Data from: Conservation genomics of anadromous Atlantic salmon across its North American range: outlier loci identify the same patterns of population structure as neutral loci
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https://datadryad.org/dataset/doi:10.5061/dryad.sb601
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Anadromous Atlantic salmon (Salmo salar) is a species of major
conservation and management concern in North America, where population
abundance has been declining over the past 30 years. Effective
conservation actions require the delineation of conservation units to
appropriately reflect the spatial scale of intraspecific variation and
local adaptation. Towards this goal, we used the most comprehensive
genetic and genomic database for Atlantic salmon to date, covering the
entire North American range of the species. The database included
microsatellite data from 9,142 individuals from 149 sampling locations and
data from a medium-density SNP array providing genotypes for >3,000
SNPs for 50 sampling locations. We used neutral and putatively selected
loci to integrate adaptive information in the definition of conservation
units. Bayesian clustering with the microsatellite dataset and with
neutral SNPs identified regional groupings largely consistent with
previously published regional assessments. The use of outlier SNPs did not
result in major differences in the regional groupings, suggesting that
neutral markers can reflect the geographic scale of local adaptation
despite not being under selection. We also performed assignment tests to
compare power obtained from microsatellites, neutral SNPs and outlier
SNPs. Using SNP data substantially improved power compared to
microsatellites, and an assignment success of 97% to the population of
origin and of 100% to the region of origin was achieved when all SNP loci
were used. Using outlier SNPs only resulted in minor improvements to
assignment success to the population of origin but improved regional
assignment. We discuss the implications of these new genetic resources for
the conservation and management of Atlantic salmon in North America.
提供机构:
Dryad
创建时间:
2014-10-23



