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A Bayesian Model Averaging approach for modelling tree mortality in relation to site, competition and climatic factors for Chinese fir plantations

Lu, Lele, Wang, Hanchen, Chhin, Sophan, Duan, Aiguo, Zhang, Jianguo, Zhang, Xiongqing
Forest ecology and management 2019 v.440 pp. 169-177
Bayesian theory, Cunninghamia lanceolata, climate change, climatic factors, model uncertainty, models, mortality, plant density, plantations, prediction, site index, temperature, tree mortality, trees
Relationships between tree mortality and endogenous factors and climate factors have emerged as important concerns, and logistic stepwise regression is widely used for modeling the relationships. However, this method subsequently ignores both the variables not selected because of insignificance, and the model uncertainty due to the variable selection process. Bayesian Model Averaging (BMA) selects all possible models and uses the posterior probabilities of these models to perform all inferences and predictions. In this study, Bayesian Model Averaging (BMA) and logistic stepwise regression were used to analyze tree mortality in relation to competition, site index, and climatic factors in Chinese fir (Cunninghamia lanceolata (Lamb.) plantations established at five initial planting densities (A: 1667, B: 3333, C: 5000, D: 6667, and E: 10,000 trees/ha). Results showed that the posterior probability of the best model acquired by stepwise regression was less than that of the best model (highest posterior probability) acquired by BMA for pooling the data and density level D. Especially in the other planting densities, the model selected by stepwise regression was not in the BMA models. It indicates that the BMA method performed better than logistic stepwise regression, because BMA gave accurate posterior probability by taking into account the uncertainty of the model. In addition, the mortality increased with high competition and decreased with increasing temperature. The research has important implications for managing Chinese fir plantations under climate change.