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ORIGINAL RESEARCH article
Front. Ecol. Evol.
Sec. Conservation and Restoration Ecology
Volume 13 - 2025 |
doi: 10.3389/fevo.2025.1539547
This article is part of the Research Topic Ecosystem Condition Assessments: Progress towards a Global Standard View all 5 articles
Assessment and Multi-Scenario Prediction of Ecosystem Services in the Yunnan-Guizhou Plateau Based on Machine Learning and the PLUS Model
Provisionally accepted- Wuhan Institute of Technology, Wuhan, China
[Introduction]: Machine learning techniques, renowned for their ability to process complex datasets and uncover key ecological patterns, have become increasingly instrumental in assessing ecosystem services.[Methods]: This study quantitatively evaluates individual services—such as water yield, carbon storage, habitat quality, and soil conservation—on the Yunnan-Guizhou 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 trade-offs 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.[Results]: 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.[Discussion]: 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.
Keywords: ecosystem services, Scenario analysis, Ecological protection, machine learning, Yunnan-Guizhou Plateau, land use
Received: 04 Dec 2024; Accepted: 28 Jan 2025.
Copyright: © 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) or licensor 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.
* Correspondence:
Yu-Ling Peng, Wuhan Institute of Technology, Wuhan, China
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