Phytoplankton Community Composition in the Surface Ocean: Methods for Detection using Optical Measurements, Pigment Concentrations, and Flow Cytometry

Phytoplankton are microscopic photoautotrophs living in the surface ocean waters and help support all life on earth via photosynthetic production of oxygen. Thousands of species make up the bulk phytoplankton community, and the spatial and temporal distribution of different types of phytoplankton has relevance for many ocean ecosystem questions including marine food web dynamics, and carbon flux and sequestration. Methods to detect phytoplankton community composition (PCC) on the vast scale of the global ocean require estimates of PCC from remote platforms, namely earth-observing satellites. The use of satellite data to observe and interpret PCC in the surface ocean requires significant effort to develop and evaluate algorithms based on measurements made in situ; the work of this thesis contributes to that effort. Information from both global and regional (North Atlantic Ocean) datasets is applied to develop methods to estimate phytoplankton pigment concentrations, phytoplankton size classes, and diatom carbon concentrations. Optical spectra, specifically hyperspectral remote-sensing reflectance, are used in the algorithm for estimating phytoplankton pigments, which resolves the concentrations of three pigments and one pigment group (chlorophylls a, b, c, and photoprotective carotenoids). This result has implications for use with hyperspectral ocean color data measured by satellite. A novel dataset of open-ocean image-in-flow cytometry is used to evaluate and improve a commonly applied phytoplankton size class algorithm, as well as to calculate diatom carbon and develop a model to map diatom carbon using environmental parameters as model input. Biases and uncertainties in the size class algorithm are reduced by our method relative to previously published work for all three size classes (pico-, nano-, and microplankton). Diatom carbon measurements from quantitative cell imagery elucidate the variability of diatom biomass as function of chlorophyll a concentration, and this novel information enables improved methods to detect diatoms from space. The findings of this thesis are relevant to large-scale studies of ocean ecosystems and are critical for algorithm development using both current and upcoming earth-observing satellite data. Additionally, the results presented here provide tools that will benefit oceanographic research on spatial scales relevant to a changing ocean climate

Plankton imagery data inform satellite-based estimates of diatom carbon

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chase, A. P., Boss, E. S., Haentjens, N., Culhane, E., Roesler, C., & Karp-Boss, L. Plankton imagery data inform satellite-based estimates of diatom carbon. Geophysical Research Letters, 49(13), (2022): e2022GL098076, https://doi.org/10.1029/2022GL098076.Estimating the biomass of phytoplankton communities via remote sensing is a key requirement for understanding global ocean ecosystems. Of particular interest is the carbon associated with diatoms given their unequivocal ecological and biogeochemical roles. Satellite-based algorithms often rely on accessory pigment proxies to define diatom biomass, despite a lack of validation against independent diatom biomass measurements. We used imaging-in-flow cytometry to quantify diatom carbon in the western North Atlantic, and compared results to those obtained from accessory pigment-based approximations. Based on this analysis, we offer a new empirical formula to estimate diatom carbon concentrations from chlorophyll a. Additionally, we developed a neural network model in which we integrated chlorophyll a and environmental information to estimate diatom carbon distributions in the western North Atlantic. The potential for improving satellite-based diatom carbon estimates by integrating environmental information into a model, compared to models that are based solely on chlorophyll a, is discussed.Funding for this work was provided by NASA grants #NNX15AE67G and #80NSSC20M0202. A. Chase is supported by a Washington Research Foundation Postdoctoral Fellowship

Assessing the skill of a high-resolution marine biophysical model using geostatistical analysis of mesoscale ocean chlorophyll variability from field observations and remote sensing

