-->PONE-D-25-26036-->-->Coral restoration alters reef soundscapes but machine learning and manual analyses suggest different recovery rates-->-->PLOS ONE

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Many thanks to Verena Schoepf for the early advice and mentoring. The authors would like to thank Nature Seychelles, through whom this study was conducted, particularly S. Ramkalawan and K. Henri, who manage the reef restoration project. We would like to thank the Seychelles Ministry of Agriculture, Climate Change and Environment, the Adaptation Fund, UNDP Mauritius and the Government of Seychelles for the project support.-->--> -->-->We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. -->-->Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. 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Reviewers' comments:

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Reviewer #1: Yes

Reviewer #2: Partly

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

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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-->5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: a) The conceptual link between reef function, ecological health, and soundscape recovery should be more clearly articulated. A concise framework or figure would help clarify the interpretation of PAM data as a proxy for ecosystem function.a) The conceptual link between reef function, ecological health, and soundscape recovery should be more clearly articulated. A concise framework or figure would help clarify the interpretation of PAM data as a proxy for ecosystem function.

b) The hypotheses could be stated more explicitly, outlining a priori expectations for both manual and machine learning analyses.

c) The use of a single restoration site should be acknowledged as a limitation affecting generalisability. Clarify whether abiotic factors were controlled across sites.

d) Justify choices in acoustic sampling design, such as the 1:4 duty cycle and selection of 10/12 recordings per phase.

e) Manual analysis by a single observer should be discussed in terms of potential bias, and whether standardised resources were used for fish call classification.

f) UMAP outputs are informative but difficult to interpret ecologically. Consider discussing potential drivers of clustering and whether acoustic indices (e.g., ACI, ADI) were considered.

g) The term “restoration-specific soundscape” is compelling but needs a clearer functional definition.

h) Expand briefly on the conservation implications of PAM-based approaches and their integration into reef monitoring.

i) Ensure consistency in reference formatting and check for updated versions of any preprint citations.

Reviewer #2: I would like to thank the authors for their effort in preparing the manuscript “Coral restoration alters reef soundscapes but machine learning and manual analyses suggest different recovery rates”. The manuscript raises a relevant topic with potential value to the field. However, I believe that in its current state, it does not provide sufficient evidence to support the claims. The described statistical analysis and machine learning approach are sound and detailed enough to be understood. Nonetheless, I believe the authors should consider adding supportive evidence or expanding the main results to strengthen their claims. I would like to thank the authors for their effort in preparing the manuscript “Coral restoration alters reef soundscapes but machine learning and manual analyses suggest different recovery rates”. The manuscript raises a relevant topic with potential value to the field. However, I believe that in its current state, it does not provide sufficient evidence to support the claims. The described statistical analysis and machine learning approach are sound and detailed enough to be understood. Nonetheless, I believe the authors should consider adding supportive evidence or expanding the main results to strengthen their claims.

L194–195. The authors state that a bandwidth filter for the low-frequency range, which typically relates to fish sounds, was applied before being used in both manual and ML analyses.

L318–321. The authors suggest that it is plausible to believe that the broader soundscape patterns seen in UMAP are due to invertebrate sounds. Do the authors mean that invertebrate sounds were also filtered in the analysis? Could you provide some evidence? It was not clear to me whether the manual analysis was focusing on a single taxon and the ML on multiple taxa.

L206–207. “We identified the individual vocalizations as likely to be produced by fishes based on their similarity to calls recorded by previous studies (11,13,49).” Could the authors provide a list with the potential matches to the previous studies? It would be useful to see, at least in the supplementary material, all the fishes that were recognized. I believe this would strengthen your claims and add to the discussion.

L276–286. I understand how demanding the manual analysis can be, but could the different results between manual and ML be a matter of sample size? Would it be possible to run the ML analysis only using the same samples as in the manual analysis to see if you get similar results? Were the same samples from the manual analysis also randomly selected and used in the ML? It would be interesting to see where the samples used in the manual analysis fall in the UMAP graphs. This could help clarify what the ML “black box” is considering.

L227. Each recording used in the ML approach was 5 seconds in length. I believe the manual approach used longer recordings. I would argue that this makes the approaches more comparative than complementary, which changes what the authors claim to achieve.

Fig 1E. The genera names should be italicized. I believe “Fungidae” is the family name. Also, the “Healthy” bar has only 10 colors, while the results indicate G = 11.

The manuscript requires stronger evidence for its claims, clarification on methodological differences, and improved presentational of results. I recommend a major revision, with the expectation that the authors can significantly improve the manuscript through additional explanation and incorporation of further supporting evidence.

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Reviewer #1: Yes: Musfera JahanMusfera Jahan

Reviewer #2: No

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Coral restoration alters reef soundscapes but machine learning and manual analyses suggest different recovery rates

PONE-D-25-26036R1

Dear Dr. Croasdale,

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Academic Editor

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