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Time-dependent probability density function for general stochastic logistic population model with harvesting effort

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  • Otunuga, Olusegun Michael

Abstract

We derive and analyze the time-dependent probability density function for the number of individuals in a population at a given time in a general logistic population model with harvesting effort using the Fokker–Planck equation. The time-dependent probability density function (obtained as the unique principal solution of the Fokker–Planck equation corresponding to certain initial value and boundary conditions) is used to describe how the distribution of the population process changes with time. We assume the environment is randomly varying and the population is subject to a continuous spectrum of disturbances, with fluctuations in the intrinsic growth rate and the harvesting effort. The randomness is expressed as independent white noise processes. The effect of changes in the intrinsic growth rate, harvesting effort, and noise intensities on the distribution is investigated. In addition, conditions for the existence of optimal harvesting policy are obtained using properties of the time-dependent probability density function. The results obtained in this work are validated using population and published parameters.

Suggested Citation

  • Otunuga, Olusegun Michael, 2021. "Time-dependent probability density function for general stochastic logistic population model with harvesting effort," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    • Handle: RePEc:eee:phsmap:v:573:y:2021:i:c:s037843712100203x
    DOI: 10.1016/j.physa.2021.125931
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    References listed on IDEAS

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    1. Bulsara, A.R. & Lindenberg, K. & Seshadri, V. & Shuler, K.E. & West, B.J., 1979. "Stochastic processes with non-additive fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 97(2), pages 234-243.
    2. West, B.J. & Bulsara, A.R. & Lindenberg, K. & Seshadri, V. & Shuler, K.E., 1979. "Stochastic processes with non-additive fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 97(2), pages 211-233.
    3. Yang, Bin & Cai, Yongli & Wang, Kai & Wang, Weiming, 2019. "Optimal harvesting policy of logistic population model in a randomly fluctuating environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
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    Cited by:

    1. Peña, Guillermo & Puente-Ajovín, Miguel & Ramos, Arturo & Sanz-Gracia, Fernando, 2022. "Log-growth rates of CO2: An empirical analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    2. Arturo Ramos & Till Massing & Atushi Ishikawa & Shouji Fujimoto & Takayuki Mizuno, 2023. "Composite distributions in the social sciences: A comparative empirical study of firms' sales distribution for France, Germany, Italy, Japan, South Korea, and Spain," Papers 2301.09438, arXiv.org.
    3. Băncescu, Irina & Chivu, Luminiţa & Massing, Till & Preda, Vasile & Puente-Ajovín, Miguel & Ramos, Arturo, 2024. "On the parametric description of log-growth rates of Romanian city sizes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).
    4. Cortés, J.-C. & Moscardó-García, A. & Villanueva, R.-J., 2022. "Uncertainty quantification for hybrid random logistic models with harvesting via density functions," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).

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