A lot of convergence, a bit of divergence: Environment and interspecific interactions shape body colour patterns in Lissotriton newts.

Coexistence with related species poses evolutionary challenges to which populations may react in diverse ways. When exposed to similar environments, sympatric populations of two species may adopt similar phenotypic trait values. However, selection may also favour trait divergence as a way to reduce competition for resources or mates. The characteristics of external body parts, such as coloration and external morphology, are involved to varying degrees in intraspecific signalling as well as in the adaptation to the environment and consequently may be diversely affected by interspecific interactions in sympatry. Here, we studied the effect of sympatry on various colour and morphological traits in males and females of two related newt species Lissotriton helveticus and L. vulgaris. Importantly, we did not only estimate how raw trait differences between species respond to sympatry, but also the marginal responses after controlling for environmental variation. We found that dorsal and caudal coloration converged in sympatry, likely reflecting their role in adaptation to local environments, especially concealment from predators. In contrast, aspects of male and female ventral coloration, which harbours sexual signals in both species, diverged in sympatry. This divergence may reduce opportunities for interspecific sexual interactions and the associated loss of energy, suggesting reproductive character displacement (RCD). Our study emphasizes the contrasting patterns of traits involved in different functions and calls for the need to consider this diversity in evolutionary studies.

Looking for Mimicry in a Snake Assemblage Using Deep Learning.

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.