Growth trends clustering: A novel method for detecting forest disturbances and extracting climate signals in tree rings.

ABSTRACT

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.