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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Partly

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

Reviewer #1: N/A

Reviewer #2: No

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: This paper addresses important, interesting, and timely questions about fishery management, and this reviewer finds the methods, at a fundamental level, appropriate and well implemented. My serious concerns are about the presentation and interpretation of the results. The authors “tie themselves into knots” by simulating myriads of possibilities and contorting results though scaling, weighting, and intractable/implicit aggregation. Due to all the transformations, I suspect the authors---like anyone reading the paper would---lost track of the correct interpretation of the results. One grave example: Scenario s5 is identified as achieving “the highest overall performance” and “but showed limited improvements in spawning biomass (Figure 6)” (Line 325). As far as this reviewer can ‘track’ the method to this result, it is technically wrong and leads to blatantly wrong/misguided interpretations. IF s5 scores “0.00” for biomass (B) (Table 5 and Figure 6) on a rescaled 0-1 scale, then the proper interpretation of zero is closer to total resource destruction than “limited improvements”!

The authors need to revisit how and what results they generate with a strong focus on simplicity, clarity, and tractability (for the reader). Simulation models provide empirically-guided conceptual results, hence the results presented do not need to cover all possible scenarios but should focus on those limited number of scenarios or permutations that produce interesting insights for fishery management! Much less, would be much more! The interpretation of those result needs to be conditioned by the assumptions/transformations along the way.

More detailed comments in no particular order:

- Com_net metric represents gross ex-vessel revenue rather than net revenue; that is disingenuous. More materially, Com_net is probably redundant (as Figure 11 also shows), as Recreational Consumer Surplus is (only) comparable with Commercial Producer Surplus. Comparing Producer Surplus to Consumer Surplus is the standard way to evaluate economic tradeoffs between commercial and recreational sectors. Adding another metric adds nothing (except confusion).

- If I understand it correctly, discards, valued at $10, can become catch, valued at $100, in some full retention scenarios (e.g., two very red boxes in Fig 8). This is conceptually flawed as discarding happens for many reasons—such as fish simply being too small or low quality to keep—not just because of regulatory limits. It’s a delicate, complicated situation---and this research might shed some light on that---or not if it's not a central result, in which case Rec_D_cs could probably be dropped from the paper entirely (again, to simplify and focus on central results without distraction, i.e., two econ metrics would probably do).

- Rescaling of metrics always involves a loss of interpretability and should be used sparingly and only when necessary. Rescaling to 0-1 loses cardinality and truncation, etc. Rescaling and then adding up is even more delicate. Related, this reviewer highly dislikes the radar plots (Figures 4 and 6), as they obscure results rather than showing any. If ONE radar plot were kept, e.g., figure 6, a status quo/baseline line/polygon MUST be added to the plot to show the direction of changes!!! (you would have seen what a complete Biomass disaster s5 is)

- Rescaling $ values (fig 8) is rescaling a ‘scale’, since $ is already a monetization of other units/dimensions. It shouldn’t be necessary, and results would be much easier to interpret, e.g., top left number in Fig 8 is “-0.1”---is this minus $100,000? Minus $10? And would “-0.2” be double the loss???

- Regarding Table 6 and associated (not well documented) analysis: Computing a single aggregated Score across all metrics relies on the very strong assumption that all five performance metrics are valued equally at 20%. Fishery management laws clearly state biomass can’t be fully compromised! The authors must carefully evaluate how to proceed with aggregation and then clearly state their assumptions (and why) in the text. I have no faith that the currently calculated Rank provides any value for fishery management (because 'equal 20%' IS a massive assumption (about policy goals), even if not documented)!

- There are too many scenarios, especially in light of the (effort) sensitivity analysis later. FOCUS. Are north/south regions or inshore/offshore really necessary for the central results of this paper? Alternatively, put the huge number of permutations into an appendix while keeping only the most interesting cases in the main text, tables, and figures.

- Figure 3: Use reverse color-coding for discards here too?

- Methodology should be in the text, not the table headings, including any rescaling, etc. Once you write it out in the text, you’ll realize how much of it does not enhance understanding…

- I think there is an internal inconsistency in Fig 5 regarding Biomass compared to Fig 3. Biomass should improve in most scenarios, no (but in Fig 5 all cells are blue)? More generally, both tables look at by species, by scenario, by metric (for a lot of permutations) but are ‘transposed’ in a different manner. Just makes it unnecessarily hard for the reader to follow. Consistency!

- Steepness of 0.99? Doesn’t that mean recruitment is basically independent of biomass? Why would managing catch and discards matter, real world, but also mathematically?

- Replace the ambiguous term "prevailing" with "base-level" or "status quo."

- Line 49: From an economic perspective, fishery management falls into two categories, “market based” and “command and control.” Output or input controls are both in the latter group. Market-based or ‘rights-based’ fishery management are ITQs, IFQs, or catch shares.

- Line 72: “closures” instead of “openings”?

- Line 17: typo ‘egive’?

- Line 226: The final “TWO” scenario”S”?

- Line 244/245: I think the base case is ‘0’ and there are 16 proposed management scenarios?

- Line 255: Is there any conversion between commercial lbs of fish and recreational numbers of fish? Is that explained in the text, and I overlooked it?

- Line 260: I think the cite should be: Liese, C. 2023. Economics of the U.S. South Atlantic Snapper-Grouper Fishery - 2018. NOAA Technical Memorandum NMFS-SEFSC-774.

- Table 5: Rec_L_cs and Rec_D-cs should be rounded to dollars, the precision implied by reporting cents does not hold for the underlying recreational research cited.

