Marine snow morphology illuminates the evolution of phytoplankton blooms and determines their subsequent vertical export

Cell size, density, and nutrient dependency of unicellular algal gravitational sinking velocities

Eukaryotic phytoplankton, also known as algae, form the basis of marine food webs and drive marine carbon sequestration. Algae must regulate their motility and gravitational sinking to balance access to light at the surface and nutrients in deeper layers. However, the regulation of gravitational sinking remains largely unknown, especially in motile species. Here, we quantify gravitational sinking velocities according to Stokes' law in diverse clades of unicellular marine microalgae to reveal the cell size, density, and nutrient dependency of sinking velocities. We identify a motile algal species, Tetraselmis sp., that sinks faster when starved due to a photosynthesis-driven accumulation of carbohydrates and a loss of intracellular water, both of which increase cell density. Moreover, the regulation of cell sinking velocities is connected to proliferation and can respond to multiple nutrients. Overall, our work elucidates how cell size and density respond to environmental conditions to drive the vertical migration of motile algae.

Multi-vehicle adaptive 3D mapping for targeted ocean sampling

Expanding spatial presentation from two-dimensional profile transects to three-dimensional ocean mapping is key for a better understanding of ocean processes. Phytoplankton distributions can be highly patchy and the accurate identification of these patches with the context, variability, and uncertainty of measurements on relevant scales is difficult to achieve. Traditional sampling methods, such as plankton nets, water samplers and in-situ vertical sensors, provide a snapshot and often miss the fine-scale horizontal and temporal variability. Here, we show how two autonomous underwater vehicles measured, adapted to, and reported real-time chlorophyll a measurements, giving insights into the spatiotemporal distribution of phytoplankton biomass and patchiness. To gain the maximum available information within their sensing scope, the vehicles moved in an adaptive fashion, looking for the regions of the highest predicted chlorophyll a concentration, the greatest uncertainty, and the least possibility of collision with other underwater vehicles and ships. The vehicles collaborated by exchanging data with each other and operators via satellite, using a common segmentation of the area to maximize information exchange over the limited bandwidth of the satellite. Importantly, the use of multiple autonomous underwater vehicles reporting real-time data combined with targeted sampling can provide better match with sampling towards understanding of plankton patchiness and ocean processes.

Impacts of extracellular polymeric substances on the behaviors of micro/nanoplastics in the water environment

Increasing pollution of microplastics (MPs) and nanoplastics (NPs) has caused widespread concern worldwide. Extracellular polymeric substances (EPS) are natural organic polymers mainly produced by microorganisms, the major components of which are polysaccharides and proteins. This review focuses on the interactions that occur between EPS and MPs/NPs in the water environment and evaluates the effects of these interactions on the behaviors of MPs/NPs. EPS-driven formation of eco-corona, biofilm, and "marine snow" can incorporate MPs and NPs into sinking aggregates, resulting in the export of MPs/NPs from the upper water column. EPS coating greatly enhances the adsorption of metals and organic pollutants by MPs due to the larger specific surface area and the abundance of functional groups such as carboxyl, hydroxyl and amide groups. EPS can weaken the physical properties of MPs. Through the synergistic action of different extracellular enzymes, MPs may be decomposed into oligomers and monomers that can enter microbial cells for further mineralization. This review contributes to a comprehensive understanding of the dynamics of MPs and NPs in the water environment and the associated ecological risks.

Morphological diversity increases with decreasing resources along a zooplankton time series

Biodiversity is studied notably because of its reciprocal relationship with ecosystem functions such as production. Diversity is traditionally described from a taxonomic, genetic or functional point of view but the diversity in organism morphology is seldom explicitly considered, except for body size. We describe morphological diversity of marine zooplankton seasonally and over 12 years using quantitative imaging of weekly plankton samples, in the northwestern Mediterranean Sea. We extract 45 morphological features on greater than 800 000 individuals, which we summarize into four main morphological traits (size, transparency, circularity and shape complexity). In this morphological space, we define objective morphological groups and, from those, compute morphological diversity indices (richness, evenness and divergence) using metrics originally defined for functional diversity. On both time scales, morphological diversity increased when nutritive resources and plankton concentrations were low, thus matching the theoretical reciprocal relationship. Over the long term at least, this diversity increase was not fully attributable to taxonomic diversity changes. The decline in the most common plankton forms and the increase in morphological variance and in extreme morphologies suggest a mechanism akin to specialization under low production, with likely consequences for trophic structure and carbon flux.

