- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Abstract and Applied Analysis
Volume 2013 (2013), Article ID 454097, 12 pages
Stability and Bifurcation Analysis for a Predator-Prey Model with Discrete and Distributed Delay
1College of Mathematics and Information Science, Shaanxi Normal University, Xi'an, Shaanxi 710062, China
2School of Mathematics and Computer Science, Shanxi Normal University, Linfen, Shanxi 041004, China
Received 3 March 2013; Accepted 27 May 2013
Academic Editor: Peixuan Weng
Copyright © 2013 Ruiqing Shi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We propose a two-dimensional predatory-prey model with discrete and distributed delay. By the use of a new variable, the original two-dimensional system transforms into an equivalent three-dimensional system. Firstly, we study the existence and local stability of equilibria of the new system. And, by choosing the time delay as a bifurcation parameter, we show that Hopf bifurcation can occur as the time delay passes through some critical values. Secondly, by the use of normal form theory and central manifold argument, we establish the direction and stability of Hopf bifurcation. At last, an example with numerical simulations is provided to verify the theoretical results. In addition, some simple discussion is also presented.
Since the pioneering theoretical works by Lotka  and Volterra , there were a lot of authors who studied all kinds of predator-prey models modeled by ordinary differential equations (ODEs). To reflect that the dynamical behavior of the models depends on the past history of the system, it is often necessary to incorporate time delays into the models. Therefore, a more realistic predator-prey model should be described by delayed differential equations (DDEs) [3–11]. In general, delay differential equations exhibit more complicated dynamics on stability, periodic structure, bifurcation, and so on [12–26]. In [27, 28], the authors investigated the effect of the discrete delay on the stability of the model. In , the effect of the distributed delay on the stability of the model was investigated. In , the authors proposed a Logistic model with discrete and distributed delays: where the parameters , , , are positive constants. The function in (1) is called the delayed kernel, which is the weight given to the population time units ago. And it was assumed that for all , together with the normalization condition which ensures that the steady states of the model (1) are unaffected by the delay. They studied the stability of the positive equilibrium and existence of Hopf bifurcations, and direction and stability of the Hopf bifurcation were also analyzed. In , the authors proposed and investigated the following predator-prey model with time delay: where and can be interpreted as the population densities of the prey and the predator at time , respectively. denotes the intrinsic growth rate of the prey, and denotes the death rate of the predator. For the convenience of computation, they chose the same as delays; the delay represents the feedback time delay of the prey species to the growth of itself in term , represents the feedback time delay of the predator species to the growth of itself in term , represents the hunting delay in term , and represents the time of the predator maturation in term . The parameters are all positive constants. They studied the stability of the positive equilibrium and existence of Hopf bifurcations.
Motivated by [7, 11, 27–29] and the references cited therein, in the present paper, we will consider the following predator-prey model with discrete and distributed delay: where and can be interpreted as the population densities of the prey and the predator at time , respectively. denotes the intrinsic growth rate of the prey and denotes the death rate of the predator; represents the feedback time delay of the prey species to the growth of itself in term , represents the feedback time delay of the predator species to the growth of itself in term , and represents the hunting delay in term ; . The function is the same as the function in system (1). Following the ideas of Cushing , we define as the following weak kernel function:
Next, we define a new variable: then using the linear chain trick technique, system (4) can be transformed into the following equivalent system:
The organization of this paper is as follows. In Section 2, we will consider the existence and stability of equilibria of system (7). The existence of Hopf bifurcation is also discussed. In Section 3, by use of normal form theory and central manifold argument, we illustrate the direction and stability of Hopf bifurcation. In Section 4, we provide an example with some numerical simulations to verify the theoretical results, and we also give some brief discussion.
2. Local Stability of Equilibria and the Existence of Hopf Bifurcations
In this section, we will finish two tasks: (a) investigating the existence and stability of equilibriums of system (7) and (b) studying the effect of time delay on the system (7); that is, we will choose as bifurcating parameter to analyze Hopf bifurcation.
Let the right equations of system (7) equal zero; we get the following algebraic equations:
By simple computation, we know that the trivial equilibrium and boundary equilibrium of system (7) always exist with values and , respectively. In addition, we have the following results.(i)The eigenvalues of characteristic equations at the trivial equilibrium are , , and , which means that this equilibrium is always unstable.(ii)The eigenvalues of characteristic equations at the boundary equilibrium are and , and other eigenvalues are determined by . When , it is easy to see that , which means that this equilibrium is locally stable; whereas when , the sign of the real part of the eigenvalues can not be determined, which means that this equilibrium may be locally stable or unstable.
