Camrin D. Braun

Movements of swordfish ( Xiphias gladius ) typically follow the diel vertical migration of mesopelagic animals, spending daylight hours at ~ 600 m and nights near the surface. However, years of satellite tagging data have also revealed infrequent extreme dives exceeding 1000 m, the motivation for which remains unknown. Here, we investigated the movements of 34 North Atlantic swordfish using data from pop-up satellite archival transmitting tags deployed for one to six months. Statistically “extreme” maximum daily depths were identified on 39 of 3666 deployment days, from which 29 extreme dives were confirmed using depth-time series. These dives were brief, predominantly daytime events, and occurred across the full geographic range of the dataset, spanning temperate to tropical waters and from shelf-break regions to the Mid-Atlantic Ridge. We evaluated competing hypotheses for these dives, including foraging, predator avoidance, and navigation. Dive geometry and mixed-effects models of environmental conditions indicated that extreme dives were most likely to occur in association with elevated sea surface height and variability in sea surface temperature, conditions associated with dynamic oceanographic features such as frontal systems and major currents, with weaker associations with bathymetric and magnetic field features. Together, these findings are most consistent with a role in environmental profiling, whereby swordfish intermittently sample vertical structure or physical cues during large-scale movements. While this profiling may contribute to navigation, the underlying mechanisms for such behavior remain unresolved. Our results highlight the behavioural complexity of swordfish and underscore the importance of the deep ocean in shaping the ecology of highly migratory pelagic predators.

Impulsive airgun transmissions collected during the New England Seamount Acoustics pilot experiments are used to investigate deep sediment structure in the Sohm abyssal plain south of the Atlantis II Seamount. Airgun signals from a moving source were recorded on a fixed vertical line array, deployed at a depth of 5050 m, which was equipped with six receivers spanning water depths from 304 to 1434 m and environmental sensors. Distinct arrivals consistent with sub-bottom eigenray paths are identified at ranges up to 15 km. Geoacoustic inversion is performed using the relative arrival time (ΔT) between single-bottom-interacting and corresponding single-sub-bottom-interacting eigenray arrivals. Modeled ΔT is computed using BELLHOP eigenrays in the water column combined with analytic formulas for layered sediment models in the sub-bottom. Constant sound speed and n2 linear parameterizations are evaluated for geoacoustic inversion. Layer parameters, including thicknesses, sound speeds, and gradients, are estimated using adaptive simplex simulated annealing, and model selection is performed using the Bayesian information criterion. Results favor a model with a constant sound speed upper layer over a deeper n2 gradient layer, indicating the presence of two resolvable sub-bottom layers with a combined thickness of approximately 500 m and sound speeds consistent with consolidated sediments.

There is a common misconception among ocean scientists and policy makers that mesopelagic (200–1000 m) food webs are an unexploited “final frontier” of living marine resources. It is true that there are currently no substantial fisheries for the small‐bodied fish species that are typically caught in the nets used in research studies. However, midwater ecosystems contain a rich biodiversity of larger fishes, including mesopredators, that largely evade scientific sampling and are absent from biogeochemical and socio‐economic models of the open ocean. Drawing on studies from around the world and decades of detailed catch data from a longline fishery, we demonstrate that this “dark web” of mid‐trophic fish species has already been exploited by industrial‐scale fisheries in plain sight for decades. The ongoing effects of these activities on a suite of critical ecosystem services, from regulating the oceanic carbon cycle to sustaining food webs, remain unquantified and largely ignored by ocean scientists and policy makers. From a policy perspective, we recommend leveraging the Biodiversity Beyond National Jurisdiction agreement and promoting cooperation among Regional Fisheries Management Organizations to standardize data collection and strengthen environmental impact assessments under the United Nations Convention on the Law of the Sea, which could facilitate sustainable use of mesopelagic resources while balancing food security and carbon storage in a changing climate.

