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Ali Chase

Senior Oceanographer






Ali Chase joined APL-UW in the summer of 2020 as a Washington Research Foundation Postdoctoral Fellow. She is collaborating with oceanographers Kyla Drushka and Peter Gaube as a member of the (Sub)mesoscale Group on several projects related to observing phytoplankton in the open ocean using optics and linking phytoplankton community composition to physical ocean features. Before beginning postdoc research in the summer of 2020, Chase earned a PhD at the University of Maine. Working there in the MISC Lab with Emmanuel Boss and Lee Karp-Boss, her dissertation was on methods to observe phytoplankton in the open ocean using optics, flow cytometry, and pigments.

During postdoc studies, Chase is working on algorithm development to detect phytoplankton community composition from ocean color satellite measurements. The approach to this challenge has two components: 1) estimate phytoplankton accessory pigment concentrations from hyperspectral remote-sensing reflectance spectra, and 2) use phytoplankton group information from imaging-in-flow cytometry to determine predictive relationships via neural networks between phytoplankton communities and ocean environmental parameters.


B.A. Geology and Environmental Studies, Bowdoin College, 2009

M.S. Oceanography, University of Maine, 2014

Ph.D. Oceanography, University of Maine, 2020

Ali Chase's Website



2000-present and while at APL-UW

A model for community-driven development of best practices: the Ocean Observatories Initiative Biogeochemical Sensor Data Best Practices and User Guide

Palevsky, H.I., and 38 others including A.P. Chase, "A model for community-driven development of best practices: the Ocean Observatories Initiative Biogeochemical Sensor Data Best Practices and User Guide," Front. Mar. Sci., 11, doi:10.3389/fmars.2024.1358591, 2024.

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3 Apr 2024

The field of oceanography is transitioning from data-poor to data-rich, thanks in part to increased deployment of in-situ platforms and sensors, such as those that instrument the US-funded Ocean Observatories Initiative (OOI). However, generating science-ready data products from these sensors, particularly those making biogeochemical measurements, often requires extensive end-user calibration and validation procedures, which can present a significant barrier. Openly available community-developed and -vetted Best Practices contribute to overcoming such barriers, but collaboratively developing user-friendly Best Practices can be challenging. Here we describe the process undertaken by the NSF-funded OOI Biogeochemical Sensor Data Working Group to develop Best Practices for creating science-ready biogeochemical data products from OOI data, culminating in the publication of the GOOS-endorsed OOI Biogeochemical Sensor Data Best Practices and User Guide. For Best Practices related to ocean observatories, engaging observatory staff is crucial, but having a "user-defined" process ensures the final product addresses user needs. Our process prioritized bringing together a diverse team and creating an inclusive environment where all participants could effectively contribute. Incorporating the perspectives of a wide range of experts and prospective end users through an iterative review process that included "Beta Testers"’ enabled us to produce a final product that combines technical information with a user-friendly structure that illustrates data analysis pipelines via flowcharts and worked examples accompanied by pseudo-code. Our process and its impact on improving the accessibility and utility of the end product provides a roadmap for other groups undertaking similar community-driven activities to develop and disseminate new Ocean Best Practices.

Phytoplankton composition from sPACE: Requirements, opportunities, and challenges

Cetinic, I., and 28 others including A.P. Chase and P. Gaube, "Phytoplankton composition from sPACE: Requirements, opportunities, and challenges," Remote Sens. Environ., 302, doi:10.1016/j.rse.2023.113964, 2024.

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1 Mar 2024

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's 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.

Toward a synthesis of phytoplankton communities composition methods for global-scale application

Kramer, S.J., and 14 others including A.P. Chase, "Toward a synthesis of phytoplankton communities composition methods for global-scale application," Linmol. Oceanogr. Methods, EOR, doi:10.1002/lom3.10602, 2024.

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23 Feb 2024

The composition of the marine phytoplankton community has been shown to impact many biogeochemical processes and marine ecosystem services. A variety of methods exist to characterize phytoplankton community composition (PCC), with varying degrees of taxonomic resolution. Accordingly, the resulting PCC determinations are dependent on the method used. Here, we use surface ocean samples collected in the North Atlantic and North Pacific Oceans to compare high-performance liquid chromatography pigment-based PCC to four other methods: quantitative cell imaging, flow cytometry, and 16S and 18S rRNA amplicon sequencing. These methods allow characterization of both prokaryotic and eukaryotic PCC across a wide range of size classes. PCC estimates of many taxa resolved at the class level (e.g., diatoms) show strong positive correlations across methods, while other groups (e.g., dinoflagellates) are not well captured by one or more methods. Since variations in phytoplankton pigment concentrations are related to changes in optical properties, this combined dataset expands the potential scope of ocean color remote sensing by associating PCC at the genus- and species-level with group- or class-level PCC from pigments. Quantifying the strengths and limitations of pigment-based PCC methods compared to PCC assessments from amplicon sequencing, imaging, and cytometry methods is the first step toward the robust validation of remote sensing approaches to quantify PCC from space.

More Publications

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center