Population genomic time series data of a natural population suggests adaptive tracking of environmental changes

Abstract

14While the evolutionary fitness of natural populations is affected by a multitude of environmental 15 factors, theory predicts that selective responses are in principle limited. However, we lack empirical 16 knowledge on the magnitude of different selection pressures natural populations adaptively track. 17Here, we developed a framework to investigate the quantitative and qualitative complexity of the 18 effectively acting selection regime using population genomic time series data. We applied the 19 approach to a natural population of the multivoltine midge Chironomus riparius. Using six seasonal 20 samples over three years from the same natural population, we could show with fitness experiments 21 that the population continuously evolved in response to a highly variable environment. Analyses of 22 genome-wide allele-frequencies revealed that tens of thousands of haplotypes responded at least 23 once to selection during the monitored period. Clustering the temporal haplotype frequency 24 trajectories revealed 46 different patterns, i.e. selection pressures. Some of these co-varied with 25 measured environmental variables known to be selective factors for the species. Our results 26 demonstrate that 1) adaptive tracking of multiple fluctuating selection pressures occurs in natural 27 populations, 2) the estimated minimum number of simultaneously acting selective pressures is quite 28 high but appears to be limited and 3) changes in intensity and direction of selective responses can be 29 frequent. This shows that adaptation in natural populations can be rapid, pervasive and complex 30

Genomic divergence landscape in recurrently hybridizingChironomussister taxa suggests stable steady state between mutual gene flow and isolation

“…First, θ is known to be unbiased under neutrality and constant population growth (population growth rate constantly >1 through time) only (Subramanian 2016). These assumptions are probably not met in multivoltine taxa under a multivariate, fluctuating selection regime that undergo seasonal boom‐bust cycles (Pfenninger and Foucault 2020). Also, LD and divergence time estimates lose accuracy under nonneutral conditions (Kumar 2005; Slatkin 2008).…”

“…Likewise, pervasive selection by adaptive tracking in conjunction with the seasonally fluctuating population size in both species with an exponential increase over several generations in summer with a setback during a single generation in winter (Pfenninger and Foucault 2020) may explain the strongly skewed TD distribution toward negative values both in isolated and nonisolated regions. Simulations have shown that demographic fluctuations alone can produce such a pattern (Fig.…”

“…Both may be linked to multivoltinity of the species in which successive generations experience very different selection regimes throughout the seasons and over time (Foucault et al. 2019; Pfenninger and Foucault 2020).…”

Spontaneous rate of clonal mutations inDaphnia galeata

“…Every possible mutation is practically always present in the population and in larger lakes, perhaps even in every clonal lineage. This almost permanent presence of exhaustive genetic variation should offer excellent opportunities for adaptive tracking of changing environmental conditions (Pfenninger and Foucault 2020), moreover since clonal reproduction should help to avoid stochastic loss of beneficial mutations (Kimura 1962;Messer and Petrov 2013). In addition, the occasional seasonal sexual reproduction allows to recombine favourable variation together.…”

Climate Change Genomics Calls for Standardized Data Reporting

“…The only solution to overcome this limitation is measuring genomic change over the actual course of climate change, i.e., tracking populations through time. Such time-for-time approaches are currently being implemented and already allowed the tracking of adaptive trajectories and quantification of the selection regime in natural populations (Pfenninger and Foucault, 2020). The GEA space for time approach can also be embedded in the time-for-time framework by building up time-series of repeated GEA.…”