Significant scholarly effort and other tools

[Fabbiologia]

While I recognize the significant effort and expertise that has gone into developing this package, I have some overarching concerns regarding its potential scholarly impact, particularly in alignment with the guidelines set by the Journal of Open Source Software (JOSS). I want to clarify that I am not an expert in salmon biology or dam impact modeling. However, as someone with general concerns about the breadth and applicability of scholarly software, I find it difficult to see how this package extends beyond a limited niche of applications and a small community of experts in this highly specific field.

One major question I would pose is: What makes this package a product that is useful on its own? While it is clear that this code supports a scientific manuscript on salmon populations, the justification for its broader utility and independent scholarly value is less evident. To align with JOSS's requirements, I believe the authors should better articulate why this package stands as a significant scholarly contribution beyond the context of its associated manuscript.

For example:

If someone wanted to adapt this package to model other species impacted by dams, how feasible would that be?
What mechanisms exist within the code or documentation to facilitate such extensions?
Could a non-expert contribute to improving or expanding this package?
These aspects are currently unclear, and addressing them would strengthen the case for publication in JOSS.

Additionally, it is worth considering whether the package is applicable to regions or systems outside the specific context it was designed for (e.g., Atlantic salmon in the Penobscot River). If the package is adaptable to other geographical areas or similar ecological problems, this adaptability should be better communicated, with examples provided in the documentation or paper.

Another point of concern is the absence of a comparison with similar tools. To fully understand the strengths and limitations of this package, it is essential to situate it within the landscape of existing tools for fisheries modeling and dam impact analysis. For example, tools like FLR, TropFishR, Mizer, and fishdynr (see below) offer related functionality for modeling population dynamics in fisheries. A discussion of how this package compares, in terms of capabilities, performance, or niche focus, would provide important context and help justify its uniqueness and utility.

In summary, I believe the following aspects should be addressed:

• A stronger justification for the package's independent scholarly impact beyond its use in a specific manuscript.
• A clear explanation of whether and how the package can be adapted for other species or regions, and how users can contribute to its improvement.
• A comparative analysis of similar tools, highlighting this package's advantages and limitations to show its value in the broader context of fisheries science.

I think these additions would not only strengthen the submission’s alignment with JOSS's guidelines but also enhance the package's appeal and usability for a wider audience.

Fisheries Modeling Tools

FLR (Fisheries Library in R)

FLR is a comprehensive framework for quantitative fisheries science, offering a suite of tools for fisheries modeling and management strategy evaluation. It emphasizes openness, flexibility, and extensibility, making it suitable for various modeling paradigms.
FLR Project

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fishdynr

This package provides functions for modeling fish growth, mortality, recruitment, and includes several models for simulating population dynamics and management scenarios. It is particularly useful for developing and testing fisheries population dynamics models.
GitHub

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TropFishR

TropFishR offers a collection of fisheries models based on the FAO manual "Introduction to Tropical Fish Stock Assessment." It focuses on the analysis of length-frequency data and is tailored for data-poor fisheries, making it applicable to a wide range of tropical fish species.
Finetwork

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Mizer

Mizer is an R package for running dynamic multi-species size-spectrum models of fish communities. Developed to model marine ecosystems subject to fishing, it provides a framework for multi-species fisheries modeling, enhancing accessibility and reproducibility.
GitHub

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ss3sim

An R package that facilitates reproducible, flexible, and rapid end-to-end simulation testing with Stock Synthesis (SS3). It allows for the exploration of structural differences between underlying truths and estimation model assumptions, aiding in model evaluation and development.
arXiv

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spatialSim

This package offers a multi-species spatiotemporal size-structured operating model for management strategy evaluation. It combines spatially continuous models of population dynamics with harvesting models, supporting simulations of fisher dynamics and catch data at true spatial scales.
arXiv

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Salmon Population Modeller

A web-based demonstration tool designed to provide insights into salmon population dynamics. It illustrates typical levels of loss at different life stages and the impact of various factors on stock viability, aiding in understanding and managing salmon populations.
Atlantic Salmon Trust