Introduction This two-part exploratory study examines whether Psychological Capital (PsyCap) can contribute to understanding individual differences in environmental actions.Methods In the first phase, 55 students (ages 21-33) completed the 12-item Psychological Capital Questionnaire (PCQ) and the 18-item Environmental Activities and Action Scale (EAA). One week later, the same participants joined focus groups to discuss how the four PsyCap components-Hope, Efficacy, Resilience, and Optimism-might shape motivations for environmental action.Results Quantitative analyses suggested that PsyCap factors explained a modest portion (approximately 26.5%) of the variance in environmental action, with Efficacy and Resilience showing the strongest unique contributions (8.6 and 8.0% of the variance, respectively). These relationships should be interpreted cautiously given the limited sample and the mixed pattern of effects across PsyCap components. The focus group discussions added nuance, indicating that Hope, Efficacy, and Resilience may function as potential motivational catalysts, whereas Optimism could in some cases reduce perceived urgency for environmental action.Discussion Although preliminary, this pilot-study highlights possible pathways through which specific psychological resources may relate to environmental action. The findings underscore the need for larger, more rigorous studies to clarify the conditions under which PsyCap supports-or fails to support-environmentally responsible behavior.

Industrial capture fisheries depend on fossil fuels, which tend to dominate both greenhouse gas emissions and operational costs of this form of seafood production. Improving energy efficiency is, in addition to shifting to alternative fuels, a crucial path towards decarbonizing fisheries. Theory suggests that healthy stocks, i.e., with higher density, should require less fuel to harvest when fishing effort and catches are correlated. This is a situation generally observed in bottom trawl fisheries. Rebuilding stocks could thus represent an important pathway for decarbonization. By analysing available time series data on fuel use intensity (FUI), fleet size and fish price in 13 European and U.S. bottom trawl fisheries, we find empirical evidence that lower FUI is associated with higher stock abundance. Lower FUI is also observed for catches with lower fish prices and with reductions in fleet size. Results suggest that rebuilding fish stocks by setting and following sustainable harvest limits combined with balancing fishing capacity with resource availability can be one part of a decarbonization strategy. However, economic incentives such as fish price and subsidies are counterproductive. Combined, this suggests that energy use and carbon emissions be considered as key fisheries management objectives. The sparse data availability of fuel use in fisheries also points to the need for standardised collection programs to allow for further research for improved understanding as well as monitoring progress towards societal objectives.

In-trawl stereo cameras can provide fine-scale spatial and temporal information on species along the trawl path and record small-sized and fragile organisms typically absent from catches. Reliable estimates of abundance and length frequency from in-trawl cameras will improve ecosystem understanding and lessen the need for physical catches on scientific surveys. However, determining these estimates from camera footage is challenging since the same individual can appear in multiple frames and swim repeatedly in and out of the camera’s field of view. The manual image analysis performed in this study provides important information on how the swimming behaviour of three abundant pelagic taxa in the Norwegian Sea, along with a camera’s field of view and frame rate, affect the number of repeated appearances. Moreover, these manual annotations serve as a valuable dataset for validating automatic image analyses. Our results show that, depending on the taxa, swimming orientation, length, density of individuals, and distance to the camera affect the extent of time an individual is observed. If the repeated appearance of individuals is not accounted for, taxa or length classes with fewer appearances are under-represented in relative abundance and lead to skewed length frequency distributions. Compared to herring and blue whiting, a large fraction of mesopelagic fishes remains undetected during automatic analysis (RetinaNet). Assessing the factors driving repeated appearances improves our understanding of in-trawl camera data and highlights the importance of integrating tracking with automatic image analysis.

