Abstract
Conservation biologists need better methods for predicting species diversity. This research investigated some new methods to analyze biodiversity patterns through the use of Geographic Information Systems and remote sensing technologies. We tested the correlation between remotely sensed habitat types and species distributions. The goal was not to do away with ground-based fieldwork, but rather to optimize and focus fieldwork by using GIS and remotely sensed data as tools for making the work more accurate and specific. Our research was conducted at a fine (30 x30 m) landscape scale using on-the ground locations of birds, butterflies, and plants in the northwest portion of the Greater Yellowstone Ecosystem. Three remotely sensed forest types (distinguished by species density and coverage) and six remotely sensed meadow types (ranging from xeric to hydric) were surveyed and coverage data were collected for grasses, shrubs, forbs and trees. Presence/absence data were collected for birds and butterflies. The objectives of this research were: 1) to determine the extent of the correlation between spectral reflectance patterns and plant or animal species distribution patterns, and 2) to test the spatial correspondence of species diversity "hotspots" among taxonomic groups. Field surveys in 1993 and 1994 validated the vegetation density, cover, and moisture gradients expected from satellite data interpretation. Both tree species composition and diameter at breast height were significant in discriminating among forest types. Twenty-two species of grasses and forbs were significant in distinguishing among meadow types. However, a smaller percentage of the animal species was significantly correlated with one habitat type. In order to find a strong correlation between species distribution patterns and remotely sensed data, a species must be moderately common and show some habitat specificity. Hotspots of species diversity coincided for shrubs, grasses, forbs, birds, and butterflies and were found in mesic meadows.