Spatiotemporal patterns and modifiable areal unit problems of the landscape ecological risk in coastal areas: A case study of the Shandong Peninsula, China

Abstract

Ecological risk assessment is the basis for sustainable land use and ecological protection and management in coastal areas. The research scale of ecological risk assessment is usually determined by empirical methods, and existing research focused on coastal areas is relatively limited. This paper aims to explore the spatiotemporal pattern of ecological risk in coastal areas, taking the Shandong Peninsula as the study area, and to determine the optimal scale of ecological risk assessment based on semivariogram analysis. The results show that the waterbodies and unused land had a relatively high landscape loss index, while the cropland had the least value. The optimal scale for landscape risk assessment in the Shandong Peninsula was 4 km. The ecological risk was generally low in the Shandong Peninsula, with the lowest risk regions and lower risk regions accounting for more than 70% of the total area. During 1990–2018, the average ecological risk dropped from 0.0819 to 0.0698, indicating a lower probability of adverse ecological effects caused by landscape changes. The spatial agglomeration pattern of the ecological risk showed low–low agglomeration in the central and northeast areas and high–high agglomeration in the northwest along the sea. Scale effects had a significant influence on the uncertainty of the assessment of ecological risks, including the study extent, size of risk units, risk level classification, and data resolutions. It is suggested that protecting cropland was important for both food safety and the stability of the ecosystem in the Shandong Peninsula. Waterbodies in this area are vulnerable due to their fragmentation and close distribution with built-up land and agricultural land. This study could offer useful information for coastal ecological risk control, and provide new perspectives regarding optimal scale selection when conducting local ecological risk assessment.