Periodic Reporting for period 4 - FluctEvol (Fluctuating selection, evolution, and plasticity in random environments)
Período documentado: 2020-09-01 hasta 2021-08-31
Temporal environmental variation in nature includes a large component of random fluctuations, the magnitude and predictability of which are modified under current climate change. These fluctuations are expected to have large eco-evolutionary impacts on natural populations, mediated by plastic and evolutionary responses to changing environments. However, understanding and predicting these responses is still hampered by lack of strong experimental evidence. The aim of FluctEvol was to shed new light on population responses to stochastic environments and facilitating their prediction, by using a unique combination of approaches. First, we designed and analysed theoretical models of evolution and demography under a randomly changing optimum phenotype, producing new quantitative predictions. Second, we performed cross-species analyses of long-term datasets from natural populations, to estimate the prevalence and magnitude of fluctuating selection in the wild. And third, we used large-scale and automated experimental evolution in stochastic salinity with the micro-alga Dunaliella salina, an extremophile that thrives at high and variable salinities. We manipulated the predictability of fluctuations in salinity, and used high-throughput phenotyping to analyse population growth and extinction risk, as well as evolution of plasticity at multiple levels of the phenotype. Our model organism D. salina combined the benefits of experimental evolution in microbes (short generations, ample replication) with a priori knowledge of ecologically relevant adaptive traits, allowing for hypothesis-driven experiments. Our results have shown that natural selection varies substantially over time in the wild, and that temporal autocorrelation in the environment is likely to have crucial effects on extinction risk, evolution of plasticity, and the dynamics of population genetic change.
The research in FluctEvol has advanced our understanding of ecological and evolutionary responses to randomly changing environments in several important ways. On the theory side, our moving optimum models, which connect genotype, phenotype, and fitness across environments, have generated qualitative and quantitative predictions for how population size (Chevin et al 2017 Am Nat) or genetic composition (Chevin 2019 Genetics) are influenced by a randomly fluctuating optimum, with an emphasis on the role of environmental autocorrelation on the variance of the stochastic population processes. On the experimental side, we have shown that memory of past environments, mediated by transgenerational plasticity, can have large effects on population size and extinction risk in a random environment (Rescan et al 2020, Nature Ecol Evol). These memory effects also influence to some extent the fluctuations in the relative frequencies of two strains in competition (Rescan et al 2021 PLoS Genetics). We have also confirmed experimentally the theoretical prediction that lower phenotypic plasticity evolves in less predictable environments (Leung et al 2020, Ecol Lett). Lastly, regarding analysis of natural populations, we have estimated patterns of temporally changing natural selection across species of birds and mammals in a way that connects to theory, namely as fluctuations of an optimum phenotype over time (de Villemereuil et al 2020, PNAS).
One of the major breakthroughs in this project has been to use a pipetting robot combined with computer simulations, to expose many experimental lines to independent random time series of salinity, with realistic continuous distributions (rather than simple high-low treatments), and controlled level of autocorrelation (which determines the timescale of predictability). Together with the high-throughput afforded by a benchtop flow cytometer, this has allowed us to track tens of lines per treatment per genotype, and thus estimate extinction rates with good precision. This has also allowed us to show, for the first time with such clarity, that phenotypic plasticity evolves in response to environmental predictability, in the direction predicted by theory. We are following up on this exceptional result by investigating the transcriptomic and epigenomic responses to salinity in these evolved lines, which was not planned originally, but represents an unprecedented opportunity to investigate the molecular mechanisms of the evolution of plasticity using experimental evolution. On another aspect of the project, the broad-scale analysis of selection on breeding time in birds and mammals constitutes the first demonstration that fluctuating optimum models can be efficiently applied to many of these datasets, and that plastic responses track movements of the optimum, as investigated in numerous theoretical models.