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Modeling the influence of competition, climate, soil, and their interaction on height to crown base for Korean pine plantations in Northeast China

  • Yunfei Yan,
  • Junjie Wang,
  • Suoming Liu,
  • Damodar Gaire,
  • Lichun Jiang

摘要

Height to crown base (HCB) is an important tree-level characteristic used to determine crown size and serves as an important input variable for forest growth and yield models. Competition between forest stands and environmental conditions are the main factors influencing HCB not only through independent effects but also through interactive effects. In addition, it is unclear whether these effects on HCB differ depending on the social status of the tree. Accordingly, we conducted a pioneering study to assess the independent and interactive effects of competition, climate, and soil on HCB using measurements from a total of 7188 Korean pine (Pinus koraiensis) trees at 98 sample plots located in northeastern China. Moreover, HCB simulations under different competitive conditions or soil gradients were provided based on the actual local climatic types to provide targeted recommendations for plantations. The results showed that stand basal area (BA), the basal area of trees larger than the subject tree (BAL), mean temperature of the coldest month (MCMT), Hargreaves climatic moisture deficit (CMD), and total nitrogen (TN) were the main factors influencing HCB. As CMD increases, the influence direction of BA and MCMT on HCB changes, and the intensity of their influence decreases. The results of hierarchical partitioning analysis showed that the impacts of climate on HCB were different depending on social status. Suppressed trees exhibit a lower climate sensitivity compared to dominant and intermediate trees. Simulation results indicate that regulating stand competition through thinning operations and adjusting soil nitrogen content by increasing understory vegetation promote crown recession, thereby increases HCB. Our findings highlight the significance of independent and interactive considering competition, climate, and soil variables to improve HCB model predictions at large spatial scales and guidance on stand management in the context of climate change.