For many marine organisms, especially large whales that cannot be studied in laboratory settings, our ability to obtain basic behavioral and physiological data is limited, because these organisms occupy offshore habitats and spend a tnajority of their time underwater. A class of multisensor, suction-cup-attached archival tags has revolutionized the study of large baleen whales, particularly with respect to the predatory strategies used by these gigantic bulk filter feeders to exploit abundant oceanic resources. By integrating these data with those from other disciplines, researchers have uncovered a diverse and extraordinary set of underwater behaviors, ratigirtg frotn acrobatic diving maneuvers to extreme feeding events during which whales engulf volumes of prey-laden water that are much larger than their own body. This research framework not only improves our knowledge of the individual pcrformatice and behavior of these keystone predators btit also informs our ability to understand the dynamics of complex marine ecosystems.

The flourishing application of drones within marine science provides more opportunity to conduct photogrammetric studies on large and varied populations of many different species. While these new platforms are increasing the size and availability of imagery datasets, established photogrammetry methods require considerable manual input, allowing individual bias in techniques to influence measurements, increasing error and magnifying the time required to apply these techniques. Here, we introduce the next generation of photogrammetry methods utilizing a convolutional neural network to demonstrate the potential of a deep learning‐based photogrammetry system for automatic species identification and measurement. We then present the same data analysed using conventional techniques to validate our automatic methods. Our results compare favorably across both techniques, correctly predicting whale species with 98% accuracy (57/58) for humpback whales, minke whales, and blue whales. Ninety percent of automated length measurements were within 5% of manual measurements, providing sufficient resolution to inform morphometric studies and establish size classes of whales automatically. The results of this study indicate that deep learning techniques applied to survey programs that collect large archives of imagery may help researchers and managers move quickly past analytical bottlenecks and provide more time for abundance estimation, distributional research, and ecological assessments.

The anatomy of large cetaceans has been well documented, mostly through dissection of dead specimens. However, the difficulty of studying the world's largest animals in their natural environment means the functions of anatomical structures must be inferred. Recently, non-invasive tracking devices have been developed that measure body position and orientation, thereby enabling the detailed reconstruction of underwater trajectories. The addition of cameras to the whale-borne tags allows the sensor data to be matched with real-time observations of how whales use their morphological structures, such as flukes, flippers, feeding apparatuses, and blowholes for the physiological functions of locomotion, feeding, and breathing. Here, we describe a new tag design with integrated video and inertial sensors and how it can be used to provide insights to the function of whale anatomy. This technology has the potential to facilitate a wide range of discoveries and comparative studies, but many challenges remain to increase the resolution and applicability of the data.

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 error 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.