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Qualitative analysis of a harvested predatorprey system with Hollingtype III functional response incorporating a prey refuge
Advances in Difference Equations volume 2012, Article number: 96 (2012)
Abstract
A predatorprey model with simplified Holling type III response function incorporating a prey refuge under sparse effect is considered. Through qualitative analysis of the model, at least two limit cycles exist around the positive equilibrium point with the result of focus value, the Hopf bifurcation under a prey refuge is obtained. We also show the influence of prey refuge. Numerical simulations are carried out to illustrate the feasibility of the obtained results and the dependence of the dynamic behavior on the prey refuge. Through the results of computer simulation, it is further shown that under certain conditions the model has three limit cycles surrounding the positive equilibrium point.
1 Introduction
The dynamical relationship between predators and their preys is one of the dominant subjects in ecology and mathematical ecology due to its universal importance, see [1–3]. Some of the empirical and theoretical work have investigated the effect of prey refuges, the refuges used by prey have a stabilizing effect on the considered interactions, and prey extinction can be prevented by the addition of refuges [4–8].
Huang et al. [9] studied the stability analysis of a preypredator model with Holling type III response function incorporating a prey refuge:
Motivated by the study of Huang et al. [9] and Ji and Wu [10], we consider the following predatorprey model with Holling type III response function incorporating a prey refuge under sparse effect:
where x(t) is the population density of the prey and y(t) is the population density of the predator at time t; r > 0 represents the intrinsic growth rate of the prey; k is the carrying capacity of the prey in the absence of predator and harvesting; m is a constant number of prey using refuges, which protects m of prey from predation; the term x^{2}/(a+x^{2}) denotes the functional response of the predator, which is known as Holling type III response function; u > 0 is the conversion factor denoting the number of newly born predators for each captured prey; d > 0 is the death rate of the predator.
From the point of view of human needs, the exploitation of biological resources and the harvest of population are commonly practiced in fishery, forestry, and wildlife management. Concerning the conservation for the longterm benefits of humanity, there is a wide range of interest in the use of bioeconomic modeling to gain insight into the scientific management of renewable resources.
The problem of predatorprey interactions under a prey refuge have been studied by some authors. For example, Hassel [2] showed that adding a large refuge to a model, which exhibited divergent oscillations in the absence of a refuge, replaced the oscillatory behavior with a stable equilibrium. McNair [11] obtained that a prey refuge with legitimate entryexit dynamics was quite capable of amplifying rather than damping predatorprey oscillations. McNair [12] showed that several kinds of refuges could exert a locally destabilizing effect and create stable, largeamplitude oscillations which would damp out if no refuge was present. Even now, prey refuges are widely believed to prevent prey extinction and damp predatorprey oscillations. For example, Kar [6] considered a LotkaVolterra type predatorprey system incorporating a constant proportion of prey using refuges m, which protects m of prey from predation, with Holling type II response function and Holling type III response function, respectively. Our results indicate that refuge had a stabilizing effect on preypredator interactions and the dynamic behavior very much depends on the prey refuge parameter m, point that increasing the amount of refuge could increase prey densities and lead to population outbreaks.
This article is organized as follows. Basic properties such as the existence, stability, and instability of the equilibria of the model and the boundedness of the solutions of the system (1.2) with positive initial values are given in Section 2. In Section 3, sufficient conditions for the global stability of the unique positive equilibrium are obtained. Section 4 is devoted to deriving the existence of limit cycle. In Section 5, we study the Hopf bifurcation of system (1.2). In Section 6, we analyze the influence of prey refuge and give numerical stimulations.
2 Basic properties of the model
Let \overline{{R}_{2}^{+}}=\left\{\left(x,\phantom{\rule{2.77695pt}{0ex}}y\right)x\ge 0,\phantom{\rule{2.77695pt}{0ex}}y\ge 0\right\}. For practical biological meaning, we simply study system (1.2) in \overline{{R}_{2}^{+}}. The main aim of this article is to study the existence and nonexistence of positive equilibrium of (1.2) by the effects of a prey refuge, that is to say, the existence and nonexistence of positive equilibrium of system (1.2) depend on the constant m ∈ [0, 1).
We make the following substitution for model (1.2)
Denoting new argument τ with t again, it gives
Solutions of system (2.1) are discussed as follows:

