DOI: 10.5061/dryad.pnvx0k6r3
title: Data for: Geographic isolation reduces genetic diversity of a wide-ranging terrestrial vertebrate, Canis lupus

description: 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 &gt;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 (distance from range center and island habitation) can act
 synergistically to erode the genetic diversity of wide-ranging terrestrial
 vertebrate populations despite the counteracting influence of long-range
 dispersal ability. These findings emphasize how geographic isolation is a
 potential threat to the genetic diversity and viability of terrestrial
 vertebrate populations even among species capable of long-range
 dispersal.  This dataset is comprised of four separate
 sub-datasets, sourced from Carmichael et al. (2007), Musiani et al.
 (2007), McNay (2006), and a manuscript in progress (referred to as
 "MacNulty" in the data files, to be first published in Frevol et
 al. 2023). The raw data are comprised of sample IDs, latitude and
 longitude points indicating where the sample was collected or recorded,
 and microsatellite genetic information. To aid in re-use, the raw genetic
 data has also been formatted and presented for use with common population
 genetics software (Genepop, MICROCHECKER, Genetix, FSTAT). The dataset
 also includes spatial data files of the sub-populations described in the
 study, allelic richness and expected heterozygosity data derived from the
 raw and spatial data, and the R script used to create the
 models.  Carmichael LE, Krizan J, Nagy JA, et al (2007)
 Historical and ecological determinants of genetic structure in arctic
 canids. Molecular Ecology 16:3466–3483.
 https://doi.org/10.1111/j.1365-294X.2007.03381.x McNay ME (2006)
 Preliminary results of parentage analysis using microsatellite markers
 from an exploited wolf population in central Alaska. Alaska Department of
 Fish and Game, Division of Wildlife Conservation, Juneau, Alaska Musiani
 M, Leonard JA, Cluff HD, et al (2007) Differentiation of tundra/taiga and
 boreal coniferous forest wolves: genetics, coat colour and association
 with migratory caribou. Molecular Ecology 16:4149–4170.
 https://doi.org/10.1111/j.1365-294X.2007.03458.x
description: 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:<br>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 Frevol et al. 2023.
description: Programs and software used: QGIS, Genepop, MICROCHECKER, Genetix,
 FSTAT, R