DETECTING SIGNALS OF SPECIES' ECOLOGICAL NICHES IN RESULTS OF STUDIES WITH DEFINED SAMPLING PROTOCOLS: EXAMPLE APPLICATION TO PATHOGEN NICHES

Ecological niches are increasingly appreciated as a long-term stable constraint on the geographic and temporal distributions of species, including species involved in disease transmission cycles (pathogens, vectors, hosts). Although considerable research effort has used correlative methodologies for characterizing niches, sampling effort (and the biases that this effort may or may not carry with it) considerations have generally not been incorporated explicitly into ecological niche modeling. In some cases, however, the sampling effort can be characterized explicitly, such as when hosts are tested for pathogens, as well as comparable situations such as when traps are deployed to capture particular species, etc. Here, we present simple methods for testing the hypothesis that non-randomness in occurrence or detection exists with respect to environmental dimensions (= a detectable signal of ecological niche); i.e., whether a pathogen occurs nonrandomly with respect to environment, given the occurrence and sampling of its host. We have implemented a set of R functions that presents an overall test for nonrandom occurrence with respect to a set of enviromnental dimensions, and, a posteriori, a set of exploratory tests that identify in which dimension(s) and in which direction or form the nonrandom occurrence is manifested. Our tools correctly detected signals of niche in most of our example cases. Although such a signal may not be detectable in cases in which the niche of interest is broader than the universe sampled, such a possibility was correctly discarded in our analyses, preventing further interpretations. This kind of testing can constitute an initial step in a process that would conclude with development of a more typical ecological niche model. The particular advantage of the analyses proposed is that they consider the biases involved in sampling, testing, and reporting, in the context of nonrandom occurrence with respect to environment before proceeding to inferential and predictive steps.