
In this paper, we investigate the complex dynamics of a classical discrete-time prey-predator system with the cost of anti-predator behaviors. We first give the existence and stability of fixed points of this system. And by using the central manifold theorem and bifurcation theory, we prove that the system will experience flip bifurcation and Neimark-Sacker bifurcation at the equilibrium points. Furthermore, we illustrate the bifurcation phenomenon and chaos characteristics via numerical simulations. The results may enrich the dynamics of the prey-predator systems.
Citation: Ceyu Lei, Xiaoling Han, Weiming Wang. Bifurcation analysis and chaos control of a discrete-time prey-predator model with fear factor[J]. Mathematical Biosciences and Engineering, 2022, 19(7): 6659-6679. doi: 10.3934/mbe.2022313
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[3] | Saheb Pal, Nikhil Pal, Sudip Samanta, Joydev Chattopadhyay . Fear effect in prey and hunting cooperation among predators in a Leslie-Gower model. Mathematical Biosciences and Engineering, 2019, 16(5): 5146-5179. doi: 10.3934/mbe.2019258 |
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[5] | A. Q. Khan, I. Ahmad, H. S. Alayachi, M. S. M. Noorani, A. Khaliq . Discrete-time predator-prey model with flip bifurcation and chaos control. Mathematical Biosciences and Engineering, 2020, 17(5): 5944-5960. doi: 10.3934/mbe.2020317 |
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[7] | Yuhong Huo, Gourav Mandal, Lakshmi Narayan Guin, Santabrata Chakravarty, Renji Han . Allee effect-driven complexity in a spatiotemporal predator-prey system with fear factor. Mathematical Biosciences and Engineering, 2023, 20(10): 18820-18860. doi: 10.3934/mbe.2023834 |
[8] | Qianqian Li, Ankur Jyoti Kashyap, Qun Zhu, Fengde Chen . Dynamical behaviours of discrete amensalism system with fear effects on first species. Mathematical Biosciences and Engineering, 2024, 21(1): 832-860. doi: 10.3934/mbe.2024035 |
[9] | Hongqiuxue Wu, Zhong Li, Mengxin He . Dynamic analysis of a Leslie-Gower predator-prey model with the fear effect and nonlinear harvesting. Mathematical Biosciences and Engineering, 2023, 20(10): 18592-18629. doi: 10.3934/mbe.2023825 |
[10] | Jiang Li, Xiaohui Liu, Chunjin Wei . The impact of fear factor and self-defence on the dynamics of predator-prey model with digestion delay. Mathematical Biosciences and Engineering, 2021, 18(5): 5478-5504. doi: 10.3934/mbe.2021277 |
In this paper, we investigate the complex dynamics of a classical discrete-time prey-predator system with the cost of anti-predator behaviors. We first give the existence and stability of fixed points of this system. And by using the central manifold theorem and bifurcation theory, we prove that the system will experience flip bifurcation and Neimark-Sacker bifurcation at the equilibrium points. Furthermore, we illustrate the bifurcation phenomenon and chaos characteristics via numerical simulations. The results may enrich the dynamics of the prey-predator systems.
In nature, various organisms are divided into different levels according to their different physiological characteristics and food sources, and there are various connections between populations at different levels. The prey-predator process is the most basic and important process in population dynamics. Under normal circumstances, on the one hand, predators can directly affect the population number of prey, and on the other, they can also affect the population change of predators themselves. The two relationships are very complex [1,2].
Mathematical modeling is an important method of scientific research. Although mathematical modeling can not completely solve the specific problems in biology, it plays an important role in describing the change law of biological population. For example, in population dynamics, the continuous model can describe the population growth law when the population number is relatively large or the generations overlap [3,4,5,6,7,8]. However, when the generations of the population do not overlap each other and their growth is discontinuous and step-by-step, the discrete model can more truly describe the change law of the population than the continuous model.
In 1976, May showed that first-order difference equations have a series of surprising dynamic behaviors in biological and economic contexts [9]. Thus, in recent years more and more literatures pay attention to the dynamical behavior of discrete-time systems [10,11,12,13,14,15,16,17]. Cheng et al. [18] studied the influence of Allee effect on the dynamic behavior of discrete predator-prey model, and analyzed the asymptotic behavior and bifurcation structure at the positive equilibrium point. He et al. [19] used the central manifold theorem and bifurcation theory to show that discrete predator-prey systems undergo flip bifurcation and Neimark-Sacker bifurcation in the interior of R2+. In addition, they had stabilized the chaotic orbits at an unstable fixed point using the feedback control method.
In 2016, Wang et al.[20] showed through experiments that prey fear of predators would lead to lower birth rate of prey, and F(k,y)=11+ky was used to represent the fear factor. Here, the parameter k reflects the level of fear which drives anti-predator behavior of the prey.
