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Data from: Mimicry refinement: Phenotypic variations tracking the local optimum

Citation

Mérot, Claire; Le Poul, Yann; Théry, Marc; Joron, Mathieu (2017), Data from: Mimicry refinement: Phenotypic variations tracking the local optimum, Dryad, Dataset, https://doi.org/10.5061/dryad.h4j6c

Abstract

1. 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 co-mimics when the local phenotypic optimum varies. 2. In this study, using analyses of wing shape, pattern and hue, we quantify multimodal phenotypic similarity between butterfly co-mimics sharing the so-called postman pattern in different localities with varying species composition. 3. We show that subtle but consistent variation between populations of the localised species, Heliconius timareta thelxinoe, enhance resemblance to the abundant co-mimics which drive the mimicry in each locality. 4. Those results suggest that rarer co-mimics 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 intra-specific 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. 5. Finally, multilocus genotyping allows estimating local hybridization rates between H. timareta and co-mimic H. melpomene in different populations, raising the hypothesis that mimicry refinement between closely-related co-mimics may be enhanced by adaptive introgression at loci modifying the accuracy of resemblance.

Usage Notes

Location

Peru (San Martin)