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Siphonophores are ubiquitous and often highly abundant members of pelagic ecosystems throughout the open ocean. They are unique among animal taxa in that they use multiple jets for propulsion. Little is known about kinematics of the individual jets produced by nectophores or how the jets are coordinated during normal swimming behavior. Using remotely operated vehicles and SCUBA, we video recorded the swimming behavior of several physonect species in their natural environment. The pulsed kinematics of the individual nectophores that comprise the siphonophore nectosome were quantified and, based on these kinematics, we examined the coordination of adjacent nectophores. We found for all species that nectophores sharing the same side of the nectosomal axis were coordinated metachronally. However, this coordination was not shared with nectophores on the opposite side of the nectosomal axis. For most species, the metachronal contraction waves of nectophores were initiated by the apical nectophores and traveled dorsally. However, the metachronal wave ofApolemia rubriversatraveled in the opposite direction. Although nectophore groups on opposite sides of the nectosome were not coordinated, they pulsed with similar frequencies. This enabled siphonophores to maintain relatively linear trajectories during swimming. The timing and characteristics of the metachronal coordination of pulsed jets affects how the jet wakes interact and may provide important insight into how interacting jets may be optimized for efficient propulsion.

Ciliated organs that pump luminal fluids are integral to animal physiology. Such organs, including the human airways and reproductive tracts, feature ciliated ducts that direct internal flows. Although cilia organization and duct morphology vary drastically in the animal kingdom, ducts are typically classified into two, seemingly disconnected, archetypes: the familiar carpet and the intriguing flame designs. The reason behind this dichotomy and how duct design relates to fluid pumping remain unclear. Here, we apply morphometric and mechanistic analyses to ciliated ducts across all animal phyla. We find that two structural parameters, lumen diameter and cilia-to-lumen ratio, organize the observed duct diversity into a continuous spectrum that connects carpets to flames. Using a unified fluid model, we discover that carpets and flames, respectively, maximize flow rate and pressure generation, which is consistent with physiological requirements for bulk transport and filtration, whereas intermediate designs along the morphological spectrum constitute optimally efficient hybrids. We propose that convergence of ciliated organ designs follows functional constraints rather than phylogenetic distance, and we present universal design rules for ciliary pumps.

Many animals build complex structures to aid in their survival, but very few are built exclusively from materials that animals create 1,2. In the midwaters of the ocean, mucoid structures are readily secreted by numerous animals, and serve many vital functions3,4. However, little is known about these mucoid structures owing to the challenges of observing them in the deep sea. Among these mucoid forms, the ‘houses’ of larvaceans are marvels of nature5, and in the ocean twilight zone giant larvaceans secrete and build mucus filtering structures that can reach diameters of more than 1 m6. Here we describe in situ laser-imaging technology7 that reconstructs three-dimensional models of mucus forms. The models provide high-resolution views of giant larvacean houses and elucidate the role that house structure has in food capture and predator avoidance. Now that tools exist to study mucus structures found throughout the ocean, we can shed light on some of nature’s most complex forms. Advanced deep-sea imaging tools yield insights into the structure and function of mucus filtration houses built by midwater giant larvaceans.

Synopsis Metachronal motion is used across a wide range of organisms for a diverse set of functions. However, despite its ubiquity, analysis of this behavior has been difficult to generalize across systems. Here we provide an overview of known commonalities and differences between systems that use metachrony to generate fluid flow. We also discuss strategies for standardizing terminology and defining future investigative directions that are analogous to other established subfields of biomechanics. Finally, we outline key challenges that are common to many metachronal systems, opportunities that have arisen due to the advent of new technology (both experimental and computational), and next steps for community development and collaboration across the nascent network of metachronal researchers.

Synopsis Aquatic animals have developed a wide array of adaptations specific to life underwater, many of which are related to moving in the water column. Different swimming methods have emerged, such as lift-based flapping, drag-based body undulations, and paddling. Patterns occur across scales and taxa, where animals with analogous body features use similar locomotory methods. Metachronal paddling is one such wide-spread propulsion mechanism, occurring in taxa as diverse as ctenophores, crustaceans, and polychaetes. Sequential movement of multiple, near identical appendages, allows for steady swimming through phase-offsets between adjacent propulsors. The soft-bodied, holopelagic polychaete Tomopteris has two rows of segmental appendages (parapodia) positioned on opposite sides along its flexible body that move in a metachronal pattern. The outer one-third of their elongate parapodia consist of two paddle-like pinnules that can be spread or, when contracted, fold together to change the effective width of the appendage. Along with metachronal paddling, tomopterid bodies undulate laterally, and by using high speed video and numerical modeling, we seek to understand how these two behaviors combine to generate effective swimming. We collected animals using deep-diving remotely operated vehicles, and recorded video data in shore- and ship-based imaging laboratories. Kinematics were analyzed using landmark tracking of features in the video data. We determined that parapodia are actively moved to generate thrust and pinnules are actively spread and contracted to create differences in drag between power and recovery strokes. At the same time, the body wave increases the parapodium stroke angle and extends the parapodia into undisturbed water adjacent to the body, enhancing thrust. Based on kinematics measurements used as input to a 1D numerical model of drag-based swimming, we found that spreading of the pinnules during the power stroke provides a significant contribution to propulsion, similar to the contribution provided by the body wave. We conclude that tomopterids combine two different propulsive modes, which are enabled by their flexible body plan. This makes their anatomy and kinematics of interest not only for biologists, but also for soft materials and robotics engineers.

