A viscosity-enhanced mechanism for biogenic ocean mixing

Recent observations of biologically generated turbulence in the ocean have led to conflicting conclusions regarding the significance of the contribution of animal swimming to ocean mixing. Measurements indicate elevated turbulent dissipation—comparable with levels caused by winds and tides—in the vicinity of large populations of planktonic animals swimming together1. However, it has also been noted that elevated turbulent dissipation is by itself insufficient proof of substantial biogenic mixing, because much of the turbulent kinetic energy of small animals is injected below the Ozmidov buoyancy length scale, where it is primarily dissipated as heat by the fluid viscosity before it can affect ocean mixing. Ongoing debate regarding biogenic mixing has focused on comparisons between animal wake turbulence and ocean turbulence. Here, we show that a second, previously neglected mechanism of fluid mixing—first described over 50 years ago by Charles Darwin — is the dominant mechanism of mixing by swimming animals. The efficiency of mixing by Darwin's mechanism is dependent on animal shape rather than fluid length scale and, unlike turbulent wake mixing, is enhanced by fluid viscosity. Therefore, it provides a means of biogenic mixing that can be equally effective in small zooplankton and large mammals. A theoretical model for the relative contributions of Darwinian mixing and turbulent wake mixing is created and validated by in situ field measurements of swimming jellyfish using a newly developed scuba-based laser velocimetry device. Extrapolation of these results to other animals is straightforward given knowledge of the animal shape and orientation during vertical migration. On the basis of calculations of a broad range of aquatic animal species, we conclude that biogenic mixing via Darwin's mechanism can be a significant contributor to ocean mixing and nutrient transport

From the surface to the seafloor: How giant larvaceans transport microplastics 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

Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus (SCUVA)

The ability to directly measure velocity fields in a fluid environment is necessary to provide empirical data for studies in fields as diverse as oceanography, ecology, biology, and fluid mechanics. Field measurements introduce practical challenges such as environmental conditions, animal availability, and the need for field-compatible measurement techniques. To avoid these challenges, scientists typically use controlled laboratory environments to study animal-fluid interactions. However, it is reasonable to question whether one can extrapolate natural behavior (i.e., that which occurs in the field) from laboratory measurements. Therefore, in situ quantitative flow measurements are needed to accurately describe animal swimming in their natural environment

The FathomNet2023 Competition Dataset

Ocean scientists have been collecting visual data to study marine organisms for decades. These images and videos are extremely valuable both for basic science and environmental monitoring tasks. There are tools for automatically processing these data, but none that are capable of handling the extreme variability in sample populations, image quality, and habitat characteristics that are common in visual sampling of the ocean. Such distribution shifts can occur over very short physical distances and in narrow time windows. Creating models that are able to recognize when an image or video sequence contains a new organism, an unusual collection of animals, or is otherwise out-of-sample is critical to fully leverage visual data in the ocean. The FathomNet2023 competition dataset presents a realistic scenario where the set of animals in the target data differs from the training data. The challenge is both to identify the organisms in a target image and assess whether it is out-of-sample.Comment: Competition was presented as part of the 10th Fine Grained Visual Categorization workshop at the 2023 Computer Vision and Pattern Recognition conference. 4 pages, 4 figure

Augmenting biologging with supervised machine learning to study in situ behavior of the medusa Chrysaora fuscescens

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Fannjiang, C., Mooney, T. A., Cones, S., Mann, D., Shorter, K. A., & Katija, K. Augmenting biologging with supervised machine learning to study in situ behavior of the medusa Chrysaora fuscescens. Journal of Experimental Biology, 222, (2019): jeb.207654, doi:10.1242/jeb.207654.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.This work was supported by the David and Lucile Packard Foundation (to K.K.), the Woods Hole Oceanographic Institution (WHOI) Green Innovation Award (to T.A.M., K.K. and K.A.S.) and National Science Foundation (NSF) DBI collaborative awards (1455593 to T.A.M. and K.A.S.; 1455501 to K.K.). Deposited in PMC for immediate release

Evolutionary traces of miniaturization in a giant—Comparative anatomy of brain and brain nerves in Bathochordaeus stygius (Tunicata, Appendicularia)

Appendicularia comprises 70 marine, invertebrate, chordate species. Appendicularians play important ecological and evolutionary roles, yet their morphological disparity remains understudied. Most appendicularians are small, develop rapidly, and with a stereotyped cell lineage, leading to the hypothesis that Appendicularia derived progenetically from an ascidian-like ancestor. Here, we describe the detailed anatomy of the central nervous system of Bathochordaeus stygius, a giant appendicularian from the mesopelagic. We show that the brain consists of a forebrain with on average smaller and more uniform cells and a hindbrain, in which cell shapes and sizes vary to a greater extent. Cell count for the brain was 102. We demonstrate the presence of three paired brain nerves. Brain nerve 1 traces into the epidermis of the upper lip region and consists of several fibers with some supportive bulb cells in its course. Brain nerve 2 innervates oral sensory organs and brain nerve 3 innervates the ciliary ring of the gill slits and lateral epidermis. Brain nerve 3 is asymmetric, with the right nerve consisting of two neurites originating posterior to the left one that contains three neurites. Similarities and differences to the anatomy of the brain of the model species Oikopleura dioica are discussed. We interpret the small number of cells in the brain of B. stygius as an evolutionary trace of miniaturization and conclude that giant appendicularians evolved from a small, progenetic ancestor that secondarily increased in size within Appendicularia.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659David and Lucile Packard Foundation http://dx.doi.org/10.13039/100000008Peer Reviewe

