Back
Full-text
Journal article

Organizing principles for vegetation dynamics.

  • Franklin O International Institute for Applied Systems Analysis, Laxenburg, Austria. franklin@iiasa.ac.at.
  • Harrison SP Department of Geography and Environmental Science, University of Reading, Reading, UK.
  • Dewar R Plant Sciences Division, Research School of Biology, The Australian National University, Canberra, Australia.
  • Farrior CE Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA.
  • Brännström Å International Institute for Applied Systems Analysis, Laxenburg, Austria.
  • Dieckmann U International Institute for Applied Systems Analysis, Laxenburg, Austria.
  • Pietsch S International Institute for Applied Systems Analysis, Laxenburg, Austria.
  • Falster D Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia.
  • Cramer W Institut Méditerranéen de Biodiversité et d'Ecologie Marine et Continentale (IMBE), Aix Marseille Université, CNRS, IRD, Avignon Université, Technopôle Arbois-Méditerranée, Aix-en-Provence, France.
  • Loreau M Centre for Biodiversity, Theory, and Modelling, Theoretical and Experimental Ecology Station, CNRS, Moulis, France.
  • Wang H Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China.
  • Mäkelä A Forest Sciences, University of Helsinki, Helsinki, Finland.
  • Rebel KT Copernicus Institute of Sustainable Development, Environmental Sciences, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands.
  • Meron E Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, Israel.
  • Schymanski SJ Department of Environmental Research and Innovation, Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg.
  • Rovenskaya E International Institute for Applied Systems Analysis, Laxenburg, Austria.
  • Stocker BD Department of Environmental Systems Sciences, ETH Zurich, Zurich, Switzerland.
  • Zaehle S Biogeochemical Integration Department, Max Planck Institute for Biogeochemistry, Jena, Germany.
  • Manzoni S Department of Physical Geography, Stockholm University, Stockholm, Sweden.
  • van Oijen M Centre for Ecology and Hydrology (CEH-Edinburgh), Bush Estate, Penicuik, UK.
  • Wright IJ Department of Biological Sciences, Macquarie University, North Ryde, New South Wales, Australia.
  • Ciais P Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ, Gif-sur-Yvette, France.
  • van Bodegom PM Environmental Biology Department, Institute of Environmental Sciences, CML, Leiden University, Leiden, The Netherlands.
  • Peñuelas J CREAF, Cerdanyola del Vallès, Spain.
  • Hofhansl F International Institute for Applied Systems Analysis, Laxenburg, Austria.
  • Terrer C Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA.
  • Soudzilovskaia NA Environmental Biology Department, Institute of Environmental Sciences, CML, Leiden University, Leiden, The Netherlands.
  • Midgley G Department Botany & Zoology, Stellenbosch University, Stellenbosch, South Africa.
  • Prentice IC Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China.
Show more…
  • 2020-05-13
Published in:
  • Nature plants. - 2020
English Plants and vegetation play a critical-but largely unpredictable-role in global environmental changes due to the multitude of contributing processes at widely different spatial and temporal scales. In this Perspective, we explore approaches to master this complexity and improve our ability to predict vegetation dynamics by explicitly taking account of principles that constrain plant and ecosystem behaviour: natural selection, self-organization and entropy maximization. These ideas are increasingly being used in vegetation models, but we argue that their full potential has yet to be realized. We demonstrate the power of natural selection-based optimality principles to predict photosynthetic and carbon allocation responses to multiple environmental drivers, as well as how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities and how entropy maximization can identify the most probable outcomes of community dynamics in space- and time-varying environments. Finally, we present a roadmap indicating how these principles could be combined in a new generation of models with stronger theoretical foundations and an improved capacity to predict complex vegetation responses to environmental change.
Language
  • English
Open access status
green
Identifiers
Persistent URL
https://sonar.ch/global/documents/221246
Statistics
Document views: 50 File downloads:
  • Full-text: 0