-->PONE-D-25-52971-->-->Proximate determinants of the frequency of mosquito sounds: separating species-specific effects from environmentally driven variations - implications for AI species recognition-->-->PLOS ONE

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Reviewer #1: Yes

Reviewer #2: Yes

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-->2. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

Reviewer #2: I Don't Know

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Reviewer #1: No

Reviewer #2: No

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-->5. Review Comments to the Author

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Reviewer #1: The manuscript “Environmental and biotic determinants of mosquito wingbeat frequency and implications for AI-based species recognition” is a timely and important study that addresses a central limitation in current mosquito acoustic monitoring: the lack of integration of environmental and biotic variability into species classification frameworks. The manuscript presents an impressive dataset (475 individuals from 15 species) and provides novel quantitative insights into within- and between-species repeatability of wingbeat frequency. The work is rigorous, relevant, and provides clear perspectives for future research, particularly regarding how biological and environmental covariates could improve AI-based classification of vector species.

The manuscript is scientifically strong and has clear potential for publication in PLOS ONE. However, revisions are necessary to improve clarity for a general audience, ensure methodological transparency, and align statements across sections. Once these issues are addressed, the paper will make an important contribution to bioacoustics and vector ecology by quantifying the influence of environmental and biotic variability on mosquito sounds and demonstrating its relevance for AI-based species recognition.

Strengths:

- Addresses a relevant and emerging intersection between bioacoustics and vector surveillance.

- Employs a well-chosen mixed-model framework with clear variance partitioning.

- Includes non-model mosquito species, expanding the taxonomic and ecological scope of acoustic studies.

- Provides actionable implications for improving AI-based mosquito recognition systems.

Specific Comments

1. Accessibility and scope of the Introduction

The Introduction is scientifically rich but currently too narrowly focused for the journal’s broad audience, i.e. briefly explain repeatability (statistical proportion of variance due to consistent among-individual or among-species differences) and why it matters for classification robustness and introduce vector species in simple terms (mosquitoes capable of transmitting pathogens).

Lines 41–70: Reorganize to introduce the ecological and applied relevance of mosquito acoustic monitoring before diving into technical details. For a general audience, it would help to first explain why acoustic species identification matters (e.g., vector surveillance & disease control).

Line 45: Briefly explain opto-acoustics for non-specialists

Lines 49–51: If they reach accuracy of 97% what is there to improve? And what is meant by underutilized and why is that important in this context? Clarify whether these results apply only under controlled laboratory conditions and why field deployment remains limited (e.g., lack of robustness, difficulty in implementation, or lack of trust). Strengthen the logical transition to “However…” in line 51.

Line 64: Quantify “data remain scarce”—for example, indicate the approximate number of studies or species covered (“data currently exist for ~6 studies on 2–3 model species”).

Tables 1 & 2: These tables are rich but create an impression that the topic is well explored, which contradicts the statement of scarcity. Consider moving them later in the Introduction or summarizing them graphically. A visual summary (e.g., species icons with circles representing investigated factors) would help illustrate the strong research bias toward Aedes aegypti.

2. Clarify the rationale for the focus on Aedes aegypti

The Introduction would benefit from a short statement explaining why Aedes aegypti dominates the literature (e.g., ease of laboratory maintenance, relevance as a disease vector). This contextualization will help readers understand the novelty of including less-studied species.

3. Methods

While the methods are thorough, several key steps need clarification to ensure reproducibility and transparency.

Line 127: Rephrase for clarity: “Wild-caught mosquitoes were recorded shortly after capture. Individuals reared from laboratory colonies did not differ in wingbeat frequency and were therefore included in the analysis.”

Lines 148–150: Clarify how acoustic stimulation works within a soundproof box. Does the operator reach into the box or use an external tool?

Table 3 (Age): Clarify that the age range reflects individuals bred from wild-collected eggs or larvae, not adult field captures. Currently it is unclear whether wild adults were recorded.

Lines 169–174: Move this paragraph earlier (before recordings) and expand on why estimating median age per cage is a reasonable approach.

Line 177: Specify the software environment of the “custom-made mosquito detection module” (e.g., R, Python, MATLAB). Indicate whether the code is publicly available and provide a repository link. If not, explain why.

Line 190: Explain selection criteria more explicitly—what was done when only one high-quality sound was available? Did usable sounds cluster temporally (e.g., early vs. late in recording)?

Line 192: Again, specify if analysis code is available.

Line 196: Explicitly name the extracted output parameter (e.g., “f1_freq = mean frequency of the first harmonic”).

Line 209: Explain what “different hierarchical levels” refers to (sounds within individuals, individuals within species) and why repeatability is the metric of interest. Define its possible range (0 = no consistency; 1 = complete consistency).

Line 211: Describe the specific parametric bootstrap procedure used (number of simulations, software function). If the scripts are not shared, reproducibility is limited—consider providing them as supplementary material.

Line 232: Adjust the Abstract to clarify that analyses were conducted on 10 species, though recordings were made for 15.

Table 4: Specify whether numbers represent all recorded individuals or those included in analyses.

Table 5: Add units (Hz), and consider presenting range and median in addition to mean ± SD. High SD values (often >10 % of the mean) merit brief comment.

Table 6: Add a short note in the Introduction explaining what repeatability values represent and how to interpret them (e.g., “values near 1 indicate high consistency across individuals or species”).

Line 269: Clarify why wing length was standardized within species and discuss potential implications of this choice on the non-significant result.

