This paper discusses the asymptotic behavior of a class of three-species stochastic model with regime switching. Using the Lyapunov function, we first obtain sufficient conditions for extinction and average time persistence. Then, we prove sufficient conditions for the existence of stationary distributions of populations, and they are ergodic. Numerical simulations are carried out to support our theoretical results.

1. Introduction

In recent years, the dynamic relationship between the predator and prey has become one of the research hotspots in ecology and mathematical ecology because of its universal importance. In particular, the predator-prey model is a typical inhibition model, which greatly changes the understanding of the existence and development of basic laws in the biological community. The following model is one of the Volterra models for the three species of the predator-prey system:where denote the densities of prey, predator, and top-predator population at time , respectively. The parameters , and are positive constants that stand for the intrinsic growth rate of the species , the death rate of the species , and the death rate of the species , respectively. The coefficient are the intraspecific competition in the resource, represent the rate of consumption, and represent the contribution of prey to the growth of predator. As a matter of fact, there are many extensive studies in the literatures concerned with three-species predator-prey systems (see ., [16]). For example, Krikorian [1] considered the Volterra predator-prey model in a three-species and explained the global properties of its solution. Zhou [2] investigated the existence and global stability of the positive periodic solutions of delayed discrete food chains with omnivory. Hsu [5] considers a three-species Lotka–Volterra food web model with omnivores, which is defined as feeding on more than one nutritional level.

In addition, the population system is always affected by environmental noise, which is very important for discovering the nature of random system from the biological point of view. Generally speaking, there are various types of environmental noise, e.g., white or color noise. First, let us consider a simple color noise, such as telegraph noise (see ., [713]). This kind of colored noise can be explained by the transformation between two or more environmental models, which can be considered to be different due to rainfall or nutrition in the population model. Therefore, we can model state switching through a finite-state Markov chain. Let be a right-continuous Markov chain on the probability space, taking values in a finite-state space with the generator given bywhere and is the transition rate from state to state and if while . Then, we can incorporate the regime switch into the three-species food chain model (1) to obtainwith initial value . Here, we assume that the coefficients are all positive for .

Next, we consider other types of environmental noise, namely, the white noise (see ., [1422]). In particular, Mao [14] showed that different structures of white noise may have different effects on the population systems; Mao et al. [15] revealed that the environmental noise can suppress a potential population explosion. So, we assume the intrinsic growth rate is disturbed withwhere are independent white noises and is a positive constant representing the intensity of the white noise. We assume that the Brownian motion is independent of the Markov chain . Therefore, system (3) becomes a three-species food chain stochastic model under regime switching:In this paper, we show that system (5) has the following properties:(i)The solution starting from anywhere in will remain in with probability 1.(ii)For any given initial value , there exists a positive constant such that the solution of system (5) has the following property: (iii)We show that if the noise is sufficiently large, the solution to system (5) will become extinct with probability 1. This is,

And we prove that the predator of system (5) will tend to extinction almost surly in some assumptions.(i)The persistent in time average is investigated under certain conditions, namely, the solution of system (5) with any initial value has the following property:(ii)In the case of noise being relatively small, there is a unique stationary distribution with ergodic property:where is the marginal stationary distribution of a solution of system (5). The key method used in this paper is to construct Lyapunov functions. This Lyapunov function analysis for stochastic differential equations has been used by many authors (see [9, 15]).

The paper is organized as follows. In Section 2, we give the unique existence and boundedness of the solution. In Section 3, we show the sufficient conditions for extinction and persistence in time average, respectively, which have closed relations with the stationary probability distribution of the Markov chain. Then, in Section 4, by using Lyapunov function, the sufficient conditions for the stationary distribution and ergodicity of the solution of system (8) are established. Finally, we illustrate our main results through an example in Section 5.

