Pacific salmon (Oncorhynchus spp.) spend a large portion of their life cycle in the open ocean, where their growth, survival, trophic interactions, and habitat selection are influenced by ocean conditions (Mueter et al. 2005; Farley et al. 2020; Litzow et al. 2020). As they travel across great distances in these offshore habitats, the vulnerability of salmon to fisheries interactions has also been a subject of concern in recent years (https://npafc.org/enforcement-activities/; Oozeki et al. 2018). Investigating salmon marine spatial dynamics through directed sampling efforts, however, is both resource-intensive and logistically difficult given the spatial scale of their marine migrations. Despite these challenges, there is a history of high seas Pacific salmon research led initially by the International North Pacific Fisheries Commission (1952–1992) and later by the North Pacific Anadromous Fish Commission (1993–Present). Through these collaborations, Pacific Rim nations have executed research programs across the North Pacific in pursuit of a variety of research objectives that shifted over time. Due to these evolving priorities, a sampling bias toward the late spring and summer, and decreased support for high seas research in recent decades, these data exhibit a highly heterogenous spatiotemporal distribution across the North Pacific. On aggregate, however, the salmon survey records cover much of the oceanic range of Pacific salmon and provide information on distribution and relative abundance throughout the seasonal cycle.

Marine apex predators are promising sentinels for detecting the ecological impacts of climate variability and change. Fishermen are increasingly recognized as marine apex predators, and there are extensive satellite-based geolocation data on fishing vessel activities. Despite this potential, the utility of fishermen as ecosystem sentinels remains unexamined. Using one million vessel positions from 600 U.S. vessels, we assess the effectiveness of fishermen as sentinels for the ecological impacts of Northeast Pacific marine heatwaves on tuna distribution and availability. Fishermen were skillful predictors of extreme northward shifts for albacore and bluefin tunas, and extreme inshore shifts for albacore. Fishermen signaled low albacore availability over a year in advance of a formal fisheries disaster declaration request. Notably, fishermen also indicated true negatives during marine heatwaves: periods of anomalous warming but stable tuna distribution and availability. This information could aid management of transboundary shifts during marine heatwaves of albacore from U.S. to Canadian waters and bluefin from Mexican to U.S. waters. Advanced warning of fisheries disasters could expedite the delivery of relief funds for struggling communities. The number of Earth-orbiting satellites is exponentially rising, generating a wealth of geospatial information on fishing vessels. This rich and growing resource can signal otherwise unobserved ecological impacts, aiding rapid management responses to climate extremes.

High-latitude ecosystems commonly experience large phytoplankton blooms in spring, which provide basal resources for a range of grazers including zooplankton, benthic consumers and fishes. Variation of the timing and intensity of the spring phytoplankton bloom influences the degree of spatial and temporal overlap with consuming organisms. In the Bering Sea, blooms occur in association with ice retreat or as pelagic open-water blooms. In the last few years, the Bering Sea shelf experienced unprecedented and widespread warming. Understanding how those climatic changes subsequently influenced phytoplankton bloom dynamics is critical for evaluating Bering Sea food web responses. We estimate spring bloom timing and type (ice-associated, open-water) across the Bering Sea shelf using a combination of data from the long-running oceanographic moorings on the eastern shelf (M2, M4, M5, M8) and satellite ocean color data from 1998 to 2022. We assess 1) if the Bering Sea shelf experienced noticeable changes in spring bloom timing or type in the last two decades, 2) whether bloom phenology was accentuated by the recent warm period (2018-2019), and 3) what influences do winds and sea surface temperatures have on spring bloom timing and where are these variables influential? Our spatial analyses reinforce the conclusion that ice retreat is the dominant forcing factor of bloom timing for the Bering Sea shelf with some influence of wind for open-water blooms. Overall, bloom timing has not shifted seasonally with climate warming in the last two decades for most of the Bering Sea shelf, except for nearshore areas and mainly in the northern Bering Sea. In warm years when ice retreats early prior to the last week of March, blooms form in open waters and bloom timing on the middle and outer shelf is delayed when wind mixing is prevalent in ice-free springs. In recent years, open-water blooms were more widespread than previously experienced, and even occurred in the northern Bering Sea during 2018-2019. A progression to more open water blooms in a future warmer climate will influence the availability of basal resources for pelagic and benthic consumers.

