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Trait Evolution and Historical Biogeography Shape Assemblages of Annual Killifish

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Trait Evolution and Historical Biogeography Shape Assemblages of Annual Killifish bioRxiv preprint doi: https://doi.org/10.1101/436808; this version posted October 5, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 ARTICLE 2 Running title: Coexistence in annual killifish 3 Title: Trait evolution and historical biogeography shape assemblages of annual killifish 4 5 Andrew J. Helmstetter1, 2, Tom J. M. Van Dooren3, 4, 5, Alexander S. T. Papadopulos6, Javier Igea7, Armand M. 6 Leroi1, and Vincent Savolainen1 7 8 1 Imperial College London, Department of Life Sciences, Silwood Park Campus, Ascot, Berkshire SL5 7PY, 9 UK 10 2 Institut de Recherche pour le Développement (IRD), UMR-DIADE, 911 Avenue Agropolis, BP 64501, 34394 11 Montpellier, France. 12 3Sorbonne University, UMR 7618, Institute of Ecology and Environmental Sciences Paris, 4 Place Jussieu, 13 75005 Paris, France 14 4CNRS, CEREEP Ecotron IleDeFrance (UMS 3194), École Normale Supérieure, 78 rue du Château, 77140 St- 15 Pierre-lès-Nemours, France 16 5Naturalis Biodiversity Center, Darwinweg 2, Leiden 2333 CR, The Netherlands 17 6 Molecular Ecology and Fisheries Genetics Laboratory, Environment Centre Wales, School of Natural 18 Sciences, Bangor University, Bangor, LL57 2UW, UK 19 7 University of Cambridge, Department of Plant Sciences, Downing Street, Cambridge, CB2 3EA, UK 20 Keywords: Annual fish, phylogenetics, trait evolution, biogeography, body size, geography 21 of speciation, Austrolebias 22 Word count: 7671 23 Corresponding Authors: [email protected], [email protected], 24 [email protected] 25 Data archival location: Genbank and Dryad 26 Elements: title page, abstract, text, literature cited, figure legends & figures and 27 supplementary materials 28 1 bioRxiv preprint doi: https://doi.org/10.1101/436808; this version posted October 5, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 29 ABSTRACT (199) 30 Reconstructions of evolutionary and historical biogeographic processes can improve our 31 understanding of how species assemblages developed and permit inference of ecological 32 drivers affecting coexistence. We explore this approach in Austrolebias, a genus of annual 33 fishes possessing a wide range of body sizes. Regional assemblages composed of different 34 species with similar size distributions are found in four areas of eastern South America. 35 Using phylogenetic trees, species distribution models and size data we show how trait 36 evolution and historical biogeography have affected the composition of species assemblages. 37 We extend age-range correlations to improve estimates of local historical biogeography. We 38 find that size variation principally arose in a single area and infer that ecological interactions 39 drove size divergence. This large-size lineage spread to two other areas. One of these 40 assemblages was likely shaped by adaptation to a new environment, but this was not 41 associated with additional size divergence. We found only weak evidence that environmental 42 filtering has been important in the construction of the remaining assemblage with the smallest 43 range of sizes. The repeated assemblage structures were the result of different evolutionary 44 and historical processes. Our approach sheds light on how species assemblages were built 45 when typical clustering approaches may fall short. 46 47 2 bioRxiv preprint doi: https://doi.org/10.1101/436808; this version posted October 5, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 48 INTRODUCTION 49 Coexistence of related species is shaped by the effects of different ecological, evolutionary 50 and historical processes. These include speciation (Nosil 2012; Warren et al. 2014; 51 Mittelbach and Schemske 2015) and extinction, local ecological processes such as 52 competition (Hardin 1960; Pigot and Tobias 2012) and environmental filtering (Mouillot et 53 al. 2007; Lebrija-Trejos et al. 2010) and processes independent of phenotype, e.g. random 54 dispersal (Gotelli and McGill 2006). Their effects on coexistence differ across spatial and 55 temporal scales (Webb et al. 2002). Ecological processes acting over short time scales (e.g., 56 resource competition) can contribute to the selective pressures that drive trait evolution and 57 ecological speciation over longer time scales (Langerhans and Riesch 2013), e.g. character 58 displacement (Brown and Wilson 1956; Losos 1990; Schluter and McPhail 1992). 