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Viral lysis of phytoplankton is one of the most common forms of death on Earth. Building on an assay used extensively to assess rates of phytoplankton loss to predation by grazers, lysis rates are increasingly quantified through dilution-based techniques. In this approach, dilution of viruses and hosts are expected to reduce infection rates and thus increase host net growth rates (i.e., accumulation rates). The difference between diluted and undiluted host growth rates is interpreted as a measurable proxy for the rate of viral lytic death. These assays are usually conducted in volumes ≥ 1 L. To increase throughput, we implemented a miniaturized, high-throughput, high-replication, flow cytometric microplate dilution assay to measure viral lysis in environmental samples sourced from a suburban pond and the North Atlantic Ocean. The most notable outcome we observed was a decline in phytoplankton densities that was exacerbated by dilution, instead of the increased growth rates expected from lowered virus-phytoplankton encounters. We sought to explain this counterintuitive outcome using theoretical, environmental, and experimental analyses. Our study shows that, while die-offs could be partly explained by a ‘plate effect’ due to small incubation volumes and cells adhering to walls, the declines in phytoplankton densities are not volume-dependent. Rather, they are driven by many density- and physiology-dependent effects of dilution on predation pressure, nutrient limitation, and growth, all of which violate the original assumptions of dilution assays. As these effects are volume-independent, these processes likely occur in all dilution assays that our analyses show to be remarkably sensitive to dilution-altered phytoplankton growth and insensitive to actual predation pressure. Incorporating altered growth as well as predation, we present a logical framework that categorizes locations by the relative dominance of these mechanisms, with general applicability to dilution-based assays.

 

Satellites have provided high-resolution ( < 100 m) water color (i.e., remote sensing reflectance) and thermal emission imagery of aquatic environments since the early 1980s; however, global operational water quality products based on these data are not readily available (e.g., temperature, chlorophyll- a , turbidity, and suspended particle matter). Currently, because of the postprocessing required, only users with expressive experience can exploit these data, limiting their utility. Here, we provide paths (recipes) for the nonspecialist to access and derive water quality products, along with examples of applications, from sensors on board Landsat-5, Landsat-7, Landsat-8, Landsat-9, Sentinel-2A, and Sentinel-2B. We emphasize that the only assured metric for success in product derivation and the assigning of uncertainties to them is via validation with in situ data. We hope that this contribution will motivate nonspecialists to use publicly available high-resolution satellite data to study new processes and monitor a variety of novel environments that have received little attention to date. 

Aquaculture of the eastern oyster, Crassostrea virginica , is an expanding industry in the US, particularly in the Gulf of Maine. High resolution ocean color satellites launched in the last decade potentially provide aquaculture-relevant water-quality parameters at farm scales. However, these parameters, such as temperature, suspended particulate matter (SPM), and Chlorophyll a (Chl a), need to be derived by interested users. Water quality parameters are derived first by applying an atmospheric correction and then estimating the target parameter with a specific algorithm. Here, we use five atmospheric correction schemes and two algorithms to derive SPM and Chl a from the Sentinel 2A&B satellites’ multispectral instrument data. The best estimates of SPM and Chl a are determined by comparison with in situ observations from buoys. Together with SST from Landsat-8, we estimated an Oyster Suitability Index (OSI) along the transects in five estuaries in the Gulf of Maine as well as applied a novel particulate organic matter algorithm, a function of Chl a and SPM in low turbidity estuaries. We then apply the optimal approaches to derive water quality parameters to study five different estuaries in Maine and find that existing high-yield oyster aquaculture farms are found in areas with elevated OSI values. Additionally, we suggest new areas, currently under-exploited, where oyster aquaculture is likely to succeed, showcasing the utility of the approach. 

In this article, we present Bio-GO-SHIP, a new ocean observing program that will incorporate sustained and consistent global biological ocean observations into the Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP). The goal of Bio-GO-SHIP is to produce systematic and consistent biological observations during global ocean repeat hydrographic surveys, with a particular focus on the planktonic ecosystem. Ocean plankton are an essential component of the earth climate system, form the base of the oceanic food web and thereby play an important role in influencing food security and contributing to the Blue Economy. Despite its importance, ocean biology is largely under-sampled in time and space compared to physical and chemical properties. This lack of information hampers our ability to understand the role of plankton in regulating biogeochemical processes and fueling higher trophic levels, now and in future ocean conditions. Traditionally, many of the methods used to quantify biological and ecosystem essential ocean variables (EOVs), measures that provide valuable information on the ecosystem, have been expensive and labor- and time-intensive, limiting their large-scale deployment. In the last two decades, new technologies have been developed and matured, making it possible to greatly expand our biological ocean observing capacity. These technologies, including cell imaging, bio-optical sensors and 'omic tools, can be combined to provide overlapping measurements of key biological and ecosystem EOVs. New developments in data management and open sharing can facilitate meaningful synthesis and integration with concurrent physical and chemical data. Here we outline how Bio-GO-SHIP leverages these technological advances to greatly expand our knowledge and understanding of the constituents and function of the global ocean plankton ecosystem. 

