Plankton Imagery Data Inform Satellite‐Based Estimates of Diatom Carbon

Abstract

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.

How many independent quantities can be extracted from ocean color?

“…UV data in particular is potentially rich with information about phytoplankton physiology and community structure, and independent of variability at other wavelengths, though there will be challenges associated with simultaneously using it for atmospheric correction and extracting biogeochemical information, and not enough data exist at present for reliable statistical analysis. In addition, it has been shown that adding other environmental variables such as SST can add useful information to inversions of phytoplantkon groups, for example, Chase et al (2022) and thus another approach to increase DoF for inversions by adding relevant and independent information (e.g., mixed-layer depth and nutrients from BGC-Argo assimilating models). It may also be fruitful to include spatiotemporal information to specify, for example, where blooms of a particular plankton type are expected.…”

Synergies Between NASA's Hyperspectral Aquatic Missions PACE, GLIMR, and SBG: Opportunities for New Science and Applications

“…PACE will observe such vast changes in phytoplankton across the Pacific Ocean during transitions between El Niño, La Niña and neutral conditions at the relevant spatial coverage from tens to thousands of kilometers and timescales from weekly to seasonal and interannual. Having near‐daily global hyperspectral imagery from PACE will allow us to observe the interplay not just of phytoplankton “biomass” estimated from chlorophyll‐ a , but of phytoplankton communities (Chase et al., 2022) leading to new understanding of biological‐physical coupling and trophic dynamics across the vast coastal and open ocean.…”

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

“…Most accessory pigments are shared between phytoplankton groups (Jeffrey et al, 2011 and references therein), making “biomarker” pigments imprecise identifiers for taxonomy. However, some pigments are used as biomarkers throughout the literature despite extensive documentation that these pigments are not limited to one taxonomic group (e.g., Fuco for diatoms; e.g., Jeffrey et al, 2011 and references therein; Chase et al, 2020, 2022). In the presentation to follow, we used commonly-applied pigment-based taxonomic designations to compare these biomarker approaches to other, higher-resolution methods.…”

“…The assumptions inherent in some methods limit the interpretation of the results, such as the challenge of unequal copy numbers of the 16S and 18S genes across taxa (e.g., Godhe et al, 2008; de Vargas et al, 2015; Needham and Fuhrman, 2016). Comparisons between and among PCC methods are relatively rare, and reveal variability when different methods are compared (e.g., Not et al, 2008; Couple et al, 2015; Gong et al, 2020; Campbell et al, 2022; Chase et al, 2022; Catlett et al, 2022; Nardelli et al, 2023). In one example, amplicon sequencing and light microscopy each provide high resolution taxonomic information for larger phytoplankton, but abundance patterns did not agree in genus- to species-level comparisons (Abad et al, 2016).…”