Species-specific and generalized allometric biomass models for eight Fagaceae species in the understory of evergreen broadleaved forests in subtropical China

Shengwang Meng1()

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Journal of Forestry Research ›› 2024, Vol. 35 ›› Issue (1) : 69. DOI: 10.1007/s11676-024-01718-6

Species-specific and generalized allometric biomass models for eight Fagaceae species in the understory of evergreen broadleaved forests in subtropical China

  • Shengwang Meng1()
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Abstract

Quantifying the biomass of saplings in the regeneration component is critical for understanding biogeochemical processes of forest ecosystems. However, accurate allometric equations have yet to be developed in sufficient detail. To develop species-specific and generalized allometric equations, 154 saplings of eight Fagaceae tree species in subtropical China’s evergreen broadleaved forests were collected. Three dendrometric variables, root collar diameter (d), height (h), and crown area (ca) were applied in the model by the weighted nonlinear seemingly unrelated regression method. Using only d as an input variable, the species-specific and generalized allometric equations estimated the aboveground biomass reasonably, with ${R}_{adj}^{2}$ values generally > 0.85. Adding h and/or ca improved the fitting of some biomass components to a certain extent. Generalized equations showed a relatively large coefficient of variation but comparable bias to species-specific equations. Only in the absence of species-specific equations at a given location are generalized equations for mixed species recommended. The developed regression equations can be used to accurately calculate the aboveground biomass of understory Fagaceae regeneration trees in China’s subtropical evergreen broadleaved forests.

Keywords

Allometry / Aboveground biomass / Additivity / Regeneration / Subtropical China

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Shengwang Meng. Species-specific and generalized allometric biomass models for eight Fagaceae species in the understory of evergreen broadleaved forests in subtropical China. Journal of Forestry Research, 2024, 35(1): 69 https://doi.org/10.1007/s11676-024-01718-6

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