1 Overview
The management of short-lived species is challenging: a small number of cohorts can generate high natural variability, data lags can prevent appropriately responsive management, environmental drivers of stock status may mask management performance and seasonality in recruitment, growth and survival may need to be accounted for.
Management Strategy Evaluation is tool widely applied in the testing of candidate management procedures but such frameworks are commonly designed on an annual resolution that is often not appropriate to the stock and fishery dynamics of short-lived species.
This project develops a suite of methods for developing operating models and designing management procedures for short-lived species that often rely on in-season data and management.
2 Project details
| Term | January 2026 - September 2028 |
| Funder | Pew Charitable Trusts |
| Stream | Pew International Fisheries |
| Contract ID #. | 39050 |
| PO # | 18501 |
| Project Partner | Blue Matter Science Ltd. |
| Blue Matter Team | Tom Carruthers, Adrian Hordyk, Quang Huynh |
| Pew Collaborators | Ashley Wilson |
3 Objective
To develop, test and document methods that facilitate the MSE testing of Management Procedures for short-lived species such as squid, shrimp and small pelagics.
Through the delivery of public-facing scientific reports, open-source tools, and capacity-building activities, the project aims to contribute directly to the advancement of ecosystem-based fisheries management for short-lived species (with an initial focus on squid stocks) on a global scale.
4 Introduction
4.1 Management Challenges of Short-Lived Species
For many fish stocks, population and fishery dynamics are approximated well by models that have an annual time step. These stocks typically have annual recruitment and annual data collection. The population comprises multiple cohorts and recent data may be informative about current stock status and management recommendations may be suitably responsive. Since management recommendations are often aligned with annual timesteps (e.g., ‘2027 TAC advice will remain constant at 2026 levels’) most stock assessment models are specified on an annual time step.
For the purposes of this project, we define a short-lived species as one that may not be approximated well by annual population and fishing dynamics and may require the use of in-season data. In broad quantitative terms this is a species that naturally lives for less than three years; has an annual instantaneous mortality of greater than 1.0, for which 95% of individuals naturally die before 36 months. Examples of such species include most stocks of shrimp, squid, octopus and anchovy.
The management challenges of such species are not related to longevity per se, but instead arise due to the relative frequencies of recruitment, data collection and management decision making. A fish that lives to one year and has monthly recruitment, monthly data collection and monthly management interventions is no different mathematically than a fish that lives to 12 years and has annual recruitment data collection and management decision making. However most short-lived species are not managed on a monthly time step. Consider a fish such as a sardine that lives to three years, recruits in a single month of the year and uses data from the previous year for management decision making. That is mathematically equivalent to a fish that lives to 36 years recruits every 12 years, sets advice from data from 12 years ago (the data point before that is 24 years ago) and for which management will be kept constant for 12 years at a time. Rephrased in those terms, it is easy to see why managing such a stock would be challenging. A retrospective analysis of TAC management for such a stock would almost certainly show unacceptable management performance due to those data lags and unresponsive management (unless a very conservative TAC was prescribed).
It follows that the ‘short-lived management problem’ is multifaceted and includes one or more of the following:
High natural variability in stock level due to few cohorts (perhaps only one) limiting forecasting certainty and lessening impact of alternative management options;
Data lags that span a high degree of uncertainty in stock status (available data may be ‘out of date’ and uninformative about an appropriate current management policy);
Management that does not occur frequently enough to adequately respond to changes in stock and fishery dynamics;
Environmental drivers of stock level may be more influential than management decisions;
Conventional fisheries management reference points that may not be suitable.
4.2 Squid Fisheries
Squid are ecologically significant species that play important roles in marine ecosystems—as predators and as critical forage for higher trophic level species. Their populations are characterized by short lifespans (typically around one year), rapid growth, semelparous reproduction, and high natural mortality. These biological characteristics lead to strong and often unpredictable fluctuations in abundance that can be closely linked/driven by environmental variability.
