Estimating the dynamics of ecosystem functions under climate change in a temperate forest region

• Published in: Ecological indicators Vol. 166; p. 112353
• Main Authors: He, Jingyuan, Lu, Liqi, He, Huaijiang, Zhang, Zhonghui, Hao, Minhui, Zhang, Chunyu, Zhao, Xiuhai, von Gadow, Klaus
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2024; Elsevier
• Subjects: Climate change; Forest ecosystem functions; Functional diversity; Machine learning; Phylogenetic diversity; Species diversity; Forest ecosystem functions; Climate change; Species diversity; Phylogenetic diversity; Functional diversity; Machine learning
• ISSN: 1470-160X
• DOI: 10.1016/j.ecolind.2024.112353

[Display omitted] •Biodiversity and forest biomass productivity decrease under future climate change.•BEF exhibit non-monotonic patterns across most SSPs.•FPD demonstrate the strongest negative correlation with ecosystem stability in SSP126.•SR display the strongest negative correlation with stability under other SSPs. Understanding the dynamics and spatial patterns of forest biodiversity and functioning is vital for effective conservation and management. This study introduces a new framework for estimating the spatio-temporal dynamics of biodiversity and ecosystem functions in northeastern China. Our novel approach evaluates 96 future scenarios, including four points in time, four socio-economic pathways, and six global climate models based on an extensive network of permanent field plots with specific bioclimatic, topographic, and soil data. Our findings indicate a future trend of diminishing biodiversity and forest biomass productivity and increasing biomass density and carbon storage. The relationship between biodiversity and ecosystem functionality indicates a non-linear response to changes in temperature and precipitation, and a stable correlation between biodiversity and ecosystem stability across most of the socio-economic pathways. Unravelling the complex relationship between biodiversity, ecosystem functions, and stability of production, in the face of global change, requires new methods of analysis that were used in this study, including machine learning and other advanced methods.