could either mean that species in the crater lakes indeed feed on different trophic levels or that food chains might be longer in Xiloá. Since it has been shown that stomach contents are rather similar across these crater lakes, the second scenario seems more plausible (Elmer et al. 2014). Additionally, these results show that in our system one has to be careful in inferring differences in trophic ecology from nitrogen isotopic signature data across environments.

In both wild-caught and laboratory-reared fish, we found suggestive evidence that guts of some crater lake species have higher levels of bacterial species richness (measured as Faith’s phylogenetic diversity) compared to A. citrinellus from the great lakes (Fig. III.3 & Fig. III.4). Similar to what we observed in Midas cichlids from the crater lakes, diet specialization in sticklebacks is associated with an increased gut microbiome diversity (Bolnick et al. 2014). Overall, biodiversity in crater lakes is reduced compared to the great lakes with much simpler trophic chains. Within the crater lakes, Midas cichlids ecologically diverged and specialized on different food items (Elmer et al. 2014), which might be reflected by the higher levels of gut microbiome diversity (Fig. III.3). Although Bolnick et al. proposed hypotheses for this observation (proliferation of few microbial strains or inhibition of a large number of bacterial strains by antimicrobial substances in a mixed diet), the question of why exactly a more specialized diet increase gut microbiome diversity remains to be answered conclusively (Bolnick et al. 2014).

Besides, we observed a consistent (albeit non-significant after correction for multiple comparisons) trend of increased variance of bacterial species richness and community composition of the gut in species from the crater lakes (Fig. III.3 & Fig. III.4). These results suggest that composition of the gut microbiome is not as tightly regulated in crater lake Midas cichlids, which might have promoted the capacity to harbor a larger variety of gut microbes. This, in turn, could be an adaptation to the

53 crater lake environment since an increased variance of gut microbiomes might have promoted the exploration of novel trophic niches after colonization of the crater lakes and facilitated the ecological diversification of Midas cichlids. However, this hypothesis is highly speculative at this point and needs to be tested in the future.

In A. citrinellus from Lake Managua, bacterial species richness was by far the lowest among all populations (Fig. III.2). The city of Managua, Nicaragua’s capitol with a population of more than 2 million, is located on the shore of Lake Managua and for decades, domestic and industrial waste water has been disposed into the lake (Lacayo et al. 1991). As a result, concentrations of mercury and other toxic substances are extremely high in the lake and are also enriched in fishes (Lacayo et al. 1991, Calero et al. 1993). Environmental pollutants, and particularly heavy metals have been shown to affect gut microbiomes in different ways (reviewed in Jin et al. 2017). Albeit speculative at this point, high levels of contamination might have decreased gut microbiome diversity of Midas cichlids from Lake Managua, thereby potentially adversely affecting the health status of these fishes. It should be noted that according to this hypothesis, the observed differences in gut microbiome diversity (bacterial species richness) among Lake Managua and crater lake Xiloá populations would be mainly driven by a recent decrease in microbiome diversity in Lake Managua rather than by an increase in crater lake Xiloá. It would be important to investigate putative effects of water contamination on gut microbiomes by including additional species from Lake Managua or by rearing fish in non-contaminated environments to see whether a more diverse microbiome can be restored.

Gut microbiome differentiation within crater lakes

One major question is whether differences in gut microbiomes are either cause or consequence of trophic divergence, however, interspecific gut microbiome differentiation is commonly studied in distantly related species with long divergence times. Thus, such studies are not altogether suited to address this question. Due to their recent evolutionary origin, Midas cichlids, especially from the young crater lakes, represent a promising system to address in which order such changes have occurred. Consistent with previous studies (Elmer et al. 2014), we found significant differentiation in main source of carbon (δ13C) and nitrogen, which reflects trophic level (δ15N), between ecomorphs (benthic vs. limnetic) and among species in each of the two crater lakes. At the same time, we only found corresponding patterns of taxonomic differentiation of bacterial community composition between ecomorphs and among species in crater lake Apoyo. On a functional level, species were differentiated within both crater lakes, but differences between ecomorphs were only detected in crater lake Apoyo. These results, particularly from crater lake Xiloá, suggest that within the short time span since this crater lake was colonized (approximately 1,300 generations; Kautt et al. 2016), Midas cichlids first diverged in feeding ecology, which initially would have led to further differentiation of the functional gut microbiome. Based on results from crater lake Apoyo, we hypothesize that taxonomic

54 differentiation of gut microbiomes would eventually also occur in crater lake Xiloá. However, comparing mean pairwise distances bacterial community composition and trophic ecology (nitrogen isotopic signature) among species within each crater lake revealed that the two measures are strongly correlated (Fig. III.5). Based on this analyses, it is not clear whether diet or gut microbiomes change first but rather that changes co-occur more or less at the same time scale.

Morphological and ecological differentiation along a benthic-limnetic axis can be found in many species of freshwater fishes, emphasizing on the importance during adaptation to different habitat types and food sources (Meyer 1990b, Landry et al. 2007, Willacker et al. 2010, Hulsey et al. 2013). When analyzing gut microbiomes from species of both crater lakes combined, we found that benthic and limnetic ecomorphs significantly differed after controlling for the effect of lake of origin. This clearly suggests that gut microbiomes of the limnetic species convergently changed after colonizing crater lakes Apoyo and Xiloá. This confirms results from a similar study on two benthic-limnetic species pairs of Midas cichlids from Apoyo and Xiloá that were reared under common conditions (Franchini et al. 2014). Our study expanded on this by including additional species from the crater lakes to obtain a more complete picture of trophic and gut microbiome divergence of the Midas cichlid species complex. These changes occurred very rapidly since crater lakes Apoyo and Xiloá were colonized only 1,700 and 1,300 generations ago, respectively (Kautt et al. 2016). Likewise, convergent changes were found in gut microbiomes of African cichlids on a larger phylogenetic scale with divergence times of 0.5 to 12 myr (Baldo et al. 2017), but other studies in whitefish and guppies did not find evidence for convergence (Sullam et al. 2015, Sevellec et al. 2018). Notably, convergent evolution has been observed in further traits of Midas cichlids from these two crater lakes, including body shape, pharyngeal jaw morphology and visual sensitivity (Elmer et al. 2014, Torres-Dowdall et al. 2017b). Convergent evolution of the gut microbiota in the two limnetic species strongly suggests that this represents an adaptation to their trophic ecology.

Genetic bases of gut microbiome differentiation

The determining factors that shape gut microbiome composition have been studied extensively in mammals, particularly in mice and humans (Turnbaugh et al. 2009a, Benson et al. 2010, Muegge et al. 2011), whereas fewer studies have addressed these issues in teleost fishes (Li et al. 2017). Studies on mammals illustrated that diet and host genotype affect gut microbiomes, however, their relative contributions remain a matter of debate (Goodrich et al. 2014, Dabrowska and Witkiewicz 2016, Rothschild et al. 2018). Accordingly, differences in gut microbiome composition observed in this study might be brought about either by genetic differentiation among host species, by a switch in diet in the novel environments, or a combination of both. In the Midas cichlid species complex, there is little genetic divergence among species due to the very recent colonization of the crater lakes (Kautt et al. 2016). Thus, differences in gut microbiota composition were expected to be rather determined by

55 dietary effects. However, contrary to this prediction, wild-caught and laboratory-reared specimens were mainly differentiated by species identity rather than by rearing environment. Along the same line, when comparing only laboratory-reared specimens from three distinct environments (great lake Nicaragua, crater lake Apoyo, crater lake Xiloá), we found that habitat of origin had a significant effect on gut microbiome composition. These results imply that gut microbiome composition is to some extent genetically controlled. Further, when compared to other Neotropical cichlids, Midas cichlids were clearly differentiated (Fig. III.6), suggesting that host genetic divergence also strongly affects the gut microbiome across larger phylogenetic scales (divergence times ranging from 5 to 8 million years based on Hulsey et al. 2010). Likewise, a study on cyprinid fishes raised under common conditions revealed that host phylogeny affected gut microbiome composition (Li et al. 2017). These results are in line with the theory of phylosymbiosis (Brooks et al. 2016), but additional species would have to be investigated to infer a robust correlation between host phylogeny and microbiome divergence in our system.

To conclude, the results of this study illustrate that (i) gut microbiomes of Midas cichlids from distinct environments are clearly differentiated and correlated with diet, (ii) within crater lakes, divergence in trophic ecology among species appeared to occur before, but is also tightly accompanied by gut microbiome divergence (iii) independently evolved limnetic species show convergence of their gut microbiomes and (iv) differences among species are, at least to some extent, controlled by host genetics.

Chapter IV

The Imperiled Fish Fauna in the Nicaragua Canal Zone

Andreas Härer, Julián Torres-Dowdall & Axel Meyer

CONSERVATION BIOLOGY, Volume 31, Issue 1, Pages 86-95, 2017

Abstract

Large-scale infrastructure projects commonly have large effects on the environment. The planned construction of the Nicaragua Canal will irreversibly alter the aquatic environment of Nicaragua in many ways. Two distinct drainage basins (San Juan and Punta Gorda) will be connected and numerous ecosystems will be altered. Considering the project’s far-reaching environmental effects, too few studies on biodiversity have been performed to date. This limits provision of robust environmental impact assessments. We explored the geographic distribution of taxonomic and genetic diversity of freshwater fish species (Poecilia spp., Amatitlania siquia, Hypsophrys nematopus, Brycon guatemalensis, and Roeboides bouchellei) across the Nicaragua Canal zone. We collected population samples in affected areas (San Juan, Punta Gorda and Escondido drainage basins), investigated species composition of two drainage basins and performed genetic analyses (genetic diversity, AMOVA) based on mitochondrial cytb. Freshwater fish faunas differed substantially between drainage basins (Jaccard similarity = 0.33). Most populations from distinct drainage basins were genetically differentiated. Removing the geographic barrier between these basins will promote biotic homogenization and the loss of unique genetic diversity. We found species in areas where they were not known to exist, including an undescribed, highly distinct clade of livebearing fish (Poecilia). Our results indicate that the Nicaragua Canal likely will have strong impacts on Nicaragua’s freshwater biodiversity. However, knowledge about the extent of these impacts is lacking, which highlights the need for more thorough investigations before the environment is altered irreversibly.

