Abstract
Identifying flying insects is a significant challenge for biologists. Entomological lidar offers a unique solution, enabling rapid identification and classification in field settings. No other method can match its speed and efficiency in identifying insects in flight. This non-intrusive tool is invaluable for assessing insect biodiversity, informing conservation planning, and evaluating efforts to address declining insect populations. Although the species richness of co-existing insects can reach tens of thousands, current photonic sensors and lidars can differentiate roughly one hundred signal types. While the retrieved number of clusters correlate with Malaise trap diversity estimates, this taxonomic specificity, the 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. Our analysis shows 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 the physical properties of the clustered observations, 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.
| Originalsprog | Engelsk |
|---|---|
| Artikelnummer | e0312770 |
| Tidsskrift | PLoS ONE |
| Vol/bind | 19 |
| Udgave nummer | 11 |
| ISSN | 1932-6203 |
| DOI | |
| Status | Udgivet - 2024 |
Bibliografisk note
Funding Information:This research work was sponsored by the European Research Council (ERC), under the European Union\u2019s Horizon 2020 research and innovation program (grant #850463, \u2018Bug-Flash\u2019). In additional the FORMAS, Swedish Research Council (2018-01061). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The lidar instrumentation were in part, kindly provided by Norsk Elektro Optikk A/S, Norway. We thank Ebba von Wachenfeldt, Zachary Nolen, Emma K\u00E4rrn\u00E4s, Magne Friberg, Jadranka Rota and in particular, Jens Rydell for assistance in field work, may he rest in peace. We thank Rachel Muheim for receiving us at the Stensoffa ecological field station. We thank Zhicheng Xu and Jacobo Salvador for discussion and initial data analysis.
Publisher Copyright:
© 2024 Bernenko et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.