Biodiversity Science ›› 2018, Vol. 26 ›› Issue (9): 941-950.doi: 10.17520/biods.2018125

• Original Papers • Previous Article     Next Article

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:2019-01-05
  • Yang Daode
  • About author:

    # Co-first authors

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

Table 1

Environmental variables represented by the four hypotheses"

Environmental variable
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
Max temperature of warmest month (℃)
Min temperature of coldest month (℃)
年均温差 Temperature annual range (℃) BIO7
Mean temperature of wettest quarter (℃)
Mean temperature of driest quarter (℃)
Mean temperature of warmest quarter (℃)
Mean temperature of coldest quarter (℃)
Mean annual sunshine (percent of daylength)
Mean annual frost-day frequency (days)
Mean annual potential evapotranspiration (mm/yr)
年均风速 Mean annual wind speed (m/s) WIND
Annual precipitation (mm/yr)
Precipitation of wettest month (mm/yr)
Precipitation of driest month (mm/yr)
季节性降雨量 Precipitation seasonality BIO15
Precipitation of wettest quarter (mm/yr)
Precipitation of driest quarter (mm/yr)
Precipitation of warmest quarter (mm/yr)
Precipitation of coldest quarter (mm/yr)
Mean annual relative humidity (%)
Productive energy
Normalized difference vegetation index
Mean annual actual evapotranspiration (mm/yr)
Mean annual solar radiation (W/m2)
海拔 Elevation ELE
Vegetation (number of vegetation
地貌类型 Landform LANDF

Table 2

Standardized regression coefficients of linear regression, t statistics, R2 and associated P-values for amphibian species richness against environmental predictors"

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

Fig. 1

Species distribution modeling of amphibians in Southwest China, based on (A) the raw data of museum collections data and (B) MaxEnt species distribution modeling. The area of each cell is 10 km × 10 km. Blank cells have no specimen based on the major collections."

Fig. 2

The relationships between amphibian richness and longitude (a) and latitude (b) based on the raw data of museum collections"

Fig. 3

The relationships between amphibian richness and longitude (a) and latitude (b) based on MaxEnt species distribution modeling"

Fig. 4

Moran’s I index correlogram for amphibian species richness and the residuals of multiple regression with environmental predictors"

Table 3

AICc value and adjusted R2 value for each hypothesis based on model selection approach"

假说 Hypothesis R2 AICc ΔAICc K Wi
Productivity energy
0.004 6,908 4,268 4 0
Habitat heterogeneity
0.005 6,898 4,258 3 0
能量假说 Ambient energy 0.173 5,462 2,822 5 0
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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