Back

Warming and predation drive rapid evolution of ecosystem functioning but not functional traits

Olazcuaga, L.; Couranjou, E.; Fargeot, L.; Raffard, A.; Bertrand, R.; Richard, M.; Prunier, J. G.; Blanchet, S.

2026-03-18 evolutionary biology
10.64898/2026.03.17.712387 bioRxiv
Show abstract

Global environmental change can rapidly reshape phenotypic trait distributions through adaptive and neutral processes. Yet, disentangling their relative contributions remains a major challenge, particularly for traits underpinning ecosystem functioning. Using a two-year mesocosm study, we experimentally evolved Asellus aquaticus under contrasting temperature and predation regimes to test how the joint influence of these global change pressures shape the evolution of traits and ecosystem functioning. We show that classic functional traits (body size and metabolic rate) showed no evidence of divergent evolution across evolutionary conditions, while ecosystem function itself, namely decomposition rate, has evolved rapidly and differently depending on evolutionary conditions, with the highest decomposition rate observed for Asellus populations exposed to high temperatures in the absence of their predator. A Pst/Fst comparison revealed that divergence in metabolic rate among evolutionary conditions was mainly driven by genetic drift, whereas, for body mass and decomposition rate, non-neutral processes (natural selection and/or plasticity) were also involved, albeit in a different way. By demonstrating that predation and temperature can influence the evolution of an ecosystem function over a few generations, this study represents an important first step toward uncovering the combined effects of global change on the eco-evolutionary dynamics of ecosystems.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.