Pacific salmon (Oncorhynchus spp.) spend much of their life near the ocean surface where climatic and oceanographic conditions affect their habitat and survival. Despite decades of study, critical knowledge gaps persist regarding their ecology and distributions. Consequently, it has been difficult to assess how environmental conditions influence the high-seas distribution and habitat use of these culturally and socioeconomically important fishes, presenting challenges to fisheries managers trying to evaluate how climate change and fishing activities may impact salmon populations. We used a recently compiled, comprehensive database of historical coastal and high-seas salmon survey data (1953-2022) in the North Pacific to fit species distribution models that (1) characterize the marine spatial distribution of six species of Oncorhynchus, (2) evaluate species-specific temperature preferences, and (3) investigate how species' temperature preferences influence distribution. Sea surface temperature, along with seasonal migrations associated with spawning and feeding, significantly affects the distribution of all species, where the warm limits of estimated preferred thermal ranges were more similar than the cold limits. Furthermore, the distributions of some species appear more responsive to temperature than others and recently observed warm conditions have likely impacted realized ranges. These models have expanded our understanding of salmon ocean distributions and thermal niches by providing a unique window into this often unobserved but important part of the life cycle. They also serve as a baseline for future investigations into the mechanisms influencing salmon spatial ecology, responses to climate change, and vulnerability to harvest across the North Pacific.

Describing the sheer scale of the global fishing industry necessitates a lot of zeros: 4,900,000 fishing vessels, 40,000,000 million workers, and an annual production of 80,000,000 tonnes of seafood valued at $141,000,000,000. Effective management of the fishing industry requires crunching these big data-while the human mind balks at such a task, the artificial mind does not. Artificial intelligence (AI) is a family of systems that allow computers to simulate human behaviors, such as learning from experience and recognizing visual patterns. This primer explains how AI is used to monitor and surveil fishing vessels from space, shore, and the seafloor and then how it is applied to process this information to meet fisheries management goals, like combating illegal fishing. The exponential rise of AI in fisheries applications over the past decade shows no signs of slowing. We reflect on how the AI of tomorrow may improve fisheries' sustainability and transparency while emphasizing the sustained need for human oversight in an increasingly automated future.

Understanding the dynamic relationship between marine species and their changing environments is critical for ecosystem based management, particularly as coastal ecosystems experience rapid change (e.g., general warming, marine heat waves). In this paper, we present a novel statistical approach to robustly estimate and track the thermal niches of 30 marine fishes along the west coast of North America. Leveraging three long-term fisheries-independent datasets, we use spatiotemporal modeling tools to capture spatiotemporal variation in species densities. Estimates from our models are then used to generate species-specific estimates of thermal niches through time at several scales: coastwide and for each of the three regions. By synthesizing data across regions and time scales, our modeling approach provides insights into how these marine species may be tracking or responding to changes in temperature. While we did not find evidence of consistent temperature-density relationships among regions, we are able to contrast differences across species: Dover sole and shortspine thornyhead have relatively broad thermal niche estimates that are static over time, whereas several semi-pelagic species (e.g., Pacific hake, walleye pollock) have niches that are both becoming warmer over time and simultaneously narrowing. This illustrates how several economically and ecologically valuable species are facing contrasting fates in a changing environment, with potential consequences for fisheries and ecosystems. Our modeling approach is flexible and can be easily extended to other species or ecosystems, as well as other environmental variables. Results from these models may be broadly useful to scientists, managers, and stakeholders — monitoring trends in the direction and variability of thermal niches may be useful in identifying species that are more susceptible to environmental change, and results of this work can form quantitative metrics that may be included in climate vulnerability assessments, estimation of dynamic essential fish habitat, and assessments of climate risk posed to fishing communities.

