Climate drivers of forest ecosystem services supply in the hilly mountainus regions of southern China based on SHAP-enhanced machine learning
Analyzing the spatiotemporal patterns of forest ecosystem services (FESs) and their climatic drivers in the hilly mountainous regions of southern China (CSHR) is crucial for advancing regional ecological conservation. In this study, we employed the InVEST model to quantify four key FES indicators fr...
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| Language: | English |
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Elsevier
2025-09-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25010179 |
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| author | Qi Mengjuan Guo Luo Liu Wenshu Wang Weiyin Jiang Chunqian Bai Yanfeng |
| author_facet | Qi Mengjuan Guo Luo Liu Wenshu Wang Weiyin Jiang Chunqian Bai Yanfeng |
| author_sort | Qi Mengjuan |
| collection | DOAJ |
| description | Analyzing the spatiotemporal patterns of forest ecosystem services (FESs) and their climatic drivers in the hilly mountainous regions of southern China (CSHR) is crucial for advancing regional ecological conservation. In this study, we employed the InVEST model to quantify four key FES indicators from 2000 to 2020: carbon storage (CS), soil conservation (SC), habitat quality (HQ), water yield (WY), and a composite ecosystem service index (CESI). Furthermore, we integrated an interpretable machine learning model, Random Forest–Shapley Additive Explanations (SHAP), to identify principal climatic drivers and characterize their nonlinear impacts on FESs. The results indicate that during the study period, SC (+5.17 %) and WY (+13.7 %) within the study area exhibited sustained increases, whereas CS (−0.47 %) and HQ (−3.87 %) exhibited a declining trend. CESI displayed a distinct spatial gradient, remaining consistently higher in the southern region compared to the northern region, whereas CESI values gradually increased towards the east. Moreover, SHAP value analysis revealed that climate-driven factors exhibited multivariate nonlinear characteristics. Specifically, temperature seasonality (Bio4) enhanced CS, the mean temperature of the warmest season (Bio10) inhibited SC, and areas with high annual precipitation (Bio12) were associated with simultaneous increases in both HQ and WY. The coupling of multiple factors affected the regulation of FESs. Among these, the interaction between temperature seasonality (Bio4) and annual precipitation (Bio12) proved particularly significant. Within this framework, WY demonstrated the strongest spatial synergy stability, with its mean bivariate spatial autocorrelation (global Moran’s I) values for Bio4 and Bio12 reaching 0.455 (p < 0.001). In this study, we combined the analysis of FES supply and its climate drivers with interpretable machine learning methods to provide scientific insights for the sustainable development and scientific management of the ecological environment in the CSHR. |
| format | Article |
| id | doaj-art-babcdec35f124f96941d2767c040e7d1 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-babcdec35f124f96941d2767c040e7d12025-08-24T05:11:38ZengElsevierEcological Indicators1470-160X2025-09-0117811408510.1016/j.ecolind.2025.114085Climate drivers of forest ecosystem services supply in the hilly mountainus regions of southern China based on SHAP-enhanced machine learningQi Mengjuan0Guo Luo1Liu Wenshu2Wang Weiyin3Jiang Chunqian4Bai Yanfeng5Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; College of Ethnology and Sociology, Minzu University of China, Beijing 100081, ChinaCollege of Life and Environmental Sciences, Minzu University of China, Beijing 100081, ChinaCollege of Ethnology and Sociology, Minzu University of China, Beijing 100081, ChinaCollege of Ethnology and Sociology, Minzu University of China, Beijing 100081, ChinaResearch Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, ChinaResearch Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; Huitong Experimental Station of Forest Ecology, Chinese Academy of Sciences, Huitong 418307, China; Corresponding author.Analyzing the spatiotemporal patterns of forest ecosystem services (FESs) and their climatic drivers in the hilly mountainous regions of southern China (CSHR) is crucial for advancing regional ecological conservation. In this study, we employed the InVEST model to quantify four key FES indicators from 2000 to 2020: carbon storage (CS), soil conservation (SC), habitat quality (HQ), water yield (WY), and a composite ecosystem service index (CESI). Furthermore, we integrated an interpretable machine learning model, Random Forest–Shapley Additive Explanations (SHAP), to identify principal climatic drivers and characterize their nonlinear impacts on FESs. The results indicate that during the study period, SC (+5.17 %) and WY (+13.7 %) within the study area exhibited sustained increases, whereas CS (−0.47 %) and HQ (−3.87 %) exhibited a declining trend. CESI displayed a distinct spatial gradient, remaining consistently higher in the southern region compared to the northern region, whereas CESI values gradually increased towards the east. Moreover, SHAP value analysis revealed that climate-driven factors exhibited multivariate nonlinear characteristics. Specifically, temperature seasonality (Bio4) enhanced CS, the mean temperature of the warmest season (Bio10) inhibited SC, and areas with high annual precipitation (Bio12) were associated with simultaneous increases in both HQ and WY. The coupling of multiple factors affected the regulation of FESs. Among these, the interaction between temperature seasonality (Bio4) and annual precipitation (Bio12) proved particularly significant. Within this framework, WY demonstrated the strongest spatial synergy stability, with its mean bivariate spatial autocorrelation (global Moran’s I) values for Bio4 and Bio12 reaching 0.455 (p < 0.001). In this study, we combined the analysis of FES supply and its climate drivers with interpretable machine learning methods to provide scientific insights for the sustainable development and scientific management of the ecological environment in the CSHR.http://www.sciencedirect.com/science/article/pii/S1470160X25010179Machine learningInVESTForest ecosystem services (FES) supplyClimate drivenHilly mountains in southern China |
| spellingShingle | Qi Mengjuan Guo Luo Liu Wenshu Wang Weiyin Jiang Chunqian Bai Yanfeng Climate drivers of forest ecosystem services supply in the hilly mountainus regions of southern China based on SHAP-enhanced machine learning Ecological Indicators Machine learning InVEST Forest ecosystem services (FES) supply Climate driven Hilly mountains in southern China |
| title | Climate drivers of forest ecosystem services supply in the hilly mountainus regions of southern China based on SHAP-enhanced machine learning |
| title_full | Climate drivers of forest ecosystem services supply in the hilly mountainus regions of southern China based on SHAP-enhanced machine learning |
| title_fullStr | Climate drivers of forest ecosystem services supply in the hilly mountainus regions of southern China based on SHAP-enhanced machine learning |
| title_full_unstemmed | Climate drivers of forest ecosystem services supply in the hilly mountainus regions of southern China based on SHAP-enhanced machine learning |
| title_short | Climate drivers of forest ecosystem services supply in the hilly mountainus regions of southern China based on SHAP-enhanced machine learning |
| title_sort | climate drivers of forest ecosystem services supply in the hilly mountainus regions of southern china based on shap enhanced machine learning |
| topic | Machine learning InVEST Forest ecosystem services (FES) supply Climate driven Hilly mountains in southern China |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25010179 |
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