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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
[Multi-scenario simulation of synergistic dynamics in ecosystem services within Shennongjia National Park]
Bin-Bin Zheng1,2, Jie Zeng1,2, Xin-Yu Cui3
1Hubei Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences (Wuhan), Wuhan 430074, China.
Abstract:
Understanding the trade-offs, synergies, and dynamics among ecosystem services (ESs) under both current and future conditions is crucial foundation for promoting holistic ecosystem conservation and scientific management of national park. Using the PLUS and InVEST models, we assessed land use and ES changes of Shennongjia National Park in 2030 and 2050 under three scenarios: Natural development, ecological conservation, and tourism development. We used Spearman correlation analysis and K-means clustering to identify trade-offs and synergies among ESs, delineate the functional structure of ES bundles, and examine their dynamics. The results showed that different ESs exhibited distinct evolutionary trends from 2020 to 2050. Recreational services remained relatively stable overall. Water yield, soil conservation, and habitat quality showed declining trends, with the most significant reductions under the tourism development scenario by 359769.00 mm, 28388.70 t, and 0.11, respectively. Carbon storage increased significantly only under the ecological conservation scenario, with a gain of 102096.71 t. Relationships among ESs under different scenarios were predominantly synergistic and showed notable changes. Under the natural development and tourism development scenarios, trade-offs weakened while synergies strengthened. The relationship between carbon storage and water yield shifted from trade-off to synergy. Under the ecological conservation scenario, trade-offs and synergies remained largely unchanged. Based on K-means clustering of ecosystem service trade-offs and synergies, four distinct types of ES bundles were identified: strict protection, science education and recreation, community livelihood, and ecological restoration. The spatial patterns of ES bundles showed minimal variation and remained relatively stable across different scenarios. Based on the distribution patterns and internal characteristics of different ES bundles, we proposed corresponding ecological management strategies to provide a scientific basis for the holistic conservation of national park ecosystems.
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