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We investigated whether accounting for land cover could improve bioclimatic models for eight species of anurans and three species of turtles at a regional scale. We then tested whether accounting for spatial autocorrelation could significantly improve bioclimatic models after statistically controlling for the effects of land cover. Nova Scotia, eastern Canada. Species distribution data were taken from a recent (1999-2003) herpetofaunal atlas. Generalized linear models were used to relate the presence or absence of each species to climate and land-cover variables at a 10-km resolution. We then accounted for spatial autocorrelation using an autocovariate or third-order trend surface of the geographical coordinates of each grid square. Finally, variance partitioning was used to explore the independent and joint contributions of climate, land cover and spatial autocorrelation. The inclusion of land cover significantly increased the explanatory power of bioclimatic models for 10 of the 11 species. Furthermore, including land cover significantly increased predictive performance for eight of the 11 species. Accounting for spatial autocorrelation improved model fit for rare species but generally did not improve prediction success. Variance partitioning demonstrated that this lack of improvement was a result of the high correlation between climate and trend-surface variables. The results of this study suggest that accounting for the effects of land cover can significantly improve the explanatory and predictive power of bioclimatic models for anurans and turtles at a regional scale. We argue that the integration of climate and land-cover data is likely to produce more accurate spatial predictions of contemporary herpetofaunal diversity. However, the use of land-cover simulations in climate-induced range-shift projections introduces additional uncertainty into the predictions of bioclimatic models. Further research is therefore needed to determine whether accounting for the effects of land cover in range-shift projections is merited.
