Evaluating <scp>LiDAR</scp>‐Derived Structural Metrics for Predicting Bee Assemblages in Managed Forests

Globally, many insects depend on forest habitat for critical nesting and floral resources. Forest structural complexity can affect the distribution of these resources and likewise alter insect assemblages within forests. Despite the importance of temperate deciduous forests for bees and their outsized contribution to pollination services within forests and beyond, the relationship between forest structure and bees has received scant attention. This is especially true in managed temperate deciduous forests, where management strategies alter forest structural complexity and may therefore affect bee communities. We investigated whether structural metrics derived from light detection and ranging (LiDAR) data could predict bee diversity and abundance, as well as bee functional trait composition within managed and unmanaged forests in the central hardwood region in southern Illinois, United States of America. We addressed three specific questions: (1) How does forest management affect structural complexity; (2) Can structural metrics predict bee diversity and abundance in spring and summer; and (3) How are structural metrics related to bee functional trait composition? We found that LiDAR-derived structural metrics could not differentiate between management types and were weak predictors of bee diversity and abundance and bee functional trait composition. Metrics related to understory and midstory vegetation structure showed the strongest association with forest bee community patterns. Specifically, vegetation density in the understory (0-2 m) had a positive effect on bee diversity and abundance in spring, while in summer, vegetation density in the mid-canopy (2-5 m) negatively affected bee communities. Our findings suggest mid- and understory vegetation structure, specifically vegetation density, may influence forest bee communities. Future studies should focus on the structural elements of these forest strata to improve understanding of how structural complexity influences bee communities within managed forests and evaluate the potential for using LiDAR-derived structural metrics to monitor and predict biodiversity patterns.