Data for: Geographic isolation reduces genetic diversity of a wide-ranging terrestrial vertebrate, Canis lupus ...

• Main Authors: Frévol, Salomé, MacNulty, Daniel, Anderson, Morgan, Carmichael, Lindsey, Cluff, H. Dean, Mech, L. David, Musiani, Marco
• Format: Dataset
• Language: English
• Published: Dryad 2022
• Subjects: Canis lupus; Expected heterozygosity; allelic richness; Conservation genetics; edge effect; geographic isolation; FOS Biological sciences; Population genetics; genetic diversity; central-marginal hypothesis; North America; microsatellite
• Online Access: https://dx.doi.org/10.5061/dryad.pnvx0k6r3; https://datadryad.org/stash/dataset/doi:10.5061/dryad.pnvx0k6r3

Genetic diversity is theorized to decrease in populations closer to a species’ range edge, where habitat may be suboptimal. However, generalist species capable of long-range dispersal may maintain sufficient gene flow to counteract this, though the presence of significant barriers to dispersal (e.g., large water bodies, human-dominated landscapes) may still lead to, and exacerbate, the edge effect. We used microsatellite data for 2,426 gray wolves (Canis lupus) from 24 sub-populations (groups) to model how allelic richness and expected heterozygosity varied with two measures of range edge (mainland-island position, latitude, and distance from range center) across >7.3 million km2 of northern North America. We found that allelic richness and expected heterozygosity of island groups was measurably less than that of mainland groups and that these differences increased with the island’s distance to the species’ range center in the study area. Our results demonstrate how multiple axes of geographic isolation ... : As this is a series of four datasets, description of methods used for collection/generation of each raw dataset are most easily found in their respective source publication. Processing:Removal of samples with low confidence or not enough genetic material designated by the above authors prior to creating raw data file. Data are separated into sub-populations (groups) based on prior knowledge of wolf genetics and ecology. Each dataset was run through Microchecker to identify scoring errors and the presence of null alleles, then run through Genepop to identify markers out of Hardy-Weinburg equilibrium or in linkage disequilibrium – these were removed unless they were otherwise known to not be physically linked. Allelic Richness and Expected Heterozygosity for each group were calculated in FSTAT and Genetix respectively. These values were then used for the creation of genetic diversity models. A more detailed explanation of the data processing stage can be found in the nested README files for each step and ...