Many species of fish, birds and mammals commonly live in human captivity; Atlantic salmon Salmo salar is one of them. The international legal status of the welfare of captive animals is slowly developing and still requires rigorous specification. For example, even though fish have complex cognition and elements of sentience, The United Nations’ animal welfare principles still take a functional health-centred perspective overlooking the cognitive-affective component. Wellbeing problems remain a major source of slow growth and high mortality in intensive aquaculture of Atlantic salmon. The value system for decision making in vertebrates is based on expectations of emotional wellbeing for the options available and is linked with the individual’s assessment of its future. We propose a new approach for monitoring and improving the welfare of salmon (or any other captive or wild vertebrate) based on modelling the salmon’s wellbeing system by digital twins, which are simulation models that implement major bodily mechanisms of the organism. Indeed, predictions on boredom, stress and wellbeing can all be captured by a computational evolutionary model of the factors underlying behaviour. We explain how such an agent-based model of salmon digital twins can be constructed by modelling a salmon’s subjective wellbeing experience along with prediction of its near future and allostasis (the bodily preparation for the expected near future). We attempt to identify the building blocks required in digital twin models to deliver early warnings about escalating issues that could eventually lead to negative effects on salmon health in aquaculture. These models would provide critical insights for optimizing production processes and could significantly reduce the reliance on animal experiments. Overall, reports of a population of digital twins could support the implementation of 3Rs - replacement, reduction, refinement - by offering actionable information to fish farmers as well as consumers, voters, politicians and regulators on relevant issues as well as guide experimental work on animal wellbeing across species.

Sustainable use of fish resources is essential, and decision makers such as the Norwegian Directorate of Fisheries (NDF) must take proactive measures to prevent Illegal, Unreported, and Unregulated (IUU) fishing activities. With access to large volumes of open datasets, machine learning (ML) models can play a key role in automating the detection of hidden patterns indicative of such activities. One valuable dataset is the collection of catch reports, where fishermen record the details of their fishing operations. Previous research has explored the use of ML models to predict expected catch quantities. By comparing these predictions with the actual reported values, potential violations of regulations can be identified. However, to ensure trust in the model’s outputs and to gain deeper insight into the data, this paper applies interpretable Artificial Intelligence (AI) methods and visualization techniques to analyze prediction errors. We investigate feature importance and examine how the most influential features affect the model's output patterns. The results are promising, demonstrating that it is possible to provide transparency in the use of ML models for fisheries data. This approach enables domain experts to better understand, trust, and make informed use of the model's findings in the future.

Fisheries and climate warming are two stressors known to induce evolutionary changes in fish life histories. While their independent effects have been well documented, their interactive effects are less charted, although likely important for sustainable fisheries management and conservation strategies. We investigated the evolutionary responses of the Northeast Arctic cod stock (Gadus morhua) to warming temperatures and fishing pressure using a mechanistic modeling approach. Our individual-based simulation model incorporates explicit energy and oxygen budgets, and a simplified genetics framework to capture the complex interactions among traits governing energy acquisition/allocation and maturation schedules. Our results provide a theoretical basis for positive consequences for this particular cod stock in a warming climate. Warmer temperatures increased the aerobic scope, which reduced natural mortality. We found that if food availability and temperature are not linked, a warming climate leads to larger population sizes. By selecting for maturation at larger sizes, adaptation to warming climate at least partially counteracts the evolutionary consequences of fishing, namely smaller body sizes and earlier maturation. Our findings emphasize the benefits of adaptive management approaches, considering fish as evolving organisms and integrating ocean warming into fisheries management strategies.

Analysis of the fatty acid composition in body tissue, which reflects the accumulated dietary intake over the last months, is a well-established method for studying trophic interactions in marine food webs. Here we present the fatty acid composition of salmon, herring, mackerel, and their prey sampled in May-Aug at marine feeding areas in the Norwegian Sea. A large proportion of the post-smolts sampled early in the summer had a high proportion of FA associated with age-0 fish. Later in the summer, when they have reached the northern Norwegian Sea, post-smolts have a higher proportion of FAs associated with calanoid copepods. The FA composition indicates of post-smolts indicate a wide feeding niche with a diet changing rapidly in time and space, and somatic growth was prioritized before lipid accumulation until the end of the first summer. Post-smolts have very low lipids levels compared to sub-adult salmon or other pelagic fish feeding in the same geographic region of the northeast Atlantic. Furthermore, the FA composition of post-smolts, which are hypothesized to compete for prey with other pelagic fish species, is partly different from the FA composition of herring and mackerel caught within the same geographic area, suggesting important diet differences between the species.