Based on the discussion above, in this paper, we consider the following discrete model
{xt+1=xt+rxt1+kyt(1−xtK)−bxtytxt+a,yt+1=yt+syt(1−ytxt), | (1.1) |
where r, k, K, a, b and s are positive, r and s are the intrinsic growth rates of the prey x and predator y populations, respectively. K is the carrying capacity for x. The functional response d(x,y)=bxx+a depending on x only. The constant b is the maximum of the predation rate when the predator will not or cannot kill more prey even when the latter is available. The constant a refers to some value of the prey population beyond which the predators attacking capability begins to saturate [21,22].
The rest of this paper is organized as follows. In Section 2, the existence and stability of fixed points are analyzed. Section 3 discusses the existence of flip bifurcation and Neimark-Sacker bifurcation. In Section 4, chaos is controlled to an unstable fixed point using the feedback control method. In Section 5, we perform numerical simulations which include the bifurcation diagrams and the phase portraits. Finally, in Section 6, we will analyze and summarize our conclusions.
In this section, we will calculate the fixed points of system (1.1) and give the conditions for the asymptotic stability of the fixed point. To find the fixed points of system (1.1), we assume that:
xt+1=xt=x,yt+1=yt=y. |
By solving the following system
{x=x+rx1+ky(1−xK)−bxyx+a,y=y+sy(1−yx), | (2.1) |
we can get the following proposition.
Proposition 1.
(A1) System (1.1) has a boundary equilibrium point E0(K,0);
(A2) System (1.1) has a unique positive equilibrium point E∗(x∗,y∗), where
x∗=y∗=−α+√α2+4raβ2β |
and
α=arK+b−r,β=rK+bk. |
The Jacobian matrix J(x,y) corresponding to system (1.1) at point (x,y) is as follows:
J(x,y)=[1+r1+ky(1−2xK)−aby(a+x)2−rkx(1+ky)2(1−xK)−bxa+xsy2x21+s−2syx]. | (2.2) |
Assume that λ1 and λ2 are two roots of the characteristic equation of the Jacobian matrix J|(x,y), and we have the following definition and conclusions.
Definition 2.1. [10] The fixed point (x, y) is called
(A1) sink if |λ1|<1 and |λ2|<1, and it is locally asymptotically stable;
(A2) source if |λ1|>1 and |λ2|>1, and it is locally unstable;
(A3) saddle if either ( |λ1|>1 and |λ2|<1 ) or ( |λ1|<1 and |λ2|>1 );
(A4) non-hyperbolic if either |λ1|=1 or |λ2|=1.
The Jacobian matrix at E0(K,0) is:
J(E0)=[1−r−bKK+a01+s]. | (2.3) |
Then, λ1=1+s,λ2=1−r. Thus, the following proposition holds.
Proposition 2. The characteristic root at the boundary equilibrium point E0(K,0) is λ1=1+s,λ2=1−r, then
(A1) E0(K,0) is a saddle point, if 0<r<2;
(A2) E0(K,0) is a source point, if r>2;
(A3) E0(K,0) is a non-hyperbolic point, if r=2.
Proof. It can be seen from (2.3) that the two characteristic roots of system (1.1) at the boundary equilibrium point are λ1=1+s,λ2=1−r. Since s>0 and r>0, then |λ1|>1. Thus from Definition 2.1, when |λ2|>1, E0(K,0) is the source point; when |λ2|<1, E0(K,0) is the saddle point. It can be known by calculation that when 0<r<2, E0(K,0) is the saddle point; when r>2, E0(K,0) is the source point; when r=2, the boundary equilibrium point E0(K,0) is non-hyperbolic.
J(x,y) evaluated at the positive equilibrium point E∗(x∗,y∗) is
J(E∗)=[1+r1+kx∗(1−2x∗K)−abx∗(a+x∗)2−rkx∗(1+kx∗)2(1−x∗K)−bx∗a+x∗s1−s]. | (2.4) |
Then the characteristic equation corresponding to J(E∗) is
F(λ)=λ2−pλ+q, | (2.5) |
where
p=tr(J)=2+r1+kx∗(1−2x∗K)−abx∗(a+x∗)2−s;q=det(J)=(1−s)[1+r1+kx∗(1−2x∗K)−abx∗(a+x∗)2]+s[rkx∗(1+kx∗)2(1−x∗K)+bx∗a+x∗]. |
Then
F(1)=1−p+q,F(−1)=1+p+q. |
Lemma 2.1. [10] Assume that F(λ)=λ2−Aλ+B, and F(1)>0 with λ1,λ2 are roots of F(λ)=0. Then the following results hold:
(A1) |λ1|<1 and |λ2|<1 if and only if F(−1)>0 and B<1;
(A2) |λ1|<1 and |λ2|>1, or |λ1|>1 and |λ2|<1 if and only if F(−1)<0;
(A3) |λ1|>1 and |λ2|>1 if and only if F(−1)>0 and B>1;
(A4) λ1=1 and λ2≠1 if and only if F(−1)=0 and B≠0,2;
(A5) λ1 and λ2 are complex and |λ1|=1 and |λ2|=1 if and only if A2−4B<0 and B=1.
