Research Article | Open Access

Zheng Wu, Hao Huang, Lianglong Wang, "Stochastic Delay Population Dynamics under Regime Switching: Global Solutions and Extinction", *Abstract and Applied Analysis*, vol. 2013, Article ID 918569, 10 pages, 2013. https://doi.org/10.1155/2013/918569

# Stochastic Delay Population Dynamics under Regime Switching: Global Solutions and Extinction

**Academic Editor:**Zhiming Guo

#### Abstract

This paper is concerned with a delay Lotka-Volterra model under regime switching diffusion in random environment. By using generalized ItÃ´ formula, Gronwall inequality and Youngâ€™s inequality, some sufficient conditions for existence of global positive solutions and stochastically ultimate boundedness are obtained, respectively. Finally, an example is given to illustrate the main results.

#### 1. Introduction

The delay differential equation has been used to model the population growth of certain species and is known as the delay Lotka-Volterra model or the delay logistic equation. The delay Lotka-Volterra model for interacting species is described by the -dimensional delay differential equation where , , , and . There is an extensive literature concerned with the dynamics of this delay model and have had lots of nice results. We here only mention Ahmad and Rao [1], Bereketoglu and GyÅ‘ri [2], Freedman and Ruan [3], and in particular, the books by Gopalsamy [4], KolmanovskiÄ and Myshkis [5], and Kuang [6], among many others.

In the equations above, the state denotes the population sizes of the species. Naturally, we focus on the positive solutions and also require the solutions not to explode at a finite time. To guarantee the positive solutions without explosion (i.e., the global positive solutions), some conditions are in general needed to impose on the system parameters. For example, it is generally assumed that , and for (1) while much more complicated conditions are required on matrices and for (2) [7] (and the references cited therein).

On the other hand, population systems are often subject to environmental noise, and the system will change significantly, which may change the dynamics of solutions significantly [8, 9]. It is therefore necessary to reveal how the noise affects the dynamics of solutions for the delay population systems. In fact, many authors have discussed population systems subject to white noise [7â€“18]. Recall that the parameter in (2) represents the intrinsic growth rate of species . In practice we usually estimate it by an average value plus an error term. According to the well-known central limit theorem, the error term follows a normal distribution. In term of mathematics, we can therefore replace the rate by , where is a white noise (i.e., is a Brownian motion) and represents the intensity of noise. As a result, (2) becomes a stochastic differential equation (SDE, in short) where . We refer to [7] for more details.

To our knowledge, much of the attention paid to environmental noise is focused on white noise. But another type of environmental noise, namely, color noise, say telegraph noise, has been studied by many authors ([19â€“25] and the references cited therein). In this context, telegraph noise can be described as a random switching between two or more environmental regimes, which differ in terms of factors such as nutrition or rain falls [23, 24]. Usually, the switching between different environments is memoryless and the waiting time for the next switch has an exponential distribution. This indicates that we may model the random environments and other random factors in the system by a continuous-time Markov chain with a finite state space . Therefore stochastic delay population system (3) in random environments can be described by the following stochastic model with regime switching: The mechanism of ecosystem described by (4) can be explained as follows. Assume that initially, the Markov chain . Then the ecosystem (4) obeys the SDE until the Markov chain jumps to another state, say, . Therefore, the ecosystem (4) satisfies the SDE for a random amount of time until the Markov chain jumps to a new state again.

It should be pointed out that the stochastic population systems under regime switching have received much attention lately. For instance, the stochastic permanence and extinction of a logistic model under regime switching were considered in [20, 24], asymptotic results of a competitive Lotka-Volterra model in random environment are obtain in [25], a new single-species model disturbed by both white noise and colored noise in a polluted environment was developed and analyzed in [26], and a general stochastic logistic system under regime switching was proposed and was treated in [27].

Equation (4) describes the dynamics of populations. This paper is concerned with the positive global solutions, ultimate boundedness and extinction. The stochastic permanence and asymptotic estimations of solutions will be investigated in the next note [28].

This paper is organized as follows. In the next section, some sufficient conditions for global positive solutions for any initial positive value are given by using generalized ItÃ´ formula, Gronwall inequality, and -function techniques. In Section 3, the stochastically ultimate boundedness of solutions is obtained by virtue of Young's inequality. Section 4 is devoted to the extinction of solutions. Finally, an example and its numerical simulation are given to illustrate our main results.

#### 2. Global Positive Solution

Throughout this paper, unless otherwise specified, let , be a complete probability space with a filtration satisfying the usual conditions (i.e., it is right continuous and contains all -null sets). Let , be a scalar standard Brownian motion defined on this probability space. We also denote by the positive cone in , that is , and denote by the nonnegative cone in , that is . If is a vector or matrix, its transpose is denoted by . If is a matrix, its trace norm is denoted by , and its operator norm is denoted by . Moreover, let and denote by the family of continuous functions from to .

In this paper we will use a lot of quadratic functions of the form for the state only. Therefore, for a symmetric matrix , we naturally introduce the following definition For more properties of , refer to the appendix in [7].

Let be a right-continuous Markov chain on the probability space, taking values in a finite state space , with the generator given by where is the transition rate from to , and if , while . We assume that the Markov chain is independent of the Brownian motion . It is well known that almost every sample path of is a right-continuous step function with a finite number of jumps in any finite subinterval of . As a standing hypothesis we assume in this paper that the Markov chain is irreducible. This is a very reasonable assumption as it means that the system can switch from any regime to any other regime. This is equivalent to the condition that for any , one can find finite numbers such that . Under this condition, the Markov chain has a unique stationary (probability) distribution which can be determined by solving the following linear equation: subject to We refer to [12, 29] for the fundamental theory of stochastic differential equations.