Lines 270–276 / Fig. 2: Provide a clearer description of random-slope results. Highlight that not all species respond similarly to temperature, and discuss how unequal sample sizes per species may influence slope estimates.

Line 284: Spell out “a good number of non-model species (n = 10 analyzed)” to emphasize novelty.

4. Results and Discussion

The Results are generally clear and statistically well presented, but some interpretation could be expanded for accessibility:

- Consider briefly summarizing expected vs. observed repeatability patterns (Table 6) for readers unfamiliar with the metric.

- The Discussion effectively highlights temperature and sex as key predictors but could benefit from a clearer paragraph summarizing the practical implications for AI-based classification (e.g., integrating temperature correction factors or including metadata in training).

- The final section on mate selection and individual quality is interesting but peripheral. Condense or explicitly link it to the study’s broader message about acoustic variability and consistency.

Reviewer #2: The authors present a study of the variation of mosquitoes' wingbeat frequency (WBF) accross species, sex, temperature and other environmental conditions. I found the study is intesresting because too many studies does not take into consideration the context where the mosquito WBF are recorded.

MAJOR COMMENTS

1) insufficient information on the "time of recording" parameter

Line 104-106, 181 : "due to the strong circadian variations in mosquitoes activity, we evaluated the impact of the time of recording"

--> no methodological informaiton on this parameter, i.e. how time of recording was controlled and which ranges it took. What time were the mosquitoes recorded in the day and when was it as compared to the mosquito circadium time.

Did you record mosquitoes at a time independnat of wether they were day or night mosquitoes?

2) Insufficient information on acoustic processing

Line 148 / 181: not clear how the authors dealt with superimposed WBF when more than 1 mosquito was flying at a time

3) Insufficient information on acoustic recording

Line 148 / 181: not clear how the authors proceed to record the mosquitoes. More information is needed to know the behaviour of the mosquitoes during the recording, e.g.:

How was managed the opening of the soundproof box to make them fly and then record them? Did the authors wait for some time after the sonudproof box was closed?

How long time do the mosquito fly in the cage in general?

What are the mean duration of an long enough flight?

Was it full darkness in the soundproof chamber? If yes, what would be the effect on WBF / mosquito behaviour?

4) Another parameters that affects male WBF is the acoustic detection of the female (table 2, ...):

See the review book chapter you cite (49): male fundamental WBFs are observed to reach up to 1 000 Hz for short periods of time in swarms (Pantoja-Sanchez et al. (2019), Garcia Castillo et al., 2021; https://doi.org/10.1242/jeb.243535 , https://doi.org/10.1126/sciadv.abl4844). Then, the behaviour could also be a factor of WBF.

MINOR COMMENTS

5) line 78 "mating does not appear to alter wingbeat frequency". WBF is altered during matin.g I guess you meant "mated status"?

6) Line 140: "N=12" : not clear if it is the number of mosquitos used per cage.

7) Table 3: Usually, good temperature sensors have a precision of ~1°C, then having 1 digit precision in table 3 can be seen as an excess of precision: what is the tempreature accuracy of Voltcraft DL-210TH? Also it seems that Voltcraft DL-210TH is a datalogguer, not a sensor (not checked carefully). In this case, you should say which sensor was plugged to the data logguer and its temperature accurary.

Humidity sensor are even far less precise than temperature sensor and a 1 digit precision is not meaningfull.

8) Table 5, line 320: 2-digits for WBF are useless and give a wrong sense of precision which does not exist on . I would advice not to put any digits, and at least to remove the 2nd digit.

9) line 300: and 302: please gives the amplitude of change in Hz/°C and statistical test results; Discuss wether it could make any difference.

10) Given a species, are the slopes the same between males and females? This could be interesting in terms of mosquito hearing; indeed, males hear the difference frequency between they own WBF and that of the nearby female (distortion product from their antennae; see Warren et al 2009) ; this would tell us whether their hearing organ tuning woul need to thave the same temperature gradiant for instance. Indeed, hearing difference-tones cancels the effect of temperature when hearing each other, as suggested by Lapshin and

Vorontsov (2017).

11) line 388: the harmonic convergence theory is somewhow out of date (e.g. see the review chapter Feugere et al 2022; Somers et al 2022; Warren et al. (2009))

12) see also Villarreal et al., 2017 for effect of temperature

13) In the abstract, I would not highlight that female's WBF are lower than males' one, as it is really well established.

NB: I have not checked the data on figshare

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Reviewer #1: No

Reviewer #2: Yes:  Lionel Feugère

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-->PONE-D-25-52971R1-->-->Proximate determinants of the frequency of mosquito sounds: separating species-specific effects from environmentally driven variations - implications for AI species recognition-->-->PLOS One

Dear Dr. Augustin,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.-->--> -->-->Please submit your revised manuscript by Feb 21 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Muzafar Riyaz, Ph.D.

Academic Editor

PLOS One

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Reviewer #2: (No Response)

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Reviewer #2: Yes

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Reviewer #2: I Don't Know

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Reviewer #2: Yes

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Reviewer #2: (No Response)

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

PONE-D-25-52971R2

Dear Dr. Augustin,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Muzafar Riyaz, Ph.D.

Academic Editor

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Reviewers' comments:

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Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #2: I Don't Know

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Reviewer #2: Yes

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Reviewer #2: (No Response)

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Reviewer #2: Yes:  Lionel Feugère

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