Research article

Approximation of invariant measure for a stochastic population model with Markov chain and diffusion in a polluted environment

  • Received: 10 June 2020 Accepted: 03 September 2020 Published: 29 September 2020
  • In the paper, we propose a novel stochastic population model with Markov chain and diffusion in a polluted environment. Under the condition that the diffusion coefficient satisfies the local Lipschitz condition, we prove the existence and uniqueness of invariant measure for the model. Moreover, we also discuss the existence and uniqueness of numerical invariance measure for stochastic population model under the discrete-time Euler-Maruyama scheme, and prove that numerical invariance measure converges to the invariance measure of the corresponding exact solution in the Wasserstein distance sense. Finally, we give the numerical simulation to show the correctness of the theoretical results.

    Citation: Ting Kang, Yanyan Du, Ming Ye, Qimin Zhang. Approximation of invariant measure for a stochastic population model with Markov chain and diffusion in a polluted environment[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 6702-6719. doi: 10.3934/mbe.2020349

    Related Papers:

  • In the paper, we propose a novel stochastic population model with Markov chain and diffusion in a polluted environment. Under the condition that the diffusion coefficient satisfies the local Lipschitz condition, we prove the existence and uniqueness of invariant measure for the model. Moreover, we also discuss the existence and uniqueness of numerical invariance measure for stochastic population model under the discrete-time Euler-Maruyama scheme, and prove that numerical invariance measure converges to the invariance measure of the corresponding exact solution in the Wasserstein distance sense. Finally, we give the numerical simulation to show the correctness of the theoretical results.


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    [1] L. Duan, Q. Lu, Z. Yang, L. Chen, Effects of diffusion on a stage-structured population in a polluted environment, Appl. Math. Comput., 02 (2004), 347-359.
    [2] A. J. Shaw, Ecological genetics of plant populations in polluted environment. Ecological Genetics and Air Pollution, Springer New York, 1991.
    [3] G. P. Samanta, A. Maiti, Dynamical model of a single-species system in a polluted environment, J. Appl. Mathe. Comput., 16 (2004), 231-242. doi: 10.1007/BF02936164
    [4] T. G. Hallam, C. E. Clark, R. R. Lassiter, Effects of toxicants on populations: a qualitative approach I. Equilibrium environmental exposure, Ecol. Model., 18 (1983), 291-304. doi: 10.1016/0304-3800(83)90019-4
    [5] T. G. Hallam, C. E. Clar, G. S. Jordan, Effects of toxicant on population: a qualitative approach II. First Order Kinetics, J. Math. Biol., 109 (1983), 411-429.
    [6] D. Mukherjee, Persistence and global stability of a population in a polluted environment with delay, J. Biol. Syst., 10 (2008), 225-232.
    [7] Z. Ma, G. Cui, W. Wang, Persistence and extinction of a population in a polluted environment, Math. Biosci., 101 (2004), 75-97.
    [8] T. G. Hallam, Z. Ma, Persistence in Population models with demographic fluctuations, J. Math. Biol., 24 (1986), 327-339. doi: 10.1007/BF00275641
    [9] J. Pan, Z. Jin, Z. Ma, Thresholds of survival for an n-dimensional Volterra mutualistic system in a polluted environment, J. Math. Anal. Appl., 252 (2000), 519-531. doi: 10.1006/jmaa.2000.6853
    [10] Z. Ma, B. J. Song, T. G. Hallam, The threshold of survival for systems in a fluctuating environment, Bull. Math. Biol., 51 (1989), 311-323. doi: 10.1016/S0092-8240(89)80078-3
    [11] M. Liu, K. Wang, Persistence and extinction of a stochastic single-species population model in a polluted environment with impulsive toxicant input, Electron. J. Differ. Equ., 230 (2013), 823-840.
    [12] X. Yu, S. Yuan, T. Zhang, Persistence and ergodicity of a stochastic single species model with Allee effect under regime switching, Commun. Nonlinear Sci. Numer. Simul., 59 (2018), 359-374. doi: 10.1016/j.cnsns.2017.11.028
    [13] F. Wei, S. A. H. Geritz, J. Cai, A stochastic single-species population model with partial pollution tolerance in a polluted environment, Appl. Math. Lett., 63 (2017), 130-136. doi: 10.1016/j.aml.2016.07.026
    [14] M. Liu, K. Wang, Persistence and extinction of a stochastic single-specie model under regime switching in a polluted environment. J. Theor. Biol., 267 (2010), 283-291.
    [15] M. Liu, K. Wang, Survival analysis of a stochastic single-species population model with jumps in a polluted environment, Int. J. Biomath., 09 (2016), 1-15.
    [16] Y. Zhao, S. Yuan, Q. Zhang, The effect of Lévy noise on the survival of a stochastic competitive model in an impulsive polluted environment, Appl. Math. Model., 40 (2016), 7583-7600. doi: 10.1016/j.apm.2016.01.056
    [17] T. C. Gard, Stability for multi-species population models in random environments, Nonlinear Anal., 10 (1986), 1411-1419. doi: 10.1016/0362-546X(86)90111-2
    [18] J. Tong, Z. Zhang, J. Bao, The stationary distribution of the facultative population model with a degenerate noise, Discrete Continuous Dyn. Syst., 83 (2013), 655-664.
    [19] M. Liu, K. Wang, Y. Wang, Long term behaviors of stochastic single-species growth models in a polluted environment, Appl. Math. Model., 35 (2011), 4438-4448. doi: 10.1016/j.apm.2011.03.014
    [20] C. Yuan, X. Mao, Stationary distributions of Euler-Maruyama-type stochastic difference equations with Markovian switching and their convergence, J. Differ. Equ. Appl., 11 (2005), 29-48. doi: 10.1080/10236190412331314150
    [21] G. Yin, X. Mao, K. Yin, Numerical approximation of invariant measures for hybrid diffusion systems, IEEE Trans. Automat. Contr., 50 (2005), 934-946. doi: 10.1109/TAC.2005.851437
    [22] J. Bao, J. Shao, C. Yuan, Approximation of invariant measures for regime-switching diffusions, Potential Anal., 44 (2016), 707-727. doi: 10.1007/s11118-015-9526-x
    [23] H. Yang, X. Li, Explicit approximations for nonlinear switching diffusion systems in finite and infinite horizons, J. Differ. Equ., 265 (2018), 2921-2967. doi: 10.1016/j.jde.2018.04.052
    [24] X. Mao, C. Yuan, Stochastic differential equations with Markovian switching, Imperial College Press, 2006.
    [25] G. Dhariwal, A. Jungel, N. Zamponi, Global martingale solutions for a stochastic population crossdiffusion system, Stoch. Process. their Appl., 129 (2019), 3792-3820. doi: 10.1016/j.spa.2018.11.001
    [26] G. Da Prato, J. Zabczyk, Ergodicity for infinite dimensional systems, Cambridge University Press, Cambridge, 1996.
    [27] G. Yin, C. Zhu, Hybrid swithching diffusion: Properties and Applications, Springer, 2010.
    [28] Y. Zhao, S. Yuan, Q. Zhang, Numerical solution of a fuzzy stochastic single-species age-structure model in a polluted environment, Appl. Math. Comput., 260 (2015), 385-396.
    [29] W J. Anderson, Continuous-time Markov chains, Springer, Berlin, 1991.
    [30] J. Tan, A. Rathinasamy, Y. Pei, Convergence of the split-step θ-method for stochastic agedependent population equations with Poisson jumps, Elsevier Science Inc, 254 (2015), 305-317.
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