Large-scale culturing of Neogloboquadrina pachyderma, its growth in, and tolerance of, variable environmental conditions

The planktic foraminifera Neogloboquadrina pachyderma is a calcifying marine protist and the dominant planktic foraminifera species in the polar oceans, making it a key species in marine polar ecosystems. The calcium carbonate shells of foraminifera are widely used in palaeoclimate studies because their chemical composition reflects the seawater conditions in which they grow. This species provides unique proxy data for past surface ocean hydrography, which can provide valuable insight to future climate scenarios. However, little is known about the response of N. pachyderma to variable and changing environmental conditions. Here, we present observations from large-scale culturing experiments where temperature, salinity and carbonate chemistry were altered independently. We observed overall low mortality, calcification of new chambers and addition of secondary calcite crust in all our treatments. In-culture asexual reproduction events also allowed us to monitor the variable growth of N. pachyderma's offspring. Several specimens had extended periods of dormancy or inactivity after which they recovered. These observations suggest that N. pachyderma can tolerate, adapt to and calcify within a wide range of environmental conditions. This has implications for the species-level response to ocean warming and acidification, for future studies aiming to culture N. pachyderma and use in palaeoenvironmental reconstruction.

Seasonality of downward carbon export in the Pacific Southern Ocean revealed by multi-year robotic observations

At high latitudes, the biological carbon pump, which exports organic matter from the surface ocean to the interior, has been attributed to the gravitational sinking of particulate organic carbon. Conspicuous deficits in ocean carbon budgets challenge this as a sole particle export pathway. Recent model estimates revealed that particle injection pumps have a comparable downward flux of particulate organic carbon to the biological gravitational pump, but with different seasonality. To date, logistical constraints have prevented concomitant and extensive observations of these mechanisms. Here, using year-round robotic observations and recent advances in bio-optical signal analysis, we concurrently investigated the functioning of two particle injection pumps, the mixed layer and eddy subduction pumps, and the gravitational pump in Southern Ocean waters. By comparing three annual cycles in contrasting physical and biogeochemical environments, we show how physical forcing, phytoplankton phenology and particle characteristics influence the magnitude and seasonality of these export pathways, with implications for carbon sequestration efficiency over the annual cycle.

Persistent reshaping of cohesive sediment towards stable flocs by turbulence

Cohesive sediment forms flocs of various sizes and structures in the natural turbulent environment. Understanding flocculation is critical in accurately predicting sediment transport and biogeochemical cycles. In addition to aggregation and breakup, turbulence also reshapes flocs toward more stable structures. An Eulerian-Lagrangian framework has been implemented to investigate the effect of turbulence on flocculation by capturing the time-evolution of individual flocs. We have identified two floc reshaping mechanisms, namely breakage-regrowth and restructuring by hydrodynamic drag. Surface erosion is found to be the primary breakup mechanism for strong flocs, while fragile flocs tend to split into fragments of similar sizes. Aggregation of flocs of sizes comparable to or greater than the Kolmogorov scale is modulated by turbulence with lower aggregation efficiency. Our findings highlight the limiting effects of turbulence on both floc size and structure.

Porous marine snow differentially benefits chemotactic, motile, and nonmotile bacteria

Particulate organic carbon settling through the marine water column is a key process that regulates the global climate by sequestering atmospheric carbon. The initial colonization of marine particles by heterotrophic bacteria represents the first step in recycling this carbon back to inorganic constituents-setting the magnitude of vertical carbon transport to the abyss. Here, we demonstrate experimentally using millifluidic devices that, although bacterial motility is essential for effective colonization of a particle leaking organic nutrients into the water column, chemotaxis specifically benefits at intermediate and higher settling velocities to navigate the particle boundary layer during the brief window of opportunity provided by a passing particle. We develop an individual-based model that simulates the encounter and attachment of bacterial cells with leaking marine particles to systematically evaluate the role of different parameters associated with bacterial run-and-tumble motility. We further use this model to explore the role of particle microstructure on the colonization efficiency of bacteria with different motility traits. We find that the porous microstructure facilitates additional colonization by chemotactic and motile bacteria, and fundamentally alters the way nonmotile cells interact with particles due to streamlines intersecting with the particle surface.

