A New Perspective on Ecological Prediction Reveals Limits to Climate Adaptation in a Temperate Tree Species.

carbon storage conservation genomics ecological modeling genomic adaptation global climate change nonstructural carbohydrates plant ecophysiology

Journal

Current biology : CB
ISSN: 1879-0445
Titre abrégé: Curr Biol
Pays: England
ID NLM: 9107782

Informations de publication

Date de publication:
20 04 2020
Historique:
received: 29 10 2019
revised: 17 12 2019
accepted: 03 02 2020
pubmed: 30 3 2020
medline: 16 7 2021
entrez: 30 3 2020
Statut: ppublish

Résumé

Forests absorb a large fraction of anthropogenic CO2 emission, but their ability to continue to act as a sink under climate change depends in part on plant species undergoing rapid adaptation. Yet models of forest response to climate change currently ignore local adaptation as a response mechanism. Thus, considering the evolution of intraspecific trait variation is necessary for reliable, long-term species and climate projections. Here, we combine ecophysiology and predictive climate modeling with analyses of genomic variation to determine whether sugar and starch storage, energy reserves for trees under extreme conditions, have the heritable variation and genetic diversity necessary to evolve in response to climate change within populations of black cottonwood (Populus trichocarpa). Despite current patterns of local adaptation and extensive range-wide heritable variation in storage, we demonstrate that adaptive evolution in response to climate change will be limited by a lack of heritable variation within northern populations and by a need for extreme genetic changes in southern populations. Our method can help design more targeted species management interventions and highlights the power of using genomic tools in ecological prediction to scale from molecular to regional processes to determine the ability of a species to respond to future climates.

Identifiants

pubmed: 32220321
pii: S0960-9822(20)30174-3
doi: 10.1016/j.cub.2020.02.001
pii:
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

1447-1453.e4

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Interests The authors declare no competing interests.

Auteurs

Meghan Blumstein (M)

Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, USA. Electronic address: blumstein@fas.harvard.edu.

Andrew Richardson (A)

Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, USA; School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA.

David Weston (D)

Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.

Jin Zhang (J)

Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.

Wellington Muchero (W)

Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.

Robin Hopkins (R)

Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, USA; The Arnold Arboretum, 1300 Centre Street, Boston, MA 02130, USA.

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Classifications MeSH