×

Asymptotic behavior of a stochastic generalized nutrient-phytoplankton-zooplankton model. (English) Zbl 07923953

Summary: In this paper, we consider the stochastic nutrient-phytoplankton-zooplankton model with nutrient cycle. In order to take stochastic fluctuations into account, we add the stochastic increments to the variations of biomass of nutrition, phytoplankton and zooplankton during time interval \(\Delta t\), thus we obtain the corresponding stochastic model. Subsequently, we explore the existence, uniqueness and stochastically ultimate boundness of global positive solution. By constructing suitable Lyapunov function, we also obtain \(V\)-geometric ergodicity of this model. In addition, the sufficient conditions of exponential extinction and persistence in the mean of plankton are established. At last, we present some numerical simulations to validate theoretical results and analyze the impacts of some important parameters.

MSC:

37A50 Dynamical systems and their relations with probability theory and stochastic processes
37H30 Stability theory for random and stochastic dynamical systems
39A50 Stochastic difference equations
60H10 Stochastic ordinary differential equations (aspects of stochastic analysis)
92-10 Mathematical modeling or simulation for problems pertaining to biology
Full Text: DOI

References:

[1] Arnold, L.: Stochastic differential equations: Theory and application (1972)
[2] Arnold, EM, Aspects of a zooplankton, phytoplankton, phosphorus system, Ecol. Model., 5, 4, 293-300, 1978 · doi:10.1016/0304-3800(78)90039-X
[3] Athreya, A.; Kolba, T.; Mattingly, J., Propagating Lyapunov functions to prove noise-induced stabilization, Electron. J. Probab., 17, 1-38, 2012 · Zbl 1308.37003 · doi:10.1214/EJP.v17-2410
[4] Bellet, L.R.: Ergodic properties of Markov processes. In: The Markovian Approach, Open Quantum Systems II, Springer (2006)
[5] Cai, Y.; Kang, Y.; Wang, W., A stochastic SIRS epidemic model with nonlinear incidence rate, Appl. Math. Comput., 305, 221-240, 2017 · Zbl 1411.92267
[6] Durrett, R.: Stochastic Calculus: A Practical Introduction, vol. 6. CRC Press (1996) · Zbl 0856.60002
[7] Hallam, TG, Structural sensitivity of grazing formulations in nutrient controlled plankton models, J. Math. Biol., 5, 3, 269-280, 1978 · Zbl 0379.92015 · doi:10.1007/BF00276122
[8] Higham, DJ, An algorithmic introduction to numerical simulation of stochastic differential equations, SIAM Rev., 43, 3, 525-546, 2001 · Zbl 0979.65007 · doi:10.1137/S0036144500378302
[9] Ikeda, N.; Watanabe, S., A comparison theorem for solutions of stochastic differential equations and its applications, Osaka J. Math., 14, 619-633, 1977 · Zbl 0376.60065
[10] Imhof, L.; Walcher, S., Exclusion and persistence in deterministic and stochastic chemostat models, J. Differ. Equ., 217, 1, 26-53, 2005 · Zbl 1089.34041 · doi:10.1016/j.jde.2005.06.017
[11] Jang, SR-J; Allen, EJ, Deterministic and stochastic nutrient-phytoplankton-zooplankton models with periodic toxin producing phytoplankton, Appl. Math. Comput., 271, 52-67, 2015 · Zbl 1410.92156
[12] Ji, C.; Jiang, D., Threshold behaviour of a stochastic SIR model, Appl. Math. Model., 38, 21-22, 5067-5079, 2014 · Zbl 1428.92109 · doi:10.1016/j.apm.2014.03.037
[13] Mandal, PS; Banerjee, M., Deterministic and stochastic dynamics of a competitive phytoplankton model with allelopathy, Differ. Equ. Dyn. Syst., 21, 341-372, 2013 · Zbl 1301.34066 · doi:10.1007/s12591-013-0170-x
[14] Mao, X.: Stochastic Differential Equations and Applications. Elsevier (2007) · Zbl 1138.60005
[15] Mattingly, JC; Stuart, AM; Higham, DJ, Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise, Stoch. Proc. Appl., 101, 2, 185-232, 2002 · Zbl 1075.60072 · doi:10.1016/S0304-4149(02)00150-3
[16] Riley, GA; Stommel, H.; Burrpus, DP, Qualitative ecology of the plankton of the western north atlantic, Bull. Bingham Oceanogr. Collect., 12, 1-169, 1949
[17] Ruan, S., Persistence and coexistence in zooplankton-phytoplankton-nutrient models with instantaneous nutrient recycling, J. Math. Biol., 31, 633-654, 1993 · Zbl 0779.92021 · doi:10.1007/BF00161202
[18] Rudnicki, R., Pichór, K., Tyran-Kamińska, M.: Markov semigroups and their applications. In: Dynamics of Dissipation, pp. 215-238. Springer (2002) · Zbl 1057.47046
[19] Smith, H.L., Waltman, P.: The Theory of the Chemostat: Dynamics of Microbial Competition, vol. 13. Cambridge University Press (1995) · Zbl 0860.92031
[20] Sun, S.; Zhang, X., Asymptotic behavior of a stochastic delayed chemostat model with nutrient storage, J. Biol. Syst., 26, 2, 225-246, 2018 · Zbl 1409.92272 · doi:10.1142/S0218339018500110
[21] Wang, L.; Jiang, D., Ergodic property of the chemostat: a stochastic model under regime switching and with general response function, Nonlinear Anal.-Hybri., 27, 341-352, 2018 · Zbl 1379.92032 · doi:10.1016/j.nahs.2017.10.001
[22] Wroblewski, JS; Sarmiento, JL; Flierl, GR, An ocean basin scale model of plankton dynamics in the north atlantic: 1. solutions for the climatological oceanographic conditions in may, Glob. Biogeochem. Cy., 2, 3, 199-218, 1988 · doi:10.1029/GB002i003p00199
[23] Xu, C.; Yuan, S., Competition in the chemostat: a stochastic multi-species model and its asymptotic behavior, Math. Biosci., 280, 1-9, 2016 · Zbl 1350.34041 · doi:10.1016/j.mbs.2016.07.008
[24] Yu, X.; Yuan, S.; Zhang, T., Asymptotic properties of stochastic nutrient-plankton food chain models with nutrient recycling, Nonlinear Anal-Hybri., 34, 209-225, 2019 · Zbl 1435.34056 · doi:10.1016/j.nahs.2019.06.005
[25] Zhang, X., A stochastic non-autonomous chemostat model with mean-reverting Ornstein-Uhlenbeck process on the washout rate, J. Dyn. Differ. Equ., 2020 · Zbl 1508.92161 · doi:10.1016/j.amc.2020.125833
[26] Zhang, X.; Yuan, R., A stochastic chemostat model with mean-reverting Ornstein-Uhlenbeck process and Monod-Haldane response function, Appl. Math. Comput., 394, 2021 · Zbl 1508.92161
[27] Zhao, Q.; Liu, S.; Niu, X., Stationary distribution and extinction of a stochastic nutrient-phytoplankton-zooplankton model with cell size, Math. Method. Appl. Sci., 43, 7, 3886-3902, 2020 · Zbl 1451.34071
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.