A simple non-parametric GIS model for predicting species distribution: endemic birds in Bioko Island, West Africa

Abstract

Species mapping is a useful conservation tool for predicting patterns of biological diversity, or identifying geographical areas of conservation significance. Mapping can also improve our understanding of the appropriateness of habitat areas for individual species. We outline a computer-based methodology, PREDICT, for the analysis of the habitat requirements of species in a combined GIS-statistical programming environment. The paper details the statistical background to the approach adopted, the program structure and input file information and then applies these techniques to bird data from Bioko Island, West Africa. It produces images and statistics that assess the potential of unstudied areas for wildlife for which presence/absence data and basic habitat information are available. Suitability for target species is determined within surveyed and non-surveyed squares by a form of weights of evidence. The program measures the degree of association between habitat factors and presence/absence of target species by means of χ² tests. The overall suitability weighting of each square, as the sum of all individual habitat factor weightings, is finally displayed in maps depicting areas of highly suitable, suitable, unsuitable and highly unsuitable habitat. Statistical relations between vegetation, rainfall and landscape features on Bioko Island and the location of 9 endemic bird taxa are presented herein. Final confirmation of the accuracy of predictions of the studied bird taxa will ensue from future field observations. However, in a series of misclassification tests of the program, actual distribution detection rate was in excess of 90%. The use of PREDICT can guide investigations of little known species in remote areas and provide a practical solution to identify areas of high rare species diversity in need of conservation.