Abstract. Phytoplankton form the base of marine food webs and playan important role in carbon cycling, making it important to quantify ratesof biomass accumulation and loss. As phytoplankton drift with oceancurrents, rates should be evaluated in a Lagrangian as opposed to an Eulerianframework. In this study, we quantify the Lagrangian (from Bio-Argo floatsand surface drifters with satellite ocean colour) and Eulerian (fromsatellite ocean colour and altimetry) statistics of mesoscale chlorophylland velocity by computing decorrelation time and length scales and relatethe frames by scaling the material derivative of chlorophyll. Because floatsprofile vertically and are not perfect Lagrangian observers, we quantify themean distance between float and surface geostrophic trajectories over thetime spanned by three consecutive profiles (quasi-planktonic index, QPI) toassess how their sampling is a function of their deviations from surfacemotion. Lagrangian and Eulerian statistics of chlorophyll are sensitive to thefiltering used to compute anomalies. Chlorophyll anomalies about a 31 dtime filter reveal an approximate equivalence of Lagrangian and Euleriantendencies, suggesting they are driven by ocean colour pixel-scale processesand sources or sinks. On the other hand, chlorophyll anomalies about aseasonal cycle have Eulerian scales similar to those of velocity, suggestingmesoscale stirring helps set distributions of biological properties, andratios of Lagrangian to Eulerian timescales depend on the magnitude ofvelocity fluctuations relative to an evolution speed of the chlorophyllfields in a manner similar to earlier theoretical results for velocityscales. The results suggest that stirring by eddies largely sets Lagrangiantime and length scales of chlorophyll anomalies at the mesoscale.

 

Measuring plankton and associated variables as part of ocean time-series stations has the potential to revolutionize our understanding of ocean biology and ecology and their ties to ocean biogeochemistry. It will open temporal scales (e.g., resolving diel cycles) not typically sampled as a function of depth. In this review we motivate the addition of biological measurements to time-series sites by detailing science questions they could help address, reviewing existing technology that could be deployed, and providing examples of time-series sites already deploying some of those technologies. We consider here the opportunities that exist through global coordination within the OceanSITES network for long-term (climate) time series station in the open ocean. Especially with respect to data management, global solutions are needed as these are critical to maximize the utility of such data. We conclude by providing recommendations for an implementation plan. 

We derived the angular response function ($W_{\mathrm{N}}$) for scattering sensors that automatically satisfies the normalization criterion and its corresponding weight ($W_{\mathrm{T}}$).$W_{\mathrm{N}}$’s, derived for two commercial sensors, HydroScat-6 (HOBI Labs) and ECO-BB (Sea-Bird Inc.), agrees well with the Monte Carlo simulation and direct measurements. The backscattering measured for microbeads of known sizes agrees better with Mie calculation when the derived$W_{\mathrm{N}}$was applied. We deduced that the reduction of$W_{\mathrm{T}}$with increasing attenuation coefficient is related to path length attenuation and showed that this theoretically derived correction factor performs better than the default methods for the two commercial backscattering sensors. The analysis conducted in this study also leads to an estimate of uncertainty budget for the two sensors. The major uncertainty for ECO-BB is associated with its angular response function because of its wide field of view, whereas the main uncertainty for the HydrScat-6 is due to attenuation correction because of its relatively long path length.

 

Background: Biogeochemical-Argo floats are collecting an unprecedented number of profiles of optical backscattering measurements in the global ocean. Backscattering (BBP) data are crucial to understanding ocean particle dynamics and the biological carbon pump. Yet, so far, no procedures have been agreed upon to quality control BBP data in real time. Methods: Here, we present a new suite of real-time quality-control tests and apply them to the current global BBP Argo dataset. The tests were developed by expert BBP users and Argo data managers and have been implemented on a snapshot of the entire Argo dataset. Results: The new tests are able to automatically flag most of the “bad” BBP profiles from the raw dataset. Conclusions: The proposed tests have been approved by the Biogeochemical-Argo Data Management Team and will be implemented by the Argo Data Assembly Centres to deliver real-time quality-controlled profiles of optical backscattering. Provided they reach a pressure of about 1000 dbar, these tests could also be applied to BBP profiles collected by other platforms. 

Background: Biogeochemical-Argo floats are collecting an unprecedented number of profiles of optical backscattering measurements in the global ocean. Backscattering (BBP) data are crucial to understanding ocean particle dynamics and the biological carbon pump. Yet, so far, no procedures have been agreed upon to quality control BBP data in real time. Methods: Here, we present a new suite of real-time quality-control tests and apply them to the current global BBP Argo dataset. The tests were developed by expert BBP users and Argo data managers and have been implemented on a snapshot of the entire Argo dataset. Results: The new tests are able to automatically flag most of the “bad” BBP profiles from the raw dataset. Conclusions: The proposed tests have been approved by the Biogeochemical-Argo Data Management Team and will be implemented by the Argo Data Assembly Centres to deliver real-time quality-controlled profiles of optical backscattering. Provided they reach a pressure of about 1000 dbar, these tests could also be applied to BBP profiles collected by other platforms.