The ocean is increasingly used for industry, energy and recreation or protected for conservation, resulting in increasing spatial restrictions for fisheries. Simultaneously, producing seafood with a low climate footprint is becoming increasingly important. Despite this, the effects of spatial restrictions on the emissions of fishing fleets are poorly known. In the Northeast Atlantic, the withdrawal of the United Kingdom from the EU (Brexit) meant that the UK regained autonomy in its Exclusive Economic Zone (EEZ). This suddenly imposed a spatial restriction for several foreign fishing fleets targeting Northeast Atlantic mackerel (Scomber scombrus). Here, we use this natural experiment and open fisheries data to investigate how Brexit affected the performance and emissions of the Norwegian mackerel fishery. As the fleet was excluded from fishing grounds in the UK, the catch per fishing trip almost halved, while the number of trips per vessel doubled. As a result, fuel use intensity (FUI) more than doubled from similar to 0.08 to similar to 0.18 L fuel per kg mackerel. We estimate that this shift required an additional 23 million liters of fuel per year, causing additional fuel costs of similar to18 million annually and emitting an additional similar to 72,000 tonnes CO2 per year(.) The policy change undid similar to 15 years of improved fuel efficiency in Norwegian pelagic fisheries. These findings provide rare empirical evidence on how spatial restrictions can undermine progress towards decreasing greenhouse gas emissions in fisheries, highlighting the need to monitor and account for emissions in fisheries management and consider these trade-offs in marine spatial management.

Conserving intraspecific trait variation is vital for maintaining the viability of species. It ensures a species to adapt to warming and increasingly stochastic environments, and to recover following extreme events. Here we investigate the selective effects of spatial management on intraspecific genetic and phenotypic variation of two sympatric but genetically distinct Atlantic cod ecotypes in a Norwegian fjord. We found that phenotypic differences between sympatric cod genotypes were mainly driven by morphological and metabolic traits. Offshore cod had higher metabolic maintenance costs at cool temperatures but lower aerobic capacity at warm acclimation than coastal ecotypes, indicative of thermal constraint of aerobic physiological processes beyond metabolic maintenance. Offshore cod also had larger and thicker peduncles and better body condition. We found that protection benefits from the no-take zone (NTZ) of the Tvedestrand marine protected area were independent of individual space-use size, but instead resulted from ecotype-specific differences in habitat occupation. Results specifically show that the current delimitations of the NTZ do not cover habitats occupied by the coastal and highly resident cod ecotype which shows greater metabolic thermal tolerance but is considered to already be in a depleted state. Our study exemplifies why protecting intraspecific diversity is directly relevant for management implementations aimed at reducing the impact of further selection pressures such as ongoing environmental change. Careful investigation of intraspecific diversity and integration of such knowledge to fisheries management and design of protected areas may prevent unwanted additional selective pressures and contribute to offer broad protection to genotypes and phenotypes.

Abstract Aquaculture of Atlantic salmon Salmo salar is in transition to precision fish farming and digitalization. As it is easier, cheaper and safer to study a digital replica than the system itself, a model of the fish can potentially improve monitoring and prediction of facilities and operations and replace live fish in many what‐if experiments. Regulators, consumers and voters also want insight into how it is like to be a salmon in aquaculture. However, such information is credible only if natural physiology and behaviour of the living fish is adequately represented. To be able to predict salmon behaviour in unfamiliar, confusing and stressful situations, the modeller must aim for a sufficiently realistic behavioural model based on the animal's proximate robustness mechanisms. We review the knowledge status and algorithms for how evolution has formed fish to control decisions and set priorities for behaviour and ontogeny. Teleost body control is through genes, hormones, nerves, muscles, sensing, cognition and behaviour, the latter being agentic, predictive and subjective, also in a man‐made environment. These are the challenges when constructing the digital salmon. This perspective is also useful for modelling other domesticated and wild animals in Anthropocene environments.

Detecting violations within fishing activity reports is crucial for ensuring the sustainable utilization of fish resources, and employing machine learning methods holds promise for uncovering hidden patterns within this complex dataset. Given that these violations are infrequent occurrences, as fishermen generally adhere to regulations, identifying them becomes akin to an anomaly outlier detection task. Since labeled data distinguishing between normal and anomalous instances is not available for catch reports from Norwegian waters, we have opted for more conventional approaches, such as clustering methods, to identify potential clusters and outliers. Moreover, the catch reports inherently exhibit randomness and noise due to environmental factors and potential errors made by fishermen during report registration which complicates the processes of scaling, clustering, and anomaly detection. Through experimentation with various scaling and clustering techniques, we have observed that many of these methods tend to group the data based on the species caught, exhibiting a high level of agreement in cluster formation, indicating the stability of the clusters. Anomaly detection methods, however, yield varying potential outliers as it is a more challenging task.

