Photonic sensors reflect variation in insect abundance and diversity across habitats
To mitigate ongoing insect biodiversity declines, there is a need for efficient yet accurate monitoring methods. The use of traditional catch-based survey methods is constrained both by costs and need for expertise for manual taxonomic identification. Emerging methods, such as eDNA and robotic sorti...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23016254 |
| _version_ | 1797371433944350720 |
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| author | Klas Rydhmer Samuel Jansson Laurence Still Brittany D. Beck Vasileia Chatzaki Karen Olsen Bennett Van Hoff Christoffer Grønne Jakob Klinge Meier Marta Montoro Inger Kappel Schmidt Carsten Kirkeby Henrik G. Smith Mikkel Brydegaard |
| author_facet | Klas Rydhmer Samuel Jansson Laurence Still Brittany D. Beck Vasileia Chatzaki Karen Olsen Bennett Van Hoff Christoffer Grønne Jakob Klinge Meier Marta Montoro Inger Kappel Schmidt Carsten Kirkeby Henrik G. Smith Mikkel Brydegaard |
| author_sort | Klas Rydhmer |
| collection | DOAJ |
| description | To mitigate ongoing insect biodiversity declines, there is a need for efficient yet accurate monitoring methods. The use of traditional catch-based survey methods is constrained both by costs and need for expertise for manual taxonomic identification. Emerging methods, such as eDNA and robotic sorting, have the potential to reduce workload but still require resource-intensive sample collection in the field. Recently, remote sensing methods such as photonic sensors have shown promise for recording large numbers of insect observations. However, accurately determining species composition in collected data remains a challenge.In this study, we investigated the potential of photonic sensors for quantifying species richness of flying insects in the field and at five sites and compared the results with estimates based on conventional Malaise traps. Firstly, we evaluated two unsupervised clustering methods using a library of measured insect signals from 42 known species. Secondly, we correlated estimated number of clusters in data recorded at five sites with species richness assessment of catches from Malaise traps. This study is based on 84,770 library- and 238,584 field individual insect recordings. Our results demonstrate that both clustering methods perform well and reflect estimates obtained by Malaise traps, indicating the potential of automated insect biodiversity monitoring. This offers the possibility of more efficient but still accurate methods for studying insect biodiversity with broader temporal and spatial coverage. |
| first_indexed | 2024-03-08T18:19:53Z |
| format | Article |
| id | doaj.art-b0e55b488e8249d6ad567d06e5974644 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-03-08T18:19:53Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-b0e55b488e8249d6ad567d06e59746442023-12-31T04:26:04ZengElsevierEcological Indicators1470-160X2024-01-01158111483Photonic sensors reflect variation in insect abundance and diversity across habitatsKlas Rydhmer0Samuel Jansson1Laurence Still2Brittany D. Beck3Vasileia Chatzaki4Karen Olsen5Bennett Van Hoff6Christoffer Grønne7Jakob Klinge Meier8Marta Montoro9Inger Kappel Schmidt10Carsten Kirkeby11Henrik G. Smith12Mikkel Brydegaard13FaunaPhotonics, Støberigade 14, 2450 Copenhagen, Denmark; Department of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen, Rolighedsvej 23, 1958 Frederiksberg C, Denmark; Corresponding author.FaunaPhotonics, Støberigade 14, 2450 Copenhagen, DenmarkFaunaPhotonics, Støberigade 14, 2450 Copenhagen, DenmarkFaunaPhotonics, Støberigade 14, 2450 Copenhagen, DenmarkFaunaPhotonics, Støberigade 14, 2450 Copenhagen, DenmarkFaunaPhotonics, Støberigade 14, 2450 Copenhagen, DenmarkFaunaPhotonics, Støberigade 14, 2450 Copenhagen, DenmarkFaunaPhotonics, Støberigade 14, 2450 Copenhagen, DenmarkFaunaPhotonics, Støberigade 14, 2450 Copenhagen, DenmarkFaunaPhotonics, Støberigade 14, 2450 Copenhagen, DenmarkDepartment of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen, Rolighedsvej 23, 1958 Frederiksberg C, DenmarkFaunaPhotonics, Støberigade 14, 2450 Copenhagen, Denmark; Department of Veterinary and Animal Sciences, Section for Production, Nutrition and Health, University of Copenhagen, Grønnegårdsvej 15, 1870 Frederiksberg C, DenmarkCentre of Environmental and Climate Science, Department of Biology, Lund University, Sölvegatan 37, 223 62 Lund, SwedenFaunaPhotonics, Støberigade 14, 2450 Copenhagen, Denmark; Department of Physics, Lund University, Sölvegatan 14c. 22362 Lund, SwedenTo mitigate ongoing insect biodiversity declines, there is a need for efficient yet accurate monitoring methods. The use of traditional catch-based survey methods is constrained both by costs and need for expertise for manual taxonomic identification. Emerging methods, such as eDNA and robotic sorting, have the potential to reduce workload but still require resource-intensive sample collection in the field. Recently, remote sensing methods such as photonic sensors have shown promise for recording large numbers of insect observations. However, accurately determining species composition in collected data remains a challenge.In this study, we investigated the potential of photonic sensors for quantifying species richness of flying insects in the field and at five sites and compared the results with estimates based on conventional Malaise traps. Firstly, we evaluated two unsupervised clustering methods using a library of measured insect signals from 42 known species. Secondly, we correlated estimated number of clusters in data recorded at five sites with species richness assessment of catches from Malaise traps. This study is based on 84,770 library- and 238,584 field individual insect recordings. Our results demonstrate that both clustering methods perform well and reflect estimates obtained by Malaise traps, indicating the potential of automated insect biodiversity monitoring. This offers the possibility of more efficient but still accurate methods for studying insect biodiversity with broader temporal and spatial coverage.http://www.sciencedirect.com/science/article/pii/S1470160X23016254InsectsBiodiversityClusteringPhotonicsEntomologyEcology |
| spellingShingle | Klas Rydhmer Samuel Jansson Laurence Still Brittany D. Beck Vasileia Chatzaki Karen Olsen Bennett Van Hoff Christoffer Grønne Jakob Klinge Meier Marta Montoro Inger Kappel Schmidt Carsten Kirkeby Henrik G. Smith Mikkel Brydegaard Photonic sensors reflect variation in insect abundance and diversity across habitats Ecological Indicators Insects Biodiversity Clustering Photonics Entomology Ecology |
| title | Photonic sensors reflect variation in insect abundance and diversity across habitats |
| title_full | Photonic sensors reflect variation in insect abundance and diversity across habitats |
| title_fullStr | Photonic sensors reflect variation in insect abundance and diversity across habitats |
| title_full_unstemmed | Photonic sensors reflect variation in insect abundance and diversity across habitats |
| title_short | Photonic sensors reflect variation in insect abundance and diversity across habitats |
| title_sort | photonic sensors reflect variation in insect abundance and diversity across habitats |
| topic | Insects Biodiversity Clustering Photonics Entomology Ecology |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23016254 |
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