Detecting concerted demographic response across community assemblages using hierarchical approximate Bayesian computation.
Identifieur interne : 000015 ( PubMed/Curation ); précédent : 000014; suivant : 000016Detecting concerted demographic response across community assemblages using hierarchical approximate Bayesian computation.
Auteurs : Yvonne L. Chan [États-Unis] ; David Schanzenbach ; Michael J. HickersonSource :
- Molecular biology and evolution [ 1537-1719 ] ; 2014.
Descripteurs français
- Wicri :
- geographic : Australie.
English descriptors
- KwdEn :
- MESH :
- geographic : Australia.
- classification : Birds.
- genetics : Birds.
- methods : Computational Biology.
- Adaptation, Biological, Animals, Bayes Theorem, Computer Simulation, Models, Statistical, Phylogeny, Phylogeography, Population Density.
Abstract
Methods that integrate population-level sampling from multiple taxa into a single community-level analysis are an essential addition to the comparative phylogeographic toolkit. Detecting how species within communities have demographically tracked each other in space and time is important for understanding the effects of future climate and landscape changes and the resulting acceleration of extinctions, biological invasions, and potential surges in adaptive evolution. Here, we present a statistical framework for such an analysis based on hierarchical approximate Bayesian computation (hABC) with the goal of detecting concerted demographic histories across an ecological assemblage. Our method combines population genetic data sets from multiple taxa into a single analysis to estimate: 1) the proportion of a community sample that demographically expanded in a temporally clustered pulse and 2) when the pulse occurred. To validate the accuracy and utility of this new approach, we use simulation cross-validation experiments and subsequently analyze an empirical data set of 32 avian populations from Australia that are hypothesized to have expanded from smaller refugia populations in the late Pleistocene. The method can accommodate data set heterogeneity such as variability in effective population size, mutation rates, and sample sizes across species and exploits the statistical strength from the simultaneous analysis of multiple species. This hABC framework used in a multitaxa demographic context can increase our understanding of the impact of historical climate change by determining what proportion of the community responded in concert or independently and can be used with a wide variety of comparative phylogeographic data sets as biota-wide DNA barcoding data sets accumulate.
DOI: 10.1093/molbev/msu187
PubMed: 24925925
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David Schanzenbach<affiliation><nlm:affiliation>Cyberinfrastructure, University of Hawai'i at Manoa.</nlm:affiliation><wicri:noCountry code="subField">University of Hawai'i at Manoa</wicri:noCountry></affiliation><affiliation><nlm:affiliation>Biology Department, City College of New YorkThe Graduate Center, City University of New York.</nlm:affiliation><wicri:noCountry code="subField">City University of New York</wicri:noCountry></affiliation>Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Detecting concerted demographic response across community assemblages using hierarchical approximate Bayesian computation.</title><author><name sortKey="Chan, Yvonne L" sort="Chan, Yvonne L" uniqKey="Chan Y" first="Yvonne L" last="Chan">Yvonne L. Chan</name><affiliation wicri:level="1"><nlm:affiliation>Hawai'i Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai'i at Manoa ylhchan@hawaii.edu.</nlm:affiliation><country wicri:rule="url">États-Unis</country><wicri:regionArea>Hawai'i Institute of Marine Biology, School of Ocean and Earth Science and Technology</wicri:regionArea></affiliation></author><author><name sortKey="Schanzenbach, David" sort="Schanzenbach, David" uniqKey="Schanzenbach D" first="David" last="Schanzenbach">David Schanzenbach</name><affiliation><nlm:affiliation>Cyberinfrastructure, University of Hawai'i at Manoa.</nlm:affiliation><wicri:noCountry code="subField">University of Hawai'i at Manoa</wicri:noCountry></affiliation></author><author><name sortKey="Hickerson, Michael J" sort="Hickerson, Michael J" uniqKey="Hickerson M" first="Michael J" last="Hickerson">Michael J. Hickerson</name><affiliation><nlm:affiliation>Biology Department, City College of New YorkThe Graduate Center, City University of New York.</nlm:affiliation><wicri:noCountry code="subField">City University of New York</wicri:noCountry></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2014">2014</date><idno type="RBID">pubmed:24925925</idno><idno type="pmid">24925925</idno><idno type="doi">10.1093/molbev/msu187</idno><idno type="wicri:Area/PubMed/Corpus">000015</idno><idno type="wicri:Area/PubMed/Curation">000015</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Detecting concerted demographic response across community assemblages using hierarchical approximate Bayesian computation.</title><author><name sortKey="Chan, Yvonne L" sort="Chan, Yvonne L" uniqKey="Chan Y" first="Yvonne L" last="Chan">Yvonne L. Chan</name><affiliation wicri:level="1"><nlm:affiliation>Hawai'i Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai'i at Manoa ylhchan@hawaii.edu.</nlm:affiliation><country wicri:rule="url">États-Unis</country><wicri:regionArea>Hawai'i Institute of Marine Biology, School of Ocean and Earth Science and Technology</wicri:regionArea></affiliation></author><author><name sortKey="Schanzenbach, David" sort="Schanzenbach, David" uniqKey="Schanzenbach D" first="David" last="Schanzenbach">David Schanzenbach</name><affiliation><nlm:affiliation>Cyberinfrastructure, University of Hawai'i at Manoa.</nlm:affiliation><wicri:noCountry code="subField">University of Hawai'i at Manoa</wicri:noCountry></affiliation></author><author><name sortKey="Hickerson, Michael J" sort="Hickerson, Michael J" uniqKey="Hickerson M" first="Michael J" last="Hickerson">Michael J. Hickerson</name><affiliation><nlm:affiliation>Biology Department, City College of New YorkThe Graduate Center, City University of New York.