Predator-prey interactions in time and space determine stock productivity, making them an important consideration when managing marine resources, rebuilding stocks or considering reopening a fishery. We analysed fine-scale diet data from surveys conducted in 2009-2010 and 2018-2019 in three fjords in northern Norway with geostatistical models investigating how predation varied in space, time and between predator species. Our focus prey species was northern shrimp (Pandalus borealis), valuable both as a commercial resource and a major food source for other important species like Atlantic cod (Gadus morhua). Diet composition of fish predators differed clearly between fjords. While predator species and size were good predictors of shrimp predation, the relationships with bathymetry, prey density and geospatial variables were complex. Our study indicates that predation of forage species, such as shrimp, varies spatially in heterogenous fjord ecosystems. Shrimp consumption was not highest in the fjord with highest predator density, indicating a higher dependency of cod on shrimp in specific areas. Realized predation is a complex combination of predator and prey densities and predator ecology that differed in each of the three fjords. Synthesis and applications. Ignoring spatial variations in predator-prey interactions may lead to an inaccurate perception of stock productivity, suboptimal management and possibly unsustainable management targets. We recommend spatially explicit assessment and management for fish stocks where predator-prey interactions vary substantially in space, such as fjords and reefs. Rovdyr-byttedyr-interaksjoner i tid og rom bestemmer produktiviteten til bestander. Det er derfor viktig a ta hensyn til dette i forvaltningen av marine ressurser, gjenoppbygging av bestander eller nar det vurderes a gjenapne et fiske. Vi analyserte finskala diettdata fra bunntraltokt utfort i 2009-2010 og 2018-2019 i tre fjorder i Nord-Norge med geostatistiske modeller, der vi undersokte hvordan predasjon varierte i rom, tid og mellom rovdyrarter. Vi fokuserte pa byttedyret dypvannsreke (Pandalus borealis), som bade er en verdifull kommersiell ressurs og et viktig bytte for andre viktige arter som atlantisk torsk (Gadus morhua). Det var en klar forskjell mellom fjordene i diettsammensetningen til rovfiskene. Mens arter av fisk og storrelsen pa dem var gode predikatorer for rekepredasjon, var sammenhengen med batymetri, byttedyrtetthet og romlige variabler komplekse. Studien var indikerer at predasjon pa typiske byttedyrarter, som reker, varierer romlig i heterogene fjordokosystemer. Rekekonsumet var ikke hoyest i fjorden med hoyest predatortetthet, noe som indikerer at torsk er mer avhengig av reker i bestemte omrader. Realisert predasjon bestar av en kompleks kombinasjon av rovdyr- og byttedyrtettheter og rovdyrokologi, som var forskjellig i hver av de tre fjordene. Syntese og applikasjoner. angstrom ignorere romlige variasjoner i interaksjoner mellom rovdyr og byttedyr kan fore til en unoyaktig forstaelse av bestandsproduktivitet, suboptimal forvaltning og muligens ikke-b AE rekraftige forvaltningsmal. Vi anbefaler en romlig eksplisitt bestandsvurdering og forvaltning for fiskebestander hvor interaksjoner mellom rovdyr og byttedyr i hoy grad varierer i rom, som i fjorder og pa rev. Ignoring spatial variations in predator-prey interactions may lead to an inaccurate perception of stock productivity, suboptimal management and possibly unsustainable management targets. We recommend spatially explicit assessment and management for fish stocks where predator-prey interactions vary substantially in space, such as fjords and reefs.image

