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Dryad

Evolutionary rate and genetic load in an emblematic Mediterranean tree following an ancient and prolonged population collapse

Cite this dataset

Gonzalez-Martinez, Santiago C et al. (2020). Evolutionary rate and genetic load in an emblematic Mediterranean tree following an ancient and prolonged population collapse [Dataset]. Dryad. https://doi.org/10.5061/dryad.59zw3r23r

Abstract

Severe bottlenecks significantly diminish the amount of genetic diversity and the speed at which it accumulates (i.e. evolutionary rate). They further compromise the efficiency of natural selection to eliminate deleterious variants, which may reach fixation in the surviving populations. Consequently, expanding and adapting to new environments may pose a significant challenge when strong bottlenecks result in genetic pauperization. Herein, we surveyed the patterns of nucleotide diversity, molecular adaptation and genetic load across hundreds of loci in a circum-Mediterranean conifer (Pinus pinea L.) that represents one of the most extreme cases of genetic pauperization in widespread outbreeding taxa. We found very little genetic variation in both hypervariable non-coding (nuSSRs) and gene-coding loci, which translated into genetic diversity estimates one order of magnitude lower than those previously reported for pines. Such values were consistent with a strong population decline that began some ~1Ma. Comparisons with the related and partially parapatric maritime pine revealed reduced rates of adaptive evolution (α and ωa) and a significant accumulation of genetic load. These did not appear to result from differences in mutation rates or linkage disequilibrium between the two species; instead they are the likely outcome of contrasting demographic histories affecting both the speed at which these taxa accumulate genetic diversity, and the global efficacy of selection. Future studies, and programs for conservation and management, should thus start testing for the effects of genetic load on fitness and integrating such effects into predictive models.