Introduction

Human induced changes to the environment are now one of the major driving forces behind evolutionary change (Palumbi 2001, Davies and Davies 2010), and they commonly have drastic effects on the integrity of natural populations and threaten the maintenance of biodiversity (Carroll et al. 2014). This applies specifically when environmental changes occur too quickly for species to adapt to, as currently seen on a large scale in what has been called Earth’s sixth mass extinction (Barnosky et al. 2011). Particularly, biodiversity in freshwater environments is far more threatened than biodiversity in terrestrial environments (Ricciardi and Rasmussen 1999, Sala et al. 2000). This is alarming because some freshwater ecosystems have exceptionally high species richness. Although freshwater habitats cover only 0.8% of Earth’s surface (Gleick 1996), they harbor approximately 6% of all species described (Hawksworth 1995). Major threats are overexploitation, water pollution, water flow modification, destruction or degradation of habitat, and establishment and spread of non-native species (Dudgeon et al. 2006). The planned construction of the Nicaragua Canal may entail all these major threats and adversely affect the aquatic fauna of Nicaragua (Meyer and Huete-Pérez 2014, Brett et al. 2015, Huete- Perez et al. 2015).

In July 2013, the Hong Kong Nicaragua Canal Development Group (HKND) of China was granted the concession to build an interoceanic shipping canal, called the Nicaragua Canal, across the Central American isthmus through Nicaragua connecting the Caribbean Sea and the Pacific Ocean (ERM 2014). The concession grants HKND rights to use any water body, including Lake Nicaragua, the largest freshwater lake in Central America (and 19th largest worldwide) with 8,400 km2 of surface area (Schwoerbel 2013). The lake’s water volume is only 80 km3 because the average depth is 15 m and the maximum depth is 50 m (Barluenga and Meyer 2010). The fish fauna of the lake is well characterized (Koenig et al. 1976, Barluenga and Meyer 2010); over 40 species have been described (Astorqui 1976, Bussing 1976). However, the level of knowledge on the fish fauna in the remote eastern regions of Nicaragua is relatively low. The prospective construction and operation of the canal are of major environmental concern due to the threat the canal poses to the fauna of Lake Nicaragua, adjacent rivers, and several natural reserves (Huete-Pérez et al. 2013, Meyer and Huete-Pérez 2014). Moreover, Lake Nicaragua’s freshwater fishes are of socioeconomic importance because fishing constitutes the livelihood of many Nicaraguans (Davies 1976, Huete-Pérez et al. 2013).

The canal will start in the Caribbean Sea at Río Punta Gorda, follow Río Tule, cross Lake Nicaragua, head westward via Río Las Lajas and enter the Pacific Ocean close to the small village of Brito (Fig. IV.1). Hence, the Punta Gorda and San Juan Basins will be connected (ERM 2014), promoting alterations in species’ distribution and ecosystem structure. Multiple routes were proposed, 2 of the rejected routes included the Escondido Basin (Fig. IV.1). The final decision on the route via Río Punta Gorda was released in the environmental and social impact assessment (ESIA) report in late 2015 and,

58 was based mainly on technical and economic grounds (ERM 2015). However, the data presented in the ESIA report, according to a panel of Nicaraguan and international scientists, do not provide sufficient information for a proper evaluation of the effects of the Nicaragua Canal on Nicaraguan biodiversity (Huete-Pérez et al. 2016). Thus, there have been appeals to conduct independent studies on a broad taxonomic scale for different ecosystems in affected areas (Huete-Perez et al. 2015).

Fig. IV.1: Proposed route (solid line) and alternative routes (dashed lines) of the Nicaragua Canal. The 3 drainage basins involved are San Juan (red), Punta Gorda (blue), and Escondido (yellow). Fish-sampling locations are marked with open diamonds.

Construction of the canal will break a geographic barrier between two distinct drainage basins, promoting homogenization of freshwater biotas across the affected regions. Biotic homogenization increases the genetic, taxonomic, and functional similarity of previously distinct biotas (Olden 2006) through introduction of non-native species, extirpation of native species, and changes to land cover (Rahel 2002). In the case of fishes (Rahel 2000, Vitule et al. 2012), biotic homogenization is associated primarily with human actions (e.g., canal building, ballast water discharge, and introduction of non- native species for recreational use), and commonly leads to a decrease in biodiversity (McKinney and Lockwood 1999). Successful establishment of invasive species is explained mainly by human disturbance of the environment (Leprieur et al. 2008). For example, in the Mediterranean Sea species invasions occur via the Suez Canal and have had adverse ecological and social consequences (Galil et al. 2015).

The aquatic landscape of Nicaragua is characterized by a number of rivers and lakes. It has been suggested that its fish fauna is similar across distinct drainage basins (Bussing 1976), which indicates

59 low levels of beta diversity. This contention has not been tested adequately. Thus, we surveyed 3 representative teleost families to determine their geographic distribution and population structure (Fig. S.IV.1, Table S.IV.4) across Escondido, Punta Gorda and San Juan Basins (Fig. IV.1). These families represent a broad taxonomic range of freshwater fishes in Nicaragua. The Punta Gorda and San Juan Basins will be part of the Nicaragua Canal zone. The studied species belong to the most species-rich and widespread teleost families in Central America. In Nicaragua, 31 Cichlidae, 7 Characidae and 8 Poeciliidae species have been described (Froese 2014). Relative to other countries in Central America, the number of poeciliid and characid species appears relatively small (Table S.IV.1). For example, only 1 poeciliid species (P. mexicana) has been described for Nicaragua, whereas 6 in Guatemala, 3 in Costa Rica and 6 in Panama have been described. These numbers indicate low species diversity across Nicaragua (Bussing 1976). In contrast, recent biogeographic investigations restricted to the Pacific slope found several species in the Poecilia sphenops and Poecilia mexicana species complexes (Alda et al. 2013, Bagley 2015). Bagley et al. (2015) detected a third genetic cluster, referred to as Poecilia sp. Tipitapa, that falls outside the P. shenops and P. mexicana species complexes. This suggests knowledge is lacking on the geographic distribution of aquatic biodiversity in Nicaragua and on how species’ distributions will be affected by the canal. Therefore, we sought to shed light on species’ distribution and patterns of population structure across three distinct drainage basins. We aimed to inform about the effects of artificially connecting distinct basins on the freshwater fauna of Nicaragua and to contribute to the evaluation of the canal’s route through Punta Gorda and Lake Nicaragua from a conservation standpoint.

Methods

Study area and sample collection

We sampled 2 poeciliids (Poecilia spp. cluster 2, Poecilia spp. cluster 4), 2 cichlids (Amatitlania siquia, Hypsophrys nematopus), and 2 characids (Brycon guatemalensis, Roeboides bouchellei). We chose these species based on their geographic distribution across Nicaragua and because their ecological characteristics (maximum size, trophic level, and reproductive strategy) differ (Table S.IV.2). Information about species’ distribution was obtained from the literature and was based on our findings in the field. We aimed to collect 25 specimens per species at each location. However, this was not achieved for all populations. Samples for interdrainage comparisons were from single-sample sites for the Punta Gorda and Escondido Basins and six sites from the San Juan Basin (Table S.IV.3 & Table S.IV.4). In August 2014, we collected fishes with seine nets (6 m long, 1-cm mesh size), gill nets (mesh sizes from 1 to 4 cm), and cast nets. We labeled each specimen with a unique identification number and stored muscle tissue or fin clips in ethanol for later DNA extraction. We identified species in the field visually and subsequently by cytochrome b (cytb) sequencing.

DNA analysis

We used polymerase chain reaction to amplify mitochondrial cytb (DreamTaq DNA Polymerase, Life Technologies, Carlsbad, California). The size of the amplified PCR products was approximately 1200 basepairs (bp), primers are listed in Table S.IV.5. Primer annealing temperatures were 54 °C (Poecilia spp.) or 58 °C (A. siquia, H. nematopus, B. guatemalensis, R. bouchellei). We sequenced purified templates on an Applied Biosystems3130xl Genetic Analyzer (Life Technologies, Carlsbad, California). The quality of sequencing reads was checked manually and reads were trimmed and assembled with SeqMan Pro (DNASTAR, Madison, Wisconsin). The trimmed DNA sequences used for all analyses had total lengths of 1086 bp (Poecilia. spp.), 1044 bp (A. siquia), 1065 bp (H. nematopus), 973 bp (B. guatemalensis), and 991 bp (R. bouchellei). The cytb sequences for phylogenetic analyses were obtained from GenBank (Table S.IV.6). For Poecilia, we assigned samples to known species based on published sequences (Fig. IV.2, Fig. S.IV.2) according to a recent phylogeny (Bagley 2015). We aligned cytb sequences and built Maximum Likelihood phylogenetic trees with SeaView version 4 (Gouy et al. 2010). We used the GTR model of nucleotide substitution to reconstruct phylogenetic trees. We created Median Joining haplotype networks with PopART version 1.7 (PopArt, Dunedin, New Zealand). Hierarchical clustering of genetic diversity was investigated using analysis of molecular variance (AMOVA) in Arlequin version 3.5.2.1 (Excoffier et al. 2005). Measures of population differentiation

(FST), haplotype diversity, and nucleotide diversity were calculated with DnaSP version 5 (Librado and Rozas 2009) and Arlequin version 3.5.2.1.

