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Abstract Pinniped species undergo uniquely amphibious life histories that make them valuable subjects for many domains of research. Pinniped research has often progressed hand‐in‐hand with technological frontiers of wildlife biology, and drones represent a leap forward for methods of aerial remote sensing, enabling data collection, and integration at new scales of biological importance. Drone methods and data types provide four key opportunities for wildlife surveillance that are already advancing pinniped research and management: 1) repeat and on‐demand surveillance, 2) high‐resolution coverage at large extents, 3) morphometric photogrammetry, and 4) computer vision and deep learning applications. Drone methods for pinniped research represent early stages of technological adoption and can reshape the field as they scale towards the full potential of their techniques. 

Abstract Human activities and climate change threaten seabirds globally, and many species are declining from already small breeding populations. Monitoring of breeding colonies can identify population trends and important conservation concerns, but it is a persistent challenge to achieve adequate coverage of remote and sensitive breeding sites. Southern giant petrels (Macronectes giganteus) exemplify this challenge: as polar, pelagic marine predators they are subject to a variety of anthropogenic threats, but they often breed in remote colonies that are highly sensitive to disturbance. Aerial remote sensing can overcome some of these difficulties to census breeding sites and explore how local environmental factors influence important characteristics such as nest-site selection and chick survival. To this end, we used drone photography to map giant petrel nests, repeatedly evaluate chick survival and quantify-associated physical and biological characteristics of the landscape at two neighboring breeding sites on Humble Island and Elephant Rocks, along the western Antarctic Peninsula in January–March 2020. Nest sites occurred in areas with relatively high elevations, gentle slopes, and high wind exposure, and statistical models predicted suitable nest-site locations based on local spatial characteristics, explaining 72.8% of deviance at these sites. These findings demonstrate the efficacy of drones as a tool to identify, map, and monitor seabird nests, and to quantify important habitat associations that may constitute species preferences or sensitivities. These may, in turn, contextualize some of the diverse population trajectories observed for this species throughout the changing Antarctic environment. 

Antarctic humpback whales forage in summer, coincident with the seasonal abundance of their primary prey, the Antarctic krill. During the feeding season, humpback whales accumulate energy stores sufficient to fuel their fasting period lasting over six months. Previous animal movement modelling work (using area-restricted search as a proxy) suggests a hyperphagic period late in the feeding season, similar in timing to some terrestrial fasting mammals. However, no direct measures of seasonal foraging behaviour existed to corroborate this hypothesis. We attached high-resolution, motion-sensing biologging tags to 69 humpback whales along the Western Antarctic Peninsula throughout the feeding season from January to June to determine how foraging effort changes throughout the season. Our results did not support existing hypotheses: we found a significant reduction in foraging presence and feeding rates from the beginning to the end of the feeding season. During the early summer period, feeding occurred during all hours at high rates. As the season progressed, foraging occurred mostly at night and at lower rates. We provide novel information on seasonal changes in foraging of humpback whales and suggest that these animals, contrary to nearly all other animals that seasonally fast, exhibit high feeding rates soon after exiting the fasting period 

Body condition is a crucial and indicative measure of an animal’s fitness, reflecting overall foraging success, habitat quality, and balance between energy intake and energetic investment toward growth, maintenance, and reproduction. Recently, drone-based photogrammetry has provided new opportunities to obtain body condition estimates of baleen whales in one, two or three dimensions (1D, 2D, and 3D, respectively) – a single width, a projected dorsal surface area, or a body volume measure, respectively. However, no study to date has yet compared variation among these methods and described how measurement uncertainty scales across these dimensions. This associated uncertainty may affect inference derived from these measurements, which can lead to misinterpretation of data, and lack of comparison across body condition measurements restricts comparison of results between studies. Here we develop a Bayesian statistical model using known-sized calibration objects to predict the length and width measurements of unknown-sized objects (e.g., a whale). We use the fitted model to predict and compare uncertainty associated with 1D, 2D, and 3D photogrammetry-based body condition measurements of blue, humpback, and Antarctic minke whales – three species of baleen whales with a range of body sizes. The model outputs a posterior predictive distribution of body condition measurements and allows for the construction of highest posterior density intervals to define measurement uncertainty. We find that uncertainty does not scale linearly across multi-dimensional measurements, with 2D and 3D uncertainty increasing by a factor of 1.45 and 1.76 compared to 1D, respectively. Each standardized body condition measurement is highly correlated with one another, yet 2D body area index (BAI) accounts for potential variation along the body for each species and was the most precise body condition metric. We hope this study will serve as a guide to help researchers select the most appropriate body condition measurement for their purposes and allow them to incorporate photogrammetric uncertainty associated with these measurements which, in turn, will facilitate comparison of results across studies. 

