Photonic sensors reflect variation in insect abundance and diversity across habitats
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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Photonic sensors reflect variation in insect abundance and diversity across habitats. / Rydhmer, Klas; Jansson, Samuel; Still, Laurence; Beck, Brittany D.; Chatzaki, Vasileia; Olsen, Karen; Van Hoff, Bennett; Grønne, Christoffer; Meier, Jakob Klinge; Montoro, Marta; Schmidt, Inger Kappel; Kirkeby, Carsten; Smith, Henrik G.; Brydegaard, Mikkel.
I: Ecological Indicators, Bind 158, 111483, 2024.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Photonic sensors reflect variation in insect abundance and diversity across habitats
AU - Rydhmer, Klas
AU - Jansson, Samuel
AU - Still, Laurence
AU - Beck, Brittany D.
AU - Chatzaki, Vasileia
AU - Olsen, Karen
AU - Van Hoff, Bennett
AU - Grønne, Christoffer
AU - Meier, Jakob Klinge
AU - Montoro, Marta
AU - Schmidt, Inger Kappel
AU - Kirkeby, Carsten
AU - Smith, Henrik G.
AU - Brydegaard, Mikkel
N1 - Publisher Copyright: © 2023
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Biodiversity
KW - Clustering
KW - Ecology
KW - Entomology
KW - Insects
KW - Modulation Spectroscopy
KW - Photonics
U2 - 10.1016/j.ecolind.2023.111483
DO - 10.1016/j.ecolind.2023.111483
M3 - Journal article
AN - SCOPUS:85181724709
VL - 158
JO - Ecological Indicators
JF - Ecological Indicators
SN - 1470-160X
M1 - 111483
ER -
ID: 381152596