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Mathematical Biosciences and Engineering

2013, Volume 10, Issue 3: 913-923. doi: 10.3934/mbe.2013.10.913
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A flexible multivariable model for Phytoplankton growth

  • 1.
    Department of Mathematical Sciences, Cameron University, Lawton, OK 73505
  • 2.
    School of Medicine, University of Alabama at Birmingham, Birmingham AL 35294
  • Received: 01 May 2012 Accepted: 29 June 2018 Published: 01 April 2013

  • MSC : 91B62, 62P10.

  • We introduce a new multivariable model to be used to studythe growth dynamics of phytoplankton as a function of both time and theconcentration of nutrients. This model is applied to a set of experimentaldata which describes the rate of growth as a function of these two variables.The form of the model allows easy extension to additional variables. Thus, themodel can be used to analyze experimental data regarding the effects ofvarious factors on phytoplankton growth rate. Such a model will also be usefulin analysis of the role of concentration of various nutrients or traceelements, temperature, and light intensity, or other important explanatoryvariables, or combinations of such variables, in analyzing phytoplanktongrowth dynamics.

    Citation: Mohammad A. Tabatabai, Wayne M. Eby, Sejong Bae, Karan P. Singh. A flexible multivariable model for Phytoplankton growth[J]. Mathematical Biosciences and Engineering, 2013, 10(3): 913-923. doi: 10.3934/mbe.2013.10.913

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  • We introduce a new multivariable model to be used to studythe growth dynamics of phytoplankton as a function of both time and theconcentration of nutrients. This model is applied to a set of experimentaldata which describes the rate of growth as a function of these two variables.The form of the model allows easy extension to additional variables. Thus, themodel can be used to analyze experimental data regarding the effects ofvarious factors on phytoplankton growth rate. Such a model will also be usefulin analysis of the role of concentration of various nutrients or traceelements, temperature, and light intensity, or other important explanatoryvariables, or combinations of such variables, in analyzing phytoplanktongrowth dynamics.


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  • This article has been cited by:

    1. Mohammad A. Tabatabai, Jean-Jacques Kengwoung-Keumo, Wayne M. Eby, Sejong Bae, Juliette T. Guemmegne, Upender Manne, Mona Fouad, Edward E. Partridge, Karan P. Singh, Kamaleshwar Singh, Disparities in Cervical Cancer Mortality Rates as Determined by the Longitudinal Hyperbolastic Mixed-Effects Type II Model, 2014, 9, 1932-6203, e107242, 10.1371/journal.pone.0107242
    2. Wayne M. Eby, Samuel O. Oyamakin, Angela U. Chukwu, A new nonlinear model applied to the height-DBH relationship in Gmelina arborea, 2017, 397, 03781127, 139, 10.1016/j.foreco.2017.04.015
    3. Jean-Jacques Kengwoung-Keumo, Dynamics of two phytoplankton populations under predation, 2014, 11, 1551-0018, 1319, 10.3934/mbe.2014.11.1319
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