Results

Geographic distribution

Based on our field data and the ESIA (ERM 2015), at least 27 species occurred in both affected drainage basins, 24 species were exclusive to Punta Gorda, and 31 species were exclusive to San Juan (Jaccard similarity coefficient of 0.33; Table S.IV.7). Two nonnative species recently introduced to Lake Nicaragua, the Nile tilapia (Oreochromis niloticus) and the devil fish (Hypostomus panamensis), were widespread across the San Juan Basin. The characids R. bouchellei and B. guatemalensis and the cichlid A. siquia occurred in all 3 drainage basins. The cichlid H. nematopus occurred in multiple locations in the San Juan Basin. We identified 4 monophyletic cluster within Poecilia, these correspond to P. gillii (P. sp. cluster 1), P. mexicana clade 8m (P. sp. cluster 2), P. mexicana clade 8a (P. sp. cluster 3), and P. sp. Tipitapa (P. sp. cluster 4) (Bagley 2015) (Fig. IV.2, Fig. S.IV.2).

Fig. IV.2: Phylogeny of Poecilia from 3 drainage basins (San Juan, Punta Gorda, and Escondido) in Nicaragua based on cytb sequence data (1086 bp) (grey shading, samples collected for this study). Species identity was assigned based on Bagley et al. (2015).

The P. sp. cluster 1 was restricted to the Punta Gorda Basin, and P. sp. cluster 3 and cluster 4 were restricted to the San Juan Basin. In contrast, P. sp. cluster 2 occurred in all three basins. In the small creek Río Caño Chiquito, a tributary to Río Punta Gorda, two genetic clusters, P. gillii (cluster 1) and P. mexicana clade 8m (cluster 2), co-occurred. In Río Las Lajas, three different clusters co-occurred, P. sp. cluster 2, P. sp. cluster 3, and P. sp. cluster 4. The still undescribed P. sp. Tipitapa (cluster 4) from Río Tipitapa at the northern shore of Lake Nicaragua (Bagley 2015) was widespread across the San Juan Basin and had high levels of haplotype diversity (Fig. IV.3f, Table S.IV.10).

Genetic diversity

In most species, a large proportion of genetic diversity was explained by drainage basin of origin (Fig. IV.3 and Table IV.1). For the cichlid A. siquia and the poeciliid P. sp. cluster 2 a significant proportion of genetic diversity, 71% and 89% respectively, was attributable to drainage basin of origin (Table IV.1). 62

Fig. IV.3: Median joining haplotype networks for freshwater fish populations from (a–c, e) 3 distinct drainage basins and from (d–f) multiple locations within the San Juan drainage Basin. Circle sizes represent sample sizes for each haplotype.

The A. siquia population from Punta Gorda was highly differentiated from those in Escondido and San Juan, whereas populations from Escondido and San Juan were less divergent. (Fig. IV.3a, Table S.IV.8). Moreover, haplotype diversity varied strongly across drainage basins, and both haplotype and nucleotide diversity were much lower in Punta Gorda (Table S.IV.9 & Table S.IV.10). In contrast, genetic diversity was less variable in the other species (Table S.IV.9). Differences in drainage basin explained a high (63%) but only marginally significant (p = 0.065) proportion of genetic diversity for the characid B. guatemalensis. Populations from Escondido and Punta Gorda possessed a single shared haplotype, whereas haplotype diversity was higher in populations from San Juan (Fig. IV.3b & Table S.IV.10). For R. bouchellei, the crystal tetra, 96% of diversity was attributable to variation within populations, whereas 1% and 3% of diversity was attributable, respectively, to variation among populations within the same basin and among populations from distinct basins (Table IV.1). This pattern of low differentiation among populations from the same drainage basins also applied to the poeciliid P. sp. cluster 4 (P. sp. Tipitapa), indicating low levels of genetic differentiation within the San Juan Basin.

However, the cichlid H. nematopus, found at three locations within the San Juan Basin had higher levels of differentiation among populations (Table IV.1).

Table IV.1: Hierarchical analyses of genetic diversity in six fish species from the Nicaragua Canal Zone. For the first four species populations from all three drainage basins (San Juan, Punta Gorda and Escondio) are compared. Additionally, the last four species are represented by multiple populations within the same basin. Significance levels: † < 0.1, * < 0.05, ** < 0.01, *** < 0.001.

Percentage of diversity attributable to variation Within Among populations Within Among Species populations within basin basins basins

A. siquia - - 29 71***

B. guatemalensis 37 0 - 63† R. bouchellei 96 1 - 3 P. mexicana “Clade 8m” - - 11 89***

H. nematopus 79 21*** - -

P. sp “Tipitapa” 97 3 - -

Populations within the same basin showed almost no genetic differentiation, whereas comparisons across basins exhibited higher differentiation. The characid B. guatemalensis (no differentiation between Punta Gorda and Escondido) and the cichlid H. nematopus (high differentiation between Río Las Lajas and other San Juan populations) were exceptions to this pattern.

Discussion

Nicaragua is part of the Mesoamerican biodiversity hotspot and has particularly high levels of vertebrate endemism (Myers et al. 2000). Moreover, the Neotropics harbor a large proportion of freshwater fishes’ global taxonomic and functional diversity (Toussaint et al. 2016). Functional diversity plays a major role in maintaining ecosystem functioning and services relevant to humans (Diaz and Cabido 2001). The Nicaragua Canal will have large effects on the environment and poses a threat to Nicaragua’s biodiversity (Meyer and Huete-Pérez 2014). In particular, connecting the Punta Gorda and San Juan drainage Basins will substantially affect the freshwater fauna. Based on the Freshwater Ecoregions of the World (FEOW) map, the San Juan and Punta Gorda Basins are within the San Juan ecoregion (Ecoregion 205; Abell et al. 2008). Ecoregions are defined as areas harboring distinct assemblages of species communities and are viewed as conservation units. Hence, it is assumed that the two drainage basins have more or less similar freshwater faunas. Yet, data from the ESIA reveals a Jaccard similarity coefficient of 0.33 for freshwater fishes (ERM 2015), suggesting that both basins have only one third of species in common (Table S.IV.7). After the canal is built, species assemblages are 64 likely to become more similar due to biotic homogenization (Olden 2006). Yet the scope of these effects remains unknown because there is still a lack of information on the distribution of taxonomic and genetic diversity of freshwater fishes in affected areas.

Habitat alteration and destruction

Water pollution, destruction or degradation of habitat, and water-flow modification are among the major threats to freshwater biodiversity (Dudgeon et al. 2006). The canal will likely lead to increased water pollution (e.g., through construction works, oil spills, and ship traffic) and cause significant alteration of habitat along its route. Multiple rivers in the Canal Zone, representing unique and distinct ecosystems, will be turned into more lake-like environments. This homogenization of habitats and connection of distinct drainage basins can increase the likelihood of biotic homogenization caused by widespread, generalist species replacing locally adapted species in affected areas (Rahel 2000, Vitule et al. 2012). Further, destruction of habitats along the route will affect populations of numerous species, possibly posing a risk to biodiversity in these areas.

For example, three different genetic clusters of Poecilia co-occur in Río Las Lajas. According to a recent phylogeny on fishes of the genus Poecilia in Central America (Bagley 2015), these genetic clusters refer to P. mexicana clade 8a, P. mexicana clade 8m and P. sp. Tipitapa (Fig. IV.2, Fig. S.IV.2). This small river on the eastern shore of Lake Nicaragua will be part of the canal and therefore disappear in its current form. Río Las Lajas harbors elevated nucleotide and haplotype diversity for most of the species we investigated (Table IV.2, Table S.IV.9, Table S.IV.10). Dredging of the river during construction works will irreversibly destroy this habitat, putting the persistence of these populations at risk. In the Punta Gorda Basin, the Poecilia sp. Cluster 1 from Río Caño Chiquito shows only one cytb haplotype. This clade may be highly threatened by rapid environmental changes because reduced genetic diversity negatively affects a population’s ability to adapt to environmental changes (Sgro et al. 2011) and decreases survival chances under stress conditions (Frankham 2005).

Invasion by non-native species

The canal will facilitate colonization of novel habitats by non-native species, as seen in similar scenarios (Kolar and Lodge 2000, Holeck et al. 2004). This may happen either through species’ range expansion after the two drainage basins are connected or through introduction of new species via ballast-water discharge. Establishment of non-native species has detrimental effects on ecosystems (e.g., eutrophication, extirpation of native species and biotic homogenization; Figueredo and Giani 2005, Menezes et al. 2012, Vitule et al. 2012). Altered environments, such as those expected for the Punta Gorda region after construction of the canal, face a higher risk of invasive species becoming established (Moyle and Light 1996, Johnson et al. 2008). Moreover, the expected homogenization of the environment is predicted to further increase the speed of species invasions (Garcia-Ramos and Rodriguez 2002). Changes in abiotic factors, especially water-flow regime, may increase the risk of 65 successful establishment of invasive species (Baltz and Moyle 1993). Non-native species can replace native species after reduction of water-flow velocity and variability caused by the construction of a dam (Marchetti and Moyle 2001). Establishment success of introduced species is also determined by propagule pressure (Williamson 1996, Lockwood et al. 2009), which reduces effects of demographic stochasticity. Propagule pressure is determined by propagule size (number of individuals) and propagule number (number of release events) (Simberloff 2009). Because the canal will form a permanent connection between distinct drainage basins, it can be assumed that propagule pressure will be high and that this will facilitate colonization and establishment.

