bioRxivEvolutionary Biology
Ancient dolphin genomes reveal rapid repeated adaptation to coastal waters
M. Louis, P. Korlevic, M. Nykanen, F. Archer, S. Berrow, A. Brownlow, E. Lorenzen, J. O'Brien, K. Post, F. Racimo, E. Rogan, P. E. Rosel, M.-H. S. Sinding, H. van der Es, N. Wales, M. C. Fontaine, O. E. Gaggiotti, A. D. Foote
Abstract
Parallel evolution provides among the strongest evidence of the role of natural selection in shaping adaptation to the local environment. Yet, the chronology, mode and tempo of the process of parallel evolution remains broadly debated and discussed in the field of evolutionary biology. In this study, we harness the temporal resolution of paleogenomics to understand the tempo and independence of parallel coastal ecotype adaptation in common bottlenose dolphins (Tursiops truncatus). For this, we generated whole genome resequencing data from subfossil dolphins (8,610-5,626 years BP) originating from around the formation time of new coastal habitat and compared them with data from contemporary populations. Genomic data revealed a shift in genetic affinity, with the oldest ancient sample being closer to the pelagic populations, while the younger samples had intermediate ancestry that showed greater affinity with the local contemporary coastal populations. We found coastal-associated genotypes in the genome of our highest coverage ancient sample, SP1060, providing rare evidence of rapid adaptation from standing genetic variation. Lastly, using admixture graph analyses, we found a reticulate evolutionary history between pelagic and coastal populations. Ancestral gene flow from coastal populations was the probable source of standing genetic variation present in the pelagic populations that enabled rapid adaptation to newly emerged coastal habitat. The genetic response to past climatic warming provides an understanding of how bottlenose dolphins will respond to ongoing directional climate change and shifting coastlines.
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- approach: statistical model
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