</nlm:affiliation><wicri:noCountry code="subField">City University of New York</wicri:noCountry></affiliation></author></analytic><series><title level="j">Molecular biology and evolution</title><idno type="eISSN">1537-1719</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adaptation, Biological</term><term>Animals</term><term>Australia</term><term>Bayes Theorem</term><term>Birds (classification)</term><term>Birds (genetics)</term><term>Computational Biology (methods)</term><term>Computer Simulation</term><term>Models, Statistical</term><term>Phylogeny</term><term>Phylogeography</term><term>Population Density</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>Australia</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Birds</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Birds</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adaptation, Biological</term><term>Animals</term><term>Bayes Theorem</term><term>Computer Simulation</term><term>Models, Statistical</term><term>Phylogeny</term><term>Phylogeography</term><term>Population Density</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Australie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Methods that integrate population-level sampling from multiple taxa into a single community-level analysis are an essential addition to the comparative phylogeographic toolkit. Detecting how species within communities have demographically tracked each other in space and time is important for understanding the effects of future climate and landscape changes and the resulting acceleration of extinctions, biological invasions, and potential surges in adaptive evolution. Here, we present a statistical framework for such an analysis based on hierarchical approximate Bayesian computation (hABC) with the goal of detecting concerted demographic histories across an ecological assemblage. Our method combines population genetic data sets from multiple taxa into a single analysis to estimate: 1) the proportion of a community sample that demographically expanded in a temporally clustered pulse and 2) when the pulse occurred. To validate the accuracy and utility of this new approach, we use simulation cross-validation experiments and subsequently analyze an empirical data set of 32 avian populations from Australia that are hypothesized to have expanded from smaller refugia populations in the late Pleistocene. The method can accommodate data set heterogeneity such as variability in effective population size, mutation rates, and sample sizes across species and exploits the statistical strength from the simultaneous analysis of multiple species. This hABC framework used in a multitaxa demographic context can increase our understanding of the impact of historical climate change by determining what proportion of the community responded in concert or independently and can be used with a wide variety of comparative phylogeographic data sets as biota-wide DNA barcoding data sets accumulate.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">24925925</PMID><DateCreated><Year>2014</Year><Month>08</Month><Day>20</Day></DateCreated><DateCompleted><Year>2016</Year><Month>03</Month><Day>07</Day></DateCompleted><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1537-1719</ISSN><JournalIssue CitedMedium="Internet"><Volume>31</Volume><Issue>9</Issue><PubDate><Year>2014</Year><Month>Sep</Month></PubDate></JournalIssue><Title>Molecular biology and evolution</Title><ISOAbbreviation>Mol. Biol. Evol.</ISOAbbreviation></Journal><ArticleTitle>Detecting concerted demographic response across community assemblages using hierarchical approximate Bayesian computation.</ArticleTitle><Pagination><MedlinePgn>2501-15</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/molbev/msu187</ELocationID><Abstract><AbstractText>Methods that integrate population-level sampling from multiple taxa into a single community-level analysis are an essential addition to the comparative phylogeographic toolkit. Detecting how species within communities have demographically tracked each other in space and time is important for understanding the effects of future climate and landscape changes and the resulting acceleration of extinctions, biological invasions, and potential surges in adaptive evolution. Here, we present a statistical framework for such an analysis based on hierarchical approximate Bayesian computation (hABC) with the goal of detecting concerted demographic histories across an ecological assemblage. Our method combines population genetic data sets from multiple taxa into a single analysis to estimate: 1) the proportion of a community sample that demographically expanded in a temporally clustered pulse and 2) when the pulse occurred. To validate the accuracy and utility of this new approach, we use simulation cross-validation experiments and subsequently analyze an empirical data set of 32 avian populations from Australia that are hypothesized to have expanded from smaller refugia populations in the late Pleistocene. The method can accommodate data set heterogeneity such as variability in effective population size, mutation rates, and sample sizes across species and exploits the statistical strength from the simultaneous analysis of multiple species. This hABC framework used in a multitaxa demographic context can increase our understanding of the impact of historical climate change by determining what proportion of the community responded in concert or independently and can be used with a wide variety of comparative phylogeographic data sets as biota-wide DNA barcoding data sets accumulate.</AbstractText><CopyrightInformation>© The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Chan</LastName><ForeName>Yvonne L</ForeName><Initials>YL</Initials><AffiliationInfo><Affiliation>Hawai'i Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai'i at Manoa ylhchan@hawaii.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Schanzenbach</LastName><ForeName>David</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Cyberinfrastructure, University of Hawai'i at Manoa.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hickerson</LastName><ForeName>Michael J</ForeName><Initials>MJ</Initials><AffiliationInfo><Affiliation>Biology Department, City College of New YorkThe Graduate Center, City University of New York.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>5P20RR016467-11</GrantID><Acronym>RR</Acronym><Agency>NCRR NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>8 P20 GM 103466-11</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>06</Month><Day>12</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Mol Biol 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Biology</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D003198">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D015233">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D010802">Phylogeny</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D058974">Phylogeography</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D011156">Population Density</DescriptorName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC4137712</OtherID><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">approximate Bayesian computation</Keyword><Keyword MajorTopicYN="N">comparative phylogeography</Keyword><Keyword MajorTopicYN="N">historical demography</Keyword><Keyword MajorTopicYN="N">response to climate 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