淮河干流越冬水鸟多样性的时空格局及其影响因素

doi:10.17520/biods.2024590

生物多样性

淮河干流越冬水鸟多样性的时空格局及其影响因素

李玉^1,2, 周立志^1,2*

1. 安徽大学资源与环境工程学院, 合肥 230601; 2. 湿地生态保护与修复安徽省重点实验室(安徽大学), 合肥 230601

收稿日期:2024-12-31 修回日期:2025-04-01 接受日期:2025-09-15
通讯作者: 周立志

Spatiotemporal patterns of wintering waterbird diversity in the mainstream of Huaihe River and their influencing factors

Yu Li^1,2, Lizhi Zhou^1,2*

1 School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China

2 Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China

Received:2024-12-31 Revised:2025-04-01 Accepted:2025-09-15
Contact: Lizhi Zhou

摘要/Abstract

摘要： 近年来, 人为扰动对河流湿地水鸟多样性的影响受到普遍关注。淮河干流人为扰动较为频繁, 各类群水鸟多样性受多方面环境因子影响, 河道水鸟多样性的研究对于优化生境格局具有重要意义。本研究于2023年10月至2024年3月逐月调查了淮河干流4类生境(农田、泥滩、林地、草滩)的水鸟及生境因子数据, 并从α和β多样性维度, 分析了河道水鸟的时空特征, 并进一步采用典范对应分析(canonical correspondence analysis, CCA)和多重回归模型(multiple regression models, MRM), 分析影响水鸟多样性的关键生境因子。结果表明, Shannon-Wiener多样性指数在草滩生境中最高, Simpson优势度指数和Pielou均匀度指数在林地生境中最高, Shannon-Wiener多样性指数、Simpson优势度指数和Pielou均匀度指数均在10月达到最高。CCA分析结果表明, 人/船流量、距道路的距离、地形湿度指数(topographic wetness index, TWI)、归一化植被指数(normalized difference vegetation index, NDVI)和水域比例对水鸟α多样性影响显著。MRM分析结果显示, 人/船流量、距道路的距离、地形湿度指数、归一化植被指数、河道宽度是影响河道水鸟β多样性的关键生境因子。同时, 总体β多样性及其组分计算结果显示物种周转组分占明显优势。研究表明, 不同生境类型下的河道水鸟群落结构存在差异, 应注重河流湿地格局的优化, 针对目标物种及类群开展水鸟生境营造与保护工作。

关键词: 群落多样性, 越冬水鸟, 生境因子, 典范对应分析, 多重回归模型, 淮河干流

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.

李玉, 周立志. 淮河干流越冬水鸟多样性的时空格局及其影响因素. 生物多样性, DOI: 10.17520/biods.2024590.

Yu Li, Lizhi Zhou. Spatiotemporal patterns of wintering waterbird diversity in the mainstream of Huaihe River and their influencing factors. Biodiversity Science, DOI: 10.17520/biods.2024590.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2024590

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