Traditional stock assessment approaches used for longer-lived finfish often fall short when applied to squid fisheries. Squid’s fast turnover, protracted spawning periods, and responsiveness to short-term environmental changes present unique challenges for developing robust, responsive management strategies. While several squid fisheries worldwide have applied models such as depletion-based models [e.g., jumbo flying squid (Dosidicus gigas) in the Gulf of California and in Chilean fisheries, Japanese common squid (Todarodes pacificus) in Japan, Argentine shortfin squid (Illex argentinus) and Patagonian longfin squid (Doryteuthis gahi) in the Falkland Islands], Bayesian hierarchical DeLury models (e.g., neon flying squid (Ommastrephes bartramii) in the North Pacific Ocean], surplus production models (e.g., SPiCT), per-recruit models and empirical/heuristic models management (see Arkhipkin et al 2021, NPFC, 2025), most still struggle with data limitations, the apparent need for near-real-time management adjustments and associated high monitoring demands.
Moreover, environmental drivers such as sea temperature, climate indices (e.g., El Niño Southern Oscillation, North Atlantic Oscillation), and spatio-temporal variability are known to influence squid abundance and distribution. Some squid species display clear offshore-coastal distribution shifts during spawning periods, which has implications for spatial management and protection of spawning aggregations.
In many instances squid fisheries do not have established monitoring and data collection programmes that facilitate management strategies that reflect their short-lived and environmentally driven fluctuations – making it challenging to have responsive/ sustainable management or even establishing suitable reference points for use by management.
4.3 Management Strategy Evaluation (MSE)
Despite recent advances in squid stock assessment science, there remain significant gaps in:
Scientific and management tools that can handle squid’s biological attributes and environmental sensitivity.
Management strategies that accommodate high-frequency data needs, in-season management adjustments and define suitable decision rules and reference points.
Frameworks that incorporate environmental variability and climate change impacts into management advice.
Accessible, and fit-for-purpose MSE tools for squid/short-lived species, especially for data-limited contexts.
Designing suitable monitoring strategies for squid and similar short-lived species.
MSE offers a promising solution to these challenges. MSE allows for the simulation and comparison of alternative management procedures under uncertainty, providing a pathway to developing harvest strategies (also known as management procedures – MPs) that are both scientifically sound and practically implementable, even when ecological processes are not fully understood.
This project builds on the growing recognition by regional fisheries management organisations (RFMOs) and the global scientific community of the need for more responsive, climate-ready fisheries management. There is a growing call for better management of the partially regulated and unregulated squid fisheries globally.
4.4 Research Goals
This research project aims to develop tools that can support the design, evaluation, and potential adoption of effective, science-based management procedures (MPs, aka ‘harvest strategies’) for squid and other species with similar short-lived, variable life histories. The project will develop an MSE package based on the openMSE platform that can be adapted for different fisheries, whether data-limited, data-moderate, or data-rich.
By leveraging existing research and developing Operating Models (OMs) tailored to squid biology and fishery dynamics, the project seeks to provide flexible, accessible MSE tools that managers and scientists can use to develop MPs for squid stocks globally, regardless of their current data or assessment capacity. These tools will enable the development and evaluation of model-based and empirical MPs, which include management considerations that may be important for squid in-season catch/effort limit adjustments, spatial measures, and environmentally responsive strategies.
A key focus will be to support the North Pacific Fisheries Commission (NPFC) and the South Pacific Regional Fisheries Management Organisation (SPRFMO) in advancing MSE for their main squid fisheries—specifically, the NPFC neon flying squid, NPFC Japanese flying squid, and the SPRFMO jumbo flying squid stocks.
The project will also explore how improved monitoring strategies (e.g., enhanced data collection, reduced data lags, and more frequent in-season updates) might reduce uncertainty and improve management outcomes for these fisheries. The project will provide:
A globally applicable short-lived MSE tool for OpenMSE.
Case study applications and MSE analyses for NPFC and SPRFMO squid fisheries.
Climate and environmental responsiveness and robustness testing methods for squid MPs.
Capacity-building opportunities through bespoke training and workshops to enhance understanding and use of these new MSE frameworks for squid and similar species.
4.5 Audience
The primary target audiences for this work include:
Scientists and managers within RFMOs (e.g., NPFC and SPRFMO).
The broader global scientific community developing stock assessment and MSE methods for squid and other short-lived species.
Stakeholders involved in sustainable management of squid and related fisheries, seeking adaptive, climate-ready, ecosystem-based approaches.
5 Methods
5.1 Overview
A collection of tools are available in a public slMSE R package that allow users to specify simulation models that can be used to test assessment methods (and operating model conditioning methods), and develop management procedures and then test those using the OpenMSE R libraries.
The intention of slMSE is to support a scientific process that begins with a collection of available data and information, leads to the consolidation of those in a range of plausible scenarios for system dynamics, this followed by a defensible assessment of the resource, and finally the development and potentially adoption of management procedures that are demonstrably robust to scientific and management uncertainties.
Data & information > (1) Simulated dynamics > (2) Assessment > (3) Management procedure
Figure 1. Suggested methodological steps.
Simulation of Fishery Dynamics
Assessment Evaluation Operating Model Conditioning
Management Procedure Development and Testing
5.2 Installation
The short-lived tools can be installed from the slMSE library:
remotes::install_github('blue-matter/slMSE')
library(slMSE)
packageVersion(slMSE)
slTest()5.3 Specifying Seasonal Operating Models for Simulating Data
library(openMSE)
packageVersion('MSEtool')
source("C:/GitHub/Short-Lived-IO/R/SLSim.R")
stock = make_SL_stock()
fleet = make_SL_fleet()
om = make_SL_om(stock=stock, fleet=fleet)
hist = Simulate(om)saveRDS(om, “C:/temp/om_agevecs.rds”)
5.3.1 A single species, single fleet model with age-specific distribution and seasonal recruitment
5.3.2 A Growth-Type-Group model with single fleet dynamics
5.4 Simulation Testing Assessment Models
5.5 Conditioning an operating model
5.6 Specifying Projection dynamics
5.7 Developing In-Season Management Procedures
5.8 Including Environmental Drivers of Stock Productivity
5.9 Quantifying Appropriate Fishery Management Reference Points
6 Examples
6.1 Neon Flying Squid
6.2 Jumbo Flying Squid
6.3 Deep Water Red Shrimp
7 References
Alexander I Arkhipkin, Lisa C Hendrickson, Ignacio Payá, Graham J Pierce, Ruben H Roa-Ureta, Jean-Paul Robin, Andreas Winter, Stock assessment and management of cephalopods: advances and challenges for short-lived fishery resources, ICES Journal of Marine Science, Volume 78, Issue 2, March 2021, Pages 714–730, https://doi.org/10.1093/icesjms/fsaa038
NPFC, 2025. NPFC-2025-SSC NFS02-WP02. Overview of Stock Assessment Methods and Management Measures Used for Squids and Other Short-Lived Species. https://www.npfc.int/system/files/2025-06/NPFC-2025-SSC%20NFS02-WP02%20Overview%20SA%20Methods%20and%20Management%20Measures.docx
NPFC, 2025. NPFC-2025-SSC NFS02-IP06Management Strategy Evaluation for Short-Lived Species with Only Annual Data: A Case Study of the Autumn Cohort of Neon Flying Squid in the North Pacific Ocean, https://www.npfc.int/system/files/2025-07/NPFC-2025-SSC%20NFS02-IP06%20Data%20moderate%20MSE%20for%20short-lived%20species.pdf
8 Acknowledgements
Funding support for this project was provided by:
The Pew Charitable Trusts
The views expressed herein are those of the authors and do not necessarily reflect the views of The Pew Charitable Trusts.
Contact point for Pew Charitable Trusts - Ashley Wilson, Science Officer, International Fisheries (awilson@pewtrusts.org).
9 Appendix: 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 feedback among the system, data, management procedure and implementation. A management procedure is a 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.
9.1 Reference Case Operating Models
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.
9.2 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.
9.3 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.
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