bioRxivEvolutionary BiologyPreprint
Population genomic time series data of a natural population suggests adaptive tracking of environmental changes
Abstract
Natural populations are constantly exposed to fluctuating environmental changes that negatively affect their fitness in unpredictable ways. While theoretical models show the possibility of counteracting these environmental changes through rapid evolutionary adaptations, there have been few empirical studies demonstrating such adaptive tracking in natural populations. Here, we analysed environmental data, fitness-related phenotyping and genomic time-series data sampled over three years from a natural Chironomus riparius (Diptera, Insecta) population to address this question. We show that the populations environment varied significantly on the time scale of the sampling in many selectively relevant dimensions, independently of each other. Similarly, phenotypic fitness components evolved significantly on the same temporal scale (mean 0.32 Haldanes), likewise independent from each other. The allele frequencies of 367,446 SNPs across the genome showed evidence of positive selection. Using temporal correlation of spatially coherent allele frequency changes revealed 35,574 haplotypes with more than one selected SNP. The mean selection coefficient for these haplotypes was 0.30 (s.d. = 0.68). The frequency changes of these haplotypes clustered in 46 different temporal patterns, indicating concerted, independent evolution of many polygenic traits. Nine of these patterns were strongly correlated with measured environmental variables. Thus, our results suggest that the natural population of C. riparius tracks environmental change through rapid polygenic adaptation in many independent dimensions. This is further evidence that natural selection is pervasive at the genomic level and that evolutionary and ecological time scales may not differ at all, at least in some organisms.
- organism: non-human primate
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- approach: statistical model
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