  Identification of insects in flight is a particular challenge for ecologists
in several settings with no other method able to count and classify insects at
the pace of entomological lidar. Thus, it can play a unique role as a
non-intrusive diagnostic tool to assess insect biodiversity, inform planning,
and evaluate mitigation efforts aimed at tackling declines in insect abundance
and diversity. While species richness of co-existing insects could reach tens
of thousands, to date, photonic sensors and lidars can differentiate roughly
one hundred signal types. This taxonomic specificity or number of discernible
signal types is currently limited by instrumentation and algorithm
sophistication. In this study we report 32,533 observations of wild flying
insects along a 500-meter transect. We report the benefits of lidar
polarization bands for differentiating species and compare the performance of
two unsupervised clustering algorithms, namely Hierarchical Cluster Analysis
and Gaussian Mixture Model. We demonstrate that polarimetric properties could
be partially predicted even with unpolarized light, thus polarimetric lidar
bands provide only a minor improvement in specificity. Finally, we use physical
properties of the clustered observation, such as wing beat frequency, daily
activity patterns, and spatial distribution, to establish a lower bound for the
number of species represented by the differentiated signal types.
