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

Area of Science:

• Microbial ecology
• Marine phytoplankton dynamics
• Biogeochemical cycling

Background:

• Microbial growth, division, and carbon loss are crucial for understanding environmental interactions and the carbon cycle.
• Current analytical methods for quantifying these microbial parameters are often invasive.
• Size-structured matrix population models (MPMs) are increasingly used to estimate microbial division rates by analyzing cell size distribution changes.

Purpose of the Study:

• To extend size-structured MPMs using a Bayesian approach to incorporate additional biological processes.
• To model the dynamics of marine phytoplankton populations over a day-night cycle.
• To improve the quantification of microbial population parameters and carbon loss.

Main Methods:

• Developed a Bayesian framework to extend size-structured MPMs.
• Integrated prior scientific knowledge into the modeling process.
• Applied the framework to data from a laboratory culture of Prochlorococcus.

Main Results:

• Successfully isolated respiratory and exudative carbon losses as critical parameters for population dynamics.
• Demonstrated the ability of the Bayesian framework to generate biologically interpretable results.
• Validated the model's utility with Prochlorococcus growth data.

Conclusions:

• The enhanced Bayesian size-structured MPM framework provides deeper insights into microbial population dynamics.
• This approach offers a powerful tool for analyzing microbial size distribution time-series data.
• The model facilitates a better understanding of microbial contributions to the carbon cycle.