A Climate-Sensitive Mixed-Effects Individual Tree Mortality Model for Masson Pine in Hunan Province, South–Central China

• Published in: Forests Vol. 15; no. 9; p. 1543
• Main Authors: Yan, Ni, He, Youjun, Chen, Keyi, Lv, Yanjie, Wang, Jianjun, Zhang, Zhenzhong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Analysis; China; climate; Climate change; Competition; Environmental protection; forest inventory; Forest management; Forest reserves; Global climate; Health aspects; Independent variables; model validation; Mortality; National forests; Pine; Pinus massoniana; Precipitation; Probability; Random variables; regression analysis; Regression models; Statistical analysis; summer; Sustainable forestry; temperature; tree mortality; Trees; Variables; Vegetation; China; Hunan China
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f15091543

Accurately assessing tree mortality probability in the context of global climate changes is important for formulating scientific and reasonable forest management scenarios. In this study, we developed a climate-sensitive individual tree mortality model for Masson pine using data from the seventh (2004), eighth (2009), and ninth (2014) Chinese National Forest Inventory (CNFI) in Hunan Province, South–Central China. A generalized linear mixed-effects model with plots as random effects based on logistic regression was applied. Additionally, a hierarchical partitioning analysis was used to disentangle the relative contributions of the variables. Among the various candidate predictors, the diameter (DBH), Gini coefficient (GC), sum of basal area for all trees larger than the subject tree (BAL), mean coldest monthly temperature (MCMT), and mean summer (May–September) precipitation (MSP) contributed significantly to changes in Masson pine mortality. The relative contribution of climate variables (MCMT and MSP) was 44.78%, larger than tree size (DBH, 32.74%), competition (BAL, 16.09%), and structure variables (GC, 6.39%). The model validation results based on independent data showed that the model performed well and suggested an influencing mechanism of tree mortality, which could improve the accuracy of forest management decisions under a changing climate.