The greater argentine is a benthopelagic fish with a northern amphi-Atlantic and southern Arctic distribution. Landings of this species have been steadily increasing since the early 2000s, mainly for ultra-processed fish food. The rising economic importance of this species begs for an accurate delineation of the management units needed to ensure the sustainability of the fishery. The alignment between management and biological units was investigated on three of the ICES stocks in the NE Atlantic (123a4, 5a14, and 5b6a) by genotyping 88 ad hoc-developed SNPs on 1299 individuals sampled along the Norwegian coast, north of Shetland, around the Faroe Islands, and in the Denmark Strait within Icelandic waters. Candidate loci to positive selection were particularly crucial for units’ delineation and supported the current ICES 5b6a and 5a14 stocks around the Faroe Islands and Iceland, respectively. However, within the third stock investigated, 123a4, which corresponded mainly to the Norwegian coast, the sample from area 3a (Skagerrak) was significantly different from all the remaining in the same stock. This differentiation advocates for reconsideration of the present policy and suggests considering ICES Area 3a (Skagerrak) as an independent management unit. The environmental conditions in the Skagerrak area have left a genetic print on other marine taxa, which could putatively be the case in the greater argentine.

Foraging behaviour is known to be a key element in ecology and evolution. Increased foraging intensity increases energy intake, which is useful for growth and reproduction but comes at the cost of higher mortality risk due to increased exposure to predators. Here, we investigate these trade-offs through an individual-based, mechanistic modelling framework adapted to the Northeast Arctic Cod. The model incorporates a series of life-history traits, survival trade-offs, and heritability, which allow evolution to occur and optimal strategies to emerge due to individual trait combinations and their fitness consequences. By altering the relationship between foraging intensity and mortality risk, we find that increased risk causes evolution towards lower foraging effort leading to lower growth and in turn, earlier maturation and a faster pace of life. These results build on previous studies by demonstrating behavioural evolution without direct anthropogenic stressors. Natural mortality among fish is poorly understood, and these results highlight an interesting point of further research that could help future modelling approaches make more accurate assumptions about natural mortality and its components.

This work is part of a design science project where the aim is to develop Machine Learning (ML) tools for analyzing tracks of fishing vessels. The ML models can potentially be used to automatically analyse Automatic Identification System (AIS) data for ships to identify fishing activity. Creating such technology is dependent on having labeled data, but the vast amounts of AIS data produced every day do not include any labels about the activities. We propose a labeling method based on verified heuristics, where we use an auxiliary source of data to label training data. In an evaluation, a series of tests have been done on the labeled data using deep learning architectures such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), 1D Convolutional Neural Network (1D CNN), and Fully Connected Neural Network (FCNN). The data consists of AIS data and daily fishing activity reports from Norwegian waters with a focus on bottom trawlers. Accuracy is higher than or equal to 87% for all deep learning models. Example applications of the trained models show how they can be used in a practical setting to identify likely unreported fishing activities.

Sexual size dimorphism (SSD) is caused by differences in selection pressures and life-history trade-offs faced by males and females. Proximate causes of SSD may involve sex-specific mortality, energy acquisition, and energy expenditure for maintenance, reproductive tissues, and reproductive behavior. Using a quantitative, individual-based, eco-genetic model parameterized for North Sea plaice, we explore the importance of these mechanisms for female-biased SSD, under which males are smaller and reach sexual maturity earlier than females (common among fish, but also arising in arthropods and mammals). We consider two mechanisms potentially serving as ultimate causes: (a) Male investments in male reproductive behavior might evolve to detract energy resources that would otherwise be available for somatic growth, and (b) diminishing returns on male reproductive investments might evolve to reduce energy acquisition. In general, both of these can bring about smaller male body sizes. We report the following findings. First, higher investments in male reproductive behavior alone cannot explain the North Sea plaice SSD. This is because such higher reproductive investments require increased energy acquisition, which would cause a delay in maturation, leading to male-biased SSD contrary to observations. When accounting for the observed differential (lower) male mortality, maturation is postponed even further, leading to even larger males. Second, diminishing returns on male reproductive investments alone can qualitatively account for the North Sea plaice SSD, even though the quantitative match is imperfect. Third, both mechanisms can be reconciled with, and thus provide a mechanistic basis for, the previously advanced Ghiselin-Reiss hypothesis, according to which smaller males will evolve if their reproductive success is dominated by scramble competition for fertilizing females, as males would consequently invest more in reproduction than growth, potentially implying lower survival rates, and thus relaxing male-male competition. Fourth, a good quantitative fit with the North Sea plaice SSD is achieved by combining both mechanisms while accounting for sex-specific costs males incur during their spawning season. Fifth, evolution caused by fishing is likely to have modified the North Sea plaice SSD.

