Contributions of Biotic and Abiotic Factors to the Spatial Heterogeneity of Aboveground Biomass in Subtropical Forests: A Case Study of Guizhou Province

Tie Zhang, Guijie Ding, Jiangping Zhang and Yujiao Qi ()
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Tie Zhang: College of Forestry, Guizhou University, Guiyang 550025, China
Guijie Ding: College of Forestry, Guizhou University, Guiyang 550025, China
Jiangping Zhang: Forest Surveying and Planning Institute of Guizhou Province, Guiyang 550003, China
Yujiao Qi: College of Forestry, Guizhou University, Guiyang 550025, China

Sustainability, 2022, vol. 14, issue 17, 1-15

Abstract: The spatial heterogeneity on a regional scale of forest biomass is caused by multiple biotic and abiotic factors. However, the contributions of biotic and abiotic factors to the spatial heterogeneity of forest biomass remain unclear. Based on the data of the National Forest Continuous Inventory (NFCI), digital elevation model (DEM), and meteorological data of Guizhou Province in 2015, we studied the spatial heterogeneity of the aboveground forest biomass in Guizhou province and evaluated the contribution rates of its influencing factors using Moran’s I , semivariogram, distance-based Moran’s eigenvector maps (dbMEMs), and variance partitioning. The results showed that the forest biomass in Guizhou province had strong spatial heterogeneity. Biotic and abiotic factors explained 34.4% and 19.2% of the spatial variation in forest biomass, respectively. Among the biotic factors, the average height of the stand had the greatest influence on forest biomass, while annual precipitation had the greatest influence on forest biomass among abiotic factors. Spatial factors only explained 0.7% of the spatial variation of forest biomass, indicating that the contribution of spatial factors can be explained by some measured abiotic factors. This study provided an effective approach to understand the underlying mechanisms of spatial allocation of forest biomass.

Keywords: forest aboveground biomass; driver analysis; variance partitioning; semivariogram; spatial autocorrelation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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