Abstract

Urbanization in China has led to land-use changes resulting in landscape fragmentation, natural habitats loss and ecological connectivity decline. Aiming to support sustainable landscape planning, we proposed an integrated method for ecological structure assessment and optimization including morphological spatial pattern analysis (MSPA), minimal cumulative resistance (MCR) model and circuit theory to assess landscape fragmentation and ecological connectivity change at regional scale. A modified connectivity index is also proposed by employing the index of relative quality of ecological patches and corridors. We applied the combined methods to Jiangsu province in eastern China for multi-temporal landscape analysis and optimization. The results showed that the combined analysis revealed both fragmentation trend of ecological patches and ecological connectivity reduction with regard to all the species as a whole in the study area from 2000 to 2018. While, the trend has been slowed down to a certain extent lately, which is related to a series of ecological protection policies implemented in the study area, including ecological redlines policy, national park construction and returning marginal farmland into forest or grassland. Furthermore, suggestions for improving the ecological network in the study area were proposed. We proposed to improve the ecological network by fostering twenty-five potential ecological sources, maintaining twenty-five pinch points, restoring eleven dredging corridors and planning fifty-three stepping stones. Overall, we argue that the combined method can reveal both structural connectivity and functional connectivity, and the time series analysis can provide dynamics situation for better understanding and planning landscape process. It can also provide planners with a valuable strategic tool that advances the practical utility of landscape ecology techniques for spatial planning, and the combined application of the method helps to reduce some of the limitations of each individual method.

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