Population dynamical behavior of a two-predator one-prey stochastic model with time delay

Meng Liu; Chuanzhi Bai; Yi Jin

doi:10.3934/dcds.2017108

Article Contents

2017, Volume 37, Issue 5: 2513-2538. Doi: 10.3934/dcds.2017108

This issue Previous Article Traveling waves and entire solutions for an epidemic model with asymmetric dispersal Next Article Fourier spectral approximations to the dynamics of 3D fractional complex Ginzburg-Landau equation

Population dynamical behavior of a two-predator one-prey stochastic model with time delay

1.
School of Mathematical Science, Huaiyin Normal University, Huaian 223300, China
2.
School of Mathematics and Statistics, Northeast Normal University, Jilin 130024, China
3.
Department of Mathematics, Harbin Institute of Technology, Weihai 264209, China

Received: October 31, 2015

Revised: November 30, 2016

Published: May 2017

Abstract Full Text(HTML) Figure(5) / Table(1) Related Papers Cited by

Abstract

In this paper, the convergence of the distributions of the solutions (CDS) of a stochastic two-predator one-prey model with time delay is considered. Some traditional methods that are used to study the CDS of stochastic population models without delay can not be applied to investigate the CDS of stochastic population models with delay. In this paper, we use an asymptotic approach to study the problem. By taking advantage of this approach, we show that under some simple conditions, there exist three numbers $ ρ_1>ρ_2>ρ_3$, which are represented by the coefficients of the model, closely related to the CDS of our model. We prove that if $ ρ_1<1$, then $ \lim\limits_{t\to +∞}N_i(t)=0$ almost surely, $ i=1,2,3;$ If $ρ_i>1>ρ_{i+1}$, $ i=1,2$, then $ \lim\limits_{t\to +∞}N_j(t)=0$ almost surely, $ j=i+1,...,3$, and the distributions of $ (N_1(t),...,N_i(t))^\mathrm{T}$ converge to a unique ergodic invariant distribution (UEID); If $ ρ_3>1$, then the distributions of $ (N_1(t),N_2(t),N_3(t))^\mathrm{T}$ converge to a UEID. We also discuss the effects of stochastic noises on the CDS and introduce several numerical examples to illustrate the theoretical results.

Keywords:

Mathematics Subject Classification: Primary: 34F05, 60H10; Secondary: 92B05, 60J27.

Citation:

Full Text(HTML)

Figure 1. Model (2) with $\sigma_1^2/2=0.3,~\sigma_2^2/2=0.05$, $~\sigma_3^2/2=0.05$, $r_1=1.2$, $r_2=-0.15$, $r_3=-0.01$, $a_{11}=1.6$, $a_{12}=1.2$, $a_{13}=0.3$, $a_{21}=-0.85$, $a_{22}=1.9$, $a_{23}=0.6$, $a_{31}=-0.4$, $a_{32}=1$, $a_{33}=2.1$, $\tau_{12}=3$, $\tau_{13}=7$, $\tau_{21}=1$, $\tau_{23}=5$, $\tau_{31}=4$, $\tau_{32}=10$, $N_1(\theta)=0.5+0.1\sin \theta$, $N_2(\theta)=0.1+0.05\sin \theta$, $N_3(\theta)=0.05+0.03\sin \theta$. (a) is the paths of $N_1(t)$, $N_2(t)$ and $N_3(t)$ and their time average; (b) is the probability density functions of $N_1(t)$, $N_2(t)$ and $N_3(t)$

Download: Full-size image PowerPoint slide

Figure 2. Model (2) with $\sigma_1^2/2=0.3,~\sigma_2^2/2=0.05,~\sigma_3^2/2=0.5$, other parameters are taken as Fig.1. (a) is the paths of $N_1(t)$, $N_2(t)$ and $N_3(t)$ and the time average of $N_1(t)$ and $N_2(t)$; (b) is the probability density functions of $N_1(t)$ and $N_2(t)$

