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The coefficient of variation in the goodness-of-fit estimates, that is, the width of the distribution in the range of log-likelihood values, suggests that environmental heterogeneity has been an important constraint on the distribution of species ranges. There is no consensus on the significance of this conclusion, because different researchers may choose different null models and hence different distributions from which the residuals are drawn. Nevertheless, our results suggest that because of the overall high level of environmental heterogeneity and the relatively low level of functional integration, the distribution of species ranges has been close to lognormal. Finally, our finding of a low level of environmental dissimilarity among the biomes suggests that species ranges have been little influenced by environmental heterogeneity among biomes, a conclusion supported by the lognormal-like fit of ranges to lognormal distributions.
This study has several limitations. First, our choice of null models and methods for assessing goodness-of-fit may influence estimates of the coefficients of variation and the form of the residual distribution. The choice of distribution from which the residuals are drawn may seem arbitrary, but in this case we used a lognormal distribution because it allows differences among the null models to be compared consistently, and because log-likelihood values of lognormal and Gaussian distributions are similar. Second, we have neglected significant horizontal variation in environmental heterogeneity among the biomes, so that any impacts of environmental heterogeneity among the biomes on species ranges are likely overestimated. Given that the significant differences in the mean dissimilarity among the biomes are due to similarities in the climates of the biomes, this may not be a significant limitation. Third, we have used data from a relatively sparse sample of environmental variables, and this may not be relevant to the larger space of environmental variables.
We have applied machine-learning algorithms to the patterns