Northern spotted owl habitat models for research and management application in Calfornia (USA)

In order to test the veracity of currently accepted ideas about Northern Spotted Owl (Strix occidentalis caurina) habitat associations in the Klamath Province of northern California (USA) we compared different habitat descriptions using predictive habitat-association models. The current description used by federal agencies and new descriptions based on research results and field, biologists' best estimates of owl nesting/roosting habitat and foraging habitat were evaluated. For each habitat description, three habitat metrics and three forms of the relationship between owl occupancy and quantities of these habitat metrics were evaluated, each at three spatial scales. Our refined descriptions of owl nesting and roosting, and foraging habitat, were better at predicting owl occupancy than the habitat description currently used by federal land managers. The best-fitting model for predicting owl occupancy was at the 200-ha scale and exhibited a pseudo-threshold relationship to nesting and roosting habitat and a quadratic relationship to foraging habitat. This model correctly classified owl-occupied sites 94% of the time for the developmental data set and between 85% and 92% of the time on four independent test data sets. The current description of owl habitat in northern California ranked among the worst in the collection of models we examined. The testing of multiple models on the four independent data sets was very important for deter: mining the goodness-of-fit and predictive capabilities of the best models. We explored the use of the best-fitting model to predict number of owls on several independent study areas and found a strong correlation between predicted and observed number of owls. The results of this study are beginning to be used to make land-management decisions regarding harvesting and prescribed-burning activities on federal forestlands and were specifically designed to be amenable to adaptive resource management.