Passive Data Research Articles

Rerouting of a major shipping lane through important harbour porpoise habitat caused no detectable change in annual occurrence or foraging patterns

Shipping is one of the largest industries globally, with well-known negative impacts on the marine environment. Despite the known negative short-term (minutes to hours) impact of shipping on individual animal behavioural responses, very little is understood about the long-term (months to years) impact on marine species presence and area use. This study took advantage of a planned rerouting of a major shipping lane leading into the Baltic Sea, to investigate the impact on the presence and foraging behaviour of a marine species known to be sensitive to underwater noise, the harbour porpoise (Phocoena phocoena). Passive acoustic monitoring data were collected from 15 stations over two years. Against predictions, no clear change occurred in monthly presence or foraging behaviour of the porpoises, despite the observed changes in noise and vessel traffic. However, long-term heightened noise levels may still impact communication, echolocation, or stress levels of individuals, and needs further investigation.

Improved sea surface salinity data for the Arctic Ocean derived from SMAP satellite data using machine learning approaches

Salinity is among the key climate characteristics of the World Ocean. During the last 15 years, sea surface salinity (SSS) is measured using satellite passive microwave sensors. Standard retrieving SSS algorithms from remote sensing data were developed and verified for the most typical temperature and salinity values of the World Ocean. However, they have far lower accuracy for the Arctic Ocean, especially its shelf areas, which are influenced by large river runoff and have low typical temperature and salinity values. In this study, an improved algorithm has been developed to retrieve SSS in the Arctic Ocean during ice-free season, based on Soil Moisture Active Passive (SMAP) mission data, and using machine learning approaches. Extensive database of in situ salinity measurements in the Russian Arctic seas collected during multiple field surveys is applied to train and validate the machine learning models. The error in SSS retrieval of the developed algorithm compared to the standard algorithm reduced from 3.15 to 2.15 psu, and the correlation with in situ data increased from 0.82 to 0.90. The obtained daily SSS fields are important to improve accurate assessment of spatial and temporal variability of large river plumes in the Arctic Ocean.

Determination of the VS profile at a “noisy” industrial site via active and passive data: The critical role of Love waves and the opportunities of multicomponent group velocity analysis

To define the procedures necessary to unambiguously define the subsurface model, a comprehensive set of active and passive seismic data was collected in an industrial area characterized by an extremely high level of background microtremors. Passive data are recorded to define three observables: the dispersion curve of the vertical component of Rayleigh waves via miniature array analysis of microtremors, the Love-wave dispersion curve via extended spatial autocorrelation, and the horizontal-to-vertical spectral ratio (HVSR). Active data used for the holistic analysis of surface waves are extracted from data recorded through a hybrid acquisition procedure accomplished with only two 3C geophones used to simultaneously define the HVSR at two points. Defined observables are combined according to three different approaches: the joint analysis of Rayleigh waves and HVSR, the joint analysis of Rayleigh and Love waves together with the HVSR, and the joint analysis of multicomponent group velocities together with the HVSR and Rayleigh-wave particle motion (RPM) curves. In agreement with the theory, data indicate that, in general, surface-wave modeling cannot be performed considering modal dispersion curves: dispersion obtained from passive data needs to be modeled considering the effective curve, whereas group velocity obtained from active data can be analyzed using the full velocity spectrum technique. Results indicate that joint inversion of Rayleigh-wave dispersion and HVSR does not necessarily ensure the correctness of the obtained S-wave velocity ([Formula: see text]) profile and that Love waves represent a key observable to fully constrain an unambiguous inversion procedure. However, the joint analysis of multicomponent group velocity spectra (from active multicomponent single-offset data) together with the HVSR and RPM curves is a further efficient way to obtain robust [Formula: see text] profiles through the active and passive data obtained by a single 3C geophone.

Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial.

