Show simple item record Bohling, Justin H. Adams, Jennifer R. Waits, Lisette P.
dc.coverage.spatial North Carolina 2012-10-10T15:57:43Z 2012-10-10T15:57:43Z 2012-19-11
dc.identifier doi:10.5061/dryad.8j486
dc.identifier.citation Bohling JH, Adams JR, Waits LP (2012) Evaluating the ability of Bayesian clustering methods to detect hybridization and introgression using an empirical red wolf dataset. Molecular Ecology 22(1): 74-86.
dc.description 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.
dc.relation.haspart doi:10.5061/dryad.8j486/1
dc.relation.haspart doi:10.5061/dryad.8j486/2
dc.relation.haspart doi:10.5061/dryad.8j486/3
dc.relation.isreferencedby doi:10.1111/mec.12109
dc.relation.isreferencedby PMID:23163531
dc.subject Conservation Biology
dc.subject Conservation Genetics
dc.subject Hybridization
dc.subject Population Genetics - Empirical
dc.subject Population Genetics - Theoretical
dc.title Data from: Evaluating the ability of Bayesian clustering methods to detect hybridization and introgression using an empirical red wolf dataset
dc.type Article *
dwc.ScientificName Canis rufus
dwc.ScientificName Canis latrans
dc.contributor.correspondingAuthor Bohling, Justin
prism.publicationName Molecular Ecology

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Title BAPS_input_files
Downloaded 62 times
Description These files are GenePop files that were used for the BAPS analyses. Each file reflects whether it contains 'Known' or 'Unknown' individuals incorporated in the BAPS analysis, along with the year and training set. Those with just a year in the file name are for the 'Update' training set, whereas those ending with '12years' were part of the '12 years' training set.
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Title Canid_genotypes_Dryad
Downloaded 89 times
Description This file contains microsatellite genotypes of all individuals used in the simulation study. The first column contains the identification number for each individual. Column two indicates which group the individual was assigned to as part of our study (note: this matches the 'Founders' training set in our publication). The third column indicates whether the individual was included as a 'known' individual in the STRUCTURE analysis. A '1' indicates that an individual's genotype was included in estimating the allele frequencies, a '0' means is was not. All the remaining columns contain genotypic information, with each column containing genotyped alleles. There are two columns per microsatellite locus.
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Title STRUCTURE_input_files
Downloaded 136 times
Description This contains all the input files used for the STRUCTURE analyses. Each file represents a separate year and training set. See our publication for more details. The ReadMe file contains the details of the parameter settings and input files.
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