Loading [Contrib]/a11y/accessibility-menu.js

A flexible multivariable model for Phytoplankton growth

  • 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

    Related Papers:

    [1] Zhenyao Sun, Da Song, Meng Fan . Dynamics of a stoichiometric phytoplankton-zooplankton model with season-driven light intensity. Mathematical Biosciences and Engineering, 2024, 21(8): 6870-6897. doi: 10.3934/mbe.2024301
    [2] Xin Zhao, Lijun Wang, Pankaj Kumar Tiwari, He Liu, Yi Wang, Jianbing Li, Min Zhao, Chuanjun Dai, Qing Guo . Investigation of a nutrient-plankton model with stochastic fluctuation and impulsive control. Mathematical Biosciences and Engineering, 2023, 20(8): 15496-15523. doi: 10.3934/mbe.2023692
    [3] Lidan Liu, Meng Fan, Yun Kang . Effect of nutrient supply on cell size evolution of marine phytoplankton. Mathematical Biosciences and Engineering, 2023, 20(3): 4714-4740. doi: 10.3934/mbe.2023218
    [4] Ming Chen, Meng Fan, Xing Yuan, Huaiping Zhu . Effect of seasonal changing temperature on the growth of phytoplankton. Mathematical Biosciences and Engineering, 2017, 14(5&6): 1091-1117. doi: 10.3934/mbe.2017057
    [5] Jean-Jacques Kengwoung-Keumo . Dynamics of two phytoplankton populations under predation. Mathematical Biosciences and Engineering, 2014, 11(6): 1319-1336. doi: 10.3934/mbe.2014.11.1319
    [6] Jean-Jacques Kengwoung-Keumo . Competition between a nonallelopathic phytoplankton and an allelopathic phytoplankton species under predation. Mathematical Biosciences and Engineering, 2016, 13(4): 787-812. doi: 10.3934/mbe.2016018
    [7] Saswati Biswas, Pankaj Kumar Tiwari, Yun Kang, Samares Pal . Effects of zooplankton selectivity on phytoplankton in an ecosystem affected by free-viruses and environmental toxins. Mathematical Biosciences and Engineering, 2020, 17(2): 1272-1317. doi: 10.3934/mbe.2020065
    [8] Sambasiva Rao Katuri, Rajesh Khanna . Kinetic growth model for hairy root cultures. Mathematical Biosciences and Engineering, 2019, 16(2): 553-571. doi: 10.3934/mbe.2019027
    [9] Alexis Erich S. Almocera, Sze-Bi Hsu, Polly W. Sy . Extinction and uniform persistence in a microbial food web with mycoloop: limiting behavior of a population model with parasitic fungi. Mathematical Biosciences and Engineering, 2019, 16(1): 516-537. doi: 10.3934/mbe.2019024
    [10] Ruiqing Shi, Jianing Ren, Cuihong Wang . Stability analysis and Hopf bifurcation of a fractional order mathematical model with time delay for nutrient-phytoplankton-zooplankton. Mathematical Biosciences and Engineering, 2020, 17(4): 3836-3868. doi: 10.3934/mbe.2020214
  • 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.


    [1] J. Plankton Research 23, (2001), 840-2001.
    [2] New Phytol., 181 (2009), 295-309.
    [3] in "2005 Proceedings of the American Statistical Association Biometrics Section [CD-ROM]" (V. A. Alexandria), (2006), American Statistical Association, 190-197.
    [4] J. Mar. Biol. Assoc. UK, 54 (1974), 825-855.
    [5] Hydrobiologia, 555 (2006), 3-18.
    [6] BMC Cancer, 10 (2010), pp. 509.
    [7] Mar. Ecol. Prog. Ser., 80 (1992), 285-290.
    [8] J. Plankton Research, 23 (2001), 977-997.
    [9] Plant Physiol, 144 (2007), 54-59.
    [10] Limnol. Oceanogr., 35 (1990), 971-972.
    [11] J. Phycol., 39 (2003), 1145-1159.
    [12] J. Plankton Research, 10 (1991), 163-172.
    [13] Bull. Math. Biol., 55 (1993), 259-275.
    [14] Bioprocess Biosyst. Eng., 27 (2005), 319-327.
    [15] J. Theor. Biol., 259 (2009), 582-588.
    [16] Annales de l'Inst. Pasteur, 79 (1950), 390-410.
    [17] Fourth Edition. Addison Wesley Publishing Company, 1996, Reading, MA.
    [18] J. Plankton Res., 12 (1990), 1207-1221.
    [19] Estuaries, 22 (1999), 92-104.
    [20] New Zealand Journal of Marine and Freshwater Research, 37 (2003), 267-272.
    [21] Limnol. Oceanogr., 34 (1989), 198-205.
    [22] PNAS, 99 (2002), 8101-8105.
    [23] Med. & Biol. Eng. & Comp., 49 (2011), 253-262.
    [24] Mathematical and Computer Modelling, 53 (2011), 755-768.
    [25] Theoretical Biology and Medical Modeling, 2 (2005), 1-13.
    [26] Japan. J. Plankton Res., 8 (1986), 1039-1049.
    [27] Limnology and Oceanography, 28 (1983), 1144-1155.
  • 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
  • Reader Comments
  • © 2013 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2943) PDF downloads(586) Cited by(3)

Article outline

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog