南岭哺乳类和鸟类物种丰富度空间分布格局及其影响因子

doi:10.17520/biods.2023026

生物多样性 ›› 2024, Vol. 32 ›› Issue (1): 23026. DOI: 10.17520/biods.2023026

• 研究报告: 动物多样性 • 下一篇

南岭哺乳类和鸟类物种丰富度空间分布格局及其影响因子

王丽媛¹^,², 胡慧建¹, 姜杰³, 胡一鸣¹^,^*()()

1.广东省科学院动物研究所, 广东省动物保护与资源利用重点实验室, 广东省野生动物保护与利用公共实验室, 广州 510260
2.中山大学生态学院, 广东深圳 518107
3.广东省林业调查规划院, 广州 510520

收稿日期:2023-08-13 接受日期:2023-12-18 出版日期:2024-01-20 发布日期:2024-01-17
通讯作者: *E-mail: huyiming@giz.gd.cn
基金资助:
国家自然科学基金(31901109);广东省2021年度自然资源事务管理—生态林业建设专项资金(2021GJGY001);中国博士后科学基金(2021M700891);广东省科学院科技发展专项(2022GDASZH-2022010105)

Species richness patterns of mammals and birds and their drivers in the Nanling Mountain Range

Liyuan Wang¹^,², Huijian Hu¹, Jie Jiang³, Yiming Hu¹^,^*()()

1 Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Guangdong Public Laboratory of Wild Animal Conservation and Utilization, Institute of Zoology, Guangdong Academy of Sciences, Guangzhou 510260
2 School of Ecology, Sun Yat-sen University, Shenzhen, Guangdong 518107
3 Guangdong Forestry Survey and Planning Institute, Guangzhou 510520

Received:2023-08-13 Accepted:2023-12-18 Online:2024-01-20 Published:2024-01-17
Contact: *E-mail: huyiming@giz.gd.cn

1. 附录.pdf(884KB)

摘要/Abstract

摘要：

作为华南地区的最大山脉和重要自然地理界线, 南岭是物种丰富度研究的热点区域之一。但是已有的物种丰富度相关研究集中在局部区域和单个生物类群, 缺乏对于整个南岭区域哺乳类和鸟类物种丰富度空间分布格局及其影响因子的研究。本研究利用南岭区域123种哺乳类和524种鸟类的地理分布数据, 构建了整个南岭山脉的哺乳类和鸟类的物种丰富度空间分布格局。此外, 我们利用路径分析和空间误差模型, 探讨多种环境因子(气候、生产力、人类活动、生境异质性和海拔)对南岭哺乳类和鸟类物种丰富度空间分布格局的影响。南岭地区哺乳类和鸟类物种丰富度空间分布格局差异较大: 哺乳类物种丰富度热点区域在南岭地区的西南部山地, 丰富度空间分布格局总体自西向东递减; 鸟类物种丰富度热点区域在南岭地区的东南部低地, 丰富度空间分布格局总体自东南向西北递减。路径分析和空间误差模型显示, 温度相关因子对于南岭哺乳类和鸟类物种丰富度空间分布格局影响最大。在温度相关的两个因子中, 年均温与哺乳类物种丰富度呈负相关, 与鸟类却呈正相关; 气温年较差与哺乳类呈正相关, 与鸟类却呈负相关。年均温和气温年较差对哺乳类和鸟类影响的差异可能是由哺乳类和鸟类在生理适应性和行为策略上的差异所导致。

关键词: 南岭, 哺乳类, 鸟类, 物种丰富度, 分布格局, 影响因子

Abstract

Aims: The Nanling Mountains are a prominent mountain range serving as a natural geographical boundary in southern China and are recognized as one of the world’s biodiversity hotspots. However, past studies on species richness in the Nanling Mountains have primarily focused on specific taxa and at localized scales. This has led to a dearth of research concerning the comprehensive spatial patterns of fauna across the entire Nanling region. The objective of this study is to identify the large-scale patterns and drivers of mammalian and avian species richness and offer insights to support the conservation of Nanling’s biodiversity.

Methods: Using geographical data for 123 mammal species and 524 bird species, we investigated the distribution of species richness within the Nanling Mountains. Additionally, we employed path analysis (PA) and spatial error models to disentangle the impacts of various predictors (climate, productivity, human activity, habitat heterogeneity, and elevation) on species richness.

Results: The hotspot of mammal species richness was in the southwestern part of the Nanling region, with a decline in richness observed from west to east. Avian species richness was highest in the southeast of Nanling, gradually decreasing from southeast to northwest. The path analysis and spatial error models demonstrated that temperature- related factors exerted the most significant influence on the spatial distribution patterns of species richness for both mammalian and avian species. Two temperature-related factors had contrasting effects on species richness. The average annual temperature had a negative impact on mammals but a positive impact on birds; however, the annual range of temperature had a positive impact on mammals but a negative impact on birds.

Conclusions: Our findings highlight the contrasting spatial distribution patterns of mammalian and avian species richness in the Nanling Mountains. The differential impact of average annual temperature and the annual range of temperature on birds and mammals may arise from physiological adaptability and behavioral strategies.