The following proposition shows the local dynamics of the unique positive equilibrium point (x∗,y∗) from Lemma 2.1.
Proposition 3. Let E∗(x∗,y∗) be the unique positive equilibrium point of system (1.1), and the following propositions hold:
(A1) E∗(x∗,y∗) is a sink point and it is locally asymptotically stable if and only if
p<|1+q|andq<1; |
(A2) E∗(x∗,y∗) is a source point and it is locally unstable if and only if
p<|1+q|andq>1; |
(A3) E∗(x∗,y∗) is a saddle point if and only if
p<min{(1+q),−(1+q)}; |
(A4) E∗(x∗,y∗) is non-hyperbolic if one of the following conditions holds:
p=−(1+q),p<0andq≠0,2, |
or
q=1and|p|<2. |
Proof. According to Lemma 2.1, E∗(x∗,y∗) is a sink point if and only if F(1)>0, F(−1)>0 and B<1, it can be obtained by calculation p<|1+q| and q<1. Therefore, Proposition 3 (A1) holds. Similarly, Proposition 3 (A2), (A3) and (A4) can be established.
In this section, we will use the central manifold theorem and bifurcation theorem [23,24,25] to discuss the existence conditions and related conclusions of flip bifurcation and Neimark-Sacker bifurcation of system (1.1) at the fixed points E0(K,0) and E∗(x∗,y∗).
The eigenvalues of J(E0) are λ1=1+s and λ2=1−r. According to Proposition 2, E0(K,0) is non-hyperbolic if r=2. If r takes that value, we have λ1=1+s and λ2=−1 with |λ1|≠1. Now, let
F.B.1={r=2,a,b,s,k,r,K>0}. |
System (1.1) around steady state E0(K,0) admits a flip bifurcation when parameters vary in a small neighborhood of F.B.1. Take parameter s as the bifurcation parameter. Then system (1.1) can be transformed into
{xt+1=xt+rxt1+kyt(1−xtK)−bxtytxt+a,yt+1=yt+(s+s∗)yt(1−ytxt), | (3.1) |
where |s∗|≪1 is a small perturbation parameter.
Let X=x−K,Y=y, then we get
{Xt+1=A11X+A12Y+A13X2+A14XY+A15Y2+A16X3+A17X2Y+A18XY2+O((|X|+|Y|)4),Yt+1=A21X+A22Y+A23X2+A24XY+A25Y2+A26X3+A27X2Y+A28XY2+B1Xs∗+B2Ys∗+B3X2s∗+B4Y2s∗+B5XYs∗+O((|X|+|Y|+|s∗|)4), | (3.2) |
where
A11=1−r, A12=−bKK+a, A13=−rK, A14=rk−ab(K+a)2,
A15=A16=0, A17=rkK+ab(K+a)3, A18=−rk2,
A21=A23=A24=A26=A27=0, A22=1+s, A25=−sK,
A28=sK2, B1=B3=B5=0, B2=1, B4=−1K.
We construct a nonsingular matrix
T=[A12A12−1−A11λ2−A11], | (3.3) |
and use the following transformation for (3.2)
[XY]=T[PQ]. |
Then (3.2) can be changed into
[Pt+1Qt+1]=[−100λ2][PtQt]+[f(P,Q,s∗)g(P,Q,s∗)], | (3.4) |
where
f(P,Q,s∗)=A13(λ2−A11)A12(1+λ2)X2+A14(λ2−aA11)A12(1+λ2)XY−A251+λ2Y2+A17(λ2−A11)A12(1+λ2)X2Y+A18(λ2−A11)−A28A12A12(1+λ2)XY2−B21+λ2Ys∗−B41+λ2Y2s∗+O((|Xt|+|Yt|+|s∗|)4),g(P,Q,s∗)=A13(1+A11)A12(1+λ2)X2+A14(1+A11)A12(1+λ2)XY+A251+λ2Y2+A17(1+a11)A12(1+λ2)X2Y+A18(1+A11)+A28A12A12(1+λ2)XY2+B21+λ2Ys∗+B41+λ2Y2s∗+O((|Xt|+|Yt|+|s∗|)4),X=A12P+a12Q,Y=−(1+A11)P+(λ2−A11)Q. |
According to the central manifold theorem, there exists a center manifold Wc(0):
Wc(0)={(P,Q,s∗)∈R3|V=z∗(P,s∗)=z1P2+z2Ps∗+z3s∗2+O((|P|+|s∗|)3)}, | (3.5) |
and satisfy
H(z∗(P,s∗))=z∗(−P+f(P,z∗(P,s∗),s∗),s∗)−λ2z∗(P,s∗)−g(P,z∗(P,s∗),s∗)=0, | (3.6) |
where P and s∗ sufficiently small.
