Abstract
1. Elevational range size patterns reflect ecological and evolutionary processes, but they are also affected by geometric constraints. The confounding effect of these constraints led to an ongoing controversy about the elevational Rapoport's rule, which postulates a positive association between the range size and elevation, and about the plausibility of the climate variability hypotheses as its causal explanation.
2. Here we used an advanced null modelling approach to disentangle the interacting effects of geometric constraints and species richness gradients on the elevational range size of vascular plants. We collected extensive field data on elevational distribution for 728 vascular plant species occurring in the Ladakh region, Western Himalaya. We supplied these regional data with subcontinental elevational ranges extracted from the literature. Moreover, we used in situ measured temperatures to quantify temperature variability along an elevational gradient to test the climate variability hypothesis.
3. Observed range size patterns were sensitive to methods used to quantify the average range size. Range truncation disproportionately affected regional ranges of low-elevation species and resulted in spurious support of elevational Rapoport's rule. However, when the confounding effects of domain boundaries and richness gradient were controlled, our null models revealed only slight deviations from the random expectations of elevational range size patterns, contrasting with the prediction of the Rapoport's rule. In line with these findings, seasonal and diurnal temperature variability did not change with elevation.
4. Synthesis. Geometric constraints combined with underlying species richness gradient create range size patterns seemingly supporting Rapoport's elevational rule. However, null models accounting for these effects indicate that the range size of vascular plants in the Himalayas does not increase with elevation. Given the universality of the geometric constraints and species richness gradient, our results suggest that these confounding factors must be controlled when testing Rapoport's rule. The null model approach described here provides an efficient tool to do that.