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

Authors: José Luís Camargo; René G. A. Boot; Christopher Baraloto; Alejandro Araujo-Murakami; Ke Zhang; Thomas E. Lovejoy; Michelle O. Johnson; +75 Authors

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

Abstract

AbstractUnderstanding 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.

Countries
France, United Kingdom, Netherlands, France, France, France, United Kingdom, United Kingdom, Australia, France, Belgium, France, France, United Kingdom, United Kingdom, Australia, Netherlands, United Kingdom, Brazil, Australia, United Kingdom, France, Germany, Brazil
Keywords

ENVIRONMENT SIMULATOR JULES, [ SDV.BV ] Life Sciences [q-bio]/Vegetal Biology, TREE MORTALITY, Bos- en Landschapsecologie, Growth, Forests, 310, Tropic Climate, Klimaatverandering, Aboveground Biomass, Trees, Theoretical, Models, forest and landscape ecology, Development And Aging, Alterra - Vegetation, Alterra - Vegetatie, [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, Forest and Landscape Ecology, Biomass, CLIMATE-CHANGE, Alterra - Klimaatverandering en adaptief land- en watermanagement, Net Primary Production, PE&RC, Alterra - Climate change and adaptive land and water management, Tree, tropical forest, Climate Change and Adaptive Land and Water Management, 570, dgvm, productivity, Climate Change, Leerstoelgroep Bosecologie en bosbeheer, DROUGHT SENSITIVITY, dynamic global vegetation model, Stem, 333, Klimaatverandering en adaptief land- en watermanagement, Carbon Cycle, forest plots, Amazonia, TROPICAL RAIN-FORESTS, allometry, Tropical Forest, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Bosecologie en Bosbeheer, Forest, Mortality, Vegetatie, 580, Allometry, Tropical Climate, Forest Ecosystem, WIMEK, Vegetation, EARTH SYSTEM, carbon, Theoretical Model, Models, Theoretical, South America, Primary Research Articles, CARBON BALANCE, Forest Ecology and Forest Management, WOOD PRODUCTIVITY, Climate Resilience, Klimaatbestendigheid, Earth and Environmental Sciences, WATER-BALANCE, cavelab, ECOSYSTEM MODEL, bos- en landschapsecologie

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
132
Top 1%
Top 10%
Top 1%
Green
hybrid