It can be obtained by calculation
z1=1+A111−λ22[A13A12+A25(1+A11)−A14(1+A11)],z2=B2(1+A11)(1+λ2)2,z3=0. |
Hence, the map restricted to the center manifold Wc(0) is given by
F:P→−P+l1P2+l2Ps∗+l3P2s∗+l4Ps∗2+l5P3+O((|P|+|s∗|)4), | (3.7) |
where
l1=11+λ2{A12A13(λ2−A11)−A14(λ2−A11)(1+A11)−A25(1+A11)2},l2=B21+λ2(1+A11),l3=11+λ2{2A13z2A12(λ2−A11)+A14z2(λ2−A11)(λ2−2A11−1)+2A25z2(1+A11)(λ2−A11)−B2z1(λ2−A11)−B4(1+A11)2},l4=−B21+λ2z2(λ2−A11),l5=11+λ2{2A13z1A12(λ2−A11)+A14z1(λ2−A11)(λ2−2A11−1)+2A25z1(λ2−A11)(1+A11),−A17A12(λ2−A11)(1+A11)+[A18(λ2−A11)−A28A12](1+A11)2}. |
Next, we define the following two nonzero real numbers:
h1=(∂2F∂P∂s∗+12∂F∂s∗∂2F∂P2)(0,0)=l2,h2=(16∂3F∂P3+(12∂2F∂P2)2)(0,0)=l5+l21. |
Therefore, based on the above analysis, we can conclude the following:
Theorem 3.1. If h1≠0,h2≠0, then system (1.1) undergoes a flip bifurcation at the boundary equilibrium point E0(K,0) when parameters vary in a small neighborhood of F.B.1. And when h2>0 (respectively, h2<0), system (1.1) bifurcates from the fixed point to a 2-periodic stable orbit(respectively, unstable).
We consider the following set:
F.B.2={p=−(1+q),q>−1andq≠0,2,a,b,s,k,r,K>0}. |
Firstly, the dynamic analysis of system (1.1) is analyzed when the parameters change in the small field of F.B.2. Select parameter (a,b,s,k,r,K)∈ F.B.2 and consider the following system:
{xt+1=xt+rxt1+kyt(1−xtK)−bxtytxt+a,yt+1=yt+(s+s∗)yt(1−ytxt), | (3.8) |
where s is the bifurcation parameter, |s∗|≪1 is a small perturbation parameter.
Let u=x−x∗,v=y−y∗. Then we get
{ut+1=C11u+C12v+C13u2+C14uv+C15v2+C16u3+C17u2v+C18uv2+O((|u|+|v|)4),vt+1=C21u+C22v+C23u2+C24uv+C25v2+C26u3+C27u2v+C28uv2+D1us∗+D2vs∗+D3u2s∗+D4v2s∗+D5uvs∗+O((|u|+|v|+|s∗|)4), | (3.9) |
where
C11=1+r1+kx∗(1−2x∗K)−abx∗(x∗+a)2, C12=−rkx∗(1+kx∗)2(1−x∗K)−bx∗x∗+a,
C13=−rK(1+kx∗)+abx∗(x∗+a)3, C14=−rk(1+kx∗)2(1−2x∗K)−ab(x∗+a)2,
C15=rk2x∗(1+kx∗)3(1−x∗K), C16=−abx∗(1+kx∗)3(1−x∗K),
C17=rkK(1+kx∗)2+ab(x∗+a)3, C18=rk2(1+kx∗)3(1−2x∗K),
C21=s, C22=1−s, C23=C24=−sx∗, C25=2sx∗, C26=C28=sx∗2,
C27=−2sx∗2, D1=1, D2=−1, D3=D4=−1x∗, D5=2x∗.
