Data from: Evaluating the ability of Bayesian clustering methods to detect hybridization and introgression using an empirical red wolf dataset

Bayesian clustering methods have emerged as a popular tool for assessing hybridization using genetic markers. Simulation studies have shown these methods perform well under certain conditions; however, these methods have not been evaluated using empirical datasets with individuals of known ancestry. We evaluated the performance of two Bayesian clustering programs, BAPS and STRUCTURE, with genetic data from a reintroduced red wolf (Canis rufus) population in North Carolina, USA. Red wolves hybridize with coyotes (C. latrans), and a single hybridization event resulted in introgression of coyote genes into the red wolf population. A detailed pedigree has been reconstructed for the wild red wolf population that includes individuals of 50–100% red wolf ancestry, providing an ideal case study for evaluating the ability of these methods to estimate admixture. Using 17 microsatellite loci, we tested the programs using different training set compositions and varying numbers of loci. STRUCTURE was more likely than BAPS to detect an admixed genotype and correctly estimate an individual’s true ancestry composition. However, STRUCTURE was more likely to misclassify a pure individual as a hybrid. Both programs were outperformed by a maximum-likelihood-based test designed specifically for this system, which never misclassified a hybrid (50-75% red wolf) as a red wolf or vice versa. Both training set composition and the number of loci had an impact on accuracy but their relative importance varied depending on the program. Our findings demonstrate the importance of evaluating methods used for evaluating hybridization in the context of endangered species management