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J Anim Ecol ; 85(4): 1056-69, 2016 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-27003742

RESUMO

Müllerian mimicry between chemically defended preys is a textbook example of natural selection favouring phenotypic convergence onto a shared warning signal. Studies of mimicry have concentrated on deciphering the ecological and genetic underpinnings of dramatic switches in mimicry association, producing a well-known mosaic distribution of mimicry patterns across geography. However, little is known about the accuracy of resemblance between natural comimics when the local phenotypic optimum varies. In this study, using analyses of wing shape, pattern and hue, we quantify multimodal phenotypic similarity between butterfly comimics sharing the so-called postman pattern in different localities with varying species composition. We show that subtle but consistent variation between populations of the localized species, Heliconius timareta thelxinoe, enhance resemblance to the abundant comimics which drive the mimicry in each locality. Those results suggest that rarer comimics track the changes in the phenotypic optimum caused by gradual changes in the composition of the mimicry community, providing insights into the process by which intraspecific diversity of mimetic pattern may arise. Furthermore, our results suggest a multimodal evolution of similarity, with coordinated convergence in different features of the phenotype such as wing outline, pattern and hue. Finally, multilocus genotyping allows estimating local hybridization rates between H. timareta and comimic H. melpomene in different populations, raising the hypothesis that mimicry refinement between closely related comimics may be enhanced by adaptive introgression at loci modifying the accuracy of resemblance.


Assuntos
Mimetismo Biológico , Borboletas/fisiologia , Fenótipo , Pigmentação , Seleção Genética , Animais , Borboletas/anatomia & histologia , Borboletas/genética , Hibridização Genética , Peru , Asas de Animais/anatomia & histologia , Asas de Animais/fisiologia
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