We construct a nonsingular matrix
˜T=[C12C12−1−C11λ2−C11], |
and use the translation
[uv]=˜T[˜P˜Q], |
then (3.9) can be changed into
[˜Pt+1˜Qt+1]=[−100λ2][˜Pt˜Qt]+[˜f(˜P,˜Q,s∗)˜g(˜P,˜Q,s∗)], | (3.10) |
where
˜f(˜P,˜Q,s∗)=C13(λ2−C11)−C23C12C12(1+λ2)u2+C14(λ2−C11)−C24C12C12(1+λ2)uv+C15(λ2−C11)−C25C12C12(1+λ2)v2+C17(λ2−C11)−C27C12C12(1+λ2)u2v+C18(λ2−C11)−C28C12C12(1+λ2)uv2+C16(λ2−C11)−C26C12C12(1+λ2)u3−D11+λ2us∗−D21+λ2vs∗−D31+λ2u2s∗−D41+λ2v2s∗−D51+λ2uvs∗+O((|ut|+|vt|+|s∗|)4),˜g(˜P,˜Q,s∗)=C13(1+C11)+C23C12C12(1+λ2)u2+C14(1+C11)+C24C12C12(1+λ2)uv+C15(1+C11)+C25C12C12(1+λ2)v2+C17(1+C11)+C27C12C12(1+λ2)u2v+C18(1+C11)+C28C12C12(1+λ2)uv2+C16(1+C11)+C26C12C12(1+λ2)u3+D11+λ2us∗+D21+λ2vs∗+D31+λ2u2s∗+D41+λ2v2s∗+D51+λ2uvs∗+O((|ut|+|vt|+|s∗|)4),u=C12˜P+C12˜Q,v=−(1+C11)˜P+(λ2−C11)˜Q. |
According to the central manifold theorem, there exists a center manifold Wc(0):
Wc1(0)={(˜P,˜Q,s∗)∈R3|˜Q=h∗(˜P,s∗)=α1˜P2+α2˜Ps∗+α3s∗2+O((|˜P|+|s∗|)3)}, | (3.11) |
and satisfy
˜H(h∗(˜P,s∗))=z∗(−˜P+˜f(˜P,h∗(˜P,s∗),s∗),s∗)−λ2h∗(˜P,s∗)−˜g(˜P,h∗(˜P,s∗),s∗)=0, | (3.12) |
where ˜P and s∗ sufficiently small.
It can be obtained by calculation
α1=1C12(1−λ22){C212[C13(1+C11)+C23C12]+(1+C11)2[C15(1+C11)+C25C12]−C12(1+C11)[C14(1+C11)+C24C12]},α2=1(1+λ2)2[D2(1+C11)−D1C12],α3=0. |
Hence, the map restricted to the center manifold Wc1(0) is given by
˜F:˜P→−˜P+k1˜P2+k2˜Ps∗+k3˜P2s∗+k4˜Ps∗2+k5˜P3+O((|˜P|+|s∗|)4), | (3.13) |
where
k1=1C12(1+λ2){C212[C13(λ2−C11)−C23C12]−C12(1+C11)[C14(λ2−C11)−C24C12]+(1+C11)2[C15(λ2−C11)−C25C12]},k2=11+λ2[−D1C12+D2(1+C11)],k3=1C12(1+λ2){2α2C212[C13(λ2−C11)−C23C12]+[C14(λ2−C11)−C24C12][α2C12(λ2−C11)−α2C12(1+C11)]−2α2(λ2−C11)(1+C11)[C15(λ2−C11)−C25C12]}+11+λ2{−α1D1C12−D2α1(λ2−C11)−D3C212−D4(1+C11)2+D5C12(1+C11)},k4=−α21+λ2[D1C12+D2(λ2−C11)],k5=11+λ2{2α1C12[C13(λ2−C11)−C23C12]+α1C12[λ2−2C11−1][C14(λ2−C11)−C24C12]+C212[C16(λ2−C11)−C26C12]−C12(1+C11)[C17(λ2−C11)−C27C12]+(1+C11)2[C18(λ2−C11)−C28C12]}−2α1(λ2−C11)(1+C11)C12(1+λ2)[C15(λ2−C11)−C25C12]. |
Remember
β1=(∂2˜F∂˜P∂s∗+12∂F∂s∗∂2F∂P2)(0,0)=k2,β2=(16∂3˜F∂˜P3+(12∂2˜F∂˜P2)2)(0,0)=k5+k21. |
If parameters β1 and β1 are not 0, system (3.13) has flip bifurcation. And we can get the following theorem:
Theorem 3.2. If β1≠0,β2≠0, then system (1.1) undergoes a flip bifurcation at the positive equilibrium point E∗(x∗,y∗) when parameters vary in a small neighborhood of F.B.2. And when β2>0 (respectively, β2<0), system (1.1) bifurcates from the fixed point E∗(x∗,y∗) to a 2-periodic stable orbit(respectively, unstable).
Next, we study the Neimark-Sacker bifurcation of positive equilibrium point of system (1.1). The characteristic polynomial (2.4) of Jacobian matrix of linearized system of (1.1) about positive equilibrium point (x∗,y∗) can be rewritten as:
F(λ)=λ2−p(x∗,y∗)λ+q(x∗,y∗), |
where
p(x∗,y∗)=2+r1+kx∗(1−2x∗K)−abx∗(a+x∗)2−s,q(x∗,y∗)=(1−s)[1+r1+kx∗(1−2x∗K)−abx∗(a+x∗)2]+s[rkx∗(1+kx∗)2(1−x∗K)+bx∗a+x∗]. |
For convenience, we assume
Θ=1+r1+kx∗(1−2x∗K)−abx∗(a+x∗)2,Λ=rkx∗(1+kx∗)2(1−x∗K)+bx∗a+x∗. |
Then
p(x∗,y∗)=1+Θ−s,q(x∗,y∗)=Θ−s(Θ−Λ). |
Let
N.S={(a,b,r,s,k,K):q=1,|p|<2andΘ≠Λ,a,b,r,s,k,K>0}. |
Then, the dynamic analysis of system (1.1) is analyzed when the parameters change in the small field of N.S. Select parameter (a,b,s,k,r,K)∈N.S and consider the following systems:
{xt+1=xt+rxt1+kyt(1−xtK)−bxtytxt+a,yt+1=yt+(s+s∗)yt(1−ytxt), |
where |s∗|≪1 is a small perturbation parameter. And we choose s as the bifurcation parameter.
