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Research article Special Issues

Jointly A-hyponormal m-tuple of commuting operators and related results

  • Received: 20 August 2024 Revised: 03 October 2024 Accepted: 10 October 2024 Published: 25 October 2024
  • MSC : 47A10, 47A12, 47A13, 47B20, 47B37

  • In this paper, we aim to investigate the class of jointly hyponormal operators related to a positive operator A on a complex Hilbert space X, which is called jointly A-hyponormal. This notion was first introduced by Guesba et al. in [Linear and Multilinear Algebra, 69(15), 2888–2907] for m-tuples of operators that admit adjoint operators with respect to A. Mainly, we prove that if B=(B1,,Bm) is a jointly A-hyponormal m-tuple of commuting operators, then B is jointly A-normaloid. This result allows us to establish, for a particular case when A is the identity operator, a sharp bound for the distance between two jointly hyponormal m-tuples of operators, expressed in terms of the difference between their Taylor spectra. We also aim to introduce and investigate the class of spherically A-p-hyponormal operators with 0<p<1. Additionally, we study the tensor product of specific classes of multivariable operators in semi-Hilbert spaces.

    Citation: Salma Aljawi, Kais Feki, Hranislav Stanković. Jointly A-hyponormal m-tuple of commuting operators and related results[J]. AIMS Mathematics, 2024, 9(11): 30348-30363. doi: 10.3934/math.20241464

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  • In this paper, we aim to investigate the class of jointly hyponormal operators related to a positive operator A on a complex Hilbert space X, which is called jointly A-hyponormal. This notion was first introduced by Guesba et al. in [Linear and Multilinear Algebra, 69(15), 2888–2907] for m-tuples of operators that admit adjoint operators with respect to A. Mainly, we prove that if B=(B1,,Bm) is a jointly A-hyponormal m-tuple of commuting operators, then B is jointly A-normaloid. This result allows us to establish, for a particular case when A is the identity operator, a sharp bound for the distance between two jointly hyponormal m-tuples of operators, expressed in terms of the difference between their Taylor spectra. We also aim to introduce and investigate the class of spherically A-p-hyponormal operators with 0<p<1. Additionally, we study the tensor product of specific classes of multivariable operators in semi-Hilbert spaces.



    In biology, discrete models of interspecific relationships in populations without generation-level overlap are more generalizable than continuous ones, which has attracted many scholars in recent years (see [1,2,3,4,5,6,7,8,9,10,11,12]). Obviously, all these works indicate that discrete systems have more complex dynamic behavior. The predator-prey model is a class of classical biomathematical model that has been studied by different scholars in terms of the evolutionary patterns of populations over time [13,14] and the effects of different functional responses on the stability of populations [15,16].

    For the predator-prey system, external factors such as food supply, climate change and population migration can affect predation and population reproduction [17,18]. However, the effects of changes in predators themselves are often overlooked, such as predator fear [19]. Frightened predators tend to eat less, which leads to reduced birth rates and increased mortality [19]. Therefore, predator fear, which cannot be quantitatively described, is more difficult to study.

    Sasmal [20] studied a predator-prey model with Allee effect and reduced reproduction of prey due to cost of fear. They discussed the effects of time scale separation between prey and predator.

    {dudt=ru(1uk)(uθ)11+fvauv(0<θ<k)dvdt=aαuvmv (1.1)

    where u and v are the prey and predator population densities, respectively. r is the maximum growth rate of the population, k is the carrying capacity, and θ represents the Allee threshold (0<θ<k), below which the population becomes extinct. f refers to the level of fear, which is due to the anti-predator response of prey. a is the predation rate, α is the conversion efficiency of predator by consuming prey, and m represents the predator's natural mortality rate.

    To simplify parameters, define N=uk, P=arkv, ϵ=aαr, θ=θk, m=maαk, f=rkfa, t=aαkt. Then, the system is given by

    {dNdt=1ϵ[N(1N)(Nθ)11+fPNP]dPdt=NPmP

    At present, there are some studies on predator-prey systems with Flip bifurcation and Hopf bifurcation [21] in continuous models, where, however, the chaotic properties have not been mentioned. For example, Khan studied a discrete-time Nicholson-Bailey host-parasitoid model and obtained the local dynamics and supercritical Neimark-Sacker bifurcation in 2017 [22]. In 2019, Li et al. investigated the discrete time predator-prey model undergoing Flip and Neimark Sacker bifurcation using the center manifold theorem and bifurcation theory [23]. In 2022, Yu et al. gave a description of the bifurcations in theory for a symmetrically coupled period-doubling system, including the Transcritical bifurcation, Pitchfork bifurcation, Flip bifurcation and Neimark-Sacker bifurcation [24]. In 2022, Chen et al. studied a discrete predator-prey system with Allee effect, which undergoes Fold bifurcation and Flip bifurcation. They obtained cascades of period-doubling bifurcation in orbits of period-2, 4, 8 and chaotic properties [25].

