the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Oct 2024
Numerical Models for Monitoring and Forecasting Ocean Ecosystems: a short description of present status
Abstract. Understanding and managing marine ecosystems under potential stress from human activities or climate change requires the development of models with different degree of sophistication in order to be capable of predicting changes in living components and environmental variables. Recent advances in ecosystem modelling are the focus of this paper, which reviews numerical approaches to analyse the characteristics of marine conditions in terms of typical units, i.e., individuals, populations, communities and ecosystems. In particular, it examines the current classification of numerical models of increasing complexity – from individuals and population and stock assessment models to models representing the whole ecosystem by covering all trophic levels – and presents examples and their operational maturity, finally demonstrating their use for supporting marine resource management, conservation, planning and mitigation actions.
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Status: open (until 19 Dec 2024)
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RC1: 'Comment on sp-2024-42', Anonymous Referee #1, 15 Nov 2024
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The paper provides an overview of six classes of marine ecosystem models and evaluates their current status in operational applications. I acknowledge that it’s a daunting task to review these many models in such limited space, and I appreciate the author’s efforts. However, I found some aspects of the paper challenging.
- I wonder who the targeted audience for this review is. I’m afraid that the current version is not quite accessible to the wider audience, partiallydue to the use of jargon and acronyms—some of which are not explained—and by lengthy sentences from time to time. I have a background in ocean ecosystem modeling, but I have to admit that it’s not always easy to follow the description of all models. If the review is intended for a wider audience, I think it could be a bit more elementary.
- The Introduction indicates that there are “available syntheses and reviews”. How does this short description complement those previous works? A brief summary highlighting the specific focus of the previous reviews in relation to this one would help readers identify which resources best align with their interests.
- As a follow-up comment, given the limited space available for detailed information on each class of model, it would be beneficial if the author could recommend key references for further reading.
- The criteria used to assess the readiness or maturity of each class of model for operational applications could be more explicitly defined. Is it based on the model complexity, computation cost, or technical challenges in implementation?
- Given the limited space, the description of each model class is brief. The only exception is the “3 Population and stock assessment models.” It presents two complex model equations, which are a bit awkward and somewhat “too detailed” in this brief review.
Some technical corrections:
Page 2, line 31. Should be “six classes”
Page 5, line 151. What does “population dynamics under technical interactions” mean?
Page 7, line 195. “These WEM models…”
Page 15. Table. What is the gww?
Page 16. Table. Missing the “of” after the “Number”
Page 17. Table. “Ensemble” instead of “Ensamble”?
Citation: https://doi.org/10.5194/sp-2024-42-RC1 -
RC2: 'Comment on sp-2024-42', Anonymous Referee #2, 28 Nov 2024
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This paper provides a classification of numerical models and toolboxes developed in recent years for marine ecosystem modelling with a view to ecosystem management and prediction. Given the limited space, the description is succinct and the overview cannot meet the objective of a full review. The readers will inevitably have to delve deeper, consulting additional reviews, synthesis or original articles to access the information they're looking for. The diversity of models available today has become very large, and the classification proposed here is useful in guiding the user not directly involved in specific model developments. Therefore, the paper should be published but some aspects need to be improved to make the contribution more impactful:
- The purpose of the paper and target audience should be clarified and made more explicit. Is it a “review of reviews” (as suggested page 2, line 31), or an attempt at classification, or a user's guide, or something else ?
- Is the classification mutually exclusive, or can there be models that fall into several classes, or combine concepts from several classes? A short discussion on possible intersections between classes should be added.
- One of the aims of the paper seems to be the assessment of the “operational maturity” of the models listed. The notion of “operational model” needs to be clarified in the text. Is it a question of high TRL ? of ability to produce information routinely ? in real time ? The title mentions “Numerical Models for Monitoring and Forecasting”, but the text suggests that the focus is more on management issues. It would be useful to identify the reasons why some models are still far from operational status, once the notion is clarified.
- The content of the sections on the 6 classes might be harmonized. Equations or GitHub references are used in some sections, but not in others. Sections 6 and 7 do not give any conclusions on the operational maturity (or TRL) of the models, unlike the others. Maybe provide one example of emblematic model for each class ? I suggest to remove the equations in section 3.
- From a user’s perspective, it will be very useful/important to identify in Table 1 (i) one reference paper for each model ; (ii) which models are open access, and how to access the code in practice (e.g. on GitHub).
Minor comments:
- page 2, line 31: five -> six classes
- page 2, line 40: “modelled dynamically or derived from energy requirement”: I don’t understand the alternative in the sentence.
- page 2, line 43: “clear empirical interpretation”: interpretation capability ?