(1)
If u ≤ d, system (2.1) possesses only two equilibrium points on the region \overline{{R}_{2}^{+}}, they are the trivial solution O(0, 0) and the semitrivial solution in the absence of predator P_{1}(k, 0);

(2)
if u > d, system (2.1) admits three equilibrium points on the region \overline{{R}_{2}^{+}}, they are the trivial solution O(0, 0), the semitrivial solution in the absence of predator P_{1}(k, 0) and the unique positive constant solution P_{2}(x_{1}, y_{1}), where
{x}_{1}=\frac{1}{1m}\sqrt{\frac{da}{ud},}{y}_{1}=r\left[1\frac{1}{k\left(1m\right)}\sqrt{\frac{da}{ud}}\right]\frac{ua}{\left(ud\right){\left(1m\right)}^{2}}.
For the existence of positive constant solution P_{2}(x_{1}, y_{1}), it is necessary to assume that 1\frac{1}{k\left(1m\right)}\sqrt{\frac{da}{ud}}>\phantom{\rule{2.77695pt}{0ex}}0, then we derive that 0\le m<1\frac{1}{k}\sqrt{\frac{da}{ud}}.
It turns out that the nonconstant positive solutions of (2.1) may exist for some ranges of the parameter m. From the first equation of system (1.2), it is easy to derive that
Lemma 2.1. The solution (x(t), y(t)) of system (1.2) with the initial value x(0) > 0, y(0) > 0 is positive and bounded for all t ≥ 0.
Proof. We see that \frac{dx}{dt}\phantom{\rule{2.77695pt}{0ex}}{}_{x=k}=\frac{{\left(1m\right)}^{2}{k}^{2}y}{a+{\left(1m\right)}^{2}{k}^{2}}<0 with y > 0, so x = k is a untangent line of system (1.2). And the positive trajectory of system (1.2) goes through from its right side to its left side when it meets the line x = k.
Construct Dulac function w(x, y) = y + ux  l, computing w = 0 along the trajectories of system (1.2)
If l > 0 is large enough, we have \frac{dw}{dt}<0, where 0 < x < k. So the line y + ux = l goes through from its upside to its downside in the region {(x, y)  0 < x < k, 0 < y < +∞}. For system (1.2), constructing a Bendixson ring \hat{OABC} including P_{2}(x_{1}, y_{1}). Define \overline{OA}, \overline{AB}, \overline{BC} as the lengths of lines L_{1} = y = 0, L_{2} = x  k = 0, L_{3} = y + ux  l, respectively. The boundary line of the Bendixson ring \hat{OABC}\prime \text{s} is J. So, the orbits of system (1.2) enter into the interior of the Bendixson ring when it meets the boundary line J.
If the initial value p which is in the first quadrant is not in the \hat{OABC}, we can construct a curve J_{ p } in the same way, denoting p ∈ J_{ p }. Thus the positive trajectory L_{ p }^{+} which is pass through p goes through into the interior of J_{ p } at the end. Note that the point P_{2}(x_{1}, y_{1}) is the unique equilibrium point in the Bendixson ring \hat{OABC}. Through the Limit collection theory, we know that the trajectory L_{ p }^{+} goes through into the interior of the Bendixson ring \hat{OABC} at the end. Therefore, all of the solutions (x(t), y(t)) of system (1.2) with the initial value x(0) > 0, y(0) > 0 are positive and bounded. This completes the proof.
Lemma 2.2. (1) O(0, 0) is a stable node point;

(2)
if u ≤ d holds, P_{1}(k, 0) is a stable focus or a stable node;

(3)
if u > d holds, then P_{1}(k, 0) is a stable focus or a stable node for 1\frac{1}{k}\sqrt{\frac{da}{ud}}<m<1; P_{1}(k, 0) is a stable node point for 0\le m<1\frac{1}{k}\sqrt{\frac{da}{ud}}; P_{1}(k, 0) is a stable node point for m=1\frac{1}{k}\sqrt{\frac{da}{ud}}. P_{2}(x_{1}, y_{1}) is an unstable focus or an unstable node for 0\le m<1\frac{u+2d}{2dk}\sqrt{\frac{da}{ud}}; P_{2}(x_{1}, y_{1}) is a stable focus or a stable node for 1\frac{u+2d}{2dk}\sqrt{\frac{da}{ud}}<m<1\frac{1}{k}\sqrt{\frac{da}{ud}}; P_{2}(x_{1}, y_{1}) is a center or a focus for m=1\frac{u+2d}{2dk}\sqrt{\frac{da}{ud}}.
Proof. The Jacobian matrix of system (2.1) is given by