Let u=x−x∗,v=y−y∗, then we get
{ut+1=C11u+C12v+C13u2+C14uv+C15v2+C16u3+C17u2v+C18uv2+O((|u|+|v|)4),vt+1=C21u+C22v+C23u2+C24uv+C25v2+C26u3+C27u2v+C28uv2+O((|u|+|v|)4), | (3.14) |
where C11,C12,C13,C14,C15,C16,C17,C18,C21,C22,C23,C24,C25,C26,C27,C28 are given in (3.14) by substituting s for s+s∗.
The characteristic equation of system (3.14) at (u,v)=(0,0) is as follows:
λ2+M(s∗)λ+N(s∗)=0, |
where
M(s∗)=−[1+Θ−(s+s∗)],N(s∗)=Θ−(s+s∗)(Θ−Λ). |
Since parameters (a,b,r,s,k,K)∈N.S, the roots of the characteristic equation are
λ1,2=−M(s∗)2±i2√4N(s∗)−M2(s∗), |
and we have
|λ1,2|=√N(s∗),L=d|λ|ds∗|s∗=0=12N−12(0)(Λ−Θ)≠0. |
In addition, it is required that s∗=0,λj1,2≠1(j=1,2,3,4) which is equivalent to M(0)≠ -2, 0, 1, 2. Because (a,b,r,s,k,K)∈N.S, thus M(0)≠ -2, 2. We only require M(0)≠0,1, so that
s≠1+Θands≠2+Θ. | (3.15) |
Therefore, eigenvalues λ1,λ2 of the fixed point (0,0) of system (3.14) do not lay in the intersection of the unit circle with the coordinate axes when s∗=0 and the condition (3.15) holds.
Let ρ=−M(0)2,ω=√4N(0)−M2(0)2, we use the following transformation:
[uv]=T′[ˆPˆQ]=[ωC11−ρ0C21][ˆPˆQ], |
and system (3.14) becomes into
[ˆPt+1ˆQt+1]=[ρ−ωωρ][ˆPtˆQt]+[¯f(ˆP,ˆQ)¯g(ˆP,ˆQ)], | (3.16) |
where
¯f(ˆP,ˆQ)=C13C21+C23(ρ−C11)C21ωu2+C14C21+C24(ρ−C11)C21ωuv+C15C21+C25(ρ−C11)C21ωv2+C16C21+C26(ρ−C11)C21ωu3+C17C21+C27(ρ−C11)C21ωu2v+C18C21+C28(ρ−C11)C21ωuv2+O((|ut|+|vt|)4),¯g(ˆP,ˆQ)=1C21{C23u2+C24uv+C25v2+C26u3+C27u2v+C28uv2}+O((|ut|+|vt|)4),u=ωˆP+(C11−ρ)ˆQ,v=C21ˆQ. |
System (3.16) undergoes the Neimark-Sacker bifurcation if the following quantity is not zero
L=−Re[(1−2λ1)λ221−λ1L11L12]−12|L11|2−|L21|2+Re(λ2L22), | (3.17) |
where
L11=14[(¯fˆPˆP+¯fˆQˆQ)+i(¯gˆPˆP+¯gˆQˆQ)],L12=18[(¯fˆPˆP−¯fˆQˆQ+2¯gˆPˆQ)+i(¯gˆPˆP−¯gˆQˆQ−2˜fˆPˆQ)],L21=18[(¯fˆPˆP−¯fˆQˆQ−2¯gˆPˆQ)+i(¯gˆPˆP−¯gˆQˆQ+2¯fˆPˆQ)],L22=116[(¯fˆPˆPˆP+¯fˆQˆQˆQ+¯gˆPˆPˆQ+¯gˆQˆQˆQ)+i(¯gˆPˆPˆP+¯gˆPˆQˆQ−¯fˆPˆPˆQ−¯fˆQˆQˆQ)]. |
Through some complicated calculations, we get
¯fˆUˆU=2ωC21[C13C21+C23(ρ−C11)],¯fˆUˆV=1C21[C14C221−C21(ρ−C11)(2C13−C24)−3C23(ρ−C11)2],¯fˆVˆV=1ωC21[2C15C221−2C221(ρ−C11)(C14−C25)−2C21(C24−C13)(ρ−C11)2+2C23(ρ−C11)3],¯fˆUˆUˆU=6ω2C21[C16C21+C26(ρ−C11)],¯fˆUˆUˆV=ωC21[2C17C221−C21(ρ−C11)(6C16−2C27)−6C26(ρ−C11)2],¯fˆUˆVˆV=1C21[2C18C221+2C221(C28−2C17)(ρ−C11)+6C21(C16−C27)(ρ−C11)2+6C26(ρ−C11)3],¯fˆVˆVˆV=6(C11−ρ)ωC21[C18C321+C221(C28−C17)(ρ−C11)+C21(C16−C27)(ρ−C11)2+C26(ρ−C11)3],¯gˆUˆU=2C23ω2C21,¯gˆUˆV=ωC21[C24C21−2C23(ρ−C11)],¯gˆVˆV=2C21[C25C221−C21C24(ρ−C11)+C23(ρ−C11)2],¯gˆUˆUˆU=6C26ω3C21,¯gˆUˆUˆV=ω2C21[2C27C21−6C26(ρ−C11)],¯gˆUˆVˆV=ωC21[2C28C221−4C27C21(ρ−C11)+6C26(ρ−C11)2],¯gˆVˆVˆV=6(C11−ρ)C21[C26C211+C27C21C11+C28C221−(2C11C26+C21C27)ρ+C26ρ2]. |
If L≠0, Neimark-Sacker bifurcation will occur in system (1.1), and the following theorem holds:
Theorem 3.3. System (1.1) undergoes a Neimark-Sacker bifurcation at the positive equilibrium point E∗(x∗,y∗) if conditions in (3.15) are satisfied and L≠0 in (3.17). Moreover, if L<0(resp., L>0), an attracting (resp., repelling) invariant closed curve bifurcates from the steady state for s>s∗ (resp., s<s∗).
