ABSTRACT
Spatial statisticians are often asked how conventional spatial statistics are relevant for animal ecology. In fact, there is an apparent gap between spatial statistics research and animal ecology research. In many cases, “spatial statistics” conventionally implies that second-order estimation or modeling is employed to characterize dependence in the data or process, perhaps in addition to the first-order estimation. Furthermore, point process models belong in the realm of spatial statistics, even though they are often only considered from a first-order perspective. Point processes appear in many different settings, including geographical and temporal settings, but they can generally arise in any multidimensional real space. The use of spatial statistics in ecological modeling is increasing and new and useful methodological developments are appearing regularly. With increasing need to analyze “big data,” dimension reduction has become popular in the spatial statistics literature recently.
