The genetic algorithm for rule-set prediction (GARP) has beensuccessfully used in modeling species' distributions with environmental data forwell-studied birds in the United States and elsewhere. GARP's efficiency hasbeen demonstrated to be robust even with incomplete occurrence and geographicdata. Thorough biological sampling in conjunction with comprehensive geographicinformation, however, is not the norm for many tropical areas where mostbiodiversity occurs. Mammals from Guyana were used as a test of the robustnessof these approaches in a worst-case scenario of uneven sampling combined withcoarse geographic data. The occurrence of species in poorly surveyed regions,such as the Pakaraima Highlands of west-central Guyana, was consistentlyunder-predicted, whereas presence in well-surveyed areas such as thesouthwestern Rupununi was usually correctly predicted. Comparisons of numbersof species and specimens collected also indicate that lowland forests in thesoutheast and coastal forests in the northwest are under-sampled. For robustdistributional predictions in Guyana, more thorough inventories are needed inthese diverse environments.
Biodiversity and Conservation – Springer Journals
Published: Oct 10, 2004
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