International Journal of Differential Equations
Volume 2017 (2017), Article ID 2653124, 11 pages
https://doi.org/10.1155/2017/2653124

Research Article

Analysis of a Predator-Prey Model with Switching and Stage-Structure for Predator

T. Suebcharoen

Center of Excellence in Mathematics and Applied Mathematics, Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand

Correspondence should be addressed to T. Suebcharoen; teeranush.s@gmail.com

Received 8 June 2017; Accepted 22 August 2017; Published 27 September 2017

Academic Editor: Yuji Liu

Copyright © 2017 T. Suebcharoen. 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.

Abstract.

This paper studies the behavior of a predator-prey model with switching and stage-structure for predator. Bounded positive solution, equilibria, and stabilities are determined for the system of delay differential equation. By choosing the delay as a bifurcation parameter, it is shown that the positive equilibrium can be destabilized through a Hopf bifurcation. Some numerical simulations are also given to illustrate our results.

1. Introduction

The predator-prey system is important in dynamical population models and has been discussed by many authors [1–15].

In the related studies, a switching predator-prey model which has the switching property of predator was introduced by [7]. It was assumed that the predators catch prey in an abundant habitat. After a decrease in prey species population, the predator moves to another abundant habitat. In [8], the authors investigated a switching model of a two-prey one-predator system and they have shown that the system undergoes a Hopf bifurcation. They used the carrying capacity of prey as the bifurcations parameter. More examples on switching models can be found in [9–11]. Saito and Takeuchi [12] proposed a stage-structure model of a species’ growth consisting of immature and mature individuals. It is assumed that the predators are divided into two-stage groups: juveniles and adults. Only the adult predators are able to catch prey species. As for the juvenile predators, they live with the adult predators. It is assumed that juveniles survive on prey already caught by adults. They live on a different resource which is available in the abundant habitat from the adult predators. Consequently, stage-structure model is more realistic than the model without stage-structure. In [14], it was further assumed that the time from juveniles to adults is itself state dependent. Qu and Wei [15] studied the asymptotic behavior of a predator-prey model with stage-structure. They found that an orbitally asymptotically stable periodic orbit exists in that model.

The purpose of the present paper is to study nonlinear delayed differential equations each of which describes a switching and stage structured predator-prey model. The present paper is organized as follows. In the next section, the main mathematical model is formulated and the positivity and boundedness of solutions are presented. In Section 3, we discuss the local stability of equilibria by analyzing the corresponding characteristic equations and we prove the existence of Hopf bifurcations for the model. Finally, numerical results and a brief discussion are provided.

2. Model

In this paper, we extend the switching predator-prey model in [8] by introducing stage structured with time delay into the model. We consider the switching with stage-structure predator-prey model of the following form:

with initial conditions

The model is formulated under the following assumptions:

(1)It is assumed that two-prey species, denoted by and , respectively, can be modelled by a logistic equation when the predator is absent. The parameter is the prey intrinsic growth rate and is its carrying capacity.(2)The prey lives in two different habitats and each prey is able to migrate among two different habitats. The parameter is the probability of successful transition from each habitat and is inverse barrier strength in going out of the first habitat and the second habitat.(3)The functions and have a characteristic property of a switching mechanism, where is capturing rate.(4)The parameter is the rate of conversion of prey to predator and is the death rate of predator.(5)The predators are derived into two-stage groups: juveniles and adults, which are divided by age , and they are denoted by and , respectively. It is assumed that juveniles take units of time to mature and is the surviving rate of juveniles to adults. Notice, we assume that the juveniles suffer a mortality rate of .

For ecological reasons, we always assume that the initial data , , , continuous on , and , , , If , , , is a solution of system (1) through that initial data, it is easy to verify that is positive on the maximum existence interval of solution. Such solutions will be called positive solution. Moreover, if such a solution is bounded above and below, it is called a positive solution. Furthermore, we discuss the bounded positive solutions of system (1) which implies a natural restriction; that is, our system (1) must have a bounded positive solution. The following theorem guarantees that our stage-structure predator-prey model (1) with initial condition (2) always has a bounded solution. Therefore, every solution to system (1) is positive and bounded.