Response mechanism of microbial community during anaerobic biotransformation of marine toxin domoic acid

Domoic acid (DA) is a potent neurotoxin produced by toxigenic Pseudo-nitzschia blooms and quickly transfers to the benthic anaerobic environment by marine snow particles. DA anaerobic biotransformation is driven by microbial interactions, in which trace amounts of DA can cause physiological stress in marine microorganisms. However, the underlying response mechanisms of microbial community to DA stress remain unclear. In this study, we utilized an anaerobic marine DA-degrading consortium GLY (using glycine as co-substrate) to systematically investigate the global response mechanisms of microbial community during DA anaerobic biotransformation.16S rRNA gene sequencing and metatranscriptomic analyses were applied to measure microbial community structure, function and metabolic responses. Results showed that DA stress markedly changed the composition of main species, with increased levels of Firmicutes and decreased levels of Proteobacteria, Cyanobacteria, Bacteroidetes and Actinobacteria. Several genera of tolerated bacteria (Bacillus and Solibacillus) were increased, while, Stenotrophomonas, Sphingomonas and Acinetobacter were decreased. Metatranscriptomic analyses indicated that DA stimulated the expression of quorum sensing, extracellular polymeric substance (EPS) production, sporulation, membrane transporters, bacterial chemotaxis, flagellar assembly and ribosome protection in community, promoting bacterial adaptation ability under DA stress. Moreover, amino acid metabolism, carbohydrate metabolism and lipid metabolism were modulated during DA anaerobic biotransformation to reduce metabolic burden, increase metabolic demands for EPS production and DA degradation. This study provides the new insights into response of microbial community to DA stress and its potential impact on benthic microorganisms in marine environments.

Disaggregation as an interaction mechanism among intestinal bacteria

The gut microbiome contains hundreds of interacting species that together influence host health and development. The mechanisms by which intestinal microbes can interact, however, remain poorly mapped and are often modeled as spatially unstructured competitions for chemical resources. Recent imaging studies examining the zebrafish gut have shown that patterns of aggregation are central to bacterial population dynamics. In this study, we focus on bacterial species of genera Aeromonas and Enterobacter. Two zebrafish gut-derived isolates, Aeromonas ZOR0001 (AE) and Enterobacter ZOR0014 (EN), when mono-associated with the host, are highly aggregated and located primarily in the intestinal midgut. An Aeromonas isolate derived from the commensal strain, Aeromonas-MB4 (AE-MB4), differs from the parental strain in that it is composed mostly of planktonic cells localized to the anterior gut. When challenged by AE-MB4, clusters of EN rapidly fragment into non-motile, slow-growing, dispersed individual cells with overall abundance two orders of magnitude lower than the mono-association value. In the presence of a certain set of additional gut bacterial species, these effects on EN are dampened. In particular, if AE-MB4 invades an already established multi-species community, EN persists in the form of large aggregates. These observations reveal an unanticipated competition mechanism based on manipulation of bacterial spatial organization, namely dissolution of aggregates, and provide evidence that multi-species communities may facilitate stable intestinal co-existence.

Machine learning techniques to characterize functional traits of plankton from image data

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

Combining multi-marker metabarcoding and digital holography to describe eukaryotic plankton across the Newfoundland Shelf

The planktonic diversity throughout the oceans is vital to ecosystem functioning and linked to environmental change. Plankton monitoring tools have advanced considerably with high-throughput in-situ digital cameras and genomic sequencing, opening new challenges for high-frequency observations of community composition, structure, and species discovery. Here, we combine multi-marker metabarcoding based on nuclear 18S (V4) and plastidial 16S (V4–V5) rRNA gene amplicons with a digital in-line holographic microscope to provide a synoptic diversity survey of eukaryotic plankton along the Newfoundland Shelf (Canada) during the winter transition phase of the North Atlantic bloom phenomenon. Metabarcoding revealed a rich eukaryotic diversity unidentifiable in the imaging samples, confirming the presence of ecologically important saprophytic protists which were unclassifiable in matching images, and detecting important groups unobserved or taxonomically unresolved during similar sequencing campaigns in the Northwest Atlantic Ocean. In turn, imaging analysis provided quantitative observations of widely prevalent plankton from every trophic level. Despite contrasting plankton compositions portrayed by each sampling method, both capture broad spatial differences between the northern and southern sectors of the Newfoundland Shelf and suggest complementary estimations of important features in eukaryotic assemblages. Future tasks will involve standardizing digital imaging and metabarcoding for wider use and consistent, comparable ocean observations.