In fact, there exists a unique positive equilibrium for system (7) provided that , holds. Here Next, we always assume that(H1) holds.
The characteristic equation for system (7) at the equilibrium takes the form where
Multiplying on both sides of (10), we obtain equivalent characteristic equation as
When , characteristic equation (10) or (12) becomes It is easy to confirm that , and . By the Routh-Hurwitz criterion we know that all the roots of (13) have negative real parts. Thus, the positive equilibrium is locally asymptotically stable for .
Next, we will consider the eigenvalues of (12) for . Suppose that there is a pure imaginary root , , then we get
Separating the real and imaginary parts, we have By simple calculation, we can obtain the following equations:
Let then , can be written as
Denote , then (20) becomes
Let then the following assumption holds true.(H2) Equation (22) has at least one positive real root.
In fact, if all the parameters of system (7) are given, it is easy to calculate the root of (22) by using a computer. Since , we conclude that if , then (22) has at least one positive real root. Without loss of generality, we assume that (22) has six positive roots, defined by , , , , , and , respectively. Then (20) has six positive roots: By (19), we get If we denote where and , then is a pair of purely imaginary roots of (12). Define Here, we use the method by [25–27], which is different from .
In order to obtain the main result, it is necessary to make the following assumption.(H3). Taking the derivative of with respect to in (12), it is easy to obtain which is equivalent to By (12), we have Take into the above equation, we get
Let Then we get
Now, we can use the following lemma to get our result.
Lemma 1 (see ). Consider the exponential polynomial where and are constants. As vary, the sum of the order of the zeros of on the open right half plane can change only if a zero appears on or crosses the imaginary axis.
Theorem 2. Suppose that (H1), (H2), and (H3) hold; then the following results hold.(i)The positive equilibrium of system (7) (or the positive equilibrium of system (4)) is asymptotically stable for .(ii)The positive equilibrium of system (7) or system (4) undergoes a Hopf bifurcation when . That is, system (7) has a branch of periodic of solutions bifurcating from the positive equilibrium near .
3. Direction and Stability of the Hopf Bifurcation
In this section, following the ideas of , we derive the explicit formulae for determining the properties of the Hopf bifurcation at critical value of by using the normal form and the center manifold theory. Throughout this section, we always assume that system (7) undergoes Hopf bifurcation at the positive equilibrium for , and then is the corresponding purely imaginary roots of the characteristic equation at the positive equilibrium .
Let , , dropping the bars for simplification of notations; then system (7) is transformed into functional differential equations in as where , , , and where . By the Riesz representation theorem, there exists a function of bounded variation for , such that In fact, we can choose where is the Dirac delta function. For , define Then system (37) is equivalent to where . For , define and a bilinear inner product where . Then and are adjoint operators. By the discussion in Section 2, we know are eigenvalues of . Thus, they are also eigenvalues of . We need to compute the eigenvector of and corresponding to and , respectively.
Suppose that is the eigenvalues of corresponding to : then . It follows from the definition of and that because ; then we get where
Similarly, let be the eigenvalues of corresponding to ; according to the definition of we get where , from which we obtain
By (45) we get then we choose such that , .
Next, we will use the ideas in  to compute the coordinates describing center manifold at . Define
On the center manifold , we have where and are local coordinates for center manifold in the direction of and . Note that is real if is real. We consider only real solutions. For the solution of (43), since , we have where From (53) and (54), we have then we can obtain From the definition of , we have Comparing the coefficients with (56), we obtain
In order to determine we need to compute and . From (43) and (53), we have where Note that on the center manifold near to the origin, thus we obtain By (62) we know that for , Comparing the coefficients with (62), we get that From (64), (66), and the definition of , we have Noting , hence where is a constant vector. Similarly, we have where is a constant vector. In the following, we will find out and . From the definition of and (64), we can obtain where . From (61) and (62) we have That is, By (39) and (53) we have
From which we can get
Thus, we can determine and from (68) and (69). Furthermore, can be expressed by the parameters and delay. Thus, we can compute the following values: which determine the qualities of bifurcating periodic solution in the center manifold at critical value ; that is, determine the direction of the Hopf bifurcation: if , then the Hopf bifurcation is supercritical (subcritical) and the bifurcating periodic solution exists for ; determines the stability of the bifurcating periodic solution: the bifurcating periodic solution is stable (unstable) if ; and determines the period of the bifurcating periodic solution: the period increases (decreases) if .
