Yogesh Girdhar

Coral reefs are under threat worldwide. Yet, in these often-challenging marine habitats, biologists lack the tools to quantify the spatial heterogeneity of mobile reef fauna at high resolution (Dendrogyra pillar coral. We used the colocated multimodal data to self-validate the hotspot’s prominence, representing a technological step forward to help understand the ecological dynamics of coral reefs.

Small photosynthetic eukaryotes are a productive and dynamic component of marine planktonic communities. Here, we investigate how seasonal changes in the abundance of these primary producers relate to changes in their community composition at a coastal site on the Northeast US Shelf. We present a 9‐year time series of 18S rRNA sequencing data and identify gradual transitions within the pico‐ and nanoplankton community that occur repeatedly over the annual cycle. We compare these compositional changes to concurrent high‐resolution in situ flow cytometry measurements of eukaryotic phytoplankton abundance and division rate. We find that the Chlorophyta contribute a large proportion of the sequences in our samples and drive much of the seasonal variability within the small phytoplankton community. Across the time series, Bathycoccus , Micromonas and Picochlorum are the dominant genera, with the first being present year‐round, while Micromonas bravo and Picochlorum are representative of the summer community. We also find a strong winter Phaeocystis signal which might be leading to flow cytometry measurements of relatively large cells in the early spring. Our results provide fundamental knowledge of the taxonomic composition of the phytoplankton community on the Northeast US Shelf, improving our understanding of the region's diversity and compositional variability over time.

Small photosynthetic eukaryotes are a productive and dynamic component of marine planktonic communities. Here, we investigate how seasonal changes in abundance of these primary producers relate to changes in their community composition at a coastal site on the Northeast U.S. Shelf. We present a 9-yr time series of 18S rRNA sequencing data and identify gradual transitions within the pico- and nanoplankton community that occur repeatedly over the annual cycle. We compare these compositional changes to concurrent high-resolution in situ flow cytometry measurements of eukaryotic phytoplank-ton abundance and division rate. We find that the Chlorophyta contribute a large proportion of the sequences in our samples and drive much of the seasonal variability within the small phytoplankton community. Across the time series, Bathycoccus, Micromonas , and Picochlorum are the dominant genera, with the first being present year round, while Micromonas bravo and Picochlorum are representative of the summer community. We also find a strong winter Phaeocystis signal which might be leading to flow cytometry measurements of relatively large cells in the early spring. Our results provide fundamental knowledge of the taxonomic composition of the phytoplankton community on the Northeast U.S. Shelf, improving our understanding of the region’s diversity and compositional variability over time.

Phytoplankton communities in the open ocean are high‐dimensional, sparse, and spatiotemporally heterogeneous. The advent of automated imaging systems has enabled high‐resolution observation of these communities, but the amounts of data and their statistical properties make analysis with traditional approaches challenging. Spatiotemporal topic models offer an unsupervised and interpretable approach to dimensionality reduction of sparse, high‐dimensional categorical data. Here we use topic modeling to analyze neural‐network‐classified phytoplankton imagery taken in and around a retentive eddy during the 2021 North Atlantic EXport Processes in the Ocean from Remote Sensing (EXPORTS) field campaign. We investigate the role physical‐biological interactions play in altering plankton community composition within the eddy. Analysis of a water mass mixing framework suggests that storm‐driven surface advection and stirring were major drivers of the progression of the eddy plankton community away from a diatom bloom over the course of the cruise.

There exists a capability gap in the design of currently available autonomous underwater vehicles (AUV). Most AUVs use a set of thrusters, and optionally control surfaces, to control their depth and pose. AUVs utilizing thrusters can be highly maneuverable, making them well-suited to operate in complex environments such as in close-proximity to coral reefs. However, they are inherently power-inefficient and produce significant noise and disturbance. Underwater gliders, on the other hand, use changes in buoyancy and center of mass, in combination with a control surface to move around. They are extremely power efficient but not very maneuverable. Gliders are designed for long-range missions that do not require precision maneuvering. Furthermore, since gliders only activate the buoyancy engine for small time intervals, they do not disturb the environment and can also be used for passive acoustic observations. In this paper we present ReefGlider, a novel AUV that uses only buoyancy for control but is still highly maneuverable from additional buoyancy control devices. ReefGlider bridges the gap between the capabilities of thruster-driven AUVs and gliders. These combined characteristics make ReefGlider ideal for tasks such as long-term visual and acoustic monitoring of coral reefs. We present the overall design and implementation of the system, as well as provide analysis of some of its capabilities.

Phytoplankton communities in the open ocean are high-dimensional, sparse, and spatiotemporally heterogeneous. The advent of automated imaging systems has enabled high-resolution observation of these communities, but the amounts of data and their statistical properties make analysis with traditional approaches challenging. Spatiotemporal topic models offer an unsupervised and interpretable approach to dimensionality reduction of sparse, high-dimensional categorical data. Here we use topic modeling to analyze neural-network-classified phytoplankton imagery taken in and around a retentive eddy during the 2021 North Atlantic EXport Processes in the Ocean from Remote Sensing (EXPORTS) field campaign. We investigate the role physical-biological interactions play in altering plankton community composition within the eddy. Analysis of a water mass mixing framework suggests that storm-driven surface advection and stirring were major drivers of the progression of the eddy plankton community away from a diatom bloom over the course of the cruise.

We present the Streaming Gaussian Dirichlet Random Field (S-GDRF) model, a novel approach for modeling a stream of spatiotemporally distributed, sparse, high-dimensional categorical observations. The proposed approach efficiently learns global and local patterns in spatiotemporal data, allowing for fast inference and querying with a bounded time complexity. Using a high-resolution data series of plankton images classified with a neural network, we demonstrate the ability of the approach to make more accurate predictions compared to a Variational Gaussian Process (VGP), and to learn a predictive distribution of observations from streaming categorical data. S-GDRFs open the door to enabling efficient informative path planning over high-dimensional categorical observations, which until now has not been feasible.

Coral reefs are fast-changing and complex ecosystems that are crucial to monitor and study. Biological hotspot detection can help coral reef managers prioritize limited resources for monitoring and intervention tasks. Here, we explore the use of autonomous underwater vehicles (AUVs) with cameras, coupled with visual detectors and photogrammetry, to map and identify these hotspots. This approach can provide high spatial resolution information in fast feedback cycles. To the best of our knowledge, we present one of the first attempts at using an AUV to gather visually-observed, fine-grain biological hotspot maps in concert with topography of a coral reefs. Our hotspot maps correlate with rugosity, an established proxy metric for coral reef biodiversity and abundance, as well as with our visual inspections of the 3D reconstruction. We also investigate issues of scaling this approach when applied to new reefs by using these visual detectors pre-trained on large public datasets.