LiDAR metrics enhance our understanding of habitat selection beyond ecoforest map habitat categories

Habitat selection studies in large mammals typically rely on photo-interpreted forest maps to link the telemetry locations of individuals to environmental data. Such forest maps mainly provide information on forest composition, age, and disturbance history but say little about the structure of stands. In contrast, airborne LiDAR (Light Detection and Ranging) can provide 3D metrics of vegetation structure, but its application to the study of wildlife–habitat relationships remains limited. We aim to determine if combining these products could improve our capacity to understand the habitat selection patterns of large mammal species representative of the eastern Canadian boreal forest: caribou ( Rangifer tarandus caribou ), moose ( Alces alces americana ) and eastern coyote ( Canis latrans ). We built resource selection functions with mixed logistic regressions to characterize habitat selection patterns, using telemetry data and the different sources of information on forest composition and structure. We evaluated model performance with a k -fold cross-validation. Our results suggest that integrating LiDAR data with forest maps substantially improves the ability to characterize habitat selection patterns across species, though benefits varied with periods and study areas. For example, vegetation structure, mostly detailed by LiDAR data, was the main determinant for caribou and eastern coyotes in the snow-covered period, whereas forest composition, described in the forest maps, was most important to characterize habitat selection patterns for moose in both periods. These differences may be partly attributed to contrasting compositions between northernmost and southernmost forests in our study area (i.e. province of Quebec), species ecology, as well as potential temporal discrepancies between data sources. When used appropriately, combining LiDAR with traditional forest maps provides richer ecological insight and a more comprehensive characterization of habitat selection patterns of large boreal mammals. Based on an average population-level description of habitat selection patterns, this integrated approach can guide practitioners to preserve sparse understory to support caribou, promote complex shrub structure for moose, and limit dense cover that may favor eastern coyotes. • Forest maps inform on composition and disturbance while LiDAR captures structure. • We combined LiDAR and forest maps to model habitat selection for three species. • We compared model parsimony and evaluated performance with k -fold cross-validation. • Combining data sources outperformed simpler models across species and seasons. • Considering stand structure variables could improve forest management for wildlife.