As digital phenotyping, the capture of active and passive data from consumer devices such as smartphones, becomes more common, the need to properly process the data and derive replicable features from it has become paramount. Cortex is an open-source data processing pipeline for digital phenotyping data, optimized for use with the mindLAMP apps, which is used by nearly 100 research teams across the world. Cortex is designed to help teams (1) assess digital phenotyping data quality in real time, (2) derive replicable clinical features from the data, and (3) enable easy-to-share data visualizations. Cortex offers many options to work with digital phenotyping data, although some common approaches are likely of value to all teams using it. This paper highlights the reasoning, code, and example steps necessary to fully work with digital phenotyping data in a streamlined manner. Covering how to work with the data, assess its quality, derive features, and visualize findings, this paper is designed to offer the reader the knowledge and skills to apply toward analyzing any digital phenotyping data set. More specifically, the paper will teach the reader the ins and outs of the Cortex Python package. This includes background information on its interaction with the mindLAMP platform, some basic commands to learn what data can be pulled and how, and more advanced use of the package mixed with basic Python with the goal of creating a correlation matrix. After the tutorial, different use cases of Cortex are discussed, along with limitations. Toward highlighting clinical applications, this paper also provides 3 easy ways to implement examples of Cortex use in real-world settings. By understanding how to work with digital phenotyping data and providing ready-to-deploy code with Cortex, the paper aims to show how the new field of digital phenotyping can be both accessible to all and rigorous in methodology.

High Arctic “hotspots” for sperm whales (Physeter macrocephalus) off western and northern Svalbard, Norway, revealed by multi-year Passive Acoustic Monitoring (PAM)

Despite the well-documented, broad global distribution of sperm whales (Physeter macrocephalus), their distributional patterns remain poorly known in Arctic regions, where year-round monitoring is challenging. Adult male sperm whales are known to migrate seasonally between nutrient-rich high latitude waters and low latitude breeding grounds. However, knowledge is limited regarding fine-scale distribution and seasonal presence at high latitudes. To investigate the acoustic occurrence of this vocally active species in the High Arctic of the Northeast Atlantic, this study combined automated and manual click detection methods to analyze passive acoustic data collected at eight locations around the Svalbard Archipelago, Norway, between 2012 and 2021. The results revealed the presence of sperm whales at six recording sites and demonstrated sperm whale “hotspots” in ice-free areas in eastern Fram Strait along the shelf break and close to the west coast of Spitsbergen from May–January, with some variation between years and locations. Although acoustic presence decreased with increasing latitude, even the northern-most location (81° N) recorded sperm whale vocal activity between August and January. This study provides a baseline for sperm whale acoustic presence in the High Arctic, which will be essential in the context of detecting future changes and also for predicting future distribution patterns in the rapidly changing Arctic marine environment.

Capturing the College Experience: A Four-Year Mobile Sensing Study of Mental Health, Resilience and Behavior of College Students during the Pandemic.

Understanding the dynamics of mental health among undergraduate students across the college years is of critical importance, particularly during a global pandemic. In our study, we track two cohorts of first-year students at Dartmouth College for four years, both on and off campus, creating the longest longitudinal mobile sensing study to date. Using passive sensor data, surveys, and interviews, we capture changing behaviors before, during, and after the COVID-19 pandemic subsides. Our findings reveal the pandemic's impact on students' mental health, gender based behavioral differences, impact of changing living conditions and evidence of persistent behavioral patterns as the pandemic subsides. We observe that while some behaviors return to normal, others remain elevated. Tracking over 200 undergraduate students from high school to graduation, our study provides invaluable insights into changing behaviors, resilience and mental health in college life. Conducting a long-term study with frequent phone OS updates poses significant challenges for mobile sensing apps, data completeness and compliance. Our results offer new insights for Human-Computer Interaction researchers, educators and administrators regarding college life pressures. We also detail the public release of the de-identified College Experience Study dataset used in this paper and discuss a number of open research questions that could be studied using the public dataset.

Urban delineation through a prism of intraday commute patterns.

Urban mobility patterns are crucial for effective urban and transportation planning. This study investigates the dynamics of urban mobility in Brno, Czech Republic, utilizing the rich dataset provided by passive mobile phone data. Understanding these patterns is essential for optimizing infrastructure and planning strategies. We developed a methodological framework that incorporates bidirectional commute flows and integrates both urban and suburban commute networks. This comprehensive approach allows for a detailed representation of Brno's mobility landscape. By employing clustering techniques, we aimed to identify distinct mobility patterns within the city. Our analysis revealed consistent structural features within Brno's mobility patterns. We identified three distinct clusters: a central business district, residential communities, and an intermediate hybrid cluster. These clusters highlight the diversity of mobility demands across different parts of the city. The study demonstrates the significant potential of passive mobile phone data in enhancing our understanding of urban mobility patterns. The insights gained from intraday mobility data are invaluable for transportation planning decisions, allowing for the optimization of infrastructure utilization. The identification of distinct mobility patterns underscores the practical utility of our methodological advancements in informing more effective and efficient transportation planning strategies.

