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Bishop, A., Grosse, R., Nugent, E., & Jorgensen, C. FERRUGINOUS HAWK AND GOLDEN EAGLE HABITAT SUITABILITY INDICES.
Nebraska’s panhandle was analyzed for potential nesting habitat of ferruginous hawks (Buteo regalis) and golden eagles (Aquila chrysaetos) using known nesting site locations to develop a Habitat Suitability Index (HSI). Landcover habitat indices used to identify potential ferruginous hawk and golden eagle nests included a topographic ruggedness index (TRI), percent grassland in the landscape within 2km radius (GRASS2km) and 5km radius window (GRASS5km), the percent of surrounding landscape that is undeveloped within 2 km radius (UNDVLP2km) and 5km radius window (UNDVLP5km), and the percent of surrounding landscape that is woodland within 2 km radius (WOOD2km) and 5km radius window (WOOD5km). The relationships were modeled using the software program CurveExpert Professional (Hyams 2010). We combined the relationships for each habitat type to create an HSI for ferruginous hawks and golden eagles.
For both ferruginous hawk and golden eagle, nest presence was related to TRI, GRASS2km, GRASS5km, UNDVLP2km, and UNDVLP5km, but neither species’ nesting locations were determined to be related to WOOD2km or WOOD5km. Final HSI models for golden eagle and ferruginous hawk nesting habitat combined weighted habitat indices based on mathematical functions for TRI suitability, GRASS2km, and UNDVLP2km.
We assessed HSI model performance by withholding a subset of data, which was used to test whether the model predicted nesting locations better than randomly created points. We used a one-tailed t-test to assess and quantify model performance, testing the hypothesis that HSI models predicted nest site occurrence significantly greater than a set of values generated from random points. Results indicated that for both species, HSI models significantly predicted nesting locations better than random (P-value <0.05).
In the future, HSI models for the ferruginous hawk and golden eagle may be used to direct conservation decisions and evaluation. These models have helped to identify habitat variables that seem to be driving nest site selection and highlight areas where the two species may be especially vulnerable. In the future these models can be refined to develop decision support tools to help prioritize areas for conservation and deter energy development in critical landscapes that maintain viable populations of species of concern.