    Based on the above analysis and discussion, we obtain the following discrete-time model:

    {Nn+1=Nn+h{1ϵ[Nn(1Nn)(Nnθ)11+fPnNnPn]}Pn+1=Pn+h[NnPnmPn] (1.2)

    where h>0 refers to the step length of (1.2), 0<θ<1.

    In this paper, we study the existence and stability of four equilibria of model (1.2) and focus on local dynamics of the unique positive equilibrium point in Section 2. In Sections 3 and 4, we study Flip bifurcation and Hopf bifurcation when bifurcation parameter h varies in a small neighborhood of the unique positive equilibrium point. In Section 5, we study chaos scenarios of Flip bifurcation. Meanwhile, some numerical simulations about bifurcations are given by maximum Lyapunov exponent and phase diagrams to verify our results. Finally, brief conclusions are given in Section 6.

    Initially, the existence and stability of the equilibrium point of model (1.2) are analyzed.

    By calculating model (1.2), obviously, the trivial equilibrium point E1=(0,0) and boundary equilibria E2=(1,0), E3=(θ,0) are obtained. Since the negative equilibrium point has no practical meaning in biology, the unique positive internal equilibrium point E4=(m,1+1+4f(1m)(mθ)2f) is chosen.

    The Jacobi matrix of the linear system of (1.2) at any equilibrium point (N,P) is given by:

    J=(1+hϵ[3N2+2(1+θ)Nθ1+fPP]hϵ[(N3(1+θ)N2+θN)f(1+fP)2N]hP1+hNmh)|(N,P)

    Now, we give some dynamical properties of four equilibria.

    Theorem 2.1. For the trivial equilibrium E1, with eigenvalues λ1=1hθϵ and λ2=1hm.

    (i) E1 is a sink if 0<h<min{2ϵθ,2m}, with eigenvalues |λ1|<1 and |λ2|<1.

    (ii) E1 is a source if h>max{2ϵθ,2m}, with eigenvalues |λ1|>1 and |λ2|>1.

    (iii) E1 is a saddle point if 2ϵθ<h<2m with eigenvalues |λ1|>1 and |λ2|<1, or 2m<h<2ϵθ with eigenvalues |λ1|<1 and |λ2|>1.

    (iv) Flip bifurcation occurs at E1 if h=2ϵθ<2m, with eigenvalues λ1=1 and |λ2|<1, or h=2m<2ϵθ, with eigenvalues λ2=1 and |λ1|<1.

    Theorem 2.2. For the boundary equilibrium E2, with eigenvalues λ1=1+hϵ(θ1) and λ2=1+h(1m).

    (i) E2 is a sink if {m>10<h<min(2ϵ1θ,2m1), with eigenvalues |λ1|<1 and |λ2|<1.

    (ii) E2 is a source if {m<1h>2ϵ1θ or {m>1h>max{2ϵ1θ,2m1}, with eigenvalues |λ1|>1 and |λ2|>1.

    (iii) E2 is a saddle point if {m>12ϵ1θ<h<2m1 or {m>12m1<h<2ϵ1θ with eigenvalues |λ1|>1 and |λ2|<1, or {m<1h<2ϵ1θ with eigenvalues |λ1|<1 and |λ2|>1.

    (iv) Flip bifurcation occurs at E2 if {m>1h=2ϵ1θ<2m1 with eigenvalues λ1=1 and |λ2|<1, or h=2m1<2ϵ1θ with eigenvalues λ2=1 and |λ1|<1.

    (v) Transcritical bifurcation occurs at E2 if {m=1h<2ϵ1θ, with eigenvalues λ2=1 and |λ1|<1.

    Theorem 2.3. For the equilibrium E3, with eigenvalues λ1=1+hϵθ(1θ) and λ2=1+h(θm).

    (i) E3 is a source if θ>m or {θ<mh>2mθ, with eigenvalues |λ1|>1 and |λ2|>1.

    (ii) E3 is a saddle point if {θ<m0<h<2mθ, with eigenvalues |λ1|>1 and |λ2|<1.

    There is no situation as |λ1|<1, i.e., stability does not exist.

