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: Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement.