生物多样性 ›› 2018, Vol. 26 ›› Issue (9): 941-950.doi: 10.17520/biods.2018125

• 研究报告 • 上一篇    下一篇

西南喀斯特地貌区两栖动物丰富度分布格局与环境因子的关系

王波1, 4, 黄勇2, 李家堂3, 戴强3, 王跃招3, 杨道德1, *()   

  1. 1 (中南林业科技大学野生动植物保护研究所, 长沙 410004)
    2 (广西中医药大学, 南宁 530200)
    3 (中国科学院成都生物研究所, 成都 610041)
    4 (广西渌金生态科技有限公司, 南宁 530028);
  • 收稿日期:2018-04-22 接受日期:2018-07-05 出版日期:2018-09-20
  • 通讯作者: 杨道德 E-mail:csfuyydd@126.com
  • 作者简介:

    # 共同第一作者

  • 基金项目:
    国家自然科学基金(31472021, 31460559)

Amphibian species richness patterns in karst regions in Southwest China and its environmental associations

Bo Wang1, 4, Yong Huang2, Jiatang Li3, Qiang Dai3, Yuezhao Wang3, Daode Yang1, *()   

  1. 1 Institute of Wildlife Conservation, Central South University of Forestry and Technology, Changsha 410004
    2 Guangxi University of Chinese Medicine, Nanning 530200
    3 Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041
    4 Guangxi Lujin Ecological Technology Company, Nanning 530028
  • Received:2018-04-22 Accepted:2018-07-05 Online:2018-09-20
  • Contact: Yang Daode E-mail:csfuyydd@126.com
  • About author:

    # Co-first authors

物种丰富度分布格局的成因机制一直是宏观生态学研究的热点问题之一。中国西南地区喀斯特地貌区(以广西、云南和贵州为主)是世界上面积最大的喀斯特地貌区, 也是全球范围内34个生物多样性热点地区之一。为了解该区域两栖动物物种丰富度分布格局及其与环境因子之间的关系, 本研究根据中国科学院成都生物研究所标本馆、中国科学院昆明动物研究所标本馆、广西壮族自治区自然博物馆和中南林业科技大学动物标本室收藏的标本数据, 以及公开发表的文献数据, 共获得18,246条两栖动物记录(219个物种), 然后运用生态位模型估测每个物种的潜在分布区, 并把每个物种的潜在分布区叠加起来, 最终得到该区域在10 km ´10 km生态位模型空间尺度上的两栖物种丰富度地理分布格局图, 最后进行多元回归和模型选择分析。结果表明: 有12种两栖动物仅在喀斯特地貌区分布, 占物种总数的5.48%; 有104种两栖动物仅在非喀斯特地貌区分布, 占物种总数的47.49%; 有103种两栖动物在喀斯特地貌区和非喀斯特地貌区均有分布, 占物种总数的47.03%; 两栖动物物种丰富度随纬度的增高而降低; 地貌类型(喀斯特地貌和非喀斯特地貌)对两栖动物物种丰富度的分布格局有显著影响(χ2 = 36.47, P < 0.0001), 但模型拟合效果差(McFadden’s Rho square = 0.0037)。影响该区域两栖动物物种丰富度分布格局最大的环境因子是年均降雨量(R2 = 0.232, P < 0.001), 其次是最干月平均降雨量(R2 = 0.221, P < 0.001)。该区域两栖动物物种丰富度的格局主要是由地貌和不同的环境因子共同相互作用的结果, 不过仍有相当一部分物种丰富度的分布格局未被解释。因此, 要更全面地认识该区域两栖动物物种丰富度格局的形成机制, 有必要加强干扰、捕食、竞争等其他生物因子的影响研究。

关键词: 生物地理学, 两栖动物, 物种多样性, 喀斯特地貌, 生态位模型

Patterns in the distribution of species richness have always been a central theme in macroecology. The karst landforms in Southwest China (mainly Guangxi, Yunnan and Guizhou provinces) are among the largest of the global biodiversity hotspots. In this study, we sought to understand spatial patterns of amphibian species richness and its relationship with environmental factors. We compiled a large dataset of 18,246 records of point location data for 219 amphibian species occurring in China. We retrieved this data from published literature, Herpetology museums of Chengdu Institute of Biology and Kunming Institute of Zoology, Chinese Academy of Sciences, Guangxi Zhuang Autonomous Region Museum of Nature and the Central South University of Forestry and Technology, and published sources. We used this data to generate the potential distributions of each species using ecological niche modeling. We combined the potential distributions maps of all species into a composite map to describe species richness patterns on the grid cell of 10 km × 10 km, and then conducted multivariate regression and model selection. Our results showed that 12 species were distributed only in karst area, accounting for 5.48% of the total species pool, 104 species were found in non-karst area (47.49% of total species), and 103 species were found in both karst area and non-karst area (47.03% of total species). Based on the raw data of museum collections data and MaxEnt species distribution modeling, we found that amphibian species richness in the study area decreased at higher latitudes. Karst landforms and non-karst landforms differed in their distribution patterns of amphibian species richness (χ2 = 36.47, P < 0.0001), but the model was a poor fit to the data (McFadden’s Rho square = 0.0037). The most significant environmental predictors of species richness were mean annual rainfall (R2 = 0.232, P < 0.001) and precipitation of driest Month (R2 = 0.221, P < 0.001). The results based on model selection showed that underlying mechanisms related to landforms and different ecological hypotheses might simultaneously explain patterns of amphibian species richness in the study area. Future research should examine other biological factors such as interference, predation, and competition to understand the mechanisms controlling patterns of amphibian species richness.

