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Chen H, Li M, Månefjord H, Travers P, Salvador J, Müller L, Dreyer D, Alison J, Høye TT, Gao Hu, Warrant E, Brydegaard M. Lidar as a potential tool for monitoring migratory insects. iScience 2024; 27:109588. [PMID: 38646171 PMCID: PMC11031831 DOI: 10.1016/j.isci.2024.109588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 01/29/2024] [Accepted: 03/25/2024] [Indexed: 04/23/2024] Open
Abstract
The seasonal migrations of insects involve a substantial displacement of biomass with significant ecological and economic consequences for regions of departure and arrival. Remote sensors have played a pivotal role in revealing the magnitude and general direction of bioflows above 150 m. Nevertheless, the takeoff and descent activity of insects below this height is poorly understood. Our lidar observations elucidate the low-height dusk movements and detailed information of insects in southern Sweden from May to July, during the yearly northward migration period. Importantly, by filtering out moths from other insects based on optical information and wingbeat frequency, we have introduced a promising new method to monitor the flight activities of nocturnal moths near the ground, many of which participate in migration through the area. Lidar thus holds the potential to enhance the scientific understanding of insect migratory behavior and improve pest control strategies.
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Affiliation(s)
- Hui Chen
- Department of Entomology, Nanjing Agricultural University, Nanjing 210095, China
- Lund Vision Group, Department Of Biology, Lund University, Sölvegatan 35, 22362 Lund, Sweden
| | - Meng Li
- Department Physics, Lund University, Sölvegatan 14c, 22363 Lund, Sweden
| | - Hampus Månefjord
- Department Physics, Lund University, Sölvegatan 14c, 22363 Lund, Sweden
| | - Paul Travers
- Department Biological Engineering, Polytech Clermont, 2 Av. Blaise Pascal, 63100 Aubière, France
| | - Jacobo Salvador
- Department Physics, Lund University, Sölvegatan 14c, 22363 Lund, Sweden
| | - Lauro Müller
- Department Physics, Lund University, Sölvegatan 14c, 22363 Lund, Sweden
| | - David Dreyer
- Lund Vision Group, Department Of Biology, Lund University, Sölvegatan 35, 22362 Lund, Sweden
| | - Jamie Alison
- Department Ecoscience, Aarhus University, C. F. Møllers Allé 8, 8000 Aarhus C, Denmark
| | - Toke T. Høye
- Department Ecoscience, Aarhus University, C. F. Møllers Allé 8, 8000 Aarhus C, Denmark
- Arctic Research Centre, Aarhus University, Ole Worms Allé 1, 8000 Aarhus C, Denmark
| | - Gao Hu
- Department of Entomology, Nanjing Agricultural University, Nanjing 210095, China
| | - Eric Warrant
- Lund Vision Group, Department Of Biology, Lund University, Sölvegatan 35, 22362 Lund, Sweden
| | - Mikkel Brydegaard
- Department Physics, Lund University, Sölvegatan 14c, 22363 Lund, Sweden
- Department Biology, Lund University, Sölvegatan 35, 22362 Lund, Sweden
- FaunaPhotonics, Støberigade 14, 2450 Copenhagen, Denmark
- Norsk Elektro Optikk, Østensjøveien 34, 0667 Oslo, Norway
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Cannet A, Simon-Chane C, Histace A, Akhoundi M, Romain O, Souchaud M, Jacob P, Sereno D, Bousses P, Sereno D. An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning. Sci Data 2024; 11:4. [PMID: 38168517 PMCID: PMC10761744 DOI: 10.1038/s41597-023-02848-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
Several Diptera species are known to transmit pathogens of medical and veterinary interest. However, identifying these species using conventional methods can be time-consuming, labor-intensive, or expensive. A computer vision-based system that uses Wing interferential patterns (WIPs) to identify these insects could solve this problem. This study introduces a dataset for training and evaluating a recognition system for dipteran insects of medical and veterinary importance using WIPs. The dataset includes pictures of Culicidae, Calliphoridae, Muscidae, Tabanidae, Ceratopogonidae, and Psychodidae. The dataset is complemented by previously published datasets of Glossinidae and some Culicidae members. The new dataset contains 2,399 pictures of 18 genera, with each genus documented by a variable number of species and annotated as a class. The dataset covers species variation, with some genera having up to 300 samples.