Illegal, unreported, and unregulated (IUU) fishing is a major problem worldwide, often made more challenging by a lack of at-sea and shoreside monitoring of commercial fishery catches. Off the US West Coast, as in many places, a primary concern for enforcement and management is whether vessels are illegally fishing in locations where they are not permitted to fish. We explored the use of supervised machine learning analysis in a partially observed fishery to identify potentially illicit behaviors when vessels did not have observers on board. We built classification models (random forest and gradient boosting ensemble tree estimators) using labeled data from nearly 10,000 fishing trips for which we had landing records (i.e., catch data) and observer data. We identified a set of variables related to catch (e.g., catch weights and species) and delivery port that could predict, with 97% accuracy, whether vessels fished in state versus federal waters. Notably, our model performances were robust to inter-annual variability in the fishery environments during recent anomalously warm years. We applied these models to nearly 60,000 unobserved landing records and identified more than 500 instances in which vessels may have illegally fished in federal waters. This project was developed at the request of fisheries enforcement investigators, and now an automated system analyzes all new unobserved landings records to identify those in need of additional investigation for potential violations. Similar approaches informed by the spatial preferences of species landed may support monitoring and enforcement efforts in any number of partially observed, or even totally unobserved, fisheries globally.

Marine heatwaves cause widespread environmental, biological, and socio-economic impacts, placing them at the forefront of 21st-century management challenges. However, heatwaves vary in intensity and evolution, and a paucity of information on how this variability impacts marine species limits our ability to proactively manage for these extreme events. Here, we model the effects of four recent heatwaves (2014, 2015, 2019, 2020) in the Northeastern Pacific on the distributions of 14 top predator species of ecological, cultural, and commercial importance. Predicted responses were highly variable across species and heatwaves, ranging from near total loss of habitat to a two-fold increase. Heatwaves rapidly altered political bio-geographies, with up to 10% of predicted habitat across all species shifting jurisdictions during individual heatwaves. The variability in predicted responses across species and heatwaves portends the need for novel management solutions that can rapidly respond to extreme climate events. As proof-of-concept, we developed an operational dynamic ocean management tool that predicts predator distributions and responses to extreme conditions in near real-time.

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of & SIM;1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.

Background: Challenging authority through speaking up to ensure patient safety is a difficult yet essential aspect of interpersonal communication and patient care. Existing interventions for speaking up are inconsistent though prior experience appears to be important for speaking up in the future. To provide experience and improve speaking up behaviour in Respiratory Therapy students an intervention was developed that integrates a Gamified Virtual Simulation (GVS) with curriculum on patient advocacy and a high-intensity in-person simulation. GVSs have demonstrated potential for improving interprofessional collaboration and communication. Development of the intervention was guided by Kolb’s Learning Cycle.Research Questions: 1. Can a GVS improve students’ performance during a high-intensity simulation? 2. Does perceptual experience during high-intensity simulation differ based on completing a GVS? 3. Will the GVS influence learning related to speaking up. Methods: One week prior to classroom instruction on patient advocacy students were randomly assigned to a GVS or No Intervention condition. The GVS used a Choose-Your-Own-Adventure format requiring students to advocate for a patient by using CUS (Concerned, Uncomfortable, Safety Issue). One month after the conclusion of classroom instruction participants completed a high-intensity simulation requiring speaking up to ensure patient safety. Rates of speaking up were measured. After the in-person simulation participants completed a questionnaire related to the VS and classroom instruction followed by a semi-structured interview. The analysis used a mixed-methods approach to integrate both data forms. The study was approved by the NAIT Research Ethics Office (#2021-03).Results: The GVS improved rates of speaking up and the use of CUS during the in-person simulation. The GVS prepared participants for the classroom instruction on patient advocacy and supported learning and retention of classroom material. GVS condition participants believed the GVS helped during the simulation and better understood how to speak up, escalate a challenge, and appeared more confident. All participants found the simulations to be beneficial to their development as healthcare professionals.Conclusions: Integrating a GVS with classroom instruction and an in-person simulation utilizing Kolb’s Learning Cycle can improve performance during in-person simulation, providing the opportunity to gain experience speaking up and support learning on patient advocacy.

