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Integr Comp Biol ; 59(3): 604-615, 2019 09 01.
Article in English | MEDLINE | ID: mdl-31292612

ABSTRACT

Coral reef fishes constitute one of the most diverse assemblages of vertebrates on the planet. Color patterns are known to serve a number of functions including intra- and inter-specific signaling, camouflage, mimicry, and defense. However, the relative importance of these and other factors in shaping color pattern evolution is poorly understood. Here we conduct a comparative phylogenetic analysis of color pattern evolution in the butterflyfishes (Chaetodontidae). Using recently developed tools for quantifying color pattern geometry as well as machine learning approaches, we investigate the tempo of evolution of color pattern elements and test whether ecological variables relating to defense, depth, and social behavior predict color pattern evolution. Butterflyfishes exhibit high diversity in measures of chromatic conspicuousness and the degrees of fine versus gross scale color patterning. Surprisingly, most diversity in color pattern was not predicted by any of the measures of ecology in our study, although we did find a significant but weak relationship between the level of fine scale patterning and some aspects of defensive morphology. We find that the tempo of color pattern diversification in butterflyfishes has increased toward the present and suggest that rapid evolution, presumably in response to evolutionary pressures surrounding speciation and lineage divergence, has effectively decoupled color pattern geometry from some aspects of ecology. Machine learning classification of color pattern appears to rely on a set of features that are weakly correlated with current color pattern geometry descriptors, but that may be better suited for the detection of discrete components of color pattern. A key challenge for future studies lies in determining whether rapid evolution has generally decoupled color patterns from ecology, or whether convergence in function produces convergence in color pattern at phylogenetic scales.


Subject(s)
Biological Evolution , Perciformes/physiology , Pigmentation , Animals , Color , Machine Learning , Phylogeny
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