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Diel vertical migration (DVM) is a vital behavior for many pelagic marine fauna. Locomotory tactics that animals use during DVM define the metabolic costs of migrations and influence the risk of detection and capture by predators, yet, for squids, there is little understanding of the fine-scale movements and potential variability during these migrations. Vertical migratory behaviors of 5 veined squid Loligo forbesii were investigated with biologging tags (ITags) off the Azores Islands (central North Atlantic). Diel movements ranged from 400 to 5 m and were aligned with sunset and sunrise. During ascent periods, 2 squid exhibited cyclic climb-and-glide movements using primarily jet propulsion, while 3 squid ascended more continuously and at a lower vertical speed using mostly a finning gait. Descents for all 5 squid were consistently more rapid and direct. While all squid swam in both arms-first and mantle-first directions during DVM, mantle-first swimming was more common during upward movements, particularly at vertical speeds greater than 25 cm s -1 . The in situ variability of animal posture, swim direction, and gait use revealed behavioral flexibility interpreted as energy conservation, prey capture, and predator avoidance. 

The ocean is a vast three-dimensional space that is poorly explored and understood, and harbors unobserved life and processes that are vital to ecosystem function. To fully interrogate the space, novel algorithms and robotic platforms are required to scale up observations. Locating animals of interest and extended visual observations in the water column are particularly challenging objectives. Towards that end, we present a novel Machine Learning-integrated Tracking (or ML-Tracking) algorithm for underwater vehicle control that builds on the class of algorithms known as tracking-by-detection. By coupling a multi-object detector (trained on in situ underwater image data), a 3D stereo tracker, and a supervisor module to oversee the mission, we show how ML-Tracking can create robust tracks needed for long duration observations, as well as enable fully automated acquisition of objects for targeted sampling. Using a remotely operated vehicle as a proxy for an autonomous underwater vehicle, we demonstrate continuous input from the ML-Tracking algorithm to the vehicle controller during a record, 5+ hr continuous observation of a midwater gelatinous animal known as a siphonophore. These efforts clearly demonstrate the potential that tracking-by-detection algorithms can have on exploration in unexplored environments and discovery of undiscovered life in our ocean. 

Knowing the displacement capacity and mobility patterns of industrially exploited (i.e., fished) marine resources is key to establishing effective conservation management strategies in human-impacted marine ecosystems. Acquiring accurate behavioral information of deep-sea fished ecosystems is necessary to establish the sizes of marine protected areas within the framework of large international societal programs (e.g., European Community H2020, as part of the Blue Growth economic strategy). However, such information is currently scarce, and high-frequency and prolonged data collection is rarely available. Here, we report the implementation of autonomous underwater vehicles and remotely operated vehicles as an aid for acoustic long-baseline localization systems for autonomous tracking of Norway lobster (Nephrops norvegicus), one of the key living resources exploited in European waters. In combination with seafloor moored acoustic receivers, we detected and tracked the movements of 33 tagged lobsters at 400-m depth for more than 3 months. We also identified the best procedures to localize both the acoustic receivers and the tagged lobsters, based on algorithms designed for off-the-shelf acoustic tags identification. Autonomous mobile platforms that deliver data on animal behavior beyond traditional fixed platform capabilities represent an advance for prolonged, in situ monitoring of deep-sea benthic animal behavior at meter spatial scales.