Monitoring stock status using fisheries-independent methods is essential for sustainable marine resource management. These methods help identify biases in fisheries-dependent data but often require comprehensive, long-term surveys. Recent advancements in spatiotemporal models enable the integration of fisheries-independent data from multiple sources, enhancing stock status estimates. We compiled bottom trawl survey data (over 43 000 trawl hauls from 1981 to 2024) for beaked (Sebastes mentella) and golden redfish (Sebastes norvegicus) from the Institute of Marine Research databases. Using spatiotemporal generalized linear mixed-effects models, we estimated trends and spatial distributions of these species in the Barents and Norwegian Seas. Model selection, conducted through 10-fold cross-validation, highlighted depth, spatial, and spatiotemporal random fields as crucial components for estimating survey indices. Beaked redfish had the highest biomass densities in the Central Barents Sea, while golden redfish dominated along the Norwegian Coast. Nursery areas for both species were primarily located in the Barents Sea, with beaked redfish juveniles distributed further north. We also detected distribution shifts: the Central Barents Sea has gained beaked redfish biomass, while the Southern Barents Sea and the close shore Norwegian coast are increasingly important for golden redfish. Additionally, we observed reductions in golden redfish biomass on the large banks south of Lofoten and in the Western and Central Barents Sea. Biomass indices indicated a positive trend for beaked redfish and a stable trend for golden redfish. Biomass changes in beaked redfish were correlated with rising bottom temperatures; however, golden redfish were only weakly correlated. The survey indices for both species correlated with commercial catches and total stock biomass estimates from current assessments. This study demonstrates the capability to track changes in species distribution over time, improving stock assessments. The methodology presented here is applicable for monitoring and managing marine species globally.

Maturity data are required to estimate spawning stock biomass (SSB) which is the most common basis for setting quota. Such data are also important when studying stock responses to, for instance, biophysical variability and fisheries-induced evolution. The official maturity time series of the most data-rich Atlantic cod (Gadus morhua) stocks (≥40-50 years) display windows of sudden reductions in age-at-50 %-maturity (A50). Here, we addressed whether these shifts in Northeast Arctic (NEAC) and North Sea (NSC) cod time series are artefacts caused by revisions to sampling. In the complementary ecophysiological approach, the analysis was extended to other cod stocks, including Northern cod (NC). The quality-assessment of the open-access NEAC and NSC databases clarified that the abrupt shifts in A50 coincide with altered sampling rather than being rooted in biology. The exceptionally long NEAC commercial catch series did not show evidence of selective removal as a causal mechanism. A high stock age diversity in synergy with age-specific spawning time and inadequate sampling coverage heighten the risk for biased high A50. We found no significant change in the 75-year NEAC A50 series (p-value = 0.576), but so in the 44-year NSC A50 series (p-value = 0.041). Histology strengthened that NEAC A50 is less variable than frequently stated. The revised NEAC A50 led to significantly higher historic SSB, with consequences for recruitment dynamics patterns and fisheries (biological) reference points. Essentially, the various cod stocks fitted into the same maturation plasticity scheme suggested that the decision to sexually mature is steered by temperature-dependent body growth rate.

The stock assessment model SAM contains a large number of age-dependent parameters that must be manually grouped together to obtain robust inference. This can make the model selection process slow, non-extensive and highly subjective, while producing unrealistic looking parameter estimates with discrete jumps. We propose to model age-dependent SAM parameters using smoothing spline functions. This can lead to more smooth parameter estimates, while speeding up and making the model selection process more automatic and less subjective. We develop different spline models and compare them with already existing SAM models for a selection of 17 different fish stocks, using cross- and forward-validation methods. The results show that our automated spline models overall outcompete the officially developed SAM models. We also demonstrate how the developed spline models can be employed as a diagnostics tool for improving and better understanding properties of the officially developed SAM models.