59 Understanding the interplay between ecological and evolutionary forces is essential in order 60 to determine the processes affecting species coexistence. 61 62 The importance of evolutionary and historical biogeographic processes in community 63 assembly is increasingly acknowledged (Gerhold et al. 2018) but they remain relatively 64 understudied in community ecology when compared to local and recent ecological processes 65 (Warren et al. 2014; Mittelbach and Schemske 2015). In community phylogenetics (Webb et 66 al. 2002), phylogenetic reconstructions are used to characterise assemblages and to predict 67 the ecological processes at work in them. An assemblage that is phylogenetically 68 overdispersed is usually inferred to be structured by competition, while phylogenetically 69 clustered assemblages are thought be shaped by environmental filtering. However, a single 70 ecological process can have variable effects on phylogenetic relatedness and trait variation in 71 an assemblage (Cavender-Bares et al. 2009). Typical community phylogenetic approaches 72 based on phylogenetic relatedness are unable to discriminate between processes effectively as 3 bioRxiv preprint doi: https://doi.org/10.1101/436808; this version posted October 5, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 73 they implicitly assume simple evolutionary models (Weber et al. 2017). They are also 74 susceptible to interpreting historical effects on current geographic distributions as evidence 75 for ecological and evolutionary processes (Warren et al. 2014). 76 77 Trait evolution is an essential component in the formation of species assemblages (Webb et 78 al. 2002, Cavender-Bares et al. 2004; Kraft et al. 2007). However, when models of trait 79 evolution are applied, they are often relatively simple: convergence in traits is often not 80 modelled while conserved traits are modeled using a Brownian motion (BM) model of 81 evolution (e.g. Kraft et al. 2007). Tools that permit inference of interactions between 82 ecological and evolutionary processes are still in development (Weber et al. 2017), but it is 83 possible to investigate variability of evolutionary processes across a phylogenetic tree 84 without having to resort to heuristic sampling algorithms (Kraft et al. 2007). Methods can be 85 used to detect whether species traits in an assemblage are attracted to more than a single 86 phenotypic optimum, by identifying selection regime shifts (Butler and King 2004). They can 87 also be used to infer whether traits of different species converge towards the same optimum 88 (Ingram and Mahler 2013; Oke et al. 2017; Speed and Arbuckle 2017). Taking advantage of 89 these approaches is important because trait convergence can also be the result of independent, 90 random divergence from distant starting points (Webb et al. 2002; Stayton 2008). By 91 separating the history of selection regimes from evolutionary random walks we can improve 92 our understanding of the driving forces behind trait evolution and coexistence (Oke et al. 93 2017). 94 95 Historical biogeography is also intrinsic to how species assemblages form but is often 96 neglected in empirical studies (Warren et al. 2014; Mittelbach & Schemske 2015). For 97 example speciation in sympatry or parapatry (i.e. non-allopatric) produces co-occurring sister 4 bioRxiv preprint doi: https://doi.org/10.1101/436808; this version posted October 5, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 98 species while speciation in allopatry does not, although this patterns will change over time 99 due to post-speciation range shifts. The most common way biogeography is included in 100 phylogenetic studies is through ancestral range estimation (ARE) models, albeit at a coarse 101 level. Species are assigned to predefined areas, which are used to estimate different types of 102 ‘cladogenetic events’ that imply varying levels of geographic proximity during divergence. 103 For example, ‘founder-event speciation’ (long-distance dispersal followed by isolation and 104 speciation; Matzke et al. 2014) lies on one end of the geographic continuum while ‘within- 105 area speciation’ is as close as these models can get to the other end. Identifying speciation in
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