Evaluating LiDAR-Derived Structural Metrics for Predicting Bee Assemblages in Managed Forests.

Area of Science:

• Ecology
• Forestry
• Entomology

Background:

• Forest structural complexity influences insect habitat and resources.
• Temperate deciduous forests support vital bee communities and pollination services.
• The relationship between forest structure and bee communities, especially in managed forests, is understudied.

Purpose of the Study:

• To investigate if light detection and ranging (LiDAR) derived structural metrics predict bee diversity, abundance, and functional traits.
• To assess how forest management affects structural complexity.
• To determine the relationship between structural metrics and forest bee communities in managed and unmanaged forests.

Main Methods:

• Utilized LiDAR data to derive forest structural metrics.
• Collected data on bee diversity, abundance, and functional traits.
• Analyzed the predictive power of structural metrics on bee communities in spring and summer.

Main Results:

• LiDAR-derived metrics did not effectively differentiate management types or predict bee diversity and abundance.
• Understory vegetation density (0-2m) positively correlated with spring bee diversity and abundance.
• Mid-canopy vegetation density (2-5m) negatively correlated with summer bee communities.

Conclusions:

• Forest management's effect on structural complexity was not clearly detected by LiDAR metrics.
• Understory and midstory vegetation structure, particularly density, are key drivers of forest bee community patterns.
• Future research should focus on these strata to understand and monitor forest bee biodiversity.