4. Numerical Investigations and Discussion
In this paper, we propose a two-dimensional predatory-prey model with discrete and distributed delay. Then, by introducing a new variable, the original system is transformed into an equivalent three-dimensional system. In Section 2, we analyze the existence and local stability of the equilibria of the three-dimensional system. The condition for the existence of a Hopf bifurcation is also obtained. In Section 3, by the use of normal form theory and central manifold argument, we establish the formulae for the direction and the stability of the Hopf bifurcation.
In order to confirm our main results obtained in this work, we consider the following special system: By simple calculation, it is easy to see that model (84) exists a unique positive equilibrium and . Note that the parameter set provided in model (84) satisfies the conditions of Theorem 2. When , the positive equilibrium is asymptotically stable, as shown in Figures 1(a) and 1(c). It follows from the discussion in Section 2 that , , and . Thus, is stable when , as indicated in Figures 1(a) and 1(c).
When passes through the critical value , loses its stability and a Hopf bifurcation occurs, that is, a family of periodic solutions bifurcate from , as shown in Figures 1(b) and 1(d). Since and , the Hopf bifurcation is supercritical and the direction of the bifurcation is and these bifurcating periodic solutions from are stable; please see Figures 1(b) and 1(d) and Figure 2(a) for . Note that the model (84) may have very complex dynamics if we choose the time delay as a bifurcation parameter. It follows from Figure 2 that the period of periodic solution is doubled as increase, and around (Figure 2(b)). If the time delay is increasing further, a periodic solution with 4-time period appears around (Figure 2(c)). Finally, the chaotic solution exists once the time delay reaches around (Figure 2(d)).
Both our theoretical and numerical results show that the positive equilibrium is asymptotically stable if , which indicates that the dynamical behavior is simple for the considered system. However, if , bifurcation and chaos may occur, which means that the considered system can take on very complex dynamics, and this may explain some complex phenomenon in the natural world.
The authors would like to thank the referees for their helpful suggestions, which improved the quality of this paper greatly. The first author is supported by the Postdoctoral Science Foundation of China (no. 2011M501428) and Young Science Funds of Shanxi (no. 2013021002-2). The third author is supported by the National Natural Science Foundation of China (nos. 11171199 and 81161120403).
- A. J. Lotka, Elements of Physical Biology, Williams and Wilkins, Baltimore, Md, USA, 1925.
- V. Volterra, “Variazioni e fluttuazioni del numero d'individui in specie animali conviventi,” Memorie Dell'Accademia Nazionale dei Lincei, vol. 2, pp. 31–113, 1926.
- A. Leung, “Periodic solutions for a prey-predator differential delay equation,” Journal of Differential Equations, vol. 26, no. 3, pp. 391–403, 1977.
- K. Gopalsamy, “Time lags and global stability in two-species competition,” Bulletin of Mathematical Biology, vol. 42, no. 5, pp. 729–737, 1980.
- X. Wen and Z. Wang, “The existence of periodic solutions for some models with delay,” Nonlinear Analysis. Real World Applications, vol. 3, no. 4, pp. 567–581, 2002.
- X. Chen, “Periodicity in a nonlinear discrete predator-prey system with state dependent delays,” Nonlinear Analysis. Real World Applications, vol. 8, no. 2, pp. 435–446, 2007.
- X.-P. Yan and C.-H. Zhang, “Hopf bifurcation in a delayed Lokta-Volterra predator-prey system,” Nonlinear Analysis. Real World Applications, vol. 9, no. 1, pp. 114–127, 2008.
- X. He, “Stability and delays in a predator-prey system,” Journal of Mathematical Analysis and Applications, vol. 198, no. 2, pp. 355–370, 1996.
- S. Ruan, “Absolute stability, conditional stability and bifurcation in Kolmogorov-type predator-prey systems with discrete delays,” Quarterly of Applied Mathematics, vol. 59, no. 1, pp. 159–173, 2001.
