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Demand for information that can be used to manage loggerhead shrikes has recently increased because of concern over declining populations and loss of open, non-forested habitat. A previously-developed habitat model was modified to predict shrike habitat quality on Fort Riley Military Reservation (FRMR) in Kansas. Shrike habitat suitability indices were calculated based on the amount of potential and usable foraging habitat, and the number of potential nesting sites within a specified area. Interpretation of high quality digital photographs was used to delineate land cover classes, hedgerows and tree counts. These data were entered into a geographic information system (GIS) as individual data sets. The shrike habitat model was then employed to produce a GIS database predicting low, moderate, and high quality shrike habitat throughout the Reservation. Model results indicated that 67% of the Reservation was suitable habitat for loggerhead shrikes. Although over 80% of FRMR was mapped as grassland, the presence of few to several isolated trees or hedgerows was identified as a key factor in modeling habitat suitability. The accuracy of the GIS model was 82% in predicting suitable (moderate and high quality) loggerhead shrike habitat using an independent set of 66 recent shrike observations. The number of potential nesting sites and percent cover of usable foraging habitat were significantly related to habitat suitability of the sites occupied by shrikes.

Abstract. Field reconnaissance data are used in a supervised classification of a 1989 Landsat Thematic Mapper (TM) scene to create a digital database of high and low quality grasslands for northwestern Kansas. To test the classification of grassland quality, plot‐based vegetation data collected from 32 sites are analyzed for differences in species composition, and evaluated for relationships between TM data and plant diversity. Significant differences between predicted high and low quality grassland sites are identified for the following variables: cover of the dominant and common species, overall species richness, number of forbs, number of grasses, and plant diversity using Shannon's index. Linear regression analysis reveals a significant relationship (r2= 0.61) between species diversity and the prediction of grassland quality from the supervised classification. The addition of spectral data to this model did not improve the prediction of species diversity, but spectral brightness is identified as a key feature in mapping shortgrass vegetation diversity patterns with TM data.