Central to the success of partial coverage observer programs is the assumption that observed fishing behaviors are representative of unobserved fishing behaviors. Traditionally, this was a difficult assumption to test beyond comparing catch compositions for observed and unobserved trips. With the proliferation of vessel monitoring systems (VMS) however, a suite of trip- and set-level fishing characteristics can be engineered from vessel locations that facilitate quantitative comparisons between observed and unobserved behaviors. The U.S. West Coast drift gillnet (DGN) fishery for swordfish (Xiphias gladius) is a small fishery with a long history of bycatch concerns and an observer coverage rate around 20–30 %. We developed an integrated data set composed of VMS, logbook, landings, and observer data for the DGN fishery from 2013 to 2019. We used a suite of machine learning models to classify unobserved fishing behaviors and we characterized these fishing behaviors at the trip- and set-level. Analyses of the trip- and set-level metrics (e.g., trip duration, fishing depth, sea surface temperatures, distance from port, and catch per unit effort) revealed that operational characteristics of the fishery were largely similar for observer and unobserved trips and vessels, but also highly variable. In the fall season, observed trip distances were an average of 53 % (std. error 20 %) longer than unobserved trips for the same vessels. These longer trips occurred in water that was, on average, about 39 % (std. error 16 %) deeper. However, characteristics like amounts of fishing effort, revenue, and water temperatures were generally similar between observed and unobserved trips. We present a methodical workflow for data integration and feature engineering and a simple approach for assessing differences across fleet behaviors in space and time that could be applied to many fisheries with limited observer coverage. This approach could also be used to target specific strata within fisheries that may benefit from increases or shifts in observer coverage.

Abstract Purpose Healthcare teams consist of interdisciplinary groups of health professionals that tend to be hierarchically structured. Often it is necessary to challenge authority through speaking up, however, within hierarchies’ individuals tend to demonstrate obedience to authority. Speaking up is an essential skill for preventing patient harm that must be developed. Virtual Simulation integrated with curriculum using Kolb’s Learning Cycle is a promising avenue for improving interprofessional collaboration. Methods An experimental design was used to determine if a gamified VS along with instruction on patient-advocacy would increase the rate of speaking up in Respiratory Therapy students (n=34) during in-person simulation delivered one month after the classroom instruction. The in-person simulation required students to challenge a senior anesthesiologist to prevent patient harm. Effects of personality and individual differences were also examined. Results The VS resulted in speaking up at a higher rate than in the control condition (p=0.04) and used CUS more often (p<0.001). No individual differences or personality measures were predictive of speaking up. Conclusion The findings from the present study support the integration of VS with course curriculum on patient-advocacy, showing improved performance during in-person simulation one month after course delivery. Longitudinal investigation is necessary to determine if the improved performance is indicative of increased likelihood of speaking up in the future.

Over the past two decades, numerous ecosystem surveys and process studies have emerged to monitor and assess the large marine ecosystems of Alaska. Several regional collaborative integrated ecosystem research projects (IERPs) were conducted to gain understanding of fish population fluctuations in relation to the surrounding environment. The Gulf of Alaska (GOA) IERP is one example of such an effort. Products of this program include a suite of in situ observations from fully integrated ecosystem surveys, laboratory experiments of physical thresholds for fish condition, and high-resolution oceanographic, planktonic, and habitat distribution models. When coupled, the synthesis products of this program can be utilized to understand system connectivity and highlight the primary ecosystem drivers of the GOA. Much of this information was included in annual GOA ecosystem status reports through individual indicator contributions. However, assimilation of these data into single-species stock assessments has remained limited. We provide a clear and direct avenue for including the products of these IERPs through the new ecosystem and socioeconomic profile (ESP) framework that identifies mechanistic relationships and tests ecosystem linkages within the stock assessment process. We present a case study using a data synthesis of the five commercially and ecologically valuable focal species of the GOAIERP (sablefish, pollock, Pacific cod, arrowtooth flounder, and Pacific ocean perch). Information was organized along the categories of distribution, phenology, and condition by life history stage to develop life history narratives for each species. These narratives identified critical ecosystem processes that could impact survival of each species. We then used habitat distribution models, seasonal phenology, and energy allocation strategies to sequentially reduce two gridded temperature datasets to reflect the life experience of the stock. This method essentially aligns ecosystem information at a spatial and temporal scale relevant to a stock and creates informed indicators that could then be related to a stock assessment parameter of interest, such as recruitment. Informed temperature indicators differed in magnitude and variability when compared to non-informed indicators and demonstrating species and stage-specific thermal preferences. The difference between the informed indicators and the non-informed indicators can also highlight thresholds and trends in habitat preference that could be further investigated with targeted process studies or laboratory experiments. The coordinated nature of the IERP allowed for the creation of these informed indicators that would not be possible with the results of any one process study. Both the stock-specific narratives and the informed indicators can be included into the ESPs for further monitoring and development. This integration ensures that the identified ecosystem linkages are evaluated concurrently with the stock assessment and ultimately transferred to fishery managers in an efficient and effective format for informing management decisions.