Our results show that even within the well-studied genus Poecilia, too little is known about the geographic distribution of species in Nicaragua, specifically in the Punta Gorda region. For example, out of the 2 genetic clusters we found in Punta Gorda, only 1 was previously described for Nicaragua. The other cluster grouped with a clade of P. gillii from Costa Rica (Clade 5b in Bagley et al. 2015). This cluster was not found in the San Juan and Escondido Basins. A recently discovered undescribed species (P. sp. Tipitapa;Bagley 2015) is known only from the San Juan Basin. Our results show that P. sp. Tipitapa is widespread across the San Juan Basin and occurs at least in Lake Nicaragua (El Tule and Isletas), Lake Managua, and 3 adjacent rivers (Río San Juan, Río Las Lajas and Río Oyate), but it was not detected in the Punta Gorda Basin. An artificial connection of the two basins will enhance the potential for range expansions and contact between previously allopatric species. This, in turn, may cause extirpations related to competitive exclusion between ecologically similar species (Hardin 1960). Because two thirds of freshwater fish species in the San Juan and Punta Gorda Basins are restricted to one or the other of the 2 basins, removing the geographic barrier could cause strong shifts in ecological dynamics for the better part of Nicaragua’s freshwater fauna.

Several nonnative species have been introduced to Lake Nicaragua, most notably tilapia (Oreochromis niloticus, O. mossambicus and O. aureus) and devil fish (Hypostomus panamensis). Tilapia have been listed as one of the most invasive groups of species in the world (Lowe et al. 2000), and their occurrence in Lake Nicaragua is correlated with a decrease in abundance of native cichlid fishes (McKaye et al. 1995). Likewise, based on accounts of local fishers, the occurrence of the devil fish is accompanied by a decrease in abundance of native species (personal communication), especially of the commercially important guapote (Parachromis managuensis). However, the ecological impacts of this species have not been investigated formally. All the aforementioned species appear to be highly invasive in Nicaragua and have spread across the entire San Juan Basin within a few years (McKaye et al. 1995, personal observation) but have not been reported in the Punta Gorda Basin to date. Hence, the construction of the canal will facilitate colonization by removing biogeographic barriers (Rahel 2002), most likely posing a threat to the local fauna.

Hybridization between genetically distinct populations

Contact between genetically distinct populations could lead to (intra- and interspecific) hybridization and cause genetic homogenization of previously differentiated gene pools (Olden et al. 2004). This would manifest as a decrease in allelic composition, an increase in similarity of allelic frequencies, and possibly compromised local adaptation (Storfer 1999). We detected high levels of differentiation among drainage basins for taxonomically and ecologically diverse species. Assuming our results are generally applicable across a broad taxonomic range, populations from distinct basins may represent independent evolutionary significant units (Fraser and Bernatchez 2001). However, populations within the same drainage basin showed no significant genetic differentiation (Fig. IV.3d - f), even across large geographic distances of up to 180 km (e.g. from Lake Managua to El Tule or from Río Las Lajas to El Castillo). These results suggest that connecting the two basins will most likely be accompanied by genetic homogenization for multiple species in the Nicaragua Canal zone. At least 27 fish species occur in both basins (Table S.IV.7), which illustrates the large scale on which unique genetic diversity may be lost across Nicaragua’s freshwater fauna.

Political issues concerning the Nicaragua Canal

Unfortunately, environmental concerns are commonly not addressed properly in the course of political decision making. There are many cases of artificial waterways connecting distinct drainage basins in which associated risks for biodiversity were not taken into account (Zhan et al. 2015). For instance, the Brazilian government decided to transfer water between isolated river basins to combat the effects of a severe drought without accounting for environmental consequences (Vitule et al. 2015). Nicaraguan freshwater biodiversity will face serious threats once construction of the canal begins. The concession to build and operate the canal was given to the infrastructure development firm HKND by the Nicaraguan government without competitive bidding Moreover, the environmental consultancy ERM was commissioned by HKND to conduct the environmental and social impact assessment (ESIA) only after the concession was already granted. In general, the ESIA is not comprehensive. It lacks, for example, data on stratigraphy of the lake and a seismic risk assessment and does not address water quality. These problems have been pointed out by a panel of Nicaraguan and international scientists who participated in a workshop in Managua organized by the Academy of Sciences of Nicaragua in 2015 (Huete-Pérez et al. 2016). The ESIA also does not properly account for effects of the canal on distinct localized freshwater faunas in the 2 affected drainage basins or the detrimental effects of biotic homogenization for these regions. The threats this project poses to multiple ecosystems and their freshwater biodiversity are profound and their effects will be irreversible. Thus, a sustainable management concept should be implemented. The associated effects of the canal on biodiversity remain unclear. Hence, the precautionary principle should be applied; the burden of proof that an action is not harmful to the environment is on those taking action. This concept is endorsed in the UN World Charter for Nature (United 1982) and calls are being made for its application to become common 67 practice internationally, especially in the context of comparable mega projects. However, its efficacy to counteract the current loss of biodiversity and its proper incorporation into political decision making remain controversial (Myers 1993, Cooney 2004, Kanongdate et al. 2012).

We found that populations from distinct drainage basins were genetically differentiated and that species assemblages differed substantially among basins. These results highlight the risk of genetic homogenization and biotic invasion associated with the Nicaragua Canal. Breaking down the geographic barrier between the Punta Gorda and San Juan Basins, as the canal would, is worrisome from a conservation standpoint and may have drastic consequences for Nicaragua’s freshwater fauna, that is loss of unique genetic diversity on a large scale. The canal will surely imperil the status quo ante. However, due to the lack of knowledge of the biogeography of species and phylogeographic distribution of genetic variation within the Nicaragua Canal zone, it is currently impossible to adequately predict consequences of the canal on Nicaragua’s biodiversity. Therefore, it is of great urgency to determine which endemic species or genetically unique populations may be threatened by its construction. Hence, we call for more thorough and independent investigations of biodiversity in the ecosystems affected by the Nicaragua Canal. Otherwise, many more, putatively still unknown, species might be threatened by extinction.

General Discussion

This dissertation aimed to further our understanding of how organisms, in this case different species of Nicaraguan cichlid fishes, are affected by and how they adapt to novel environmental conditions. The underlying mechanisms of phenotypic diversification and local adaptation were investigated on different levels, within the lifetime of single individuals (Chapter I) or on evolutionary time scales (Chapter II and Chapter III). Further, we discussed the threat of human-mediated environmental change, the planned construction of an interoceanic shipping canal, on Nicaragua’s freshwater biodiversity (Chapter IV).

Environmentally induced changes in given traits of organisms, i.e., phenotypic plasticity, are a major source of phenotypic variation, particularly during development. The visual system of Midas cichlids proves ideal for studying environmental effects on phenotypic diversification because cone opsin expression (responsible for color vision) changes during ontogeny and there is a good understanding of the molecular mechanisms underlying this trait. (Carleton et al. 2016, Torres-Dowdall et al. 2017b). Midas cichlids adapted to the prevailing light environments in different aquatic habitats by changing cone opsin expression patterns (Torres-Dowdall et al. 2017b), but, the source of this phenotypic diversity that selection acted upon was unknown. Yet, a good deal is known about the colonization history of the Midas cichlid species complex (Barluenga and Meyer 2010, Kautt et al. 2016). Midas cichlids colonized young and clear crater lakes from ancestral and turbid great lakes. Assuming that the light environments of the great lakes have been more or less constant over time, this setting allows to compare derived phenotypes (crater lake Midas cichlids) to inferred ancestral phenotypes (great lake Midas cichlids). In this respect, Midas cichlids provide a better system to address this question than the adaptive radiations of cichlids from the African great lakes (Lakes Malawi, Tanganyika and Victoria). Although African cichlids show a spectacular diversity of the visual system (Carleton et al. 2016), divergence times are commonly much longer and phylogenetic relationships among species are more complex than for Midas cichlids. This complicates inferring ancestral phenotypes in African cichlids, but it has been done by O’Quin et al (2010).

The results from Chapter I demonstrate that Midas cichlids, similar to many African cichlids, change cone opsin expression during ontogeny, suggesting that this is a common mechanism that produces phenotypic variation during an individual’s lifetime in both African and Neotropical cichlid fishes (Carleton et al. 2008). Based on knowledge about the history of Midas cichlids, we infer that Amphilophus astorquii from crater lake Apoyo shows a peadomorphic visual phenotype. During the evolution of Midas cichlids’ visual system, this paedomorphic phenotype was most likely brought about by slowing down the ontogenetic progression in cone opsin expression of A. astorquii and most likely represents an adaptation to the light environment of the crater lake that substantially differs from the great lakes (Torres-Dowdall et al. 2017b; see also Chapter II). These changes evolved within less than 69

1,700 generations (Kautt et al. 2016), which, in turn, illustrates that this adaptation of the visual system to a novel light environment was mediated by changes in developmental progression and occurred very rapidly. Thus, Chapter I presents an example of how alterations of developmental progression can produce an adaptive adult phenotype.

Cone opsin expression patterns of Midas cichlids are also affected by the ambient light environment, as further shown in Chapter I. These environmentally-induced changes increased visual sensitivity under the prevailing light conditions, indicating that phenotypic plasticity of Midas cichlids’ visual system is indeed adaptive, in the sense that it tracks the dominant light environment. So, this adaptive plasticity in the ancestral population of Midas cichlids might have facilitated colonization of the crater lakes, characterized by their short wavelength shifted photic environments. Then, there might have been further selection acting on the phenotype resulting in local adaptation of A. astorquii’s visual system and divergence from its source population in Lake Nicaragua (Pigliucci et al. 2006, Crispo 2007, Ghalambor et al. 2007). In fact, A. astorquii from crater lake Apoyo produced its derived phenotype even in the absence of the stimulus (short wavelength light). Yet, another interesting observation was that plasticity itself evolved. At the age of six months, phenotypes were canalized in the ancestral population as light environment did not result in a phenotypic change, but the visual system of A. astorquii from crater lake Apoyo remained plastic. When exposed to red light mimicking the ancestral environment, A. astorquii expressed a set of cone opsins resembling the phenotype of Midas cichlids from the great lake. This is in agreement with plasticity evolving as a by- product of local adaptation (Via and Lande 1985, Lande 2009). The more plastic phenotypes would be selected in the novel environment as they produce the most adaptive phenotypes resulting not only in the evolution of adaptive changes but also in an increase of plasticity (Crispo 2007). Taken together, the results from Chapter I illustrate that evolution of Midas cichlids’ visual system was facilitated by adaptive developmental plasticity.