(1)
Since detJ(0, 0) = 0, O(0, 0) is a higherorder singular point. We make a transformation d\tau =\frac{1}{da}dt. Substituting this into (2.1), then replacing τ with t gives
\left\{\begin{array}{c}\frac{dx}{dt}=\frac{r}{d}{x}^{2}+\frac{r}{dk}{x}^{3}\frac{r{\left(1m\right)}^{2}}{da}{x}^{4}+\frac{r{\left(1m\right)}^{2}}{dak}{x}^{5}+\frac{{\left(1m\right)}^{2}}{da}{x}^{2}y\equiv {P}_{2}\left(x,y\right),\hfill \\ \frac{dy}{dt}=y\frac{\left(ud\right){\left(1m\right)}^{2}}{da}{x}^{2}y=y+{Q}_{2}\left(x,\phantom{\rule{2.77695pt}{0ex}}y\right).\hfill \end{array}\right.(2.2)
From y + Q_{2}(x, y) = 0, we can solve that y = φ(x) ≡ 0. Furthermore, we can derive that {P}_{2}\left(x,\phantom{\rule{2.77695pt}{0ex}}\phi \left(x\right)\right)=\frac{r}{d}{x}^{2}+\frac{r}{dk}{x}^{3}\frac{r{\left(1m\right)}^{2}}{da}{x}^{4}+\frac{r{\left(1m\right)}^{2}}{dak}{x}^{5}, so we have {\mathrm{\Delta}}_{n}=\frac{r}{d}\ne 0, n = 2. According to Zhang et al. [13], we can derive that O(0, 0) is a stable node point.

(2)
The Jacobian matrix of system (2.1) for the equilibrium point P_{1}(k, 0) is given by
J\left(k,\phantom{\rule{2.77695pt}{0ex}}0\right)=\left(\begin{array}{cc}r\left[ak{\left(1m\right)}^{2}{k}^{3}\right]\hfill & {\left(1m\right)}^{2}{k}^{2}\hfill \\ 0\hfill & da+\left(ud\right){\left(1m\right)}^{2}{k}^{2}\hfill \end{array}\right).
If u ≤ d, the eigenvalues of matrix r[ak  (1  m)^{2}k^{3}] and da + (u  d) (1  m)^{2}k^{2} are negative, hence P_{1}(k, 0) is a stable focus or a stable node.

(3)
If u > d, according to (2), we know that if da + (u  d)(1  m)^{2}k^{2} < 0, namely 1\frac{1}{k}\sqrt{\frac{da}{ud}}<m<1, then p = detJ(k, 0) > 0, hence P_{1}(k, 0) is a stable focus or a stable node; If da + (u  d) (1  m)^{2}k^{2} > 0, namely 0\le m<1\frac{1}{k}\sqrt{\frac{da}{ud}}, then detJ(k, 0) < 0, hence P_{1}(k, 0) is a saddle point. If m=1\frac{1}{k}\sqrt{\frac{da}{ud}}, then detJ(k, 0) = 0, thus P_{1}(k, 0) is a higherorder singular point. In this situation P_{1}(k, 0) and point P_{2}(x_{1}, y_{1}) are the same point. So P_{1}(k, 0) is a stable node point.
As detJ\left({x}_{1},\phantom{\rule{2.77695pt}{0ex}}{y}_{1}\right)=2\left(ud\right){\left(1m\right)}^{4}{x}_{1}^{3}{y}_{1}>0,
if 2\left(1m\right)\sqrt{\frac{da}{ud}}\frac{au+2da}{k\left(ud\right)}>0, namely 0\le m<1\frac{u+2d}{2dk}\sqrt{\frac{da}{ud}}, we derive that P < 0, then P_{2} (x_{1}, y_{1}) is an unstable focus or an unstable node; if 2\left(1m\right)\sqrt{\frac{da}{ud}}\frac{au+2da}{k\left(ud\right)}<0, namely 1\frac{u+2d}{2dk}\sqrt{\frac{da}{ud}}<m<1\frac{1}{k}\sqrt{\frac{da}{ud}}, we derive that P > 0, then P_{2}(x_{1}, y_{1}) is a stable focus or a stable node; If 2\left(1m\right)\sqrt{\frac{da}{ud}}\frac{au+2da}{k\left(ud\right)}=0, namely P = 0, then we can derive that P_{2}(x_{1}, y_{1}) is a center or a focus. The proof is completed.
From Lemma 2.2, if P=r{x}_{1}^{2}\left[2\left(1m\right)\sqrt{\frac{da}{ud}}\frac{au+2da}{k\left(ud\right)}\right]=0 holds, namely m=1\frac{u+2d}{2dk}\sqrt{\frac{da}{ud}}, P_{2}(x_{1}, y_{1}) is a center focus. We can make further conclusions:
Lemma 2.3. (1) if C_{0} > 0 holds, P_{2}(x_{1}, y_{1}) is a stable fine focus with order one;

