The importance of topographically corrected null models for analyzing ecological point processes.

Abstract

Analyses of point process patterns and related techniques (e.g., MaxEnt) make use of the expected number of occurrences per unit area and second-order statistics based on the distance between occurrences. Ecologists working with point process data often assume that points exist on a two-dimensional x-y plane or within a three-dimensional volume, when in fact many observed point patterns are generated on a two-dimensional surface existing within three-dimensional space. For many surfaces, however, such as the topography of landscapes, the projection from the surface to the x-y plane preserves neither area nor distance. As such, when these point patterns are implicitly projected to and analyzed in the x-y plane, our expectations of the point pattern's statistical properties may not be met. When used in hypothesis testing, we find that the failure to account for the topography of the generating surface may bias statistical tests that incorrectly identify clustering and, furthermore, may bias coefficients in inhomogeneous point process models that incorporate slope as a covariate. We demonstrate the circumstances under which this bias is significant, and present simple methods that allow point processes to be simulated with corrections for topography. These point patterns can then be used to generate "topographically corrected" null models against which observed point processes can be compared.