Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model

Aim: The mechanisms of plant trait adaptation and acclimation are still poorly understood and, consequently, lack a consistent representation in terrestrial biosphere models (TBMs). Despite the increasing availability of geo-referenced trait observations, current databases are still insufficient to cover all vegetation types and environmental conditions. In parallel, the growing number of continuous eddy-covariance observations of energy and CO2 fluxes has enabled modellers to optimize TBMs with these data. Past attempts to optimize TBM parameters mostly focused on model performance, overlooking the ecological properties of ecosystems. The aim of this study was to assess the ecological consistency of optimized trait-related parameters while improving the model performances for gross primary productivity (GPP) at sites. Location: Worldwide. Time period: 1992–2012. Major taxa studied: Trees and C3 grasses. Methods: We optimized parameters of the ORCHIDEE model against 371 site-years of GPP estimates from the FLUXNET network, and we looked at global covariation among parameters and with climate. Results: The optimized parameter values were shown to be consistent with leaf-scale traits, in particular, with well-known trade-offs observed at the leaf level, echoing the leaf economic spectrum theory. Results showed a marked sensitivity of trait-related parameters to local bioclimatic variables and reproduced the observed relationships between traits and climate. Main conclusions: Our approach validates some biological processes implemented in the model and enables us to study ecological properties of vegetation at the canopy level, in addition to some traits that are difficult to observe experimentally. This study stresses the need for: (a) implementing explicit trade-offs and acclimation processes in TBMs; (b) improving the representation of processes to avoid model-specific parameterization; and (c) performing systematic measurements of traits at FLUXNET sites in order to gather information on plant ecophysiology and plant diversity, together with micro-meteorological conditions. © 2019 John Wiley & Sons Ltd

Peaucelle M.1, 2 , Bacour C.3 , Ciais P.1 , Vuichard N.1 , Kuppel S.4 , Peñuelas J.2, 5 , Belelli Marchesini L. , Blanken P.D.8 , Buchmann N.9 , Chen J.10 , Delpierre N.11 , Desai A.R.12 , Dufrene E.11 , Gianelle D.6 , Gimeno-Colera C.13 , Gruening C.14 , Helfter C.15 , Hörtnagl L.9 , Ibrom A.16 , Joffre R.17 , Kato T.18, 19 , Kolb T.E.20 , Law B.21 , Lindroth A.22 , Mammarella I.23 , Merbold L.24 , Minerbi S.25 , Montagnani L.25, 26 , Šigut L.27 , Sutton M.15 , Varlagin A.28 , Vesala T.29, 30 , Wohlfahrt G.31 , Wolf S.32 , Yakir D.33 , Viovy N.1
Blackwell Publishing Ltd
  • 1 Laboratoire des Sciences du Climat et de l'Environnement, CEA/CNRS/UVSQ, Gif-sur-Yvette, France
  • 2 CREAF, Barcelona, Spain
  • 3 NOVELTIS, Labège, France
  • 4 Northern Rivers Institute, University of Aberdeen, Aberdeen, United Kingdom
  • 5 CSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Spain
  • 6 Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy
  • 7 Department of Landscape Design and Sustainable Ecosystems, Agrarian-Technological Institute, RUDN University, Moscow, Russian Federation
  • 8 Department of Geography, University of Colorado, Boulder, CO, United States
  • 9 Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
  • 10 LEES Lab, Department of Geography and Spatial Sciences, Michigan State University, East Lansing, MI, United States
  • 11 Ecologie Systématique Evolution, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Orsay, France
  • 12 Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, WI, United States
  • 13 Fundación CEAM, Parque Tecnológico, Paterna, Spain
  • 14 European Commission, Joint Research Centre, Ispra, Italy
  • 15 Centre for Ecology and Hydrology, Penicuik, United Kingdom
  • 16 Department of Environmental Engineering, Technical University of Denmark, Lyngby, Denmark
  • 17 CEFE, CNRS – Université de Montpellier – Université Paul-Valéry Montpellier – EPHE – IRD, Montpellier, France
  • 18 Research Faculty of Agriculture, Hokkaido University, Sapporo, Japan
  • 19 Global Station for Food, Land and Water Resources (GSF), GI-CoRE, Hokkaido University, Sapporo, Japan
  • 20 School of Forestry, Northern Arizona University, Flagstaff, AZ, United States
  • 21 Forest Ecosystems and Society Dept, College of Forestry, Oregon State University, Corvallis, OR, United States
  • 22 Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
  • 23 Institute for Atmosphere and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
  • 24 Mazingira Centre, International Livestock Research Institute (ILRI), Nairobi, Kenya
  • 25 Forest Services, Autonomous Province of Bolzano, Bolzano, Italy
  • 26 Faculty of Science and Technology, Free University of Bolzano, Piazza Università, Bolzano, Italy
  • 27 Department of Matter and Energy Fluxes, Global Change Research Institute CAS, Brno, Czech Republic
  • 28 A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, Russian Federation
  • 29 Institute for Atmosphere and Earth System Research/Forest Sciences, Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland
  • 30 Viikki Plant Science, University of Helsinki, Helsinki, Finland
  • 31 Institute for Ecology, University of Innsbruck, Innsbruck, Austria
  • 32 Institute of Terrestrial Ecosystems, ETH Zurich, Zurich, Switzerland
  • 33 Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, Israel
Ключевые слова
data assimilation; optimization; ORCHIDEE; plant acclimation; plant functional traits; terrestrial model
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