Key points are not available for this paper at this time.
ABSTRACT Current research on ecological security pattern (ESP) primarily emphasizes preserving the overall connectivity of ecosystems. However, a significant gap exists in systematically identifying ecologically degraded areas and optimizing regional ecological networks. In particular, the southwestern region of China, characterized by complex terrain and diverse ecosystems, lacks comprehensive research on the dynamic changes in regional ESP. This study uses machine learning to identify the primary drivers of spatial patterns in the remote sensing ecological index (RSEI) and establishes a corresponding ecological resistance surface. Through integrating morphological spatial pattern analysis with circuit theory, we developed a “point‐line‐area” combined ESP for Yunnan Province and proposed recommendations for optimizing regional ecological protection. Results show that 66 ecological sources were identified in 2000, decreasing to 52 in 2023, with the area increasing from 29,730.57 to 43,122.06 km 2 , primarily in the southwest. Additionally, the study identified 69 ecological corridors in 2000 and 52 in 2023, with total length reducing from 5934 to 4813 km. Ecological connectivity in the southwestern region improved significantly; however, ecological corridors in the northeast remain sparse, contributing to ecological pressure. Furthermore, 71 and 65 ecological pinch points, along with 46 and 53 ecological barriers, were identified. Overall, ecological source areas in Yunnan Province increased significantly, with substantial connectivity improvements, especially in the southwest. However, areas such as the Kunming economic circle exhibit limited ecological functionality. Future efforts should focus on expanding buffer zones and restoring corridors to enhance connectivity and stability.
Lai et al. (Fri,) studied this question.