Britten, G. L. | bioRxiv Science

bioRxiv2023

Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass

Despite phytoplankton contributing roughly half of the photosynthesis on earth and fueling marine food-webs, field measurements of phytoplankton biomass remain scarce. The particulate backscattering coefficient (bbp) has often been used as an optical proxy to estimate phytoplankton carbon biomass (Cphyto). However, total observed bbp is impacted by phytoplankton size, cell composition, and non-algal particles. The lack of phytoplankton field data has prevented the quantification of uncertainties driven by these factors. Here, we first review and discuss existing bbp algorithms by applying them to bbp data from the BGC-Argo array in surface waters (<10m). We find a bbp threshold where estimated Cphyto differs by more than an order of magnitude. Next, we use a global ocean circulation model (the MITgcm Biogeochemical and Optical model) that simulates plankton dynamics and associated inherent optical properties to quantify and understand uncertainties from bbp-based algorithms in surface waters. We do so by developing and calibrating an algorithm to the model. Simulated error-estimations show that bbp-based algorithms overestimate/underestimate Cphyto between 5% and 100% in surface waters, depending on the location and time. This is achieved in the ideal scenario where Cphyto and bbp are known precisely. This is not the case for algorithms derived from observations, where the largest source of uncertainty is the scarcity of phytoplankton biomass data and related methodological inconsistencies. If these other uncertainties are reduced, the model shows that bbp could be a relatively good proxy for phytoplankton carbon biomass, with errors close to 20% in most regions. Plain Language SummaryPhytoplankton contribute roughly half of the photosynthesis on earth and fuel fisheries around the globe. Yet, few direct measurements of phytoplankton concentration are available. Frequently, concentrations of phytoplankton are instead estimated using the optical properties of water. Backscattering is one of these optical properties, representing the light being scattered backwards. Previous studies have suggested that backscattering could be a good method to estimate phytoplankton concentration. However, other particles that are present in the ocean also contribute to backscattering. In this paper we examine how well backscattering can be used to estimate phytoplankton. To address this question, we use data from drifting instruments that are spread across the ocean and a computer model that simulates phytoplankton and backscattering over the global oceans. We find that by using backscattering, phytoplankton can be overestimated/underestimated on average by [~]20%. This error differs between regions, and can be larger than 100% at high latitudes. Computer simulations allowed us to quantify spatial and temporal variability in backscattering signal composition, and thereby understand potential errors in inferring phytoplankton with backscattering, which could not have been done before due to the lack of phytoplankton data. Key PointsO_LIPhytoplankton carbon bbp-based algorithms can differ up to an order of magnitude at low bbp values. C_LIO_LIAn algorithm fitted to a global model output shows biases ranging between 15% and 40% in most regions. C_LIO_LIMost uncertainties are due to the relative contribution of phytoplankton to total bbp. C_LI

ecology↗

bioRxiv2021

A flexible Bayesian approach to estimating size-structured matrix population models

The rates of cell growth, division, and carbon loss of microbial populations are key parameters for understanding how organisms interact with their environment and how they contribute to the carbon cycle. However, the invasive nature of current analytical methods has hindered efforts to reliably quantify these parameters. In recent years, size-structured matrix population models (MPMs) have gained popularity for estimating rate parameters of microbial populations by mechanistically describing changes in microbial cell size distributions over time. And yet, the construction, analysis, and biological interpretation of these models are underdeveloped, as current implementations do not adequately constrain or assess the biological feasibility of parameter values, leading to inference which may provide a good fit to observed size distributions but does not necessarily reflect realistic physiological dynamics. Here we present a flexible Bayesian extension of size-structured MPMs for testing underlying assumptions describing the dynamics of a marine phytoplankton population over the day-night cycle. Our Bayesian framework takes prior scientific knowledge into account and generates biologically interpretable results. Using data from an exponentially growing laboratory culture of the cyanobacterium Prochlorococcus, we herein demonstrate the performance improvements of our approach over current models and isolate previously ignored biological processes, such as respiratory and exudative carbon losses, as critical parameters for the modeling of microbial population dynamics. The results demonstrate that this modeling framework can provide deeper insights into microbial population dynamics provided by flow-cytometry time-series data. Author summaryIdentifying the growth and population dynamics of marine microorganisms in their natural habitat is crucial to understanding the flow of carbon in the oceans but remains a grand challenge due to the invasive nature of current measurement methods. As time-series observations of population size structure have become more commonplace in aquatic environments, matrix population models (MPMs), which aim to mechanistically describe the change in size structure of these populations over time, have gained in popularity over the last decade. However, the underlying assumptions and behavior of MPMs have not been adequately scrutinized, and parameter values are difficult to interpret biologically, leading to inference that may not reflect plausible physiological dynamics. Here, we develop a Bayesian extension of the MPM framework to examine biological assumptions, improve interpretability of model output, and account for additional biological processes. We evaluated the performance of our models on a publicly available dataset of laboratory experiment time-series measurements of the cyanobacterium Prochlorococcus, Earths most abundant photosynthetic organisms, demonstrated the performance improvements of our approach over current models, and isolated previously ignored respiratory and exudative carbon losses as critical parameters for the modeling of microbial population dynamics.

microbiology↗