The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

article i nfo Wildlifehabitatmapping has evolved at a rapidpace over thelastfew decades. Beginningwith simple, oftensub- jective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often essential for producing range wide maps. Habitat monitoring for northern spotted owls (Strix occidentalis caurina), whose geographic covers about 23 million ha, is based on SDMs that use Landsat Thematic Mapper imagery to create forest vegetation data layers using gradient nearest neighbor (GNN) methods. Vegetation data layers derived from GNN are modeled relationships between forest inventory plot data, climate and topographic data, and the spectral signatures acquired by the satellite. When used as pre- dictor variables for SDMs, there is some transference of the GNN modeling error to the final habitat map. Recent increasesinthe use of light detection and ranging (lidar) data, coupled with the need to produce spatially accurate and detailed forest vegetation maps have spurred interest in its use for SDMs and habitat mapping. In- stead of modeling predictor variables from remotely sensed spectral data, lidar provides direct measurements of vegetationheightforuseinSDMs.WeexpectaSDMhabitatmapproducedfromdirectlymeasuredpredictorvar- iables to be more accurate than one produced from modeled predictors. We used maximum entropy (Maxent) SDM modeling software to compare predictive performance and esti- mates of habitat area between Landsat-based and lidar-based northern spotted owl SDMs and habitat maps. We explored the differences and similarities between these maps, and to a pre-existing aerial photo- interpreted habitat map produced by local wildlife biologists. The lidar-based map had the highest predictive performance based on 10 bootstrapped replicate models (AUC = 0.809 ± 0.011), but the performance of the Landsat-based map was within acceptable limits (AUC = 0.717 ± 0.021). As is common with photo- interpreted maps, there was no accuracy assessment available for comparison. The photo-interpreted map pro- duced the highest and lowest estimates of habitat area, depending on which habitat classes were included (nesting, roosting, and foraging habitat = 9962 ha, nesting habitat only = 6036 ha). The Landsat-based map produced an estimate of habitat area that was within this range (95% CI: 6679-9592 ha), while the lidar-based map produced an area estimate similar to what was interpreted by local wildlife biologists as nesting (i.e., high quality) habitat using aerial imagery (95% CI: 5453-7216). Confidence intervals of habitat area estimates from the SDMs based on Landsat and lidar overlapped. Weconcludedthatboth Landsat- and lidar-based SDMs produced reasonablemaps and areaestimatesfor north- ern spotted owl habitat within the study area. The lidar-based map was more precise and spatially similar to whatlocal wildlife biologistsconsideredspottedowl nesting habitat.The Landsat-basedmap provideda lesspre- cisespatial representation of habitatwithin therelatively small geographic confines ofthe study area,but habitat area estimates were similar to both the photo-interpreted and lidar-based maps.