Theorem 1. Every solution of system (1) with initial condition (2) is bounded for all and all of these solutions are ultimately bounded.

Proof. Let . By calculating the derivative of with respect to along the positive solution of the system of system (1), we have

Let . We have

Hence, there exists a positive constant , such that

Thus, we get

Therefore, is ultimately bounded; that is, each solution of system (1) is ultimately bounded.

3. Local Stability and Existence of Hopf Bifurcation

The main goal in this section is to investigate the stability of a positive equilibrium and the existence of a Hopf bifurcation.

Because of the last equation of system (1), is completely determined by , , . Therefore, in the rest of this paper, we will study the following system:

with the initial conditions , , continuous on and , , .

Before we proceed further, let us scale (7) by putting

and dropping the bars for the sake of simplicity. We obtain the following system containing dimensionless quantities:

Next, we find equilibria of system (9) by equating the derivatives on the left-hand sides to zero. The equilibria are solutions of the system

This gives two possible equilibria which are

(i)boundary equilibrium , which is corresponding to extinction of the predator, where is a real positive root of the cubic equation

(ii)positive equilibrium , which is corresponding to coexistence of prey and predator and

 Here is a real positive root of the cubic equation

 or

 Obviously, is the one real positive root of   (13). The other two values of will be real and positive if

We now analyze the stability of each equilibrium.

Let be any arbitrary equilibrium. The characteristic equation about is given by

The next lemma gives conditions for the stability of equilibrium .

Theorem 2. The equilibrium is

(i)unstable if ;(ii)locally asymptotically stable if

Proof. We consider the characteristic equation of (16) at the equilibrium . It follows that

Hence, one characteristic root is the solution of the equation

If , then , and Therefore, has at least one positive root and the equilibrium is unstable.
On the other hand, let ; that is,

Then and . Thus, a root of has negative real part. Hence, the other characteristic roots are the solution of the equation

that is,

Since is a real positive root of the cubic equation , we have . We, then, consider the last few terms from (21)

Thus, all the roots of characteristic equation have negative real part. The equilibrium is locally asymptotically stable.

Now, we analyze the stability of positive equilibrium . The associated characteristic equation is

where

In the following, we study the Hopf bifurcation for system (9), using the time delay as the bifurcation parameter. We assume that is a root of the characteristic equation (23). Then we get

By separating real part and imaginary part, we obtain

By squaring both sides of the equations and using the property that , we can simplify the above equation. As a result,

Denote , , , and . Then (27) becomes

By the Routh-Hurwitz criterion, we conclude that if

(23) has no positive real roots. Therefore, we get the following results.

Theorem 3. Suppose conditions in (29) hold and , Then the equilibrium is locally asymptotically stable.

Proof. For defined in (28), we have

and the zeros of (30) are

If , then . Hence, and are negative. Thus, has no positive root. Since , it follows that has no positive roots. Therefore, the equilibrium is locally asymptotically stable.

Theorem 4. Suppose that conditions in (29) hold and that

(i)either ,(ii)or , and ,

where satisfies with given in (23). Then the equilibrium is locally asymptotically stable if and is unstable if , where

Furthermore, when , a Hopf bifurcation occurs; that is, a family of periodic solutions are bifurcated from as passes through the critical value .

Proof. If , then it follows from (28) that and . Thus, (27) has at least one positive root. If , then is one positive root of . Since , it follows that has at least one positive root. As a consequence, (27) has a positive root . This implies that the characteristic equation (23) has a pair of purely imaginary roots.
Let be the eigenvalue of (23) such that and . If there exists , such that Then by the first equation of (26), we have

and then

By taking the derivative of the characteristic equation (23) with respect to , we have

Thus,

We can also verify the following transversality condition [16]:

Therefore, if , then a Hopf bifurcation occurs; that is, a family of periodic solutions appear as passes through the critical value .

