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
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<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>
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<CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Evolution. 2008 Dec;62(12):3117-34</RefSource>
<PMID Version="1">19087188</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>BMC Evol Biol. 2008;8:322</RefSource>
<PMID Version="1">19038027</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Science. 2009 Feb 6;323(5915):785-9</RefSource>
<PMID Version="1">19197066</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol. 2009 Apr;18(7):1310-30</RefSource>
<PMID Version="1">19281471</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol. 2009 Nov;18(21):4390-7</RefSource>
<PMID Version="1">19735451</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mitochondrion. 2010 Jan;10(1):1-11</RefSource>
<PMID Version="1">19788938</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Phylogenet Evol. 2010 Jan;54(1):291-301</RefSource>
<PMID Version="1">19755165</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Trends Ecol Evol. 2010 Jul;25(7):410-8</RefSource>
<PMID Version="1">20488578</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Genetics. 2010 Jun;185(2):587-602</RefSource>
<PMID Version="1">20382835</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol. 2010 Jul;19(13):2609-25</RefSource>
<PMID Version="1">20561199</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol. 2010 Jul;19(14):2980-94</RefSource>
<PMID Version="1">20609078</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Proc Biol Sci. 2010 Sep 7;277(1694):2675-81</RefSource>
<PMID Version="1">20410037</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2010;11:401</RefSource>
<PMID Version="1">20667077</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol. 2010 Oct;19(20):4505-19</RefSource>
<PMID Version="1">20735734</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Annu Rev Entomol. 2011;56:143-59</RefSource>
<PMID Version="1">20809802</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2011;12:1</RefSource>
<PMID Version="1">21199577</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol Resour. 2011 May;11(3):423-34</RefSource>
<PMID Version="1">21481200</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Philos Trans R Soc Lond B Biol Sci. 2011 Aug 27;366(1576):2391-402</RefSource>
<PMID Version="1">21768154</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Philos Trans R Soc Lond B Biol Sci. 2011 Aug 27;366(1576):2403-13</RefSource>
<PMID Version="1">21768155</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2011 Sep 13;108(37):15112-7</RefSource>
<PMID Version="1">21876135</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol. 2011 Nov;20(21):4533-49</RefSource>
<PMID Version="1">21981746</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Biol Evol. 1999 Dec;16(12):1791-8</RefSource>
<PMID Version="1">10605120</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Nature. 2000 Jun 22;405(6789):907-13</RefSource>
<PMID Version="1">10879524</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2000 Aug 1;97(16):9115-20</RefSource>
<PMID Version="1">10922067</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Heredity (Edinb). 2000 Aug;85 ( Pt 2):167-76</RefSource>
<PMID Version="1">11012719</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Science. 2001 Apr 27;292(5517):673-9</RefSource>
<PMID Version="1">11326089</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Evolution. 2001 Nov 11;55(11):2161-9</RefSource>
<PMID Version="1">11794777</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol. 2002 Apr;11(4):829-37</RefSource>
<PMID Version="1">11972768</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Biol Evol. 2003 Jan;20(1):76-86</RefSource>
<PMID Version="1">12519909</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol. 2004 Apr;13(4):853-64</RefSource>
<PMID Version="1">15012760</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Philos Trans R Soc Lond B Biol Sci. 2004 Feb 29;359(1442):183-95; discussion 195</RefSource>
<PMID Version="1">15101575</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Nat Rev Genet. 2004 Apr;5(4):251-61</RefSource>
<PMID Version="1">15131649</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Science. 2004 Oct 1;306(5693):70-5</RefSource>
<PMID Version="1">15459379</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Biol Evol. 1992 May;9(3):552-69</RefSource>
<PMID Version="1">1316531</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Genetics. 1997 Feb;145(2):505-18</RefSource>
<PMID Version="1">9071603</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Genetics. 1998 May;149(1):429-34</RefSource>
<PMID Version="1">9584114</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol. 1998 Apr;7(4):453-64</RefSource>
<PMID Version="1">9628000</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Genetics. 1999 Jul;152(3):1079-89</RefSource>
<PMID Version="1">10388826</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol. 2005 Apr;14(5):1485-96</RefSource>
<PMID Version="1">15813786</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Bioinformatics. 2005 Apr 15;21(8):1733-4</RefSource>
<PMID Version="1">15564305</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Science. 2006 Apr 28;312(5773):570-2</RefSource>
<PMID Version="1">16645093</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol. 2006 Dec;15(14):4261-93</RefSource>
<PMID Version="1">17107465</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Evolution. 2006 Dec;60(12):2435-53</RefSource>
<PMID Version="1">17263107</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Biol Evol. 2008 Jan;25(1):120-30</RefSource>
<PMID Version="1">17998254</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Proc Biol Sci. 2008 Nov 7;275(1650):2431-40</RefSource>
<PMID Version="1">18664434</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Trends Ecol Evol. 2008 Nov;23(11):619-30</RefSource>
<PMID Version="1">18823678</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol. 2008 Oct;17(20):4480-8</RefSource>
<PMID Version="1">18986494</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Nature. 2011 Nov 17;479(7373):359-64</RefSource>
<PMID Version="1">22048313</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Ecol. 2011 Dec;20(23):5042-59</RefSource>
<PMID Version="1">22060632</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Phylogenet Evol. 2012 Jan;62(1):286-95</RefSource>
<PMID Version="1">22040766</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Curr Biol. 2012 Mar 20;22(6):532-7</RefSource>
<PMID Version="1">22405865</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Mol Phylogenet Evol. 2012 Jun;63(3):668-78</RefSource>
<PMID Version="1">22426434</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Evol Biol. 2012 Aug;25(8):1636-49</RefSource>
<PMID Version="1">22686622</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>PLoS One. 2012;7(10):e47710</RefSource>
<PMID Version="1">23118892</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>PLoS Comput Biol. 2013;9(1):e1002803</RefSource>
<PMID Version="1">23341757</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>PLoS One. 2013;8(5):e62992</RefSource>
<PMID Version="1">23667558</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Evolution. 2013 Dec;67(12):3412-28</RefSource>
<PMID Version="1">24299397</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Evolution. 2014 Jan;68(1):284-94</RefSource>
<PMID Version="1">24102483</PMID>
</CommentsCorrections>
</CommentsCorrectionsList>
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