In this section, we will use the feedback control method [26,27,28] to control the chaos of system (1.1). Specifically, a feedback control term is added to system (1.1) to stabilize the chaotic orbit of system (1.1) at the equilibrium point. Thus, system (1.1) becomes the following form:
{xt+1=xt+rxt1+kyt(1−xtK)−bxtytxt+a−u(xt,yt)=f(xt,yt),yt+1=yt+syt(1−ytxt)=g(xt,yt), | (4.1) |
where
u(xt,yt)=r1(xt−x∗)+r2(yt−y∗) | (4.2) |
is feedback controlling force, r1 and r2 are feedback gains, and (x∗,y∗) the unique positive equilibrium point of system (1.1). Moreover f(x∗,y∗)=x∗ and g(x∗,y∗)=y∗.
The Jacobian matrix of system (4.1) at equilibrium point (x∗,y∗) is as follows:
J′(x∗,y∗)=[C11−r1C12−r2C21C22], |
where
C11=1+r1+kx∗(1−2x∗K)−abx∗(x∗+a)2, C12=−rkx∗(1+kx∗)2(1−x∗K)−bx∗x∗+a, C21=s, C22=1−s.
Thus, the characteristic equation corresponding to J′(x∗,y∗) is:
λ2−(C11+C22−r1)λ+C22(C11−r1)−C21(C12−r2)=0. | (4.3) |
Let λ1 and λ2 be the eigenvalues of characteristic equation (4.3), then
λ1+λ2=C11+C22−r1 | (4.4) |
and
λ1λ2=C22(C11−r1)−C21(C12−r2). | (4.5) |
In order to make the absolute values of λ1 and λ2 less than 1, we assume that λ1=±1 and λ1λ2=1 hold.
Assume that λ1λ2=1, and we have
L1:r1C22−r2C21=C11C22−C21C12−1. | (4.6) |
Assume that λ1=1, and we get
L2:r1(1−C22)+r2C21=C11+C22+C21C12−C11C22−1. | (4.7) |
Assume that λ1=−1, and we obtain
L3:r1(1+C22)−r2C21=C11+C22−C21C12+C11C22+1. | (4.8) |
Then stable eigenvalues lie within the triangular region bounded by the straight lines L1,L2,L3. Therefore, when the control parameters r1 and r1 take values in the triangular region, system (4.1) will not produce chaos.
In this section, we will use numerical simulation to verify the previous theoretical results and show the dynamic behavior of the discrete system (1.1) at the positive equilibrium point E∗(x∗,y∗). This is due to the presence of prey only at the boundary equilibrium point E0(K,0) and the extinction of predators.
Firstly, we assume that the fear factor k=0 and take s as the bifurcation parameter to analyze the dynamic behavior of system (1.1) at the positive equilibrium point. We consider the parameter values as
r=2.2,b=0.2,a=4,K=6, | (5.1) |
and the initial value is taken as (x0,y0)=(2.5,2). Figure 1 is the Neimark-Sacker bifurcation diagram of system (1.1) at the positive equilibrium point. In Figure 3, system (1.1) undergoes Neimark-Sacker bifurcation when the parameter values above are taken. The critical value of s=1.7802 for the bifurcation to occur can be calculated. Combined with the maximum Lyapunov exponents diagram (Figure 4(a)), when s<1.7802, system (1.1) is in equilibrium, and when s>1.7802, the phase diagram corresponding to system (1.1) appears closed track, and thus the periodic solution appears. However, when s continues to increase, the value of the maximum Lyapunov exponents corresponding to system (1.1) is greater than 0, and thus chaos will occur, i.e., the solution of system (1.1) is arbitrarily periodic (Figure 3).