(2)
if C_{0} < 0 holds, P_{2}(x_{1}, y_{1}) is an unstable fine focus with order one;

(3)
if C_{0} = 0 and C_{1} > 0 hold, P_{2}(x_{1}, y_{1}) is a stable fine focus with secondorder;

(4)
if C_{0} = 0 and C_{1} < 0 hold, P_{2}(x_{1}, y_{1}) is an unstable fine focus with secondorder.
where {C}_{0}=\frac{\mathrm{\Pi}}{2}\left(3G+3F+I+2DE5EG3DHG\right) and
Proof. First use the coordinate translation, that is translation the origin of coordinates into the point P_{2}(x_{1}, y_{1}). Then we assume
Replacing \stackrel{\u0304}{x}, \mathit{\u0233}, \stackrel{\u0304}{t} with x, y, t, respectively, it gives
We denoteA=\sqrt{\frac{2\left(ud\right){y}_{1}}{{x}_{1}}}>0 and make the following transformations u = x, v=\frac{1}{A}y, dτ = Adt, and replacing u, v, τ with x, y, t, respectively, we have
where
It is obvious that D^{2} = 4N and HD = 4I. Then we make use of the method Poincare to calculate the focus value.
Construct a form progression F\left(x,\phantom{\rule{2.77695pt}{0ex}}y\right)={x}^{2}+{y}^{2}+{\sum}_{k=3}^{\infty}{F}_{k}\left(x,\phantom{\rule{2.77695pt}{0ex}}y\right), where F_{ k }(x, y) is the k th homogeneous multinomials with x and y.
Considering \frac{dF}{dt}\left(2.4\right)=\frac{\partial F}{\partial t}\cdot \widehat{P}\left(x,\phantom{\rule{2.77695pt}{0ex}}y\right)+\frac{\partial F}{\partial t}\widehat{Q}\left(x,\phantom{\rule{2.77695pt}{0ex}}y\right)=0, we can obtain that three multinomials and four multinomials of F (x, y) are equal to zero separately.
Noting that 2xP_{2}(x, y) + 2yQ_{2}(x, y) = H_{3}(x, y) we can obtain
Let F_{3}(x, y) = a_{0}x^{3} + a_{1}x^{2}y + a_{2}xy^{2} + a_{3}y^{3}, then we can obtain the following form:
From y\frac{\partial {F}_{3}}{\partial x}x\frac{\partial {F}_{3}}{\partial y}={H}_{3}, we can derive that
Through the comparison method of correlates, we can obtain that
then {F}_{3}\left(x,\phantom{\rule{2.77695pt}{0ex}}y\right)=\frac{2G2D}{3}{x}^{3}2E{x}^{2}y\frac{2H+4E}{3}{y}^{3} and
Let x = r cos θ, y = r sin θ, then we can derive that
Hence, then point O(0, 0) of system (2.3) is an unstable fine focus with order one when C_{0} > 0. But considering the time change dτ = Adt, P_{2}(x_{1}, y_{1}) is a stable fine focus with order one. And P_{2}(x_{1}, y_{1}) is an unstable fine focus with order one when C_{0} < 0. If C_{0} = 0, namely 3G + 3F + I + 2DE  5EG  3D  HG = 0, we denote
By substituting F_{4}(x, y) = b_{0}x^{4} + b_{1}x^{3}y + b_{2}x^{2}y^{2} + b_{3}xy^{3} + b_{4}y^{4} into y\frac{\partial {F}_{4}}{\partial x}x\frac{\partial {F}_{4}}{\partial y}={H}_{4},
Through the comparison method of correlates, we can obtain that
then we have
Also
Noting F_{5}(x, y) = d_{0}x^{5} + d_{1}x^{4}y + d_{2}x^{3}y ^{2} + d_{3}x^{2}y^{3} + d_{4}xy^{4} + d_{5}y^{5} into y\frac{\partial {F}_{5}}{\partial x}x\frac{\partial {F}_{5}}{\partial y}={H}_{5}, through the comparison method of correlates, we can obtain that
Substituting (2.5) into
we can derive that
Hence point O(0, 0) of system (2.3) is an unstable fine focus with secondorder when C_{1} > 0. But considering the time change dτ = Adt, we know that P_{2}(x_{1}, y_{1}) is a stable fine focus with secondorder. And P_{2}(x_{1}, y_{1}) is an unstable fine focus with secondorder when C_{1} < 0. The proof is completed.
Remark: If C_{1} = 0 holds, P_{2}(x_{1}, y_{1}) is possible a fine focus with thirdorder.
3 Global stability of the unique positive equilibrium
Theorem 3.1. Suppose that 1\frac{\sqrt{3a}}{k}\le m<1 holds and there is no close orbit around system (2.1) in the first quadrant. Assume that:
(H_{1}) d<u\le \frac{4}{3}d;
(H_{2}) u>\frac{4}{3}d and u ≠ 4d, 1\frac{\sqrt{3a}}{k}\le m<1\frac{1}{k}\sqrt{\frac{da}{ud}};
(H_{3}) u = 4d, 1\frac{\sqrt{3a}}{k}<m<1\frac{\sqrt{3a}}{3k}.
Then the equilibrium P_{1}(k, 0) of system (2.1) is globally asymptotically stable on the first quadrant, if (H_{1}) holds. And the positive equilibrium P_{2}(x_{1}, y_{1}) of system (2.1) is globally asymptotically stable, if one of (H 2) and (H 3) holds.
Proof. By Lemmas 2.1 and 2.2, the solution (x(t), y(t)) of system (1.2) with the initial values x(0) > 0, y(0) > 0 is unanimous bounded for all t ≥ 0 and the point P_{2}(x_{1}, y_{1}) is globally asymptotically stable, we should proof that the system (2.1) is not exist limit cycle if 1\frac{\sqrt{3a}}{k}\le m<1 holds.
Define a Dulac function B(x, y) = x^{2}y^{1}, then from system (2.1), we have
Hence if m\ge 1\frac{\sqrt{3a}}{k} and D < 0 for all x ≥ 0, system (2.1) does not exist any close orbit. Then we can obtain that if 1\frac{1}{k}\sqrt{\frac{ad}{ud}}\le 1\frac{\sqrt{3a}}{k} holds, namely d<u\le \frac{4}{3}d, the positive equilibrium P_{2}(x_{1}, y_{1}) does not exist and P_{1}(k, 0) of system (2.1) is globally asymptotically stable; If u>\frac{4}{3}d and u ≠ 4d, then 1\frac{\sqrt{3a}}{k}>1\frac{u+2d}{2k}\sqrt{\frac{a}{d\left(ud\right)}} or if u = 4d, then 1\frac{\sqrt{3a}}{k}=1\frac{u+2d}{2k}\sqrt{\frac{a}{d\left(ud\right)}}. So the positive equilibrium P_{2}(x_{1}, y_{1}) of system (2.1) is globally asymptotically stable, if one of (H 2) and (H 3) holds. The proof is completed.
4 Existence of limit cycle
Theorem 4.1. Suppose that 0\le m<1\frac{u+2d}{2k}\sqrt{\frac{a}{d\left(ud\right)}}. Then system (2.1) exists at least one limit cycle in the first quadrant.
Proof. In the proof of Lemma 2.1, we can obtain the boundary line J of the Bendixson ring B = {(x, y)  0 < x < k, 0 < y < l  ux}. Let J be the outer boundary of system (2.1). Due to the Lemma 2.1, we know that there exists an unique unstable singular point P_{2}(x_{1}, y_{1}) in the Bendixson ring B. By PoincareBendixson theorem, System (2.1) exists at least one limit cycle in the first quadrant. This completes the proof.
5 Hopf bifurcation
By the study of Lou et al. [14] and the Lemma 2.3 we have the following theorem:
Theorem 5.1. (1) If C_{0} > 0 and 0<1\frac{u+2d}{2d}\phantom{\rule{2.77695pt}{0ex}}\cdot \phantom{\rule{2.77695pt}{0ex}}\frac{1}{k}\sqrt{\frac{da}{ud}}m\ll 1 hold, then system (2.1) exists a limit cycle around the small neighborhood of P_{2}(x_{1}, y_{1}). Further if the limit cycle is unique, then it is stable.

