Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models

Johnson, Michelle; Galbraith, David; Gloor, Manuel; De Deaurwaerder, Hannes; Guimberteau, Mattieu; Rammig, Anja; Thonicke, Kristin; Verbeeck, Hans; von Randow, Celso; Monteagudo Mendoza, Abel; Phillips, Oliver L.; Brienen, Roel; Feldpausch, Ted R.; Lopez Gonzales, Gabriela; Fauset, Sophie; Quesada, Carlos; Christofferson, Bradley; Ciais, Phillippe; Sampaio, Gilvan; Kruijt, Bart; Meir, Patrick; Moorcroft, Paul; Zhang, Ke; Alvarez-Davila, Esteban; Alves de Oliveira, Atila; Amaral, Ieda; Andrade, Ana; Aragao, Luiz; Araujo-Murakami, Alejandro; Arets, Eric; Arroyo, Luzmila; Aymard, Gerardo; Baraloto, Christopher; Barroso, Jocely; Bonal, Damien; Boot, Rene; Camargo, Jose; Chave, Jerome; Cogollo, Alvaro; Cornejo, Fernando; Lola da Costa, Antonio; Di Fiore, Anthony; Ferreira, Leandro; Higuchi, Niro; Honorio, Euridice; Killeen, Timothy; Laurance, Susan; Laurance, William; Licona, Juan; Lovejoy, Thomas; Malhi, Yadvinder; Marimon, Bia; Marimon Junior, Ben Hur; Matos, Darley; Mendoza, Casimiro; Ne...

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.

Citation

Johnson, M., Galbraith, D., Gloor, M., De Deaurwaerder, H., Guimberteau, M., Rammig, A., Thonicke, K., Verbeeck, H., von Randow, C., Monteagudo Mendoza, A., Phillips, O. L., Brienen, R., Feldpausch, T. R., Lopez Gonzales, G., Fauset, S., Quesada, C., Christofferson, B., Ciais, P., Sampaio, G., Kruijt, B., …Baker, T. (in press). Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models. Global Change Biology, https://doi.org/10.1111/gcb.13315

• Journal Article Type: Article
• Acceptance Date: Mar 1, 2016
• Online Publication Date: May 19, 2016
• Deposit Date: May 25, 2016
• Publicly Available Date: May 25, 2016
• Journal: Global Change Biology
• Print ISSN: 1354-1013
• Electronic ISSN: 1365-2486
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1111/gcb.13315
• Public URL: https://nottingham-repository.worktribe.com/output/789386
• Publisher URL: http://onlinelibrary.wiley.com/doi/10.1111/gcb.13315/abstract;jsessionid=A05AA58C2F6A21DFB4EC1D861F6E5B01.f01t03
• Contract Date: May 25, 2016