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Assessing the prevalence of hybridization between sympatric Canis species surrounding the red wolf (Canis rufus) recovery area in North Carolina
- Bohling, Justin H., Waits, Lisette P.
- Molecular ecology 2011 v.20 no.10 pp. 2142-2156
- Bayesian theory, Canis latrans, Canis lupus, ancestry, dogs, genetic techniques and protocols, hybrids, introgression, microsatellite repeats, mitochondrial DNA, sympatry, winter, wolves, North Carolina
- Predicting spatial patterns of hybridization is important for evolutionary and conservation biology yet are hampered by poor understanding of how hybridizing species can interact. This is especially pertinent in contact zones where hybridizing populations are sympatric. In this study, we examined the extent of red wolf (Canis rufus) colonization and introgression where the species contacts a coyote (C. latrans) population in North Carolina, USA. We surveyed 22 000 km² in the winter of 2008 for scat and identified individual canids through genetic analysis. Of 614 collected scats, 250 were assigned to canids by mitochondrial DNA (mtDNA) sequencing. Canid samples were genotyped at 6-17 microsatellite loci (nDNA) and assigned to species using three admixture criteria implemented in two Bayesian clustering programs. We genotyped 82 individuals but none were identified as red wolves. Two individuals had red wolf mtDNA but no significant red wolf nDNA ancestry. One individual possessed significant red wolf nDNA ancestry (approximately 30%) using all criteria, although seven other individuals showed evidence of red wolf ancestry (11-21%) using the relaxed criterion. Overall, seven individuals were classified as hybrids using the conservative criteria and 37 using the relaxed criterion. We found evidence of dog (C. familiaris) and gray wolf (C. lupus) introgression into the coyote population. We compared the performance of different methods and criteria by analyzing known red wolves and hybrids. These results suggest that red wolf colonization and introgression in North Carolina is minimal and provide insights into the utility of Bayesian clustering methods to detect hybridization.