ALBERT

Description: Phenotypic traits and their associated trade-offs have been shown to have globally consistent effects on individual plant physiological functions, but how these effects scale up to influence competition, a key driver of community assembly in terrestrial vegetation, has remained unclear. Here we use growth data from more than 3 million trees in over 140,000 plots across the world to show how three key functional traits--wood density, specific leaf area and maximum height--consistently influence competitive interactions. Fast maximum growth of a species was correlated negatively with its wood density in all biomes, and positively with its specific leaf area in most biomes. Low wood density was also correlated with a low ability to tolerate competition and a low competitive effect on neighbours, while high specific leaf area was correlated with a low competitive effect. Thus, traits generate trade-offs between performance with competition versus performance without competition, a fundamental ingredient in the classical hypothesis that the coexistence of plant species is enabled via differentiation in their successional strategies. Competition within species was stronger than between species, but an increase in trait dissimilarity between species had little influence in weakening competition. No benefit of dissimilarity was detected for specific leaf area or wood density, and only a weak benefit for maximum height. Our trait-based approach to modelling competition makes generalization possible across the forest ecosystems of the world and their highly diverse species composition.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Kunstler, Georges -- Falster, Daniel -- Coomes, David A -- Hui, Francis -- Kooyman, Robert M -- Laughlin, Daniel C -- Poorter, Lourens -- Vanderwel, Mark -- Vieilledent, Ghislain -- Wright, S Joseph -- Aiba, Masahiro -- Baraloto, Christopher -- Caspersen, John -- Cornelissen, J Hans C -- Gourlet-Fleury, Sylvie -- Hanewinkel, Marc -- Herault, Bruno -- Kattge, Jens -- Kurokawa, Hiroko -- Onoda, Yusuke -- Penuelas, Josep -- Poorter, Hendrik -- Uriarte, Maria -- Richardson, Sarah -- Ruiz-Benito, Paloma -- Sun, I-Fang -- Stahl, Goran -- Swenson, Nathan G -- Thompson, Jill -- Westerlund, Bertil -- Wirth, Christian -- Zavala, Miguel A -- Zeng, Hongcheng -- Zimmerman, Jess K -- Zimmermann, Niklaus E -- Westoby, Mark -- England -- Nature. 2016 Jan 14;529(7585):204-7. doi: 10.1038/nature16476. Epub 2015 Dec 23.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Irstea, UR EMGR, 2 rue de la Papeterie BP-76, F-38402, St-Martin-d'Heres, France. ; Univ. Grenoble Alpes, F-38402 Grenoble, France. ; Department of Biological Sciences, Macquarie University, New South Wales 2109, Australia. ; Forest Ecology and Conservation Group, Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK. ; Mathematical Sciences Institute, The Australian National University, Canberra 0200, Australia. ; National Herbarium of New South Wales, Royal Botanic Gardens and Domain Trust, Sydney 2000, New South Wales, Australia. ; Environmental Research Institute, School of Science, University of Waikato, Hamilton 3240, New Zealand. ; Forest Ecology and Forest Management Group, Wageningen University, 6708 PB Wageningen, The Netherlands. ; Department of Biology, University of Regina, 3737 Wascana Pkwy, Regina SK S4S 0A2, Canada. ; Cirad, UPR BSEF, F-34398 Montpellier, France. ; Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, Republic of Panama. ; Graduate School of Life Sciences, Tohoku University, Sendai 980-8578, Japan. ; INRA, UMR Ecologie des Forets de Guyane, BP 709, 97387 Kourou Cedex, France. ; International Center for Tropical Botany, Department of Biological Sciences, Florida International University, Miami, Florida 33199, USA. ; Faculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto, Ontario M5S 3B3, Canada. ; Swiss Federal Research Institute WSL, Landscape Dynamics Unit, CH-8903 Birmensdorf, Switzerland. ; Systems Ecology, Department of Ecological Science, Vrije Universiteit, Amsterdam 1081 HV, The Netherlands. ; Swiss Federal Research Institute WSL, Forest Resources and Management Unit, CH-8903 Birmensdorf, Switzerland. ; University of Freiburg, Chair of Forestry Economics and Planning, D-79106 Freiburg, Germany. ; Cirad, UMR Ecologie des Forets de Guyane, Campus Agronomique, BP 701, 97387 Kourou, France. ; Max Planck Institute for Biogeochemistry, Hans Knoll Str. 10, 07745 Jena, Germany. ; German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Deutscher Platz 5e 04103 Leipzig, Germany. ; Graduate School of Agriculture, Kyoto University, Kyoto, 606-8502 Japan. ; CSIC, Global Ecology Unit CREAF-CSIC-UAB, Cerdanyola del Valles 08193, Catalonia, Spain. ; CREAF, Cerdanyola del Valles, 08193 Barcelona, Catalonia, Spain. ; Plant Sciences (IBG-2), Forschungszentrum Julich GmbH, D-52425 Julich, Germany. ; Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, New York 10027, USA. ; Landcare Research, PO Box 40, Lincoln 7640, New Zealand. ; Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, Stirling FK9 4LA, UK. ; Forest Ecology and Restoration Group, Department of Life Sciences, Science Building, University of Alcala, Campus Universitario, 28805 Alcala de Henares (Madrid), Spain. ; Department of Natural Resources and Environmental Studies, National Dong Hwa University, Hualien 97401, Taiwan. ; Department of Forest Resource Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgrand, 901 83 Umea, Sweden. ; Department of Biology, University of Maryland, College Park, Maryland 20742, USA. ; Centre for Ecology and Hydrology, Bush Estate, Penicuik, Midlothian EH26 0QB, UK. ; Department of Environmental Sciences, University of Puerto Rico, Rio Piedras Campus PO Box 70377 San Juan, Puerto Rico 00936-8377, USA. ; Institute for Systematic, Botany and Functional Biodiversity, University of Leipzig, Johannisallee 21 04103 Leipzig, Germany.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26700807" target="_blank"〉PubMed〈/a〉

