A Bayesian network model to disentangle the effects of stand and climate factors on tree mortality of Chinese fir plantations

IF 2.7 3区 农林科学 Q2 ECOLOGY
Yihang Jiang, Zhen Wang, Hanyue Chen, Yuxin Hu, Yancheng Qu, Sophan Chhin, Jianguo Zhang, Xiongqing Zhang
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Abstract

Tree mortality is a complex process that not only be affected by the various factors, such as stand and climate factors, but also the various long-term effects of the factors to each other. In this study, based on the long-term spacing trials of Chinese fir in four regions of southern China, a Bayesian network was used to model tree mortality in response to stand and climate factors, as well as comparing this approach with logistic regression and random forest method. The results showed that the Bayesian network method had the highest accuracy in predicting tree mortality. In addition, the Bayesian network approach could find the dependency in the relationship between data and provide a theoretical framework for modeling uncertainty by using probabilistic calculus and underlying graph structure. Sensitivity analysis showed relative diameter was the most important factor, and temperature was the most important climate factor. Furthermore, climate factors not only directly affected tree mortality, but also indirectly affected tree mortality through affecting relative diameter, stand density and Gini coefficient. We also found that stand competition, structural heterogeneity and age affected tree mortality under climate change, and a moderate level of competition condition and stand structure heterogeneity weakened the negative impact of climate factors on tree mortality. Old trees were more sensitive to climate change than young trees, especially under extreme climate conditions. Besides, we found that tree mortality was negatively correlated with moderate annual precipitation, winter mean minimum temperature, and stand structure (Gini), and low age, but positively correlated with low relative diameter, high density and age. The results will provide adaptive options for effective forest management of Chinese fir plantations under the backdrop of global climate change in the future.
杉木人工林林分和气候因子对树木死亡率影响的贝叶斯网络模型
树木死亡是一个复杂的过程,既受林分、气候等多种因素的影响,又受多种因素相互之间的长期影响。本研究基于中国南方4个地区杉木的长期行距试验,采用贝叶斯网络模型对林分和气候因子对杉木死亡率的响应进行了建模,并与logistic回归和随机森林方法进行了比较。结果表明,贝叶斯网络方法对树木死亡率的预测精度最高。此外,贝叶斯网络方法可以发现数据之间的依赖关系,并利用概率演算和底层图结构为不确定性建模提供理论框架。敏感性分析表明,相对直径是最重要的气候因子,温度是最重要的气候因子。此外,气候因子不仅直接影响树木死亡率,还通过影响相对直径、林分密度和基尼系数间接影响树木死亡率。气候变化条件下,林分竞争、林分结构异质性和林龄对林分死亡率均有影响,适度的竞争条件和林分结构异质性减弱了气候因子对林分死亡率的负面影响。古树比幼树对气候变化更敏感,尤其是在极端气候条件下。此外,树木死亡率与中等年降水量、冬季平均最低气温、林分结构(Gini)和低树龄呈负相关,而与低相对直径、高密度和树龄呈正相关。研究结果将为未来全球气候变化背景下杉木人工林的有效森林管理提供适应性选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.50
自引率
6.20%
发文量
256
审稿时长
12 weeks
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