Chapter II is, to our knowledge, the first study that conclusively shows convergence of the visual system in cichlid fishes in response to the same environmental conditions. It is also one of the first ones to show that RNA-Seq is a valid method to obtain reliable cone opsin expression data for a large number of individuals. What makes the visual system particularly appropriate to study convergent evolution is that the colonization history of the Nicaraguan aquatic landscape is well-known (Barluenga and Meyer 2010, Kautt et al. 2016), and that populations of multiple cichlid species occur in habitats that differ strongly in their photic environments (Torres-Dowdall et al. 2017b; Chapter II). Further, the vertebrate visual system has been extensively studied, which revealed valuable insights into the adaptive value of molecular and phenotypic changes. Taken together, this knowledge enabled to predict the direction of phenotypic change after colonization of novel environments. This major question of how deterministic evolution is has been vigorously debated for almost 30 years (Gould 1989, Conway Morris 2003, Losos 2017). In contrast to Stephen Jay Gould’s view that evolutionary 70 change is strongly affected by contingency over long time scales; when natural selection is strong and when one observes evolution over short time scales, evolutionary change could indeed be predictable, especially if the nature of selection is known (Nosil et al. 2018). While it is commonly challenging to conclusively show that natural selection is acting on a given trait (Endler 1986), when such a trait evolves repeatedly in independent lineages, i.e., convergent evolution (Arendt and Reznick 2008), one can plausibly infer that this is due to shared selection pressures. Convergent evolution has been described in many instances (Schluter and McPhail 1992, Colosimo et al. 2005, Mahler et al. 2013, Elmer et al. 2014), however, it should be noted that in most of these cases organisms colonized similar, but not identical novel environments, and hidden environmental heterogeneity might affect the extent of convergence (Fitzpatrick et al. 2014, Stuart et al. 2017). The study presented in Chapter II circumvents this potential problem as all study species share exactly the same environments, which combined with knowledge on colonization history, light environments and adaptive value of changes to the visual system, makes this system an excellent one to study convergent evolution (Rosenblum 2006). Moreover, the advancements in sequencing technology allow us to nowadays not only study convergence on a phenotypic level but also to determine the underlying molecular mechanisms (Elmer and Meyer 2011). Again, the visual system is particularly suited to ask whether phenotypic convergence is also reflected by convergence of molecular mechanisms because the phenotypic effects of molecular changes (i.e., coding sequence variation and differential expression) are well- understood. Interestingly, the results from Chapter II revealed that differential expression of a varying subset of cone opsin genes produced phenotypic convergence of visual sensitivities. Since it is known from African cichlids that gene expression of cone opsins is regulated largely independently (Carleton et al. 2010, Schulte et al. 2014, Nandamuri et al. 2017a), it can be speculated that the differences in gene expression patterns observed in Nicaraguan cichlid fishes are regulated by different molecular bases. Thus, we conclude that the convergent evolution of visual sensitivity resulted from non- convergent patterns of cone opsin gene expression.

Chapter III shifts the focus from a particular trait of cichlids, the visual system, to a different level of biological organization, the holobiont (Brucker and Bordenstein 2013a). The holobiont is characterized by the interaction between a host and its associated microbiome, and in this study focus was set on the gut microbiome. There is an ongoing debate about the contributions of host genetics and diet in shaping the gut microbiome (Goodrich et al. 2014, Rothschild et al. 2018). In Midas cichlids, diet (measured as stable isotope signatures of carbon and nitrogen) was correlated with gut microbiome composition but when fish were reared under common conditions (including the same diet), gut microbiome differences among species were maintained. This implies that diet influenced gut microbiome divergence among species and that these differences have become assimilated with time so that they now persist, even when fish have the same diet. These are interesting results since sympatric species of Midas cichlids found in crater lakes have short divergence times (less than 900

71 generations for the crater lake species) (Kautt et al. 2016) and, thus, show low levels of genetic divergence. Still, even in this young system host genetic background seems to contribute to regulating the gut microbiome. In each of the two crater lakes examined in this study, Apoyo and Xiloá, Midas cichlids independently diverged along a benthic-limnetic axis and distinct species evolved in sympatry (Barluenga and Meyer 2004, Barluenga et al. 2006, Elmer et al. 2014, Kautt et al. 2016). Benthic and limnetic species from both crater lakes differ in habitat use but also convergently in diet (Elmer et al. 2010, Elmer et al. 2014). Notably, the two limnetic species (A. zaliosus from Apoyo and A. sagittae from Xiloá) changed their gut microbiomes in the same direction, suggesting that these convergent changes were either produced by or are adaptations to changes in diet. However, it is commonly uncertain whether changes of the gut microbiome either allow trophic diversification or are the consequence of changes in diet. Since Midas cichlids of the crater lakes only recently diverged but show strong differentiation in trophic ecology, they are suited to address this issue. As Chapter III revealed that differentiation of gut microbiome composition was strongly correlated with trophic divergence among crater lakes species, this suggests that divergence of diet and the gut microbiome evolve, or better co-evolve, at similar rates. However, only in crater lake Apoyo have sympatric species significantly diverged both in trophic ecology and in gut microbiome composition whereas in crater lake Xiloá only divergence in trophic ecology was detected. The results from crater lake Xiloá imply that in this setting trophic divergence likely evolved first but there might have been too little time for divergence of the gut microbiome. However, divergence times among sympatric species within each of the two crater lakes are more or less identical (approximately 900 generations ago) (Kautt et al. 2016). This implies that time alone cannot explain why gut microbiomes changed in one but not the other crater lake, but given that the level of differentiation in trophic ecology among sympatric species is similar in both lakes, diet alone cannot explain it either. Thus, the factors that determine divergence of the gut microbiome and cause the differences among the fishes of the crater lakes remain to be investigated in the future.

Finally, the study presented in Chapter IV focused on the impact of human-mediated actions on biological diversity from a conservation standpoint. Specifically, the potential consequences of a large scale infrastructure project, the construction of the Nicaragua Canal, on Nicaragua’s freshwater fish fauna were examined. One potential effect is the connection of previously isolated habitats, thereby bringing into contact locally adapted and genetically distinct populations of the same species, but also different species that have not encountered each other in the past. To this end, we first collected information on species distribution and systematically compared freshwater faunas of two distinct drainage basins that will be affected by the Nicaragua Canal. The San Juan and Punta Gorda drainage basins have been merged in the San Juan ecoregion according to the Freshwater Ecoregions of the World map (Abell et al. 2008), which suggests that these drainage basins harbor similar species communities. However, our results revealed that only one third of all freshwater fish species are

72 shared among San Juan and Punta Gorda, which clearly shows that these two drainage basins indeed have highly distinct species communities and should be treated as separate conservation units. Moreover, the geographic distribution of genetic diversity was determined for a subset of representative species from different teleost families. These analyses revealed that populations from San Juan and Punta Gorda are genetically differentiated, which emphasizes on the risk that genetic diversity might be lost once the Nicaragua Canal will produce an artificial connection of the two distinct drainage basins. Further, by reporting species of the genus Poecilia, a well-studied group of Central American freshwater fishes, from areas where they had not been described before, Chapter IV illustrates the lack of knowledge on biodiversity in the affected areas. To summarize, the results of this study give information that is essential to adequately evaluate the effects of the Nicaragua Canal on Nicaragua’s freshwater fauna. This is of particular importance since the decision for the route of the canal via the San Juan and Punta Gorda drainage basins, that was chosen in the frame of the official ecological and social impact assessment (ESIA), was not based on ecological grounds but rather on technical and economic grounds (ERM 2015). Another problem is that the environmental consultancy firm (ERM) that performed the ESIA was commissioned by HKND, the company that is supposed to build the canal, which inherently poses a conflict of interest. To our knowledge, the study presented in Chapter IV, together with another one on terrestrial mammals inhabiting the Caribbean Coast of Nicaragua (Jordan et al. 2016), is the only independent investigation of the ecological consequences of the Nicaragua Canal on biodiversity.

To summarize, one of the main conclusions across the chapters of this dissertation is that phenotypic change in Nicaraguan cichlids is largely predictable, at least on the time scales observed in our system, as exemplified in the visual system as well as in the gut microbiome. Moreover, these convergent phenotypic changes were, at least to some extent, genetically determined, and occurred over very short periods of time, providing an interesting system to study rapid adaptive evolution. However, biological diversity is at risk in the current times where human activity tends to homogenize the natural world.

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Acknowledgements

General acknowledgements

First and foremost, I would like to express my gratitude to Prof. Axel Meyer for giving me the opportunity to conduct my research in his lab and for supporting me throughout the last years. Being a PhD student in Axel’s lab allowed me to work in a highly stimulating environment, meet interesting people at conferences and travel to beautiful Nicaragua and I sincerely value all this. I am particular thankful that Axel allowed, or rather encouraged, me to develop my own research ideas, to think critically and also to never forget the most important question: “Why is this interesting?”

I would also like to thank Prof. Louis Bernatchez for agreeing to be a member of my thesis committee and for valuable comments on my work and his general support. Unfortunately, Louis had to resign from my committee due to scheduling reasons.

Prof. Cameron Ghalambor agreed to replace Louis on my thesis committee at the last minute and I am more than grateful for that. I also really enjoy the fact that Cameron was Julián’s supervisor for his doctoral work, which somehow makes Julián my scientific brother.

Further, I would like to thank Prof. Mark van Kleunen for agreeing to chair my PhD defense.