Key words: biogeography, amphibia, species diversity, karst landforms, MaxEnt model

表1

4个假说表征的环境变量"

相关假说
Hypothesis
环境变量
Environmental variable
变量缩写
Abbreviation
能量假说
Energy
availability
年均温度 Annual mean temperature (℃) BIO1
平均日温差 Mean diurnal range (℃) BIO2
等温性 Isothermality (℃) BIO3
季节性温度变化 Temperature seasonality BIO4
最高气温
Max temperature of warmest month (℃)
BIO5
最低气温
Min temperature of coldest month (℃)
BIO6
年均温差 Temperature annual range (℃) BIO7
最湿季平均温度
Mean temperature of wettest quarter (℃)
BIO8
最干季平均温度
Mean temperature of driest quarter (℃)
BIO9
最暖季平均温度
Mean temperature of warmest quarter (℃)
BIO10
最冷季平均温度
Mean temperature of coldest quarter (℃)
BIO11
年均日照时数(白昼长的百分比)
Mean annual sunshine (percent of daylength)
SUNP
年均霜日频率
Mean annual frost-day frequency (days)
FF
年均潜在蒸散量
Mean annual potential evapotranspiration (mm/yr)
PET
年均风速 Mean annual wind speed (m/s) WIND
水分假说
Water
availability
年均降雨量
Annual precipitation (mm/yr)
BIO12
最湿月平均降雨量
Precipitation of wettest month (mm/yr)
BIO13
最干月平均降雨量
Precipitation of driest month (mm/yr)
BIO14
季节性降雨量 Precipitation seasonality BIO15
最湿季平均降雨量
Precipitation of wettest quarter (mm/yr)
BIO16
最干季平均降雨量
Precipitation of driest quarter (mm/yr)
BIO17
最暖季平均降雨量
Precipitation of warmest quarter (mm/yr)
BIO18
最冷季平均降雨量
Precipitation of coldest quarter (mm/yr)
BIO19
年均相对湿度
Mean annual relative humidity (%)
REH
生产力假说
Productive energy
归一化植被指数
Normalized difference vegetation index
NDVI
年均实际蒸散量
Mean annual actual evapotranspiration (mm/yr)
AET
年均太阳辐射量
Mean annual solar radiation (W/m2)
RAD
生境异质性
假说
Habitat
heterogeneity
海拔 Elevation ELE
植被类型数
Vegetation (number of vegetation
classes/quadrat)
VEG
地貌类型 Landform LANDF

表2

两栖类物种丰富度和变量的线性回归分析的AICc值、统计值(t)、R2和P值"

环境变量 Enviromental predictor AICc ΔAICc Wi t R2 P
年均降雨量 Annual precipitation (mm/yr) 41,710 0 1 48.465 0.232 < 0.001
最干月平均降雨量 Precipitation of driest month (mm/yr) 41,820 110 0 46.963 0.221 < 0.001
日照时数(白昼长的百分比) Mean annual sunshine (percent of daylength) 43,021 1,311 0 -27.903 0.091 0
年均实际蒸散量 Mean annual actual evapotranspiration (mm/yr) 43,071 1,361 0 26.895 0.085 < 0.001
年均相对湿度 Mean annual relative humidity (%) 43,479 1,769 0 17.021 0.036 0
年均风速 Mean annual wind speed 43,666 1,956 0 -9.861 0.012 < 0.001
年均潜在蒸散量 Mean annual potential evapotranspiration (mm/yr) 43,753 2,043 0 3.129 0.001 0.002
年均温度 Annual mean temperature (℃) 43,568 1,858 0 14.043 0.025 < 0.001
植被类型数 Vegetation (number of vegetation classes/quadrat) 43,758 2,048 0 -2.251 < 0.001 0.024
归一化植被指数 Normalized difference vegetation index 43,761 2,051 0 1.38 < 0.001 0.168
年均太阳辐射量 Mean annual solar radiation (W/m2) 43,761 2,051 0 -1.453 < 0.001 0.146

图1

西南喀斯特两栖动物物种丰富度格局图。A: 基于实际收集的数据; B: 基于MaxEnt模型预测。每个栅格面积为10 km × 10 km, 空白栅格表示无物种分布记录或未收集到数据。"

图2

基于实际收集到的经纬度点数据的两栖类物种丰富度与经度(a)和纬度(b)的关系"

图3

基于MaxEnt模型预测的两栖类物种丰富度与经度(a)和纬度(b)的关系"

图4

两栖类的物种丰富度和环境变量的回归残差的Moran’s I指数图"

表3

基于模型选择方法的每种假说的AICc值和调整R2值"

假说 Hypothesis R2 AICc ΔAICc K Wi
生产力假说
Productivity energy
0.004 6,908 4,268 4 0
生境异质性假说
Habitat heterogeneity
0.005 6,898 4,258 3 0
能量假说 Ambient energy 0.173 5,462 2,822 5 0
水分-能量假说
Water-energy balance hypothesis
0.34 3,706 1,066 4 0
混合模型 Mixed model* 0.425 2,640 0 12 1
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