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Please use this identifier to cite or link to this item: http://hdl.handle.net/1834/705

Title: A Simple Non-Parametric Gis Model For Predicting Species Distribution: Endemic Birds In Bioko Island
Authors: Fa, J.E.
Lenton, S.M.
Del Val, J.P.
ASFA Terms: GIS
Biodiversity
Fish species
Issue Date: 2000
Publisher: Springer - Kluwer Acad.
Citation: Biodiversity and Conservation , 9(7), p. 869-885
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 developed a new model, PREDICT, for mapping habitat suitability of plant and animal species from incomplete field survey data. PREDICT is a statistical program written for use within a GIS (geographic information system). 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 a 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 chi-squared 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. The program is corroborated with endemic bird distributions in the island of Bioko, West Africa. Statistical relations between vegetation, rainfall and landscape features of the island and the predicted location of 9 endemic bird taxa are presented. 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.
URI: http://hdl.handle.net/1834/705
Related document: http://dx.doi.org/10.1023/A:1008980910283
ISSN: 0960-3115
Appears in Collections:Miscellaneous
AfReMaS

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