I don’t even know where to start thanking Julián. But one thing I know for sure is that I owe Julián more than I can express here. At an early phase of my PhD, during which I was struggling with the direction of my research, one day Julián asked me to help him out with some qPCRs on opsin genes and the rest is history. Ever since this fortunate event, Julián has supported me in so many ways and always gave me the feeling that we are on the same level. While he might think that I regularly got frustrated by the plethora of comments on my manuscripts, proposals or presentation, in fact I absolutely valued his input and I grudgingly have to admit that most of the time his suggestions significantly improved my work. I also absolutely loved to listen to his baby stories over a glass of smoky whisky. Over the last couple of years, Julián has become a true friend. Thanks for everything big brother!

Of course, none of this could have been achieved without the valuable help and advice from all my friends and colleagues in the lab. Working together with such bright and dedicated people was really inspiring and joyful. I will not go into more detail here but you know who you are.

Nidal, thanks for your amazing work and for being a great guy. There is no doubt that you will have a bright future in science. Just don’t forget that there is also a world outside the lab.

There are so many wonderful people that made the last couple of years in Konstanz quite a memorable time. Special thanks go to Sylvia, Joe and Sales (thanks for being so nice and quiet while I am writing this), Hendrik, Karl, Martin and Elena, Andi Graumann, Claudius, Lars, Dori and many more.

Most of all, I am thanking my parents who have always supported me in any possible way throughout the years. I truly appreciate everything they have done for me.

Finally, I would like to thank Tanja. Thanks for being my best friend and for making life so much better every day.

Chapter-specific acknowledgements

Chapter I

I would like to thank N. Karagic and M. Kaupp for support with molecular work and data analysis, R. F. Schneider for fruitful discussions and N. Karagic for valuable comments on the manuscript. All experiments were performed under University of Konstanz permit (Aktenzeichen 35-9185.81/G- 16/07). This work was supported by the European Research Council through an ERC-advanced grant (grant number 293700-GenAdap to AM), the Deutsche Forschungsgemeinschaft (grant number 914/2- 1 to J.T.D.) and the Young Scholar Fund of the University of Konstanz (grant number FP 794/15 to JTD).

Chapter II

I would like to thank the Ministry of Natural Resources (MARENA) in Nicaragua for collection and export permits. For logistic support, we thank G. Schnepel, J. Huete-Pérez and the Universidad Centroamericana in Managua. I thank L. Paíz-Medina and R. Rayo for help in the field, D. Monné Parera for help in the laboratory and N. Karagić for fruitful discussions. This work was supported by the European Research Council through ERC-advanced (grant number 293700-GenAdap to AM), the Deutsche Forschungsgemeinschaft (grant number TO 914/2-1 to JTD) and the Young Scholar Fund of the University of Konstanz (grant number FP 794/15 to JTD).

Chapter III

I thank the Ministry of Natural Resources (MARENA) in Nicaragua for collection and export permits. Further, I would like to thank L. Paíz-Medina and R. Rayo for help in the field. This work was supported by the European Research Council through ERC-advanced (grant number293700-GenAdap to A.M.) and the University of Konstanz.

Chapter IV

I thank the Ministry of Natural Resources (MARENA) in Nicaragua for collection permits. For logistic support, I thank the Universidad Centroamericana in Managua. L. Páiz Medina, R. Rayo, and L. Reyes Gutierrez helped in the field. The field trip to Nicaragua was funded by the “Stiftung Umwelt und

Wohnen an der Universität Konstanz.” This work was supported by the European Research Council through ERC-advanced (grant number293700-GenAdap to A.M.) and the University of Konstanz.

Author contributions

Chapter I

AH and JTD designed the experiments, collected the data, and performed the analyses. AH wrote the first draft of the manuscript with contributions from JTD. All the authors read and edited the final version.

Chapter II

All authors conceived the idea and collected the samples; AH prepared the libraries, analyzed the data and wrote the manuscript with revisions from AM and JTD.

Chapter III

AH, JTD and AM developed the project; AH, JTD and GMS collected samples in the field. AH extracted DNA, prepared the libraries and analyzed the sequencing data. AH and SR prepared samples for stable isotope analyses. AH wrote manuscript with revisions from JTD and SR.

Chapter IV

All authors developed the project, AH collected all samples in the field and further did all molecular work and analyses under supervision of JTD. AH wrote the manuscript with revisions from AM and JTD. All the authors read and edited the final version.

Supplementary Material

Chapter I

Fig. S.I.1: Light spectrum of the standard fluorescence lamp light condition. All fish were reared in this light environment before introduction to the respective light treatments.

Fig. S.I.2: Proportional Quantum Catch for cone opsin genes in the four light treatments. Proportional Quantum Catch values for each cone opsin are relative to overall cone opsin expression, i.e., quantum catch values sum up to 100% for each light treatment. In the blue light treatment, short and medium wavelength sensitive cone opsins (SWS2B, SWS2A and RH2B) capture the highest proportions of light, in all other light treatments the long wavelength sensitive LWS captures most of the light.

Fig. S.I.3: Diagnostic plot of the linear regression model for A. citrinellus predicted single cone sensitivities during ontogeny.

Fig. S.I.4: Relative expression of two deiodinase genes (dio2 and dio3) and two cone opsin genes in controls (grey bars) and thyroxine (T4) treatments (black bars). As expected based on previous studies, TH treatment downregulated dio2 and upregulated dio3 gene expression (Top Row). TH treatment also mediated an increased expression of the cone opsins most sensitive to the longest wavelengths in single (sws2a) and double cones (lws). Fish larvae at the age of 7 days were introduced to the treatments that lasted 2 weeks. Expression is normalized with the geometric mean of two housekeeping genes (imp2 and gapdh2); normalized expression values were compared between treatments for each gene (Wilcoxon rank sum test, ** p < 0.01, * p < 0.05, † p < 0.1).

Fig. S.I.5: Statistically significant correlations (p < 0.05) between dio and cone opsin gene expression were determined using the Pearson Correlation Coefficient. sws2a, rh2aβ and lws expression were positively correlated with dio3 expression and sws2b expression was positively correlated with dio2 expression, as expected based on the assumption that TH signaling leads to a shift towards long wavelength sensitivity. However, there are some caveats in this analysis. To obtain an appropriate sample size for the correlation analyses, expression data of great lake Midas (black triangles) and crater lake Midas (grey circles) and all age classes were combined. Nonetheless, we have some evidence that species and age affect dio gene expression differently, and therefore these results should be taken with caution. We are currently working on a more comprehensive study that will overcome these issues and further address the effects of TH signaling on opsin expression and development of the visual system.

Fig. S.I.6: Proportional cone opsin expression for great lake Midas cichlids (left column) and crater lake Midas cichlids (right column) exposed to different light treatments. Both species showed phenotypic plasticity as response to light treatments at 7 days and 14 days. At 6 months, cone opsin expression is only affected by light treatments in crater lake Midas cichlids.

Table S.I.1: LEDs used for the light treatments in this study. Further information about the properties of the LEDs can be found under the denoted URLs. Light treatment Type of LEDs Web URL http://www.leds.de/High-Power- Cree XP-E, royal blue, 500 Blue LEDs/Cree-High-Power-LEDs/Cree-XP- mW E-royalblau-500mW.html http://www.leds.de/High-Power- Cree MX6 Q5, white, 278 LEDs/Cree-High-Power-LEDs/Cree- Cold-white Lumen MX6-Q5-weiss-278-Lumen-mit- Platine-Star.html http://www.leds.de/High-Power- Cree MX6 Q3, warm-white, LEDs/Cree-High-Power-LEDs/Cree- Warm-white 242 Lumen MX6-Q3-warmweiss-242-Lumen-mit- Platine-Star.html http://www.leds.de/High-Power- Red Cree XP-E, red-orange LEDs/Cree-High-Power-LEDs/Cree-XP- E-rot-orange-154-Lumen.html

Table S.IV.2: List of qPCR primer used in this study.

Primer Sequence SWS1_F2 GATATACCTGCTCCAGCCAAAG SWS1_R2 GGTCATCTGTAAACCATTTGGAG

SWS2B_F1 CACGAACGGAACTGCTTATTG SWS2B_R1 GCCATTTGACCTGAGACTGG

SWS2A_F1.1 GAAACTTCGATCCCACCTCA SWS2A_R4 GGCTTACCATACCACCGAGT

RH2B_F1 GCTTGCAGCAGCCTTCAC RH2B_R1 CCTGTGGACCGGATTACTACAC

RH2Abeta_F3 TGCTGGAGTACTCTTCACATGG RH2A_R3 CTCATTGTTGAAGCCTGGAG

RH2Aalpha_F2.1 TGGTGCTGGGGTACTTTTG RH2A_R3 CTCATTGTTGAAGCCTGGAG

LWS_F1.1 GGCGGTACCATGAAGATACAAC LWS_R4.1 ACCACGAAAAGCATCCAGAG

DIO2_F1 GCATTGTTGTCCATGCAGTC DIO2_R1 CAATGGGTCCTTGCTCTTTC

DIO3_F2 TGAGATCCGATACCCCTCAG DIO3_R1 CATGGAAAACTCCTCCAACG

Cyp27c1_F1 AATTGCAGAGCTGGAGATGC Cyp27c1_R1 TGGCTTTGATGTCAGGAGTG

IMP2_F1 GCCTGGAGCATGTTGACC IMP2_R1 CGAAGTGACGGATCTTACGG

GAPDH2_F1 TGCCCATACAAACATCATTCC GAPDH2_R1 GGCATGTCAGATCCACCACT

Chapter II

Fig. S.II.1: Proportional expression of six cone opsins based on quantitative Real-Time PCR (qPCR; y- axes) and RNA-Seq (x-axes) data. All cone opsins with substantial expression levels (particularly the highly expressed sws2a, rh2a and lws) showed strong correlations between the two methods based on Spearman's rank correlation coefficient, thereby validating RNA-Seq as an appropriate method to estimate opsin gene expression levels. Note that expression data of rh2aα and rh2aβ was combined to overall rh2a expression since the two paralogs could not be distinguished by qPCR in previous studies. All qPCR data was obtained from Torres Dowdall et al. (2017).