With a biomass of similar to 4 million tonnes, and annual catches of 900 000 tonnes, the northeast Atlantic (NEA) cod stock in the Barents Sea is the world's largest. Scientists have been trying to explain the variability in recruitment of this stock for over 100 years, in particular connecting it to spawning stock biomass and environmental factors such as temperature. It has been suggested that the combination of ocean acidification and global warming will lead to a significant decrease in the spawning stock biomass and an eventual (end of this century) collapse of the NEA cod stock in the Barents Sea. We show that a temperature- and OA-driven decline in recruits will likely lead to a smaller cod stock, but not to a collapse. Instead, the level of fishing pressure and, not least, the choice of the recruitment function applied in simulations and how it relates to temperature, is extremely important when making such forecasts. Applying a non-linear relationship between temperature and spawning stock biomass-as has been done in studies that predict a collapse of the NEA cod stock-does not improve accuracy and, in addition, adds a large decrease in number of recruits that is not biologically supported.

Spatio-temporal models are essential tools for estimating abundance indices and quantifying the associated uncertainty. Time series of index uncertainties can be used to objectively determine the influence each index has on the assessment. This can involve reducing the influence of indices in years with limited data. However, incorporating uncertainty in age-length conversion into assessment models has remained a challenge. In this research, we propose an index estimation approach that combines an abundance-at-length model with a model for age-at-length to generate age-specific abundance indices. By jointly modeling abundance-at-length and age-at-length, we address uncertainties in both components of the index-at-age. Using North East Arctic haddock (Melanogrammus aeglefinus) as a case study, we validate the uncertainty of the indices by integrating them into the state space assessment model SAM. The results indicate that the uncertainty estimates are realistic, and we further demonstrate that incorporating uncertainty in age conversion has effects on the assessment results. Our case study demonstrates that incorporating the uncertainty in age-at-length data improves the characterization of uncertainty in stock assessment, and hence better accounts for risk in precautionary management.

Marine fishes are heterogeneously distributed across their ranges according to population dynamics governed by complex spatiotemporal relationships between ontogenetic habitat usage, species interactions, environmental variability, and harvest patterns. However, few stock assessments incorporate spatial population structure in the determination of population status and sustainable catch limits. A small number of generalized stock assessment software platforms are utilized worldwide to assess a large number of marine fish populations. Although each platform relies on similar underlying population dynamics, the spatial capabilities and functionality often differ among them. We catalogue spatial dynamics and capabilities across stock assessment platforms to leverage collective experiences and identify future needs for next generation assessment software packages. Despite commonalities across platforms (e.g., most models allow for a single population with spatial heterogeneity, apportionment of recruitment, and age-varying connectivity), no single platform is flexible enough to address the full breadth of spatial dynamics observed for managed marine fish species. Our review clarifies spatial assessment design and modeling ‘good practices’, while emphasizing the need for more generalizable and modular next generation assessment platforms that can account for the spatiotemporal complexity of marine resources (such as natal homing and spawning migrations, ontogenetic movement patterns, metapopulation structure, and complex fleet dynamics). Generalized, spatially-integrated assessment platforms will be key decision-tools to account for spatiotemporal species and fishery interactions, particularly as managers attempt to address climate change and implement ecosystem-based fisheries management.

Multispecies models have existed in a fisheries context since at least the 1970s, but despite much exploration, advancement, and consideration of multispecies models, there remain limited examples of their operational use in fishery management. Given that species and fleet interactions are inherently multispecies problems and the push towards ecosystem-based fisheries management, the lack of more regular operational use is both surprising and compelling. We identify impediments hampering the regular operational use of multispecies models and provide recommendations to address those impediments. These recommendations are: (1) engage stakeholders and managers early and often; (2) improve messaging and communication about the various uses of multispecies models; (3) move forward with multispecies management under current authorities while exploring more inclusive governance structures and flexible decision-making frameworks for handling tradeoffs; (4) evaluate when a multispecies modelling approach may be more appropriate; (5) tailor the multispecies model to a clearly defined purpose; (6) develop interdisciplinary solutions to promoting multispecies model applications; (7) make guidelines available for multispecies model review and application; and (8) ensure code and models are well documented and reproducible. These recommendations draw from a global assemblage of subject matter experts who participated in a workshop entitled "Multispecies Modeling Applications in Fisheries Management".

