Data from: Genetic diversity maintained among fragmented populations of a tree undergoing range contraction
Borrell, James S.; Wang, Nian; Nichols, Richard A.; Buggs, Richard J. A. (2018), Data from: Genetic diversity maintained among fragmented populations of a tree undergoing range contraction, Dryad, Dataset, https://doi.org/10.5061/dryad.v75rj24
Dwarf birch (Betula nana) has a widespread boreal distribution but has declined significantly in Britain where populations are now highly fragmented. We analysed the genetic diversity of these fragmented populations using markers that differ in mutation rate: conventional microsatellites markers (PCR-SSRs), RADseq generated transition and transversion SNPs (RAD-SNPs), and microsatellite markers mined from RADseq reads (RAD-SSRs). We estimated the current population sizes by census and indirectly, from the linkage disequilibrium found in the genetic surveys. The two types of estimate were highly correlated. Overall we found genetic diversity to be only slightly lower in Britain than across a comparable area in Scandinavia where populations are large and continuous. Whilst the ensemble of British fragments maintain diversity levels close to Scandinavian populations, individually they have drifted apart and lost diversity; particularly the smaller populations. An ABC analysis, based on coalescent models, favours demographic scenarios in which Britain maintained high levels of genetic diversity through post-glacial recolonisation. This diversity has subsequently been partitioned into population fragments that have recently lost diversity at a rate corresponding to the current population-size estimates. We conclude that the British population fragments retain sufficient genetic resources to be the basis of conservation and re-planting programmes. Use of markers with different mutation rates gives us greater confidence and insight than one marker set could have alone, and we suggest that RAD-SSRs are particularly useful as high mutation rate marker set with a well-specified ascertainment bias, which are widely available yet often neglected in existing RAD datasets.