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Track: Natural Resources and Conservation
Bennett Sandler
Stanford University
Department of Biological Sciences
Stanford, CA 94305-5020
Telephone: 415-725-5585
Fax: 415-723-5920
E-mail: bennett@bing.stanford.edu
Peter Pearman, Mauricio Guerrero, Karen Levy
Using GIS To Assess the Spatial Scale of Taxonomic Richness in Amazonian Ecuador
Defining Issue: Multitaxonomic sampling is a common tool for quantifying effects of disturbance on biological diversity. Results and subsequent management implications are significantly affected by the spatial configuration of the sampling design employed. To assess disturbance effects, sampling should control for variability inherent in species counts as well as variation due to natural habitat gradients. GIS Solution: The Center for Conservation Biology at Stanford University has developed a methodological approach using GIS to determine a scale-sensitive sampling scheme for measuring changes in taxonomic diversity along disturbance gradients. Sample data were collected at Jatun Sacha Biological Preserve in Napo Province, Ecuador, where primary tropical forest conversion is advancing at an alarming rate due to human colonization. A GIS was used to extract a variety of landscape variables, including "embeddedness" in primary forest, land cover diversity, elevation, and solar insolation, at increasing
distances from sample sites, to test for predictive value of species richness. Results from this research offer explicit spatial decision rules for designing monitoring studies to test predicted effects of human disturbance on taxonomic diversity. Software: The ARC GRID module in ArcInfo was used to extract a series of landscape metrics at varying distances from sample site centroids where intensive species surveys were conducted. In addition, ArcInfo was used to generate a digital elevation model from which many of the landscape variables were derived. PCI image processing software was used to classify land cover from Landsat Thematic Mapper data. Pfinder software was used to differentially correct GPS data. SAS and JMP were used for statistical tests.
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