Efficient quality assurance and quality control for passive acoustic monitoring data: reducing and documenting false-positive and false-negative errors

ABSTRACT Autonomous Recording Units (ARUs) are widely used to survey for a variety of taxa. This survey method allows for high spatial and temporal coverage but will typically include identification errors that can bias estimates of occupancy. In some instances, verifying all individual detections is prohibitive. To direct verification effort, we developed a model to estimate the probability that transcribers would agree on an identification. Agreement probability was positively influenced by transcriber skill, identification confidence, species commonness and some song types. In contrast, agreement probability was lower when an acoustic signal was classified as a trill. We evaluated our model on independent data where all species detections were verified, and verification effort (time) was quantified. Our model performed well at predicting transcriber agreement on independent data (AUC = 0.71). We applied the model to randomised subsets of the independent data to compare the cost benefit of three approaches to verification under varying effort. We show how modelling probability of transcriber agreement can be used to more efficiently direct verification of species acoustic tags. Our approach could be adapted elsewhere to quantify and reduce species misidentifications in unverified passive acoustic monitoring data for either manual processing or detections from automated classifiers.

Spatiotemporal patterns of fish chorusing in California National Marine Sanctuaries

In aggregations, some male fish will sing together in “chorus” for hours, to attract female mates. Through analyzing passive acoustic data, one can determine which, when, and where fish are chorusing, to identify breeding grounds, species distributions, and mating seasons. National marine sanctuaries aim to protect marine ecosystems, particularly breeding and feeding grounds for at risk species. Through the Sanctuary Soundscape Monitoring project, a cumulative 17.9 years of passive acoustic data were manually analyzed for fish chorusing in Monterey Bay, Channel Islands, and proposed Chumash Heritage National Marine Sanctuaries. We aimed to determine which fish were chorusing within the sanctuaries, where and when they occurred, and if fish were engaging in acoustic niche partitioning (minimizing overlap in time/frequency to reduce competition). We found five unique fish choruses including plainfin midshipman, bocaccio rockfish, white seabass, and two unidentified fish choruses, which showed distinct diel, seasonal, and spatial preferences. Fish were found to partition acoustic space in frequency but not in time. Understanding fish reproductive habitat and behavior within sanctuaries is crucial for safeguarding threatened fish species. Moreover, studying fish chorusing is a powerful tool to noninvasively monitor fish to understand their reproductive habits, with goals of better protecting them.

Risk of myocarditis after three doses of COVID-19 mRNA vaccines in the United States, 2020-2022: A self-controlled case series study.

Myocarditis is a recognized safety concern following COVID-19 mRNA vaccination. However, there is limited research quantifying the risk associated with the third dose or comparing the risk between the three doses. The US Vaccine Adverse Event Reporting System (VAERS) is a passive surveillance system that monitors rare adverse events after US-licensed vaccination. However, studies analyzing VAERS data have often faced criticism for underreporting cases and lacking a control group to assess the increase in baseline risk. The temporal association between myocarditis onset and COVID-19 vaccination was studied. To overcome limitations, a novel modified self-controlled case series method was employed, explicitly modeling the case reporting process in VAERS data. We found an increased risk of myocarditis during the 1- to 3-day period following the second and third doses of both the BNT162b2 vaccine and the mRNA-1273 vaccine. Following the second dose, the relative incidence (RI) was 4.89 (95% confidence interval (CI), 2.39-10.08) for the BNT162b2 vaccine and 2.86 (95% CI: 1.18-7.03) for the mRNA-1273 vaccine. Similarly, following the third dose, the RI was 9.04 (95% CI: 2.79-40.99) for the BNT162b2 vaccine and 4.71 (95% CI: 1.42-19.09) for the mRNA-1273 vaccine. No significant increase in risk was observed during other periods. Notably, our analysis also identified a similar increased risk of myocarditis among individuals aged below 30. These findings raise safety concerns regarding COVID-19 mRNA vaccines, provide insights into the quantification of myocarditis risk at different postvaccination periods, and offer a novel approach to interpreting passive surveillance system data.

