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Cited by 2Mosquito-borne diseases and controlDiptera species taxonomy and behaviorAnimal Vocal Communication and Behavior

Proximate determinants of the frequency of mosquito sounds: separating species-specific effects from environmentally driven variations - Implications for AI species recognition

Open access
Augustin, Julie, Zsebők, Sándor, Kovács, Dorottya, Jánki, Zoltán, Bánhalmi, András, Soltész, Zoltán, Seffer, Péter, Bilicki, Vilmos +1 more·2026·PLOS One
DOI 10.1371/journal.pone.0343060PMC PMC12959652
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Abstract

In recent years, several technologies have been developed for the monitoring and control of insect vector species. Many of them aim to use mosquito wingbeat frequency in the form of sound or opto-acoustic measurements to identify mosquito species, often through the training of AI classification models. However, these models often struggle to be accurate in real-life conditions, as the training data rarely captures the variability range of different species across many individual and environmental conditions, or does not explicitly control for it. Here, we use lab recordings of mosquito sounds to evaluate the impact of several environmental and life history factors on the mean frequency of the first harmonic of mosquito sounds. We recorded 475 individuals of 15 species in several environmental conditions, varying in temperature and humidity, while we also characterized the effect of body size (wing length), sex and age on the frequency of wingbeat sound at the among-individual level. Only species that comprised at least 2 recorded individuals were included in the analysis (N = 10 species). Variances at the within-individual and within-species level varied consistently, as the repeatability of the trait was 0.411 and 0.466, respectively. However, when we controlled for morphological and environmental effects, the proportion of between-individual variance decreased, while the between-species component increased (repeatabilities: 0.267 and 0.630). This suggests that species-specific signals in the sound are more robust once factors introducing variances due to real life conditions are involved in the models. Sex and temperature both had a significant effect on mosquito sound: an increase in temperature led to an increase in wingbeat frequency. In addition, the random slope analysis showed that response to temperature differ between species, with strong between-species differences, especially for males. Therefore, advancing AI species recognition requires that biotic and environmental variables be either explicitly integrated into classification models or sufficiently represented in training data to reflect real-life variability.

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  • Mosquito wingbeat frequencies-often used for AI-based species recognition-are strongly influenced by temperature, sex, and body size, which can reduce classification reliability if such variability is not represented in training data.

    “mosquito wingbeat frequencies—often used for AI-based species recognition—are strongly influenced by factors such as temperature, sex, and body size, which can reduce classification reliability if such variability is not represented in training data”

    From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Augustin et al. 2026

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Research works printed but not linked: 6

  1. [30]Kim J, Oh J, Heo TY. Acoustic classification of mosquitoes using convolutional neural networks combined with activity circadian rhythm information. Int J Interact Multimed Artif Intell. 2021; 7: 59– 65.
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Authors
Augustin, J., Zsebők, S., Kovács, D., Jánki, Z., Bánhalmi, A., Soltész, Z., Seffer, P., Bilicki, V., & Garamszegi, L. Z.
Affiliations
  • Institute of Ecology and Botany (HU)
  • Eötvös Loránd University (HU)
  • University of Szeged (HU)
Corresponding author
Augustin, Julie
ORCID
Venue
PLOS One
Publisher
Public Library of Science (PLoS)
Published
2026-03-04
Volume
21
Issue
3
Pages
e0343060
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0343060
PMC
PMC12959652
OpenAlex
W7133518125
Cited by
2
References
90
Citations by year
2026: 2
FWCI
12.81
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • SZTE Publicatio Repozitórium (University of Szeged) (acceptedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Hungary's National Research, Development and Innovation Office (K135841, RRF-2.3.1-21-2022-00006, ADVANCED 152427)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mosquito-borne diseases and control
  • Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Diptera species taxonomy and behavior
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Developmental Biology › Animal Vocal Communication and Behavior
Keywords
wingbeat frequency, species recognition, acoustic biomarkers, temperature effects, humidity, body size, sex differences, age effects, repeatability, artificial intelligence classification, training data variability, vector control
MeSH terms
Animals, Environment, Female, Male, Culicidae, Sound, Species Specificity, Temperature, Wings, Animal

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