A multi-level, multi-scale comparison of LiDAR- and LANDSAT-based habitat selection models of Mexican spotted owls in a post-fire landscape

Abstract

The increasing frequency and severity of wildfires pose significant challenges for habitat conservation, particularly in post-fire landscapes. This study evaluates the habitat selection of the Mexican spotted owl (Strix occidentalis lucida) in a post-fire environment using multi-level and multi-scale models derived from LANDSAT and LiDAR data. By focusing on 2nd order (home range selection) and 3rd order (microhabitat selection) habitat use, we assessed the predictive performance and ecological relevance of these datasets. Optimizing predictors across spatial scales revealed that large trees, high canopy cover, and mixed-conifer forests were consistently critical for habitat selection, regardless of the data source. When optimized for spatial scale, LANDSAT- and LiDAR-based models exhibited comparable predictive accuracy (AUC = 0.976 and 0.975, respectively), emphasizing the critical role of scale in model performance. Both models had low out-of-bag (OOB) error rates (0.037 for LANDSAT and 0.050 for LiDAR), indicating high classification reliability. High-severity fire burned 36.6 % of the study area, negatively impacting owl habitat at fine scales around nest and roost sites, whereas a mosaic of burned and unburned patches provided foraging opportunities. Spatial disagreement analysis revealed notable differences in predicted habitat suitability between LANDSAT and LiDAR models, particularly in areas with complex topography and forest composition. These findings underscore the complementary strengths of both datasets, with LiDAR excelling in fine-scale structural detail and LANDSAT providing broad-scale compositional insights. Integrating these technologies offers a scalable and cost-effective framework for monitoring habitat recovery and guiding conservation strategies in fire-affected landscapes.