Download: Full-size image PowerPoint slide

Figure 3. Model (2) with $\sigma_1^2/2=0.3,~\sigma_2^2/2=0.47$, $~\sigma_3^2/2=0.5$, other parameters are taken as Fig.1. (a) is the paths of $N_1(t)$, $N_2(t)$ and $N_3(t)$ and the time average of $N_1(t)$; (b) is the probability density functions of $N_1(t)$

Download: Full-size image PowerPoint slide

Figure 4. The paths of $N_1(t)$, $N_2(t)$ and $N_3(t)$ of model (2) with $\sigma_1^2/2=2,~\sigma_2^2/2=0.47,~\sigma_3^2/2=0.5$, other parameters are taken as Fig.1

Download: Full-size image PowerPoint slide

Figure 5. Solutions of model (2) with $a_{12}=1.32$, other parameters are taken as Fig.1(a), initial values $N_1(\theta)=0.5+0.1\sin \theta$, $N_2(\theta)=0.1+0.05\sin \theta$, $N_3(\theta)=0.05+0.03\sin \theta$, $M_1(\theta)=0.4+0.1\sin \theta$, $M_2(\theta)=0.3+0.05\sin \theta$, $M_3(\theta)=0.1+0.05\sin \theta$

Download: Full-size image PowerPoint slide

$(i')~~1>\rho_1$	$\lim\limits_{t\rightarrow+\infty}N_i(t)=0,~i=1,2,3,~~a.s.$
$(ii')~~\rho_1>1>\rho_2$	$\lim\limits_{t\rightarrow+\infty}\langle N_1(t)\rangle=\frac{b_1}{a_{11}},~\lim\limits_{t\rightarrow+\infty}N_2(t)=\lim\limits_{t\rightarrow+\infty}N_3(t)=0,~~a.s.$
$(iii')~\rho_2>1>\rho_3$	$\lim\limits_{t\rightarrow+\infty}\langle N_1(t)\rangle=\frac{\Delta_1-\tilde{\Delta}_1}{A_{33}},~\lim\limits_{t\rightarrow+\infty}\langle N_2(t)\rangle=\frac{\Delta_2-\tilde{\Delta}_2}{A_{33}},~\lim\limits_{t\rightarrow+\infty}N_3(t)=0,~~a.s.$
$(iv')~~\rho_3>1$	$\lim\limits_{t\rightarrow+\infty}\langle N_i(t)\rangle=\frac{A_i-\tilde{A}_i}{A},~i=1,2,3,~~a.s.$

