The Science

This study provides several key benchmarks for vegetation models in the Amazon basin via (1) spatial pattern maps of mortality, woody net primary productivity (NPP), and aboveground biomass (AGB) and (2) the underlying mechanisms controlling these patterns.

The Impact

Previous work had supposed that spatial patterns in AGB in Amazon forests were mediated by a positive association between woody NPP and stem mortality rates inducing reductions in AGB. In contrast, the scientists found that woody NPP and stem mortality are not correlated and, instead, that spatial variability in AGB is controlled primarily by stem mortality (not woody biomass loss).

Summary

Understanding the processes that determine 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 NPP and the rate at which carbon is lost through tree mortality. Here, the scientists 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. 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, the study finds that woody NPP was not correlated with stem mortality rates and is weakly positively correlated with AGB. The spatial pattern maps of mortality, NPP, and AGB, as well as the underlying mechanisms controlling these patterns, provide key benchmark targets for DGVMs in Amazonia.

Principal Investigator

Brad Christoffersen
Los Alamos National Laboratory
bradley@lanl.go

Program Manager

Daniel Stover
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
daniel.stover@science.doe.gov

Funding

This paper is a product of the European Union’s Seventh Framework Programme AMAZALERT project (282664). The field data used in this study have been generated by the RAINFOR network, which has been supported by a Gordon and Betty Moore Foundation grant, the European Union’s Seventh Framework Programme projects 283080, ‘GEOCARBON’; and 282664, “AMAZALERT”; ERC grant “Tropical Forests in the Changing Earth System”), and Natural Environment Research Council (NERC) Urgency, Consortium and Standard Grants “AMAZONICA”(NE/F005806/1), “TROBIT” (NE/D005590/1) and “Niche Evolution of South American Trees” (NE/I028122/1). Additional data were included from the Tropical Ecology Assessment and Monitoring (TEAM) Network – a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partly funded by these institutions, the Gordon and Betty Moore Foundation, and other donors. Fieldwork was also partially supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico of Brazil (CNPq), project Programa de Pesquisas Ecológicas de Longa Duração (PELD-403725/2012-7). A.R. acknowledges funding from the Helmholtz Alliance “Remote Sensing and Earth System Dynamics”; L.P., M.P.C., E.A., and M.T. are partially funded by the EU FP7 project “ROBIN” (283093), with co-funding for E.A. from the Dutch Ministry of Economic Affairs (KB-14-003-030); B.C. was supported in part by the Office of Biological and Environmental Research, within the U.S. Department of Energy Office of Science, through its Next-Generation Ecosystem Experiments (NGEE)–Tropics project (subcontract to Los Alamos National Laboratory). O.L.P. is supported by an ERC Advanced Grant and is a Royal Society-Wolfson Research Merit Award holder. P.M. acknowledges support from ARC grant FT110100457 and NERC grants NE/J011002/1, and T.R.B. acknowledges support from a Leverhulme Trust Research Fellowship.

References