1 Disclaimer

The following work is preliminary and intended only as tool for eliciting feedback on data, modelling and other aspects of this fishery.

None of these results are final.

These analyses do not necessarily reflect the point of view of NOAA and in no way anticipate NOAA future policy.

2 Objective

Develop an MSE framework for the Atlantic Dolphinfish (Mahi Mahi) fishery to test candidate management procedures and inform other management decision making including research prioritization, assessment methodology, specification of fishing regulations and enforcement.

3 Project details

Title: ‘Lead Analyst for the Dolphin Management Strategy Evaluation Project IAW the Tasks included in the Statement of Work’

Term June 2024 - May 2025, June 2025 - Feb 2026
Funding body U.S. National Oceanic and Atmospheric Administration
Funding stream Sam.gov
Solicitation No. # 1305M324Q0309, NA
Contract No. 1305M324P0270, NA
Project Partners Blue Matter Science Ltd.
Blue Matter Team Tom Carruthers, Adrian Hordyk, Quang Huynh
NOAA Collaborators Cassidy Peterson, (Matt Damiano)

4 Progress

The Prerequisite, Foundation, Initial and Revision phases have been completed (Figure 1). These form the basis for the completed MSE framework which is now subject to CIE peer-review (July 2026).

Important methodological components of the Refinement and Adoption phases have been completed such as proposed tuning targets and tuning methods, description of all quantitative performance metrics and the production of an interactive Slick object for viewing MSE results.

Figure 1. Progress in the MSE roadmap. For more information about the roadmap and the various steps see the supporting document

5 For the Peer - Review Team

5.1 Trial Specification Document

The principle document that describes the technical aspects of the MSE framework is the Trial Specifications Document (TSD) - a comprehensive guide to MSE framework development. This is probably the most important source material for your review as it describes and links all of the code, supporting documentation and equations.

5.2 Presentations for Review Meeting

The presentations of the review meeting roughly follow the steps of the Trial Specifications Document but they include direct reference to the Terms of Reference. These may or may not be of interest.

1. Context

2. Introduction

3. Model Inputs

4. OM Conditioning Approach

5. Operating Models

6. Data Simulation

7. Management Procedures

8. MP Performance Summary

5.3 Performance Work Statement (inc Terms of Reference)

The Performance Work Statement for SEDAR 104 contains relevant review information such as background, scope, tasks, reporting requirements, terms of reference and a preliminary agenda.

5.4 Demonstration MSE results

  1. Download the Slick object with demonstration MSE results
  2. Go to the Slick App Online
  3. Load up the slick object from your computer and explore results.

6 Code and Software

6.1 Dolphinfish MSE Code

DolphinMSE Github Repository (Private)

  • https://github.com/Blue-Matter/DolphinMSE
  • Private ‘workhorse’ repo with live data and analyses.
  • Data, reports, notes
  • Code for processing data, spatial modelling (VAST), conditioning operating models
  • Supporting analyses (e.g., exploratory tests of trip limits, VAST specifications etc)
  • Makes all the inputs to mahiMSE R package (OMs, MPs, etc)

mahiMSE GitHub Repository and R Package (Public)

  • https://github.com/Blue-Matter/mahiMSE
  • mahiMSE Manual
  • Public-facing R package with manuals, documentation
  • How to design MPs, use the mahiMP, run MSE simulations and plot results.
  • Includes some small demonstration OM objects and example data sets
  • Custom code for organizing results and plotting results.

mahiRefSet GitHub Repository and R Package (Public)

mahiRobSet GitHub Repository and R Package (Public)

6.2 MSE Computation and Results Presentation

OpenMSE (Hordyk et la. 2026, v1.01)

MSEtool (Hordyk et al. 2026, v4.0.0)

  • MSEtool GitHub
  • OM specification
  • Closed loop simulation
  • Empirical MPs

SAMtool (Huynh et al. 2026, v1.9.1)