- L. Wang, W. Li, and P. Zhao, “Existence and global stability of positive periodic solutions of discrete predator-prey system with delays,” Advances in Difference Equations, vol. 4, pp. 321–336, 2004.
- Y. Song and Y. Peng, “Stability and bifurcation analysis on a logistic model with discrete and distributed delays,” Applied Mathematics and Computation, vol. 181, no. 2, pp. 1745–1757, 2006.
- W. Ma and Y. Takeuchi, “Stability analysis on a predator-prey system with distributed delays,” Journal of Computational and Applied Mathematics, vol. 88, no. 1, pp. 79–94, 1998.
- B. Liu, Z. Teng, and L. Chen, “Analysis of a predator-prey model with Holling II functional response concerning impulsive control strategy,” Journal of Computational and Applied Mathematics, vol. 193, no. 1, pp. 347–362, 2006.
- R. Xu and Z. Wang, “Periodic solutions of a nonautonomous predator-prey system with stage structure and time delays,” Journal of Computational and Applied Mathematics, vol. 196, no. 1, pp. 70–86, 2006.
- X.-P. Yan and Y.-D. Chu, “Stability and bifurcation analysis for a delayed Lotka-Volterra predator-prey system,” Journal of Computational and Applied Mathematics, vol. 196, no. 1, pp. 198–210, 2006.
- Z. Teng and M. Rehim, “Persistence in nonautonomous predator-prey systems with infinite delays,” Journal of Computational and Applied Mathematics, vol. 197, no. 2, pp. 302–321, 2006.
- G. Jiang and Q. Lu, “Impulsive state feedback control of a predator-prey model,” Journal of Computational and Applied Mathematics, vol. 200, no. 1, pp. 193–207, 2007.
- Y. Kuang, Delay Differential Equations with Applications in Population Dynamics, vol. 191, Academic Press, Boston, Mass, USA, 1993.
- L. Zhou, Y. Tang, and S. Hussein, “Stability and Hopf bifurcation for a delay competition diffusion system,” Chaos, Solitons & Fractals, vol. 14, no. 8, pp. 1201–1225, 2002.
- S. Krise and S. Roy Choudhury, “Bifurcations and chaos in a predator-prey model with delay and a laser-diode system with self-sustained pulsations,” Chaos, Solitons and Fractals, vol. 16, no. 1, pp. 59–77, 2003.
- X. Liao and G. Chen, “Hopf bifurcation and chaos analysis of Chen's system with distributed delays,” Chaos, Solitons & Fractals, vol. 25, no. 1, pp. 197–220, 2005.
- Z. Liu and R. Yuan, “Stability and bifurcation in a harvested one-predator–two-prey model with delays,” Chaos, Solitons and Fractals, vol. 27, no. 5, pp. 1395–1407, 2006.
- S. Zhang, D. Tan, and L. Chen, “Chaotic behavior of a chemostat model with Beddington-DeAngelis functional response and periodically impulsive invasion,” Chaos, Solitons and Fractals, vol. 29, no. 2, pp. 474–482, 2006.
- X. Liu and D. Xiao, “Complex dynamic behaviors of a discrete-time predator-prey system,” Chaos, Solitons & Fractals, vol. 32, no. 1, pp. 80–94, 2007.
- F. Wang and G. Zeng, “Chaos in a Lotka-Volterra predator-prey system with periodically impulsive ratio-harvesting the prey and time delays,” Chaos, Solitons and Fractals, vol. 32, no. 4, pp. 1499–1512, 2007.
- C. Sun, M. Han, Y. Lin, and Y. Chen, “Global qualitative analysis for a predator-prey system with delay,” Chaos, Solitons & Fractals, vol. 32, no. 4, pp. 1582–1596, 2007.
- C. Çelik, “The stability and Hopf bifurcation for a predator-prey system with time delay,” Chaos, Solitons and Fractals, vol. 37, no. 1, pp. 87–99, 2008.
- C. Çelik, “Hopf bifurcation of a ratio-dependent predator-prey system with time delay,” Chaos, Solitons and Fractals, vol. 42, no. 3, pp. 1474–1484, 2009.
- C. Çelik, “Dynamical behavior of a ratio dependent predator-prey system with distributed delay,” Discrete and Continuous Dynamical Systems B, vol. 16, no. 3, pp. 719–738, 2011.