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Looking for Mimicry in a Snake Assemblage Using Deep Learning.
Am Nat. 2020 Jul; 196(1):74-86.AN

Abstract

Batesian mimicry is a canonical example of evolution by natural selection, popularized by highly colorful species resembling unrelated models with astonishing precision. However, Batesian mimicry could also occur in inconspicuous species and rely on subtle resemblance. Although potentially widespread, such instances have been rarely investigated, such that the real frequency of Batesian mimicry has remained largely unknown. To fill this gap, we developed a new approach using deep learning to quantify the visual resemblance between putative mimics and models from photographs. We applied this method to Western Palearctic snakes. Potential nonvenomous mimics were revealed by an excess of resemblance to sympatric venomous snakes compared with random expectations. We found that 8% of the nonvenomous species were potential mimics, although they resembled their models imperfectly. This study is the first to quantify the frequency of Batesian mimicry in a whole community of vertebrates, and it shows that even concealed species can act as potential models. Our approach should prove useful for detecting mimicry in other communities, and more generally it highlights the benefits of deep learning for quantitative studies of phenotypic resemblance.

Authors

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Pub Type(s)

Journal Article

Language

eng

PubMed ID

32552103

Citation

de Solan, Thomas, et al. "Looking for Mimicry in a Snake Assemblage Using Deep Learning." The American Naturalist, vol. 196, no. 1, 2020, pp. 74-86.
de Solan T, Renoult JP, Geniez P, et al. Looking for Mimicry in a Snake Assemblage Using Deep Learning. Am Nat. 2020;196(1):74-86.
de Solan, T., Renoult, J. P., Geniez, P., David, P., & Crochet, P. A. (2020). Looking for Mimicry in a Snake Assemblage Using Deep Learning. The American Naturalist, 196(1), 74-86. https://doi.org/10.1086/708763
de Solan T, et al. Looking for Mimicry in a Snake Assemblage Using Deep Learning. Am Nat. 2020;196(1):74-86. PubMed PMID: 32552103.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Looking for Mimicry in a Snake Assemblage Using Deep Learning. AU - de Solan,Thomas, AU - Renoult,Julien Pierre, AU - Geniez,Philippe, AU - David,Patrice, AU - Crochet,Pierre-André, Y1 - 2020/05/27/ PY - 2020/6/20/entrez PY - 2020/6/20/pubmed PY - 2020/6/20/medline KW - Batesian mimicry KW - camouflage KW - deep neural network KW - imperfect mimicry SP - 74 EP - 86 JF - The American naturalist JO - Am. Nat. VL - 196 IS - 1 N2 - Batesian mimicry is a canonical example of evolution by natural selection, popularized by highly colorful species resembling unrelated models with astonishing precision. However, Batesian mimicry could also occur in inconspicuous species and rely on subtle resemblance. Although potentially widespread, such instances have been rarely investigated, such that the real frequency of Batesian mimicry has remained largely unknown. To fill this gap, we developed a new approach using deep learning to quantify the visual resemblance between putative mimics and models from photographs. We applied this method to Western Palearctic snakes. Potential nonvenomous mimics were revealed by an excess of resemblance to sympatric venomous snakes compared with random expectations. We found that 8% of the nonvenomous species were potential mimics, although they resembled their models imperfectly. This study is the first to quantify the frequency of Batesian mimicry in a whole community of vertebrates, and it shows that even concealed species can act as potential models. Our approach should prove useful for detecting mimicry in other communities, and more generally it highlights the benefits of deep learning for quantitative studies of phenotypic resemblance. SN - 1537-5323 UR - https://www.unboundmedicine.com/medline/citation/32552103/Looking_for_Mimicry_in_a_Snake_Assemblage_Using_Deep_Learning L2 - https://doi.org/10.1086/708763 DB - PRIME DP - Unbound Medicine ER -
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