Bird community diversity patterns and their drivers in the Qinghai region of Kunlun Mountains

doi:10.17520/biods.2023406

Abstract

Abstract:

Aims: Kunlun Mountains is a crucial part of Qinghai-Xizang Plateau that presents a challenging environment for wildlife surveys and community research due to its high-elevation, cold, and hypoxic environment. This study aims to reveal patterns of avian community diversity across different regions, and their drivers.

Method: Surveys were conducted on wild birds within the Qinghai region of the Kunlun Mountains in the December 2021 and July 2022 using the line transects method. A total of 102 grids, each measuring 10 km × 10 km, with different altitudinal gradients and habitat types were investigated. Bird community diversity across different regions was calculated using R software. Subsequently, environmental variables were extracted using ArcGIS software, and a generalized additive model (GAM) was employed to fit the potential non-linear relationships between diversity indices and various variables.

Results: A total of 87 bird species representing 14 orders, 31 families, and 59 genera were recorded. Among these, 5 species were classified as first-class national protected wildlife, 11 species were classified as second-class national protected wildlife, and 8 species were identified as local new distributions. The analysis of distribution patterns demonstrated higher avian community diversity during summer compared to winter, with grassland and meadow habitats showing significantly greater diversity than other habitats. Furthermore, bird community diversity during both winter and summer exhibited similar single peak patterns along the altitudinal gradient. Climate variables revealed seasonal differences in bird diversity, with precipitation of coldest quarter significantly impacting richness during summer and annual precipitation during winter. Additionally, isothermality and temperature seasonality had the most substantial impact on the Shannon-Wiener and Simpson diversity indices in both winter and summer. These findings underscored the significance of water resources and stable climatic conditions on bird community diversity in the Qinghai region of the Kunlun Mountains.

Conclusion: This study provides a scientific foundation for the conservation of the bird resources in the Kunlun Mountains, and offers crucial data for the planning and establishment of national park in future.

Key words: Kunlun Mountains, bird community diversity, elevation gradient

Bin Li, Pengfei Song, Haifeng Gu, Bo Xu, Daoxin Liu, Feng Jiang, Chengbo Liang, Meng Zhang, Hongmei Gao, Zhenyuan Cai, Tongzuo Zhang. Bird community diversity patterns and their drivers in the Qinghai region of Kunlun Mountains[J]. Biodiv Sci, 2024, 32(4): 23406.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2023406

https://www.biodiversity-science.net/EN/Y2024/V32/I4/23406

Figures/Tables 6

Fig. 1 Survey grids of bird community diversity in the Qinghai region of Kunlun Mountains. Summer grids were surveyed exclusively during the summer season. Winter grids were surveyed exclusively during the winter season. Both season grids were surveyed during both the winter and summer.

Table 1 Proportion of surveyed to effective grid across vegetation types at different elevation gradients in the Qinghai region of Kunlun Mountains (%)

海拔梯度 Elevation gradient	草甸 Meadow	草原 Grassland	高山植被 Alpine	荒漠 Desert	其他植被 Other	总计 Total
3,000-3,500 m	0	50.0	0	27.8	11.5	19.6
3,500-4,000 m	16.7	31.3	0	21.2	7.7	22.3
4,000-4,500 m	19.6	30.3	0	22.6	11.8	24.0
4,500-5,000 m	3.8	13.6	20.0	0	10.5	8.7
> 5,000 m	0	0	0	0	8.3	4.5
总计 Total	10.5	24.5	11.8	61.7	10.3	17.7

Fig. 2 Species accumulation curves of bird surveys in the Qinghai region of Kunlun Mountains

Fig. 3 Bird community diversity distribution pattern of different season (a-c), different elevation (d-f), and different habitat (g-i) in the Qinghai region of Kunlun Mountains. The significance of intergroup differences was assessed using the Wilcoxon rank-sum test. * P < 0.05; ** P < 0.01; *** P < 0.001; ns, P > 0.05.

Fig. 4 The importance of variables for the bird community diversity across seasons was assessed using GAM models based on adjusted R² (coefficient of determination). bio1, Annual mean temperature; bio2, Mean diurnal range; bio3, Isothermality; bio4, Temperature seasonality; bio5, Max temperature of warmest month; bio6, Min temperature of coldest month; bio7, Temperature annual range; bio8, Mean temperature of wettest quarter; bio9, Mean temperature of driest quarter; bio10, Mean temperature of warmest quarter; bio11, Mean temperature of coldest quarter; bio12, Annual precipitation; bio13, Precipitation of wettest month; bio14, Precipitation of driest month; bio15, Precipitation seasonality; bio16, Precipitation of wettest quarter; bio17, Precipitation of driest quarter; bio18, Precipitation of warmest quarter; bio19, Precipitation of coldest quarter; precs, Average precipitation of summer; precw, Average precipitation of winter; tavgs, Average temperature of summer; tavgw, Average temperature of winter; GDP, Gross domestic product; pop, Population density.

Fig. 5 The nonlinear impact of environmental factors with the highest explanatory power on bird community diversity in the Qinghai region of Kunlun Mountains across different seasons. (a-c): Summer bird community diversity; (d-f): Winter bird community diversity. P values represent the significance test results for each predictor variable.

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