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Rangewide population differentiation and population substructure in Quercus rubra L.
- Borkowski, Daniel S., Hoban, Sean M., Chatwin, Warren, Romero-Severson, Jeanne
- Tree genetics & genomes 2017 v.13 no.3 pp. 67
- Bayesian theory, Quercus rubra, ancestry, biogeography, climatic factors, genes, genetic variation, genotyping, latitude, models, trees
- Genetic diversity and differentiation in Quercus rubra remains undescribed for most of the native range. Using 10 highly informative gSSR markers, we genotyped 23 populations (980 trees), from across the native range, at local and rangewide spatial scales. We used a hierarchical Bayesian method to generate population-specific F ₛₜ estimates. The posterior probabilities of 3 spatial and 19 climate factors revealed that latitude and precipitation in the warmest quarter were the only plausible models for population-specific F ₛₜ estimates. Population-specific F ₛₜ estimates increased as a linear function of population latitude (R ² = 0.745, p < 0.0001), the expected effect of postglacial range expansion. Measures of shared ancestry (pairwise local differentiation) among populations sampled at fine spatial scales within locations also increased with the latitude of the location (R ² = 0.495, p = 0.014), suggesting an influence on differentiation that could not be attributed to postglacial range expansion. As precipitation in the warmest quarter drives radial growth rate in this species, we propose that growth rate influences generation time, which determines the time required to re-establish migration-drift equilibrium among local regenerating stands. Population substructure analysis detected four groups, only one of which was geographically coherent. Most other populations were moderately admixed. Sampling designs that include both fine and coarse spatial scales can begin to disentangle the effect of past range expansions from the effect of recent disturbances. Accounting for population substructure in this species will require additional studies involving functional genes, interspecific interactions, and species distribution modeling.