Within forests susceptible to wildfire and insect infestations, land managers need to balance dead tree removal and habitat requirements for wildlife species associated with snags. We used Mahalanobis distance methods to develop predictive models of white‐headed woodpecker (Picoides albolarvatus) nesting habitat in postfire ponderosa pine (Pinus ponderosa)‐dominated landscapes on the Fremont‐Winema National Forests in south central Oregon, USA. The 1‐km radius (314 ha) surrounding 45 nest sites was open‐canopied before fire and a mosaic of burn severities after wildfire. The 1‐ha surrounding nests of white‐headed woodpeckers had fewer live trees per hectare and more decayed and larger diameter snags than at non‐nest sites. The leading cause of nest failure seemed to be predation. Habitat and abiotic features were not associated with nest survival. High daily survival rates and little variation within habitat features among nest locations suggest white‐headed woodpeckers were consistently selecting high suitability habitats. Management activities that open the forest canopy and create conditions conducive to a mosaic burn pattern will probably provide suitable white‐headed woodpecker nesting habitat after wildfire. When making postfire salvage logging decisions, we suggest that retention of larger, more decayed snags will provide nesting habitat in recently burned forests.

Salvage logging in burned forests can negatively affect habitat for white-headed woodpeckers (Dryobates albolarvatus), a species of conservation concern, but also meets socioeconomic demands for timber and human safety. Habitat suitability index (HSI) models can inform forest management activities to help meet habitat conservation objectives. Informing post-fire forest management, however, involves model application at new locations as wildfires occur, requiring evaluation of predictive performance across locations. We developed HSI models for white-headed woodpeckers using nest sites from two burned-forest locations in Oregon, the Toolbox (2002) and Canyon Creek (2015) fires. We measured predictive performance by developing one model at each of the two locations and quantifying discrimination of nest from reference sites at two other wildfire locations where the model had not been developed (either Toolbox or Canyon Creek, and the Barry Point Fire [2011]). We developed and evaluated Maxent models based on remotely sensed environmental metrics to support habitat mapping, and weighted logistic regression (WLR) models that combined remotely sensed and field-collected metrics to inform management prescriptions. Both Maxent and WLR models developed either at Canyon Creek or Toolbox performed adequately to inform management when applied at the alternate Toolbox or Canyon Creek location, respectively (area under the receiver-operating-characteristic curve [AUC] range = 0.61-0.72) but poorly when applied at Barry Point (AUC = 0.53-0.57). The final HSI models fitted to Toolbox and Canyon Creek data quantified suitable nesting habitat as severely burned or open sites adjacent to lower severity and closed canopy sites, where foraging presumably occurs. We suggest these models are applicable at locations similar to development locations but not at locations resembling Barry Point, which were characterized by more (pre-fire) canopy openings, larger diameter trees, less ponderosa pine (Pinus ponderosa), and more juniper (Juniperus occidentalis). Considering our results, we recommend

No abstract

A common forest restoration goal is to achieve a spatial distribution of trees consistent with historical forest structure, which can be characterized by the distribution of individuals, clumps, and openings (ICO). With the stated goal of restoring historical spatial patterns comes a need for effectiveness monitoring at appropriate spatial scales. Airborne light detection and ranging (LiDAR) can be used to identify individual tree locations and collect data at landscape scales, offering a method of analyzing tree spatial distributions over the scales at which forest restoration is conducted. In this study, we investigated whether tree locations identified by airborne LiDAR data can be used with existing spatial analysis methods to quantify ICO distributions for use in restoration effectiveness monitoring. Results showed fewer large clumps and large openings, and more small clumps and small openings relative to historical spatial patterns, suggesting that the methods investigated in this study can be used to monitor whether restoration efforts are successful at achieving desired tree spatial patterns. Study Implications: Achieving a desired spatial pattern is often a goal of forest restoration. Monitoring for spatial pattern, however, can be complex and time-consuming in the field. LiDAR technology offers the ability to analyze spatial pattern at landscape scales. Preexisting methods for evaluation of the distribution of individuals, clumps, and openings were used in this study along with LiDAR individual tree detection methodology to assess whether a forest restoration project implemented in a Southern Oregon landscape achieved desired spatial patterns.

No abstract