Both Landsat‐ and LiDAR‐derived measures predict forest bee response to large‐scale wildfire

Abstract Large‐scale disturbances such as wildfire can have profound impacts on the composition, structure, and functioning of ecosystems. Bees are critical pollinators in natural settings and often respond positively to wildfires, particularly in forests where wildfire leads to more open conditions and increased floral resources. The use of Light Detection and Ranging (LiDAR) provides opportunities for quantifying habitat features across large spatial scales and is increasingly available to scientists and land managers for post‐fire habitat assessment. We evaluated the extent to which LiDAR‐derived forest structure measurements can predict forest bee communities after a large, mixed‐severity fire. We hypothesized that LiDAR measurements linked to post‐fire forest structure would improve our ability to predict bee abundance and species richness when compared to satellite‐based maps of burn severity. To test this hypothesis, we sampled wild bee communities within the Douglas Fire Complex in southwestern Oregon, USA. We then used LiDAR and Landsat data to quantify forest structure and burn severity, respectively, across bee sampling locations. We found that the LiDAR forest structure model was the best predictor of abundance, whereas the Landsat burn severity model had better predictive ability for species richness. Furthermore, the Landsat burn severity model was better at predicting the presence and species richness of bumble bees (Bombus spp.), an ecologically distinct and economically important group within the Pacific Northwest. We posit that the divergent responses of the two modeling approaches are due to distinct responses by bee taxa to variation in forest structure as mediated by wildfire, with bumble bees in particular depending on closed‐canopy forest for some portions of their life cycle. Our study demonstrates that LiDAR data can provide information regarding the drivers of bee abundance in post‐wildfire conifer forest, and that both remote sensing approaches are useful for predicting components of wild bee diversity after large‐scale wildfire.