In this study, a multispecies gadget model (GadCap) simulating the interactions among the Flemish Cap cod (Gadus morhua), redfish (Sebastes spp.), and shrimp (Pandalus borealis) has been incorporated as the operating model in a management strategy evaluation (MSE) framework (a4a-FLR) to test the performance of multiple combinations of harvest control rules (HCRs) for the three stocks when recruitment uncertainty and assessment error are accounted for. The results indicate that due to the strong trophic interactions, it is not possible to achieve the precautionary exploitation of all the stocks at the same time. Maintaining shrimp biomass above the limit reference point (B lim ) would require unsustainable fishing pressure on cod and redfish to reduce predation mortality. In contrast, maintaining cod biomass above B lim would involve high predation on and high risk of collapse of the shrimp and redfish stocks. The implementation of alternative two-stage HCRs would reduce predation, resulting in higher productivity and lower probability of collapse for cod and redfish. The results of this study support the need of accounting for species interactions when designing management strategies for a group of interdependent commercial stocks.

The correct prediction of the shape and strength of density dependence in productivity is key to predicting future stock development and providing the best possible long- term fisheries management advice. Here, we identify unbiased estimators of the relationship between somatic growth, recruitment and density, and apply these to 80 stocks in the Northeast Atlantic. The analyses revealed density- dependent recruitment in 68% of the stocks. Excluding pelagic stocks exhibiting significant trends in spawning stock biomass, the probability of significant density dependence was even higher at 78%. The relationships demonstrated that at the commonly used biomass limit of 0.2 times maximum spawning stock size, only 32% of the stocks attained three quarters of their maximum recruitment. This leaves 68% of the stocks with less than three quarters of their maximum recruitment at this biomass limit. Significantly lower recruitment at high stock size than at intermediate stock size was seen in 38% of the stocks. Density dependence in late growth occurred in 54% of the stocks, whereas early growth was generally density- independent. Pelagic stocks were less likely to exhibit density dependence in recruitment than demersal and benthic stocks. We recommend that both the degree to which productivity is related to density and the

We simulate the combined natural and pollutant-induced survival of early life stages of NEA cod and haddock, and the impact on the adult populations in response to the time of a major oil spill in a single year. Our sim-ulations reveal how dynamic ocean processes, controlling both oil transport and fate and the frequency of in-teractions of oil with drifting fish eggs and larvae, mediate the magnitude of population losses due to an oil spill. The largest impacts on fish early life stages occurred for spills initiated in Feb-Mar, concomitant with the initial rise in marine productivity and the earliest phase of the spawning season. The reproductive health of the adult fish populations was maintained in all scenarios. The study demonstrates the application of a simulation system that provides managers with information for the planning of development activities and for the protection of fisheries resources from potential impacts.

Biased estimates of population status are a pervasive conservation problem. This problem has plagued assessments of commercial exploitation of marine species and can threaten the sustainability of both populations and fisheries. We develop a computer-intensive approach to minimize adverse effects of persistent estimation bias in assessments by optimizing operational harvest measures (harvest control rules) with closed-loop simulation of resource-management feedback systems: management strategy evaluation. Using saithe (Pollachius virens), a bottom water, apex predator in the North Sea, as a real-world case study, we illustrate the approach by first diagnosing robustness of the existing harvest control rule and then optimizing it through propagation of biases (overestimated stock abundance and underestimated fishing pressure) along with select process and observation uncertainties. Analyses showed that severe biases lead to overly optimistic catch limits and then progressively magnify the amplitude of catch fluctuation, thereby posing unacceptably high overharvest risks. Consistent performance of management strategies to conserve the resource can be achieved by developing more robust control rules. These rules explicitly account for estimation bias through a computational grid search for a set of control parameters (threshold abundance that triggers management action, B-trigger, and target exploitation rate, F-target) that maximize yield while keeping stock abundance above a precautionary level. When the biases become too severe, optimized control parameters-for saithe, raising B-trigger and lowering F-target-would safeguard against a overharvest risk (<3.5% probability of stock depletion) and provide short-term stability in catch limit (<20% year-to-year variation), thereby minimizing disruption to fishing communities. The precautionary approach to fine-tuning adaptive risk management through management strategy evaluation offers a powerful tool to better shape sustainable harvest boundaries for exploited resource populations when estimation bias persists. By explicitly accounting for emergent sources of uncertainty, our proposed approach ensures effective conservation and sustainable exploitation of living marine resources even under profound uncertainty.