While there have been recent improvements in reducing bycatch in many fisheries, bycatch remains a threat for numerous species around the globe. Static spatial and temporal closures are used in many places as a tool to reduce bycatch. However, their effectiveness in achieving this goal is uncertain, particularly for highly mobile species. We evaluated evidence for the effects of temporal, static, and dynamic area closures on the bycatch and target catch of 15 fisheries around the world. Assuming perfect knowledge of where the catch and bycatch occurs and a closure of 30% of the fishing area, we found that dynamic area closures could reduce bycatch by an average of 57% without sacrificing catch of target species, compared to 16% reductions in bycatch achievable by static closures. The degree of bycatch reduction achievable for a certain quantity of target catch was related to the correlation in space and time between target and bycatch species. If the correlation was high, it was harder to find an area to reduce bycatch without sacrificing catch of target species. If the goal of spatial closures is to reduce bycatch, our results suggest that dynamic management provides substantially better outcomes than classic static marine area closures. The use of dynamic ocean management might be difficult to implement and enforce in many regions. Nevertheless, dynamic approaches will be increasingly valuable as climate change drives species and fisheries into new habitats or extended ranges, altering species -fishery interactions and underscoring the need for more responsive and flexible regulatory mechanisms.

Sustainability is a common goal and catchphrase used in conjunction with seafood, but the metrics used to determine the level of sustainability are poorly defined. Although the conservation statuses of target or nontarget fish stocks associated with fisheries have been scrutinized, the relative climate impacts of different fisheries are often overlooked. Although an increasing body of research seeks to understand and mitigate the climate forcing associated with different fisheries, little effort has sought to integrate these disparate disciplines to examine the synergies and trade-offs between conservation efforts and efforts to reduce climate impacts. We quantified the climate forcing per unit of fish protein associated with several different U.S. tuna fishing fleets, among the most important capture fisheries by both volume and value. We found that skipjack tuna caught by purse seine, a gear type that is often associated with relatively high bycatch of nontarget species, results in lower climate forcing than all other sources of proteins examined with the exception of plants. Conversely, skipjack tuna caught by trolling, a gear type that is often associated with relatively low bycatch of nontarget species, generates higher climate forcing than most other protein sources with the exception of beef. Because there is a range of selectivity and climate forcing impacts associated with fishing gears, examining the trade-offs associated with bycatch and climate forcing provides an opportunity for broadening the discourse about the sustainability of seafood. A central goal of more sustainable seafood practices is to minimize environmental impacts, thus mitigation efforts-whether they target conservation, habitat preservation, or climate impacts-should consider the unintended consequences on fisheries conservation.