© The Author(s), 2021. This article is distributed under the terms of the Creaive Commons Attribution License. The definitive version was published in Eveleth, R., Glover, D. M., Long, M. C., Lima, I. D., Chase, A. P., & Doney, S. C. . Assessing the skill of a high-resolution marine biophysical model using geostatistical analysis of mesoscale ocean chlorophyll variability from field observations and remote sensing. Frontiers in Marine Science, 8, (2021): 612764, https://doi.org/10.3389/fmars.2021.612764.High-resolution ocean biophysical models are now routinely being conducted at basin and global-scale, opening opportunities to deepen our understanding of the mechanistic coupling of physical and biological processes at the mesoscale. Prior to using these models to test scientific questions, we need to assess their skill. While progress has been made in validating the mean field, little work has been done to evaluate skill of the simulated mesoscale variability. Here we use geostatistical 2-D variograms to quantify the magnitude and spatial scale of chlorophyll a patchiness in a 1/10th-degree eddy-resolving coupled Community Earth System Model simulation. We compare results from satellite remote sensing and ship underway observations in the North Atlantic Ocean, where there is a large seasonal phytoplankton bloom. The coefficients of variation, i.e., the arithmetic standard deviation divided by the mean, from the two observational data sets are approximately invariant across a large range of mean chlorophyll a values from oligotrophic and winter to subpolar bloom conditions. This relationship between the chlorophyll a mesoscale variability and the mean field appears to reflect an emergent property of marine biophysics, and the high-resolution simulation does poorly in capturing this skill metric, with the model underestimating observed variability under low chlorophyll a conditions such as in the subtropics.This work was supported in part by the National Aeronautics and Space Administration (NASA) as part of the North Atlantic Aerosol and Marine Ecosystems Study (NAAMES; NASA grant 80NSSC18K0018). The CESM project is supported by the National Science Foundation and the Office of Science (BER) of the United States Department of Energy. Computing resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory (CISL), sponsored by the National Science Foundation and other agencies. This research was enabled by CISL compute and storage resources

Evaluation of diagnostic pigments to estimate phytoplankton size classes

Limnology and Oceanography: Methods published by Wiley Periodicals LLC. on behalf of Association for the Sciences of Limnology and Oceanography. Phytoplankton accessory pigments are commonly used to estimate phytoplankton size classes, particularly during development and validation of biogeochemical models and satellite ocean color-based algorithms. The diagnostic pigment analysis (DPA) is based on bulk measurements of pigment concentrations and relies on assumptions regarding the presence of specific pigments in different phytoplankton taxonomic groups. Three size classes are defined by the DPA: picoplankton, nanoplankton, and microplankton. Until now, the DPA has not been evaluated against an independent approach that provides phytoplankton size calculated on a per-cell basis. Automated quantitative cell imagery of microplankton and some nanoplankton, used in combination with conventional flow cytometry for enumeration of picoplankton and nanoplankton, provide a novel opportunity to perform an independent evaluation of the DPA. Here, we use a data set from the North Atlantic Ocean that encompasses all seasons and a wide range of chlorophyll concentrations (0.18–5.14 mg m−3). Results show that the DPA overestimates microplankton and picoplankton when compared to cytometry data, and subsequently underestimates the contribution of nanoplankton to total biomass. In contrast to the assumption made by the DPA that the microplankton size class is largely made up of diatoms and dinoflagellates, imaging-in-flow cytometry shows significant presence of diatoms and dinoflagellates in the nanoplankton size class. Additionally, chlorophyll b is commonly attributed solely to picoplankton by the DPA, but Chl b-containing phytoplankton are observed with imaging in both nanoplankton and microplankton size classes. We suggest revisions to the DPA equations and application of uncertainties when calculating size classes from diagnostic pigments

Inversion of Multiangular Polarimetric Measurements Over Open and Coastal Ocean Waters: A Joint Retrieval Algorithm for Aerosol and Water-Leaving Radiance Properties

