DOI: 10.5061/dryad.j6q573nq2
title: Data from: Evaluating LiDAR-derived structural metrics for predicting bee assemblages in managed forests

description: 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.
description: # Evaluating LiDAR-derived structural metrics for predicting bee
 assemblages in managed forests
 \[[https://doi.org/10.5061/dryad.j6q573nq2](https://doi.org/10.5061/dryad.j6q573nq2)]([https://doi.org/10.5061/dryad.j6q573nq2](https://doi.org/10.5061/dryad.j6q573nq2)) **Description of the data and file structure** 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? **Files and variables** **File: site_traits_full_21_22_abundance_match_LiDAR_data.csv** **Variables** \* site: site name \* polylectic: lecty - generalist (# of generalists) \* oligolectic: lecty - specialist (# of specialists) \* cleptoparasitic: whether a bee is a cleptoparasite (# of cleptoparasitic bees) \* non-cleptoparasitic: whether a bee is a cleptoparasite (# of non-cleptoparasitic bees bees) \* solitary: sociality (# of solitary bees) \* subsocial: sociality (# of subsocial bees) \* social: sociality (# of social bees) \* ground-excavator: nesting (# of ground-excavating bees) \* cavity-excavator: nesting (# of cavity-excavating bees) \* cavity-renter: nesting (# of cavity-renting bees) \* diverse-nesting: nesting (# of diverse-nesting bees) **File: BeeSpeciesTraits.csv** **Variables** \* species: bee species \* body size: intertegular span measurement in millimeters \* lecty: specialization designation (oligolectic or polylectic) \* nesting: nesting strategy (ground, cavity-renting, cavity-excavating, diverse) \* parasitism: cleptoparsitic bee or non-cleptoparasitic bee \* sociality: sociality (solitary, subsocial, primitively eusocial) \* scopa location: # of distinct locations on a bee's body where a scopa is located \* tongue length: tongue length of a bee in millimeters \* flight phenology: # of a months a bee is foraging on the landscape as an adult \** NA values in dataset do not have associated data **File: LiDARStructuralMetrics.csv** **Variables** \* ORIG_FID: original unique identifier \* FID_: unique identifier after GIS analyses \* plot: plot # \* site: site name \* lat: latitude \* long: longitude \* treatment: management type \* bee_div: bee diversity overall \* bee_div_spring: spring bee diversity \* bee_div_summer: summer bee diversity \* bee_abun: bee abundnace \* bee_abun_spring: spring bee abundance \* bee_abun_summer: summer bee abundance \* Dist_Ag_KM: distance to ag in kilometers \* total_bloom_spring: total bloom abundance in spring \* total_bloom_summer: total bloom abundance in summer \* total_bloom: total bloom abudnance \* Perc_Under: percentage of vegetation in understory \* Perc_Can_1: percent vegetation in the canopy \* Perc_Mix: percent of vegetation in both the understory and canopy, as well as mid-story \* VHmean_new: mean vertical height in meters \* VHsd_new: standard deviation of vertical height \* Dist_Ag_m: distance to ag in meters \* Perc_Canop: percent canopy  \* Perc_Gap: percent of canopy gaps \* VD_CV: vegetation density coefficient of variation \* VD_MEAN: mean vegetation density \* VH_CV: vegetation height coefficient of variation (meters) \* VH_Mean: mean vegetation height in meters \* DEM_CV: DEM coefficient of variation \* DEM_MEAN: mean DEM \* VD_2m_CV: vegetation density 0-2 meters coefficient of variation \* VD_2m_MEAN: mean vegetation density 0-2 meters \* VD_2_5m_CV: vegetation density 2-5 meters coefficient of variation \* VD_2_5m_MEAN: mean vegetation density 2-5 meters \* VD_5_15m_CV: vegetation density 5-15 meters coefficient of variation \* VD_5_15m_MEAN: mean vegetation density 5-15 meters \* VD_Above_15m_CV: vegetation density above 15 meters coefficient of variation \* VD_Above_15m_MEAN: mean vegetation density above 15 meters **Code/software** Statistical analyses were conducted with R 4.2.1 (R Core Team 2022). **Access information** Other publicly accessible locations of the data: \* Illinois Geospatial Data Clearinghouse Data were derived from the following sources: \* Illinois Geospatial Data Clearinghouse