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Spatiotemporal dynamics and future predictions of ecosystem service synergies and trade-offs in China
Xiaohui Bai1, Shilong Ren1, Shuping Ji2
1Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao, 266003, China.
Abstract:
Ecosystems provide extensive benefits to human well-being through food production, climate regulation, and cultural services. Climate change and socioeconomic development have substantially altered the capacity of ecosystem services and pose ongoing risks to their long-term sustainability. However, our understanding of the dynamic evolution patterns of ecosystem service interactions and their underlying mechanisms remains limited. This study comprehensively explored the spatiotemporal characteristics of ecosystem services and their synergistic/trade-off relationships over China by combining historical assessments (2001-2022) with future predictions (2030-2100) through machine learning algorithms. Results show that carbon sequestration (CS) and food production (FP) have significantly increased since 2001 in forest, grassland, and cropland. Future projections indicate that CS and soil retention (SR) will continue to rise after 2030 under different scenarios, while FP will keep relatively stable. A dominant synergy pattern was detected for all pairs of ecosystem services, distributed mainly in Northeast China, indicating mutual promotion of ecosystem functions. However, segmented trend analysis reveals that the synergies among ecosystem services have significantly declined since 2004, except for CS & water yield (WY) and FP & WY. Concerningly, these synergies will continue weakening during 2030-2100, while trade-offs will expand significantly for CS & FP and FP & SR. Although geoclimatic factors are vital in controlling service supply, overlapping implementation of engineering projects has also largely contributed to ecosystem service growth. Our findings highlight the necessity for implementing sophisticated intervention policies to navigate increasing trade-off risks and ensure optimal ecosystem service provision.
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