title: Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models.
abstract: Understanding the processes that determine above-ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basin-wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs.
authors.affiliation: School of Geography, University of Leeds, Leeds, LS6 2QT, UK.
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authors.affiliation: School of Geography, University of Leeds, Leeds, LS6 2QT, UK.
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authors.affiliation: School of Geography, University of Leeds, Leeds, LS6 2QT, UK.
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authors.affiliation: CAVElab Computational & Applied Vegetation Ecology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, B-9000, Gent, Belgium.
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authors.affiliation: Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, F-91191, Gif-sur-Yvette, France.|UMR 7619 METIS, IPSL, Sorbonne Universités, UPMC, CNRS, EPHE, 75252, Paris, France.
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authors.affiliation: Potsdam Institute for Climate Impact Research (PIK), Telegrafenberg A62, PO Box 60 12 03, D-14412, Potsdam, Germany.
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authors.identifier: 0000-0001-5283-4937
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authors.affiliation: School of Geography, University of Leeds, Leeds, LS6 2QT, UK.
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authors.affiliation: School of Geography, University of Leeds, Leeds, LS6 2QT, UK.
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mesh_terms: D018533:Biomass; D065928:Forests; D008962:Models, Theoretical; D013020:South America; D014197:Trees / Q000254:growth & development*; D014329:Tropical Climate
keywords: allometry; carbon; dynamic global vegetation model; forest plots; productivity; tropical forest