Now, we consider the parameter values as
r=0.8,b=0.1,a=2.5,K=7, | (5.2) |
and k=0. In Figure 2, system (1.1) undergoes a period-doubling bifurcation (flip bifurcation), and it is stable when s<2.1142 and when s>2.1142, system (1.1) oscillates with periods of 2,22,23,⋅⋅⋅. It can be seen from Figure 4(b) that when the bifurcation parameters s continue to increase, chaos will occur in system (1.1).
We assume that fear factor k>0 and make bifurcation diagrams and maximum Lyapunov exponents diagrams (Figure 5) for k=0.5 and k=10 respectively with s as the bifurcation parameter on the basis of (4.1). In Figures 1 and 5, when k>0, the dynamic behavior of system (1.1) will change significantly. When k=0.5, system (1.1) changes from Neimark-Sacker bifurcation to period-doubling bifurcation (Figure 5(a), (b)), and will produce chaos (Figure 5(c)). However, when k=10, system (1.1) will produce not only period-doubling bifurcation and chaos (Figure 5(d)–(f)), but also the equilibrium point be lowered. So it can be concluded that fear factor will have a significant impact on the dynamic behavior of system (1.1).
Here, we consider the parameter values as
r=2.2,b=0.2,a=4,K=6,s=2, | (5.3) |
and fear factor k>0. At this time, the bifurcation phenomenon of system (1.1) will not occur. In Figure 6, when the fear factor k increases, the density of predators and preys will continue to decrease and tend to 0, which leads to the collapse of the population system and the extinction of predators and prey.
Furthermore, when the parameter value is r=3.5,b=1.5,a=1,K=5,s=0.5, k is bifurcation parameter. Figure 7 shows the occurrence of Neimark-Sacker bifurcation as the value of parameter k varies. According to the calculation, the critical value for Neimark-Sacker bifurcation can be determined as k_{0} = 0.1865 . As shown in the x-y space, when k < k_{0} , the stable stationary state is stable. Moreover, the maximum Lyapunov exponents are plotted in Figure 7(c), Thus Neimark-Sacker bifurcation occurs at k = k_{0} = 0.1865 and [0.1865, 0.7] behaves like chaotic region as k varies in this interval.
Next, we will conduct numerical simulation of chaos control. Parameter values are fixed as r = 2.2, \; K = 6, \; k = 10, \; b = 0.2, \; a = 4 , and the initial value is (x_{0}, y_{0}) = (2.5, 2) . In Figure 5(f) when the bifurcation parameter s = 2.8 , system (1.1) will produce chaos. When the feedback gains are r_{1} = 2 and r_{2} = -0.8 , Figure 7(a), (b) show that a chaotic trajectory is stabilized at the fixed point (2.0443, 2.0443). In Figures 8 and 9, when the parameters r_{1} and r_{2} are controlled in the triangular region surrounded by three straight lines L_{1}, \; L_{2} , and L_{3} , the chaos generated by system (4.1) will be controlled near the fixed point and become an asymptotically stable state.
Research shows that the dynamic behavior of discrete systems is richer and more complex than that of continuous systems [9,10]. Therefore, based on the previous research work, this paper studies the dynamic behavior and nonlinear characteristics of a class of discrete predator-prey systems with the fear effect. Based on the findings of the research, we can obtain the following mathematical and ecological results:
(1) System (1.1) has two fixed points, in which the only stable fixed point is positive, which reflects the stable coexistence of predators and prey.
(2) System (1.1) has flip bifurcation at the boundary equilibrium point, and flip bifurcation and Neimark-Sacker bifurcation at the positive fixed point. It can be found from Figures 1, 2 and 4 that when k = 0 , the Neimark-Sacker bifurcation at the positive equilibrium point will produce chaos, and the flip bifurcation will also produce chaos. We can also find the orbits of periods 2, 4, and 8 periodic windows.
(3) When fear k is larger, the number of both predators and prey decreases. It is worth noting that the cost of fear cannot induce the extinction of predators but the extinction of prey. And the system will change from Neimark-Sacker bifurcation to flip bifurcation when k increases (see Figures 1 and 5). Therefore, fear k is very important in analyzing the change of population size.
This work was supported by the National Natural Science Foundation of China(Grant Nos. 12161079, 12171192) and Natural Science Foundation of Gansu Province(No.20JR10RA086).
The authors declare that there are no conflicts of interest regarding the publication of this paper.
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