Articles | Volume 17, issue 8
https://doi.org/10.5194/gmd-17-3235-2024
© Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License.
Special issue:
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
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- Final revised paper (published on 25 Apr 2024)
- Preprint (discussion started on 02 Nov 2023)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
-
RC1: 'Comment on egusphere-2023-2503', Juan Rocha, 16 Dec 2023
- AC1: 'Reply on RC1', Fabian Stenzel, 19 Jan 2024
- RC2: 'Comment on egusphere-2023-2503', Anonymous Referee #2, 02 Feb 2024
Peer review completion
AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Fabian Stenzel on behalf of the Authors (23 Feb 2024)
Author's response
Author's tracked changes
Manuscript
ED: Publish as is (28 Feb 2024) by Carlos Sierra
AR by Fabian Stenzel on behalf of the Authors (01 Mar 2024)
Manuscript
Thanks for the opportunity to read your work. The manuscript is set to describe a new R package to calculate metrics of integrity and stability in ecosystems. The paper is clear and methods well explained with some simulation results coming from LPJmL models.
The paper, however, falls short in introducing the software, its requirements and documentation for a larger adoption by the community. If the central goal of the paper is to introduce the `biosphericmetrics` package, more attention should be given to the software documentation. For example, the package does not contain vignettes or demos that help the user understand what are the main functionalities. Since the package is meant to be used as companion of LPJmL model output, the authors should make available a small datasets that the users can test to understand the usage of functions (the equivalent of mtcars in other packages, not the 3GB in Zenodo). The user should be able to install the package, read the help of functions, and be able to run a demo with some results. I understand the output can be large files, but you can get inspiration of other packages such as stars or terra in making available small spatial data cubes (NetCDF files) for testing.
The GitHub repository has some useful information on for example what functions to use when working on small datasets, sending jobs to servers, or plotting. However, the package documentation is not stand alone. For example, it seems one needs json files to use `calc_biocol()`, but that dependency is not documented on the help files of the function. Most functions on the package do not have examples and are not working out of the box, many assumes the user has access to Fabian’s folder structure. Thus I can install the package, but as a reviewer I cannot test if it works. The package also lacks testing and better documentation on scalability.
I strongly recommend to check the standards for software development in specialised journals such as Journal of Open Source Software, and the Journal of Statistical Software (JStatSoft). Both of them recommend the workflow and standards of the rOpenSci consortia: https://stats-devguide.ropensci.org
At minimum the package should contain a vignette explaining its functionality and a demo data that users can use to test its functionality.
Best regards,
Juan
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