Description: The feedback between climate and the terrestrial carbon cycle will be a key determinant of the dynamics of the Earth System over the coming decades and centuries. However Earth System Model projections of the terrestrial carbon-balance vary widely over these timescales. This is largely due to differences in their carbon cycle models. A major goal in biogeosciences is therefore to improve understanding of the terrestrial carbon cycle to enable better constrained projections. Essential to achieving this goal will be assessing the empirical support for alternative models of component processes, identifying key uncertainties and inconsistencies, and ultimately identifying the models that are most consistent with empirical evidence. To begin meeting these requirements we data-constrained all parameters of all component processes within a global terrestrial carbon model. Our goals were to assess the climate dependencies obtained for different component processes when all parameters have been inferred from empirical data, assess whether these were consistent with current knowledge and understanding, assess the importance of different data sets and the model structure for inferring those dependencies, assess the predictive accuracy of the model, and to identify a methodology by which alternative component models could be compared within the same framework in future. Although formulated as differential equations describing carbon fluxes through plant and soil pools, the model was fitted assuming the carbon pools were in states of dynamic equilibrium (input rates equal output rates). Thus, the parameterised model is of the equilibrium terrestrial carbon cycle. All but 2 of the 12 component processes to the model were inferred to have strong climate dependencies although it was not possible to data-constrain all parameters indicating some potentially redundant details. Similar climate dependencies were obtained for most processes whether inferred individually from their corresponding data sets or using the full terrestrial carbon model and all available data sets, indicating a strong overall consistency in the information provided by different data sets under the assumed model formulation. A notable exception was plant mortality, in which qualitatively different climate dependencies were inferred depending on the model formulation and data sets used, highlighting this component as the major structural uncertainty in the model. All but two component processes predicted empirical data better than a null model in which no climate dependency was assumed. Equilibrium plant carbon was predicted especially well (explaining around 70% of the variation in the withheld evaluation data). We discuss the advantages of our approach in relation to advancing our understanding of the carbon cycle and enabling Earth System Models make better constrained projections.

Description: The feedback between climate and the terrestrial carbon cycle will be a key determinant of the dynamics of the Earth System (the thin layer that contains and supports life) over the coming decades and centuries. However, Earth System Model projections of the terrestrial carbon-balance vary widely over these timescales. This is largely due to differences in their terrestrial carbon cycle models. A major goal in biogeosciences is therefore to improve understanding of the terrestrial carbon cycle to enable better constrained projections. Utilising empirical data to constrain and assess component processes in terrestrial carbon cycle models will be essential to achieving this goal. We used a new model construction method to data-constrain all parameters of all component processes within a global terrestrial carbon model, employing as data constraints a collection of 12 empirical data sets characterising global patterns of carbon stocks and flows. Our goals were to assess the climate dependencies inferred for all component processes, assess whether these were consistent with current knowledge and understanding, assess the importance of different data sets and the model structure for inferring those dependencies, assess the predictive accuracy of the model and ultimately to identify a methodology by which alternative component models could be compared within the same framework in the future. Although formulated as differential equations describing carbon fluxes through plant and soil pools, the model was fitted assuming the carbon pools were in states of dynamic equilibrium (input rates equal output rates). Thus, the parameterised model is of the equilibrium terrestrial carbon cycle. All but 2 of the 12 component processes to the model were inferred to have strong climate dependencies, although it was not possible to data-constrain all parameters, indicating some potentially redundant details. Similar climate dependencies were obtained for most processes, whether inferred individually from their corresponding data sets or using the full terrestrial carbon model and all available data sets, indicating a strong overall consistency in the information provided by different data sets under the assumed model formulation. A notable exception was plant mortality, in which qualitatively different climate dependencies were inferred depending on the model formulation and data sets used, highlighting this component as the major structural uncertainty in the model. All but two component processes predicted empirical data better than a null model in which no climate dependency was assumed. Equilibrium plant carbon was predicted especially well (explaining around 70% of the variation in the withheld evaluation data). We discuss the advantages of our approach in relation to advancing our understanding of the carbon cycle and enabling Earth System Models to make better constrained projections.