Assessment of ecosystem services in new perspective: A comprehensive ecosystem service index (CESI) as a proxy to integrate multiple ecosystem services. (May 2022)

• Record Type: Journal Article
• Authors: Wu, Linlin; Fan, Fenglei
• Is Part Of: Ecological indicators. Volume 138(2022)
• Publication Date: 2022-05
• Subjects: Multiple ecosystem services index -- InVEST models -- Water supply, Carbon storage, Soil conservation -- Guangdong-Hongkong-Macao Greater Bay; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2022.108800
• Languages: English
• ISSNs: 1470-160X

Abstract

Graphical abstract: Highlights: A comprehensive ecosystem service index (CESI) by the multiplicative method had been proposed. The CESI was built to assess multiple ecosystem services delivering based on carbon storage service, water supply service and soil conservation service. The CESI had been tested in the Guangdong-Hongkong-Macao Greater Bay Areas (GBA) and compared with the performance of previous methods. The results showed that the CESI was well applied in GBA and got rid of the limitation of single ES. Abstract: A comprehensive understanding of multiple ecosystem services (ES) across the landscape is a key highlight of ecosystem management. There still remain a weakness to integrate multiple ESs for mirroring the capability of the ecosystems to deliver services in a broad perspective. Here, we proposed a comprehensive ecosystem services index (CESI) for integrating multiple ESs based on multiplicative method. Water supply service, carbon storage service and soil conservation service were assessed using multi-sources remote sensing datasets and InVEST models to build CESI. To examine the suitability and performance of CESI, we took the Guangdong-Hongkong-Macao Greater Bay Areas (GBA) as a test region. Meanwhile, we applied two previous methods used commonly for assessing multiple ESs: i) cumulative method and ii) maximum value composite method to construct comparative indexes for evaluating the performance of CESI with the help of spatial autocorrelation method and regression analysis method from the spatial pattern and numerical distribution perspective. The results showed that the CESI was well applied in GBA and got rid of the limitation of single ES. The spatial pattern of CESI was observed a high-value clustering in central areas and a low-value clustering in outer regions. A comparison of CESI and comparative indexes were illustrated on the results of linear regression and spatial autocorrelation, which showed a good linear correlation between each method and a similar spatial pattern in a high-value clustering, but had subtle different in low-value clustering. Specifically, the performance of CESI in expressing the low-value clustering was better than other indexes which could indicate the multiplicative method benefits to manifest the lagging in the provision of ESs better than cumulative method and maximum value composite method. This study could pave a way for a new approach to evaluate the provision of multiple ESs from the socio-ecological systems with a comprehensive perspective.