Back

Heritable morphology-environment correlations among lake populations of threespine stickleback

Yeung, A.; Flanagan, B. A.; Alexander, H.; Choi, E.; Berini, J.; Albright, A.; Szajda, C.; Vargas, N.; Flanagan, J.; Contreras, E. R.; Cooper, P.; Shahid, M.; Steffen, P. R.; Gilani, F.; Santacruz, A.; Watts, V.; Polard, E.; Rochon, K.; Redfield, E.; Hite, J.; Hund, A. K.; Bolnick, D. I.

2026-08-22 evolutionary biology
10.64898/2026.08.20.745995 bioRxiv
Show abstract

Phenotypic differences among populations can arise through heritable genetic divergence, phenotypic plasticity, or both, making it difficult to determine whether trait-environment correlations observed in nature reflect adaptive evolution. Within threespine stickleback (Gasterosteus aculeatus) studies, numerous document morphological differences among allopatric-, parapatric-, and even sympatric populations. These phenotypic differences among populations are often correlated with diet and lake habitat (e.g., lake size), suggesting an adaptive value to the population differences. However, many studies of ecomorphological divergence in stickleback use wild-caught stickleback, which may differ due to evolution or plasticity. Although common garden experiments have confirmed that population differences can be heritable, such experiments typically entail small numbers of populations. Consequently, we still do not know to what extent well-known trait-environment correlations in stickleback are a result of evolution. To address this gap, we reared stickleback embryos from 27 lake populations on Vancouver Island, in a laboratory environment. Morphological differences among populations persist in common-garden fish, confirming a large role for divergent evolution. These heritable differences were associated with environmental variation among lakes, implying an adaptive value. However, some well-known trait-environment relationships in stickleback did not persist in common-garden fish and may be primarily plastic.

Matching journals

The top 4 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.