Additive genetic variance and selection are the key ingredients for evolution. In wild populations, however, predicting evolutionary trajectories is difficult, potentially by an unrecognised underlying environment dependency of both (additive) genetic variance and selection (i.e. G×E and S×E). Particularly, if GxE and SxE lead to covariation of heritability and selection, this can substantially increase or decrease the rate of evolutionary change. To date, evidence for both G×E and S×E within the same wild population remains limited to only two studies. Thus, to assess the prevalence of G×E and S×E across wild populations, we searched for Open Access data in 12 data repositories. Of the 63 data sets found, 20 contain useful data sets for our analysis. These multiannual datasets were from 17 pedigreed populations, covering 12 species. Using random regression animal models, we were able to identify G×E in a variety of traits and populations in 24 of these data sets. In some cases, the G×E coincided with the S×E. Evidence that trait heritability and selection covaried was inconsistent, as was evidence that G×E hampered or increased predicted rates of adaptation. Nevertheless, our study shows that the use of Open Access data can be used to address a timely topic in ecology and evolution and is able to include significantly more studies than a traditional meta-analysis.
|Publication status||Published - 2017|
Ramakers, J. J. C., Culina, A., Visser, M. E., & Gienapp, P. (2017). Predicting evolutionary responses when genetic variance and selection covary with the environment: a large-scale Open Access Data approach.