Effects of stand factors on tree growth of Chinese fir in the subtropics of China depends on climate conditions from predictions of a deep learning algorithm: A long-term spacing trial
Effects of stand factors on tree growth of Chinese fir in the subtropics of China depends on climate conditions from predictions of a deep learning algorithm: A long-term spacing trial
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DOI：
10.1016/j.foreco.2022.120363
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发表时间：
2022-09
影响因子：
3.7
通讯作者：
Zhen Wang;Xiongqing Zhang;Jianguo Zhang;S. Chhin
中科院分区：
文献类型：
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作者：
Zhen Wang;Xiongqing Zhang;Jianguo Zhang;S. Chhin
林分和气候相关变量是控制个体树木生长的主要驱动力。使用深度学习和随机森林两种机器学习算法来探索年直径增长如何随林分和气候变量变化。数据来自对中国南方四个省的杉木（Cunninghamia lanceolata(Lamb.) Hook.）人工林的长期间距跟踪。模型对比结果显示，8个隐藏层、每个隐藏层90个神经元的深度学习模型性能最佳，RF模型在9个入选模型中排名第4。此外，敏感性分析表明，单株生长随着基尼系数的增加而增加，而随着林龄（A）和大树断面积（BAL）的增加而减少。直径增长与夏季平均最高气温（SMMT）以及冬季平均最低气温（WMMT）和年降水量（AP）之间的关系并不是恒定的，这取决于每个气候因子的取值范围。在所有变量中，BAL 对直径增长的影响最大。通过交互分析，我们发现气候因素加剧了竞争对增长的负面影响。气候变化促进了年轻树木的生长，但抑制了老树的生长。考虑气候变量，高林分结构异质性和中林分结构异质性下的树木生长相似，并且明显高于低林分结构异质性的树木生长。气候的积极影响往往会在竞争较低的情况下促进树木生长，而老年个体更容易受到 WMMT 变化的影响。我们的研究结果增强了我们对在未来气候不确定性的情况下驱动中国南方杉木个体生长的机制的理解。
Stand and climate related variables are the main driving forces controlling individual tree growth. Two machine learning algorithms called deep learning and random forest were used to explore how annual diameter growth varied with stand and climatic variables. Data was obtained from a long-term spacing trail of Chinese fir (Cunninghamia lanceolata(Lamb.) Hook.) plantations in four provinces of southern China. Results from model comparisons showed the deep learning model with 8 hidden layers and 90 neurons in each hidden layer achieved the best performance, and the RF model ranked 4th among 9 selected models. In addition, sensitivity analysis showed that individual tree growth increased with an increase in Gini coefficient, while growth decreased with an increase in stand age (A) and the basal area of larger trees (BAL). The relationships between diameter growth and summer mean maximum temperature (SMMT), as well as winter mean minimum temperature (WMMT) and annual precipitation (AP) were not constant, which depended on the range of values of each climate factor. BAL had the greatest influence on diameter growth among all the variables. From an interaction analysis, we found that climate factors exacerbated the negative effects of competition on growth. Climate change promoted the growth of younger trees but restrained the growth of older trees. With climate variables considered, tree growth under high and middle stand structural heterogeneity were similar, and observably higher than that with low stand structural heterogeneity. Positive influences of climate tended to promote tree growth under lower competition and older individuals were more vulnerable to WMMT changes. Our findings enhance our understanding of the mechanisms driving individual Chinese fir growth in southern China in the face of future climate uncertainty.