  • SAMtool GitHub
  • Data-rich stock assessment methods
  • OM conditioning using Rapid Conditioning Model (RCM)
  • Model-based MPs

RCM (Huynh 2026)

Slick (Hordyk et al. 2026, v1.0.2)

ECP (Carruthers 2024, v1.0.5)

7 Current Priorities

Table 3. MSE framework development priorities

Limitation / new feature / bug Description
Carry out peer-review of MSE framework Are the current OMs, MPs and performance metrics sufficient to meet ToRs? Can the process now progress to formal MP testing and potentially adoption?
Alternative Indices of Abundance for MPs Formalization of PR_tourn, MRIP, ENSO, Blue-Blob
Effort dynamics model To model opportunity a model is needed to predict whether ACLs will be reached given available fishing characteristics.
Slick improvements Add landings by fleet to time series plots
Slick improvements Make robustness slick or add to existing

8 MSE Resources

The Ocean Foundation provides excellent introductory materials on MSE and management procedures (harvest strategies) at their harvest strategies website.

The openMSE website contains extensive documentation about openMSE and approaches such as RCM.

10 Reports

Stakeholder workshop report. Stakeholder workshop meeting materials are appended.

Dolphin / wahoo participatory modeling workshop report. Note that this was pre-MSE stakeholder workshops, but includes relevant background info.

11 Recent Presentations

April SSC Meeting. Dolphin management strategy evaluation: SSC review 1.

April Dolphin Wahoo AP Meeting. Update on the dolphin management strategy evaluation.

December Council Meeting. Update on the dolphin management strategy evaluation.

June Council Meeting. Update on the dolphin management strategy evaluation.

December Council Meeting. Update on the dolphin management strategy evaluation.

November Dolphin Wahoo AP Meeting. Dolphin management strategy evaluation: plans and progress.

June Council Meeting. Update on the dolphinfish management strategy evaluation.

December Council Meeting. Update on the dolphinfish management strategy evaluation stakeholder workshops.

12 References

Damiano, M., Karnauskas, M., Merten, W., Cao, J. 2024. Spatiotemporal dynamics of dolphinfish (Coryphaena hippurus) in the western Atlantic Ocean. Fish Bull. 2024

Hadley, J., Mehta, K.N. 2021. Amendment 10 to the Fishery Management Plan for the Dolphin and Wahoo Fishery of the Atlantic. Available from https://repository.library.noaa.gov/view/noaa/32989

Hordyk, A. 2025. Slick decision analysis. Available from: https://harveststrategies.org/slick-user-guide.html

Hordyk, A., Huynh, Q., Carruthers, T. 2025. OpenMSE: An open-source R package for Management strategy evaluation, available from: https://openmse.com

Huynh, Q., 2025. Rapid Conditioning model. Available from https://openmse.com/tutorial-rcm/

Peterson, C., Karnauskas, M., McPherson, M., Hadley, J., Blake, S., Byrd, J. 2024. Report of the South Atlantic dolphinfish management strategy evaluation stakeholder workshops. NOAA Tech. Memo. NMFS-SEFSC-781, 35 p. Available from https://repository.library.noaa.gov/view/noaa/61285

Punt, A.E., Butterworth, D.S., de Moor, C.L., De Oliveira, J.A.A., and Haddon, M. 2016. Management strategy evaluation: Best practices. Fish Fish. 17(2): 303–334. doi:10.1111/faf.12104.

13 Acknowledgements

Many thanks to the hard work of our project collaborators.

Stakeholder Science Team: Matt Damiano, Mandy Karnauskas, Matt McPherson, Suzana Blake, Julia Byrd, John Hadley, Nikhil Mehta, Wess Merten, Lela Schlenker.

MSE Modelling Technical Team: Matt Damiano, Kyle Shertzer, John Walter, Cassidy Peterson

Blue Matter: Tom Carruthers, Adrian Hordyk, Quang Huynh

OpenMSE was developed with support from the Natural Resources Defense Council (NRDC), the Gordon and Betty Moore Foundation, the Packard Foundation, the Marine Stewardship Council, Fisheries and Oceans Canada (DFO), the U.S. National Oceanic and Atmospheric Administration, the International Commission for the Conservation of Atlantic Tunas (ICCAT) and The Ocean Foundation.

