Disentangling unique site-specific and shared habitat-level adaptation in a classic system of repeated evolution.

Authors

Marius Roesti, Hannes Roesti, Hiranya Sudasinghe, Nicole Nesvadba, Verena Saladin, Catherine L Peichel

Published in

Proceedings of the National Academy of Sciences of the United States of America. Volume 123. Issue 36. Pages e2612038123. Sep 08, 2026. Epub Sep 04, 2026.

Abstract

Repeated divergence across contrasting habitats is widely used to infer natural selection and adaptation. Such comparative inferences, however, remain inherently correlative and capture only adaptation shared among populations from the same habitat type, thereby missing site-specific adaptation unique to individual populations. Field transplant experiments test adaptation directly by measuring fitness in nature but are typically limited to pairwise reciprocal exchanges between populations and therefore cannot distinguish between habitat-level and site-specific adaptation. Here, we extend the typical transplant framework to include multiple populations from both within and across habitat types, allowing fitness variation to be partitioned into shared habitat-level and unique site-specific components. We apply this framework to lake-stream stickleback, a classic system for studying adaptation via repeated divergence. Specifically, we transplanted laboratory-reared fish from a panmictic lake population and four independent stream populations across one lake and two stream sites. Stream fish outperformed lake fish in streams and vice versa, demonstrating adaptive divergence across the lake-stream divide. However, at both stream sites, local stream fish also outperformed foreign stream fish. Strikingly, this site-specific advantage was twice as large as the advantage of shared stream adaptation reflected by the fitness benefit of foreign stream fish over foreign lake fish. These results show that most fitness-relevant evolutionary variation in this system is unique to individual populations and therefore invisible to approaches that rely on repeated evolution to infer adaptation. More broadly, our work underscores the importance of ecological scale for understanding adaptation and evolutionary predictability.

PMID:
42696538
Bibliographic data and abstract were imported from PubMed on 05 Sep 2026.

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