Many animal species colonize recently burned forests, where resources generated by wildfire allow populations to proliferate. In particular, woodpeckers benefit from trees that are killed (i.e. snags) or weakened by fire for nesting and foraging.
Salvage logging in recently burned forests provides timber and improves human safety. However, it also removes relatively large snags, a key resource for fire-associated species like white-headed woodpeckers (Dryobates albolarvatus), a species of conservation concern.
Forest managers often seek to balance the socio-economic benefits of salvage logging with the need to maintain post-fire habitat for wildlife. Increased size and severity of wildfire with warming temperatures may further increase opportunities for salvage logging, making this balancing act even more important.
Habitat suitability index (HSI) models can inform forest management activities to help meet multiple objectives. Informing post-fire forest management, however, involves model application at new locations as wildfires occur, requiring evaluation of how well the model can predict nesting sites across locations (predictive performance).
We developed HSI models for white-headed woodpeckers using nest sites from two burned-forest locations in Oregon. We developed and evaluated models based on remotely sensed environmental metrics to support habitat mapping, and models that combined remotely sensed and field-collected metrics to inform management prescriptions. We measured predictive performance by developing one model at each of the two locations and quantifying how well the model distinguished nest sites from reference sites at two other wildfire locations where the model had not been developed.