What question did this study set out to answer?

The study aims to understand the interrelationships among ecosystem services and their driving factors in the Taihu Lake Basin.

March 19, 2026Open Access

Exploring the Multiscale Spatiotemporal Dynamics of Ecosystem Service Interactions and Their Driving Factors in the Taihu Lake Basin, China

Key Points

The study aims to understand the interrelationships among ecosystem services and their driving factors in the Taihu Lake Basin.
Analyzed five ecosystem services: water yield, carbon sequestration, soil retention, habitat quality, crop production.
Utilized the InVEST model, food production model, and ArcGIS for spatial distribution analysis.
Employed Spearman correlation analysis and K-means clustering to assess trade-offs and synergies among services.
Applied GeoDetector to identify driving mechanisms affecting ecosystem services.
Significant changes in spatial distribution of ecosystem services from 2000 to 2020, with water yield and soil retention increasing by approximately 49% and 52%.
Habitat quality, carbon sequestration, and crop production decreased by 17.2%, 15.5%, and 47.6%, respectively.
Ecosystem service interactions transitioned from trade-offs to synergies as spatial scale increased.
Identification of six ecosystem service bundles with varying stability across different regions.
Land use/land cover emerged as a significant factor influencing ecosystem services at varying spatial scales.

Abstract

Understanding the intricate interrelationships among ecosystem services (ESs) is fundamental to advancing sustainable ecological management. This study focuses on the Taihu Basin and examines five representative ESs, including water yield (WY), carbon sequestration (CS), soil retention (SR), habitat quality (HQ), and crop production (CP), for the years 2000, 2010, and 2020. Spatial distribution characteristics and spatiotemporal dynamics were quantified through the combined application of the InVEST model, a food production model, and ArcGIS. Spearman correlation analysis and K-means clustering were then applied to characterize trade-offs and synergies among ESs and to delineate ecosystem service bundles at multiple spatial scales, including 1 km × 1 km grids, 10 km × 10 km grids, and the county level, while GeoDetector was used to identify the associated driving mechanisms. The results indicated that (1) between 2000 and 2020, the spatial distribution pattern of the ESs in the Taihu Basin underwent significant changes, with WY and SR increasing by 48.97% and 51.89%, respectively, while HQ, CS, and CP decreased by 17.2%, 15.5%, and 47.6%. (2) From an overall perspective of trade-offs and synergies, the interactions among ESs shifted from trade-offs (r 0) as the scale increased. From the perspective of the spatial characteristics of trade-offs and synergies, the intensity of these interactions varied significantly with increasing scale, but the trend remained relatively stable. (3) The Taihu Basin can be categorized into six ES bundles (ESBs). ESB 1, ESB 3, ESB 4, and ESB 5 have relatively stable ES structures, whereas ESBs 2 and 6 display significant variations. (4) The primary factors influencing ESs vary significantly across different spatial scales, with land use/land cover (LULC) and the proportions of arable land, forestland, and buildings exhibiting strong explanatory power. This highlights the critical role of coupled natural and anthropogenic processes in shaping the spatial patterns of ESs. This study considers the spatiotemporal variation and scale dependence of ecosystem services, providing management recommendations tailored to different regions and spatial scales, and offering a scientific basis for regional ecological planning and watershed governance.

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