Data from: Genetic and environmental similarities drive repeated genomic evolution in island lizards

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Authors: Stéphanie Sherpa, Viola Di Canio, Martina Muraro, Benedetta Barzaghi, Emanuele Berrilli, Pierluigi Bombi, Andrea Dalpasso, Mattia Falaschi, Benedetta Gambioli, Raoul Manenti, Silvio Marta, Paolo Momigliano
+8 more Elia Lo Parrino, Stefano Scali, Federico Storniolo, Leonardo Vignoli, Marco A. L. Zuffi, Roberto Sacchi, Daniele Salvi, Gentile Francesco Ficetola
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Abstract

Repeated adaptation provides valuable insights into the predictability of evolution. Population history, selection and stochastic processes can concur to generate a continuum from distinct to highly parallel evolutionary trajectories across replicate populations. Yet, the role of genetic and environmental factors in shaping this continuum remains underexplored. We quantified repeated genetic adaptation in lizards that colonized multiple islands with comparable environmental gradients, investigating whether environmental-dependent and divergence-dependent processes can explain the degree of repeated adaptation (genetic reuse and trajectory similarity). We found 149 genes exhibiting repeated adaptation in multiple islands, some of which are likely involved in thermal physiology and developmental processes. Genetic reuse was stronger at the functional level than at the mutation level and exceeded random expectations, highlighting that different genetic combinations can generate similar functional outcomes. Adaptive trajectories were more similar between islands with low genetic differentiation and similar environmental conditions, but the effects of genetic and environmental factors vary across the diverse facets of repeatability. Overall, our findings reveal the extent and conditions under which local adaptation is, in part, predictable.

Fields of Study & Subjects

Genetic diversity and population structure Evolution and Genetic Dynamics Animal Behavior and Reproduction

Publication details

Journal / source
DRYAD
Publication date
12 January 2026
Language
EN
Document type
Dataset