Few genetic and environmental correlations between life history and stress resistance traits affect adaptation to fluctuating thermal regimes.

Abstract

Laboratory selection in thermal regimes that differed in the amplitude and the predictability of daily fluctuations had a marked effect on stress resistance and life history traits in Drosophila simulans. The observed evolutionary changes are expected to be the result of both direct and correlated responses to selection. Thus, a given trait might not evolve independently from other traits because of genetic correlations among these traits. Moreover, different test environments can induce novel genetic correlations because of the activation of environmentally dependent genes. To test whether and how genetic correlations among stress resistance and life history traits constrain evolutionary adaptation, we used three populations of D. simulans selected for 20 generations in constant, predictable and unpredictable daily fluctuating thermal regimes and tested each of these selected populations in the same three thermal regimes. We explored the relationship between genetic correlations between traits and the evolutionary potential of D. simulans by comparing genetic correlation matrices in flies selected and tested in different thermal test regimes. We observed genetic correlations mainly between productivity, body size, starvation and desiccation tolerance, suggesting that adaptation to the three thermal regimes was affected by correlations between these traits. We also found that the correlations between some traits such as body size and productivity or starvation tolerance and productivity were determined by test regime rather than selection regime that is expected to limit genetic adaptation to thermal regimes in these traits. The results of this study suggest that several traits and several environments are needed to explore adaptive responses, as genetic and environmentally induced correlations between traits as results obtained in one environment cannot be used to predict the response of the same population in another environment.