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Pronghorn population genomics show connectivity at the core of their range

Cite this dataset

LaCava, Melanie E.F. et al. (2020). Pronghorn population genomics show connectivity at the core of their range [Dataset]. Dryad.


Preserving connectivity in the core of a species’ range is crucial for long-term persistence. However, a combination of ecological characteristics, social behavior, and landscape features can reduce connectivity among wildlife populations and lead to genetic structure. Pronghorn (Antilocapra americana), for example, exhibit fluctuating herd dynamics and variable seasonal migration strategies, but GPS-tracking studies show that landscape features such as highways impede their movements, leading to conflicting hypotheses about expected levels of genetic structure. Given that pronghorn populations declined significantly in the early 1900s, have only partially recovered, and are experiencing modern threats from landscape modification, conserving connectivity among populations is important for their long-term persistence in North America. To assess the genetic structure and diversity of pronghorn in the core of their range, we genotyped 4,949 genome-wide single nucleotide polymorphisms and 11 microsatellites from 398 individuals throughout the state of Wyoming. We found no evidence of genetic subdivision and minimal evidence of isolation by distance despite a range that spans hundreds of kilometers, multiple mountain ranges, and three interstate highways. In addition, a rare variant analysis using putatively recent mutations found no genetic division between pronghorn on either side of a major highway corridor. Although we found no evidence that barriers to daily and seasonal movements of pronghorn impede gene flow, we suggest periodic monitoring of genetic structure and diversity as a part of management strategies to identify changes in connectivity.

Usage notes

This directory contains data, protocols, and scripts associated with our publication. The data include sample metadata, microsatellite genotypes, SNP genotypes, SNP genotype likelihoods, and the consensus genome used for de novo assembly of our raw sequencing reads. The protocols include our genotyping-by-sequencing library preparation protocol. We include Unix-based scripts for our step-by-step bioinformatics workflow, for parsing raw sequence data by sample barcode, for data reduction before CD-HIT assembly, for running bwa in parallel, for converting vcf genotypes into a genotype likelihood matrix, and for converting a genotype likelihood matrix into a text file. We also include R-based scripts for performing PCA on SNP data, for performing our rare variant analysis, and for detecting loci with excess levels of heterozygosity.


National Institute of General Medical Sciences, Award: 2P20GM103432

University of Wyoming Program in Ecology

Y Cross Ranch Endowment

Wyoming Excellence Chair Funds