Amphibian species richness generally increases towards the equator, with high concentrations in tropical moist forests, particularly the Amazon Basin (). Other regions of high richness include the Congo Basin and Southeast Asia. While these patterns broadly correspond to those of birds (Orme et al. 2005
) and mammals (Ceballos et al. 2005
), amphibians are unique in being species rich in the eastern United States. Amphibian richness is substantially higher in the eastern US than in Europe. In contrast, much of Asia and North America is depauperate of amphibian species.
Global amphibian species richness. Richness was compiled within equal area quadrats equivalent to 0.5° in size by overlaying species' distribution maps (Global Amphibian Assessment).
Examining amphibian richness patterns in relation to single environmental variables reveals triangular or scattered patterns (). The most notable triangular relationship is that for temperature. The 10, 50 and 90% quantile regression slopes for the relationship between log(mean annual temperature) (K) and log(SR) are 5.22±0.37, 17.55±0.37 and 23.18±0.29, respectively. This suggests that a given level of an environmental parameter is necessary, but not sufficient, for achieving a given level of species richness. Multiple factors appear to work in concert to constrain species richness for amphibians, more so than for mammals and birds (Currie 1991
; Jetz & Rahbek 2002
). Temperature alone is a relatively weak predictor of species richness (r2
=0.28). Notably, the observed triangular relationship contrasts the linear relationship predicted by metabolic theory which was used to predict gradients of amphibian richness under the untested assumption of uniform abundance (Allen et al. 2002
). However, the slope of the 90% quantile regression for the relationship between the inverse of temperature (1000
) and the natural log of species richness (6.53±0.06) is remotely in the vicinity of that predicted (predicted slope=9.0; F[1,40312]
Figure 2 Amphibian species richness is constrained by multiple environmental variables. Bivariate plots of environmental effects on richness across 40315 equal area quadrats equivalent to 0.5° size covering the world except islands showing 10, (more ...)
As expected, given the overall importance of water for this taxon, annual precipitation has a strong positive effect on amphibian richness (r2
=0.57, slope 0.81±0.01 in log–log space). Energy supply, as indicated by NPPmin
or AET, encapsulates both temperature and precipitation and is the strongest single predictor of amphibian richness. NPPmin
explains a considerable 76 and 86% of the variation in the non-spatial and spatial model, respectively (). The strong influence of NPPmin
is indicated by its overall accelerating relationship with amphibian richness (slope 1.13±0.01 in log–log space). In combination, the balance of water and temperature has slightly more explanatory power than single energy variables with an r2
of 0.77 in the general linear model without interactions (). Model fit is consistent between the spatial and non-spatial models. Including a second-order term substantially improved the explanatory power of most environmental variables. The analysis confirms for the broad spatial scale what has been extensively documented at smaller scales (Lips et al. 2003
)—the crucial importance of water availability.
Table 1 Environmental and historical models for global amphibian richness patterns. (The potential of the following variables to account for global amphibian richness using general linear models (GLM, n=40315) and spatially autoregressive linear models (more ...)
We next consider the potential influence of the history of speciation and extinction on the regional species pool. The number of species available to colonize an area can interact with climatic constraints on establishment to determine richness patterns (Qian & Ricklefs 2000
; Ricklefs 2004
). We use biogeographic realm as an indicator of the importance of history. While realm alone is a relatively weak predictor of amphibian species richness () that furthermore is collinear with environmental variables, it adds predictive power to already strong core environmental predictors (6% additional variance explained in case of the combined temperature–precipitation models; ). This demonstrates that the environment and the regional history constrain amphibian richness in conjunction. First, the model fit is improved in all cases when biogeographic realm is considered along with the environmental variables. Second, accounting for region strikingly disentangles the triangular relationships observed at the global scale, particularly for annual temperature. Annual temperature accounts for less than 10% of the variation in amphibian richness in all realms except the Nearctic and Neotropics. When considering these regions independently, temperature accounts for more variability (r2
=0.75 and 0.67 for the Nearctic and Neotropics, respectively) and the quantile regression slopes are much more consistent and close to the median slope throughout all parts of the data than when considering realms together. For the Nearctic, the 10, 50 and 90% quantile regression slopes for the relationship between log(Annual Temperature) and log(SR) are 21.0±1.00, 27.4±0.88 and 32.9±1.18, respectively. For the Neotropics, the corresponding slopes are 46.5±3.25, 45.3±1.97 and 35.4±3.29, respectively.
We use minimum net primary productivity, the strongest single predictor variable, to illustrate how accounting for history can reveal regional differences in the richness relationship (). The slopes of the relationships between energy availability and richness are broadly similar (ranging from 0.73 to 1.51 in log–log space), but the intercepts differ when considering regions independently (see for intercept values). These findings confirm and strengthen the asserted importance of contemporary environmental conditions. More broadly, they illustrate how a typical triangular or scattered macroecological relationship can be strengthened by going beyond contemporary drivers and accounting for historical contingencies.
Figure 3 Regional species pools influence environmental constraints on amphibian richness. The relationships between three-month mean minimum net primary productivity (NPPmin) and richness is relatively consistent between the six biogeographic realms (mean(95% (more ...)
When accounting for biogeographic realm, the balance of temperature and precipitation is the best overall predictor, accounting for 88% of the variation in global amphibian richness (). Minimum NPP and mean annual NPP are the next best predictors when incorporating realm. Controlling for realm consistently improves the model fit, indicating that, for a given value of an environmental variable, richness is consistently greater in some regions. Including the interaction between the environmental variables and realm improved the model fit, suggesting that, to a small degree at least, the shapes of the relationships between the environment and history vary between regions (). However, the relative importance of environmental constraints is consistent across realms despite inter-realm differences in species pools. The water–temperature balance is the primary determinant of richness patterns for all realms except the Palearctic, where energy (NPPmin) is a better predictor (by AIC value). Energy and the temperature–water balance are the top two predictors in all realms excepting the Nearctic, where temperature is an important factor.
The best fitting model for amphibian species richness is consistent, but the scatter increases, at coarser grid sizes. Accounting for biogeographic realm, the balance of temperature and precipitation is a better predictor than AET or minimum NPP
at grain sizes of 2 and 4° (, see tables 3 and 4 of electronic supplementary material). The balance of temperature and precipitation is also the best model when accounting for spatial autocorrelation. The models' increasing predictive power with finer spatial resolution is unexpected for multi-scale assessments of biodiversity patterns (Rahbek & Graves 2001
), but supports amphibian's close tracking of environmental conditions. The consistency of determinants towards coarser grid sizes corroborates their importance irrespective of potential range map interpolations that may compromise high resolution analyses.