Abstract and Applied Analysis

Volume 2013 (2013), Article ID 129072, 11 pages

http://dx.doi.org/10.1155/2013/129072

Research Article

Stochastic Delay Population Dynamics under Regime Switching: Permanence and Asymptotic Estimation

Zheng Wu, Hao Huang, and Lianglong Wang

School of Mathematical Sciences, Anhui University, Hefei 230039, China

Received 30 January 2013; Accepted 14 March 2013

Academic Editor: Yuming Chen

Copyright © 2013 Zheng Wu 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.

Abstract.

This paper is concerned with a delay Lotka-Volterra model under regime switching diffusion in random environment. Permanence and asymptotic estimations of solutions are investigated by virtue of -function technique, -matrix method, and Chebyshev's inequality. 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 , , , . There is an extensive literature concerned with the dynamics of this delay model and have had lots of nice results and we here only mention the work of Ahmad and Rao [1], Bereketoglu and Győri [2], and Freedman and Ruan [3], and in particular, the books by Gopalsamy [4], Kolmanovskiĭ 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 behavior 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 [7–18]. 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 terms 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 attention 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 (see [19–25] 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 obtained 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].

In [28], some results have been obtained for (4), such as existence of global positive solutions, stochastically ultimate boundedness, and extinction. In contrast to the existing results, our new contributions in this paper are as follows.

(i)The stochastic permanence of solutions is derived.(ii)The asymptotic estimations of the solutions are obtained, which is related to the stationary probability distribution of the Markov chain.

The rest of the paper is arranged as follows. For convenience of the reader, we briefly recall the main result of [28] in Section 2. The main results of this paper are arranged in Sections 3 and 4. Section 3 is devoted to the stochastic permanence. The asymptotic estimations of the solutions are obtained in Section 4. Finally, an example is given to illustrate our main results.

2. Properties of the 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, for all and denote by the nonnegative cone in , that is, for all . If is a vector or matrix, its transpose is denoted be . If is a matrix, its trace norm is denoted by , whilst it 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 , please see 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

For the fundamental theory of stochastic differential equations, one can refer to [12, 29].

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

To proceed, we first state a result, whose proof can be found in [28].

Assumption 1. Assume that there exist positive numbers such that

where .

Assumption 2. Assume that there exist positive numbers such that

where .

Assumption 3. Assume that there exist positive numbers such that

where and .

Theorem 1. 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 almost surely.

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

Solutions of (4) are stochastically ultimately bounded under Assumption 2; that is, for any , there exists a positive constants , such that the solutions of (4) with any positive initial value have the property that

Under Assumption 3, for any given initial data , the solution of (4) has the properties that

where . Particularly, if  , then

That is, the population will become extinct exponentially with probability 1.

3. Stochastic Permanence

Definition 2. Equation (4) is said to be stochastically permanent if, for any , there exist positive constants , such that

where is the solution of (4) with any positive initial value.

It is obvious that if a stochastic equation is stochastically permanent, its solutions must be stochastically ultimately bounded. For convenience, let

and we impose the following assumptions.

Assumption 4. For some , (for all ).

Assumption 5. .

Assumption 6. For each , .

Let be a vector or matrix. By , we mean all elements of are positive, and by , we mean all elements of are nonnegative. We also adopt here the traditional notation by letting

Lemma 3 (see [29]). If has all of its row sums positive, that is,

then .

Lemma 4 (see [29]). If , then the following statements are equivalent:

(1)is a nonsingular -matrix.(2)All of the principal minors of are positive; that is,

(3) is semipositive; that is, there exists in such that .

Lemma 5 (see [23]). Assumptions 4 and 5 imply that there exists a constant such that the matrix

is a nonsingular -matrix, where

Lemma 6 (see [23]). Assumption 6 implies that there exists a constant such that the matrix is a nonsingular -matrix.

Lemma 7. If there exists a constant such that is a nonsingular -matrix and , then the global positive solution of (4) has the property that

where is a fixed positive constant (defined by (42) in the proof).

Proof. Define on . Then

Define also

Let . Applying the generalized It formula, we derive from (28) that

dropping from , and from , , respectively. By Lemma 4, for given , there is a vector such that

namely,

Define the function by . It follows from the generalized It formula that

where

It is easy to see that, for all ,

where is a positive constant, while

Consequently,

Substituting (37) into (34) yields

Now, choose a constant sufficiently small such that it satisfies , that is,

Then, by the generalized Itô formula again,

It is computed that

where

This implies

For , note that . Consequently,

The required assertion (27) is obtained.

