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Eco-evolutionary optimality as a means to improve vegetation and land-surface models.
Harrison, Sandy P; Cramer, Wolfgang; Franklin, Oskar; Prentice, Iain Colin; Wang, Han; Brännström, Åke; de Boer, Hugo; Dieckmann, Ulf; Joshi, Jaideep; Keenan, Trevor F; Lavergne, Aliénor; Manzoni, Stefano; Mengoli, Giulia; Morfopoulos, Catherine; Peñuelas, Josep; Pietsch, Stephan; Rebel, Karin T; Ryu, Youngryel; Smith, Nicholas G; Stocker, Benjamin D; Wright, Ian J.
Afiliação
  • Harrison SP; Department of Geography and Environmental Science, University of Reading, Reading, RG6 6AB, UK.
  • Cramer W; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Franklin O; Institut Méditerranéen de Biodiversité et d'Ecologie Marine et Continentale, Aix Marseille Université, CNRS, IRD, Avignon Université, Technopôle Arbois-Méditerranée, Aix-en-Provence Cedex 04, F-13545, France.
  • Prentice IC; International Institute for Applied Systems Analysis, Schlossplatz 1, Laxenburg, 2361, Austria.
  • Wang H; Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, 90183, Sweden.
  • Brännström Å; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • de Boer H; Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, SL5 7PY, UK.
  • Dieckmann U; Department of Biological Sciences, Macquarie University, North Ryde, NSW, 2109, Australia.
  • Joshi J; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Keenan TF; International Institute for Applied Systems Analysis, Schlossplatz 1, Laxenburg, 2361, Austria.
  • Lavergne A; Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, 901 87, Sweden.
  • Manzoni S; Copernicus Institute of Sustainable Development, Environmental Sciences, Faculty of Geosciences, Utrecht University, Vening Meinesz Building, Princetonlaan 8a, Utrecht, 3584 CB, the Netherlands.
  • Mengoli G; International Institute for Applied Systems Analysis, Schlossplatz 1, Laxenburg, 2361, Austria.
  • Morfopoulos C; Department of Evolutionary Studies of Biosystems, The Graduate University for Advanced Studies (Sokendai), Hayama, Kanagawa, 240-0193, Japan.
  • Peñuelas J; International Institute for Applied Systems Analysis, Schlossplatz 1, Laxenburg, 2361, Austria.
  • Pietsch S; Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA.
  • Rebel KT; Department of Environmental Science, Policy and Management, University of California Berkeley, Berkeley, CA, 94720, USA.
  • Ryu Y; Department of Physics, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
  • Smith NG; Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, SE-106 91, Stockholm, Sweden.
  • Stocker BD; Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, SL5 7PY, UK.
  • Wright IJ; Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, SL5 7PY, UK.
New Phytol ; 231(6): 2125-2141, 2021 09.
Article em En | MEDLINE | ID: mdl-34131932
ABSTRACT
Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Plantas / Ecossistema Idioma: En Revista: New Phytol Assunto da revista: BOTANICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Plantas / Ecossistema Idioma: En Revista: New Phytol Assunto da revista: BOTANICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Reino Unido