| Show Table

DownLoad: CSV

Related Papers

Cited by

References

[1]	S. Ahmad and I. M. Stamova, Almost necessary and sufficient conditions for survival of species, Nonlinear Anal. Real World Appl., 5 (2004), 219-229. doi: 10.1016/S1468-1218(03)00037-3.
[2]	J. Bao, X. Mao, G. Yin and C. Yuan, Competitive Lotka-Volterra population dynamics with jumps, Nonlinear Anal., 74 (2011), 6601-6616. doi: 10.1016/j.na.2011.06.043.
[3]	I. Barbalat, Systems dequations differentielles d'oscillations nonlineaires, Rev. Roumaine Math. Pures Appl., 4 (1959), 267-270.
[4]	J. R. Beddington and R. M. May, Harvesting natural populations in a randomly fluctuating environment, Science, 197 (1977), 463-465.
[5]	C. A. Braumann, Variable effort harvesting models in random environments: Generalization to density-dependent noise intensities, Math. Biosci., 177/178 (2002), 229-245.
[6]	C. A. Braumann, Itô versus Stratonovich calculus in random population growth, Math. Biosci., 206 (2007), 81-107.
[7]	Y. Cai, Y. Kang, M. Banerjee and W. Wang, A stochastic SIRS epidemic model with infectious force under intervention strategies, J. Differential Equations, 259 (2015), 7463-7502.
[8]	J. Connell, On the prevalence and relative importance of interspecific competition: evidence from field experiments, Am. Nat., 115 (2011), 351-370.
[9]	N. H. Dang, N. H. Du and T. V. Ton, Asymptotic behavior of predator-prey systems perturbed by white noise, Acta Appl. Math., 115 (2011), 351-370. doi: 10.1007/s10440-011-9628-4.
[10]	M. Farkas and H. Freedman, Stability conditions for two predator one prey systems, Acta. Appl. Math., 14 (1989), 3-10.
[11]	K. Gopalsamy, Stability and Oscillations in Delay Differential Equations of Population Dynamics, Kluwer Academic, Dordrecht, 1992. doi: 10.1007/978-94-015-7920-9.
[12]	R. Z. Hasminskii, Stochastic Stability of Differential Equations, Netherlands, 1980. doi: 10.1007/978-94-009-9121-7.
[13]	A. Hastings and T. Powell, Chaos in a three-species food chain, Ecology, 72 (1991), 896-903. doi: 10.2307/1940591.
[14]	D. J. Higham, An algorithmic introduction to numerical simulation of stochastic differential equations, SIAM Rev., 43 (2001), 525-546. doi: 10.1137/S0036144500378302.
[15]	L. C. Hung, Stochastic delay population systems, Appl. Anal., 88 (2009), 1303-1320.
[16]	N. Ikeda and S. Watanabe, A comparison theorem for solutions of stochastic differential equations and its applications, Osaka J. Math., 14 (1977), 619-633.
[17]	C. Ji, D. Jiang and N. Shi, A note on a predator-prey model with modified Leslie-Gower and Holling-type Ⅱ schemes with stochastic perturbation, J. Math. Anal. Appl., 377 (2011), 435-440.
[18]	D. Jiang and N. Shi, A note on nonautonomous logistic equation with random perturbation, J. Math. Anal. Appl., 303 (2005), 164-172.
[19]	A. L. Koch, Competitive coexistence of two predators utilizing the same prey unde rconstant environmental conditions, J. Theor. Biol., 44 (1974), 387-395.
[20]	Y. Kuang, Delay Differential Equations with Applications in Population Dynamics, Academic Press, Boston, 1993.
[21]	X. Li and X. Mao, Population dynamical behavior of non-autonomous Lotks-Volterra competitive system with random perturbation, Discrete Contin. Dyn. Syst., 24 (2009), 523-545.
[22]	M. Y. Li and Z. Shuai, Global-stability problem for coupled systems of differential equations on networks, J. Differential Equations, 248 (2010), 1-20.
[23]	W. Li, X. Zhang and C. Zhang, A new method for exponential stability of coupled reaction diffusion systems with mixed delays: Combining Razumikhin method with graph theory, J. Franklin Inst., 352 (2015), 1169-1191.
[24]	M. Liu, Global asymptotic stability of stochastic Lotka-Volterra systems with infinite delays, IMA J. Appl. Math., 80 (2015), 1431-1453.
[25]	M. Liu and C. Bai, Analysis of a stochastic tri-trophic food-chain model with harvesting, J. Math. Biol., 73 (2016), 597-625.
[26]	M. Liu and C. Bai, Optimal harvesting of a stochastic mutualism model with Lévy jumps, Appl. Math. Comput., 276 (2016), 301-309.
[27]	M. Liu and M. Fan, Permanence of stochastic lotka-volterra systems, J. Nonlinear Sci. , (2016). doi: 10.1007/s00332-016-9337-2.