Ocean color remote sensing is a challenging task over coastal waters due to the complex optical properties of aerosols and hydrosols. In order to conduct accurate atmospheric correction, we previously implemented a joint retrieval algorithm, hereafter referred to as the Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm, to obtain the aerosol and water-leaving signal simultaneously. The MAPOL algorithm has been validated with synthetic data generated by a vector radiative transfer model, and good retrieval performance has been demonstrated in terms of both aerosol and ocean water optical properties (Gao et al., 2018). In this work we applied the algorithm to airborne polarimetric measurements from the Research Scanning Polarimeter (RSP) over both open and coastal ocean waters acquired in two field campaigns: the Ship-Aircraft Bio-Optical Research (SABOR) in 2014 and the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) in 2015 and 2016. Two different yet related bio-optical models are designed for ocean water properties. One model aligns with traditional open ocean water bio-optical models that parameterize the ocean optical properties in terms of the concentration of chlorophyll a. The other is a generalized bio-optical model for coastal waters that includes seven free parameters to describe the absorption and scattering by phytoplankton, colored dissolved organic matter, and nonalgal particles. The retrieval errors of both aerosol optical depth and the water-leaving radiance are evaluated. Through the comparisons with ocean color data products from both in situ measurements and the Moderate Resolution Imaging Spectroradiometer (MODIS), and the aerosol product from both the High Spectral Resolution Lidar (HSRL) and the Aerosol Robotic Network (AERONET), the MAPOL algorithm demonstrates both flexibility and accuracy in retrieving aerosol and water-leaving radiance properties under various aerosol and ocean water conditions

Small phytoplankton dominate western North Atlantic biomass

The North Atlantic phytoplankton spring bloom is the pinnacle in an annual cycle that is driven by physical, chemical, and biological seasonality. Despite its important contributions to the global carbon cycle, transitions in plankton community composition between the winter and spring have been scarcely examined in the North Atlantic. Phytoplankton composition in early winter was compared with latitudinal transects that captured the subsequent spring bloom climax. Amplicon sequence variants (ASVs), imaging flow cytometry, and flow-cytometry provided a synoptic view of phytoplankton diversity. Phytoplankton communities were not uniform across the sites studied, but rather mapped with apparent fidelity onto subpolar- and subtropical-influenced water masses of the North Atlantic. At most stations, cells < 20-µm diameter were the main contributors to phytoplankton biomass. Winter phytoplankton communities were dominated by cyanobacteria and pico-phytoeukaryotes. These transitioned to more diverse and dynamic spring communities in which pico- and nano-phytoeukaryotes, including many prasinophyte algae, dominated. Diatoms, which are often assumed to be the dominant phytoplankton in blooms, were contributors but not the major component of biomass. We show that diverse, small phytoplankton taxa are unexpectedly common in the western North Atlantic and that regional influences play a large role in modulating community transitions during the seasonal progression of blooms

OOI Biogeochemical Sensor Data: Best Practices and User Guide. Version 1.0.0.

The OOI Biogeochemical Sensor Data Best Practices and User Guide is intended to provide current and prospective users of data generated by biogeochemical sensors deployed on the Ocean Observatories Initiative (OOI) arrays with the information and guidance needed for them to ensure that the data is science-ready. This guide is aimed at researchers with an interest or some experience in ocean biogeochemical processes. We expect that users of this guide will have some background in oceanography, however we do not assume any prior experience working with biogeochemical sensors or their data. While initially envisioned as a “cookbook” for end users seeking to work with OOI biogeochemical (BGC) sensor data, our Working Group and Beta Testers realized that the processing required to meet the specific needs of all end users across a wide range of potential scientific applications and combinations of OOI BGC data from different sensors and platforms couldn’t be synthesized into a single “recipe”. We therefore provide here the background information and principles needed for the end user to successfully identify and understand all the available “ingredients” (data), the types of “cooking” (end user processing) that are recommended to prepare them, and a few sample “recipes” (worked examples) to support end users in developing their own “recipes” consistent with the best practices presented here. This is not intended to be an exhaustive guide to each of these sensors, but rather a synthesis of the key information to support OOI BGC sensor data users in preparing science-ready data products. In instances when more in-depth information might be helpful, references and links have been provided both within each chapter and in the Appendix