Extended use of laboratory and field courses makes biology a discipline considering itself as a habitual practitioner of active learning strategies. We investigated how widely the faculty at the Department of Biological Sciences (BIO) at the University of Bergen (UiB, Norway) uses active learning methods. Thirty-six members of the teaching staff answered our web-based questionnaire, and we carried out in-depth interviews of 7 faculty members. Our results show that almost all BIO-teachers use at least some active learning methods, and plan to use them in their teaching in the near future. The teachers use active learning methods mostly because they want their students to achieve deeper learning, but also because they want to develop themselves as teachers. This self-motivation is obvious, as over 90% of the teachers identified self-motivation as the strongest incentive, while colleagues, the department, and the university were less important. A vast majority of the teachers also think that it is their own responsibility to adopt active learning methods, while fewer faculty members assume institutional responsibility from BIO. The major bottlenecks identified were large class size and difficulties related to evaluating and grading student performance when using active learning methods. The teachers would use more active learning methods if the availability of active learning rooms was increased. Our in-depth interviews suggest that the most suitable time window for adopting more student-active learning methods is either when new courses are established, or when teachers are taking over courses new to them. We therefore suggest that if educational institutes wish to increase the proportion of active teaching and learning methods, they should provide extra support in such transition periods.

This case study explores educational practices and processes in an interdisciplinary summer course addressing SDG14 (Life below water), SDG13 (Climate action), SDG4 (Education), SDG3 (Good health and wellbeing), and SDG17 (Partnerships). From May to August in 2022, students from 12 countries participated in an undergraduate summer course (SDG 200 Ocean–Climate–Society) on the sailship Statsraad Lehmkuhl as part of the One Ocean Expedition. Sustainability, marine biology, behavioral science, and sail training were core aspects of the daily assignments for the 86 students during the Pacific crossing from Chile to Tahiti. The students took part in watch duties 24–7 and were assigned to 18 working groups in their academic studies. Active learning approaches such as team-based learning and storytelling proved essential to engage students in interdisciplinary exchange on sustainability issues. A major challenge was to strike a balance between the academic work and the requirements from sea duties and life on board a sailship. Student feedback and assessment contribute to contextualize the learning experiences and personal development during the first five weeks on board. This case study provides an example of how life on a sailship can present a formative learning experience and an interdisciplinary laboratory to study and live in alignment with SDGs and with the overall mandate of the Global Agenda for Sustainable Development.

Growth is a key component of population dynamics and, thus, fisheries management, yet drivers of its variations are often poorly understood. Using individual data collected over 80 years, we explored how environmental drivers affect growth in a major population of Atlantic herring (Clupea harengus). The results confirm that intrinsic factors (age and maturation) determine growth to a large degree but also that extrinsic factors such as temperature have some influence. While the role of intrinsic factors was independent of time series length, the importance of extrinsic drivers varies strongly with the analysed time period. It remains unclear whether this is caused by data inconsistencies back in time, spurious correlations appearing in shorter time series, shifts in population dynamics, or dynamic interactions between variables that cannot be determined with current data. Generally, environmental effects on growth became less clear and relevant with increasing time series length. What drives variation in growth may therefore change over time, potentially due to impacts such as fishing or climate change. It also underlines that seemingly clear correlations can break down or change their sign over time; hence, caution is advised when interpreting results from time series of 20–40 years.

Density-dependent growth, which might influence the effects of fisheries on a population, is often ignored when management strategies are evaluated, mainly due to a lack of appropriate models readily available to be implemented. To improve on this, we investigated if somatic growth in Norwegian spring-spawning herring (Clupea harengus) depends on cohort density using a formulation of the von Bertalanffy growth function on cohorts from 1921 to 2014 and found a significant negative correlation between estimated asymptotic length and density. This clearly indicates density-dependent effects on growth, and we propose a model that can be used to predict the size-at-age of Norwegian spring-spawning herring as a function of herring density (the abundance of two successive cohorts) in short-term predictions of catch advice, and in Management strategy evaluations, including estimation of their reference points such as F-MSY.