4. Numerical Simulations and Discussion

In this section, we present some numerical simulation of system (9) at different parameters to illustrate our analytic results.

Example 5. Let and we consider the following system:

In this case, we obtain only one boundary equilibrium , and the conditions of (ii) in Theorem 2 are satisfied. Therefore, the equilibrium is locally asymptotically stable. The behaviors of , , and with respect to are shown in Figure 1. According to the graph in Figure 1, the predator population decreases and eventually the predator species becomes extinct. As for prey species, the population of both species reaches the equilibrium as the predator population approaches zero.

Figure 1: The behavior of , , and with respect to for Example 5.

Example 6. As an example, consider the following system:

There is a positive equilibrium . By direct calculation, we have , , and . From Theorem 4, there is a critical value , and the equilibrium is locally asymptotically stable as . A Hopf bifurcation occurs as and the equilibrium becomes unstable and stable periodic solutions exist for . Figures 2 and 3 show the solutions of that system corresponding to and . Furthermore, a bifurcation diagram for Example 6 is shown in Figure 4. This is an example when the predator and prey coexist permanently. If the time that juvenile takes to be mature is less than , then both predators and prey population reach the nonzero equilibrium. They can coexist permanently. On the other hand, if the time that juvenile predators takes to become mature and ready to hunt is longer than , then the population of both predator and prey species becomes unstable and periodic.

Figure 2: The behavior of , , and with respect to for Example 6 with .

Figure 3: The behavior of , , and with respect to for Example 6 with .

Figure 4: Bifurcation diagram for Example 6.

Example 7. As an example, consider the following system:

In this case, we obtain three positive equilibria , , and . By Theorem 4, we know that the positive equilibrium is locally asymptotically stable when and unstable when , and the system can also undergo a Hopf bifurcation at the equilibrium when crosses through the critical value ; see Figures 5 and 6. Similarly, at the positive equilibrium , a Hopf bifurcation occurs as . Hence, the positive equilibrium is locally asymptotically stable when and unstable when ; see Figures 7 and 8. For equilibrium , (27) has no positive real root. Hence, equilibrium is locally asymptotically stable and no stability switches can occur; see Figure 9. On this last example, what occurs at the first two equilibria and is similar to the previous example where Hopf bifurcations occur at ’s and the stable limit cycle exists. The predator and prey species coexist. As for the other equilibrium , the system is locally asymptotically stable where predator and prey species also coexist. Finally, the bifurcation diagram for Example 7 is shown in Figure 10.

Figure 5: The behavior of , , and with respect to for equilibrium with .

Figure 6: The behavior of , , and with respect to for equilibrium with .

Figure 7: The behavior of , , and with respect to for equilibrium with .

Figure 8: The behavior of , , and with respect to for equilibrium with .

Figure 9: The behavior of , , and with respect to for equilibrium with .

Figure 10: Bifurcation diagram for Example 7.

5. Concluding Remarks

In this paper, we find that system (7) has complex dynamics behavior. By Theorem 2, our results show that the predator and prey coexist permanently if ; that is, the adult predators’ reproductive rate at the peak of prey abundance is larger than its death rate. On the other hand, the predator faces extinction, if , which implies that the predator’s possible highest reproductive rate is less than its death rate. We also find the stability switches of the positive equilibrium due to the increase of . Our results show that when there is no time delay or the time delay is very small, the positive equilibrium is locally asymptotically stable. As the time delay increases to the critical value, it can cause a stable equilibrium to become unstable and Hope bifurcation can occur.

Conflicts of Interest

The author declares that there are no conflicts of interest regarding the publication of this article.

Acknowledgments

This research was supported by Chiang Mai University, Thailand.

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