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To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study was to investigate the use of reinforcement learning as a tool for range-only underwater target-tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Unlike most medusae that forage with tentacles trailing behind their bells, several species forage upstream of their bells using aborally located tentacles. It has been hypothesized that these medusae forage as stealth predators by placing their tentacles in more quiescent regions of flow around their bells. Consequently, they are able to capture highly mobile, sensitive prey. We used digital particle image velocimetry (DPIV) to quantitatively characterize the flow field around Craspedacusta sowerbyi, a freshwater upstream-foraging hydromedusa, to evaluate the mechanics of its stealth predation. We found that fluid velocities were minimal in front and along the sides of the bell where the tentacles are located. As a result, the deformation rates in the regions where the tentacles are located were low, below the threshold rates required to elicit an escape response in several species of copepods. Estimates of their encounter volume rates were examined on the basis of flow past the tentacles, and trade-offs associated with tentacle characteristics were evaluated. [ABSTRACT FROM AUTHOR]

It is generally accepted that animal-fluid interactions have shaped the evolution of animals that swim and fly. However, the functional ecological advantages associated with those adaptations are currently difficult to predict on the basis of measurements of the animal-fluid interactions. We report the identification of a robust, fluid dynamic correlate of distinct ecological functions in seven jellyfish species that represent a broad range of morphologies and foraging modes. Since the comparative study is based on properties of the vortex wake - specifically, a fluid dynamical concept called optimal vortex formation - and not on details of animal morphology or phylogeny, we propose that higher organisms can also be understood in terms of these fluid dynamic organizing principles. This enables a quantitative, physically based understanding of how alterations in the fluid dynamics of aquatic and aerial animals throughout their evolution can result in distinct ecological functions. [ABSTRACT FROM AUTHOR]