Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)

Abstract The rapid expansion of wild pigs (Sus scrofa) throughout the United States has been fueled by unlawful introductions, with invasive populations causing extensive crop losses, damaging native ecosystems, and serving as a reservoir for disease. Multiple states have passed laws prohibiting the...

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Main Authors: Timothy J. Smyser, Peter Pfaffelhuber, Rachael M. Giglio, Matthew G. DeSaix, Amy J. Davis, Courtney F. Bowden, Michael A. Tabak, Arianna Manunza, Valentin Adrian Bâlteanu, Marcel Amills, Laura Iacolina, Pamela Walker, Carl Lessard, Antoinette J. Piaggio
Format: Article
Language:English
Published: Wiley 2024-02-01
Series:Ecosphere
Subjects:
Online Access:https://doi.org/10.1002/ecs2.4774
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author Timothy J. Smyser
Peter Pfaffelhuber
Rachael M. Giglio
Matthew G. DeSaix
Amy J. Davis
Courtney F. Bowden
Michael A. Tabak
Arianna Manunza
Valentin Adrian Bâlteanu
Marcel Amills
Laura Iacolina
Pamela Walker
Carl Lessard
Antoinette J. Piaggio
author_facet Timothy J. Smyser
Peter Pfaffelhuber
Rachael M. Giglio
Matthew G. DeSaix
Amy J. Davis
Courtney F. Bowden
Michael A. Tabak
Arianna Manunza
Valentin Adrian Bâlteanu
Marcel Amills
Laura Iacolina
Pamela Walker
Carl Lessard
Antoinette J. Piaggio
author_sort Timothy J. Smyser
collection DOAJ
description Abstract The rapid expansion of wild pigs (Sus scrofa) throughout the United States has been fueled by unlawful introductions, with invasive populations causing extensive crop losses, damaging native ecosystems, and serving as a reservoir for disease. Multiple states have passed laws prohibiting the possession or transport of wild pigs. However, genetic and phenotypic similarities between domestic pigs and invasive wild pigs—which overwhelmingly represent domestic pig and wild boar hybrids—pose a challenge for the enforcement of such regulations. We sought to exploit wild boar ancestry as a common attribute among the vast majority of invasive wild pigs as a means of genetically differentiating wild pigs from breeds of domestic pig found within the United States. We organized reference high‐density single nucleotide polymorphism genotypes (1039 samples from 33 domestic breeds and 382 samples from 16 wild boar populations) into five genetically cohesive reference groups: mixed‐commercial breeds, Durocs, heritage breeds, primitive breeds, and wild boar. Building upon well‐established genetic clustering approaches, we structured the test statistic to describe the difference in the likelihood of a given genotype's ancestry vectors (sensu genetic clustering analysis) if derived strictly from the four described domestic pig reference groups versus allowing for admixture from the wild boar group. By fitting statistical distributions to test statistics of reference domestic pigs, we characterized the distribution of the null hypothesis that a given genotype descends strictly from domestic pig reference groups. We tested the approach with simulated genotypes and empirical data from an additional 29 breeds of domestic pig represented by 435 unique genotypes; all associated test statistics for simulated and empirical domestic pig challenge sets fell within the distribution of reference domestic pigs. We then evaluated 6566 invasive wild pigs sampled across the contiguous United States, of which 63% exceeded the maximum threshold for domestic pigs and could be statistically classified as possessing wild boar ancestry. This approach provides a scientific foundation to enforce regulations prohibiting the possession of this destructive invasive species. Further, this computationally efficient and generalizable approach could be readily adapted to quantify gene flow among ecological systems of conservation or management concern.
