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[REVIEW]: The sspm R package: spatial surplus production models for the management of northern shrimp fisheries #4724
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👋 👋 👋 @VLucet, @quang-huynh, @kellijohnson-NOAA this is the review thread for the paper. All of our communications will happen here from now on. As a reviewer, the first step is to create a checklist for your review by entering
as the top of a new comment in this thread. These checklists contain the JOSS requirements. As you go over the submission, please check any items that you feel have been satisfied. The first comment in this thread also contains links to the JOSS reviewer guidelines. The JOSS review is different from most other journals. Our goal is to work with the authors to help them meet our criteria instead of merely passing judgment on the submission. As such, the reviewers are encouraged to submit issues and pull requests on the software repository. When doing so, please mention We aim for reviews to be completed within about 2-4 weeks. Please let me know if any of you require some more time. We can also use EditorialBot (our bot) to set automatic reminders if you know you'll be away for a known period of time. Please feel free to ping me (@Bisaloo) if you have any questions/concerns. |
@editorialbot generate my checklist |
@quang-huynh, could you try re-running this command please? Something went wrong on our end. |
Review checklist for @quang-huynhConflict of interest
Code of Conduct
General checks
Functionality
Documentation
Software paper
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I reviewed the software by installing the package on my local desktop and going through the example in the An_example_with_simulated_data vignette. I was able to replicate the steps in the vignette on my R console and replicate the figures. Documentation suggestions: I noticed that many figures in the vignette are too small, so the figure width needs to be wider in the Rmarkdown document. I also see that the smoothened density estimates (step 8) are identical in all four panels. Is this correct? Do P1-4 represent SFA? The package is well organized for those familiar with S4 class system. I would recommend reducing the number of methods for generics whenever possible but this is not a major concern. For example, Are model diagnostic tools available to evaluate goodness of fit? I see that A summary method for the Functionality documentation The help documentation can be improved without much additional effort. For example, the help for Community guidelines A quick statement in the README.md to recommending users to report bugs in Github issues will meet this requirement. Paper suggestions: State of the field There's not much reference to similar software. There are various software packages, e.g., spict, aspic, jabba, for surplus production models that estimate intrinsic rate of increase and carrying capacity. These model a unit stock without spatial considerations. However, sspm does not appear to estimate the inherent productivity of a population. Rather, sspm appears to use GAMs to estimate surplus production and productivity In a fisheries context, sspm reminds me more of the VAST and sdmTMB packages that implement spatiotemporal GLMMs to predict response variables across a spatial field over time. It may be possible to somewhat replicate sspm in sdmTMB, but estimation in sspm will be much, much faster (compared to the use of Gaussian Markov random fields in the other packages), and sspm conveniently addresses a specific use-case for fisheries assessment. I recommend reference and comparison of these packages with sspm. References The Pedersen et al and Prager reference needs to be edited to meet this requirement. Other paper suggestions: Line 13: Fisheries managers typically do not use analytical methods but receive advice from analysts. Perhaps "fisheries analysts" should be used instead of 'fisheries managers'? |
Review checklist for @kellijohnson-NOAAConflict of interest
Code of Conduct
General checks
Functionality
Documentation
Software paper
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Thanks a lot @kellijohnson-NOAA & @quang-huynh for your reviews! @VLucet, do you have a timeline of when you could be able to address these comments? |
Thanks a lot to @kellijohnson-NOAA and @quang-huynh for your thoughtful reviews. |
Hi all, my apologies for underestimating the amount of time it is taking me to address all the comments. I wanted to thank again @kellijohnson-NOAA and @quang-huynh for the detailed and helpful comments (the list of tasks they have given and the linked commits for addressing them are here: pedersen-fisheries-lab/sspm#54 and pedersen-fisheries-lab/sspm#59). All of @kellijohnson-NOAA's have been addressed. @quang-huynh I also wanted to let you know that the research document is finally out here, in case there is more you wanted to add to your comment which mentions being unable to access it yet: pedersen-fisheries-lab/sspm#125) |
@VLucet Thanks for the link to the research document. The closest alternative to sspm appears to be VAST for spatial surplus production modeling: https://james-thorson-noaa.github.io/docs/tutorials/surplus-production/ The associated paper is https://doi.org/10.1111/1365-2664.12664 which the authors could use for comparison The major benefit of sspm over VAST appears to be run time, the longer model runtime is VAST is associated with estimating the spatial random field. I've also found a big learning curve is needed with VAST. sspm addresses a much narrower use case which makes it easier to learn for a new user. |
Hi @VLucet, happy new year! Do you have an updated timeline of when you could go over the last remaining review comments? |
Hi @Bisaloo ! Happy new year as well. @eric-pedersen and I have been discussing how to address the last couple of comment relating to the text (pedersen-fisheries-lab/sspm#125 and pedersen-fisheries-lab/sspm#126). We need a little more time to finalize that. All the other comments and technical issues have been addressed in pedersen-fisheries-lab/sspm#58. |
Hi @VLucet, what is the status of your submission please? Do you have an estimated date where you aim at finalizing your updates. It would be great to not wait too much as to make sure your work is still fresh in the reviewer's memory. |
Thanks, I submitted one last PR (pedersen-fisheries-lab/sspm#144) to fix the format for co-first authors and everything is ready for acceptance after this! |
just saw, I merged it |
@editorialbot generate pdf |
@editorialbot recommend-accept |
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ID ref-thorson_importance_2015 already defined |
I see the error, the ref |
It should be fixed now. |
@editorialbot recommend-accept |
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👋 @openjournals/ese-eics, this paper is ready to be accepted and published. Check final proof 👉📄 Download article If the paper PDF and the deposit XML files look good in openjournals/joss-papers#4328, then you can now move forward with accepting the submission by compiling again with the command |
Looks good to me. |
Here is my to do list:
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@editorialbot accept |
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Congratulations @VLucet @eric-pedersen 🎉🎉! Thanks a lot for your thorough and informative reviews @kellijohnson-NOAA @quang-huynh 🙏🙏! |
Yay! Thanks to you @Bisaloo as well! |
Congrats on your new publication @VLucet!! Many thanks to editor @Bisaloo and reviewers @quang-huynh and @kellijohnson-NOAA for your time, hard work, and expertise!! |
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Submitting author: @VLucet (Valentin Lucet)
Repository: https://github.com/pedersen-fisheries-lab/sspm
Branch with paper.md (empty if default branch): main
Version: v1.0.0
Editor: @Bisaloo
Reviewers: @quang-huynh, @kellijohnson-NOAA
Archive: 10.5281/zenodo.8015102
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