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Fig. S.II.2: Hypothetical data of two species to illustrate our vector analysis for convergent evolution. Two-dimensional vectors connect population means from river, great lake and crater lake for each species. The angle between two vectors is indicated by γ. The length of each vector (L) and the pairwise difference in length (ΔL) are further shown. γ and ΔL were calculated for each species pair (total of 21 pairwise comparisons) and each colonization event separately. The sums of these 21 pairwise comparisons were calculated for γ and ΔL. When both colonization events were analyzed together, the sums of for γ and ΔL from both colonization events were added. For statistical analyses, species identity was randomized within each habitat and 21 pairwise comparisons of γ and ΔL were performed. 999 random sets were created and for each, the sums of these pairwise comparisons were added and compared to the original data set. If the sums of the original data set were smaller than 5% of the random data sets, we regarded this as evidence for parallelism.

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Fig. S.II.3: As previously shown with quantitative Real-Time PCR (Härer et al. 2017, Torres-Dowdall et al. 2017b), Midas cichlids (A. astorquii) from crater lake Apoyo (Top Row) express sws2b and rh2b, similar to A. siquia from crater lake Xiloá (Bottom Row).

Fig. S.II.4: Expression ratio of green-sensitive paralogs (rh2aβ/total rh2a) differed among habitats in all four species that expressed both paralogs (A. centrarchus, A. siquia, H. nicaraguensis and A. rostratus). (Kruskal-Wallis test, * p < 0.05, ** p < 0.01, FDR corrected).

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Fig. S.II.5: Proportional expression values for all seven cone opsins of wild-caught specimens from river, great lake and crater lake (white, grey and black bars) as well as laboratory-reared specimens (orange bars). Hatched bars indicate populations from the same habitat of origin. For A. cf. citrinellus and A. centrarchus, laboratory-reared specimens were originally from the great lake, and from the crater lake for A. siquia and H. nematopus. Note that gene expression data of laboratory-reared specimens of A. cf. citrinellus was generated by a different molecular method (qPCR) and is obtained from Torres Dowdall et al. (2017).

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Fig. S.II.6: Principal component analyses was performed using the prcomp function of the stats package in R v3.2.3 (R Core Team 2015). As input, we used proportional cone opsin expression data (see Fig. S.II.5) of wild-caught specimens (black and grey symbols) and laboratory-reared specimens (orange symbols) from the same habitat of origin for four species. Laboratory-reared specimens of A. cf. citrinellus and A. centrarchus were originally from the great lake A. siquia and H. nematopus from the crater lake. Along the main axis of variation (PC1, explaining approximately 80% of the total variance), specimens cluster by habitat of origin, independent of rearing condition. These results suggest that cone opsin expression patterns are, at least to some extent, genetically determined. However, there is also evidence for phenotypic plasticity, e.g. in A. siquia, where wild-caught and laboratory-reared specimens segregate along PC2. Note that for laboratory-reared A. cf. citrinellus, qPCR data is shown that was obtained from Torres-Dowdall et al. (2017).

Table S.II.1: Morphological and ecological features of all study species. Within a food web, the position of organisms is defined by the trophic level based on their feeding behavior. A value of 2 is

104 characteristics for herbivores, values of 3 and 4 are characteristic of primary and secondary carnivores. Data was obtained from www.fishbase.org.

Species Max length [cm] Habitat Trophic level Polychromatism A. cf. citrinellus 24,4 Benthopelagic 3,2 ± 0,47 Dark & Gold morphs A. centrarchus 11,0 Benthopelagic 2,6 ± 0,28 - A. siquia 7,9 Benthopelagic 2,3 ± 0,2 Sexual dimorphism P. managuensis 55,0 Benthopelagic 4,0 ± 0,59 - H. nematopus 14,0 Demersal 2,0 ± 0,00 - H. nicaraguensis 16,5 Benthopelagic 2,7 ± 0,34 Sexual dimorphism A. rostratus 18,5 Benthopelagic 2,6 ± 0,27 -

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Table S.II.2: Sampling locations and sample sizes for all species.

Crater lake Xiloá Lake Nicaragua Lake Managua San Juan River Punta Gorda River GPS coordinates N 12°12.502' N 11°55.090' N 12°13.197' N 10°56.556' N 11°29.538' W 86°18.573' W 85°55.012' W 86°17.313' W 83°43.599' W 84°28.405' A. cf. citrinellus 5 4 6 A. centrarchus 6 3 6 A. siquia 6 6 6 6 P. managuensis 5 6 6 H. nematopus 6 6 5 H. nicaraguensis 6 6 6 A. rostratus 6 5 6

Table S.II.3 (provide in Online Supplementary Material: https://onlinelibrary.wiley.com/doi/abs/10.1111/mec.14289): Total number of raw reads, reads mapped to the Midas cichlid reference genome and reads mapped to each cone opsin and cyp27c1.

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Table S.II.4: Variable sites within species leading to amino acid substitutions in RH2Aβ and LWS opsin proteins. In SWS2A, non-synonymous substitutions were not found in any species. Grey boxes indicate sites under positive selection based on random site models of codon evolution in PAML. Numbering of amino acid sites is based on bovine RH1.

protein RH2Aβ LWS nucleotide position 559 664 767 77 157 172 280 656 680 688 898 Species n A. cf. citrinellus 15 A. centrarchus 15 a/g a/g A. siquia 24 t/g c/t P. managuensis 14 H. nematopus 17 t/g c/t t/g c/t c/t a/g H. nicaraguensis 18 a/g c/t A. rostratus 17 a/c amino acid substitution I179L V214F R248K A10V S40A F45L V81I V206A I214T G217S A287T location TM5 TM6 TM1 TM1 TM2 TM5 TM5 TM5 TM7

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Table S.II.5: Variable SWS2A residues across species. Numbering of amino acid sites is based on bovine RH1.

protein SWS2A amino acid residue 13 42 47 50 101 131 155 220 232 248 Species n A. cf. citrinellus 15 V A V F R I A I M K A. centrarchus 15 V A V F R I A I M K A. siquia 24 V A V F R I A L M K P. managuensis 17 V A V F R I A I M K H. nematopus 17 V V V F R V A L M K H. nicaraguensis 18 V V I F R V A L M K A. rostratus 17 I A I C K I V I L R location TM1 TM1 TM1 E-1 TM3 TM4 TM5 C-3 TM6

Table S.II.6: Variable RH2Aβ residues across species. Numbering of amino acid sites is based on bovine RH1.

protein RH2Aβ amino acid residue 39 104 151 179 214 228 248 Species n A. cf. citrinellus 15 L I T L V M R A. centrarchus 15 L I T I V M R/K A. siquia 24 L I T L V M K P. managuensis 17 L I T L V L R H. nematopus 17 F V S I V/F M K H. nicaraguensis 18 F V S I V M K A. rostratus 17 L I T I/L V M K location TM1 E-1 TM4 E-2 TM5 C-3 TM6

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Table S.II.7: Variable LWS residues across species. Numbering of amino acid sites is based on bovine RH1.

protein LWS amino acid residue 40 45 52 63 88 92 111 115 155 162 164 166 169 213 214 217 218 282 Species n A. cf. citrinellus 15 S L T M L S I Y A V A F S I I S I A A. centrarchus 15 S L T A L T V F S V S F A I I G/S V A A. siquia 24 S/A F/L T M L S I Y A V S F A I I G V A P. managuensis 17 A F V A V S I Y A V S F A I I G/A I A H. nematopus 17 S/A F/L T M I S I Y S A A F A I I S I S H. nicaraguensis 18 S L T M L S I Y A A A F A I I/T G I S A. rostratus 17 S L V A I T V Y G V S V A F I G V A location TM1 TM1 TM1 TM1 TM2 TM2 TM3 TM3 TM4 TM4 TM4 TM4 TM4 TM5 TM5 TM5 TM5 E-3

Table S.II.8: LRT of positive selection (random sites model in PAML) for three cone opsin coding sequences.

Loglikelihood ratio tests Parameters under M2 a Gene no le M0 tree length ωM0 M3/M0 M2/M1 ω0(p0) ω2(p2) Positively selected sites (M2, BEB) sws2a 137 1,053 0.118 0.212 1.474NS 0.0001NS 0.003(0.79) 1(0.21) rh2aβ 138 1,059 0.131 0.101 10.989* 3.004NS 0.064(0.996) 9.857(0.004) 179 lws 156 1,071 0.199 0.534 85.142*** 43.665*** 0.133(0.963) 12.773(0.037) 40,45,52,63,88,155,164b,217,218 no: number of sequences, le: length of sequences. *P < 0.05; ***P < 0.001; NSP > 0.05. aOnly sites with a posterior probability higher than 80% are reported. If the posterior probability of a site belonging to the positively selected class is higher than 0.9, it is shown in bold. Numbering of amino acid sites is based on bovine RH1. bSites directed into the chromophore binding pocket.

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Chapter III

Fig S.III.1: Abundances of the eight most common bacterial phyla found in the guts of our study species (> 1% of overall sequencing reads). Overall, these eight phyla constitute 98 - 99.8% of the gut microbiome.

Fig S.III.2: Bacterial species richness estimates at different rarefaction depths (PD whole tree diversity index). The investigated sequencing depths range from 10 to 25,000 reads. At a sampling depth of 25,000 reads, a large proportion of the microbial diversity is captured.

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Chapter IV

Fig. S.IV.1: Representative pictures of specimens from species included in this study. The two poecilids depicted (c & f) are males.

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Fig. S.IV.2: Full Poecilia phylogeny showing all clades of the P. sphenops species complex as described by Bagley et al. (2015). Different haplotypes of our study species are shown in the tree (Species name_haplotype_number).

