A Bayesian approach to modeling phytoplankton population dynamics from size distribution time series

Abstract

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 division rates of microbial populations by mechanistically describing changes in microbial cell size distributions over time. Motivated by the mechanistic structure of these models, we employ a Bayesian approach to extend size-structured MPMs to capture additional biological processes describing the dynamics of a marine phytoplankton population over the day-night cycle. Our Bayesian framework is able to take prior scientific knowledge into account and generate biologically interpretable results. Using data from an exponentially growing laboratory culture of the cyanobacterium Prochlorococcus, we isolate respiratory and exudative carbon losses as critical parameters for the modeling of their population dynamics. The results suggest that this modeling framework can provide deeper insights into microbial population dynamics provided by size distribution time-series data.

Disentangling top-down drivers of mortality underlying diel population dynamics ofProchlorococcusin the North Pacific Subtropical Gyre

“…Unlike other modelling efforts focused on picoplankton which include generic loss terms, e.g. [2, 11, 13, 114, 115], ECLIP can be used to infer how losses are partitioned between grazing, lysis, and other processes. On the other hand ECLIP does not explicitly capture size-structured processes which are important drivers of growth [2, 11, 13, 114, 115] and other ecological interactions [17].…”

“…[2, 11, 13, 114, 115], ECLIP can be used to infer how losses are partitioned between grazing, lysis, and other processes. On the other hand ECLIP does not explicitly capture size-structured processes which are important drivers of growth [2, 11, 13, 114, 115] and other ecological interactions [17]. Additionally, light-driven forcing of division does not fully account for light-driven and diurnal-timescale variability in processes such as nutrient content [60, 116, 117], and metabolic state [60].…”

“…We implemented the parameter estimation in two steps. First, we estimated the three parameter posterior distributions ( µ ave , δ µ and δ t ) for the division rate function (see equation S19) using a modelinferred Prochlorococcus division rate estimated for the same oceanographic campaign by [27]. In a second step, we used the division parameter posteriors as priors for another parameter inference to estimate the whole set of model parameters ( µ ave , δ µ , δ t , m P , m V , m G , ϕ, β, LP , and Ψ ) and the abundance measurements for total and infected Prochlorococcus cells, cyanophages and heterotrophic grazers.…”

“…Highly resolved surface ocean observations suggest that diel light rhythms drive repeatable changes in the abundance of ubiquitous picocyanobacteria at the base of the marine microbial food web, including both Prochlorococcus and Synechococcus [1, 2]. Phytoplankton population dynamics are influenced by both nutrient and light availability [2–11] as well as by density- and size-dependent feedback processes with other components of the community [12–18]. These interactions lead to diel oscillations in ecological rates, including in grazing rates, viral abundances, viral infection rates, and viral activity [19–24].…”

“…This type of identifiability problem is inherent to prior efforts to model phytoplankton growth with a generic loss term, e.g. [2, 11–13]. The identifiability problem is compounded by the fact that daily rhythms of microbes are also entrained, in part, with oscillatory day-night light forcing.…”