Although frequently suggested as a goal for ecosystem-based fisheries management, incorporating ecosystem information into fisheries stock assessments has proven challenging. The uncertainty of input data, coupled with the structural uncertainty of complex multi-species models, currently makes the use of absolute values from such models contentious for short-term single-species fisheries management advice. Here, we propose a different approach where the standard assessment methodologies can be enhanced using ecosystem model derived information. Using a case study of the Irish Sea, we illustrate how stock-specific ecosystem indicators can be used to set an ecosystem-based fishing mortality reference point (F ECO ) within the “Pretty Good Yield” ranges for fishing mortality which form the present precautionary approach adopted in Europe by the International Council for the Exploration of the Sea (ICES). We propose that this new target, F ECO , can be used to scale fishing mortality down when the ecosystem conditions for the stock are poor and up when conditions are good. This approach provides a streamlined quantitative way of incorporating ecosystem information into catch advice and provides an opportunity to operationalize ecosystem models and empirical indicators, while retaining the integrity of current assessment models and the F MSY -based advice process.

Sustainable human exploitation of living marine resources stems from a delicate balance between yield stability and population persistence to achieve socioeconomic and conservation goals. But our imperfect knowledge of how oceanic oscillations regulate temporal variation in an exploited species can obscure the risk of missing management targets. We illustrate how applying a management policy to suppress fluctuations in fishery yield in variable environments (prey density and regional climate) can present unintended outcomes in harvested predators and the sustainability of harvesting. Using Atlantic cod (Gadus morhua, an apex predatory fish) in the Barents Sea as a case study we simulate age-structured population and harvest dynamics through time-varying, density-dependent and density-independent processes with a stochastic, process-based model informed by 27-year monitoring data. In this model, capelin (Mallotus villosus, a pelagic forage fish), a primary prey of cod, fluctuations modulate the strength of density-dependent regulation primarily through cannibalistic pressure on juvenile cod survival; sea temperature fluctuations modulate thermal regulation of cod feeding, growth, maturation, and reproduction. We first explore how capelin and temperature fluctuations filtered through cod intrinsic dynamics modify catch stability and then evaluate how management to suppress short-term variability in catch targets alters overharvest risk. Analyses revealed that suppressing year-to-year catch variability impedes management responses to adjust fishing pressure, which becomes progressively out of sync with variations in cod abundance. This asynchrony becomes amplified in fluctuating environments, magnifying the amplitudes of both fishing pressure and cod abundance and then intensifying the density-dependent regulation of juvenile survival through cannibalism. Although these transient dynamics theoretically give higher average catches, emergent, quasicyclic behaviors of the population would increase long-term yield variability and elevate overharvest risk. Management strategies that overlook the interplay of extrinsic (fishing and environment) and intrinsic (life history and demography) fluctuations thus can inadvertently destabilize fish stocks, thereby jeopardizing the sustainability of harvesting. These policy implications underscore the value of ecosystem approaches to designing management measures to sustainably harvest ecologically connected resources while achieving socioeconomic security.

