Modelling wildlife distributions: Logistic multiple regression vs overlap analysis

We compare the results, benefits and disadvantages of two techniques for mo delling wildlife species distribution: Logistic Regression and Overlap Anal ysis. While Logistic Regression uses mathematic equations to correlate vari ables with presence/absence of the species, Overlap Analysis simply combine variables with the presence points, eliminating the non-explanatory variab les and recombining the others. Both techniques were performed in a Geograp hic Information System and we attempted to minimise the spatial autocorrela tion of data. The species used was the Schreiber's green lizard Lacerta sch reiberi and the study area was Portugal, using 10 x 10 km UTM squares. Both techniques identified the same group of variables as the most important fo r explaining the distribution of the species. Both techniques gave high ave rage correct classification rates for the squares with presence of the spec ies (79% for Logistic Regression and 92% for Overlap Analysis). Correct abs ence classification was higher with Logistic Regression (73%) than with Ove rlap Analysis (32%). Overlap Analysis tends to maximise the potential area of occurrence of the species, which induces a reduced correct classificatio n of absences, since many absences will fall in the potential area. This is because a single presence in a given class of a variable makes all the are a of that class to be considered as potential. The technique does not consi der that the species may occasionally occupy an unfavourable region. Althou gh, in Logistic Regression, modelling procedures are more complex and time- consuming, the results are more statistically robust. Moreover, Logistic Re gression has the capability of associating probability of occurrence to the potential area. Overlap Analysis is very simple in building procedures and swift in obtaining reliable potential areas. It is a valid technique espec ially in exploratory analysis of species distributions or in the initial st ages of research when data may be scarce.