Search for: All records

Morphological features are the primary identifying properties of most animals and key to many comparative physiological studies, yet current techniques for preservation and documentation of soft-bodied marine animals are limited in terms of quality and accessibility. Digital records can complement physical specimens, with a wide array of applications ranging from species description to kinematics modeling, but options are lacking for creating models of soft-bodied semi-transparent underwater animals. We developed a lab-based technique that can live-scan semi-transparent, submerged animals, and objects within seconds. To demonstrate the method, we generated full three-dimensional reconstructions (3DRs) of an object of known dimensions for verification, as well as two live marine animals—a siphonophore and an amphipod—allowing detailed measurements on each. Techniques like these pave the way for faster data capture, integrative and comparative quantitative approaches, and more accessible collections of fragile and rare biological samples.

 

Siphonophores are ubiquitous and often highly abundant members of pelagic ecosystems throughout the open ocean. They are unique among animal taxa in that many species use multiple jets for propulsion. Little is known about kinematics of the individual jets produced by nectophores or whether 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 that, for the 5 species considered, nectophores located along same side of the nectosomal axis (i.e.; axially aligned) were coordinated and their timing was offset such that they pulsed metachronally. However, this level of coordination did not extend across the nectosome and no coordination was evident between nectophores on opposite sides 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 of Apolemia rubriversa traveled 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.

 

Ocean scientists studying diverse organisms and phenomena increasingly rely on imaging devices for their research. These scientists have many tools to collect their data, but few resources for automated analysis. In this paper, we report on discussions with diverse stakeholders to identify community needs and develop a set of functional requirements for the ongoing development of ocean science-specific analysis tools. We conducted 36 in-depth interviews with individuals working in the Blue Economy space, revealing four central issues inhibiting the development of effective imaging analysis monitoring tools for marine science. We also identified twelve user archetypes that will engage with these services. Additionally, we held a workshop with 246 participants from 35 countries centered around FathomNet, a web-based open-source annotated image database for marine research. Findings from these discussions are being used to define the feature set and interface design of Ocean Vision AI, a suite of tools and services to advance observational capabilities of life in the ocean. 

Image-based machine learning methods are becoming among the most widely-used forms of data analysis across science, technology, engineering, and industry. These methods are powerful because they can rapidly and automatically extract rich contextual and spatial information from images, a process that has historically required a large amount of human labor. A wide range of recent scientific applications have demonstrated the potential of these methods to change how researchers study the ocean. However, despite their promise, machine learning tools are still under-exploited in many domains including species and environmental monitoring, biodiversity surveys, fisheries abundance and size estimation, rare event and species detection, the study of animal behavior, and citizen science. Our objective in this article is to provide an approachable, end-to-end guide to help researchers apply image-based machine learning methods effectively to their own research problems. Using a case study, we describe how to prepare data, train and deploy models, and overcome common issues that can cause models to underperform. Importantly, we discuss how to diagnose problems that can cause poor model performance on new imagery to build robust tools that can vastly accelerate data acquisition in the marine realm. Code to perform analyses is provided at https://github.com/heinsense2/AIO_CaseStudy . 

Abstract The ocean is experiencing unprecedented rapid change, and visually monitoring marine biota at the spatiotemporal scales needed for responsible stewardship is a formidable task. As baselines are sought by the research community, the volume and rate of this required data collection rapidly outpaces our abilities to process and analyze them. Recent advances in machine learning enables fast, sophisticated analysis of visual data, but have had limited success in the ocean due to lack of data standardization, insufficient formatting, and demand for large, labeled datasets. To address this need, we built FathomNet, an open-source image database that standardizes and aggregates expertly curated labeled data. FathomNet has been seeded with existing iconic and non-iconic imagery of marine animals, underwater equipment, debris, and other concepts, and allows for future contributions from distributed data sources. We demonstrate how FathomNet data can be used to train and deploy models on other institutional video to reduce annotation effort, and enable automated tracking of underwater concepts when integrated with robotic vehicles. As FathomNet continues to grow and incorporate more labeled data from the community, we can accelerate the processing of visual data to achieve a healthy and sustainable global ocean. 

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. 

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.Mesobothas the ability to survey and track slow-moving animals and to correlate the animals’ movements with critical environmental measurements.Mesobotwill 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 (Solmissussp.) 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.