Abstract:

Aims: In recent years, the impact of human disturbance on waterbird diversity in riverine wetlands has attracted wide attention. The mainstream of the Huaihe River experiences frequent human disturbances, and the diversity of various waterbird assemblages is influenced by multiple environmental factors. Understanding riverine waterbird diversity is critical for optimizing habitat configurations. 

Methods: From October 2023 to March 2024, monthly data were conducted across four habitat types (farmland, flat, woodland, grassland) along the mainstream of the Huaihe River, total of 6 investigations. Waterbird observations and associated habitat data were collected. The spatiotemporal characteristics of waterbird diversity were analyzed from the perspectives of α and β diversity. Canonical correspondence analysis (CCA) and multiple regression models (MRM) were employed to identify key habitat factors influencing waterbird diversity. 

Results: The Shannon-Wiener diversity index was highest in the grassland habitat, while the Pielou and Simpson indices peaked in woodland habitats. All three indices reached their maximum value in October. CCA analysis indicated that flow of people/ships. Distance to roads, topographic wetness index (TWI), normalized difference vegetation index (NDVI), and water area significantly affected waterbird α diversity. MRM results revealed that flow of people/ships, distance to roads, TWI, NDVI, and width were the key habitat factors influencing river waterbird β diversity. Additionally, decomposition of overall β diversity and its components showed that species turnover was the dominant component. 

Conclusion: The study highlights that waterbird community structure varies across different habitat types. It is worth noting that efforts to optimize riverine wetland patterns should be emphasized, and targeted habitat creation and conservation strategies should be implemented based on the needs of species and functional groups.

Key words: community diversity, wintering waterbird, habitat factors, canonical correspondence analysis, multiple regression model, Huai River.