Assumption 7. Assume that there exist positive numbers such that

where . Moreover for each , .

Theorem 8. Under Assumptions 4, 5, and 7, (4) is stochastically permanent.

The proof is a simple application of the Chebyshev’s inequality, Lemmas 5 and 7, and Theorem 1(3). Similarly, we have the following result.

Theorem 9. Under Assumptions 6 and 7, (4) is stochastically permanent.

4. Asymptotic Properties

Lemma 10. Under Assumption 2, for any given initial data , the solution of (4) with any positive initial value has the property

Proof. By Theorem 1 (1), the solution will remain in for all with probability 1. Denote , on . It is known that

We can also derive from this that

From (15), we know that and . By the well-known BDG’s inequality [29] and the Hölder’s inequality, we derive that

Combining the inequality above with

we get that

Recalling the following inequality for any  , we obtain

It is following from (52) that there is a positive constant such that

Let be arbitrary. Then, by Chebyshev’s inequality, we have

Applying the well-known Borel-Cantelli lemma [24], we obtain that for almost all

holds for all but finitely many . Hence, there exists a , for almost all , for which (55) holds whenever . Consequently, for almost all , if and ,

Therefore, . Letting , we obtain the desired assertion (46).

Lemma 11. If there exists a constant such that is a nonsingular -matrix and for each , , then the global positive solution of SDE (4) has the property that

Proof. Let be the same as defined by (29); for convenience, we write . Applying the generalized It formula, for the fixed constant , we derive from (37) that

By (43), there exists a positive constant such that

Let be sufficiently small such that

Then (58) implies that

It is computed that

On the other hand, by the BDG’s inequality, we derive that

Substituting this and (62) into (61) gives

Making use of (59) and (60), we obtain that

Let be arbitrary. Then, by Chebyshev inequality, we have

Applying the Borel-Cantelli lemma, we obtain that for almost all

holds for all but finitely many . Hence, there exists an integer , for almost all , for which (67) holds whenever . Consequently, for almost all , if and ,

Therefore . Let , we obtain the desired assertion

Recalling the definition of , this yields , which further implies

This is our required assertion (57).

Assumption 8. Assume that there exist positive numbers such that

where and . Moreover for each , .

Theorem 12. Under Assumptions 4, 5, and 8, for any given initial data , the solution of (4) obeys

where .

Proof. By Theorem 1(1), the solution will remain in for all with probability 1. Define , for . By generalized It formula, one has

From Lemmas 5, 10, and 11, it follows that

By (73), it has

Meanwhile,

Substituting (76) into (75) yields

Hence,

Applying the strong law of large numbers for martingales, we have

Dividing both sides of (78) by and letting , we obtain that

which implies the required assertion (72).

On the other hand, it is observed from (75)-(76) that

Hence,

Consequently, one gets that

which implies the other required assertion (4.12).

Similarly, using Lemmas 6, 10, and 11, we can show the following

Theorem 13. Under Assumptions 5 and 8, for any given initial data , the solution of (4) obeys

where .

5. Examples

In this section, an example is given to illustrate our main results.

Example 1. Consider the two-species Lotka-Volterra system with regime switching described by

where , , , ,

and is a right-continuous Markov chain taking values in , and and are independent. Here

Let . It is easy to compute that

Then

Moreover, , , , and .

By Theorem 1(1), the solution of (85) will remain in for all with probability 1. Let the generator of the Markov chain be

By solving the linear equation , we obtain the unique stationary (probability) distribution . Then

Therefore, by Theorems 8 and 12, (85) is stochastically permanent and the solutions have the following properties:

Acknowledgments

The authors are grateful to Editor Proffessor Yuming Chen for the diligent work. This work is supported by Research Fund for Doctor Station of Ministry of Education of China (no. 20113401110001, no. 20103401120002), TIAN YUAN Series of Natural Science Foundation of China (no. 11126177, no. 11226247), Key Natural Science Foundation (no. KJ2009A49), 211 Project of Anhui University (no. KJJQ1101), Anhui Provincial Nature Science Foundation (no. 1308085MA01, no. 1308085QA15, and no. 1208085QA15), and Foundation for Young Talents in College of Anhui Province (no. 2012SQRL021).

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