[28]	M. Liu and M. Fan, Stability in distribution of a three-species stochastic cascade predator-prey system with time delays, IMA J Appl Math, (2016). doi: 10.1093/imamat/hxw057.
[29]	M. Liu and P. S. Mandal, Dynamical behavior of a one-prey two-predator model with random perturbations, Commun. Nonlinear Sci. Numer. Simul., 28 (2015), 123-137.
[30]	M. Liu, H. Qiu and K. Wang, A remark on a stochastic predator-prey system with time delays, Appl. Math. Lett., 26 (2013), 318-323.
[31]	M. Liu, K. Wang and Q. Wu, Survival analysis of stochastic competitive models in a polluted environment and stochastic competitive exclusion principle, Bull. Math. Biol., 73 (2011), 1969-2012.
[32]	Q. Luo and X. Mao, Stochastic population dynamics under regime switching Ⅱ, J. Math. Anal. Appl., 355 (2009), 577-593.
[33]	X. Mao, Stationary distribution of stochastic population systems, Systems Control Lett., 60 (2011), 398-405.
[34]	X. Mao and C. Yuan, Stochastic Differential Equations with Markovian Switching, Imperial College Press, 2006.
[35]	R. M. May, Stability and Complexity in Model Ecosystems, Princeton Univ., 1973.
[36]	R. M. May and W. J. Leonard, Nonlinear aspects of competition between three species, SIAM J. Appl. Math., 29 (1975), 243-253.
[37]	J. W. Moon, Counting Labelled Tress, Canadian Mathematical Congress, Montreal, 1970.
[38]	J. D. Murray, Mathematical Biology I, An introduction, Third edition. Interdisciplinary Applied Mathematics, 17. Springer-Verlag, New York, 2002.
[39]	B. Øendal, Stochastic Differential Equations: An Introduction with Applications, 5^th edition, Springer, Berlin, 1998.
[40]	R. T. Paine, Road maps of interactions or grist for theoretical development, Ecology, 69 (1988), 1648-1654.
[41]	S. Pasquali, The stochastic logistic equation: stationary solutions and their stability, Rend. Sem. Mat. Univ. Padova, 106 (2001), 165-183.
[42]	D. PratoZabczyk and J. Zabczyk, Ergodicity for Infinite Dimensional Systems, Cambridge University Press, Cambridge, 1996.
[43]	P. W. Price, C. W. Bouton, P. Gross and B. A. McPheron, Interactions among the three trophic levels: Influence of plants on interactions between insect herbivores and natural enemies, Annu. Rev. Ecol. Evol. S., 11 (1980), 41-65.
[44]	R. Rudnicki, Long-time behaviour of a stochastic prey-predator model, Stoch. Process. Appl., 108 (2003), 93-107. doi: 10.1016/S0304-4149(03)00090-5.
[45]	R. Rudnicki and K. Pichór, Influence of stochastic perturbation on prey-predator systems, Math. Biosci., 206 (2007), 108-119.
[46]	S. J. Schreiber, M. Benaïm and K.A.S. Atchadé, Persistence in fluctuating environments, J. Math. Biol., 62 (2011), 655-683.
[47]	L. Turyn, Persistence in models of 3 interacting predator prey populations-remarks, Math. Biosci., 110 (1992), 125-130.
[48]	P. Xia, D. Jiang and X. Li, Dynamical behavior of the stochastic delay mutualism system, Abstract and Applied Analysis, 2014 (2014), Art. ID 781394, 19 pp.
[49]	C. Zhang, W. Li and K. Wang, Graph-theoretic method on exponential synchronization of stochastic coupled networks with Markovian switching, Nonlinear Anal. Hybrid Syst., 15 (2015), 37-51.
[50]	C. Zhang, W. Li and K. Wang, Graph-theoretic approach to stability of multi-group models with dispersal, Discrete Contin. Dyn. Syst. Ser. B, 20 (2015), 259-280.
[51]	C. Zhang, W. Li and K. Wang, Graph theory-based approach for stability analysis of stochastic coupled systems with Lévy noise on networks, IEEE Transactions on Neural Networks and Learning Systems, 26 (2015), 1698-1709.
[52]	Q. Zhang and D. Jiang, The coexistence of a stochastic Lotka-Volterra model with two predators competing for one prey, Appl. Math. Comput., 269 (2015), 288-300.
[53]	Y. Zhao, S. Yuan and J. Ma, Survival and stationary distribution analysis of a stochastic competitive model of three species in a polluted environment, Bull. Math. Biol., 77 (2015), 1285-1326.
[54]	C. Zhu and G. Yin, On hybrid competitive Lotka-Volterra ecosystems, Nonlinear Anal., 71 (2009), e1370-e1379.
[55]	X. Zou, D. Fan and K. Wang, Stationary distribution and stochastic Hopf bifurcation for a predator-prey system with noises, Discrete Contin. Dyn. Syst. Ser. B, 18 (2013), 1507-1519.