Concentration and distribution of phytoplankton nitrogen and carbon in the Northwest Atlantic and Indian Ocean: A simple model with applications in satellite remote sensing

This is the final version. Available on open access from Frontiers Media via the DOI in this record Data availability statement: The in-situ datasets and code used for data processing can be found in the following GitHub repository https://github.com/rjbrewin/POC-PON-TChl-analysis. This includes an...

Full description

Bibliographic Details
Published in:Frontiers in Marine Science
Main Authors: Maniaci, G, Brewin, RJW, Sathyendranath, S
Format: Article in Journal/Newspaper
Language:English
Published: Frontiers Media 2022
Subjects:
Online Access:http://hdl.handle.net/10871/131773
https://doi.org/10.3389/fmars.2022.1035399
Description
Summary:This is the final version. Available on open access from Frontiers Media via the DOI in this record Data availability statement: The in-situ datasets and code used for data processing can be found in the following GitHub repository https://github.com/rjbrewin/POC-PON-TChl-analysis. This includes an Jupyter Notebook Python Script, that can be run through binder (https://mybinder.org) without having to install Python software. Datasets from satellite observations of ocean colour are publicly accessible from https://www.oceancolour.org. Despite the critical role phytoplankton play in marine biogeochemical cycles, direct methods for determining the content of two key elements in natural phytoplankton samples, nitrogen (N) and carbon (C), remain difficult, and such observations are sparse. Here, we extend an existing approach to derive phytoplankton N and C indirectly from a large dataset of in-situ particulate N and C, and Turner fluorometric chlorophyll-a (Chl-a), gathered in the off-shore waters of the Northwest Atlantic and the Arabian Sea. This method uses quantile regression (QR) to partition particulate C and N into autotrophic and non-autotrophic fractions. Both the phytoplankton C and N estimates were combined to compute the C:N ratio. The algal contributions to total N and C increased with increasing Chl-a, whilst the C:N ratio decreased with increasing Chl-a. However, the C:N ratio remained close to the Redfield ratio over the entire Chl-a range. Five different phytoplankton taxa within the samples were identified using data from high-performance liquid chromatography pigment analysis. All algal groups had a C:N ratio higher than Redfield, but for diatoms, the ratio was closer to the Redfield ratio, whereas for Prochlorococcus, other cyanobacteria and green algae, the ratio was significantly higher. The model was applied to remotely-sensed estimates of Chl-a to map the geographical distribution of phytoplankton C, N, and C:N in the two regions from where the data were acquired. Estimates of ...