For convenience and simplicity in the following discussion, for any constant sequence let

As in system (4) denotes populations size at time , it should be nonnegative. Thus for further study, we must give some condition under which (4) has a unique global positive solution.

Theorem 1. *Assume that there are positive numbers and such that
**
where . Then for any given initial data , there is a unique solution to (4) on and the solution will remain in with probability 1, namely, for all a.s.*

* Proof. *Since the coefficients of the equation are locally Lipschitz continuous, for any given initial data , there is a unique maximal local solution on , where is the explosion time. To show that the solution is global, we need to show that a.s.

Let be sufficiently lager such that
For each integer , define the stopping time
where throughout this paper we set (as usual denotes the empty set). Clearly, is increasing as . Set , where a.s. If a.s., then a.s. and a.s. for all . In other words, to complete the proof, one should show that a.s. Define by
The nonnegativity of this function can be seen from . Let and be arbitrary. For , it is easy to see by the generalized ItÃ´ formula that
where is defined by
and . Using condition (12) we compute

Moreover, there is a constant such that

Substituting these inequalities into (17) yields
Noticing that , we compute

It follows from (20) and (21) that
where is a positive constant. Substituting this inequality into (16) yields
Compute
and, similarly
Substituting these inequalities into (23) gives
where .

By the Gronwall inequality, we obtain that
Noting that for every ,
one has by (27) that
where is the indicator function of . Letting gives and hence . Since is arbitrary, we must have , so as required.

*Assumption 1. *Assume that there exist positive numbers such that
where .

The following theorem is easy to verify in applications, which will be used in the sections below.

Theorem 2. *Under Assumption 1, for any given initial data , there is a unique solution to (4) on and the solution will remain in with probability 1, namely, for all a.s.*

* Proof. *Define by . The non-negativity of this function can be seen from , and then we have (16) and (17).

If , then . Consequently
Otherwise for . In this case, we also have that
Thus,
Denote . By (33) and Assumption 1, one has
The rest of the proof is similar to that of Theorem 1 and omitted.

#### 3. Ultimate Boundness

Theorem 2 shows that solutions of the SDE (4) will remain in the positive cone . This nice property provides us with a great opportunity to discuss how solutions vary in in detail. In this section, we give the definitions of stochastically ultimate boundedness of the SDE (4) and some sufficient conditions under which solutions of SDE (4) are stochastically ultimate bounded.

*Definition 3. *The solutions of (4) are called stochastically ultimately bounded, if for any , there exists a positive constant , such that the solutions of (4) with any positive initial value have the property that

*Assumption 2. *Assume that there exist positive numbers such that
where .

Theorem 4. *Under Assumption 2, for any given initial data and any given positive constant , there are two positive constants and , such that the solution of (4) has the properties that
*

*Proof. *By Theorem 2, the solution will remain in for all with probability 1. If , we let and . Define . It has by the generalized ItÃ´ formula that
where is defined by
Meanwhile, by Assumption 2 and Youngâ€™s inequality, one gets
where
On the other hand,
by (41) and (43), we obtain that
which yields
Since , it has and the desired assertion (37) follows by setting . It is easy to verify this result, if . We omit its proof here.

Define . By the generalized ItÃ´ formula, it follows
where is defined by
By Assumption 2 and Youngâ€™s inequality again,
It is easy to compute
Moreover,
hence, we get
where . This implies immediately that
and the desired assertion (38) follows by setting .

*Remark 5. *From (37) of Theorem 4, there is a such that
Since is continuous, there is a such that
Let , we have
which implies that the th moment of any positive solution of (4) is bounded.

*Remark 6. *Conclusion (38) of Theorem 4 shows that the average in time of the th () moment of solutions of (4) will be bounded.

Theorem 7. *Solutions of (4) are stochastically ultimately bounded under Assumption 2.*

* Proof. *This can be easily verified by Chebyshevâ€™s inequality and Theorem 4.

#### 4. Extinction

*Assumption 3. *Assume that there exist positive numbers such that
where and .

Theorem 8. *Under Assumption 3, for any given initial data , the solution of (4) has the properties that
**
where . Particularly, , then
**
That is, the population will become extinct exponentially with probability 1.*

* Proof. *By Theorem 2, the solution will remain in for all with probability 1. Define
where . Then
By the generalized ItÃ´ formula,
It is computed
Substituting these two inequalities into (61) yields
where is a martingale defined by
The quadratic variation of this martingale is
hence
Applying the strong law of large numbers for martingales [29], we therefore have
It finally follows from (64) by dividing on the both sides and then letting that
which is the required assertion (57).

Similarly, we can prove the following conclusions.

Theorem 9. *Assume that Assumption 3 holds. Assume moreover that the noise intensities are sufficiently large in the sense that
**
then for any given initial data , the solution of (4) has the properties that
**
where
**
That is, the population will become extinct exponentially with probability 1.*

* Proof. *Let be the same as defined in the proof of Theorem 8, so we have (60), (61), and (62). It is also computed
where and . Substituting (62) and (73) into (61) yields
Note that , the th element of the matrix is positive by (70). It is therefore easy to verify
where has been defined in the statement of the theorem. Substituting this inequality into (74) yields
The rest of the proof is similar to that of Theorem 8 and omitted.

#### 5. Examples

In this section, an example and corresponding numerical simulations are given to illustrate our main results.

*Example 10. *Consider the two-species Lotka-Volterra system with regime switching described by
where , , ,
and is a right-contiuous Markov chain taking values in , and and are independent. Here