Modelling the impact of wood density dependent tree mortality on the spatial distribution of Amazonian vegetation carbon
Abstract. Spatially heterogeneous plant mortality rates are an important predictor of the distribution of vegetation carbon in Amazonia. Reproducing the spatial gradients of vegetation carbon in Amazonia and the observed decline in the intact Amazonian carbon sink since 1990 is a challenge faced by dynamic global vegetation models (DGVMs). In this paper, we implement spatially variable mortality rates in TRIFFID, the DGVM currently coupled to the Joint UK Land Environment Simulator (JULES), and compare with the standard model which assumes a homogeneous mortality rate. Spatially variable gridded fields of Amazonian tree mortality are created using a well-known relationship between mortality and wood density, and three independent wood density maps. The diversified mortality scheme substantially improves the representation of vegetation carbon in TRIFFID when compared to observations, with a 90 % reduction in model bias and an increase in the Pearson correlation coefficient with observed biomass. JULES now captures the observed variability of both mortality and vegetation carbon to a greater extent, demonstrating the potential of using easily-measured traits, like wood density, to add spatial and functional diversity into DGVMs. Despite this, the spatial variation of vegetation carbon simulated with the new mortality fields (with standard deviation 15 MgCha-1) is still less than half of the variation in the observed data (standard deviation 35 MgCha-1). Future work should consider the effects of additional processes, like fire, drought and the phosphorus cycle, on the simulated distribution of vegetation carbon in the Amazon.
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Mathilda Hancock et al.
Mathilda Hancock et al.
Data for: Modelling the impact of wood density dependent tree mortality on the spatial distribution of Amazonian vegetation carbon https://doi.org/10.5281/zenodo.6388019
Mathilda Hancock et al.
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This is a very well-written paper on a topic that should be of wide-interest across biogeosciences discussions. The authors included the tree mortality rate through the wood density-mortality relationship as a predictor of the carbon distribution of vegetation in the Amazon. Different dynamic global vegetation models (DGVMs) and four different mortality equations were used to compare with the standard models, which assume homogeneous mortality rates throughout the Amazon basin. This approach brought important improvements in the representation of the spatial dynamics of Carbon in the vegetation of the Amazon, showing a greater correlation between the model with variable mortality and the observed biomass.
Wood density is an important trait in the determination of mortality, and it is relatively easy to obtain, but even so, it does not fully represent the mortality patterns of trees. This is demonstrated by the low variation of the mortality data estimated in this paper, in relation to the actual values ââobserved. Although there is a well-known relationship between wood density and mortality rate, we know that tree mortality results from the interaction between extrinsic environmental conditions, such as climate and other tree ecological traits. Local soil conditions, topography, occurrence of lightning, drought, fire and other environmental and climatic factors also affect mortality patterns, making prediction for such a heterogeneous Amazon basin difficult (e.g. New Phytologist (2019) doi: 10.1111/nph. 16260). As well as extrinsic conditions affect mortality rates, other functional traits also play a key role in determining mortality rates across the basin, such as the phenological behavior of species (e.g. Nat. Clim. Chang. (2019) https: //doi.org/10.1038/s41558-019-0458-0). These factors add even more complexity to the modeling of mortality, making this variable difficult to represent in DGVMs.
Likewise, mortality is an important process that determines the stock of biomass in Amazonian forests, but it is not sufficient by itself to explain the distribution of vegetation along the basin.
Despite these challenges, the authors did a great job of testing different mortality models and equations, carefully explaining the effects that each variable had on biomass estimates. It is an important advance that can be very useful when applied to the science of climate change and effects on Amazonian biomass. The methodology used opens the way for the use of other traits (such as phenology) in mortality estimates, as well as the use of processes other than mortality in modeling the spatial distribution of Amazon Carbon.
The article brings a detailed and very rich discussion about the main points of interest of the scientific community, increasing even more the importance of this manuscript. The methods are presented very clearly, despite the complexity of the subject. It also shows where there are some data gaps where researchers should focus efforts to increase our ability to understand the carbon dynamics of this important Amazon forest ecosystem.
Combining mortality variations as a result of wood density is a path that proved to be very useful and easy to implement to improve biomass stock estimates, although it needs special care in obtaining data and equations used.