2. Preliminaries

Throughout this paper, unless otherwise specified, let be a complete probability space with a filtration satisfying the usual conditions (i.e., it is increasing and right continuous while contains all P-null sets). Let denote the positive cone of , namely, , and denote the nonnegative cone of , this is, . For convenience and simplicity in the following discussion, denote . If is a vector or matrix, its transpose is denoted by . Let denote the family of all nonnegative real-value function which are continuously twice differentiable in and once in .

Furthermore, as a standing hypothesis, we assume that the Markov chain is in this paper. This is very reasonable as it means that the system will switch from any regime to any other regime. This is equivalent to the condition that, for any , one can find finite numbers such that . Note that always has an eigenvalue 0. The algebraic interpretation of irreducibility is rank . Under this condition, the Markov chain has a unique stationary distribution , which can be determined by solving the following linear equation subject to .

For convenience, system (5) can be rewritten into the following form:where

For any twice continuously differentiable function , we define bywhere

From the biological sense, we are only interested in the positive solutions of equation (5). Therefore, we first prove the existence and uniqueness of the global positive solution.

Theorem 1. For any given initial value , there is a unique positive solution of system (5), and the solution will remain in with probability 1.

Proof. Note that the coefficients of system (5) are local Lipschitz continuous for the given initial value . So, there is a unique maximal local solution on , where is the explosion time (see [8, 15]). To show this solution is global, we need to show that a.s. Since the initial value is positive and bounded, there is a number large enough such that . For each integer , define the stopping time:where (as usual denotes the empty set). Clearly, is increasing as . Set , hence a.s. If we can show that a.s., then a.s. and a.s. for all . In other words, to complete the proof all we need to show that a.s. If this statement is false, there is a pair of constants and such that . Hence, there is an integer such thatDefine the -function bywhere are positive constants. The nonnegativity of this function can be seen from on . Then, from the generalized Itô formula (10), we haveLet . By the definition of , for any , we haveThus,In addition, it is straightforward to see that and there exists a constant such that . Then, it follows from (15) and (17) thatwhere is a positive constant. Then, from Itô formula (10), we haveThe Gronwall inequality [9] implies thatSet for and by (13), we know . Note that, for every , there is some which equals either or ; hence, is no less than or , where .
Consequently,It then follows from (20) thatwhere is the indicator function of . Letting leads to the contradictionThis completes the proof.

Theorem 2. For any given initial value , there is a constant such that the solution of system (5) satisfies

Proof. By Theorem 1, the solution will remain in for all with probability 1.
Define a function:where are positive constants. By virtue of the generalized Itô’s formula (10), we haveThis isFurthermore, for any given positive constant , we haveNote thatSimilar to (17), we obtainCombining (28)–(30), we can obtainHence,By comparison theorem [9], we obtainwhich implies that there is a such thatIn addition, is continuous and there exists a such thatLet , thenThe proof is complete.

3. Extinction and Persistence in Time Average

In the previous section, we have proved that the solution of system (5) with a positive initial value remains in the positive cone . In order to further study asymptotic properties of system (5), in this section, we investigate the persistence in time average and extinction of system (5) under a certain condition. We first give some assumptions and related definitions.

Assumption 1. .

Assumption 2. .

Assumption 3. ,

Definition 1. The three species are extinct if .
The three species are persistent in time average if.

Theorem 3. If Assumption 1 holds, then for any given initial value , the solution of system (5) has the property that

Particularly, if holds, then

Proof. By the generalized Itô formula (10), we yieldBy Assumption 1 and basic inequality , integrating (40) from 0 to , we obtainwhereNoticing thatby the strong law of large numbers for martingales [9, 15], we therefore haveSimilarly, .
It finally follows from (41), by dividing by on both sides and then letting , and we obtainSo, we obtain the desired assertion.
Let us consider the following system:According to Luo [11], we have shown thatBy the comparison principle, we know that .

Theorem 4. If Assumption 2 holds, then for any initial value , the predator of system (5) will tend to extinction almost surly.

Proof. Note thatBy the comparison principle and (47), we haveTaking Itô formula to the second equation of system (5), we obtainIntegrating both sides of (50) results inMerging (49) to (51), by Assumption 2 and , we obtain