OpenMSE continues to be developed with the support of the The Ocean Foundation.

14 Glossary

Table 3. Glossary of MSE terminology and acronyms

Term Description
MSE Management Strategy Evaluation: a participatory process to establish management procedures (harvest strategies) that are robust to uncertainties in fishery and population dynamics.
OM Operating Model: a mathematical description of fishery and population dynamics codified in a simulation framework for the robustness testing of candidate management procedures.
MP Management Procedure (harvest strategy): a algorithm that calculates management advice from data (real or simulated).
CMP Candidate Management Procedure. One of multiple possible management procedures that is to be comparatively evaluated by MSE.
MSE framework The process, membership, meetings, documents, software package, management objectives and exceptional circumstances protocols that support the adoption of a management procedures.
Closed-loop simulation The engine at the heart of MSE simulations: a codified representation of fishery and population dynamics (operating model) linked to an observation error model (data generation) a candidate management procedure, an implementation model (controls adherence to management advice) which accounts for feedback between the fishery system, data, recommendations and management actions to quantify management performance.
TSD Trial Specifications Document: a description of the methodology of the MSE framework that ensures reproducibility including all decisions, background information and equations.
Reference Case A single operating model familiar to the working group that can be used for didactive purposes such as exploring ideas, demonstrating concepts / sensitivities.
Reference Set A set of operating models, sometimes represented by an orthogonal grid of operating models that represent the core uncertainties that CMPs should be robust to: the primary basis for the evaluation of CMPs.
Robustness Set A secondary set of operating models used to further distinguish between CMPs that otherwise perform similarly for the reference set of OMs. These may include hypotheses that have a relatively weak empirical basis or uncertain future conditions for projections.
Data guillotine A date after which new data will not be accepted for use in operating model or management procedure development.
OM conditioning The process of fitting operating models to observed data statistically (similar to fitting of stock assessment models).
EC Protocols Exceptional Circumstances protocols: an empirical check that observed data are consistent with those data expected to be observed when the MP is in use (a basis for detecting departures in systems dynamics away from the operating models for which the MP was demonstrated to be robust).

14.1 More on Operating Models

An operating model is a theoretical description of fishery and population dynamics used for the testing of management strategies that could include, for example, data collection protocols, stock assessment methods, harvest control rules, enforcement policies and reference points. In fisheries, operating models are used in closed-loop simulation to test management procedures (aka. harvest strategy) accounting for feedbacks between the system, data, management procedure and implementation. A management procedure is any codifable rule that calculates management advice from data. Management Strategy Evaluation uses closed-loop simulation of management procedures as a core technical component but is a wider process of stakeholder and manager engagement that identifies system uncertainties, performance metrics, viable management procedures, ultimately aiming to adopt an MP for the provision of management advice for an established time period.

 

14.1.1 Reference Case Operating Model

The reference case operating model is used as the single ‘base’ operating model from which reference set and robustness set operating models are specified. Reference and robustness tests are typically 1-factor departures from the reference case OM, however sometimes reference set OMs are organized in a factorial grid across primary axes of uncertainty.

 

14.1.2 Reference Set Operating Models

Reference set operating models span a plausible range of the core uncertainties for states of nature. These are often the types of alternative parameterizations or assumptions that would be included in a stock assessment sensitivity analysis.

The role of the reference set operating models is to provide the central basis for evaluating the performance of candidate management procedures, for example rejecting badly performing harvest strategies.

 

14.1.3 Robustness Set Operating Models

Robustness set operating models are intended to include additional sources of uncertainty for providing further discrimination among management procedures that perform comparably among reference set operating models.

Robustness operating models often represent system states of nature that are not empirically informed or are hypotheses of a subset of stakeholders.