A Simple Non-Parametric Gis Model For Predicting Species Distribution: Endemic Birds In Bioko Island
Average rating
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Star rating
Your vote was cast
Thank you for your feedback
Thank you for your feedback
Date
2000
Metadata
Show full item recordAbstract
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.Journal
Biodiversity And ConservationVolume
9Issue/Article Nr
7Publisher or University
Springer - Kluwer Acad.Resource/Dataset Location
http://dx.doi.org/10.1023/A:1008980910283