We examined the role scale plays in determining the predictive power of bald eagle (Haliaeetus leucocephalus) habitat models. We used a bald eagle roost habitat database that included 35 roost sites and 123 random sites located and characterized on the Chesapeake Bay from 1985-1988. A micro-habitat model, based on 6 micro-scale variables correctly classified 80% of the roost sites. A macro-habitat model, based on 10 macro-scale variables, correctly classified only 63% of the roost sites. A mixed model, incorporating the significant micro- and macro-scale variables, correctly classified 89% of the roost sites. Our results suggest there is a tradeoff between model performance (predictive power), model development costs, and model application.