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Affiliation(s)
- Arnaud Cannet
- Direction des affaires sanitaires et sociales de la Nouvelle-Calédonie, Nouméa, France
| | | | - Aymeric Histace
- ETIS UMR 8051, Cergy Paris University, ENSEA, CNRS, F-95000, Cergy, France
| | | | - Olivier Romain
- ETIS UMR 8051, Cergy Paris University, ENSEA, CNRS, F-95000, Cergy, France
| | - Marc Souchaud
- ETIS UMR 8051, Cergy Paris University, ENSEA, CNRS, F-95000, Cergy, France
| | - Pierre Jacob
- ETIS UMR 8051, Cergy Paris University, ENSEA, CNRS, F-95000, Cergy, France
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, F-33400, Talence, France
| | - Darian Sereno
- InterTryp, Univ Montpellier, IRD-CIRAD, Infectiology, Entomology and One Health Research Group, Montpellier, France
| | | | - Denis Sereno
- InterTryp, Univ Montpellier, IRD-CIRAD, Infectiology, Entomology and One Health Research Group, Montpellier, France.
- MIVEGEC, Univ Montpellier, CNRS, IRD, Montpellier, France.
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Li M, Runemark A, Hernandez J, Rota J, Bygebjerg R, Brydegaard M. Discrimination of Hover Fly Species and Sexes by Wing Interference Signals. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2304657. [PMID: 37847885 DOI: 10.1002/advs.202304657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/08/2023] [Indexed: 10/19/2023]
Abstract
Remote automated surveillance of insect abundance and diversity is poised to revolutionize insect decline studies. The study reveals spectral analysis of thin-film wing interference signals (WISs) can discriminate free-flying insects beyond what can be accomplished by machine vision. Detectable by photonic sensors, WISs are robust indicators enabling species and sex identification. The first quantitative survey of insect wing thickness and modulation through shortwave-infrared hyperspectral imaging of 600 wings from 30 hover fly species is presented. Fringy spectral reflectance of WIS can be explained by four optical parameters, including membrane thickness. Using a Naïve Bayes Classifier with five parameters that can be retrieved remotely, 91% is achieved accuracy in identification of species and sexes. WIS-based surveillance is therefore a potent tool for remote insect identification and surveillance.
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Affiliation(s)
- Meng Li
- Department of Physics, Lund University, Sölvegatan 14c, Lund, 22363, Sweden
| | - Anna Runemark
- Department of Biology, Lund University, Sölvegatan 35, Lund, 22362, Sweden
| | | | - Jadranka Rota
- Biological Museum, Department of Biology, Lund University, Sölvegatan 37, Lund, 22362, Sweden
| | - Rune Bygebjerg
- Biological Museum, Department of Biology, Lund University, Sölvegatan 37, Lund, 22362, Sweden
| | - Mikkel Brydegaard
- Department of Physics, Lund University, Sölvegatan 14c, Lund, 22363, Sweden
- Department of Biology, Lund University, Sölvegatan 35, Lund, 22362, Sweden
- Norsk Elektro Optikk, Østensjøveien 34, Oslo, 0667, Norway
- FaunaPhotonics, Støberigade 14, Copenhagen, 2450, Denmark
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Saha T, Genoud AP, Williams GM, Thomas BP. Monitoring the abundance of flying insects and atmospheric conditions during a 9-month campaign using an entomological optical sensor. Sci Rep 2023; 13:15606. [PMID: 37731042 PMCID: PMC10511543 DOI: 10.1038/s41598-023-42884-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 09/15/2023] [Indexed: 09/22/2023] Open
Abstract
Monitoring the dynamics of insect populations is key to assessing the impact of human activities on insect populations. However, traditional methodologies relying on physical traps have inherent limitations in accurately monitoring insect abundance. Here, we present findings from a 9-month campaign conducted in New Jersey, USA, utilizing a near-infrared optical sensor known as eBoss. From April to December 2022, the eBoss derived the aerial density (insect/m3) and biomass density (mg/m3) with a 1-min resolution from a total of 302,093 insect observations. The data collected were analyzed in relation to air temperature, relative humidity, and wind speed. The results revealed that the abundance of flying insects exhibited an initial increase from April to June, reaching a peak of 0.094 insect/m3 and 1.34 mg/m3, followed by a subsequent decline towards the end of the year. Our investigation showed a surge in insect abundance above 12.5 °C, with particularly high levels observed between 19 and 31 °C. The impact of relative humidity and wind speed on insect populations was also explored. Overall, this campaign demonstrated the efficacy of photonic sensors in gathering novel and extensive data for the field of entomology, paving the way for improved understanding and management of insect populations.
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Affiliation(s)
- Topu Saha
- Department of Physics, New Jersey Institute of Technology, Newark, NJ, USA
| | - Adrien P Genoud
- Institute of Light and Matter, Claude Bernard University, Lyon, France
| | | | - Benjamin P Thomas
- Department of Physics, New Jersey Institute of Technology, Newark, NJ, USA.
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