Abstract
Adequately understanding the spatial multi-scale relationships of ecosystem services (ES) is an important step for environmental management decision-making. Here, we used spatially explicit methods to estimate five critical ES (nitrogen and phosphorous purifications, crop production, water supply and soil retention) related to non-point source (NPS) pollution in the Taihu Basin region of eastern China. Then a factorial kriging analysis and stepwise multiple regression were performed to identify the spatial multi-scale relationships of ES and their dominant factors at each scale. The spatial variations in ES were characterized at the 12 km and 83 km scales and the result indicated that the relationships of these services were scale dependent. It was inferred that at the 12 km scale, ES were controlled by anthropogenic activities and their relationships were dependent on socio-economic factors. At the 83 km scale, we suggested that ES were primarily dominated by the physical environment. Moreover, the policy implications of ES relationships and their dominant factors were discussed for the multi-level governance of NPS pollution. Overall, this study presents an optimized approach to identifying ES relationships at multiple spatial scales and illustrates how appropriate information can help guide water management.
Highlights
Ecosystem services (ES) contribute to human wellbeing and have drawn considerable attention from governments and the public around the world[1, 2]
The soil retention service (SR) value ranged from 0 to 82.54 t/ha, with the low value areas showing a wide distribution and the high value areas showing a limited distribution, primarily in areas covered with forest and grass (Figure S3)
As our study aimed to reveal potential ecosystem services (ES) relationships at multiple spatial scales, the nugget effect was not considered in the subsequent analyses
Summary
Ecosystem services (ES) contribute to human wellbeing and have drawn considerable attention from governments and the public around the world[1, 2]. To the best of our knowledge, classical geostatistical methods (e.g., ordinary kriging, indicator kriging, etc.) have rarely been used in the study of ES30, and FKA has never been applied to examine the spatial multi-scale relationships among ES. Identifying the factors that influence ES is a key step in understanding the spatial scale dependence of ES relationships and essential to managing multiple ES to improve ecosystem functions[31, 32]. Biophysical and socio-economic factors can be considered the main types of drivers that affect the spatial heterogeneity of ES12 These factors have scale-dependent impacts on ecological processes because of their different functional ranges[34, 35] and drive changes in ecosystem functions, which result in variations in ES supply. The relationships of ES are characterized by spatial scales and tend to vary from scale to scale
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