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spelling doaj.art-18a946b90e734dab9218d5d3c5b7be652024-03-21T02:06:54ZengWileyEcosphere2150-89252024-02-01152n/an/a10.1002/ecs2.4774Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)Timothy J. Smyser0Peter Pfaffelhuber1Rachael M. Giglio2Matthew G. DeSaix3Amy J. Davis4Courtney F. Bowden5Michael A. Tabak6Arianna Manunza7Valentin Adrian Bâlteanu8Marcel Amills9Laura Iacolina10Pamela Walker11Carl Lessard12Antoinette J. Piaggio13United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center Fort Collins Colorado USADepartment of Mathematical Stochastics University of Freiburg Freiburg im Breisgau Baden‐Württemberg GermanyUnited States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center Fort Collins Colorado USAUnited States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center Fort Collins Colorado USAUnited States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center Fort Collins Colorado USAUnited States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center Fort Collins Colorado USAQuantitative Science Consulting, LLC Melancthon Ontario CanadaNational Research Council Institute of Agricultural Biology and Biotechnology Milan ItalyLaboratory of Genomics, Animal Breeding, Biodiversity and Molecular Pathology University of Agricultural Sciences and Veterinary Medicine Cluj‐Napoca Cluj‐Napoca RomaniaCentre for Research in Agricultural Genomics (CRAG), CSIC‐IRTA‐UAB‐UB, Universitat Autònoma de Barcelona Bellaterra Catalonia SpainFaculty of Mathematics, Natural Sciences and Information Technologies University of Primorska Koper SloveniaAnimal Genetic Resources of Canada, Agriculture and Agri‐Food Canada Saskatoon Saskatchewan CanadaAnimal Genetic Resources of Canada, Agriculture and Agri‐Food Canada Saskatoon Saskatchewan CanadaUnited States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center Fort Collins Colorado USAAbstract The rapid expansion of wild pigs (Sus scrofa) throughout the United States has been fueled by unlawful introductions, with invasive populations causing extensive crop losses, damaging native ecosystems, and serving as a reservoir for disease. Multiple states have passed laws prohibiting the possession or transport of wild pigs. However, genetic and phenotypic similarities between domestic pigs and invasive wild pigs—which overwhelmingly represent domestic pig and wild boar hybrids—pose a challenge for the enforcement of such regulations. We sought to exploit wild boar ancestry as a common attribute among the vast majority of invasive wild pigs as a means of genetically differentiating wild pigs from breeds of domestic pig found within the United States. We organized reference high‐density single nucleotide polymorphism genotypes (1039 samples from 33 domestic breeds and 382 samples from 16 wild boar populations) into five genetically cohesive reference groups: mixed‐commercial breeds, Durocs, heritage breeds, primitive breeds, and wild boar. Building upon well‐established genetic clustering approaches, we structured the test statistic to describe the difference in the likelihood of a given genotype's ancestry vectors (sensu genetic clustering analysis) if derived strictly from the four described domestic pig reference groups versus allowing for admixture from the wild boar group. By fitting statistical distributions to test statistics of reference domestic pigs, we characterized the distribution of the null hypothesis that a given genotype descends strictly from domestic pig reference groups. We tested the approach with simulated genotypes and empirical data from an additional 29 breeds of domestic pig represented by 435 unique genotypes; all associated test statistics for simulated and empirical domestic pig challenge sets fell within the distribution of reference domestic pigs. We then evaluated 6566 invasive wild pigs sampled across the contiguous United States, of which 63% exceeded the maximum threshold for domestic pigs and could be statistically classified as possessing wild boar ancestry. This approach provides a scientific foundation to enforce regulations prohibiting the possession of this destructive invasive species. Further, this computationally efficient and generalizable approach could be readily adapted to quantify gene flow among ecological systems of conservation or management concern.https://doi.org/10.1002/ecs2.4774ancestryexoferalizationferal swinegenetic clusteringgenetic identificationhybridization
spellingShingle Timothy J. Smyser
Peter Pfaffelhuber
Rachael M. Giglio
Matthew G. DeSaix
Amy J. Davis
Courtney F. Bowden
Michael A. Tabak
Arianna Manunza
Valentin Adrian Bâlteanu
Marcel Amills
Laura Iacolina
Pamela Walker
Carl Lessard
Antoinette J. Piaggio
Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)
Ecosphere
ancestry
exoferalization
feral swine
genetic clustering
genetic identification
hybridization
title Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)
title_full Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)
title_fullStr Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)
title_full_unstemmed Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)
title_short Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)
title_sort probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs sus scrofa
topic ancestry
exoferalization
feral swine
genetic clustering
genetic identification
hybridization
url https://doi.org/10.1002/ecs2.4774
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