Abstract Background Advances in biologging technology allow researchers access to previously unobservable behavioral states and movement patterns of marine animals. To relate behaviors with environmental variables, features must be evaluated at scales relevant to the animal or behavior. Remotely sensed environmental data, collected via satellites, often suffers from the effects of cloud cover and lacks the spatial or temporal resolution to adequately link with individual animal behaviors or behavioral bouts. This study establishes a new method for remotely and continuously quantifying surface ice concentration (SIC) at a scale relevant to individual whales using on-animal tag video data. Results Motion-sensing and video-recording suction cup tags were deployed on 7 Antarctic minke whales ( Balaenoptera bonaerensis ) around the Antarctic Peninsula in February and March of 2018. To compare the scale of camera-tag observations with satellite imagery, the area of view was simulated using camera-tag parameters. For expected conditions, we found the visible area maximum to be ~ 100m 2 which indicates that observations occur at an equivalent or finer scale than a single pixel of high-resolution visible spectrum satellite imagery. SIC was classified into one of six bins (0%, 1–20%, 21–40%, 41–60%, 61–80%, 81–100%) by two independent observers for the initial and final surfacing between dives. In the event of a disagreement, a third independent observer was introduced, and the median of the three observer’s values was used. Initial results ( n  = 6) show that Antarctic minke whales in the coastal bays of the Antarctic Peninsula spend 52% of their time in open water, and only 15% of their time in water with SIC greater than 20%. Over time, we find significant variation in observed SIC, indicating that Antarctic minke occupy an extremely dynamic environment. Sentinel-2 satellite-based approaches of sea ice assessment were not possible because of persistent cloud cover during the study period. Conclusion Tag-video offers a means to evaluate ice concentration at spatial and temporal scales relevant to the individual. Combined with information on underwater behavior, our ability to quantify SIC continuously at the scale of the animal will improve upon current remote sensing methods to understand the link between animal behavior and these dynamic environmental variables. 

Abstract Understanding the interplay of ocean physics and biology at the submesoscale and below (<30 km) is an ongoing challenge in oceanography. While poorly constrained, these scales may be of critical importance for understanding how changing ocean dynamics will impact marine ecosystems. Fronts in the ocean, regions where two disparate water masses meet and isopycnals become tilted toward vertical, are considered hotspots for biophysical interaction, but there is limited observational evidence at the appropriate scales to assess their importance. Fronts around western boundary currents like the Gulf Stream are of particular interest as these dynamic physical regions are thought to influence both productivity and composition of primary producers; however, how exactly this plays out is not well known. Using satellite data and 2 years of in situ observations across the Gulf Stream front near Cape Hatteras, North Carolina, we investigate how submesoscale frontal dynamics could affect biological communities and generate hotspots of productivity and export. We assess the seasonality and phenology of the region, generalize the kilometer‐scale structure of the front, and analyze 69 transects to assess two physical processes of potential biogeochemical importance: cold shelf filament subduction and high salinity Sargasso Sea obduction. We link these processes observationally to meanders in the Gulf Stream and discuss how cold filament subduction could be exporting carbon and how obduction of high salinity water from depth is connected with high chlorophyll‐a. Finally, we report on phytoplankton community composition in each of these features and integrate these observations into our understanding of frontal submesoscale dynamics.