Synaptic Tau Seeding Precedes Tau Pathology in Human Alzheimer's Disease Brain

Alzheimer's disease (AD) is defined by the presence of intraneuronal neurofibrillary tangles (NFTs) composed of hyperphosphorylated tau aggregates as well as extracellular amyloid-beta plaques. The presence and spread of tau pathology through the brain is classified by Braak stages and thought to correlate with the progression of AD. Several in vitro and in vivo studies have examined the ability of tau pathology to move from one neuron to the next, suggesting a “prion-like” spread of tau aggregates may be an underlying cause of Braak tau staging in AD. Using the HEK293 TauRD-P301S-CFP/YFP expressing biosensor cells as a highly sensitive and specific tool to identify the presence of seed competent aggregated tau in brain lysate—i.e., tau aggregates that are capable of recruiting and misfolding monomeric tau—, we detected substantial tau seeding levels in the entorhinal cortex from human cases with only very rare NFTs, suggesting that soluble tau aggregates can exist prior to the development of overt tau pathology. We next looked at tau seeding levels in human brains of varying Braak stages along six regions of the Braak Tau Pathway. Tau seeding levels were detected not only in the brain regions impacted by pathology, but also in the subsequent non-pathology containing region along the Braak pathway. These data imply that pathogenic tau aggregates precede overt tau pathology in a manner that is consistent with transneuronal spread of tau aggregates. We then detected tau seeding in frontal white matter tracts and the optic nerve, two brain regions comprised of axons that contain little to no neuronal cell bodies, implying that tau aggregates can indeed traverse along axons. Finally, we isolated cytosolic and synaptosome fractions along the Braak Tau Pathway from brains of varying Braak stages. Phosphorylated and seed competent tau was significantly enriched in the synaptic fraction of brain regions that did not have extensive cellular tau pathology, further suggesting that aggregated tau seeds move through the human brain along synaptically connected neurons. Together, these data provide further evidence that the spread of tau aggregates through the human brain along synaptically connected networks results in the pathogenesis of human Alzheimer's disease

Phytoplankton composition from sPACE: Requirements, opportunities, and challenges

Ocean color satellites have provided a synoptic view of global phytoplankton for over 25 years through near surface measurements of the concentration of chlorophyll a. While remote sensing of ocean color has revolutionized our understanding of phytoplankton and their role in the oceanic and freshwater ecosystems, it is important to consider both total phytoplankton biomass and changes in phytoplankton community composition in order to fully understand the dynamics of the aquatic ecosystems. With the upcoming launch of NASA\u27s Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission, we will be entering into a new era of global hyperspectral data, and with it, increased capabilities to monitor phytoplankton diversity from space. In this paper, we analyze the needs of the user community, review existing approaches for detecting phytoplankton community composition in situ and from space, and highlight the benefits that the PACE mission will bring. Using this three-pronged approach, we highlight the challenges and gaps to be addressed by the community going forward, while offering a vision of what global phytoplankton community composition will look like through the “eyes” of PACE

Collaborative Sociological Practice: the Case of Nine Urban Biotopes

This paper examines the socially engaged art project Nine Urban Biotopes (9UB), an international exchange between European and South African cultural organisations. Two artist residencies offer case studies of collaborative arts and research practice. The ways that these case studies are read as ‘failures’ and ‘successes’ illustrate the complexities of North- South collaborations. This project, the partnership that sustained it and the residencies that were central to it, exemplify, in modest ways, how public sociology can be realised in modest ways in a global context. This paper shows, with examples, that whilst partnership and collaboration are emphasised in institutional and policy discourse, in practice these arrangements are filled with tension and unequal power relations between partners. An evaluative methodology premised on sociological practice allows the tensions that are inherent in partnership and collaboration to be recognised and productively interrogated. It also allows us to reimagine what ‘success’ and ‘failure’ looks like in research partnerships by working with the antagonisms that are integral to collaboration