Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation.
Identifieur interne : 001E13 ( Main/Exploration ); précédent : 001E12; suivant : 001E14Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation.
Auteurs : Matthew C. Fitzpatrick [États-Unis] ; Stephen R. KellerSource :
- Ecology letters [ 1461-0248 ] ; 2015.
Descripteurs français
- KwdFr :
- Adaptation physiologique (génétique), Analyse spatiale (MeSH), Biodiversité (MeSH), Changement climatique (MeSH), Génomique (méthodes), Modèles biologiques (MeSH), Modèles statistiques (MeSH), Polymorphisme de nucléotide simple (MeSH), Populus (génétique), Protéines CLOCK (génétique), Protéines végétales (génétique), Variation génétique (MeSH).
- MESH :
English descriptors
- KwdEn :
- Adaptation, Physiological (genetics), Biodiversity (MeSH), CLOCK Proteins (genetics), Climate Change (MeSH), Genetic Variation (MeSH), Genomics (methods), Models, Biological (MeSH), Models, Statistical (MeSH), Plant Proteins (genetics), Polymorphism, Single Nucleotide (MeSH), Populus (genetics), Spatial Analysis (MeSH).
- MESH :
- chemical , genetics : CLOCK Proteins, Plant Proteins.
- genetics : Adaptation, Physiological, Populus.
- methods : Genomics.
- Biodiversity, Climate Change, Genetic Variation, Models, Biological, Models, Statistical, Polymorphism, Single Nucleotide, Spatial Analysis.
Abstract
Local adaptation is a central feature of most species occupying spatially heterogeneous environments, and may factor critically in responses to environmental change. However, most efforts to model the response of species to climate change ignore intraspecific variation due to local adaptation. Here, we present a new perspective on spatial modelling of organism-environment relationships that combines genomic data and community-level modelling to develop scenarios regarding the geographic distribution of genomic variation in response to environmental change. Rather than modelling species within communities, we use these techniques to model large numbers of loci across genomes. Using balsam poplar (Populus balsamifera) as a case study, we demonstrate how our framework can accommodate nonlinear responses of loci to environmental gradients. We identify a threshold response to temperature in the circadian clock gene GIGANTEA-5 (GI5), suggesting that this gene has experienced strong local adaptation to temperature. We also demonstrate how these methods can map ecological adaptation from genomic data, including the identification of predicted differences in the genetic composition of populations under current and future climates. Community-level modelling of genomic variation represents an important advance in landscape genomics and spatial modelling of biodiversity that moves beyond species-level assessments of climate change vulnerability.
DOI: 10.1111/ele.12376
PubMed: 25270536
Affiliations:
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Keller</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2015">2015</date><idno type="RBID">pubmed:25270536</idno><idno type="pmid">25270536</idno><idno type="doi">10.1111/ele.12376</idno><idno type="wicri:Area/Main/Corpus">001F83</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">001F83</idno><idno type="wicri:Area/Main/Curation">001F83</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">001F83</idno><idno type="wicri:Area/Main/Exploration">001F83</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation.</title><author><name sortKey="Fitzpatrick, Matthew C" sort="Fitzpatrick, Matthew C" uniqKey="Fitzpatrick M" first="Matthew C" last="Fitzpatrick">Matthew C. Fitzpatrick</name><affiliation wicri:level="2"><nlm:affiliation>Appalachian Lab, University of Maryland Center for Environmental Science, Frostburg, MD, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Appalachian Lab, University of Maryland Center for Environmental Science, Frostburg, MD</wicri:regionArea><placeName><region type="state">Maryland</region></placeName></affiliation></author><author><name sortKey="Keller, Stephen R" sort="Keller, Stephen R" uniqKey="Keller S" first="Stephen R" last="Keller">Stephen R. Keller</name></author></analytic><series><title level="j">Ecology letters</title><idno type="eISSN">1461-0248</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adaptation, Physiological (genetics)</term><term>Biodiversity (MeSH)</term><term>CLOCK Proteins (genetics)</term><term>Climate Change (MeSH)</term><term>Genetic Variation (MeSH)</term><term>Genomics (methods)</term><term>Models, Biological (MeSH)</term><term>Models, Statistical (MeSH)</term><term>Plant Proteins (genetics)</term><term>Polymorphism, Single Nucleotide (MeSH)</term><term>Populus (genetics)</term><term>Spatial Analysis (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adaptation physiologique (génétique)</term><term>Analyse spatiale (MeSH)</term><term>Biodiversité (MeSH)</term><term>Changement climatique (MeSH)</term><term>Génomique (méthodes)</term><term>Modèles biologiques (MeSH)</term><term>Modèles statistiques (MeSH)</term><term>Polymorphisme de nucléotide simple (MeSH)</term><term>Populus (génétique)</term><term>Protéines CLOCK (génétique)</term><term>Protéines végétales (génétique)</term><term>Variation génétique (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>CLOCK Proteins</term><term>Plant Proteins</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Adaptation, Physiological</term><term>Populus</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Adaptation physiologique</term><term>Populus</term><term>Protéines CLOCK</term><term>Protéines végétales</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Genomics</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Génomique</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Biodiversity</term><term>Climate Change</term><term>Genetic Variation</term><term>Models, Biological</term><term>Models, Statistical</term><term>Polymorphism, Single Nucleotide</term><term>Spatial Analysis</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse spatiale</term><term>Biodiversité</term><term>Changement climatique</term><term>Modèles biologiques</term><term>Modèles statistiques</term><term>Polymorphisme de nucléotide simple</term><term>Variation génétique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Local adaptation is a central feature of most species occupying spatially heterogeneous environments, and may factor critically in responses to environmental change. However, most efforts to model the response of species to climate change ignore intraspecific variation due to local adaptation. Here, we present a new perspective on spatial modelling of organism-environment relationships that combines genomic data and community-level modelling to develop scenarios regarding the geographic distribution of genomic variation in response to environmental change. Rather than modelling species within communities, we use these techniques to model large numbers of loci across genomes. Using balsam poplar (Populus balsamifera) as a case study, we demonstrate how our framework can accommodate nonlinear responses of loci to environmental gradients. We identify a threshold response to temperature in the circadian clock gene GIGANTEA-5 (GI5), suggesting that this gene has experienced strong local adaptation to temperature. We also demonstrate how these methods can map ecological adaptation from genomic data, including the identification of predicted differences in the genetic composition of populations under current and future climates. Community-level modelling of genomic variation represents an important advance in landscape genomics and spatial modelling of biodiversity that moves beyond species-level assessments of climate change vulnerability. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25270536</PMID><DateCompleted><Year>2015</Year><Month>04</Month><Day>11</Day></DateCompleted><DateRevised><Year>2014</Year><Month>12</Month><Day>16</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1461-0248</ISSN><JournalIssue CitedMedium="Internet"><Volume>18</Volume><Issue>1</Issue><PubDate><Year>2015</Year><Month>Jan</Month></PubDate></JournalIssue><Title>Ecology letters</Title><ISOAbbreviation>Ecol Lett</ISOAbbreviation></Journal><ArticleTitle>Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation.</ArticleTitle><Pagination><MedlinePgn>1-16</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1111/ele.12376</ELocationID><Abstract><AbstractText>Local adaptation is a central feature of most species occupying spatially heterogeneous environments, and may factor critically in responses to environmental change. However, most efforts to model the response of species to climate change ignore intraspecific variation due to local adaptation. Here, we present a new perspective on spatial modelling of organism-environment relationships that combines genomic data and community-level modelling to develop scenarios regarding the geographic distribution of genomic variation in response to environmental change. Rather than modelling species within communities, we use these techniques to model large numbers of loci across genomes. Using balsam poplar (Populus balsamifera) as a case study, we demonstrate how our framework can accommodate nonlinear responses of loci to environmental gradients. We identify a threshold response to temperature in the circadian clock gene GIGANTEA-5 (GI5), suggesting that this gene has experienced strong local adaptation to temperature. We also demonstrate how these methods can map ecological adaptation from genomic data, including the identification of predicted differences in the genetic composition of populations under current and future climates. Community-level modelling of genomic variation represents an important advance in landscape genomics and spatial modelling of biodiversity that moves beyond species-level assessments of climate change vulnerability. </AbstractText><CopyrightInformation>© 2014 John Wiley & Sons Ltd/CNRS.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Fitzpatrick</LastName><ForeName>Matthew C</ForeName><Initials>MC</Initials><AffiliationInfo><Affiliation>Appalachian Lab, University of Maryland Center for Environmental Science, Frostburg, MD, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Keller</LastName><ForeName>Stephen R</ForeName><Initials>SR</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</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>09</Month><Day>30</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Ecol Lett</MedlineTA><NlmUniqueID>101121949</NlmUniqueID><ISSNLinking>1461-023X</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D010940">Plant Proteins</NameOfSubstance></Chemical><Chemical><RegistryNumber>EC 2.3.1.48</RegistryNumber><NameOfSubstance UI="D056926">CLOCK Proteins</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000222" MajorTopicYN="N">Adaptation, Physiological</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D044822" MajorTopicYN="Y">Biodiversity</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D056926" MajorTopicYN="N">CLOCK Proteins</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D057231" MajorTopicYN="Y">Climate Change</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014644" MajorTopicYN="N">Genetic Variation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D023281" MajorTopicYN="N">Genomics</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008954" MajorTopicYN="Y">Models, Biological</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="N">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010940" MajorTopicYN="N">Plant Proteins</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D020641" MajorTopicYN="N">Polymorphism, Single Nucleotide</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="N">Populus</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D062206" MajorTopicYN="N">Spatial Analysis</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Biodiversity</Keyword><Keyword MajorTopicYN="N">Populus balsamifera</Keyword><Keyword MajorTopicYN="N">Single-nucleotide polymorphism</Keyword><Keyword MajorTopicYN="N">climate change</Keyword><Keyword MajorTopicYN="N">generalised dissimilarity modelling</Keyword><Keyword MajorTopicYN="N">gradient forests</Keyword><Keyword MajorTopicYN="N">intraspecific variation</Keyword><Keyword MajorTopicYN="N">landscape genetics</Keyword><Keyword MajorTopicYN="N">local adaptation</Keyword><Keyword MajorTopicYN="N">species distribution modelling</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2014</Year><Month>05</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2014</Year><Month>06</Month><Day>17</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2014</Year><Month>08</Month><Day>19</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>08</Month><Day>21</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>10</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>10</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>4</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25270536</ArticleId><ArticleId IdType="doi">10.1111/ele.12376</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Maryland</li></region></list><tree><noCountry><name sortKey="Keller, Stephen R" sort="Keller, Stephen R" uniqKey="Keller S" first="Stephen R" last="Keller">Stephen R. Keller</name></noCountry><country name="États-Unis"><region name="Maryland"><name sortKey="Fitzpatrick, Matthew C" sort="Fitzpatrick, Matthew C" uniqKey="Fitzpatrick M" first="Matthew C" last="Fitzpatrick">Matthew C. Fitzpatrick</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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