Flexible propulsors are ubiquitous in aquatic and flying organisms and are of great interest for bioinspired engineering. However, many animal models, especially those found in the deep sea, remain inaccessible to direct observation in the laboratory. We address this challenge by conducting an integrative study of the giant larvacean, an invertebrate swimmer and “fluid pump” of the mesopelagic zone. We demonstrate a workflow involving deep sea robots, advanced imaging tools, and numerical modeling to assess the kinematics and resulting fluid transport of the larvacean’s beating tail. A computational model of the tail was developed to simulate the local fluid environment and the tail kinematics using embedded passive (elastic) and active (muscular) material properties. The model examines how varying the extent of muscular activation affects the resulting kinematics and fluid transport rates. We find that muscle activation in two-thirds of the tail’s length, which corresponds to the observed kinematics in giant larvaceans, generates a greater average downstream flow speed than other designs with the same power input. Our results suggest that the active and passive material properties of the larvacean tail are tuned to produce efficient fluid transport for swimming and feeding, as well as provide new insight into the role of flexibility in biological propulsors.

Many transparent animals rely for survival on structures built from their own mucus. To study such organisms deep underwater, researchers are using robots outfitted with lasers.

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.

15 pages, 6 figures, 2 tables, supplementary material https://doi.org/10.1126/scirobotics.abc3701.-- This manuscript has been accepted for publication in Science Robotics. This version has not undergone final editing. Please refer to the complete version of record at https://robotics.sciencemag.org/content/5/48/eabc3701. The manuscript may not be reproduced or used in any manner that does not fall within the fair use provisions of the Copyright Act without the prior, written permission of AAAS, 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, This work received financial support from different research projects of the Spanish Ministerio de Economía y Competitividad (RESNEP: CTM2017-82991-C2-1-R; RESBIO: TEC2017-87861-R; and SASES: RTI2018-095112-B-I00), of the Generalitat de Catalunya “Sistemas de Adquisición Remota de datos y Tratamiento de la Información en el Medio Marino (SARTI-MAR)” 2017 SGR 371, and NSF-IDBR (#145501 to K.K.). J.N. was funded by the Spanish National Program Ramón y Cajal (RYC-2015-17809). This project has also received support from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie–Individual Fellowship grant agreement AIforUTracking No. 893089, With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

Mesobot, an autonomous underwater vehicle, addresses specific unmet needs for observing and sampling a variety of phenomena in the ocean's midwaters. The midwater hosts a vast biomass, has a role in regulating climate, and may soon be exploited commercially, yet our scientific understanding of it is incomplete. Mesobot has the ability to survey and track slow-moving animals and to correlate the animals' movements with critical environmental measurements. Mesobot will complement existing oceanographic assets such as towed, remotely operated, and autonomous vehicles; shipboard acoustic sensors; and net tows. Its potential to perform behavioral studies unobtrusively over long periods with substantial autonomy provides a capability that is not presently available to midwater researchers. The 250-kilogram marine robot can be teleoperated through a lightweight fiber optic tether and can also operate untethered with full autonomy while minimizing environmental disturbance. We present recent results illustrating the vehicle's ability to automatically track free-swimming hydromedusae (Solmissus sp.) and larvaceans (Bathochordaeus stygius) at depths of 200 meters in Monterey Bay, USA. In addition to these tracking missions, the vehicle can execute preprogrammed missions collecting image and sensor data while also carrying substantial auxiliary payloads such as cameras, sonars, and samplers.

