The lesser sandeel (Ammodytes marinus) is a vital forage species in the North Sea, supporting numerous seabird, fish, and marine mammal populations. Its life cycle is characterized by benthic dwelling during juvenile and adult stages and a pelagic larval stage, which makes it especially sensitive to environmental disturbances. This study investigated the sublethal effects of crude oil exposure on early-stage lesser sandeel larvae (2 days post hatch), with a focus on developmental morphology, cardiac function, pigmentation, gene expression, and lipid content. Larvae were exposed to environmentally relevant concentrations of oil (15-99 µg total hydrocarbon (THC)/L) from 2 to 16 days post-hatch. Even at the lowest concentration (15 µg/L), larvae exhibited significant morphological abnormalities in jaw structures. Cardiac assessments revealed bradycardia, atrioventricular block, and silent ventricles in exposed groups, indicating impaired cardiac function. Fatty acid profiling of dissected tissues revealed altered lipid content in the eyes, which may result in visual development impairments. These findings suggest that even minimal oil concentrations can cause functional and developmental impairments in lesser sandeel larvae, potentially reducing survival during this critical life stage. Given the species' ecological importance and population declines, these results underscore the need for species- and stage-specific risk assessments in environmental management and conservation strategies.

The capelin is a dominant forage fish in the Barents Sea and is also harvested commercially. With its short life cycle, death after one spawning and a fishery targeting only maturing fish close to spawning, it is a challenging stock to manage and assess. The only abundance estimate used for stock assessment is from September-6 months prior to spawning, and a reliable survey closer to the time of the spawning could therefore improve the assessment. Here, we evaluate the results from a five-year series of capelin monitoring in early March during pre-spawning along the Norwegian coast. The confidence range of the survey results and the stock assessment forecast overlapped for 7 of the 8 survey coverages, but in 6 of 8 cases mean biomass from the survey was below the 25% quantile of the forecast. A lower measured than predicted biomass is probably at least partly caused by the lack of survey coverage in the east. The survey abundance estimates were associated with high uncertainty and relative sampling errors above 0.25 for 6 of the 8 estimates, likely due to the patchy distribution of capelin. The survey in its present form is hence more suited as a fallback in case the autumn survey fails than for direct implementation in stock advice. We found weight-at-length of capelin to be lower during pre-spawning than in autumn, which is presently not considered in the assessment. This highlights the importance of a pre-spawning survey as validation of key parameters in the stock forecast.

Entrainment is a process in schooling migratory fish whereby routes to suitable habitats are transferred from repeat spawners to recruits over generations through social learning1. Selective fisheries targeting older fish may therefore result in collective memory loss and disrupted migration culture2. The world's largest herring (Clupea harengus) population has traditionally migrated up to 1,300 km southward from wintering areas in northern Norwegian waters to spawn at the west coast. This conservative strategy is proposed to be a trade-off between high energetic swimming costs and enhanced larval survival under improved growth conditions3. Here an analysis of extensive data from fisheries, scientific surveys and tagging experiments demonstrates an abrupt approximately 800-km poleward shift in main spawning. The new migration was established by a large cohort recruiting when the abundance of older fish was critically low due to age-selective fisheries. The threshold of memory required for cultural transfer was probably not met-a situation that was further exacerbated by reduced spatiotemporal overlap between older fish and recruits driven by migration constraints and climate change. Finally, a minority of survivors from older generations adopted the migration culture from the recruits instead of the historically opposite. This may have profound consequences for production and coastal ecology, challenging the management of migratory schooling fish.

Scientific acoustic-trawl surveys collect data that are used to track fish and zooplankton populations over time. Most rely on manual annotation during acoustic target classification, but automated methods have been proposed. Here, we report on a framework for testing deep learning-based acoustic classification models and integrating them into the survey estimation process. The approach was applied to North Sea lesser sandeel (Ammodytes marinus) surveys from 2009 to 2024. Three U-Net-based models were tested: a baseline model, a depth-aware model, and a model trained with similarity-based sampling for the foreground class. A threshold based on the training years was applied to the models’ SoftMax outputs. The official sandeel estimation process was used as a starting point, replacing input data with model predictions. The biomass estimates were generally similar between manual annotations and model-based estimates, but variation existed across years. The baseline model misclassified a surface layer as sandeel and was prone to bottom contamination, causing larger deviations from official estimates. Discrepancies between the similarity-based model and the official estimates resulted from an incorrectly applied SoftMax threshold, leading to missing school interiors and indicating threshold sensitivity. Unlike traditional F1 score evaluations commonly used in image-based classification, our comparison assessed predictions in a survey-relevant context. The evaluation indicated that full automation was not yet feasible, but the predictions could be used as starting points for manual scrutiny. Annotating a subset of the data to refine thresholds or employing more advanced active learning approaches could enhance efficiency. These methods could enable faster, more consistent survey annotation.