Rapid changes in ocean climate, circulation, and ecosystem structure are driving large-scale shifts in marine species distributions—challenging biodiversity conservation and the sustainability of fisheries worldwide. Anticipating these shifts is central to climate-ready ecosystem management, yet many existing species distribution modeling (SDM) approaches struggle to capture non-linear ecological responses, multi-scale environmental drivers, and emergent behaviors that arise under rapid climate change. To support proactive, climate-ready management, we developed an interpretable AI framework that integrates satellite remote sensing, high-resolution oceanographic reanalyses, and in situ data streams from a range of species spanning forage fishes to top predators. We use this framework to forecast the dynamic spatial and temporal (re)distribution of marine species in the Northwest Atlantic Ocean under variable oceanographic conditions. By combining multi-sensor observations with explainable machine learning methods, our approach provides both high predictive accuracy and transparent, ecologically grounded insights into the drivers of species movement. We compare the performance and usability of interpretable AI models with traditional statistical approaches. Whereas conventional SDMs typically assume smooth, stationary relationships between species occurrence and environmental predictors, interpretable AI models more flexibly capture threshold effects, interacting climate drivers, and regionally varying habitat relationships. We demonstrate how this system identifies climate-resilient habitats, reveals emerging hotspots of overlap between fisheries and vulnerable species, and dynamically informs spatial management measures such as time–area closures, protected area design, and climate sentinel site monitoring. Working directly with management agencies and protected area networks, we co-develop actionable metrics of ecosystem climate vulnerability and translate model outputs into operational decision support tools. This work illustrates a scalable pathway for uniting remote sensing, AI, and in situ observing networks to monitor biodiversity change and its drivers. By aligning technological advances with ecological understanding and management needs, our approach contributes directly to adaptive marine spatial planning and supports implementation of related domestic and global policy targets.

The biological impacts of climate change on ocean ecosystems and fisheries are an increasing global concern, influencing fish distribution, phenology, and productivity. To address these challenges, fisheries management agencies are developing climate-informed strategies to build resilience in marine species, ecosystems, and fisheries. Climate vulnerability assessments (CVAs) have been widely used to evaluate the vulnerabilities of species, ecosystems, fisheries, and communities to climate change, but their uptake in decision making has been limited. Here, we explore the factors that are hindering broader adoption and integration of CVA results into active fisheries management. We identify the critical components of a next-generation CVA framework that is dynamic, adaptive, and better suited to support climate responses and adaptation by fishers, fisheries managers, and coastal communities. These elements will serve to improve applicability of CVAs while emphasizing the integration of qualitative and quantitative data, including experiential knowledge, embracing dynamic models, and quantifying uncertainty to create impactful spatially and temporally explicit vulnerability assessments. The proposed changes to existing CVA approaches aim to improve fisheries management by more explicitly linking ecological and socio-economic systems and providing actionable insights for climate-resilient fisheries. These components also better align CVAs with ongoing efforts by various government initiatives and offer a path forward for improving the climate resilience of fisheries and coastal communities.

Due to their large ecological niche and high commercial value, highly migratory species are exposed to fishing pressure across the Atlantic Ocean. Although international management collects spatial catch and effort data, accuracy and representativeness are difficult to verify. Fleet behavior can increasingly be monitored using ship tracking technologies. Simultaneously, electronic tagging provides location estimates as well as broader insights into seasonal habitat suitability. Here, we combined electronic tagging datasets from 778 satellite and archival tags with environmental data to model distributions for Atlantic Bluefin Tuna, Blue Sharks, Swordfish, and Blue Marlin. We overlaid these maps with longline effort estimates derived from AIS data to identify times and regions of high overlap between species and fleet. Comparing with species-specific catch per unit efforts (CPUEs) reported to management, we identified regions where, for specific species and seasons, reported catches were low compared to species density predicted from presence data and habitat models. This data science approach may be useful to management enforcement agencies and compliance committees, potentially identifying regions where fleets may be under-reporting or other management assumptions may be invalid.

Echograms generated using active shipboard acoustics data provide visually rich insights into the structure and behaviour of interior pelagic communities, yet current numerical approaches underuse this complexity. Here, we develop a novel approach for analysing diel echogram imagery using convolutional autoencoders (CAEs). We apply this approach to 1 596 daily echograms collected using uncalibrated Acoustic Doppler Current Profilers (ADCPs) from 306 cruises in the Pacific Ocean between 2001 and 2018 over nearly 500,000 km. By compressing each echogram into a low-dimensional feature space, we identify recurring structural patterns—such as diel vertical migrations and scattering layers—and use these features to cluster the echograms into six coherent regional modes across the basin. These modes, or acoustic “fingerprints”, provide a new, interpretable framework for classifying pelagic ecosystems based on structure rather than backscattering intensity. When mapped and predicted to the entire Pacific basin using environmental covariates, our classification aligns closely with previous biogeographies derived from calibrated scientific echosounder data, demonstrating the potential of unsupervised deep learning to extend ecosystem monitoring using widely available ADCP archives. The Pacific ADCP archive used in this study is nearly 40% larger than any of the 38 kHz archives used in previous global studies: by enabling analysis of uncalibrated acoustic data, this approach unlocks a vast global archive for ecological classification, expanding opportunities for ecosystem monitoring, biogeography, and change detection.

