Abstract
Maps of potential habitat distribution are needed for regional population models of rare species, but reliable information from ground surveys is not always available. Existing data sources from disciplines other than ecological research often are underused. In this article, we discuss the development of a geographic information system (GIS) model that predicts potential habitats from ecosystem information contained in the US soil classification and soil survey. Soil classification and survey were used in the GIS model in an earlier study on the US Department of Energy's Oak Ridge Reservation, Tennessee, to predict threatened calcareous habitats. The model predicts potential habitats from the combination of (a) soil taxon as an indication of long-term ecosystem processes; (b) geologic parent material; and (c) slope class. Satellite imagery was added to indicate current successional state. In this study, we tested the model's predictive ability by using data from the Cedar Creek Slope Glades Preserve at the 44,000-ha US Department of Defense Fort Knox Military Reservation, Kentucky. We then used the model to predict occurrences of potential suitable habitat on the remainder of the Fort Knox reservation, including heavily impacted ordnance and tank training areas that are unsafe for public access. The soil component of the model also was applied to a 1.2 × 106–km2 region of the US, by using the US Department of Agriculture–National Resources Conservation Service (USDA-NRCS) State Soil Geographic Database (STATSGO) combined with official soil series descriptions. Soil taxa from the USDA-NRCS Soil Taxonomy were demonstrated to be associated with threatened calcareous habitats of rare plant species. These soil taxa were lithic mollisols (rendolls and udolls; Food and Agriculture Organization of the United Nations (FAO) rendzinas and chernozems) and alfisols (udalfs; FAO luvisols). The combined soil/geology/slope GIS approach has potential for prediction of rare ecosystems with narrow edaphic constraints. The approach would be useful in long-term planning for conservation management and restoration, especially where intensive ground surveys are expensive and/or impractical and where disturbance history obscures patterns of historical distribution.
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