Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation.
Identifieur interne : 001F83 ( Main/Curation ); précédent : 001F82; suivant : 001F84Ecological 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
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pubmed:25270536Le document en format XML
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<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>
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<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>
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<term>Plant Proteins</term>
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<term>Populus</term>
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<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>
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<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>
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<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>
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<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>
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