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59 (2014) APPLICATIONS OF MATHEMATICS No. 3, 331–343
PERSISTENCE AND EXTINCTION OF A STOCHASTIC DELAY
PREDATOR-PREY MODEL UNDER REGIME SWITCHING
Zhen Hai Liu, Qun Liu, Nanning
(Received July 17, 2012)
Abstract. The paper is concerned with a stochastic delay predator-prey model under
regime switching. Sufficient conditions for extinction and non-persistence in the mean
of the system are established. The threshold between persistence and extinction is also
obtained for each population. Some numerical simulations are introduced to support our
main results.
Keywords: persistence; extinction; Markov switching; delay; stochastic perturbations
MSC 2010 : 34B16, 34C25
1. Introduction
The deter ministic delay predator-prey model can be expr e ssed as follows:
dx(t)/dt = x(t)[r
1
(t) − a
11
(t)x(t) − a
12
(t)x
θ
11
(t − τ
1
) −a
13
(t)y
θ
12
(t)],(1)
dy(t)/dt = y(t)[−r
2
(t) + a
21
(t)x(t) − a
22
(t)y(t) − a
23
(t)y
θ
21
(t − τ
2
)],
where x(t) and y(t) are the prey population density and the predator p opulation
density at time t, respectively; r
1
(t) and r
2
(t) represent the intrinsic growth rates
of the prey and the preda tor at time t, respe c tively; a
11
(t) and a
22
(t) denote the
density-dependent coefficients of the prey and the predator, respectively; a
12
(t) is the
capturing r ate of the predator and a
21
(t) denotes the rate of c onversion of nutrients
into the reproduction of the pr e dator; a
13
(t) provides a measure of intra-specific
interference and a
23
(t) pr ovides a measure of inter-specific interference; τ
1
and τ
2
are
two positive constants which stand for the time delays; θ
11
, θ
12
, θ
21
> 0 and θ
11
, θ
21
The research has been supported by NNSF of China Grant Nos. 11271087, 61263006.
331
![](https://csdnimg.cn/release/download_crawler_static/15539074/bg2.jpg)
provide nonlinear measures of intra-sp e c ific interference, θ
21
provides a nonlinear
measure of inter-specific interference; r
i
(t) and a
ij
(t) are positive continuous bounded
functions on R
+
= [0 , +∞), i = 1, 2; j = 1, 2, 3.
On the other hand, in the real world, population system is inevitably affected
by the environmental noise (see e.g. [2], [13], [11], [12]). As we all know, there are
various types of environmental nois es. First, we sha ll consider a classical colored
noise, say the telegraph no ise. May [10 ] pointed out that due to the environmental
noise, the birth rate, carrying capacity, competition c oefficient and other parameters
involved in the sys tem are often affected by the telegraph noise. Several authors
(see e.g. [9 ], [7], [14], [4]) have revealed that we can model the telegraph noise by a
continuous-time Markov chain γ(t), t > 0 with a finite-state space S = {1, 2, . . . , m}.
Let γ(t) be generated by Q = (q
ij
), that is,
P{γ(t + ∆t) = j|γ(t) = i} =
(
q
ij
∆t + o(∆t) if j 6= i;
1 + q
ii
∆t + o(∆t) if j = i,
where q
ij
> 0 for i, j = 1, 2, . . . , m with j 6= i and
m
P
j=1
q
ij
= 0 for i = 1, 2, . . . , m.
Then model (1) will become
(2)
dx(t)/dt = x(t)[r
1
(γ(t)) − a
11
(γ(t))x(t) − a
12
(γ(t))x
θ
11
(t − τ
1
) − a
13
(γ(t))y
θ
12
(t)],
dy(t)/dt = y(t)[−r
2
(γ(t)) + a
21
(γ(t))x(t) − a
22
(γ(t))y(t) − a
23
(γ(t))y
θ
21
(t − τ
2
)].
The mechanism of system (2) is explained as follows. Assume that γ(0) = κ ∈ S ,
then (2) satisfies
dx(t)/dt = x(t)[r
1
(κ) − a
11
(κ)x(t) − a
12
(κ)x
θ
11
(t − τ
1
) − a
13
(κ)y
θ
12
(t)],
dy(t)/dt = y(t)[−r
2
(κ) + a
21
(κ)x(t) − a
22
(κ)y(t) − a
23
(κ)y
θ
21
(t − τ
2
)]
for a random amount of time until γ(t) jumps to another state, say ς ∈ S . Then
the system obeys
dx(t)/dt = x(t)[r
1
(ς) − a
11
(ς)x(t) − a
12
(ς)x
θ
11
(t − τ
1
) − a
13
(ς)y
θ
12
(t)],
dy(t)/dt = y(t)[−r
2
(ς) + a
21
(ς)x(t) − a
22
(ς)y(t) − a
23
(ς)y
θ
21
(t − τ
2
)]
for a random amount of time until γ(t) jumps to a new state again.
Further , let us consider the white nois e. Recall that r
1
(i) r e presents the intrinsic
growth rate in regime i (i ∈ S ). We estimate it by an error term plus an average
332
![](https://csdnimg.cn/release/download_crawler_static/15539074/bg3.jpg)
value. Sometimes, the error term follows a normal dis tribution. Consequently, we
can replace r
1
(i) by r
1
(i) + σ
11
(i)
˙
B
11
(t) (see e.g. [7], [14], [4]), where
˙
B
11
(t) is a
white noise and σ
2
11
(i) stands for the intensity of the white noise. In the same way,
−a
11
(i), −a
12
(i), −a
13
(i), −r
2
(i), a
21
(i), −a
22
(i) and −a
23
(i) will become −a
11
(i) +
σ
12
(i)
˙
B
12
(t), −a
12
(i) + σ
13
(i)
˙
B
13
(t), −a
13
(i) + σ
14
(i)
˙
B
14
(t), −r
2
(i) + σ
21
(i)
˙
B
21
(t),
−a
21
(i)+σ
22
(i)
˙
B
22
(t), −a
22
(i)+σ
23
(i)
˙
B
23
(t) and −a
23
(i)+σ
24
(i)
˙
B
24
(t) (see e.g. [8]).
Then we obtain the following stochastic delay predator-prey model under regime
switching:
dx(t) = x(t)[r
1
(γ(t)) − a
11
(γ(t))x(t) − a
12
(γ(t))x
θ
11
(t − τ
1
)(3)
− a
13
(γ(t))y
θ
12
(t)] dt + σ
11
(γ(t))x(t) dB
11
(t) + σ
12
(γ(t))x
2
(t) dB
12
(t)
+ σ
13
(γ(t))x(t)x
θ
11
(t − τ
1
) dB
13
(t) + σ
14
(γ(t))x(t)y
θ
12
(t) dB
14
(t),
dy(t) = y(t)[−r
2
(γ(t)) + a
21
(γ(t))x(t) − a
22
(γ(t))y(t) − a
23
(γ(t))y
θ
21
(t − τ
2
)] dt
+ σ
21
(γ(t))y(t) dB
21
(t) + σ
22
(γ(t))x(t)y(t) dB
22
(t)
+ σ
23
(γ(t))y
2
(t) dB
23
(t) + σ
24
(γ(t))y(t)y
θ
21
(t − τ
2
) dB
24
(t),
where B(t) =
B
11
(t) B
12
(t) B
13
(t) B
14
(t)
B
21
(t) B
22
(t) B
23
(t) B
24
(t)
is a given 2×4 dimensional Brow-
nian motion defined on a complete probability space (Ω, F , P) with a filtration
{F
t
}
t∈R
+
satisfying the usual conditions. Suppose that the Markov chain γ(·) is
independent of B(t). As the standing hypothesis, we a ssume that γ(·) has a unique
stationary distribution π = (π
1
, π
2
, . . . , π
m
) which can b e o bta ined by solving the
linear equation πQ = 0 subject to
m
P
i=1
π
i
= 1 and π
i
> 0, i ∈ S . Throughout this
article, we assume that min
i∈S
a
jj
(i) > 0, min
i∈S
a
jk
(i) > 0, min
i∈S
r
2
(i) > 0, min
i∈S
σ
2
jl
(i) > 0,
j 6= k, j = 1, 2; k = 1, 2, 3; l = 1, 2, 3, 4 and define ˆν = max
i∈S
ν(i), ˘ν = min
i∈S
ν(i).
To begin with, we give the following useful definition.
Definition 1. 1. The population x(t) is said to go to extinction if lim
t→+∞
x(t) = 0.
2. The population x(t) is said to be nonpersis tent in the mean if hx(t)i
∗
= 0,
where hf(t)i =
R
t
0
f(s) ds/t, f
∗
= lim sup
t→+∞
f(t), f
∗
= lim inf
t→+∞
f(t).
The organization of this paper is as follows: In Section 2, we analyze the persistence
and extinction of a stochastic delay predator-prey model under regime switching.
Some simulation figures are provided to illustra te our main results in Section 3.
Finally we give some conclusions and discussio n.
333
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