Environmental fluctuations promote intraspecific diversity and population persistence via inflationary effects

Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment

Most natural environments exhibit a substantial component of random variation, with a degree of temporal autocorrelation that defines the color of environmental noise. Such environmental fluctuations cause random fluctuations in natural selection, affecting the predictability of evolution. But despite long-standing theoretical interest in population genetics in stochastic environments, there is a dearth of empirical estimation of underlying parameters of this theory. More importantly, it is still an open question whether evolution in fluctuating environments can be predicted indirectly using simpler measures, which combine environmental time series with population estimates in constant environments. Here we address these questions by using an automated experimental evolution approach. We used a liquid-handling robot to expose over a hundred lines of the micro-alga Dunaliella salina to randomly fluctuating salinity over a continuous range, with controlled mean, variance, and autocorrelation. We then tracked the frequencies of two competing strains through amplicon sequencing of nuclear and choloroplastic barcode sequences. We show that the magnitude of environmental fluctuations (determined by their variance), but also their predictability (determined by their autocorrelation), had large impacts on the average selection coefficient. The variance in frequency change, which quantifies randomness in population genetics, was substantially higher in a fluctuating environment. The reaction norm of selection coefficients against constant salinity yielded accurate predictions for the mean selection coefficient in a fluctuating environment. This selection reaction norm was in turn well predicted by environmental tolerance curves, with population growth rate against salinity. However, both the selection reaction norm and tolerance curves underestimated the variance in selection caused by random environmental fluctuations. Overall, our results provide exceptional insights into the prospects for understanding and predicting genetic evolution in randomly fluctuating environments.

Being able to predict evolution under natural selection is important for many applied fields of biology, ranging from agriculture to medicine or conservation. However, this endeavor is complicated by factors that inherently limit our ability to predict the future, such as random fluctuations in the environment. Population genetic theory indicates that probabilistic predictions can still be made in this context, but the extent to which this holds empirically, and whether these predictions can be based on simple measurements, are still open questions. Making progress on answering these questions can be achieved by capitalizing on experiments where the environment is precisely controlled over many generations. Here, we used a pipetting robot to generate random time series of salinities with controlled patterns of fluctuations, which we imposed on a microalga, Dunaliella salina. Tracking the frequencies of two genotypes in a mixture by sequencing two short barcode sequences, we were able to show how patterns of fluctuating selection relate to the fluctuating environment. Interestingly, parts of these responses, but not all, could be predicted by simpler measurements in constant environments, allowing precise characterization the limits and prospects for predicting evolution in fluctuating environments.

The distribution and role of functional abundance in cross-scale resilience

The cross-scale resilience model suggests that system-level ecological resilience emerges from the distribution of species' functions within and across the spatial and temporal scales of a system. It has provided a quantitative method for calculating the resilience of a given system and so has been a valuable contribution to a largely qualitative field. As it is currently laid out, the model accounts for the spatial and temporal scales at which environmental resources and species are present and the functional roles species play but does not inform us about how much resource is present or how much function is provided. In short, it does not account for abundance in the distribution of species and their functional roles within and across the scales of a system. We detail the ways in which we would expect species' abundance to be relevant to the cross-scale resilience model based on the extensive abundance literature in ecology. We also put forward a series of testable hypotheses that would improve our ability to anticipate and quantify how resilience is generated, and how ecosystems will (or will not) buffer recent rapid global changes. This stream of research may provide an improved foundation for the quantitative evaluation of ecological resilience.

The temporal structure of the environment may influence range expansions during climate warming

Understanding the processes that influence range expansions during climate warming is paramount for predicting population extirpations and preparing for the arrival of non-native species. While climate warming occurs over a background of variation due to cyclical processes and irregular events, the temporal structure of the thermal environment is largely ignored when forecasting the dynamics of non-native species. Ecological theory predicts that high levels of temporal autocorrelation in the environment - relatedness between conditions occurring in close temporal proximity - will favor populations that would otherwise have an average negative growth rate by increasing the duration of favorable environmental periods. Here, we invoke such theory to explain the success of biological invasions and evaluate the hypothesis that sustained periods of high environmental temperature can act synergistically with increases in mean temperature to favor the establishment of non-native species. We conduct a 60-day field mesocosm experiment to measure the population dynamics of the non-native cladoceran zooplankter Daphnia lumholtzi and a native congener Daphnia pulex in ambient temperature environments (control), warmed with recurrent periods of high environmental temperatures (uncorrelated-warmed), or warmed with sustained periods of high environmental temperatures (autocorrelated-warmed), such that both warmed treatments exhibited the same mean temperature but exhibited different temporal structures of their thermal environments. Maximum D. lumholtzi densities in the warmed-autocorrelated treatment were threefold and eightfold higher relative to warmed-uncorrelated and control treatments, respectively. Yet, D. lumholtzi performed poorly across all experimental treatment(s) relative to D. pulex and were undetectable (by) the end of the experiment. Using mathematical models, we show that this increase in performance can occur alongside increasing temporal autocorrelation and should occur over a broad range of warming scenarios. These results provide both empirical and theoretical evidence that the temporal structure of the environment can influence the performance of species undergoing range expansions due to climate warming.