Dataset Information

Application of the geographic population structure (GPS) algorithm for biogeographical analyses of wild and captive gorillas.

ABSTRACT: BACKGROUND:The utilization of high resolution genome data has important implications for the phylogeographical evaluation of non-human species. Biogeographical analyses can yield detailed understanding of their population biology and facilitate the geo-localization of individuals to promote their efficacious management, particularly when bred in captivity. The Geographic Population Structure (GPS) algorithm is an admixture based tool for inference of biogeographical affinities and has been employed for the geo-localization of various human populations worldwide. Here, we applied the GPS tool for biogeographical analyses and localization of the ancestral origins of wild and captive gorilla genomes, of unknown geographic source, available in the Great Ape Genome Project (GAGP), employing Gorillas with known ancestral origin as the reference data. RESULTS:Our findings suggest that GPS was successful in recapitulating the population history and estimating the geographic origins of all gorilla genomes queried and localized the wild gorillas with unknown geographical origin

SUBMITTER: Das R

PROVIDER: S-EPMC6362561 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

Application of the geographic population structure (GPS) algorithm for biogeographical analyses of wild and captive gorillas.

Das Ranajit R Upadhyai Priyanka P

BMC bioinformatics 20190205 Suppl 1

<h4>Background</h4>The utilization of high resolution genome data has important implications for the phylogeographical evaluation of non-human species. Biogeographical analyses can yield detailed understanding of their population biology and facilitate the geo-localization of individuals to promote their efficacious management, particularly when bred in captivity. The Geographic Population Structure (GPS) algorithm is an admixture based tool for inference of biogeographical affinities and has be ...[more]

PMID: 30717677

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Project description:Simple Summary Wildlife trafficking is a major contributor to global biodiversity loss, especially reptiles, which are confiscated by law enforcement more than any other vertebrate class. Wildlife forensic experts can use chemicals from animal tissues to determine the origin of confiscated animals. Such physical evidence can help law enforcement prosecute wildlife traffickers in court and hold poachers accountable. In this study, we developed a statistical tool that can be used to determine if a confiscated wood turtle (Glyptemys insculpta) from Maine came from the wild or captivity. We used carbon and nitrogen stable isotopes from wood turtle claw tips to construct a statistical model that correctly classified all wild turtles as wild and nearly all captive turtles as captive (predictive accuracy 97.2%). Results from our study can be used to assist law enforcement in Maine and to develop a forensics tool used to help combat the illegal turtle trade. Abstract Wildlife traffickers often claim that confiscated animals were captive-bred rather than wild-caught to launder wild animals and escape prosecution. We used stable isotopes (δ13C and δ15N) derived from the claw tips of wild wood turtles from Maine and captive wood turtles throughout the eastern U.S. to develop a predictive model used to classify confiscated wood turtles as wild or captive. We found that the claw tips of wild and captive wood turtles (Glyptemys insculpta) were isotopically distinct. Captive turtles had significantly higher δ13C and δ15N values than wild turtles. Our model correctly classified all wild turtles as wild (100%) and nearly all captive turtles as captive (94%). All but two of the 71 turtles tested were successfully predicted as wild or captive (97.2% accuracy), yielding a misclassification rate of 2.8%. In addition to our model being useful to law enforcement in Maine, we aim to develop a multi-species model to assist conservation law enforcement efforts to curb illegal turtle trafficking from locations across the eastern United States and Canada.

Dataset Information

Application of the geographic population structure (GPS) algorithm for biogeographical analyses of wild and captive gorillas.

Publications

Application of the geographic population structure (GPS) algorithm for biogeographical analyses of wild and captive gorillas.

Similar Datasets

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