    Theorem 2.4. For the internal equilibrium E4,

    (i) E4 is a sink if

    {1+hϵ[m(1+θ2m)1+1+4f(1m)(mθ)]+h2ϵ[m(1m)(mθ)1+4f(1m)(mθ)[1+1+4f(1m)(mθ)]2]>0m(1m)(mθ)1+4f(1m)(mθ)>01+θ2m1+1+4f(1m)(mθ)+h[2(1m)(mθ)1+4f(1m)(mθ)[1+1+4f(1m)(mθ)]2]<0

    (ii) Transcritical bifurcation occurs at E4 if

    {m=1 or m=θ2ϵ<h[2m(1+θ2m)1+1+4f(1m)(mθ)]<0.

    (iii) Flip bifurcation occurs at E4 if

    {1+hϵ[m(1+θ2m)1+1+4f(1m)(mθ)]+h2ϵ[m(1m)(mθ)1+4f(1m)(mθ)[1+1+4f(1m)(mθ)]2]=04<hϵ[m(1+θ2m)1+1+4f(1m)(mθ)]<2.

    (iv) Hopf bifurcation occurs at E4 if

    {h=(2mθ1)[1+1+4f(1m)(mθ)]2(1m)(mθ)1+4f(1m)(mθ)4<hϵ[m(1+θ2m)1+1+4f(1m)(mθ)]<0.

    Proof. The Jacobi matrix of (1.2) at point E4=(N0,P0) is

    J(N0,P0)=(1+hϵ[2m(1+θ2m)1+1+4f(1m)(mθ)]   hϵ[2m+1+4f(1m)(mθ)1+1+4f(1m)(mθ)]h(1+1+4f(1m)(mθ))2f   1) (2.1)

    where n=1+1+4f(1m)(mθ).

    Meanwhile, the characteristic polynomial of J(N0,P0) is given by:

    f(λ)=λ2(A+1)λ+(A+BC)=0 (2.2)

    where

    A=1+hϵ[2m(1+θ2m)1+1+4f(1m)(mθ)],
    B=hϵ[2m+1+4f(1m)(mθ)1+1+4f(1m)(mθ)],C=h(1+1+4f(1m)(mθ))2f.

    Then, eigenvalues of J(N0,P0) are λ1=A+1+Δ2 and λ2=A+1Δ2, where Δ=(A1)24BC.

    (i) (N0,P0) is a sink, if |λ1|<1 and |λ2|<1. From the Jury Criterion [26], we obtain {1+(A+1)+(A+BC)>01(A+1)+(A+BC)>01(A+BC)>0.

    (ii) If Δ>0, λ1=1 and |λ2|<1 or λ2=1 and |λ1|<1, then we have {BC=0|A|<1.

    (iii) If Δ>0, λ1=1 and |λ2|<1 or λ2=1 and |λ1|<1, then we have {2A+BC+2=0|A+2|<1.

    (iv) If Δ<0, |λ1|=|λ2|=1, which is a pair of conjugate complex roots, then we have {A+BC=10<|A+1|<2.

    When λ1 and λ2 is a pair of conjugate complex roots, we assume λ1=a+bi and λ2=abi (a2+b2=1 b0), i.e., 0<|a|<1. Since (λλ1)(λλ2)=λ2(λ1+λ2)λ+λ1λ2=0, obviously we have λ1+λ2=A+1=2a and λ1λ2=A+BC=1.

    All of the expressions A,B,C can be brought into equations mentioned above to obtain the corresponding conclusions.

    In this section, we discuss how system (1.2) undergoes Flip bifurcation around its internal equilibrium E4=(m,1+1+4f(1m)(mθ)2f) when h is chosen as bifurcation parameter. The necessary conditions for Flip bifurcation to occur is given by the following curve:

    U={(m,n,θ,f,ϵ)R5+:h=h=2f(2m1θ)+2f2(1+θ2m)2+1m(n1)(n2)ϵfnm(n1)(n2) ,|D|<1}

    where D=hϵ(m(1+θ2m)1+1+4f(1m)(mθ))+3 and n=1+1+4f(1m)(mθ).

    The Jacobi matrix of the linear system of (1.2) at the equilibrium point (N0,P0) is given by:

    J(N0,P0)=(1+hϵ[2m(1+θ2m)n] hϵ[2m(n1)n]h(n2)2f 1) (3.1)

    where n=1+1+4f(1m)(mθ).

