Ecological Indicators (Feb 2024)

Characterising spatial effects of individual tree and component biomass for three typical tree species in Yunnan, China

  • Qinling Fan,
  • Hui Xu,
  • Dapeng Luo,
  • Yong Wu,
  • Xiaoli Zhang,
  • Guoqi Chen,
  • Sitong Qin,
  • Zhi Liu,
  • Chunxiao Liu,
  • Guanglong Ou

Journal volume & issue
Vol. 159
p. 111705

Abstract

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The trees in a given stand compete with each other for light, water and nutrients, producing spatial effects. To understand spatial effects, it is critical to characterise the spatial distributions and patterns of the aboveground biomass (AGB) of individual trees and the biomass of their components (stem, bark, branches and foliage). This study investigated the spatial effects by examining the AGB of individual trees and the biomass of their components in the context of typical sub-tropical tree species in Yunnan, China, including a Pinus kesiya var. langbianensis natural forest (PN), a Pinus kesiya var. langbianensis plantation (PP), and a Eucalyptus spp. plantation (EP), using datasets for three clear-cutting plots. Ripley’s L function was employed to identify the spatial distributions and patterns of the AGB of individual trees and the biomass of their components. Then, Global and Local Moran’s I indices were utilised to analyse spatial autocorrelation, with Intra-group variance being calculated to quantify spatial heterogeneity. This led to following findings. Firstly, with increasing distance, the spatial heterogeneity of the three stands increased before stabilising at a distance of 10 m. Secondly, excluding bark, all components of the tree biomass in the PN stand exhibited significant dispersion. In contrast, all the components in the PP stand showed significant dispersion. The spatial distributions and patterns of trees and their biomass in the EP stand indicated the coexistence of clustering and dispersion. Moreover, the significant spatial autocorrelations of tree AGB and components biomass could be noticed but depending on the stands, components and distances. These findings provide theoretical support for sustainable forest management based on biomass and carbon stocks.

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