Spatial structural characteristics of forests dominated by Pinus tabulaeformis Carr

PLoS One. 2018 Apr 13;13(4):e0194710. doi: 10.1371/journal.pone.0194710. eCollection 2018.

Abstract

The Chinese pine (Pinus tabulaeformis Carr.) is an ecologically and economically important evergreen coniferous tree which dominates warm temperate forests throughout northern China. We established two permanent plots within the Chinese pine forest in the Jiulong Mountains, Beijing, China. To understand the structural characteristics and dynamics of these plots, we analyzed the spatial structural characteristics within nearest-neighbor relationships using the bivariate distributions of the stand spatial structural parameters: uniform angle index, W; mingling index, M; dominance index, U; and crowding index, C. Results revealed that most trees in the forest were randomly distributed. The predominant individuals and randomly arranged trees were in very dense areas and surrounded by the same species. In addition, both plots exhibited a uniform size differentiation pattern. The two plots differed mainly in the level of species mixture and dominance. The majority of reference trees in the pure Chinese pine forest (plot 1) exhibited poor species mingling and low dominance, whereas trees in the mixed Chinese pine forest (plot 2) were evenly distributed in each mingling class and most trees were of intermediate dominance. The study results are useful for optimizing forest management activities in the studied stands, promoting tree growth, regeneration and habitat diversity, and improving forest quality at a fine scale.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Biodiversity
  • China
  • Ecosystem
  • Forests*
  • Geography
  • Models, Theoretical
  • Pinus*
  • Spatial Analysis*

Grants and funding

This paper was financially supported by the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (Grant No. CAFYBB2014QA035) and the National Key Research and Development Program of China (Grant No.2016YFD0600203).