(2)
If C_{0} < 0 and 0<m\left(1\frac{u+2d}{2d}\phantom{\rule{2.77695pt}{0ex}}\cdot \phantom{\rule{2.77695pt}{0ex}}\frac{1}{k}\sqrt{\frac{da}{ud}}\right)\ll 1 hold, then system (2.1) exists a limit cycle around the small neighborhood of P_{2}(x_{1}, y_{1}). Further if the limit cycle is unique, then it is unstable.

(3)
If C_{1} < 0, 0 < δ_{3} ≪ 1, 0 < δ_{4} < δ_{3} ≪ 1 and 0 < C_{0} < δ_{3}, δ_{4} < P < 0 hold, then system (2.1) exists at least two limit cycles around the small neighborhood of P_{2}(x_{1}, y_{1}). By selecting the suitable values of the parameters, we can obtain these two limit cycle.

(4)
If C_{1} > 0, 0 < δ_{5} ≪ 1, 0 < δ_{6} < δ_{5} ≪ 1 and δ_{5} < C_{0} < 0, 0 < P < δ_{6} hold, then system (2.1) exists at least two limit cycles around the small neighborhood of P_{2}(x_{1}, y_{1}). By selecting the suitable values of the parameters, we can obtain these two limit cycle.
6 The effect of prey refuge and harvesting efforts and examples
6.1 The influence of prey refuge on model (1.2)
By the variable transformation dt = a + (1  m)^{2}x^{2}dτ, the positive equilibrium P_{2}(x_{1}, y_{1}) of system (1.2) takes the form {x}_{1}=\frac{1}{1m}\sqrt{\frac{da}{ud}}, and {y}_{1}=r\left[1\frac{1}{k\left(1m\right)}\sqrt{\frac{da}{ud}}\right]\frac{ua}{\left(ud\right){\left(1m\right)}^{2}}, where 0 < x_{1} < k.
For x_{1} < k, we have \frac{1}{1m}\sqrt{\frac{da}{ud}}<k, namely 0<m<1\frac{1}{k}\sqrt{\frac{da}{ud}}. Then we obtain
The above inequality shows that x_{1} is a strictly increasing function with respect to the parameter m and that the increasing of the prey refuge increases the density of the prey.
One could see that y_{1} is also a continuous differential function of the parameter m.
Simple computation shows that
We discuss (6.2) in the following two cases.
Case 1: Assume that the inequality 1\frac{3}{2k}\sqrt{\frac{da}{ud}}<m<1\frac{1}{k}\sqrt{\frac{da}{ud}} holds, then \frac{d{y}_{1}}{dm}<0 for all m > 0, thus y_{1} is a strictly decreasing function with respect to the parameter m. That is, increasing the amount of prey refuge can decrease the density of the predator. In this case, y_{1} reaches the maximum value r\left[1\frac{1}{k}\sqrt{\frac{da}{ud}}\right]\frac{ua}{ud} at m = 0.
Case 2: Assume that the inequality 0<m<1\frac{3}{2k}\sqrt{\frac{da}{ud}} holds, then \frac{d{y}_{1}}{dm}>0 for all m > 0, thus y_{1} is a strictly increasing function of parameter m. That is, increase the amount of prey refuge can increase the density the predator. This analysis shows that increasing the amount of prey refuge can increase the density of the predator due to the predator still has enough food for predation with m being small.
6.2 Example and simulations
Example 1. Let r = 0.05, k = 4, a = 3, d = 2, u = 4 in system (2.1). By simple computation, we have
We known that 1\frac{\sqrt{3a}}{k}<m<1\frac{1}{k}\sqrt{\frac{da}{ud}}, if we take m = 0.49 and m = 0.43 separately. By Theorem 3.1, system (2.1) admits a globally asymptotically stable equilibrium P_{2}(x_{1}, y_{1}) in the region {R}_{2}^{+}, which is shown in Figures 1 and 2.
If we take m = 0.134 and m = 0.113 separately, by Theorem 5.1, system (2.1) admits one and two limit cycles surrounding equilibrium P_{2}(x_{1}, y_{1}), which is shown in Figures 3 and 4.
Example 2. Let r = 0.05, k = 4, a=\frac{10}{3}, d = 2, u = 4 in the system (2.1). By simple computation, we have
If we take m = 0.065, by Theorem 5.1, system (2.1) admits three limit cycles surrounding equilibrium P_{2}(x_{1}, y_{1}), which is shown in Figure 5.
Figures 1, 2, 3, 4, and 5 show the dependence of the dynamic behavior of system (1.2) on the prey refuge m. Figures 2, 3, 4, and 5 show that when m is small enough, there are three or two limit cycles surrounding the unique positive equilibrium, when m is large there is a stable limit cycle surrounding the unique positive equilibrium and when m is large enough, the limit cycle is broken and both the prey and predator population converge to their equilibrium values, respectively, which means that if we change the value of m, it is possible to prevent the cyclic behavior of the predatorprey system and to drive it to a required stable state.
7 Conclusion
This article considers a predatorprey model with Holling type III response function incorporating a prey refuge under sparse effect. We give the complete qualitative analysis of the instability and global stability properties of the equilibria and the existence of limit cycles for the model. Our results, examples, and simulations indicate that dynamic behavior of the model very much depends on the prey refuge parameter m and increasing the amount of refuge could increase prey densities and lead to population outbreaks.
References
Holling CS: Some characteristics of simple 240 types of predation and parasitism. Can Entomol 1959, 91: 385–398. 10.4039/Ent913857
Hassell MP: The Dynamics of Arthropod PredatorPrey Systems. Princeton University Press, Princeton; 1978.
Hoy MA: Almonds (California). In Spider Mites: Their Biology, Natural Enemies and Control. World Crop Pests. Volume vol. 1B. Edited by: Helle W, Sabelis MW. Elsevier, Amsterdam; 1985.
Sih A: Prey refuges and predatorprey stability. Theoret Popul Biol 1987, 31: 1–12. 10.1016/00405809(87)900190
Krivan V: Effects of optimal antipredator behavior of prey on predatorprey dynamics: the role of refuges. Theoret Popul Biol 1998, 53: 131–142. 10.1006/tpbi.1998.1351
Kar TK: Stability analysis of a preypredator model incorporating a prey refuge. Commun Nonlinear Sci Numer Simul 2005, 10: 681–691. 10.1016/j.cnsns.2003.08.006
Ko W, Ryu K: Qualitative analysis of a predatorprey model with Holling type II functional response incorporating a prey refuge. J Diff Equ 2006, 231: 534–550. 10.1016/j.jde.2006.08.001
Collings JB: Bifurcation and stability analysis of a temperaturedependent mite predatorprey interaction model incorporating a prey refuge. B Math Biol 1995, 57: 63–76.
Huang Y, Chen F, Zhong L: Stability analysis of a preypredator model with Holling type III response function incorporating a prey refuge. Appl Math Comput 2006, 182: 672–683. 10.1016/j.amc.2006.04.030
Ji L, Wu C: Qualitative analysis of a predatorprey model with constantrate prey refuge. Nonlinear Anal Real World Appl 2010, 11: 2285–2295. 10.1016/j.nonrwa.2009.07.003
McNair JN: Stability effects of prey refuges with entryexit dynamics. J Theoret Biol 1987, 125: 449–464. 10.1016/S00225193(87)802138
McNair JN: The effects of refuges on predatorprey interactions: a reconsideration. Theoret Popul Biol 1986, 29: 38–63. 10.1016/00405809(86)900043
Zhang ZF, Ding TR, Huang WZ, Dong ZX: Qualitative Theory of Differential Equations. 1st edition. Science Publishing Company, Beijing; 1985.
Lou DJ, Zhxng X, Dong MF: Qualitative and Branch Theory of the Dynamic System. Science Publishing Company, Beijing; 1999.
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Wang, J., Pan, L. Qualitative analysis of a harvested predatorprey system with Hollingtype III functional response incorporating a prey refuge. Adv Differ Equ 2012, 96 (2012). https://doi.org/10.1186/16871847201296
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DOI: https://doi.org/10.1186/16871847201296
Keywords
 limit cycles
 Holling type III
 prey refuge
 predatorprey model