Olav Nikolai Risdal Breivik

A spatial model implemented in TMB was presented (mslt). The model was recently published in JASA (https://doi.org/10.1080/01621459.2025.2566422) . It treats whale sightings as arising from a thinned Cox process, with intensity driven by a latent Gaussian Markov random field (using the SPDE approach) and a two-state Markov-modulated Poisson process (MMPP). The SPDE component is intended to capture long-range spatial structures, wheras the MMPP accounts for short-range structure. It was demonstrated that including the MMPP component typically increases the spatial correlation length of the SPDE component. This implies that the model can propagate the SPDE process further away from the transects when the MMPP is successfully included. The model jointly estimates the detection function, group intensity, and group size, ensuring that uncertainty is propagated automatically through the delta method implemented in TMB.

On 30 March 2022 all Russian participation in ICES was temporally suspended. Althoughthe announcement of the suspension stressed the role of ICES as a “multilateral scienceorganization”, this suspension applied not only to research activities but also to the ICESwork providing fisheries advice for the sustainable management of fish stocks andecosystems. On 9 December 2024 Russia announced its intention to leave ICES by 9December 2025. As a result, since 2022 the ICES AFWG has provided advice only for saithe, coastal cod north, coastal cod south, and golden redfish. Northeast Arctic (NEA) cod, haddock, beaked redfish, Greenland halibut, and capelin assessments have beenconducted outside of ICES in a bilateral Joint Russian-Norwegian Working Group on ArcticFisheries (JRN-AFWG). Although this work has been conducted independently of ICES, the methodologies agreed at ICES benchmarks and agreed HCRs (Harvest Control Rules) have been followed in providing this advice. The chapter numbering in this report is not continuous (Chapter 2,5 and 7 are missing). This is done in order to be consistent with the chapter numbering in ICES AFWG reports. In 2025 we are giving advice for NEA cod and haddock. There is a 2-year advice for bothGreenland halibut and beaked redfish, with the next advice release planned for 2026. Thecapelin advice is conducted and released in the autumn via a separate report. The beakedredfish model is planned for a method revision prior to the next advice. We would note that both cod and haddock had TAC quota set above advice in 2025. Bothstocks are projected to increase provided the HCRs are followed. However, any further fishing above scientific advice would imperil the improvements for these stocks, and for NEA cod in particular would increase the risk of the stock falling below Blim.

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.

Retrospective patterns are commonly investigated to validate fish stock assessment models. A widely applied measure for retrospective bias is Mohn’s ρ and corresponding retrospective plots. However, retrospective patterns can be interpreted differently by experts. To make decisions regarding significant retrospective patterns less subjective, we proposed a post-sample Mohn’s ρ significance test. As case studies, we applied the state space assessment model SAM with data on Northeast Arctic cod and Norwegian coastal cod north of 67°N. We showed that the acceptance regions of Mohn’s ρ depends on both the data available and the assessment model complexity. We also assessed the test power under a range of assumption violations and conclude that Mohn’s ρ is useful for detecting violations associated with bias, but not for violations associated with variances and correlations.

On 30th March 2022, all Russian participation in ICES was suspended. Although the announcement of the suspension stressed the role of ICES as a “multilateral science organization”, this suspension applied not only to research activities but also to the ICES work providing fisheries advice for the sustainable management of fish stocks and ecosystems. As a result of the suspension, it is not possible to run ICES stock assessments or provide ICES advice for the Barents Sea stocks of NEA cod, NEA haddock, Sebastes mentella or Greenland Halibut, as management and data collection for these stocks are shared between Norway and Russia. There are therefore no AFWG stock assessments for these stocks this year. This is especially unfortunate as NEA cod is currently declining, and updated assessments are required to ensure an appropriate management response. It is to be hoped that the political decision to exclude Russia from the ICES advice process which underlies our sustainable fisheries management does not lead to mismanagement of the shared stocks and the consequent ecological harm.

In many situations, species distribution models need to make use of multiple data sources to address their objectives. We developed a spatio-temporal modelling framework that integrates research survey data and data collected by observers onboard fishing vessels while accounting for physical barriers (islands, convoluted coastlines). We demonstrated our framework for two bycatch species in New Zealand deepwater fisheries: spiny dogfish (Squalus acanthias) and javelinfish (Lepidorhynchus denticulatus). Results indicated that employing observer-only data or integrated data is necessary to map fish biomass at the scale of the New Zealand exclusive economic zone, and to interpolate local biomass indices (e.g., for the east coast of the South Island) in years with no survey but available observer data. Results also showed that, if enough survey data are available, fisheries analysts should: (1) develop both an integrated model and a model relying on survey-only data; and (2) for a given geographic area, ultimately choose the index produced with integrated data or the index produced with survey-only data based on the reliability of the interannual variability of the index. We also conducted a simulation experiment, which indicated that the predictions of our spatio-temporal models are virtually insensitive to the consideration of physical barriers.

