Julio B.J. Harvey

Data-driven robotic sampling for marine ecosystem monitoring

Robotic sampling is attractive in many field robotics applications that require persistent collection of physical samples for ex-situ analysis. Examples abound in the earth sciences in studies involving the collection of rock, soil, and water samples for laboratory analysis. In our test domain, marine ecosystem monitoring, detailed understanding of plankton ecology requires laboratory analysis of water samples, but predictions using physical and chemical properties measured in real-time by sensors aboard an autonomous underwater vehicle (AUV) can guide sample collection decisions. In this paper, we present a data-driven and opportunistic sampling strategy to minimize cumulative regret for batches of plankton samples acquired by an AUV over multiple surveys. Samples are labeled at the end of each survey, and used to update a probabilistic model that guides sampling during subsequent surveys. During a survey, the AUV makes irrevocable sample collection decisions online for a sequential stream of candidates, with no knowledge of the quality of future samples. In addition to extensive simulations using historical field data, we present results from a one-day field trial where beginning with a prior model learned from data collected and labeled in an earlier campaign, the AUV collected water samples with a high abundance of a pre-specified planktonic target. This is the first time such a field experiment has been carried out in its entirety in a data-driven fashion, in effect “closing the loop” on a significant and relevant ecosystem monitoring problem while allowing domain experts (marine ecologists) to specify the mission at a relatively high level.

A hybrid zone between Bathymodiolus mussel lineages from eastern Pacific hydrothermal vents

BackgroundThe inhabitants of deep-sea hydrothermal vents occupy ephemeral island-like habitats distributed sporadically along tectonic spreading-centers, back-arc basins, and volcanically active seamounts. The majority of vent taxa undergo a pelagic larval phase, and thus varying degrees of geographical subdivision, ranging from no impedance of dispersal to complete isolation, often exist among taxa that span common geomorphological boundaries. Two lineages of Bathymodiolus mussels segregate on either side of the Easter Microplate, a boundary that separates the East Pacific Rise from spreading centers connected to the Pacific-Antarctic Ridge.ResultsA recent sample from the northwest flank of the Easter Microplate contained an admixture of northern and southern mitochondrial haplotypes and corresponding alleles at five nuclear gene loci. Genotypic frequencies in this sample did not fit random mating expectation. Significant heterozygote deficiencies at nuclear loci and gametic disequilibria between loci suggested that this transitional region might be a ‘Tension Zone’ maintained by immigration of parental types and possibly hybrid unfitness. An analysis of recombination history in the nuclear genes suggests a prolonged history of parapatric contact between the two mussel lineages. We hereby elevate the southern lineage to species status as Bathymodiolus antarcticus n. sp. and restrict the use of Bathymodiolus thermophilus to the northern lineage.ConclusionsBecause B. thermophilus s.s. exhibits no evidence for subdivision or isolation-by-distance across its 4000 km range along the EPR axis and Galápagos Rift, partial isolation of B. antarcticus n. sp. requires explanation. The time needed to produce the observed degree of mitochondrial differentiation is consistent with the age of the Easter Microplate (2.5 to 5.3 million years). The complex geomorphology of the Easter Microplate region forces strong cross-axis currents that might disrupt self-recruitment of mussels by removing planktotrophic larvae from the ridge axis. Furthermore, frequent local extinction events in this tectonically dynamic region might produce a demographic sink rather than a source for dispersing mussel larvae. Historical changes in tectonic rates and current patterns appear to permit intermittent contact and introgression between the two species.

Hierarchical probabilistic regression for AUV-based adaptive sampling of marine phenomena

Marine phenomena such as algal blooms can be detected using in situ measurements onboard autonomous underwater vehicles (AUVs), but understanding plankton ecology and community structure requires retrieval and analysis of water specimens. This process requires shipboard or manual sample collection, followed by onshore lab analysis which is time-consuming. Better understanding of the relationship between the observable environmental features and organism abundance would allow more precisely targeted sampling and thereby save time. In this work, we present an approach to learn and improve models that predict this relationship. Coupled with recent advances in AUV technology allowing selective retrieval of water samples, this constitutes a new paradigm in biological sampling. We use organism abundance models along with spatial models of environmental features learned immediately after AUV deployments to compute spatial distributions of organisms in the coastal ocean purely from in situ AUV data. We use Gaussian process regression along with the unscented transform to fuse the two models, obtaining both the mean and variance of the organism abundance estimates. The uncertainty in organism abundance predictions is used in a sampling strategy to selectively acquire new water specimens that improves the organism abundance models. Simulation results are presented demonstrating the advantage of performing hierarchical probabilistic regression. After the validation through simulation, we show predictions of organism abundance from models learned on lab-analyzed water sample data, and AUV survey data.

A 96-Well Plate Format for Detection of Marine Zooplankton with the Sandwich Hybridization Assay

The sandwich hybridization assay (SHA) is a ribosomal RNA (rRNA) targeted molecular method used to detect specific target organisms from diverse communities found in environmental water samples. This sensitive, robust assay is particularly useful for detecting zooplankton, including copepod grazers or reproductive propagules from broadcast spawning invertebrates. Herein, I describe the most basic application of this flexible methodology-a 96-well plate format for analysis of water samples in the laboratory. A microarray format SHA is also available and uses the same basic chemistry for remote, robotically mediated, in situ target detection. Traditionally produced only in the laboratory, preassembled SHA reagents and consumables are now also available for purchase.

Using STOQS (The spatial temporal oceanographic query system) to manage, visualize, and understand AUV, glider, and mooring data

The Monterey Bay Aquarium Research Institute (MBARI) uses the Spatial Temporal Oceanographic Query System (STOQS) to manage data from its muli-platform observational campaigns. By using geospatial relational database technology STOQS solves the fundamental problem of providing efficient access to mobile platform data, while also handling in situ measurements from stationary platforms. Because it embraces existing standards and conventions it easily integrates into existing workflows. STOQS includes a modern web-based user interface providing sophisticated tools for deep exploration of multidisciplinary data sets. Direct programmatic access using the Python programming language allows for detailed visualization and analysis of large and diverse data sets. STOQS is a 100% open source project, free for anyone to use.