Cold threat and moisture deficit induced individual tree mortality via 25-year monitoring in seminatural mixed forests, northeastern China.

Autor: Shen C; Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Key Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Beijing 100091, China; Hubei Zigui Three Gorges Reservoir National Forest Ecosystem Observation and Research Station, Zigui 443600, China., Lei X; State Key Laboratory of Efficient Production of Forest Resources, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China. Electronic address: xdlei@ifrit.ac.cn., Huang Z; Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Key Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Beijing 100091, China; Hubei Zigui Three Gorges Reservoir National Forest Ecosystem Observation and Research Station, Zigui 443600, China; Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2024 Nov 25; Vol. 953, pp. 176048. Date of Electronic Publication: 2024 Sep 05.
DOI: 10.1016/j.scitotenv.2024.176048
Abstrakt: Accurately predicting tree mortality in mixed forests sets a challenge for conventional models because of large uncertainty, especially under changing climate. Machine learning algorithms had potential for predicting individual tree mortality with higher accuracy via filtering the relevant climatic and environmental factors. In this study, the sensitivity of individual tree mortality to regional climate was validated by modeling in seminatural mixed coniferous forests based on 25-year observations in northeast of China. Three advanced machine learning and deep learning algorithms were employed, including support vector machines, multi-layer perceptron, and random forests. Mortality was predicted by the effects of multiple inherent and environmental factors, including tree size and growth, topography, competition, stand structure and regional climate. All three types of models performed satisfactorily with their values of the areas under receiving operating characteristic curve (AUC) > 0.9. With tree growth, competition and regional climate as input variables, a model based on random forests showed the highest values of the explained variance score (0.862) and AUC (0.914). Since the trees were vulnerable despite their species, mortality could occur after growth limit induced by insufficient or excessive sun radiation during growing seasons, cold threat caused thermal insufficiency in winters, and annual moisture constraints in these mixed coniferous forests. Our findings could enrich basic knowledge on individual tree mortality caused by water and heat inadequacy with the negative impacts of global warming. Successful individual tree mortality modeling via advanced algorithms in mixed forests could assist in adaptive forest ecology modeling in large areas.
Competing Interests: Declaration of competing interest The authors declare no conflict of interest.
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Databáze: MEDLINE
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