Evolution Characteristics and Optimization of Urban Ecological Network in Chaoyang District, Beijing

Objective Ecological network is an organic structure integrating landscape patches and corridors, and improving landscape connectivity can effectively maintain ecosystem health. This research aims to improve the urban ecological network of Chaoyang District to promote the construction of urban green spaces under the background of ecological civilization strategy.Methods Taking Chaoyang District as the research area, the research selects four time nodes of 2005, 2010, 2015 and 2020 for a landscape connectivity analysis based on Graphab, a graph theory principle tool, and explores the evolution characteristics of ecological network and the application of graph theory in network pattern optimization.Results From 2005 to 2020, the scale of green space in Chaoyang District continued to increase; the total number of patches increased, while the average patch area decreased by 27.3%, leading to increasingly fragmented ecological network; the overall landscape connectivity of ecological network improved,with the probability of connectivity(PC) increasing by 35.4%; with the increase of size of the largest component(SLC) and the decrease of mean size of the components(MSC), the dominance of core landscape connectivity components is enhanced, while the mean landscape connectivity is degraded, making the ecological network tend to be polarized; the spatial distribution of landscape connectivity grades is uneven at the individual level, presenting a pattern high in the north while low in the south.Conclusion The research realizes the diagnosis and optimization of the ecological network of Chaoyang District based on graph theory, and identifies the positions of four important potential ecological patches, increasing PC by 11.9%. The results provide decision-making references for the planning and development of urban green spaces, and reflects the application value of graph theory in the construction and optimization of urban ecological network.