Broadband frequency-modulated signals are believed to improve acoustic spectral-based target classification. Efficient use of uncrewed surface vehicles (USV) for fisheries science applications, with no possibility for biological sampling, is believed to be facilitated by use of broadband signals with methods for target classification. If the broadband frequency response used to train automated target classifiers are obtained from conventional research vessels (RVs), due to potential vessel avoidance, the swimming angle distribution may be different than for USVs. This may have consequences for target classification if the model is trained with RV data. The aim of this study was to assess whether the frequency response differs between platforms due to avoidance. Broadband acoustic data were collected with a conventional RV and a small USV. The broadband frequency response of Norwegian spring spawning herring obtained with the USV and RV was found to be significantly different for shallow herring layers in the 200 kHz band. This indicates that broadband frequency response has potential as a tool for real-time monitoring of behaviour reactions to vessels and to provide insight into fish behaviour in general. When using broadband frequency response for target classification, the potential platform-dependent broadband frequency response should be considered.

Lesser sandeel (Ammodytes marinus) exhibits a peculiar diel vertical migration (DVM) during the feeding season, burying into the seabed at night and emerging during daytime to form schools that feed on zooplankton. Large schools may consist of a pelagic component searching for prey and a bottom component connected by collective bridge-like formations. However, the temporal variation in the schools' vertical distribution is poorly understood. In this study, 38 and 200 kHz acoustic data recorded with Saildrones were used to examine the schooling dynamics during their main feeding season in May-June. A total of 1497 sandeel schools that were identified by linear discriminant analysis displayed two distinct vertical components throughout the season: one in the pelagic zone and one near the seabed. The pelagic component was distributed deepest at noon and had a similar pattern to zooplankton DVM, suggesting that sandeel followed the vertical distribution of their prey. Their diurnal ascension was greater in both distance and hours in May than June, suggesting a decline in feeding motivation towards the end of the feeding season. These findings were made possible with the long-term monitoring by silent Saildrones, which did not seem to affect the natural behaviour of sandeel schools.

Uncrewed surface vehicles (USVs) equipped with echosounders have the potential to replace or enhance acoustic observations from conventional research vessels (RVs), increase spatial and temporal coverage, and reduce cost and carbon emission. We discuss the objectives, system requirements, infrastructure, and regulations for using USVs with echosounders to conduct ecological experiments, acoustic-trawl surveys, and long-term monitoring. We present four example applications of USVs with lengths <8 m, and highlight some advantages and disadvantages relative to RV-based data acquisitions. Sail-driven USVs operate continuously for months and are more mature than motorized USVs, but they are slower. To maintain the pace of an RV, multiple sail-powered USVs sample in coordination. In comparison, motorized USVs can travel as fast as RVs and therefore may facilitate a combined survey, interleaving USV and RV transects, with RV-based biological sampling. Important considerations for all USVs include platform design, noise and transducer motion mitigation, communications and operations infrastructure, onboard data processing, biological sampling approach, and legal requirements. This technology is evolving and applied in multiple disciplines, but further development and institutional commitment are needed to allow USVs equipped with echosounders to become ubiquitous and useful components of a worldwide network of autonomous ocean observation platforms.

