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

2022 ◽  
Vol 18 (1) ◽  
pp. e1009733
Author(s):  
Jann Paul Mattern ◽  
Kristof Glauninger ◽  
Gregory L. Britten ◽  
John R. Casey ◽  
Sangwon Hyun ◽  
...  

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.

2021 ◽  
Author(s):  
Jann Paul Mattern ◽  
Kristof Glauninger ◽  
Gregory L Britten ◽  
John Casey ◽  
Sangwon Hyun ◽  
...  

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.


2021 ◽  
Author(s):  
Eberhard Voit ◽  
Jacob Davis ◽  
Daniel Olivenca

Abstract For close to a century, Lotka-Volterra (LV) models have been used to investigate interactions among populations of different species. For a few species, these investigations are straightforward. However, with the arrival of large and complex microbiomes, unprecedently rich data have become available and await analysis. In particular, these data require us to ask which microbial populations of a mixed community affect other populations, whether these influences are activating or inhibiting and how the interactions change over time. Here we present two new inference strategies for interaction parameters that are based on a new algebraic LV inference (ALVI) method. One strategy uses different survivor profiles of communities grown under similar conditions, while the other pertains to time series data. In addition, we address the question of whether observation data are compliant with the LV structure or require a richer modeling format.


2019 ◽  
Vol 37 (4) ◽  
pp. 461-468 ◽  
Author(s):  
David S. Fischer ◽  
Anna K. Fiedler ◽  
Eric M. Kernfeld ◽  
Ryan M. J. Genga ◽  
Aimée Bastidas-Ponce ◽  
...  

2007 ◽  
Vol 64 (6) ◽  
pp. 899-910 ◽  
Author(s):  
Eric J Ward ◽  
Ray Hilborn ◽  
Rod G Towell ◽  
Leah Gerber

Catastrophic events are considered a major contributor to extinction threats, yet are rarely explicitly estimated in population models. We extend the basic state–space population dynamics model to include a mixture distribution for the process error. The mixture distribution consists of a "normal" component, representing regular process error variability, and a "catastrophic" component, representing rare events that negatively affect the population. Direct estimation of parameters is rarely possible using a single time series; however, estimation is possible when time series are combined in hierarchical models. We apply the catastrophic state–space model to simulated time series of abundance from simple, nonlinear population dynamics models. Applications of the model to these simulated time series indicate that population parameters (such as the carrying capacity or growth rate) and observation and process errors are estimated robustly when appropriate time series are available. Our simulations indicate that the power to detect a catastrophe is also a function of the strength of catastrophes and the magnitude of observation and process errors. To illustrate one potential application of this model, we apply the state–space catastrophic model to four west coast populations of northern fur seals (Callorhinus ursinus).


Ecosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
Author(s):  
Brenda J. Hanley ◽  
André A. Dhondt ◽  
Brian Dennis ◽  
Krysten L. Schuler

2020 ◽  
Author(s):  
Hung Chak Ho ◽  
Guangqing Chi

Abstract. Land vulnerability and development can be restricted by both land policy and geophysical limits. Land vulnerability and development cannot be simply quantified by land cover/use change, because growth related to population dynamics is not horizontal. Particularly, time-series data with a higher flexibility considering the ability of land to be developed should be used to identify areas of spatiotemporal change. By considering the policy aspects of land development, this approach will allow one to further identify the lands facing population stress, socioeconomic burdens, and health risks. Here the concept of “land developability” is expanded to include policy-driven factors and land vulnerability to better reconcile developability with socio-environmental justice. The first phrase of policy-driven land developability mapping is implemented in estimating land information across the contiguous United States in 2001, 2006, and 2011. Multiscale data products for state-, county- and census-tract-levels are provided from this estimation. The extension of this approach can be applied to other countries with modifications for their specific scenarios. The data generated from this work are available at https://doi.org/10.7910/DVN/AMZMWH (Chi and Ho, 2019).


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