Cetacean presence and ambient sound level analysis in Bermuda: A comparison to historical records

Marine Protected Areas (MPAs) are established to conserve nature while also preserving ecosystem services and cultural values. Many ongoing MPA discussions are centered on offshore islands, including Bermuda, a British Overseas Territory in the North Atlantic Ocean, which is currently assessing a management plan to protect 20% of its EEZ. Marine mammals are essential targets to monitor as part of effective MPA management because they are keystone species. In addition, as human activities raise ocean noise levels worldwide, monitoring ambient sound levels is essential to evaluating impacts on marine ecosystems. We collected nine months of passive acoustic data (at 250 kHz sampling rate and with 24-bit resolution) between December 2021 and September 2022, approximately 24 nm south of Bermuda, close to a location where previous studies were conducted in 1966 and 2013–2014. The cetacean presence and ambient sound levels derived from our dataset will be compared to these historical datasets. Our comparative analysis focused on humpback whales (Megaptera novaeangliae), fin whales (Balaenoptera physalus), minke whales (Balaenoptera acutorostrata), blue whales (Balaenoptera musculus), sei whales (Balaenoptera borealis), and beaked whales (family Ziphiidae). Results will illustrate how Bermuda’s offshore underwater soundscape has changed over nearly six decades.

The past, present and future of underwater passive acoustic monitoring

We review the history of underwater passive acoustic monitoring and predict the future trajectory. Over the past three decades, advances in digital data storage capacity and low power electronics have made it possible to collect autonomous long-term broadband passive acoustic monitoring data. Concomitant advances have taken place in data analysis and curation. Standardized spectra are an excellent first-step in analysis, calculated for all data using multiple frequency bands. These spectra allow in-situ instrument calibration based on an understanding of underwater ambient noise. Software for efficient manual scanning and signal discovery is used for verification and error estimation. Automatic detectors/classifiers are obtained from both supervised and unsupervised machine learning. Detections are aggregated into a database that allows the combination of multiple datasets and association with environmental or other data. Future work will increasing use of arrays of acoustics sensors, the integration of passive acoustics with other sensors, and machine learning that integrates these data streams to provide better understanding of anthropogenic, biological and physical processes in the ocean.

Using passive acoustic data to understand sustainable vessel operations

Marine vessels support diverse ocean economic sectors. As society works towards a sustainable ocean economy, a variety of vessel initiatives are emerging. Passive acoustic monitoring (PAM) offers a method to evaluate these initiatives. Here we review efforts related to vessel noise management in marine protected areas (MPA) and benefits of vessel speed reduction programs (VSRs). Monitoring marine vessel activity and related underwater noise across a network of protected areas, like the U.S. National Marine Sanctuary system, helps managers ensure the quality of habitats used by a wide range of species. Network-wide comparisons of vessel noise revealed a spectrum of conditions, providing robust metrics to help prioritize management and inform condition assessments. These same vessel noise metrics provided insight on noise reduction related to changes in vessel operations during multiple VSRs. The metrics are complementary to other noise reduction metrics currently used to evaluate VSRs. With the growth in VSRs, there is a need to communicate noise reduction at scales relevant to the targeted vessels, typically larger than individual VSRs. Coordinated efforts are advancing to meet these needs. With strategic, systematic, and sustained efforts, PAM can continue to provide key insight on efforts to realize sustainable marine vessel operations.

Ambient Noise Interferometry Using Ocean Bottom Seismometer Data From Active Source Experiments Conducted in the Southernmost Mariana Trench

AbstractOcean bottom seismometers (OBSs) have been used to detect submarine structural and tectonic information for decades. According to signal source controllability, OBS data have generally been classified into active and passive source data categories. The former mainly focuses on the compressional wave (P‐wave) velocity inversion and always lacks valid information about the shear wave (S‐wave) velocity structure. While the latter provides structural information with limited resolution due to the aperture of the stations. Overcoming the barriers between processing these two data types will allow the reuse of a vast amount of data from active source experiments to explore the submarine S‐wave velocity structural properties. Here, we creatively applied ambient noise interferometry to invert the S‐wave velocity structure using data from active source OBS deployment conducted in the southernmost Mariana subduction zone, which had already been utilized to detect submarine P‐wave velocity structure. Considering the short time duration and relatively low quality of this type of data, a combined method of short‐segment cross‐correlation and selected time‐frequency domain phase‐weighted stacking was adopted to obtain stable cross‐correlation functions, which were subsequently used to invert S‐wave velocity structures. Compared to previous studies using different methods, our result sheds new light on the crust and upper mantle structure of the southernmost Mariana subduction zone. This method could be used to detect more information based on the reutilization of existing active source OBS data.

A primer on terrestrial passive acoustic monitoring data management: Best practices and guidelines

Recent advances in recording technology and data storage capabilities have revolutionized how we monitor vocal animals and their habitats. Recently, there has been an explosion of interest in the use of terrestrial passive acoustic monitoring (PAM) which relies on autonomous acoustic recording units (ARU), but terrestrial PAM studies are limited by a lack of standardized protocols for data collection, analysis, and archiving. Here, we draw on collective experiences of recording, analyzing, and archiving, hundreds of years of PAM data to provide guidelines and recommendations for PAM practitioners. First, we highlight some of the major pitfalls in field data collection, including inappropriate recording settings for the research question and incomplete metadata. We then provide recommendations for data management, including suggestions for backing up, compressing, and archiving the data. Our goal is to provide the community with standardized working guidelines that will help facilitate comparative, large-scale terrestrial studies using PAM.

Listening to the acidity of the ocean: Inversion of passive deep sea acoustic data for pH

Ocean acidification is an ongoing concern due to its impact on the marine ecosystem. The volume integrated pH of sea water can be determined from the depth-dependence of ambient sound, which depends on the acoustic absorption properties of seawater. For a wind-driven noise in the ocean over the band 1–10 kHz, two main contributions to sound attenuation are associated with the ionic relaxation of boric acid (<3 kHz), related to pH, and magnesium sulfate (>3 kHz), unrelated to pH. When local winds are strong (>10 m/s), the ambient noise is dominated by locally generated surface noise and has a depth-independent directionality and a weakly frequency and depth-dependent intensity, due to sound absorption. By measuring the attenuation of sound in a wide frequency band, it is possible to estimate pH by comparing the experimentally measured attenuation with an analytical theory of passive acoustic absorption spectroscopy. Measurements of the depth-dependent ambient sound field were carried out in the Philippine Sea, Mariana Trench, and Tonga Trench throughout 2009—2021. The wideband (5 Hz—30 kHz) acoustic data were recorded with untethered free-falling autonomous recording systems carrying two or four hydrophones.

Sediment thickness across Australia from passive seismic methods

SUMMARY Around the world the Earth’s crust is blanketed to various extents by sediment. For continental regions, knowledge of the distribution and thickness of sediments is crucial for a wide range of applications including seismic hazard, resource potential and our ability to constrain the deeper crustal geology. Excellent constraints on the sediment thickness can be obtained from borehole drilling or active seismic surveys. However, these approaches are expensive and impractical in remote continental interiors such as central Australia. Recently, a method for estimating the sediment thickness using passive seismic data, the collection of which is relatively simple and low cost, was developed and applied to seismic stations in South Australia. This method uses receiver functions, specifically the time delay of the P-to-S converted phase generated at the sediment-basement interface, relative to the direct-P arrival, to generate a first-order estimate of the thickness of sediments. In this work, we expand the analysis to the vast array of over 1500 seismic stations across Australia, covering an entire continent and numerous sedimentary basins that span the entire range from Precambrian to present day. We compare with an established yet separate method to estimate the sediment thickness, which utilizes the autocorrelation of the radial receiver functions to ascertain the two-way traveltime of shear waves reverberating in a sedimentary layer. Across the Australian continent the new results match the broad pattern of expected sedimentary features based on the various geological provinces. We are able to delineate the boundaries of many sedimentary basins, such as the Eucla and Murray Basins, which are Cenozoic, and the boundary between the Karumba Basin and the mineral rich Mount Isa Province. Contrasts in seismic delay time across these boundaries are upwards of 0.4 $\, \mathrm{s}$. The delay signal is found to diminish to <0.1 $\, \mathrm{s}$ for older Proterozoic basins, likely due to compaction and metamorphism of the sediments over time. As an application of the method, a comparison with measurements of sediment thickness from local boreholes allows for a straightforward predictive relationship between the delay time and the cover thickness to be defined. This offers future widespread potential, providing a simple and cheap way to characterize the sediment thickness in underexplored areas from passive seismic data.