- page 3, line 67: how B differs from Bt ? Ct not in (eq 1)
- page 3, line 79: meaning of SS3, a4a, XSA ? please remove unnecessary acronyms.
- page 4, line 119: would it make sense to cite ichthyop (Lett 2008) explicitly ? https://ichthyop.org/ which has been used in a variety of projects ? (at least to be added to Table 1)
- page 5, line 147: please provide the GitHub reference
- page 6, line 174: is DBEM the same as the one cited in Section 5 (species distribution models) ? Is this an example of hybrid class model ?
- page 7, line 204: “general improvement of realism but also lower accuracies”: this is confusing. What is meant by “realism” ?
- Table 1: the legend should be checked and further developed to explain to meaning of each column (e.g. time units ? is it time scale ? “Name” should be “Model” and Model should better be “Acronym” ?)
- Table 1: a new column indicating open access, web site and (one) reference paper would be very helpful
- Table 1: the last row (Apecosm) should be filled in
Citation: https://doi.org/10.5194/sp-2024-42-RC2 -
RC3: 'Comment on sp-2024-42', Anonymous Referee #3, 02 Dec 2024
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The author reviews current numerical marine modeling approaches, from bioenergetic models for individual organisms to ecosystem-scale models, with a stated focus on understanding and managing marine systems under stress from human activities and climate change. The manuscript attempts the difficult task of briefly assessing the maturity of various modeling approaches while discussing their applications in marine resource management.
The manuscript's assessment of model maturity in the earlier sections (2-4) appears subjective and would benefit from more rigorous supporting evidence and citations. Often, it is not even clear what factor contributed to the assessment. An example is the apparent contradiction in the utility of spatial explicitness and oceanographic forcing for stock assessment models and connectivity models that need to be resolved or better explained: "Although in most cases, stock assessment models are not spatially explicit and do not consider explicitly oceanographic forcings they are routinely used in formal assessments for management and might be considered as ready for operational oceanographic applications." (l. 101) Yet, the text describing connectivity models seems to suggest that adding the missing features makes the models less mature: "Connected with oceanographic variables and spatially explicit these models however, appear less mature for operational applications." Perhaps rather than attempting maturity assessments, I would suggest using the approach taken in Section 5, which focuses on describing actual use cases and operational status.
In general, I would suggest revising each section to follow a consistent format: beginning with a brief description of the model class, followed by current use cases -- if available -- specifically related to human activities and climate change. Each section could conclude with an assessment of which current environmental challenges are already being addressed or need attention. This would be more valuable than the current approach, which primarily describes existing models and their structure. The list of models in Table 1 already serves this purpose, allowing the main text to focus more on the present status and outstanding issues in the field.
In Section 2, the description switches from a general description of bioenergetic models to the DEB subclass. If all current bioenergetic models are DEB models, this should be pointed out in the manuscript. If not, what are the alternatives and when are they used?
Starting from the title to about the last sentence of the abstract, a reader might think that the topic of the manuscript are coupled physical-biogeochemical models, as these are often referred to as ecosystem models. Here it would be useful to differentiate the type of model earlier, preferentially in the title.
In this context, it would be helpful to more clearly state what kind of organisms are being modeled using the approaches. Neither the abstract nor the introduction explicitly mention what is being modeled: The abstract speaks of "individuals, populations, communities and ecosystems", the introduction mentions "marine biological resources" without providing any examples. As stated above, I would suggest making use cases of each type of model more explicit.# specific comments
L 22: "numerical models can be divided into six broad classes [...] These five classes of models are reviewed in the following sections ...": It should be six classes in both cases.
L 51: the "DEB" abbreviation was introduced in the preceding sentence.
L 53: "abstract concepts that are more challenging to measure empirically": What would be an example of that?
L 54: "The presence of the storage": Here it would be easier for the reader to refer to "storage tissue" again, using the exact term introduced earlier.
Eq 1: What is n here?
L 67: "fishing mortality": So this type of model is only used to model populations of species that are being fished? Why not mention this early on explicitly?
L 79: "such as SS3, a4a, XSA, etc.": Listing these names/abbreviations here is not useful. Readers unfamiliar with these approaches are only given 3 letter abbreviations without any context or citations, and readers who know these approaches probably don't need the see the abbreviations again. Here, it would be much more useful to describe these sophisticated approaches in words.
Eq. 2: I think it is not useful to show the equation here, it can be more easily explained in words. If it is kept, the initial N should have a subscript t, and the "a=" is missing from some of the subscripts.
L 153: "considering the specific problem": What is the specific problem?
L 157: "MICE can simultaneously represent focal populations in age-structured classes, while others take a surplus production approach": This could be described better, what is the surplus production approach, and how does it compare to an age-structured approach?
Citation: https://doi.org/10.5194/sp-2024-42-RC3
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