The ability to remotely identify habitats with high biodiversity is becoming increasingly useful in directing management and conservation efforts by natural resource planners and governmental agencies. However, methods at the watershed scale have not been developed since watersheds span large land areas, encompass a connected range of stream sizes, and integrate natural and altered properties of a drainage area. Methods are needed to identify the locations of high biodiversity in watersheds, compare aquatic biodiversity distributions among regions, and provide watershed-scale information useful for targeting conservation measures. The United States Geological Survey (USGS) in cooperation with other Federal and State agencies developed a geographic information system (GIS) methodology called gap analysis to identify the distribution of biodiversity over large spatial areas. To date, it has been used primarily to address terrestrial conservation needs. We developed an aquatic version of gap analysis in the Allegheny River and Fall Creek (1:100,000-scales) and French Creek (1:24,000-scale) watersheds of New York State to define the methodology and evaluate the feasibility of predicting biodiversity distribution at the watershed scale.
Geographic Information Systems (GIS) models were developed for the prediction of aquatic biodiversity at the community and species levels for two spatial scales. In the automated model, habitat attribution for the landscape attributes stream size, habitat quality, water quality, riparian forest cover, and stream gradient was achieved with fully automated GIS Arc/Info macros from digital maps. Through extensive literature searches, fish species were associated with habitat types using information on preferences and tolerances for stream size, degree of habitat specialization, and tolerance to water pollution. Macroinvertebrate families were associated with habitat types using information on feeding guild, life habit and tolerance to water pollution. Predictions of habitat types and associated fish species and macroinvertebrate family diversity levels were performed and gaps in protection located. In the calibrated model, additional landscape attributes were added providing information on point source pollution, surficial geology, bedrock geology, depth to bedrock, and priority water status. Previously collected fish collections were used with all ten landscape attributes to statistically optimize the prediction of likely high fish diversity habitat on a stream segment basis. Species-specific optimizations were also performed using the same methodology. Standardized collections of habitat, fish and macroinvertebrate data were performed at 39 stream sites in the Allegheny River watershed in 1998 for use in testing these predictions.
The automated model succeeded at predicting stream size and gradient with accuracy, however, our adaptation of the nonpoint source pollution model was unable to predict relative pollutant levels for short time periods on all parameters but total nitrogen. Tests of predicted habitat quality and riparian forest cover indicated more than chance agreement with observed data. Validation of the automated and calibrated models in the Allegheny River watershed revealed near equivalent significant correlation between predicted and observed fish diversity values in both models. Macroinvertebrate diversity predictions were also well correlated with observed diversity in both the Allegheny River and French Creek watersheds. However, predictions at the 1:24,000-scale in the French Creek watershed were uniformly lower in accuracy than those at the 1:100,000-scale for both fish and macroinvertebrate diversity for both models, indicating that the models have more strength at the coarser scale level. Fish species occurrences predictions were very weak using the calibrated model demonstrating a need for more refined methodologies for species level predictions. It is clear that the community level modeling procedures presented here have potential as coarse but feasible methods in identifying high diversity habitats that should receive priority conservation attention at the watershed scale.