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Above-ground biomass accumulation in Cerradão managed by the mass ratio

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Background: Forests have a huge potential to mitigate global warming through CO2 accumulation in their living biomass. Thus, understanding the functioning of these ecosystems is crucial for revealing factors that drive biomass accumulation. Functional diversity helps us understand ecosystem services, including biomass accumulation. Within this context, this work aimed to investigate the role of functional diversity in biomass accumulation for woody vegetation of the Cerradão. Methods: We estimated above-ground biomass (AGB) of 50 Cerradão stands in Brazil and measured five different functional traits associated with tree species’ survival and growth. For each stand, we calculated five community-weighted means (CWMs), based on each functional trait, and three functional diversity metrics. We specifically explored the relationship between CWMs describing functional diversity metrics and AGB. After that, exponential regressions were adjusted, using the variables that presented significant correlation as independent variables and AGB as a dependent variable. Regressions with more than one independent variable were fitted in a way that avoided collinearity. Based on the strongest correlation coefficient (r) and the lowest Akaike value (AIC), we chose the best regression to explain the majority of the variance in AGB. Results: Our results showed the role of function in determining AGB. In particular, bivariate correlations show that four CWM functional traits and two functional diversity metrics are significantly associated with AGB production, with CWM showing AGB estimation variables. However, multiple regression analyses show that maximum height is the only trait significantly associated with AGB and it alone provides the best model fit. Conclusions: The accumulation of AGB in Cerradão is explained by the mass ratio theory, and this ecosystem services is directly related to the presence and abundance of species with greater potential tree height (Htmax).
Title: Above-ground biomass accumulation in Cerradão managed by the mass ratio
Description:
Background: Forests have a huge potential to mitigate global warming through CO2 accumulation in their living biomass.
Thus, understanding the functioning of these ecosystems is crucial for revealing factors that drive biomass accumulation.
Functional diversity helps us understand ecosystem services, including biomass accumulation.
Within this context, this work aimed to investigate the role of functional diversity in biomass accumulation for woody vegetation of the Cerradão.
Methods: We estimated above-ground biomass (AGB) of 50 Cerradão stands in Brazil and measured five different functional traits associated with tree species’ survival and growth.
For each stand, we calculated five community-weighted means (CWMs), based on each functional trait, and three functional diversity metrics.
We specifically explored the relationship between CWMs describing functional diversity metrics and AGB.
After that, exponential regressions were adjusted, using the variables that presented significant correlation as independent variables and AGB as a dependent variable.
Regressions with more than one independent variable were fitted in a way that avoided collinearity.
Based on the strongest correlation coefficient (r) and the lowest Akaike value (AIC), we chose the best regression to explain the majority of the variance in AGB.
Results: Our results showed the role of function in determining AGB.
In particular, bivariate correlations show that four CWM functional traits and two functional diversity metrics are significantly associated with AGB production, with CWM showing AGB estimation variables.
However, multiple regression analyses show that maximum height is the only trait significantly associated with AGB and it alone provides the best model fit.
Conclusions: The accumulation of AGB in Cerradão is explained by the mass ratio theory, and this ecosystem services is directly related to the presence and abundance of species with greater potential tree height (Htmax).

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