Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Photonic sensors reflect variation in insect abundance and diversity across habitats

Rydhmer, Klas ; Jansson, Samuel LU ; Still, Laurence ; Beck, Brittany D. ; Chatzaki, Vasileia ; Olsen, Karen ; Van Hoff, Bennett ; Grønne, Christoffer ; Meier, Jakob Klinge and Montoro, Marta , et al. (2024) In Ecological Indicators 158.
Abstract

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... (More)

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.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and , et al. (More)
; ; ; ; ; ; ; ; ; ; ; ; and (Less)
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Biodiversity, Clustering, Ecology, Entomology, Insects, Modulation Spectroscopy, Photonics
in
Ecological Indicators
volume
158
article number
111483
pages
12 pages
publisher
Elsevier
external identifiers
  • scopus:85181724709
ISSN
1470-160X
DOI
10.1016/j.ecolind.2023.111483
language
English
LU publication?
yes
id
c04e100a-11df-4c63-b28b-cd763f994882
date added to LUP
2024-02-06 12:06:16
date last changed
2024-03-06 15:33:00
@article{c04e100a-11df-4c63-b28b-cd763f994882,
  abstract     = {{<p>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.</p>}},
  author       = {{Rydhmer, Klas and Jansson, Samuel and Still, Laurence and Beck, Brittany D. and Chatzaki, Vasileia and Olsen, Karen and Van Hoff, Bennett and Grønne, Christoffer and Meier, Jakob Klinge and Montoro, Marta and Schmidt, Inger Kappel and Kirkeby, Carsten and Smith, Henrik G. and Brydegaard, Mikkel}},
  issn         = {{1470-160X}},
  keywords     = {{Biodiversity; Clustering; Ecology; Entomology; Insects; Modulation Spectroscopy; Photonics}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Ecological Indicators}},
  title        = {{Photonic sensors reflect variation in insect abundance and diversity across habitats}},
  url          = {{http://dx.doi.org/10.1016/j.ecolind.2023.111483}},
  doi          = {{10.1016/j.ecolind.2023.111483}},
  volume       = {{158}},
  year         = {{2024}},
}