The oil industry's expansion and increased operational activity at older installations, along with their demolition, contribute to rising cumulative pollution and a heightened risk of accidental oil spills. The lesser sandeel (Ammodytes marinus) is a keystone prey species in the North Sea and coastal systems. Their eggs adhere to the seabed substrate making them particularly vulnerable to oil exposure during embryonic development. We evaluated the sensitivity of sandeel embryos to crude oil in a laboratory by exposing them to dispersed oil at concentrations of 0, 15, 50, and 150 µg/L oil between 2 and 16 days post-fertilization. We assessed water and tissue concentrations of THC and tPAH, cyp1a expression, lipid distribution in the eyes, head and trunk, and morphological and functional deformities. Oil droplets accumulated on the eggshell in all oil treatment groups, to which the embryo responded by a dose-dependent rise in cyp1a expression. The oil exposure led to only minor sublethal deformities in the upper jaw and otic vesicle. The findings suggest that lesser sandeel embryos are resilient to crude oil exposure. The lowest observed effect level documented in this study was 36 µg THC/L and 3 µg tPAH/L. The inclusion of these species-specific data in risk assessment models will enhance the precision of risk evaluations for the North Atlantic ecosystems.

An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.

BACKGROUND:Lesser sandeel (Ammodytes marinus) is widely distributed in North Sea ecosystems. Sandeel acts as a critical trophic link between zooplankton and top predators (fish, mammals, sea birds). Because they live buried in the sand, sandeel may be directly affected by the rapid expansion of anthropogenic activities linked to their habitat on the sea bottom (e.g., hydrocarbon extraction, offshore renewable energy, and subsea mining). It is, therefore, important to understand the impact of cumulative environmental and anthropogenic stressors on this species. A detailed description of the ontogenetic timeline and developmental staging for this species is lacking limiting the possibilities for comparative developmental studies assessing, e.g., the impact of various environmental stressors.RESULTS:A detailed description of the morphological development of lesser sandeel and their developmental trajectory, obtained through visual observations and microscopic techniques, is presented. Methods for gamete stripping and intensive culture of the early life stages are also provided.CONCLUSION:This work provides a basis for future research to understand the effect of cumulative environmental and anthropogenic stressors on development in the early life stages of lesser sandeel.

In the North Sea, the number and size of offshore wind (OW) turbines, together with the associated network of High Voltage Direct Current (HVDC) subsea cables, will increase rapidly over the coming years. HVDC cables produce magnetic fields (MFs) that might have an impact on marine animals that encounter them. One of the fish species that is at risk of exposure to MF associated with OW is the lesser sandeel (Ammodytes marinus), a keystone species of the North Sea basin. Lesser sandeel could be exposed to MF as larvae, when they drift in proximity of OW turbines. Whether MFs impact the behavior of lesser sandeel larvae, with possible downstream effects on their dispersal and survival, is unknown. We tested the behavior of 56 lesser sandeel larvae, using a setup designed to simulate the scenario of larvae drifting past a DC cable. We exposed the larvae to a MF intensity gradient (150-50 μT) that is within the range of MFs produced by HVDC subsea cables. Exposure to the MF gradient did not affect the spatial distribution of lesser sandeel larvae in a raceway tank 50 cm long, 7 cm wide and 3.5 cm deep. Nor did the MF alter their swimming speed, acceleration or distance moved. These results show that static MF from DC cables would not impact behavior of lesser sandeel larvae during the larval period of their life although it does not exclude the possibility that later life stages could be affected.

Impacts of climate change on ocean productivity sustaining world fisheries are predominantly negative but vary greatly among regions. We assessed how 39 fisheries resources-ranging from data-poor to data-rich stocks-in the North East Atlantic are most likely affected under the intermediate climate emission scenario RCP4.5 towards 2050. This region is one of the most productive waters in the world but subjected to pronounced climate change, especially in the northernmost part. In this climate impact assessment, we applied a hybrid solution combining expert opinions (scorings)-supported by an extensive literature review-with mechanistic approaches, considering stocks in three different large marine ecosystems, the North, Norwegian and Barents Seas. This approach enabled calculation of the directional effect as a function of climate exposure and sensitivity attributes (life-history schedules), focusing on local stocks (conspecifics) across latitudes rather than the species in general. The resulting synopsis (50-82 degrees N) contributes substantially to global assessments of major fisheries (FAO, The State of World Fisheries and Aquaculture, 2020), complementing related studies off northeast United States (35-45 degrees N) (Hare et al., PLoS One, 2016, 11, e0146756) and Portugal (37-42 degrees N) (Bueno-Pardo et al., Scientific Reports, 2021, 11, 2958). Contrary to prevailing fisheries forecasts elsewhere, we found that most assessed stocks respond positively. However, the underlying, extensive environmental clines implied that North East Atlantic stocks will develop entirely different depending upon the encountered stressors: cold-temperate stocks at the southern and Arctic stocks at the northern fringes appeared severely negatively impacted, whereas warm-temperate stocks expanding from south were found to do well along with cold-temperate stocks currently inhabiting below-optimal temperatures in the northern subregion.

