Ecological Indicators (Dec 2022)
Impacts of landscape pattern on ecological network evolution in Changsha-Zhuzhou-Xiangtan Urban Agglomeration, China
Abstract
As cities develop multidimensionally, regional landscape patterns and ecological network structure considerably change. Understanding spatial relationships between landscape patterns and ecological networks is important for optimising urban ecological patterns. However, actual landscape connectivity in ecological sources remain unknown, the spatial structure of ecological networks has not been quantified. In this study, the impacts of landscape pattern on ecological network evolution of the Changsha-Zhuzhou-Xiangtan urban agglomeration (CZXUA) in China was investigated. Ecological sources were identified based on morphological spatial pattern analysis, and identification methods were optimized through of threshold experiments in landscape connectivity distances in the years 1990, 2000, 2010, and 2020. Ecological resistance surfaces were constructed by introducing patch-type factors, and ecological corridors were extracted based on the minimum cumulative resistance model. The ecological networks were evaluated using the gravity model, network closure index (α), network connectivity index (β), and network connectivity rate index (γ). Ecological network quality index (ENQI) quantified by the ratio of ecological security to ecological resistance was proposed. The impacts of landscape pattern on ENQI during different periods were evaluated using geographically weighted regression model. The results show that: (1) The landscape connectivity distance thresholds of CZXUA in 1990, 2000, 2010, and 2020 were 1100 m, 1100 m, 1000 m, and 1400 m, respectively. The numbers of ecological sources and corridors were 16, 15, 14, and 14, and 120, 105, 91, and 91, respectively. During the period 1990–2020, delta of probability of connectivity (dPC) decreased while ecological resistance increased, the ecological sources and corridors in the centre of the study area decreased considerably. (2) The proportion of important corridors decreased from 40.83 % to 20.88 %, the highest ENQI value decreased from 6.0410 to 5.7513. (3) The aggregation index (AI) decrease observed for the central area of the study reduced its negative impact on ENQI. The negative impact region of largest patch index (LPI) on ENQI gradually connected, while the increase in patch density (PD) in habitat patches increased ENQI. The enclave-like outward expansion of construction land followed by internal mosaic filling initially strengthened, then weakened the impact of the mean perimeter-to-area ratio (PARA_MN) on ENQI. The homogenization of patches in the central area of the study extended the Shannon’s diversity index (SHDI)-positive impact range on ENQI. These results are expected to serve as a reference for studies of urban ecological networks evaluation.
