[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128718-en":3,"doc-seo-128718-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128718,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Assessment and multi-scenario prediction of ecosystem services in the Yunnan-Guizhou Plateau based on machine learning and the PLUS model","Machine learning is leveraged to quantify and forecast ecosystem service dynamics in the Yunnan-Guizhou Plateau, focusing on water yield, carbon storage, habitat quality, and soil conservation for 2000, 2010, and 2020. An ecosystem service index characterizes spatiotemporal variation, while models detect key drivers behind service trade-offs and synergies. Land-use trajectories to 2035 are generated with the PLUS model under three scenarios, then InVEST is used to evaluate services. Results show pronounced fluctuations from 2000–2020, with land use and vegetation cover dominating overall capacity.","TYPE Original Research PUBLISHED 18 February 2025 DOI 10.3389/fevo.2025.1539547  \nOPEN ACCESS  \nEDITED BY  \nAthanasios Kallimanis,  \nAristotle University of Thessaloniki, Greece  \nREVIEWED BY  \nAliya Baidourela,  \nXinjiang Agricultural University, China Peng Du,  \nLiaoning Normal University, China  \n*CORRESPONDENCE  \nYu-Ling Peng  \n[yulingpengwit@163.com](yulingpengwit@163.com)  \nRECEIVED 04 December 2024  \nACCEPTED 28 January 2025  \nPUBLISHED 18 February 2025  \nCITATION  \nLi Y, Peng Y-L, Peng H-N and Cheng W-Y (2025) Assessment and multi-scenario prediction of ecosystem services in the Yunnan-Guizhou Plateau based on machine learning and the PLUS model.  \nFront. Ecol. Evol. 13:1539547 .  \ndoi: 10.3389/fevo.2025.1539547  \nCOPYRIGHT  \n© 2025 Li, Peng, Peng and Cheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nAssessment and multi-scenario prediction of ecosystem services in the Yunnan-Guizhou Plateau based on machine learning and the PLUS model  \nYuan Li 1,2, Yu-Ling Peng 1,2*, Hao-Na Peng 1,2 and Wei-Ying Cheng 1,2  \n1School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan, China, 2Village Culture and Human Settlements Research Center, Wuhan Institute of Technology, Wuhan, China  \nIntroduction: Machine learning techniques, renowned for their ability to process complex datasets and uncover key ecological patterns, have become increasingly instrumental in assessing ecosystem services.  \nMethods: This study quantitatively evaluates individual services—such as water yield, carbon storage, habitat quality, and soil conservation—on the YunnanGuizhou Plateau for the years 2000, 2010, and 2020 . A comprehensive ecosystem service index is employed to assess the overall ecological service capacity, revealing spatiotemporal variations in services and exploring the tradeoffs and synergies among them. Additionally, machine learning models identify the key drivers influencing ecosystem services, informing the design of future scenarios. The PLUS model is used to project land use changes by 2035 under three scenarios—natural development, planning-oriented, and ecological priority. Based on the land use simulation results for these scenarios, the InVEST model is applied to evaluate various ecosystem services.  \nResults: During 2000-2020, ecosystem services on the Yunnan-Guizhou Plateau exhibited significant fluctuations, driven by complex trade-offs and synergies. Land use and vegetation cover were the primary factors affecting overall ecosystem services, with the ecological priority scenario demonstrating the best performance across all services.  \nDiscussion: The research integrates machine learning with the PLUS model, providing more efficient data interpretation and more precise scenario design, offering new insights and methodologies for managing and optimizing ecosystem services on the Yunnan-Guizhou Plateau. These findings contribute to the development of more effective ecological protection and sustainable development strategies, applicable to both the plateau and similar regions.  \nKEYWORDS  \necosystem services, scenario analysis, ecological protection, machine learning, YunnanGuizhou Plateau, land use  \nFrontiers in Ecology and Evolution 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nEcosystem services (ESs) are the diverse benefits provided by natural ecosystems to human societies (Liu et al., 2023). As global climate change and human activities increasingly affect ecosystems, understanding the spatiotemporal dynamics of ecosystem services has become essential. It is critical to examine the interacti","cbCaim6wtWyTn8uj","https://ap.wps.com/l/cbCaim6wtWyTn8uj","pdf",7817903,2,1,18,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion","[{\"question\":\"Which ecosystem services are assessed in the Yunnan-Guizhou Plateau study?\",\"answer\":\"The study evaluates water yield, carbon storage, habitat quality, and soil conservation, and also derives an overall ecosystem service index to represent total ecological service capacity.\"},{\"question\":\"How are future scenarios and land-use changes projected to 2035?\",\"answer\":\"Land-use changes are simulated with the PLUS model under three scenarios: natural development, planning-oriented development, and ecological priority.\"},{\"question\":\"What are the main drivers of ecosystem service changes between 2000 and 2020?\",\"answer\":\"Land use and vegetation cover are identified as the primary factors influencing overall ecosystem services, with ecological priority showing the best performance across services.\"}]","Assessment and multi-scenario prediction of ecosystem services in the Yunnan-Guizhou Plateau based on machine learning and the PLUS model | 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ecosystem services are assessed in the Yunnan-Guizhou Plateau study?","Question",{"text":76,"@type":77},"The study evaluates water yield, carbon storage, habitat quality, and soil conservation, and also derives an overall ecosystem service index to represent total ecological service capacity.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are future scenarios and land-use changes projected to 2035?",{"text":81,"@type":77},"Land-use changes are simulated with the PLUS model under three scenarios: natural development, planning-oriented development, and ecological priority.",{"name":83,"@type":74,"acceptedAnswer":84},"What are the main drivers of ecosystem service changes between 2000 and 2020?",{"text":85,"@type":77},"Land use and vegetation cover are identified as the primary factors influencing overall ecosystem services, with ecological priority showing the best performance across 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