Michelle O. Johnson 1 , David Galbraith 1 , Manuel Gloor 1 , Hannes De Deurwaerder 2 , Matthieu Guimberteau 3 , 4 , Anja Rammig 5 , 6 , Kirsten Thonicke 6 , Hans Verbeeck 2 , Celso von Randow 7 , Abel Monteagudo 8 , Oliver L. Phillips 1 , Roel J. W. Brienen 1 , Ted R. Feldpausch 9 , Gabriela Lopez Gonzalez 1 , Sophie Fauset 1 , Carlos A. Quesada 10 , Bradley Christoffersen 11 , 12 , Philippe Ciais 3 , Gilvan Sampaio 7 , Bart Kruijt 13 , Patrick Meir 11 , 14 , Paul Moorcroft 15 , Ke Zhang 16 , Esteban Alvarez‐Davila 17 , Atila Alves de Oliveira 10 , Ieda Amaral 10 , Ana Andrade 10 , Luiz E. O. C. Aragao 8 , Alejandro Araujo‐Murakami 18 , Eric J. M. M. Arets 13 , Luzmila Arroyo 18 , Gerardo A. Aymard 19 , Christopher Baraloto 20 , Jocely Barroso 21 , Damien Bonal 22 , Rene Boot 23 , Jose Camargo 10 , Jerome Chave 24 , Alvaro Cogollo 25 , Fernando Cornejo Valverde 26 , Antonio C. Lola da Costa 27 , Anthony Di Fiore 28 , Leandro Ferreira 29 , Niro Higuchi 10 , Euridice N. Honorio 30 , Tim J. Killeen 31 , Susan G. Laurance 32 , William F. Laurance 32 , Juan Licona 33 , Thomas Lovejoy 34 , Yadvinder Malhi 35 , Bia Marimon 36 , Ben Hur Marimon Junior 36 , Darley C. L. Matos 29 , Casimiro Mendoza 37 , David A. Neill 38 , Guido Pardo 39 , Marielos Peña‐Claros 33 , 40 , Nigel C. A. Pitman 41 , Lourens Poorter 40 , Adriana Prieto 42 , Hirma Ramirez‐Angulo 43 , Anand Roopsind 44 , Agustin Rudas 42 , Rafael P. Salomao 29 , Marcos Silveira 45 , Juliana Stropp 46 , Hans ter Steege 47 , John Terborgh 41 , Raquel Thomas 44 , Marisol Toledo 33 , Armando Torres‐Lezama 43 , Geertje M. F. van der Heijden 48 , Rodolfo Vasquez 9 , Ima Cèlia Guimarães Vieira 29 , Emilio Vilanova 43 , Vincent A. Vos 49 , 50 , Timothy R. Baker 1 ,
19 May 2016
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.