Open ocean ecosystems represent the largest habitat on Earth and are highly dynamic in time and space. Mesoscale eddies are a primary driver of this variability and serve a key structural role in ocean ecosystems. Eddies modulate marine biodiversity beyond their impacts on plankton, influencing many ecologically and commercially important predators that may preferentially occupy anticyclonic eddies. However, how animal‐eddy interactions scale across predator species and the mechanistic drivers of these relationships remain an area of active research. We integrated satellite tracking data for sharks with observations of mesoscale eddies to determine how four shark species interact with eddies in the Gulf Stream region. Based on over 24,000 tracking days, we found that blue, white, and shortfin mako sharks selected for the cores of anticyclones while the use of eddies by tiger sharks was less conspicuous. Some particularly large and long‐lived anticyclones were occupied by tagged sharks for multiple weeks suggesting that these eddies may serve as hotspots for pelagic predators.

Marine megafauna species are affected by a wide range of anthropogenic threats. To evaluate the risk of such threats, species’ vulnerability to each threat must first be determined. We build on the existing threats classification scheme and ranking system of the International Union for Conservation of Nature (IUCN) Red List of Threatened Species by assessing the vulnerability of 256 marine megafauna species to 23 at‐sea threats. The threats we considered included individual fishing gear types, climate‐change‐related subthreats not previously assessed, and threats associated with coastal impacts and maritime disturbances. Our ratings resulted in 70 species having high vulnerability ( v > 0.778 out of 1) to at least 1 threat, primarily drifting longlines, temperature extremes, or fixed gear. These 3 threats were also considered to have the most severe effects (i.e., steepest population declines). Overall, temperature extremes and plastics and other solid waste were rated as affecting the largest proportion of populations. Penguins, pinnipeds, and polar bears had the highest vulnerability to temperature extremes. Bony fishes had the highest vulnerability to drifting longlines and plastics and other solid waste; pelagic cetaceans to 4 maritime disturbance threats; elasmobranchs to 5 fishing threats; and flying birds to drifting longlines and 2 maritime disturbance threats. Sirenians and turtles had the highest vulnerability to at least one threat from all 4 categories. Despite not necessarily having severe effects for most taxonomic groups, temperature extremes were rated among the top threats for all taxa except bony fishes. The vulnerability scores we provide are an important first step in estimating the risk of threats to marine megafauna. Importantly, they help differentiate scope from severity, which is key to identifying threats that should be prioritized for mitigation.

In the western North Atlantic (WNA), the common thresher shark Alopias vulpinus is captured by several fisheries, but its population status has not formally been assessed, and its ecology and population structure are poorly understood. A total of 61 pop-up satellite archival transmitting tags were deployed to study the species’ horizontal and vertical movement patterns and habitat use in the WNA and to inform the formulation of fishery management policy. Tracking data from 48 individuals ranging from 122 to 259 cm fork length revealed widespread horizontal movements throughout the WNA between northeastern Florida north and east to the Grand Banks of Newfoundland. Seasonal migrations across continental shelf and off-shelf habitats were identified in both juveniles and adults. Tagged common thresher sharks inhabited a wide temperature range in the WNA (-0.5 to 25.6°C), but spent ~90% of their time in waters between 14 and 20°C. Depth distribution ranged from the surface to 1822 m, with ~87% of time spent at depths shallower than 50 m. Deeper depths were achieved during the winter, spring, and fall than during the summer. These results will assist with the identification of important geographic locations of occurrence for both juvenile and adult common thresher sharks, help forecast the effects of environmental change on the species’ distribution, and inform the relevant spatial scales for fishery management policies and stock assessment.