Species that migrate long distances or between distinct habitats- for example, anadromous or catadromous fish-experience the consequences of climate change in each habitat and are therefore particularly at risk in a changing world. Studies of anadromous species often focus on freshwater despite the ocean's disproportionate influence on survival and growth. To understand a prominent anadromous species' response to ocean climate, we use a new spatio-temporal model jointly estimating the ocean distribution of all major fall-run Chinook salmon (Oncorhynchus tshawytscha, Salmonidae) stocks from California to British Columbia over 40 years. We model hundreds of millions of tagged individuals, finding that different stocks have fundamentally different ocean distributions, distinct associations with sea surface temperature (SST), and contrasting distributional responses to historical ocean SST variation. We show species-level estimates of ocean distribution that ignore among-stock variation will lead to errant predictions of spatial distribution. Using future (2030-2090) SST projections to model focal stocks of fisheries importance we predict substantial ocean redistribution in response to SST change. Predicted aggregate distributional changes do not follow a simple, poleward shift. Instead, we predict net movement into some ocean regions (British Columbia, central California) but net movement out of others (northern California, Washington). Distribution shifts have implications for both major fisheries and marine mammal predators of Chinook salmon. We focus on the consequences of spatial changes in ocean distribution, but our approach provides a general structure to link marine and freshwater components of anadromous species under climate change.

Minimal vessel traffic and cold water temperatures are believed to limit non-indigenous species (NIS) in high-latitude ecosystems. We evaluated whether suitable conditions exist in the Bering Sea for the survival and reproduction of NIS. We compiled temperature and salinity thresholds of NIS and compared these to ocean conditions projected during two study periods: recent (2003-2012) and mid-century (2030-2039). We also explored patterns of vessel traffic and connectivity for US Bering Sea ports. We found that the southeastern Bering Sea had suitable conditions for the year-round survival of 80% of NIS assessed (n = 42). This highly suitable area is home to the port of Dutch Harbor, which received the most vessel arrivals and ballast water discharge in the US Bering Sea. Conditions north of 58 degrees N that include sub-zero winter water temperatures were unsuitable for most NIS. While mid-century models predicted a northward expansion of suitable conditions, conditions for reproduction remained marginal. Only 40% of NIS assessed (n = 25) had 6 or more weeks where conditions were suitable for reproduction. Our findings illustrate the potential vulnerability of a commercially important subarctic ecosystem and highlight the need to consider life stages beyond adult survival when evaluating limits to NIS establishment.

The northern Bering Sea and Chukchi Sea represent the gateway from the Pacific to the Arctic. This contiguous marine system encompasses one of the largest continental shelves in the world and serves as the sole point of connection between the North Pacific and Arctic Ocean. This region has unique attributes and complex dynamics, driven by the convergence of distinct water masses, dynamic currents, advection between Pacific and Arctic systems, and important latitudinal gradients relevant to stratification and water mass structure, water temperature, and seasonal ice cover. Many processes and interactions in the region appear to be changing with important implications for both hydrography and ecology. Our analyses access remote and local data sources in US and Russian waters to characterize oceanographic conditions and analyze the implications of dramatic shifts in recent years. Previously, this region appeared resistant to trends apparent elsewhere in the greater Arctic. Now, the Pacific Arctic also appears to be in rapid transition. The conditions observed in 2017–2019 are unprecedented. We note important shifts in the phenology and magnitude of physical variables, including sea-ice extent, concentration, and duration, as well as extreme reduction in the extent and intensity of the related Bering Sea cold pool. We also note distinct regional dynamics in sea surface temperature in the Bering-Chukchi system, distinguishing western, eastern and northern areas of the Bering Sea. Specifically, our analyses distinguish the northern Bering Sea as an important transition zone between the Pacific and Arctic with higher frequency variability in sea surface temperature anomalies. Our results suggest that the strength and position of the Aleutian Low may be linked to warm and cold phases in the Bering Sea and has an important role in large-scale circulation. While cold winds out of the north are necessary to form ice in the northern Bering Sea, strong winds may be associated with weak sea ice, as wind action may break ice and enhance vertical mixing, counteracting enhanced sea-ice production from the advection of cold air. Research in this important region is complicated by international borders but may be enhanced through international collaboration. This analysis represents an attempt to integrate data across Russian and US waters to more fully represent system-wide processes, to contrast regional trends, and to better understand physical interactions.