Data from: The influence of contemporary and historic landscape features on the genetic structure of the sand dune endemic, Cirsium pitcheri (Asteraceae)
Cite this dataset
Fant, Jeremie B. et al. (2013). Data from: The influence of contemporary and historic landscape features on the genetic structure of the sand dune endemic, Cirsium pitcheri (Asteraceae) [Dataset]. Dryad. https://doi.org/10.5061/dryad.cf165
Narrow endemics are at risk from climate change due to their restricted habitat preferences, lower colonization ability and dispersal distances. Landscape genetics combines new tools and analyses that allow us to test how both past and present landscape features have facilitated or hindered previous range expansion and local migration patterns, and thereby identifying potential limitations to future range shifts. We have compared current and historic habitat corridors in Cirsium pitcheri, an endemic of the linear dune ecosystem of the Great Lakes, to determine the relative contributions of contemporary migration and post-glacial range expansion on genetic structure. We used seven microsatellite loci to characterize the genetic structure for 24 populations of Cirsium pitcheri, spanning the center to periphery of the range. We tested genetic distance against different measures of geographic distance and landscape permeability, based on contemporary and historic landscape features. We found moderate genetic structure (ave Fst =0.14), and a north -south pattern to the distribution of genetic diversity and inbreeding, with northern populations having the highest diversity and lowest levels of inbreeding. High allelic diversity, small average pairwise distances and mixed genetic clusters identified in Structure suggest populations in the center of the range represent the point of entry to the Lake Michigan and a refugia of diversity for this species. A strong association between genetic distances and lake level changes suggests that historic lake fluctuations best explain the broad geographic patterns, and sandy habitat best explain local patterns or movement.