Selective effects of a short transient environmental fluctuation on a natural population

Abstract

Natural populations experience continuous and often transient changes of environmental conditions. These in turn may result in fluctuating selection pressures leading to variable demographic and evolutionary population responses. Rapid adaptation as short-term response to a sudden environmental change has in several cases been attributed to polygenic traits, but the underlying genomic dynamics and architecture are poorly understood. In this study, took advantage of a natural experiment in an insect population by monitoring genome-wide allele frequencies before and after a cold snap event. Whole genome pooled sequencing of time series samples revealed ten selected haplotypes carrying ancient polymorphisms, partially with signatures of balancing selection. By constantly cold exposing genetically variable individuals in the laboratory, we could demonstrate with whole genome resequencing i) among the survivors, the same alleles rose in frequency as in the wild and ii) that the identified variants additively predicted fitness (survival time) of its bearers. Finally, by simultaneously sequencing the genome and the transcriptome of cold exposed individuals we could tentatively link some of the selected SNPs to the cis- and trans-regulation of genes and pathways known to be involved in cold response of insects, like Cytochrome P450 and fatty acid metabolism. Altogether, our results shed light on the strength and speed of selection in natural populations and the genomic architecture of its underlying polygenic trait. Population genomic time series data thus appear as promising tool for measuring the selective tracking of fluctuating selection in natural populations.

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

“…Most importantly for the diagnosis of adaptive tracking was that phenotypic evolution occurred on the same temporal scale as the environmental change. However, we did not test for a direct link between any environmental parameter and a phenotypic trait, because i) the measured traits are rather highly inclusive fitness components, thus integrating the influence of many lower level traits, ii) transient, short term extreme events were not recorded by the long-term averages, but are known to exert important selective pressure (Pfenninger et al ., 2022). All measured traits have strong links to fitness, which also varied over the monitored period.…”

“…A short range haplotype structure is a precondition for the observed short distance between haplotypes responding to selection. Since only haplotypes with more than a single SNP were considered, this may even present an underestimate, because ancient selected polymorphisms may very well be completely separated from their background by recombination (Pfenninger et al ., 2022). In some instances, even different parts of the same gene followed different evolutionary trajectories.…”

“…In a recent article Pfenninger et al (2022) could show with population genomic time series data and experiments that a natural population of the non-biting midge Chironomus riparius (Meigen, 1803) selectively tracked a short-term weather event. Moreover, long term genomic time series data suggested that the spatially unlinked alleles involved in the investigated polygenic trait under selection changed temporally in a correlated fashion over a longer period (Pfenninger et al ., 2022). This raised the question whether there are more spatially unlinked haplotypes in the genome that change over time in correlated fashion, thus indicating independent responses to the respectively same selective forces.…”

On the use of population genomic time series for environmental monitoring

“…Long before species demographically decline or actually vanish, stressor effects on population fitness manifests on the molecular level: transcription patterns and mutation rates change (Singh et al, 2020). Last but not least, adaptive processes are triggered (Kosakivska et al, 2021) that result in measurable changes of allele frequencies at affected loci throughout the genome (Pfenninger et al, 2022).…”

“…It is therefore plausible that temporal changes in environmental conditions likewise change the phenotypic optimum, which is then tracked by respective changes in allelic frequency to reach the new equilibrium frequency. Such adaptation from standing genetic variation to track temporally moving phenotypic optima is theoretically possible nearly in real time (Matuszewski et al, 2015) and has recently been shown empirically (Pfenninger et al, 2022). Monitoring the selectively driven changes of the associated genetic variation is therefore the basis of the proposed approach.…”