Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale.

International audience The temperature response of photosynthesis is one of the key factors determining predicted responses to warming in global vegetation models (GVMs). The response may vary geographically, owing to genetic adaptation to climate, and temporally, as a result of acclimation to chang...

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Bibliographic Details
Published in:New Phytologist
Main Authors: Kumarathunge, Dushan P, Medlyn, Belinda E, Drake, John E, Tjoelker, Mark G, Aspinwall, Michael J, Battaglia, Michael, Cano, Francisco J, Carter, Kelsey R, Cavaleri, Molly A, Cernusak, Lucas A, Chambers, Jeffrey Q, Crous, Kristine y, de Kauwe, Martin G, Dillaway, Dylan N, Dreyer, Erwin, Ellsworth, David S, Ghannoum, Oula, Han, Qingmin, Hikosaka, Kouki, Jensen, Anna M, Kelly, Jeff W G, Kruger, Eric L, Mercado, Lina M, Onoda, Yusuke, Reich, Peter B, Rogers, Alistair, Slot, Martijn, Smith, Nicholas G, Tarvainen, Lasse, Tissue, David T, Togashi, Henrique F, Tribuzy, Edgard S, Uddling, Johan, Varhammar, Angelica, Wallin, Göran, Warren, Jeffrey M, Way, Danielle A
Other Authors: Western Sydney University, New York University, New York University New York (NYU), NYU System (NYU)-NYU System (NYU), University of North Florida Jacksonville (UNF), Commonwealth Scientific and Industrial Research Organisation Canberra (CSIRO), Michigan Technological University (MTU), James Cook University (JCU), University of California Berkeley (UC Berkeley), University of California (UC), University of New South Wales Sydney (UNSW), Thomashow Learning Laboratories, Partenaires INRAE, SILVA (SILVA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech-Université de Lorraine (UL), Forestry and Forest Products Research Institute (FFPRI), Tohoku University Sendai, Linnaeus University, University of Washington Seattle, University of Wisconsin-Madison, University of Exeter, Centre for Ecology and Hydrology, Kyoto University, Brookhaven National Laboratory Upton, NY (BNL), UT-Battelle, LLC-Stony Brook University SUNY (SBU), State University of New York (SUNY)-State University of New York (SUNY)-U.S. Department of Energy Washington (DOE), Smithsonian Tropical Research Institute, TexasTech University, Swedish University of Agricultural Sciences (SLU), Macquarie University, Universidade Federal do Oeste do Pará, University of Gothenburg (GU), Oak Ridge National Laboratory, University of Western Ontario (UWO), Duke University Durham
Format: Article in Journal/Newspaper
Language:English
Published: HAL CCSD 2019
Subjects:
Online Access:https://hal.inrae.fr/hal-02628795
https://doi.org/10.1111/nph.15668
Description
Summary:International audience The temperature response of photosynthesis is one of the key factors determining predicted responses to warming in global vegetation models (GVMs). The response may vary geographically, owing to genetic adaptation to climate, and temporally, as a result of acclimation to changes in ambient temperature. Our goal was to develop a robust quantitative global model representing acclimation and adaptation of photosynthetic temperature responses. We quantified and modelled key mechanisms responsible for photosynthetic temperature acclimation and adaptation using a global dataset of photosynthetic CO2 response curves, including data from 141 C3 species from tropical rainforest to Arctic tundra. We separated temperature acclimation and adaptation processes by considering seasonal and common‐garden datasets, respectively. The observed global variation in the temperature optimum of photosynthesis was primarily explained by biochemical limitations to photosynthesis, rather than stomatal conductance or respiration. We found acclimation to growth temperature to be a stronger driver of this variation than adaptation to temperature at climate of origin. We developed a summary model to represent photosynthetic temperature responses and showed that it predicted the observed global variation in optimal temperatures with high accuracy. This novel algorithm should enable improved prediction of the function of global ecosystems in a warming climate.