Integrating ecological risk, ecosystem health, and ecosystem services for assessing regional ecological security and its driving factors: Insights from a large river basin in China

• Published in: Ecological indicators Vol. 155; p. 110954
• Main Authors: Zhu, Qing, Cai, Yongli
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2023; Elsevier
• Subjects: Assessment system; China; Driving factors; ecosystems; environmental health; Huaihe River Basin; Regional ecological security; risk; Spatial heterogeneity; spatial variation; sustainable development; urbanization; vegetation; watersheds; China; RSEI; ESs; ESsI; EHI; NDVI; SPLIT; DIVISION; DFR; GDP; ESI; ACD; EH; Regional ecological security; SPEI; Driving factors; POI; ER; ES; PSR; PD; Huaihe River Basin; Spatial heterogeneity; PCL; HRB; Assessment system; ERI
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2023.110954

[Display omitted] •The new assessment index could reflect the regional ecological security (RES) status.•RES included three basic characteristics: risk, health, and services.•The multidimensional characteristics of RES had spatial heterogeneity and dependence.•Various drivers impacted the multidimensional characteristics of RES differently.•Main tasks to ensure RES: control urban expansion and enhance vegetation cover. Watershed ecological security (ES), which is fundamental to regional sustainable development, consists of ecological risk (ER), ecosystem health (EH), and ecosystem services (ESs). It remains unclear how to describe the status of regional ES by integrating the three key characteristics, and little is known about the differences among the factors driving ER, EH, ESs, and ES. Here, we established an integrated assessment system of regional ES based on the cause–effect relationship of ER, EH, and ESs. We quantified the ES characteristics in the Huaihe River Basin of China in 2020 and pinpointed the factors (i.e., natural environment, urbanization, accessibility) driving their spatial differentiation using the Geodetector model. Results showed that: (1) in the study area, ER and EH were at the average level, and ESs and ES were at the high level. (2) ER, EH, ESs, and ES had positive spatial correlations (Moran’ I > 0.60), with ER and EH having the strongest spatial dependence. (3) Natural environmental factors predominantly influenced ER, EH, and ESs. Slope was the primary driver for ER and ESs (q-value = 0.492 and 0.395, respectively), and vegetation coverage was the major driver for EH (q-value = 0.506). (4) ES was mainly influenced by urbanization factors, the proportion of construction land area exhibiting the highest explanatory power (q-value = 0.751), and the interaction with vegetation coverage being the strongest (q-value = 0.836). This study provides new ideas and methods for the assessment of regional ES, and holds positive implications for the protection and management of regional ecosystems.