Ocean-going platforms and instruments are integrating cameras for observation and navigation, producing a deluge of visual data. The volume of this data collection can rapidly outpace researchers' abilities to process and analyze them. Recent advances in artificial intelligence enable fast, sophisticated analysis of visual data, but have had limited success in the oceanographic world due to lack of dataset standardization, sparse annotation tools, and insufficient formatting and aggregation of existing, expertly curated imagery for use by data scientists. To address this need, we are building FathomNet, a public platform that makes use of existing (and future), expertly curated data to know what is in the ocean and where it is for effective and responsible marine stewardship. This platform is modeled after popular terrestrial datasets (e.g., ImageNet, COCO) that enabled rapid advances in automated visual analysis. FathomNet seeks to engage a wide audience, from the general public to subject-matter experts, to further augment, contribute to, and utilize the training data set. FathomNet will accelerate development of novel algorithms to automate the analysis of underwater visual data, thereby enabling scientists, explorers, policymakers, storytellers, and the public, to learn, understand, and care more about our ocean and its inhabitants.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Many animals build complex structures to aid in their survival, but very few are built exclusively from materials that animals create

Plastic waste has been documented in nearly all types of marine environments and has been found in species spanning all levels of marine food webs. Within these marine environments, deep pelagic waters encompass the largest ecosystems on Earth. We lack a comprehensive understanding of the concentrations, cycling, and fate of plastic waste in sub-surface waters, constraining our ability to implement effective, large-scale policy and conservation strategies. We used remotely operated vehicles and engineered purpose-built samplers to collect and examine the distribution of microplastics in the Monterey Bay pelagic ecosystem at water column depths ranging from 5 to 1000 m. Laser Raman spectroscopy was used to identify microplastic particles collected from throughout the deep pelagic water column, with the highest concentrations present at depths between 200 and 600 m. Examination of two abundant particle feeders in this ecosystem, pelagic red crabs (Pleuroncodes planipes) and giant larvaceans (Bathochordaeus stygius), showed that microplastic particles readily flow from the environment into coupled water column and seafloor food webs. Our findings suggest that one of the largest and currently underappreciated reservoirs of marine microplastics may be contained within the water column and animal communities of the deep sea.

Zooplankton occupy critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood due to the difficulty of studying individualsin situ. Here we combine biologging with supervised machine learning (ML) to demonstrate a pipeline for studyingin situbehavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on 8Chrysaora fuscescensin Monterey Bay, using the tether method for retrieval. Using simultaneous video footage of the tagged jellyfish, we develop ML methods to 1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and 2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and provide evidence that developing behavioral classifiers onin siturather than laboratory data is essential.Summary StatementHigh-resolution motion sensors paired with supervised machine learning can be used to infer fine-scalein situbehavior of zooplankton for long durations.

Zooplankton play critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood because of the difficulty in studying individuals in situ. Here, we combine biologging with supervised machine learning (ML) to propose a pipeline for studying in situ behavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on eight Chrysaora fuscescens in Monterey Bay, using the tether method for retrieval. By analyzing simultaneous video footage of the tagged jellyfish, we developed ML methods to: (1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and (2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and we conclude that it is essential to develop behavioral classifiers on in situ rather than laboratory data., Summary: High-resolution motion sensors paired with supervised machine learning can be used to infer fine-scale in situ behavior of zooplankton over long durations.

Additional file 1: Figure S1. Motion details of captive Aurelia aurita. (A) Consecutive still images of a 4.5 cm diameter Aurelia aurita swimming through a laser sheet in the laboratory used to identify the body centroid location over two swimming cycles. Stars and diamonds indicate frames that capture peak contraction and relaxation of the bell during swimming, respectively. The body centroid position was then used to determine the swimming speed (B, black line) and the swimming acceleration (B, gray line) during consecutive swimming cycles. These methods are described in more detail in Katija and Jiang (2013).

Zooplankton occupy critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood due to the difficulty of studying individuals in situ. Here we combine biologging with supervised machine learning (ML) to demonstrate a pipeline for studying in situ behavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on 8 Chrysaora fuscescens in Monterey Bay, using the tether method for retrieval. Using simultaneous video footage of the tagged jellyfish, we developed ML methods to 1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and 2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and provide evidence that developing behavioral classifiers on in situ rather than laboratory data is essential.

OCEANS 2019, 17-20 June 2019, Marseille.-- 9 pages, 6 figures, 1 table, The use of autonomous underwater vehicles for ocean research has increased as they have a better cost/performance ratio than crewed oceanographic vessels. For example, autonomous vehicles (e.g. a Wave Glider) can be used to localise and track underwater targets. Whereas other researchers have been focused on target tracking using acoustic modems, here we present a novelty method called area-only target tracking. This method works with commercially available acoustic tags, thereby reducing the costs and complexity over other tracking systems. Moreover, this method can be used to track small targets such as jellyfishes due to the tag's size. The methodology behind the area-only technique is shown, and results from the first field tests conducted in Monterey Bay area are also presented.

