Estimating Admixture at the Population Scale: Taking Imperfect Detectability and Uncertainty in Hybrid Classification Seriously

• Published in: The Journal of Wildlife Management
• Main Authors: Santostasi, Nina L., Gimenez, Olivier, Caniglia, Romolo, Fabbri, Elena, Molinari, Luigi, Reggioni, Willy, Ciucci, Paolo
• Other Authors: European Commission, Sapienza Università di Roma, Agence Nationale de la Recherche
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2021
• Subjects: Canis lupus
• Online Access: http://dx.doi.org/10.1002/jwmg.22038; https://onlinelibrary.wiley.com/doi/pdf/10.1002/jwmg.22038; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/jwmg.22038

ABSTRACT Introgressive hybridization between domestic dogs and wolves ( Canis lupus ) represents an emblematic case of anthropogenic hybridization and is increasingly threatening the genomic integrity of wolf populations expanding into human‐modified landscapes. But studies formally estimating prevalence and accounting for imperfect detectability and uncertainty in hybrid classification are lacking. Our goal was to present an approach to formally estimate the proportion of admixture by using a capture‐recapture (CR) framework applied to individual multilocus genotypes detected from non‐invasive samples collected from a protected wolf population in Italy. We scored individual multilocus genotypes using a panel of 12 microsatellites and assigned genotypes to reference wolf and dog populations through Bayesian clustering procedures. Based on 152 samples, our dataset comprised the capture histories of 39 individuals sampled in 7 wolf packs and was organized in bi‐monthly sampling occasions (Aug 2015−May 2016). We fitted CR models using a multievent formulation to explicitly handle uncertainty in individual classification, and accordingly examined 2 model scenarios: one reflecting a traditional approach to classifying individuals (i.e., minimizing the misclassification of wolves as hybrids; Type 1 error), and the other using a more stringent criterion aimed to balance Type 1 and Type 2 error rates (i.e., the misclassification of hybrids as wolves). Compared to the sample proportion of admixed individuals in the dataset (43.6%), formally estimated prevalence was 50% under the first and 70% under the second scenario, with 71.4% and 85.7% of admixed packs, respectively. At the individual level, the proportion of dog ancestry in the wolf population averaged 7.8% (95% CI = 4.4−11%). Balancing between Type 1 and 2 error rates in assignment tests, our second scenario produced an estimate of prevalence 40% higher compared to the alternative scenario, corresponding to a 65% decrease in Type 2 and no increase in Type 1 error ...