Key words: Nanling, mammal, bird, species richness, distribution pattern, impact factor

中图分类号:

Q958

王丽媛, 胡慧建, 姜杰, 胡一鸣 (2024) 南岭哺乳类和鸟类物种丰富度空间分布格局及其影响因子. 生物多样性, 32, 23026. DOI: 10.17520/biods.2023026.

Liyuan Wang, Huijian Hu, Jie Jiang, Yiming Hu (2024) Species richness patterns of mammals and birds and their drivers in the Nanling Mountain Range. Biodiversity Science, 32, 23026. DOI: 10.17520/biods.2023026.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2023026

https://www.biodiversity-science.net/CN/Y2024/V32/I1/23026

图/表 5

图1 南岭山脉及其周边区域地形图

Fig. 1 Generalized topographic map of the Nanling Mountains and surrounding area

图2 南岭地区物种丰富度空间分布格局(GCS WGS 1984坐标系投影)。a: 哺乳类; b: 鸟类。

Fig. 2 Spatial distribution patterns of species richness in the Nanling Mountain Range (projected with GCS WGS 1984 coordinate system). a, Mammal; b, Bird.

图3 各变量(年均温、气温年较差、年均降水量、人类足迹指数、海拔、生境异质性和归一化植被指数)对南岭地区物种丰富度空间分布格局的直接和间接效应路径图。a: 哺乳类; GFI = 0.987; CFI = 0.978; IFI = 0.978。b: 鸟类; GFI = 0.997; CFI = 0.989; IFI = 0.989。红色和黑色箭头分别表示正效应和负效应; 箭头边的数字表示标准化系数, 且所有系数的P值均小于0.05; 图中标明了三个模型集的R2。

Fig. 3 The path diagram of direct and indirect effects of variables (average annual temperature, annual range of temperature, average annual precipitation, human footprint index, elevation, habitat heterogeneity, normalized difference vegetation index) on the spatial distribution pattern of species richness in the Nanling region. a, Mammal; GFI = 0.987; CFI = 0.978; IFI = 0.978. b, Bird; GFI = 0.997; CFI = 0.989; IFI = 0.989. Red and black arrows indicate positive and negative effects respectively; numbers along the arrows represent standardized coefficients and the P-values of all coefficients were less than 0.05; R2 of the three model sets is given in the figure.

表1 基于路径分析的哺乳类和鸟类物种丰富度空间分布格局影响因子的结果

Table 1 The results of factors influencing the spatial distribution pattern of mammal and bird species richness based on path analysis

类群 Group	变量 Variables	总影响 Total effect	直接影响 Direct effect	间接影响 Indirect effect
哺乳类 Mammal	年均温 Average annual temperature	-0.537	-0.424	-0.113
	气温年较差 Annual range of temperature	0.344	0.361	-0.017
	年均降水量 Average annual precipitation	0.279	0.248	0.031
	归一化植被指数 Normalized difference vegetation index	-0.306	-0.306	0
	人类足迹指数 Human footprint index	0.056	0.017	0.039
	生境异质性 Habitat heterogeneity	-0.124	-0.124	0
	海拔 Elevation	-0.157	-0.208	0.051
鸟类 Bird	年均温 Average annual temperature	0.525	0.502	0.023
	气温年较差 Annual range of temperature	-0.597	-0.615	0.018
	年均降水量 Average annual precipitation	0.089	0.094	-0.005
	归一化植被指数 Normalized difference vegetation index	-0.030	-0.030	0
	人类足迹指数 Human footprint index	0.031	0.019	0.012
	生境异质性 Habitat heterogeneity	0.047	0.047	0
	海拔 Elevation	0.209	0.201	0.008

表2 基于空间误差模型的哺乳类和鸟类物种丰富度空间分布格局影响因子的结果

Table 2 The results of factors influencing the spatial distribution pattern of mammal and bird species richness based on spatial error model

类群 Group	变量 Variables	空间误差模型 Spatial error model
类群 Group	变量 Variables	标准估计值 Standard estimate (β)	标准误差 Standard error	z检验概率值 Pr(>\|z\|)
哺乳类 Mammal	年均温 Average annual temperature	-0.096	0.009	< 0.01
	气温年较差 Annual range of temperature	0.138	0.009	< 0.01
	年均降水量 Average annual precipitation	-0.039	0.010	< 0.01
	归一化植被指数 Normalized difference vegetation index	-0.021	0.003	< 0.01
	人类足迹指数 Human footprint index	0.000	0.003	0.92
	生境异质性 Habitat heterogeneity	-0.045	0.004	< 0.01
	海拔 Elevation	-0.013	0.006	0.03
鸟类 Bird	年均温 Average annual temperature	0.398	0.008	< 0.01
	气温年较差 Annual range of temperature	-0.454	0.009	< 0.01
	年均降水量 Average annual precipitation	0.235	0.009	< 0.01
	归一化植被指数 Normalized difference vegetation index	-0.001	0.003	0.68
	人类足迹指数 Human footprint index	0.006	0.003	0.03
	生境异质性 Habitat heterogeneity	0.014	0.004	< 0.01
	海拔 Elevation	0.037	0.006	< 0.01

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南岭哺乳类和鸟类物种丰富度空间分布格局及其影响因子

Species richness patterns of mammals and birds and their drivers in the Nanling Mountain Range

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