WKBARFAR 2021 met online to revise the advice basis of three stocks: Northeast Arctic (NEA) cod, Norwegian coastal cod north of 62 degrees, and Faroese ling. During the course of the meeting, it was decided to split the Norwegian coastal cod into two separate stocks: a data-rich stock north of 67 degrees north, and a data-poor stock between 62 and 67 degrees north. The NEA cod received a revision to the existing Category 1 SAM (State-space Assessment Model) model, Norwegian coastal cod south will receive advice based on the “3-over-2” rule, while both Norwegian coastal cod north and Faroese ling move from Category 3 to Category 1 stocks with SAM assessments. NEA cod: A revised version of the Category 1 SAM assessment was approved for the advice basis for NEA cod. Given the use of density-dependence in estimating reference points, the benchmark was not able to revise these estimates. Tests were conducted indicating the biomass limit (Blim) remained appropriate, that the existing Harvest Control Rule (HCR) remained precautionary, and that the performance of the HCR is not expected to be substantially impacted by the revision in the assessment model. Reference points will be fully re-evaluated at a forthcoming HCR evaluation, likely in 2022. Faroese Ling: This stock will now be assessed with a Category 1 SAM model, previously the stock was Category 3 with no analytic assessment. Full reference points are estimated, although the actual management is currently based on days at sea controls. Norwegian coastal cod north of 67°N: Category 1 SAM model, the first accepted analytic assessment for this stock. Evaluation of the Spawning–stock biomass (SSB) to recruit plot indicated a continued increase in recruitment across the range of SSBs observed during the model period. Consequently, Blim is provisionally set to the highest observed SSB, with a strong recommendation to conduct further investigations and put in place a recovery plan for this stock. Without evidence of recruitment performance above Blim, no reliable estimates could be provided for F reference points. Norwegian coastal cod between 62 and 67°N: Advice for this stock will be using Category 3, with a “3-over-2” rule based on the Catch Per Unit of Effort (CPUE) index from the reference fleet, with a Length-Based Spawners Per Recruit (LBSPR) model used to indicate if a precautionary buffer is required. It is likely that a SPiCT (Production model in Continuous Time) model may be a viable future assessment method, once the reference fleet CPUE time-series has been extended sufficiently.

The state-space assessment model (SAM) is extended by allowing a functional relationship between observation variance and the corresponding prediction. An estimated relationship between observation variance and predicted value for each individual observation allows the model to assign smaller (or larger) variance to predicted larger log-observations. This relation is different from the usual assumption of constant variance of log-observations within age groups. The prediction–variance link is implemented and compared to the usual constant variance assumption for the official assessments of North East Arctic cod and haddock. For both of these stocks, the prediction–variance link is found to give a significant improvement.

A general spatio-temporal abundance index model is introduced and applied on a case study for North East Arctic cod in the Barents Sea. We demonstrate that the model can predict abundance indices by length and identify a significant population density shift in northeast direction for North East Arctic cod. Varying survey coverage is a general concern when constructing standardized time series of abundance indices, which is challenging in ecosystems impacted by climate change and spatial variable population distributions. The applied model provides an objective framework that accommodates for missing data by predicting abundance indices in areas with poor or no survey coverage using latent spatio-temporal Gaussian random fields. The model is validated, and no violations are observed.

The REDUS project (2016-2020) has been a strategic project at the Institute of Marine Research (IMR) aimed at quantifying and reducing the uncertainty in data-rich and age-structured stock assessments (e.g., cod, herring, haddock, capelin). Work was organized in four topical work-packages: Fisheries-dependent (catch) surveys and assessment modeling (WP1), Fishery-independent (scientific) surveys (WP2), Evaluating and testing of long-term management strategies (WP3), and Communication of uncertainty, dissemination of project results and capacity building (WP4). The Norwegian Computing Center (NR) was contracted in as a strategic partner in statistical modeling and analysis, contributing mainly to WP1 and WP2, but found the research of fundamental interest therefore also allocating internal (NR) funding to develop the statistical science base of several of the methods.

The North Sea cod stock assessment is based on indices of abundance-at-age from fishery-independent bottom trawl surveys. The age structure of the catch is estimated by sampling fish for otoliths collection in a length-stratified manner from trawl hauls. Since age determination of fish is costly and time consuming, only a fraction of fish is sampled for age from a larger sample of the length distribution and an age–length key (ALK) is then used to obtain the age distribution. In this study, we evaluate ALK estimators for calculating the indices of abundance-at-age, with and without the assumption of constant age–length structures over relatively large areas. We show that the ALK estimators give similar point estimates of abundance-at-age and yield similar performance with respect to precision. We also quantify the uncertainty of indices of abundance and examine the effect of reducing the number of fish sampled for age determination on precision. For various subsampling strategies of otoliths collection, we show that one fish per 5-cm-length group width per trawl haul is sufficient and the total number of fish subsampled for age from trawl surveys could be reduced by at least half (50%) without appreciable loss in precision.