Phytoplankton communities residing in the open ocean, the largest habitat on Earth, play a key role in global primary production. Through their influence on nutrient supply to the euphotic zone, open-ocean eddies impact the magnitude of primary production and its spatial and temporal distributions. It is important to gain a deeper understanding of the microbial ecology of marine ecosystems under the influence of eddy physics with the aid of advanced technologies. In March and April 2018, we deployed autonomous underwater and surface vehicles in a cyclonic eddy in the North Pacific Subtropical Gyre to investigate the variability of the microbial community in the deep chlorophyll maximum (DCM) layer. One long-range autonomous underwater vehicle (LRAUV) carrying a third-generation Environmental Sample Processor (3G-ESP) autonomously tracked and sampled the DCM layer for four days without surfacing. The sampling LRAUV's vertical position in the DCM layer was maintained by locking onto the isotherm corresponding to the chlorophyll peak. The vehicle ran on tight circles while drifting with the eddy current. This mode of operation enabled a quasi-Lagrangian time series focused on sampling the temporal variation of the DCM population. A companion LRAUV surveyed a cylindrical volume around the sampling LRAUV to monitor Manuscript

For robots to succeed in complex missions, they must be reliable in the face of subsystem failures and environmental challenges. In this paper, we focus on autonomous underwater vehicle (AUV) autonomy as it pertains to self-perception and health monitoring, and we argue that automatic classification of state-sensor data represents an important enabling capability. We apply an online Bayesian nonparametric topic modeling technique to AUV sensor data in order to automatically characterize its performance patterns, then demonstrate how in combination with operatorsupplied semantic labels these patterns can be used for fault detection and diagnosis by means of a nearest-neighbor classifier. The method is evaluated using data collected by the Monterey Bay Aquarium Research Institute's Tethys long-range AUV in three separate field deployments. Our results show that the proposed method is able to accurately identify and characterize patterns that correspond to various states of the AUV, and classify faults at a high rate of correct detection with a very low false detection rate.

No abstract

Many pelagic animals, such as krill, lanternfish, and cephalopods, migrate to deep water at dawn to avoid visual predators during daylight hours and move up toward the sea surface at dusk to search for food. This behavior is termed "diel vertical migration." Migrating animals graze on phytoplankton or zooplankton and in turn serve as food for higher trophic levels, hence providing a key mechanism for carbon export via this migration. These animals are often observed as sound-scattering layers by echosounders, but the animals causing the acoustic scattering are difficult to identify using acoustics alone. In a spring 2019 experiment in Monterey Bay, we deployed autonomous underwater and surface vehicles over a seabed-mounted upward-looking echosounder to collect environmental DNA (eDNA) with the goal of identifying the vertically migrating animals. The echosounder was installed at 890-m depth on the Monterey Accelerated Research System (MARS) seabed cabled ocean observatory, providing realtime data of acoustic backscatter from the full water column. One long-range autonomous underwater vehicle (LRAUV) carrying a Third-Generation Environmental Sample Processor (3G-ESP) acquired water samples from a sequence of layers from near surface down to ∼290 m as directed by the distribution of animals observed by the echosounder. During the sampling of each layer, the LRAUV ran on a tight circular yo-yo trajectory directly above the echosounder, remaining in its beam by acoustically tracking a station-keeping Wave Glider on the sea surface marking the echosounder's latitude and longitude. The persistent and simultaneous acoustic observation and eDNA acquisition enables identification of animals at precise locations to better understand their vertical migration behaviors. We present the methods and the system performance in the experiment.

Phytoplankton (microscopic algae) play an important role in marine ecology. Resulting from a combination of physical, chemical, and biological processes, the distribution of phytoplankton is patchy, particularly in coastal marine ecosystems. Patches of high chlorophyll represent areas where enhanced primary productivity and biogeochemical cycling can occur. The scientific goal is to place observations within these biological hotspots to enable more extensive characterization of the environment and plankton populations. Aerial or satellite remote sensing can detect optical signal originating from phytoplankton within a limited depth range only near the ocean surface, and application of remote sensing is limited by atmospheric clarity. To observe the development of patchy phytoplankton communities in situ, we need the ability to locate and track individual patches. In this article, we present a method for an autonomous underwater vehicle (AUV) to autonomously find and climb on a positive horizontal gradient of chlorophyll to locate and track a phytoplankton patch. In two experiments in 2021, a Tethys-class long-range AUV autonomously located and tracked phytoplankton patches in southern Monterey Bay, CA, USA. The experiments demonstrated effectiveness of the method and pointed to the need for increased onboard adaptiveness in autonomous patch finding and tracking.