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Table S.IV.1: Numbers of reliably described species for three different teleost families across Central America. Data was taken from www.fishbase.org.

Cichlidae Characidae Poeciliidae Guatemala 40 6 33 Belize 15 3 14 Honduras 23 2 12 El Salvador 10 1 3 Nicaragua 31 7 8 Costa Rica 26 19 23 Panama 23 29 21

Table S.IV.2: Ecology and reproductive features of the study species. Information for Poecilia spp. is based on Poecilia mexicana. Data for trophic level and maximum length was taken from www.fishbase.org.

Trophic level Max. length (cm) Reproduction mode Parental care Amatitlania siquia 3.1 ± 0.5 7.9 Oviparous Yes Hypsophrys nematopus 2.0 ± 0.0 14 Oviparous Yes Brycon guatemalensis 2.3 ± 0.27 59 Oviparous No Roeboides bouchellei 3.9 ± 0.64 8.2 Oviparous No Poecilia spp. 2.0 ± 0.0 11 Viviparous No

Table S.IV.3: Sample sizes for all species and drainage basins. Only species included in interdrainage comparisons are shown.

San Juan Punta Gorda Escondido Amatitlania siquia 30 22 26 Brycon guatemalensis 43 27 7 Roeboides bouchellei 65 24 18 Poecilia sp. “Cluster 2” 19 17 25

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Table S.IV.4: List of sampling locations with GPS coordinates.

Sampling site Drainage basin Latitude Longitude Isletas San Juan N 11°55.141' W 85°55.091' El Tule San Juan N 11°19.883' W 84°52.178' Lake Managua San Juan N 12°14.172' W 86°23.391' Río Oyate San Juan N 11°43.993' W 84°58.627' Río Las Lajas San Juan N 11°23.543' W 85°46.471' Río San Juan San Juan N 11°01.177' W 84°23.736' Caño Chiquito Punta Gorda N 11°38.730' W 84°10.135' Río Rama Escondido N 11°53.269' W 84°38.535'

Table S.IV.5: Cytochrome b primers used for amplification and sequencing of PCR products.

Primer name Primer sequence (5' -> 3') Taxon Reference LNF GACTTGAAAAACCAYCGTTGT Characidae (Oliveira et al. 2011) H08R2 GCTTTGGGAGTTAGDGGTGGGAGTTAGAATC Characidae (Oliveira et al. 2011) GLuDG.L TGA CTT GAA RAA CCA YCG TTG Cichlidae (Palumbi et al. 1991) H15915 AAC TGC CAG TCA TCT CCG GGT TAC AAG AC Cichlidae (Irwin et al. 1991) L14725 GAYTTGAARAACCAYCGTTG Poecilia (Hrbek et al. 2007) H15982 CCTAGCTTTGGGAGYTAGG Poecilia (Hrbek et al. 2007)

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Table S.IV.6: GenBank accession numbers of cytochrome b sequences used for phylogenetic analysis of Poecilia.

Species Accession number Reference P. sp. “Tipitapa” KP700379.1 (Bagley et al. 2015) P. sphenops KJ696840 (Pollux et al. 2014) P. mexicana mexicana KJ696835.1 (Pollux et al. 2014) P. gillii KJ696831.1 (Pollux et al. 2014) P. orri KJ696836.1 (Pollux et al. 2014) P. latipinna KJ696833.1 (Pollux et al. 2014) P. velifera KJ696841.1 (Pollux et al. 2014) P. petenensis KJ696838.1 (Pollux et al. 2014) P. butleri KJ696829.1 (Pollux et al. 2014) P. salvatoris KJ696839.1 (Pollux et al. 2014) P. vivipara HQ857431.1 (Meredith et al. 2011) P. latipunctata EF017539.1 (Hrbek et al. 2007) P. reticulata EF017536.1 (Hrbek et al. 2007) G. affinis EF017514.1 (Hrbek et al. 2007)

Table S.IV.7: Freshwater fish species occurrence in the two drainage basins affected by the Nicaragua Canal. Data is obtained from the official environmental and social impact assessment (ERM 2015).

San Juan Punta Gorda Agnostomus monticola yes yes Alfaro culturatus yes yes Amatitlania siquia yes yes Amphilophus alfari yes yes Amphilophus citrinellus yes no Amphilophus labiatus yes no Amphilophus longimanus yes yes Amphilophus rostratus yes no Anchoa lyoleps no yes Antherinimorus stipes no yes

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Arius seemanni no yes Astyanax aeneus yes yes Astyanax cocibolca yes no Atherinella hubbsi yes no Atherinella milleri yes yes Atherinella sardina yes no Atractosteus tropicus yes no Awaous banana no yes Bagre marinus no yes Batrachoides surinamensis no yes Belonesox belizanus yes no Brachyraphis sp. yes yes Bramocharax bransfordii yes no Brycon guatemalensis yes yes Bryconamericus scleroparius yes no Caranx latus no yes Carcharhinus leucas yes no Carlana eigenmanni yes no Centropomus medius no yes Centropomus parallelus yes yes Centropomus pectinatus yes yes Chaedotiperus faber no yes Cryptoheros septemfasciatus yes no Cynodonichthys isthmensis yes no Dorosoma chavesi yes no Eleotris amblyopsis no yes Eleotris picta no yes Eugerres plumieri yes yes Gambusia nicaraguensis no yes Gobiomorus dormitor yes yes Gymnotus cylindricus yes yes Gymnotus maculosus yes no Heterotilapia multispinosa yes yes Hyphessobrycon tortuguerae yes no

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Hypostomus panamensis yes no Hypsophrys nematopus yes yes Hypsophrys nicaraguensis yes no Joturus pichardi no yes Megalops atlanticus yes no Melaniris sp. no yes Mugil curema no yes Oligoliptes palometa no yes Oreochromis aureus yes no Oreochromis niloticus yes no Oostethus brachyurus no yes Parachromis dovii yes yes Parachromis loisellei yes no Parachromis managuensis yes yes Paraneetroplus maculicauda yes yes Phallichthys amates yes yes Phallichthys tico yes no Platybelone argalis no yes Poecilia sp. “Tipitapa” yes no Poecilia gillii no yes Poecilia mexicana 8a yes no Poecilia mexicana 8m yes yes Pomadasys bayanus no yes Pomadasys crocro yes yes Priapichthys annectens yes no Pristis perotteti yes no Pristis pristis yes no Rhamdia guatemalensis yes yes Rhamdia nicaraguensis yes yes Rhamdia laticauda yes no Rivulus rubripunctata no yes Roeboides bouchellei yes yes Sciaena sp. no yes Selene vomer no yes

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Synbranchus marmoratus yes yes Tomocichla tuba yes yes Trinectes paulistanus no yes Xenophallus umbratilis yes no

Table S.IV.8: FST values showing high levels of genetic differentiation between populations from distinct

drainage basins. Significance levels: * < 0.05, ** < 0.01, *** < 0.001.

Amatitlania siquia Roeboides bouchellei Brycon guatemalensis Poecilia sp. “Cluster 2”

San Juan - Punta Gorda 0.77705*** 0.01287* 0.63198*** 0.90610***

San Juan - Escondido 0.09758*** 0.04542*** 0.53497*** 0.92427***

Punta Gorda - Escondido 0.88672*** 0.09237* 0 0.92195***

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Table S.IV.9: Nucleotide diversity for populations from three distinct drainage basins The population with the highest nucleotide diversity for each species is indicated in bold. A. siquia H. nematopus R. bouchellei B. guatemalensis P. sp. “Cluster 2” P. sp. “Cluster 4” San Juan 0.00212 ± 0.00134 0.00101 ± 0.00077 0.00071 ± 0.00060 0.00227 ± 0.00142 0.00047 ± 0.00049 0.00534 ± 0.00293

Río Las Lajas 0.00212 ± 0.00134 0.00100 ± 0.00077 0.00086 ± 0.00071 - 0.00047 ± 0.00049 0.00953 ± 0.00503

El Tule - 0.00107 ± 0.00081 0.00079 ± 0.00067 0.00225 ± 0.00145 - 0.00115 ± 0.00084

Río San Juan - 0.00125 ± 0.00091 0.00048 ± 0.00048 0.00235 ± 0.00150 - -

Escondido 0.00098 ± 0.00076 - 0.00037 ± 0.00042 0 0.00025 ± 0.00031 -

Punta Gorda 0.00017 ± 0.00026 - 0.00057 ± 0.00053 0 0.00020 ± 0.00028 -

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Table S.IV.10: Haplotype diversity for populations from three distinct drainage basins. Total numbers of haplotypes are indicated in brackets and the population with the highest haplotype diversity for each species is indicated in bold.

A. siquia H. nematopus R. bouchellei B. guatemalensis P. sp. “Cluster 2” P. sp. “Cluster 4”

San Juan 0.899 ± 0.029 (11) 0.598 ± 0.112 (15) 0.442 ± 0.095 (12) 0.631 ± 0.058 (7) 0.205 ± 0.119 (3) 0.804 ± 0.071 (14)

Río Las Lajas 0.899 ± 0.029 (11) 0.338 ± 0.120 (3) 0.568 ± 0.129 (8) - 0.205 ± 0.119 (3) 0.818 ± 0.073 (10)

El Tule - 0.731 ± 0.109 (9) 0.324 ± 0.124 (5) 0.586 ± 0.079 (4) - 0.788 ± 0.068 (8)

Río San Juan - 0.725 ± 0.107 (8) 0.427 ± 0.134 (6) 0.693 ± 0.081 (6) - -

Escondido 0.526 ± 0.118 (8) - 0.366 ± 0.112 (2) 0 (1) 0.28 ± 0.101 (2) -

Punta Gorda 0.178 ±0.106 (3) - 0.496 ± 0.119 (6) 0 (1) 0.221 ± 0.12 (2) -

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