Drivers of aboveground biomass in Quercus wutaishanica Mayr forests based on random forest and structural equation modeling: A cross-scale analysis

Shuaishuai Ma, Huayong Zhang, Zhongyu Wang, Hengchao Zou and Xiaona Xu

Ecological Modelling, 2025, vol. 505, issue C

Abstract: A comprehensive understanding of vegetation biomass is essential to gain deeper insights into the global carbon cycle. Quercus wutaishanica Mayr forests are the dominant and established species in temperate deciduous broad-leaved forests in China. They play a significant role in the ecological functions and carbon sequestration of forests. At present, the drivers and influencing mechanisms of aboveground biomass (AGB) of Q. wutaishanica forests have not been clearly quantified across different scales. In this study, we examined the effects of abiotic factors, encompassing climatic conditions and soil properties, and biotic factors that involve grass coverage and forest age, and disturbances on the AGB of Q. wutaishanica forests at overall and 15 vegetation regionalization scales in China using random forest (RF) and piecewise structural equation modeling (piecewiseSEM). The results from the RF model and piecewiseSEM explained 68 % and 47 % of the variance in AGB, respectively, at the overall scale. Climatic factors represented by temperature seasonality (TS) and climate moisture index (CMI) primarily shaped community biomass patterns overall. These factors affected AGB directly and indirectly by influencing edaphic and biotic factors, respectively. In addition, the AGB was mainly constrained by significant seasonal temperature variations or inadequate moisture. The direct effect of biotic factors, specifically forestage and grass coverage, on AGB was more pronounced at the regional than at the overall scale. The dominant factors in the adjacent vegetation zones exhibited a degree of geographical continuity. These findings represented an essential reference for carbon cycling mechanisms within terrestrial ecosystems.

Keywords: Aboveground biomass; Scale; Random forest; Structural equation modeling; Temperature seasonality; Climate moisture index (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:505:y:2025:i:c:s0304380025000997

DOI: 10.1016/j.ecolmodel.2025.111113

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