Assessing Representation Learning and Clustering Algorithms for Computer-Assisted Image Annotation-Simulating and Benchmarking MorphoCluster

Image annotation is a time-consuming and costly task. Previously, we published MorphoCluster as a novel image annotation tool to address problems of conventional, classifier-based image annotation approaches: their limited efficiency, training set bias and lack of novelty detection. MorphoCluster uses clustering and similarity search to enable efficient, computer-assisted image annotation. In this work, we provide a deeper analysis of this approach. We simulate the actions of a MorphoCluster user to avoid extensive manual annotation runs. This simulation is used to test supervised, unsupervised and transfer representation learning approaches. Furthermore, shrunken k-means and partially labeled k-means, two new clustering algorithms that are tailored specifically for the MorphoCluster approach, are compared to the previously used HDBSCAN*. We find that labeled training data improve the image representations, that unsupervised learning beats transfer learning and that all three clustering algorithms are viable options, depending on whether completeness, efficiency or runtime is the priority. The simulation results support our earlier finding that MorphoCluster is very efficient and precise. Within the simulation, more than five objects per simulated click are being annotated with 95% precision.

The Underwater Vision Profiler 6: an imaging sensor of particle size spectra and plankton, for autonomous and cabled platforms

Autonomous and cabled platforms are revolutionizing our understanding of ocean systems by providing 4D monitoring of the water column, thus going beyond the reach of ship-based surveys and increasing the depth of remotely sensed observations. However, very few commercially available sensors for such platforms are capable of monitoring large particulate matter (100-2000 μm) and plankton despite their important roles in the biological carbon pump and as trophic links from phytoplankton to fish. Here, we provide details of a new, commercially available scientific camera-based particle counter, specifically designed to be deployed on autonomous and cabled platforms: the Underwater Vision Profiler 6 (UVP6). Indeed, the UVP6 camera-and-lighting and processing system, while small in size and requiring low power, provides data of quality comparable to that of previous much larger UVPs deployed from ships. We detail the UVP6 camera settings, its performance when acquiring data on aquatic particles and plankton, their quality control, analysis of its recordings, and streaming from in situ acquisition to users. In addition, we explain how the UVP6 has already been integrated into platforms such as BGC-Argo floats, gliders and long-term mooring systems (autonomous platforms). Finally, we use results from actual deployments to illustrate how UVP6 data can contribute to addressing longstanding questions in marine science, and also suggest new avenues that can be explored using UVP6-equipped autonomous platforms.

Machine Learning for the Study of Plankton and Marine Snow from Images

Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit this wealth of information, machine learning is necessary to automate tasks such as taxonomic classification. Through a review of the literature, we highlight the progress of those machine classifiers and what they can and still cannot be trusted for. Several examples showcase how the combination of quantitative imaging with machine learning has brought insights on pelagic ecology. They also highlight what is still missing and how images could be exploited further through trait-based approaches. In the future, we suggest deeper interactions with the computer sciences community, the adoption of data standards, and the more systematic sharing of databases to build a global community of pelagic image providers and users.

From Nano-Gels to Marine Snow: A Synthesis of Gel Formation Processes and Modeling Efforts Involved with Particle Flux in the Ocean

Marine gels (nano-, micro-, macro-) and marine snow play important roles in regulating global and basin-scale ocean biogeochemical cycling. Exopolymeric substances (EPS) including transparent exopolymer particles (TEP) that form from nano-gel precursors are abundant materials in the ocean, accounting for an estimated 700 Gt of carbon in seawater. This supports local microbial communities that play a critical role in the cycling of carbon and other macro- and micro-elements in the ocean. Recent studies have furthered our understanding of the formation and properties of these materials, but the relationship between the microbial polymers released into the ocean and marine snow remains unclear. Recent studies suggest developing a (relatively) simple model that is tractable and related to the available data will enable us to step forward into new research by following marine snow formation under different conditions. In this review, we synthesize the chemical and physical processes. We emphasize where these connections may lead to a predictive, mechanistic understanding of the role of gels in marine snow formation and the biogeochemical functioning of the ocean.