Reviewer #2: This study used a simulation model to examine the effectiveness of alternative temporal and spatial management strategies on the multispecies snapper-grouper fishery in the southeast United States Atlantic. It evaluated performance metrics including spawning biomass, commercial and recreational landings, commercial and recreational discards, and economic indicators for six species within the complex. The results show that different management strategies work better for different species, highlighting the challenges inherent to managing a multispecies fishery. The paper is written clearly and concisely.

The paper includes multiple unrealistic assumptions that the authors readily acknowledge, including (i) a focus on equilibrium outcomes in deterministic simulations without explicit treatment of uncertainty and (ii) an assumption that there would be no reallocation of fishing effort under almost all of the alternative temporal and spatial management scenarios. Both assumptions are addressed extensively in the fisheries literature, and it is well known that violation of these assumptions commonly leads to qualitatively different outcomes and inferences. The use of point-estimate parameters (Table 1) is especially concerning. While I understand that this study is being published posthumously for one of the co-lead authors and it may not be possible to augment the analytical framework, these issues are major concerns.

I note that all scenarios lead to extremely poor performance for recreational and commercial landings (Figs 4-5). This result is potentially a consequence of assuming no reallocation of fishing effort. More importantly, it is therefore unclear why fisheries managers and the fishing industries would consider these scenarios. Is it possible to identify and explore a greater range of scenarios, including those where recreational and commercial landings might be similar to or better than the status quo? If not, why not, and can the authors provide some justification for where the legal mandate(s) and/or political will might emerge to pursue the scenarios they chose?

The paper did not explicitly consider the potential for interactions (eg, competition) between the 6 species under consideration, or the effects of habitat (eg, structure), on species dynamics or the outcomes of the alternative management strategies. This omission strikes me as a missed opportunity, and could be suggested as a future direction.

The figures require a lot of the reader, with so many scenarios and response variables represented by cryptic acronyms and the application of difficult color palettes to represent the scenarios in the radar plots. I encourage the authors to heavily revise the figures so that the acronyms are spelled out in the figures and the distinctions among scenarios in the radar plots are made more obvious.

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what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

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Navigating Complexity: Evaluating Management Measures for Multispecies Snapper-Grouper Fisheries in the Southeast U.S. Atlantic

PONE-D-26-09475R1

Dear Dr. Cao,

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,

Abdul Azeez Pokkathappada, Ph.D.

Academic Editor

PLOS One

Additional Editor Comments (optional):

Kindly complete the minor correction suggested by reviewer.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Partly

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: The authors have addressed many of my original concerns. But the one central one is still not addressed: The presentation of too many results, with varying graphical tools and organization, requires too much of any reader. There are still 16 scenarios evaluated, and then 3 sensitivity analyses and both approaches serve the same purpose.

The 16 scenarios provide results for 9 measures (5 bio, 4 econ) across 6 species and overall. That’s 1008 data points for the reader to comprehend. That probably is already too much. For instance, Figure 6, which breaks out economic measures at the species level, gets just 2 sentences in the text (380-384). The sensitivity (to effort changes) under 3 differing assumptions (rec effort only-no-full retention; rec effort only-full retention; rec and com effort-full retention) provides results over continuous effort (10%-90%) for the 9 measures across 6 species. Hence the reader needs to comprehend 162 continuous lines…

Worse, the authors inexplicably switch the method of presentation. Fig 3, 4, 5, and 6 all present the 16 scenarios across the various measures and species. WHY is Figure 5 a dot-like plot when all the other figures are amounts (% or $, I think) with consistent color coding? Absolute WORST: Fig 5 reverses the order that the scenarios!!! Figure 5 needs to follow the same pattern as the other figures! [Aside, Figure 5 also clearly shows why including both commercial revenue and producer surplus is redundant: The dot cloud is exactly the same---just rescaled. Hence, I had recommended to just use one metric, also to limit the workload for the reader.]

Figures 7, 8, and 9 took me a long while to decipher: My major recommendation is “transposing” the figure and reordering the column panels. Logically, for left to right readers, the ‘least treatment’ scenario is the currently third one on the right (rec sector only, NO full retention). It really should be the first/left column/scenario and, internally, it should start with ‘100% effort remaining’ on the left or, better, 0% reduction, 10% reduction, 20% reduction. Notice that currently all lines converge on 0 all the way on the right of the figure! That should be the starting point. The second column should be ‘rec sector only, FULL retention, as it is most comparable to ‘rec sector only, NO full retention’. The third column should be ‘Both sectors, FULL’. So going from one column to the next adds treatments incrementally. All x-axis should go from 100% effort remaining to 10%, i.e., more intense treatment, or departure from the status quo, as the lines go to the right.

It looks like the two FULL retention scenarios (current middle and right side panels) compare the impact of the effort reduction to the global status quo/baseline. Hence at 90% Effort the lines appear far from “0” to which they should converge! Hence these are not really effort sensitivity curves at all---they are effort sensitivity AND the shift by going to full retention. Should these measures not be the percentage change from 100% Effort UNDER FULL RETETNION?

All this is why I think so many of the results are so redundant. Scenarios 1-6 look at various effort reduction and catch retention combinations---at 25% and 75% for both sectors or rec sector with or without full retention. Then the sensitivity analysis does it all again (always for 9 measures and 6 species). You could add another scenario at 50% effort and drop the sensitivity analyses?

Ideally, the authors would reduce the amount of results presented. At the very least, they need to make the presentation of their results consistent and intuitive to help the reader process so much data.

Reviewer #2: This revision represents a huge improvement!

The technical details (e.g., equations) about how fishing effort is redistributed under the various scenarios in the regional and depth closure scenarios remain unclear and likely unrealistic. I am willing to live with unrealistic, but increased specificity is important.

In the figures, it surprised me that red colors indicated increases and blue decreases. Consider reversing the color scale.

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what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review?  For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #2: No

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