Detecting Concerted Demographic Response across Community Assemblages Using Hierarchical Approximate Bayesian Computation
Identifieur interne : 000210 ( Pmc/Checkpoint ); précédent : 000209; suivant : 000211Detecting Concerted Demographic Response across Community Assemblages Using Hierarchical Approximate Bayesian Computation
Auteurs : Yvonne L. Chan ; David Schanzenbach ; Michael J. HickersonSource :
- Molecular Biology and Evolution [ 0737-4038 ] ; 2014.
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.
Url:
DOI: 10.1093/molbev/msu187
PubMed: 24925925
PubMed Central: 4137712
Affiliations:
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Detecting Concerted Demographic Response across Community Assemblages Using Hierarchical Approximate Bayesian Computation</title>
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<front><div type="abstract" xml:lang="en"><p>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.</p>
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</TEI>
<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Mol Biol Evol</journal-id>
<journal-id journal-id-type="iso-abbrev">Mol. Biol. Evol</journal-id>
<journal-id journal-id-type="publisher-id">molbev</journal-id>
<journal-id journal-id-type="hwp">molbiolevol</journal-id>
<journal-title-group><journal-title>Molecular Biology and Evolution</journal-title>
</journal-title-group>
<issn pub-type="ppub">0737-4038</issn>
<issn pub-type="epub">1537-1719</issn>
<publisher><publisher-name>Oxford University Press</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">24925925</article-id>
<article-id pub-id-type="pmc">4137712</article-id>
<article-id pub-id-type="doi">10.1093/molbev/msu187</article-id>
<article-id pub-id-type="publisher-id">msu187</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Methods</subject>
</subj-group>
</article-categories>
<title-group><article-title>Detecting Concerted Demographic Response across Community Assemblages Using Hierarchical Approximate Bayesian Computation</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Chan</surname>
<given-names>Yvonne L.</given-names>
</name>
<xref ref-type="corresp" rid="msu187-COR1">*</xref>
<xref ref-type="author-notes" rid="msu187-FN1"><sup>†</sup>
</xref>
<xref ref-type="aff" rid="msu187-AFF1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Schanzenbach</surname>
<given-names>David</given-names>
</name>
<xref ref-type="aff" rid="msu187-AFF2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Hickerson</surname>
<given-names>Michael J.</given-names>
</name>
<xref ref-type="aff" rid="msu187-AFF3"><sup>3</sup>
</xref>
<xref ref-type="aff" rid="msu187-AFF4"><sup>4</sup>
</xref>
</contrib>
<aff id="msu187-AFF1"><sup>1</sup>
Hawai′i Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai'i at Manoa</aff>
<aff id="msu187-AFF2"><sup>2</sup>
Cyberinfrastructure, University of Hawai'i at Manoa</aff>
<aff id="msu187-AFF3"><sup>3</sup>
Biology Department, City College of New York</aff>
<aff id="msu187-AFF4"><sup>4</sup>
The Graduate Center, City University of New York</aff>
</contrib-group>
<author-notes><fn id="msu187-FN1"><p>†Present address: Department of Genetics and Bioinformatics, Swedish Museum of Natural History, Stockholm, Sweden</p>
</fn>
<corresp id="msu187-COR1"><bold>*Corresponding author:</bold>
E-mail: <email>ylhchan@hawaii.edu</email>
.</corresp>
<fn id="msu187-FN2"><p><bold>Associate editor:</bold>
Noah Rosenberg</p>
</fn>
</author-notes>
<pub-date pub-type="ppub"><month>9</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="epub"><day>12</day>
<month>6</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>12</day>
<month>6</month>
<year>2014</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
. </pmc-comment>
<volume>31</volume>
<issue>9</issue>
<fpage>2501</fpage>
<lpage>2515</lpage>
<permissions><copyright-statement>© The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.</copyright-statement>
<copyright-year>2014</copyright-year>
<license license-type="creative-commons" xlink:href="http://creativecommons.org/licenses/by-nc/3.0/"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/3.0/">http://creativecommons.org/licenses/by-nc/3.0/</ext-link>
), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com</license-p>
</license>
</permissions>
<abstract><p>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.</p>
</abstract>
<kwd-group><kwd>comparative phylogeography</kwd>
<kwd>approximate Bayesian computation</kwd>
<kwd>historical demography</kwd>
<kwd>response to climate change</kwd>
</kwd-group>
<counts><page-count count="15"></page-count>
</counts>
</article-meta>
</front>
</pmc>
<affiliations><list></list>
<tree><noCountry><name sortKey="Chan, Yvonne L" sort="Chan, Yvonne L" uniqKey="Chan Y" first="Yvonne L." last="Chan">Yvonne L. Chan</name>
<name sortKey="Hickerson, Michael J" sort="Hickerson, Michael J" uniqKey="Hickerson M" first="Michael J." last="Hickerson">Michael J. Hickerson</name>
<name sortKey="Schanzenbach, David" sort="Schanzenbach, David" uniqKey="Schanzenbach D" first="David" last="Schanzenbach">David Schanzenbach</name>
</noCountry>
</tree>
</affiliations>
</record>
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