Improving the ecological network optimization with landscape connectivity: a case study of Neijiang City, Sichuan Province

Rapid urbanization intensifies the fragmentation of landscape patches and affects the stability of ecosystems. The construction of an ecological network can effectively promote the connection of important ecological spaces and improve the landscape integrity. However, the landscape connectivity, directly affecting the stability of ecological network, was less considered in the ecological network construction of recent researches, which easily caused the instability of constructed ecological network. Therefore, this study introduced landscape connectivity index to establish a modified ecological network optimization method based on the minimum cumulative resistance (MCR) model. The results showed that, compared with the traditional model, the modified model focused on the spatially detailed measurement of regional connectivity, and emphasized the impact of human disturbance on ecosystem stability at the landscape scale. The constructed corridors in the optimized ecological network of the modified model not only effectively improved the connection degree between important ecological sources but also avoided the areas with low landscape connectivity and high obstacles to ecological flow, especially in the counties of Zizhong, Dongxing, and Longchang within the focal study area. The ecological network established by the traditional model and modified model generated 19 and 20 ecological corridors with lengths of 334.49 km and 364.35 km, respectively, and the number of ecological nodes was 18 and 22. Evaluated by the Gravity method, the modified model identified the important ecological corridors in the ecological network, and the energy transfer efficiency of the network was improved. This study provided an effective way to improve the structural stability of ecological network construction and can provide scientific support for regional landscape pattern optimization and ecological security construction.