Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China; University of Chinese Academy of Sciences, 19(A) Yuquan Road, Shijingshan, District, Beijing 100049, China.
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China.
Sci Total Environ. 2024 Nov 10;950:175174. doi: 10.1016/j.scitotenv.2024.175174. Epub 2024 Jul 31.
Tree-ring widths contain valuable historical information related to both forest disturbances and climate variability and changes within forests. However, current methods are still unable to accurately distinguish between disturbances and climate signals in tree rings, especially in the case of climate anomalies. To address this issue, we developed a novel method, called Growth Trends Clustering (GTC) that uses the distribution characteristics of tree-ring widths within a stand to distinguish the effects of climate and other forest disturbances. GTC employed a Gaussian mixture model to fit the probability density distribution of annual ring-width index (RWI) in a stand. Discriminative criteria were established to cluster diverse sub-distributions from the Gaussian mixture model into categories of growth release, suppression, or normal trends. This approach allowed us to identify the occurrence, duration, and severity of forest disturbances based on percentage changes in the growth release or suppression categories of trees. And the effect of climate on tree growth was assessed according to the mean statistics of the growth normal categories. Using common forest disturbances such as defoliating insects and thinning as examples, we validated our method using tree-ring collections from six sites in British Columbia and Quebec, Canada. We found that the GTC method was superior to traditional time-series analysis methods (e.g., Radial Growth Averaging, Boundary Line, Absolute Increase, and Curve Intervention Detection) for detecting past forest disturbances and was able to significantly enhance climate signals. In summary, the GTC method presented in this study introduces a novel statistical approach for accurately distinguishing between forest disturbances and climate signals in tree rings. This is particularly important for understanding forest disturbance regimes under climate change and for developing future disturbance mitigation strategies.
树木年轮宽度包含有关森林干扰和气候变异性以及森林内部变化的宝贵历史信息。然而，目前的方法仍然无法准确区分树木年轮中的干扰和气候信号，特别是在气候异常的情况下。为了解决这个问题，我们开发了一种新的方法，称为生长趋势聚类（GTC），它利用林分内树木年轮宽度的分布特征来区分气候和其他森林干扰的影响。GTC 使用高斯混合模型来拟合林分内年轮宽度指数（RWI）的概率密度分布。建立了判别标准，将高斯混合模型中的多个子分布聚类为生长释放、抑制或正常趋势类别。这种方法允许我们根据树木生长释放或抑制类别的百分比变化来识别森林干扰的发生、持续时间和严重程度。并且根据生长正常类别的平均值统计数据来评估气候对树木生长的影响。使用常见的森林干扰，如食叶昆虫和疏伐作为示例，我们使用来自加拿大不列颠哥伦比亚省和魁北克省的六个地点的树木年轮数据验证了我们的方法。我们发现，GTC 方法在检测过去的森林干扰方面优于传统的时间序列分析方法（例如，径向生长平均、边界线、绝对增加和曲线干预检测），并且能够显著增强气候信号。总之，本研究提出的 GTC 方法为准确区分树木年轮中的森林干扰和气候信号引入了一种新的统计方法。这对于理解气候变化下的森林干扰机制以及制定未来的干扰缓解策略非常重要。