MMF-CNN: a multimodal fusion CNN network for winter wheat extraction incorporating active and passive time series data

ABSTRACT Timely and accurate acquisition of winter wheat planting areas is crucial for food security. In this study, Sentinel-1 and Sentinel-2 time-series data are integrated at the feature level to enhance the accuracy of winter wheat extraction. However, existing feature-level fusion models suffer from insufficient feature extraction and lack of feature completeness, thereby overlooking the complementarity and correlation between these two modalities. A Multimodal Fusion Convolutional Neural Network (MMF-CNN) model is proposed to address the issues above. Firstly, the images of Sentinel-1 and Sentinel-2 are processed to obtain the NDVI and backscatter characteristics of the winter wheat time series life cycle. A single feature and a combination of two features are then imported into each end of the model. The model adds a feature fusion module, which can fully extract the feature information. At the same time, the original features are retained in the process of multiscale feature fusion, which avoids the loss of the original information. Finally, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Visual Geometry Group (VGG) are selected as the comparison models for comparative experiments, and the classification results of remote sensing images are obtained. These results demonstrate that the joint utilization of SAR and optical data yields the highest classification accuracy, with an F1 score of 97.42% for winter wheat. The overall accuracy (OA) of the proposed MMF-CNN method in this study is 96.87%, representing a 1.86% improvement compared to the Conv1D-CNN model. This improvement signifies adaptive feature learning at different hierarchical levels. Comparing the accuracy with other mainstream methods, the OA improves by 1.75%–4.37%, reveals finer ground details, and demonstrates faster performance. This study can provide methodological references for crop extraction studies based on multi-source data and time series analysis.

Clinical Presentation, Risk Factors, and Comparison of Laboratory Diagnostics for Seasonal Influenza Virus Among Cambodians From 2007 to 2020.

Despite its global significance, challenges associated with understanding the epidemiology and accurately detecting, measuring, and characterizing the true burden of seasonal influenza remain in many resource-poor settings. A prospective observational study was conducted in Cambodia at 28 health facilities between 2007 and 2020 utilizing passive surveillance data of patients presenting with acute undifferentiated febrile illness (AUFI) to describe the prevalence of influenza A and B and characterize associated risk factors and symptoms using a questionnaire. A comparison of rapid influenza diagnostic tests (RIDTs) and real-time reverse transcription polymerase chain reaction (rRT-PCR) results was also conducted. Of 30 586 total participants, 5634 (18.4%) tested positive for either influenza A or B, with 3557 (11.6%) positive for influenza A and 2288 (7.5%) positive for influenza B during the study. Influenza A and B were strongly associated with the rainy season (odds ratio [OR], 2.30; P < .001) and being from an urban area (OR, 1.45; P < .001). Analysis of individual symptoms identified cough (OR, 2.8; P < .001), chills (OR, 1.4; P < .001), and sore throat (OR, 1.4; P < .001) as having the strongest positive associations with influenza among patients with AUFI. Analysis comparing RIDTs and rRT-PCR calculated the overall sensitivity of rapid tests to be 0.492 (95% CI, 0.479-0.505) and specificity to be 0.993 (95% CI, 0.992-0.994) for both influenza type A and B. Findings from this 14-year study include describing the epidemiology of seasonal influenza over a prolonged time period and identifying key risk factors and clinical symptoms associated with infection; we also demonstrate the poor sensitivity of RIDTs in Cambodia.

Basin‐Wide Shift in Bowhead Whale Migration in the Pacific Arctic

AbstractIn a rapidly changing Arctic, multiple lines of evidence suggest that bowhead whale migration is changing. To explore these changes further, we used passive acoustic data to examine bowhead whale presence in the western Beaufort Sea (12 years) and Chukchi Plateau (11 years) spanning 2008 to 2022. Departure from the western Beaufort Sea shifted 45 days later over the 12‐year period. Summer presence increased at both sites, suggesting feeding areas within the Chukchi Sea are becoming more favorable. Likewise, findings from the Bering Strait suggest that some whales are remaining north of the Bering Strait for the winter instead of in the Bering Sea. These Pacific Arctic‐wide changes to migration have occurred over only one decade. Questions remain about prey availability in the Chukchi Sea, implications of migratory changes, such as a northward shift in the core overwintering area, and impact to communities south of the Bering Strait.