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  1. Abstract Bio-logging devices equipped with inertial measurement units—particularly accelerometers, magnetometers, and pressure sensors—have revolutionized our ability to study animals as necessary electronics have gotten smaller and more affordable over the last two decades. These animal-attached tags allow for fine scale determination of behavior in the absence of direct observation, particularly useful in the marine realm, where direct observation is often impossible, and recent devices can integrate more power hungry and sensitive instruments, such as hydrophones, cameras, and physiological sensors. To convert the raw voltages recorded by bio-logging sensors into biologically meaningful metrics of orientation (e.g., pitch, roll and heading), motionmore »(e.g., speed, specific acceleration) and position (e.g., depth and spatial coordinates), we developed a series of MATLAB tools and online instructional tutorials. Our tools are adaptable for a variety of devices, though we focus specifically on the integration of video, audio, 3-axis accelerometers, 3-axis magnetometers, 3-axis gyroscopes, pressure, temperature, light and GPS data that are the standard outputs from Customized Animal Tracking Solutions (CATS) video tags. Our tools were developed and tested on cetacean data but are designed to be modular and adaptable for a variety of marine and terrestrial species. In this text, we describe how to use these tools, the theories and ideas behind their development, and ideas and additional tools for applying the outputs of the process to biological research. We additionally explore and address common errors that can occur during processing and discuss future applications. All code is provided open source and is designed to be useful to both novice and experienced programmers.« less
    Free, publicly-accessible full text available December 1, 2022
  2. 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 derivedmore »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.« less
    Free, publicly-accessible full text available November 26, 2022
  3. Free, publicly-accessible full text available November 4, 2022
  4. Increasingly, drone-based photogrammetry has been used to measure size and body condition changes in marine megafauna. A broad range of platforms, sensors, and altimeters are being applied for these purposes, but there is no unified way to predict photogrammetric uncertainty across this methodological spectrum. As such, it is difficult to make robust comparisons across studies, disrupting collaborations amongst researchers using platforms with varying levels of measurement accuracy. Here we built off previous studies quantifying uncertainty and used an experimental approach to train a Bayesian statistical model using a known-sized object floating at the water’s surface to quantify how measurement errormore »scales with altitude for several different drones equipped with different cameras, focal length lenses, and altimeters. We then applied the fitted model to predict the length distributions and estimate age classes of unknown-sized humpback whales Megaptera novaeangliae , as well as to predict the population-level morphological relationship between rostrum to blowhole distance and total body length of Antarctic minke whales Balaenoptera bonaerensis . This statistical framework jointly estimates errors from altitude and length measurements from multiple observations and accounts for altitudes measured with both barometers and laser altimeters while incorporating errors specific to each. This Bayesian model outputs a posterior predictive distribution of measurement uncertainty around length measurements and allows for the construction of highest posterior density intervals to define measurement uncertainty, which allows one to make probabilistic statements and stronger inferences pertaining to morphometric features critical for understanding life history patterns and potential impacts from anthropogenically altered habitats.« less
    Free, publicly-accessible full text available September 2, 2022
  5. Understanding how closely related, sympatric species distribute themselves relative to their environment is critical to understanding ecosystem structure and function and predicting effects of environmental variation. The Antarctic Peninsula supports high densities of krill and krill consumers; however, the region is warming rapidly, with unknown consequences. Humpback whales Megaptera novaeangliae and Antarctic minke whales Balaenoptera bonaerensis are the largest krill consumers here, yet key data gaps remain about their distribution, behavior, and interactions and how these will be impacted by changing conditions. Using satellite telemetry and novel spatial point-process modeling techniques, we quantified habitat use of each species relative tomore »dynamic environmental variables and determined overlap in core habitat areas during summer months when sea ice is at a minimum. We found that humpback whales ranged broadly over continental shelf waters, utilizing nearshore bays, while minke whales restricted their movements to sheltered bays and areas where ice is present. This presents a scenario where minke whale core habitat overlaps substantially with the broader home ranges of humpback whales. While there is no indication that prey is limiting in this ecosystem, increased overlap between these species may arise as climate-driven changes that affect the extent, timing, and duration of seasonal sea ice decrease the amount of preferred foraging habitat for minke whales while concurrently increasing it for humpback whales. Our results provide the first quantitative assessment of behaviorally based habitat use and sympatry between these 2 krill consumers and offers insight into the potential effects of a rapidly changing environment on the structure and function of a polar ecosystem.« less
  6. 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 andmore »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.« less
  7. Fundamental scaling relationships influence the physiology of vital rates, which in turn shape the ecology and evolution of organisms. For diving mammals, benefits conferred by large body size include reduced transport costs and enhanced breath-holding capacity, thereby increasing overall foraging efficiency. Rorqual whales feed by engulfing a large mass of prey-laden water at high speed and filtering it through baleen plates. However, as engulfment capacity increases with body length (Engulfment Volume ∝ Body Length 3.57), the surface area of the baleen filter does not increase proportionally (Baleen Area ∝ Body Length1.82), and thus the filtration time of larger rorquals predictablymore »increases as the baleen surface area must filter a disproportionally large amount of water. We predicted that filtration time should scale with body length to the power of 1.75 (Filter Time ∝ Body Length1.75). We tested this hypothesis on four rorqual species using multi-sensor tags with corresponding unoccupied aircraft systems (UAS) -based body length estimates. We found that filter time scales with body length to the power of 1.79 (95% CI: 1.61 - 1.97). This result highlights a scale-dependent trade-off between engulfment capacity and baleen area that creates a biomechanical constraint to foraging through increased filtration time. Consequently, larger whales must target high density prey patches commensurate to the gulp size to meet their increased energetic demands. If these optimal patches are absent, larger rorquals may experience reduced foraging efficiency compared to smaller whales if they do not match their engulfment capacity to the size of targeted prey aggregations.« less
  8. The largest animals are marine filter feeders, but the underlying mechanism of their large size remains unexplained. We measured feeding performance and prey quality to demonstrate how whale gigantism is driven by the interplay of prey abundance and harvesting mechanisms that increase prey capture rates and energy intake. The foraging efficiency of toothed whales that feed on single prey is constrained by the abundance of large prey, whereas filter-feeding baleen whales seasonally exploit vast swarms of small prey at high efficiencies. Given temporally and spatially aggregated prey, filter feeding provides an evolutionary pathway to extremes in body size that aremore »not available to lineages that must feed on one prey at a time. Maximum size in filter feeders is likely constrained by prey availability across space and time.« less