Abstract

The difference between rapid morphological evolutionary changes observed in populations and the long periods of stasis detected in the fossil record has raised a decade-long debate about the exact role played by intraspecific mechanisms at the interspecific level. Although they represent different scales of the same evolutionary process, micro- and macroevolution are rarely studied together and few empirical studies have compared the rates of evolution and the selective pressures between both scales. Here, we analyse morphological, genetic and ecological traits in clownfishes at different evolutionary scales and demonstrate that the tempo of molecular and morphological evolution at the species level can be, to some extent, predicted from parameters estimated below the species level, such as the effective population size or the rate of evolution within populations. We also show that similar codons in the gene of the rhodopsin RH1, a light-sensitive receptor protein, are under positive selection at the intra and interspecific scales, suggesting that similar selective pressures are acting at both levels.

Highlights

  • Understanding the evolutionary process necessitates the integration of multiple biological scales that are rarely studied together

  • Linking micro- and macroevolution remains one of the greatest current challenges in evolutionary biology [8,9,10,11,12,13,14] and relatively few empirical studies have provided mechanisms that can explain the two evolutionary scales; such as natural and sexual selection in stick insects [15], selection related to the beak morphology in Darwin finches [16], or sexual preferences related to the colour of cichlids fishes [17]

  • Using newly generated sequences of one gene potentially under selection (RH1, implicated in dim light vision in deep sea [24,25]) and five morphological traits important for fish ecology, we tested the three following predictions: (i) rate of evolution should be accelerated within species compared to between species. (ii) Genetic and morphological evolutionary rates at the species level may be inferred with the parameters estimated below the species level, such as the effective population size or the microevolutionary rate. (iii) The major selective forces should be acting at both the population and the species level

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Summary

Introduction

Understanding the evolutionary process necessitates the integration of multiple biological scales that are rarely studied together. Palaeontologists and phylogeneticists have been interested in the dynamics of diversification and the rate of phenotypic evolution above or at the level of the species, what is commonly referred to as macroevolution [4,5] These different timescales challenge our understanding of evolutionary biology and question whether the mechanisms of microevolution could explain the rate of evolution at the macroevolutionary scale. It has been proposed that if there was a continuum of divergence from populations to the species level [23], similar selective pressures (ecological or evolutionary factors) might act at both micro- and macroevolutionary scales All these predictions still remain to be tested empirically with datasets encompassing morphological and genetic data for a reasonable number of species and individuals within species [13]. Using newly generated sequences of one gene potentially under selection (RH1, implicated in dim light vision in deep sea [24,25]) and five morphological traits important for fish ecology (electronic supplementary material, S1), we tested the three following predictions: (i) rate of evolution should be accelerated within species compared to between species. (ii) Genetic and morphological evolutionary rates at the species level may be inferred with the parameters estimated below the species level, such as the effective population size or the microevolutionary rate. (iii) The major selective forces (i.e. related to water depth here) should be acting at both the population and the species level

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15. Riesch R et al 2017 Transitions between phases of
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18. Ho SY et al 2011 Time-dependent rates of
57. Schabauer H et al 2012 SlimCodeML: an optimized
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