Data from: Evaluating LiDAR-derived structural metrics for predicting bee assemblages in managed forests

Aim: Globally, insects depend on forest habitats for shelter from
disturbances and 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.Location: Illinois, United States of
AmericaMethods: 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 forest lands. 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?Results: 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 the 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.Main conclusions: Our findings suggest mid- and
understory vegetation structure may have an important
influence on 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.