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, basinwide 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.

A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: an example from the Amazon region

“…Several studies, that evaluated patterns of interpolated maps from plot data and model simulations, concluded that the observed and simulated spatial patterns do not match (e.g. Johnson et al 2016). Here, we complement Geosci.…”

“…Model runs were performed based on the standardized Moore Foundation Andes-Amazon Initiative (AAI) modelling protocol (Zhang et al, 2015). The same set of models and output variables was analysed in Johnson et al (2016). 25…”

“…Johnson et al, 2016;Malhi et al, 2006) should be treated with 25 caution since plot observations may not be representative at the landscape-scale (Chave et al, 2004). As a result, a direct and meaningful comparison of observed and simulated maps is currently not feasible but reliable biomass estimates are necessary for implementation of protection incentives and future projections of biomass development.…”

“…To correctly capture carbon dynamics in tropical forests, DGVMs need to improve the simulation approach of drought-related mortality and other types of tree mortality that control stand density (Pillet et al 2018), and they need to incorporate how nutrient availability limits woody productivity (Quesada et al 20 2012;Johnson et al 2016). At the same time, model evaluation based on available data is necessary for which the primary source are ground-based observations of AGB obtained from forest census data (Lopez-Gonzalez et al 2011;Brienen et al 2014;Lopez-Gonzalez et al 2014;Mitchard et al 2014;Brienen et al 2015;Johnson et al 2016). When conducting datamodel comparisons at the plot scale, the spatial resolution of both get into focus.…”

“…Similar to Johnson et al (2016), we focus on three ecosystem properties that are well defined and represented in both, DGVMs and forest inventories: (i) aboveground biomass (AGB, in Mg C ha -1 ); (ii) aboveground woody productivity (WP, in Mg C ha -1 yr -1 );…”