A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehicles

Underwater target localization using range-only and single-beacon (ROSB) techniques with autonomous vehicles has been used recently to improve the limitations of more complex methods, such as long baseline and ultra-short baseline systems. Nonetheless, in ROSB target localization methods, the trajectory of the tracking vehicle near the localized target plays an important role in obtaining the best accuracy of the predicted target position. Here, we investigate a Reinforcement Learning (RL) approach to find the optimal path that an autonomous vehicle should follow in order to increase and optimize the overall accuracy of the predicted target localization, while reducing time and power consumption. To accomplish this objective, different experimental tests have been designed using state-of-the-art deep RL algorithms. Our study also compares the results obtained with the analytical Fisher information matrix approach used in previous studies. The results revealed that the policy learned by the RL agent outperforms trajectories based on these analytical solutions, e.g. the median predicted error at the beginning of the target’s localisation is 17% less. These findings suggest that using deep RL for localizing acoustic targets could be successfully applied to in-water applications that include tracking of acoustically tagged marine animals by autonomous underwater vehicles. This is envisioned as a first necessary step to validate the use of RL to tackle such problems, which could be used later on in a more complex scenarios.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 893089. This work also received financial support from the Spanish Ministerio de Economía y Competitividad (SASES: RTI2018-095112-B-I00; BITER-ECO: PID2020-114732RB C31). This work acknowledges the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), and from the Generalitat de Catalunya ”Sistemas de Adquisicion Remota de datos y Tratamiento de la Informacion en el Medio Marino (SARTI-MAR)” 2017 SGR 376. We gratefully acknowledge the David and Lucile Packard Foundation.Peer ReviewedPostprint (author's final draft

ITAG : an eco-sensor for fine-scale behavioral measurements of soft-bodied marine invertebrates

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Animal Biotelemetry 3 (2015): 31, doi:10.1186/s40317-015-0076-1.Soft-bodied marine invertebrates comprise a keystone component of ocean ecosystems; however, we know little of their behaviors and physiological responses within their natural habitat. Quantifying ocean conditions and measuring organismal responses to the physical environment is vital to understanding the species or ecosystem-level influences of a changing ocean. Here we describe a novel, soft-bodied invertebrate eco-sensor tag (the ITAG), its trial attachments to squid and jellyfish, and the fine-scale behavioral measurements recorded on captive animals. Tags were deployed on five jellyfish (Aurelia aurita) and eight squid (Loligo forbesi) in laboratory conditions for up to 24 h. Using concurrent video and tag data, movement signatures for specific behaviors were identified. These behaviors included straight swimming (for jellyfish), and finning, jetting, direction reversal and turning (for squid). Overall activity levels were quantified using the root-mean-squared magnitude of acceleration, and finning was found to be the dominant squid swimming gait during captive squid experiments. External light sensors on the ITAG were used to compare squid swimming activity relative to ambient light across a ca. 20-h trial. The deployments revealed that while swimming was continuous for captive squid, energetically costly swimming behaviors (i.e., jetting and rapid direction reversals) occurred infrequently. These data reflect the usefulness of the ITAG to study trade-offs between behavior and energy expenditure in captive and wild animals. These data demonstrate that eco-sensors with sufficiently high sampling rates can be applied to quantify behavior of soft-bodied taxa and changes in behavior due to interactions with the surrounding environment. The methods and tool described here open the door for substantial lab and field-based measurements of fine-scale behavior, physiology, and concurrent environmental parameters that will inform fisheries management, and elucidate the ecology of these important keystone taxa.This work was supported by WHOI’s Ocean Life Institute and the Innovative Technology Program, Hopkins Marine Station’s Marine Life Observatory (to KK), as well as the National Science Foundation’s Ocean Acidification Program (to TAM) and NSF’s Program for Innovative Development of Biological Research (to TAM, KK and KAS)

Quantifying the swimming gaits of veined squid (Loligo forbesi) using bio-logging tags

Author Posting. © Company of Biologists, 2019. This article is posted here by permission of Company of Biologists for personal use, not for redistribution. The definitive version was published in Journal of Experimental Biology 222 (2019):jeb.198226, doi: 10.1242/jeb.198226.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 tank's 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.This work was supported by Woods Hole Oceanographic Institution’s Ocean Life Institute and the Innovative Technology Program, Hopkins Marine Station’s Marine Life Observatory (to K.K.), as well as the National Science Foundation Program for Instrument Development for Biological Research (award no. 1455593 to T.A.M., K.K. and K.A.S.). F.C. thanks the Presidentís International Fellowship Initiative (PIFI) of the Chinese Academy of Science. G.E.F. thanks the National Science Foundation GRFP and National Science Foundation REU programs for support of this research.2020-10-2