Squid are mobile, diverse, ecologically important marine organisms whose behavior and habitat use can have substantial impacts on ecosystems and fisheries. However, as a consequence in part of the inherent challenges of monitoring squid in their natural marine environment, fine-scale behavioral observations of these free-swimming, soft-bodied animals are rare. Bio-logging tags provide an emerging way to remotely study squid behavior in their natural environments. Here, we applied a novel, high-resolution bio-logging tag (ITAG) to seven veined squid, Loligo forbesii, in a controlled experimental environment to quantify their short-term (24 h) behavioral patterns. Tag accelerometer, magnetometer and pressure data were used to develop automated gait classification algorithms based on overall dynamic body acceleration, and a subset of the events were assessed and confirmed using concurrently collected video data. Finning, flapping and jetting gaits were observed, with the low-acceleration finning gaits detected most often. The animals routinely used a finning gait to ascend (climb) and then glide during descent with fins extended in the tank9s water column, a possible strategy to improve swimming efficiency for these negatively buoyant animals. Arms- and mantle-first directional swimming were observed in approximately equal proportions, and the squid were slightly but significantly more active at night. These tag-based observations are novel for squid and indicate a more efficient mode of movement than suggested by some previous observations. The combination of sensing, classification and estimation developed and applied here will enable the quantification of squid activity patterns in the wild to provide new biological information, such as in situ identification of behavioral states, temporal patterns, habitat requirements, energy expenditure and interactions of squid through space–time in the wild.

Squid are mobile, diverse, ecologically important marine organisms whose behavior and habitat use can have substantial impacts on ecosystems and fisheries. However, as a consequence in part of the inherent challenges of monitoring squid in their natural marine environment, fine-scale behavioral observations of these free-swimming, soft-bodied animals are rare. Bio-logging tags provide an emerging way to remotely study squid behavior in their natural environments. Here, we applied a novel, high-resolution bio-logging tag (ITAG) to seven veined squid

Mesobot, a new class of autonomous underwater vehicle, will address specific unmet needs for observing slow-moving targets in the midwater ocean. Mesobot will track targets such as zooplankton, fish, and descending particle aggregates using a control system based on stereo cameras and a combination of thrusters and a variable buoyancy system. The vehicle will also be able to collect biogeochemical and environmental DNA (eDNA) samples using a pumped filter sampler.

Mixing in the ocean is opposed by the stratification of fluid, such that density of seawater increases with greater depth. The mechanisms by which mixing occurs have been attributed largely to physical processes that include atmospheric forcing, tides, and internal waves. Biogenic mixing, another potential source of mixing in the ocean, may generate significant transport of fluid during diel vertical migrations of organisms. Biogenic mixing is not limited to the near-surface or to regions of rough bottom topography, as are other physical mixing processes, and may contribute significantly to the energy budget of mixing in mid-ocean. "Fluid drift", a mechanism first described by Charles Galton Darwin, has been identified as a mechanism that allows for long-distance, vertical transport of fluid by the smallest of swimming organisms. However, little is known about how fluid drift varies with morphology and behavior of swimming organisms. We conducted numerical simulations of theoretical and experimentally measured flows of swimming medusae (Phyllorhiza sp.), and compared the volume of the drift induced by these flows. Our numerical simulations of fluid drift showed that morphology coupled with swimming behavior alters the transport of fluid both spatially and temporally. Given empirical velocity field data, the methods presented here allow us to systematically compare fluid transport across taxa, and enable us to deduce the potential of swimming organisms to influence fluid transport.

Larvaceans can filter microplastics from the water and package them into their fecal pellets, transporting them into the deep sea., Plastic waste is a pervasive feature of marine environments, yet little is empirically known about the biological and physical processes that transport plastics through marine ecosystems. To address this need, we conducted in situ feeding studies of microplastic particles (10 to 600 μm in diameter) with the giant larvacean Bathochordaeus stygius. Larvaceans are abundant components of global zooplankton assemblages, regularly build mucus “houses” to filter particulate matter from the surrounding water, and later abandon these structures when clogged. By conducting in situ feeding experiments with remotely operated vehicles, we show that giant larvaceans are able to filter a range of microplastic particles from the water column, ingest, and then package microplastics into their fecal pellets. Microplastics also readily affix to their houses, which have been shown to sink quickly to the seafloor and deliver pulses of carbon to benthic ecosystems. Thus, giant larvaceans can contribute to the vertical flux of microplastics through the rapid sinking of fecal pellets and discarded houses. Larvaceans, and potentially other abundant pelagic filter feeders, may thus comprise a novel biological transport mechanism delivering microplastics from surface waters, through the water column, and to the seafloor. Our findings necessitate the development of tools and sampling methodologies to quantify concentrations and identify environmental microplastics throughout the water column.

Unlike most medusae that forage with tenta- cles trailing behind their bells, several species forage up- stream 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 re- gions of flow around their bells. Consequently, they are able to capture highly mobile, sensitive prey. We used digital particle image velocimetry (DPIV) to quantitatively charac- terize 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 defor- mation 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 ten- tacle characteristics were evaluated.