Potential distribution patterns and species richness of avifauna in rapidly urbanizing East China.

In recent years, increased species extinction and habitat loss have significantly reduced biodiversity, posing a serious threat to both nature and human survival. Environmental factors strongly influence bird distribution and diversity. The potential distribution patterns and species richness offer a conservation modeling framework for policymakers to assess the effectiveness of natural protected areas (PAs) and optimize their existing ones. Very few such studies have been published that cover a large and complete taxonomic group with fine resolution at regional scale. Here, using birds as a study group, the maximum entropy model (MaxEnt) was used to analyze the pattern of bird species richness in Jiangsu Province. Using an unparalleled amount of occurrence data, we created species distribution models (SDMs) for 312 bird species to explore emerging diversity patterns at a resolution of 1 km 2 . The gradient of species richness is steep, decreasing sharply away from water bodies, particularly in the northern part of Jiangsu Province. The migratory status and feeding habits of birds also significantly influence the spatial distribution of avian species richness. This study reveals that the regions with high potential bird species richness are primarily distributed in three areas: the eastern coastal region, the surrounding area of the lower reaches of the Yangtze River, and the surrounding area of Taihu Lake. Compared with species richness hotspots and existing PAs, we found that the majority of hotspots are well-protected. However, only a small portion of the regions, such as coastal areas of Sheyang County in Yancheng City, as well as some regions along the Yangtze River in Nanjing and Zhenjiang, currently have relatively weak protection. Using stacked SDMs, our study reveals effective insights into diversity patterns, directly informing conservation policies and contributing to macroecological research advancements.