Advancements in technologies have led to a rapid development of unmanned surface vehicles (USV) for marine ecosystem monitoring. The design, size, and scientific payload of the USVs differ as they are built for different purposes. Here, we present the design criteria and detailed technical solutions of a prototype USV which has been built to fulfill the following experimental and operational needs; the USV should be used for inshore and shallow water acoustic monitoring, offshore comparison of echo sounder recordings from the USV and research vessels, monitor natural fish schooling behavior and seabird-fish behavioral interactions. The prototype has been built over a period of 5 years with steadily quality improvements. As the hull is based on an expedition double kayak, the USV is named Kayak Drone, and we aimed at building the Kayak Drone using of-the-shelf hardware and existing open-source software. This allowed for the development of a modular and well-functioning USV at a relatively low cost. The Kayak Drone produces very little noise and in situ experiments show that the Kayak Drone can record echo sounder data of fish near the surface without disturbing their natural distribution and behavior. One in situ study shows that the Kayak Drone could navigate within a couple of meters from swimming puffin and other seabirds without triggering escape. These results demonstrate that the Kayak Drone can be utilized to produce unbiased survey estimates for fish distributed in shallow waters and near the surface, which is very important for many fish stock assessments and managements. Furthermore, it can also be used as a tool to observe the predation by seabirds on fish schools without interfering with their natural interspecific behavior, which traditionally has been very difficult. The use of the Kayak Drone is not restricted to these tasks, and we foresee that the Kayak Drone can be utilized in many different experiments where a silent platform is needed.

The authors wish to correct the following error in the original paper [...].

Killer whales Orcinus orca have a cosmopolitan distribution with a broad diet ranging from fish to marine mammals. In Norway, killer whales are regularly observed feeding on overwintering Norwegian spring-spawning (NSS) herring Clupea harengus inside the fjords. However, their offshore foraging behavior and distribution are less well understood. In particular, it is not known to what degree they rely on the NSS herring stock when the herring move to deeper offshore waters. Satellite telemetry data from 29 male killer whales were analyzed to assess whether their offshore foraging behavior is linked to herring distribution. Unlike most marine predator-prey studies that use indirect proxies for prey abundance and distribution, our study utilized 2 herring density estimates based on (1) direct observations from acoustic trawl survey data and (2) simulations from a fully coupled ecosystem model. Mixed effects models were used to infer the effect of herring density and light intensity on whale movement patterns. Our results suggest that killer whales follow NSS herring over long distances along the coast from their inshore overwintering areas to offshore spawning grounds. All whales changed from fast, directed, to slow, non-directed movement when herring density increased, although individuals had different propensities towards movement. Our data indicated that whales continue to feed on herring along the Norwegian shelf. We conclude that NSS herring constitute an important prey resource for at least some killer whales in the northeastern Atlantic, not only during the herring overwintering period, but also subsequently throughout the herring spawning migration.

This presentation gives an overview of recent achievements in fisheries acoustics associated with the Center for Research Based Innovation in Marine Acoustic Abundance Estimation and Backscatter Classification (CRIMAC), Norway. We present a data processing pipeline from raw acoustic data to survey indices of abundance for fisheries assessments as a test bench. This includes the raw data as well as manually worked up labels for acoustic target classification (ATC). We also collect data from dedicated CRIMAC surveys and organize broadband data across a range of species, using different echo sounder setting, to find optimal settings for various standard surveys. We have trained deep neural networks on labelled acoustic data for ATC and tested this in the pipeline. Semi-supervised methods are also developed, requiring only 10% of the training data compared to the supervised models. IMR has a developed a strategy for phasing in unmanned platforms in our surveys. The process is stepwise, where we first augment existing surveys to maintain time series integrity; this approach has been tested on our sand eel and sprat surveys. We also embed our deep learning models in containers to facilitate simple deployment. This allows adaptive survey strategies, which is a step towards fully autonomous acoustic surveys.

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.

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)