A new approach for estimating the fishing mortality benchmark Fmsy (fishing pressure that corresponds to maximum sustainable yield) is proposed. The approach includes density-dependent factors. The analysis considers 53 data-rich fish stocks in the Northeast Atlantic. The new Fmsy values are estimated from an ensemble of data sources: (i) applying traditional surplus production models on time-series of historic stock sizes, fishing mortalities, and catches from the current annual assessments; (ii) dynamic pool model (e.g. age-structured models) estimation for stocks where data on density-dependent growth, maturity, and mortality are available; (iii) extracts from multispecies and ecosystem literature for stocks where well-tested estimates are available; (iv) the “Great Experiment” where fishing pressure on the demersal stocks in the Northeast Atlantic slowly increased for half a century; and (v) linking Fmsy to life history parameters. The new Fmsy values are substantially higher (average equal to 0.38 year−1) than the current Fmsy values (average equal to 0.26 year−1) estimated in stock assessments and used by management, similar to the fishing pressure in the 1960s, and about 30% lower than the fishing pressure in 1970–2000.

Although many countries have formally committed to Ecosystem-Based Fisheries Management (EBFM), actual progress toward these goals has been slow. This paper presents two independent case studies that have combined strategic advice from ecosystem modeling with the tactical advice of single-species assessment models to provide practical ecosystem-based management advice. With this approach, stock status, reference points, and initial target F are computed from a single-species model, then an ecosystem model rescales the target F according to ecosystem indicators without crossing pre-calculated single-species precautionary limits. Finally, the single-species model computes the quota advice from the rescaled target F, termed here F-eco. Such a methodology incorporates both the detailed population reconstructions of the single-species model and the broader ecosystem perspective from ecosystem-based modeling, and fits into existing management schemes. The advocated method has arisen from independent work on EBFM in two international fisheries management systems: (1) Atlantic menhaden in the United States and (2) the multi species fisheries of the Irish Sea, in the Celtic Seas ecoregion. In the Atlantic menhaden example, the objective was to develop ecological reference points (ERPs) that account for the effect of menhaden harvest on predator populations and the tradeoffs associated with forage fish management. In the Irish Sea, the objective was to account for ecosystem variability when setting quotas for the individual target species. These two exercises were aimed at different management needs, but both arrived at a process of adjusting the target F used within the current single-species management. Although the approach has limitations, it represents a practical step toward EBFM, which can be adapted to a range of ecosystem objectives and applied within current management systems.

Management Strategy Evaluation (MSE) is a framework to explore the tradeoffs amongst fishing strategies and assess the consequences for achieving management goals provided sources of uncertainty by means of simulation models (referred to as operating models). Single-species stock assessment often implements simulations for MSE, but the operating models often omit the dynamics of key biological interactions. This could be a disadvantage for the evaluation of tradeoffs as species interactions could have an impact on the performance of harvesting strategies. Tools for conducting ecosystem-based fisheries management (EBFM), such as integrated ecosystem assessments, include executing MSEs with ecosystem models, many of which explicitly include biological interactions. Although the support for EBFM has grown over the years, the amount of information provided by MSEs based on ecosystem models appears to be limited. A clear summary of such efforts would provide beneficial information for future efforts for EBFM. Herein, an inventory of applications simulating MSEs with ecosystem models that explicitly include biological interactions was developed based on findings from a literature review. First, the methodologies and foci across all identified applications are analyzed. Next, summaries of each application are provided. Lastly, general observations are provided along with recommendations for future applications.

Erik Olsen (HI), Sondre Aanes Norwegian Computing Center, Magne Aldrin Norwegian Computing Center, Olav Nikolai Breivik Norwegian Computing Center, Edvin Fuglebakk, Daisuke Goto, Nils Olav Handegard, Cecilie Hansen, Arne Johannes Holmin, Daniel Howell, Espen Johnsen, Natoya Jourdain, Knut Korsbrekke, Ono Kotaro, Håkon Otterå, Holly Ann Perryman, Samuel Subbey, Guldborg Søvik, Ibrahim Umar, Sindre Vatnehol og Jon Helge Vølstad (HI)