The University of Arizona

Comparing Ecological Site Descriptions to Habitat Characteristics Influencing Greater Sage-Grouse Nest Site Occurrence and Success

Kevin E. Doherty, Jeffrey L. Beck, David E. Naugle


We used 119 greater sage-grouse (Centrocercus urophasianus) nests located in the Powder River Basin of northeastern Wyoming during 2004–2007 to assess the ability of US Department of Agriculture–Natural Resource Conservation Service (USDA–NRCS) ecological site descriptions (ESDs) to predict nest occurrence and success. We used nesting data from a regional study in the Powder River Basin that documented effects of local and landscape scale habitat characteristics on nest occurrence and success. We compared ESD metrics to these predictive local and landscape habitat variables where NRCS ESD field surveys overlapped our regional nest data set. We specifically asked three questions: 1) Are ESDs useful in predicting sage-grouse nest site occurrence and success as a univariate explanatory variable? 2) Can ESD information refine predictions of local scale nest site occurrence and success models? 3) Can ESD information refine landscape scale nest site occurrence models by serving as a surrogate for local scale information that cannot be mapped in a geographic information system (GIS)? Our results demonstrated that all models using ESD information were within 6 2 Akaike’s Information Criterion points of a constant only model (i.e., null model) for local-scale data, or a baseline model where local- and landscape-scale habitat metrics were held constant while allowing ESD models to compete for remaining variation. No ESD metrics were statistically significant at the 95% level (P , 0.05), although some were significant at the 80–90% level (P 5 0.09–0.14). Our study does not support the use of ESDs to predict habitat use or base sage-grouse management decisions in the Powder River Basin, but in some instances the refutation was weak. Local and landscape based habitat metrics showed high discrimination between null models with highly significant relationships on the subset data.

Full Text: