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Enhancing forest insect outbreak detection by integrating tree-ring and climate variables

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Enhancing forest insect outbreak detection by integrating tree-ring and climate variables
Annual tree rings are widely recognized as valu-able tools for quantifying and reconstructing historical forest disturbances.However,the influence of climate can compli-cate the detection of disturbance signals,leading to limited accuracy in existing methods.In this study,we propose a random under-sampling boosting(RUB)classifier that inte-grates both tree-ring and climate variables to enhance the detection of forest insect outbreaks.The study focused on 32 sites in Alberta,Canada,which documented insect outbreaks from 1939 to 2010.Through thorough feature engineering,model development,and tenfold cross-validation,multiple machine learning(ML)models were constructed.These models used ring width indices(RWIs)and climate variables within an 11-year window as input features,with outbreak and non-outbreak occurrences as the corresponding output variables.Our results reveal that the RUB model consist-ently demonstrated superior overall performance and stabil-ity,with an accuracy of 88.1％,which surpassed that of the other ML models.In addition,the relative importance of the feature variables followed the order RWIs＞mean maximum temperature(Tmax)from May to July＞mean total precipita-tion(Pmean)in July＞mean minimum temperature(Tmin)in October.More importantly,the dfoliatR(an R package for detecting insect defoliation)and curve intervention detec-tion methods were inferior to the RUB model.Our findings underscore that integrating tree-ring width and climate vari-ables as predictors in machine learning offers a promising avenue for enhancing the accuracy of detecting forest insect outbreaks.

Forest disturbance Insect outbreaksMachine learningTree-ring analysis

Yao Jiang、Zhou Wang、Zhongrui Zhang、Xiaogang Ding、Shaowei Jiang、Jianguo Huang、Tao Xu

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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,People's Republic of China

University of Chinese Academy of Sciences,Beijing 100049,People's Republic of China

Ministry of Emergency Management of China,National Institute of Natural Hazards,Beijing 100085,People's Republic of China

Guangdong Academy of Forestry,Guangzhou 510520,People's Republic of China

MOE Key Laboratory of Biosystems Homeostasis and Protection,College of Life Sciences,Zhejiang University,Hangzhou 310000,People's Republic of China

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Forest disturbance Insect outbreaks Machine learning Tree-ring analysis

2024

10.1007/s11676-024-01759-x

林业研究(英文版)
东北林业大学,中国生态学学会

林业研究(英文版)

CSTPCDEI
影响因子：0.365
ISSN：1007-662X
年,卷(期)：2024.35(6)