Multi-Scale Spatiotemporal Dynamics of Ecosystem Services and Detection of Their Driving Mechanisms in Southeast Coastal China

Haoran Zhang, Xin Fu (), Jin Huang, Zhenghe Xu and Yu Wu
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Haoran Zhang: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
Xin Fu: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
Jin Huang: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
Zhenghe Xu: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
Yu Wu: School of Earth System Science, Tianjin University, Tianjin 300072, China

Land, 2025, vol. 14, issue 11, 1-27

Abstract: Intensive human interference has severely disrupted the natural and ecological environments of coastal areas, threatening ecosystem services (ESs). Meanwhile, the relationships between ESs exhibit certain variations across different spatial scales. Therefore, identifying the scale effects of interrelationships among ESs and their underlying driving mechanisms will better support scientific decision-making for the hierarchical and sustainable management of coastal ecosystems. Therefore, employing the Integrated Valuation of ESs and Tradeoffs (InVEST) model combined with GIS spatial visualization techniques, this investigation systematically examined the spatiotemporal distribution of four ESs across three scales (grid, county, and city) during 2000–2020. Complementary statistical approaches (Spearman’s correlation analysis and bivariate Moran’s I) were integrated to systematically quantify evolving ES trade-off/synergy patterns and reveal their spatial self-correlation characteristics. The geographical detector model (GeoDetector) was used to identify the main driving factors affecting ESs at different scales, and combined with bivariate Moran’s I to further visualize the spatial differentiation patterns of these key drivers. The results indicated that: (1) ESs (except for Water yield) generally increased from coastal regions to inland areas, and their spatial distribution tended to become more clustered as the scale increased. (2) Relationships between ESs became stronger at larger scales across all three study levels. These ESs connections showed stronger links at the middle scale (county). (3) Natural factors had the greatest impact on ESs than anthropogenic factors, with both demonstrating increased explanatory power as the scale enlarges. The interactions between factors of the same type generally yield stronger explanatory power than any single factor alone. (4) The spatial aggregation patterns of ESs with different driving factors varied significantly, while the spatial aggregation patterns of ESs with the same driving factor were highly similar across different spatial scales. These findings confirm that natural and social factors exhibit scale dependency and spatial heterogeneity, emphasizing the need for policies to be tailored to specific scales and adapted to local conditions. It provides a basis for future research on multi-scale and region-specific precision regulation of ecosystems.

Keywords: scale effects; InVEST model; ecosystem synergies and trade-offs; spatiotemporal patterns; driving factors; southeast coastal China (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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