    In order to obtain the bifurcation properties, we consider translations ¯Nn+1=Nn+1N0, ¯Pn+1=Pn+1P0 for shifting (N0,P0) to the origin. Then, the model (1.2) is given by

    {¯Nn+1=¯Nn+h{1ϵ[(¯Nn+N0)(1¯NnN0)(¯Nn+N0θ)11+f(¯Pn+P0)(¯Nn+N0)(¯Pn+P0)]}¯Pn+1=¯Pn+h[(¯Nn+N0)(¯Pn+P0)m(¯Pn+P0)] (3.2)

    Using Taylor expansion at the point E4(N0,P0) yields the following expression :

    (¯Nn+1¯Pn+1)=J(N0,P0)(¯Nn¯Pn)+h(1ϵψ1(¯Nn,¯Pn)ψ2(¯Nn,¯Pn)) (3.3)

    where

    ψ1(¯Nn,¯Pn)=b1¯Nn2+b2¯Nn¯Pn+b3¯Pn2+b4¯Nn3+b5¯Nn2¯Pn+b6¯Nn¯Pn2+b7¯Pn3+O((|¯Nn|+|¯Pn|)4),

    ψ2(¯Nn,¯Pn)=¯Nn¯Pn,

    b1=3N0+1+θ1+fP0, b2=[3N202(1+θ)N0+θ]f(1+fP0)21, b3=[N30+(1+θ)N20θN0]f2(1+fP0)3, b4=11+fP0, b5=(3N01θ)f(1+fP0)2,

    b6=[3N202(1+θ)N0+θ]f2(1+fP0)3, b7=[N30(1+θ)N20+θN0]f3(1+fP0)4.

    From the characteristic polynomial (2.2), we obtain f(1)=h2ϵ[m(n1)(n2)fn]+hϵ[4m(1+θ2m)n]+4.

    Since step size h>0, the bifurcation parameter is chosen as

    h=2f(2m1θ)+2f2(1+θ2m)2+1m(n1)(n2)ϵfnm(n1)(n2)

    where parameter m satisfies 1+θ2<m<1.

    Consider parameter h with a small perturbation δ, i.e., h=h+δ, |δ|1, and the system (3.3) becomes

    (¯Nn+1¯Pn+1)=(1+(h+δ)ϵ[2m(1+θ2m)n](h+δ)ϵ[2m(n1)n](h+δ)(n2)2f1)(¯Nn¯Pn)+(h+δ)(1ϵψ1(¯Nn,¯Pn)ψ2(¯Nn,¯Pn)) (3.4)

    The characteristic polynomial of (3.4) is given by

    g(λ)=λ2[2+h+δϵ[2m(1+θ2m)n]]λ+1+h+δϵ[2m(1+θ2m)n]+(h+δ)2ϵ[m(n1)(n2)fn]

    The transversal condition at (N0,P0) is

    dg(λ)dδλ=1,δ=0=42mh(1+θ2m)ϵn

    If dg(λ)dδλ=1,δ=00, then Flip bifurcation will occur at E4.

    To facilitate discussion, define A=(1+hϵ[2m(1+θ2m)n]hϵ[2m(n1)n]h(n2)2f1).

    If the eigenvalue of A goes for λ=1, the corresponding eigenvector is given as:

    T1=(hϵ[2m(n1)n]2hϵ[2m(1+θ2m)n])

    If the eigenvalue of A goes for λ=λ2, the corresponding eigenvector is given as:

    T2=(hϵ[2m(n1)n]λ21hϵ[2m(1+θ2m)n])

    Then, we have the invertible matrix

    T=(T1,T2)=(hϵ[2m(n1)n]hϵ[2m(n1)n]2hϵ[2m(1+θ2m)n]  λ21hϵ[2m(1+θ2m)n])

    Using translation (xnyn)=T1(¯Nn¯Pn), system (3.4) turns into

    (xn+1yn+1)=(100λ2)(xnyn)+(f1(¯Nn,¯Pn,δ)g1(¯Nn,¯Pn,δ)) (3.5)

    where

    f1(¯Nn,¯Pn,δ)=a11¯Nnδ+a12¯Pnδ+b11¯Nn2+b12¯Nn¯Pn+b13¯Pn2+b14¯Nn3+b15¯Nn2¯Pn+b16¯Nn¯Pn2+b17¯Pn3+c11¯Nn2δ+c12¯Nn¯Pnδ+c13¯Pn2δ+c14¯Nn3δ+c15¯Nn2¯Pnδ+c16¯Nn¯Pn2δ+c17¯Pn3δ
    g1(¯Nn,¯Pn,δ)=a21¯Nnδ+a22¯Pnδ+b21¯Nn2+b22¯Nn¯Pn+b23¯Pn2+b24¯Nn3+b25¯Nn2¯Pn+b26¯Nn¯Pn2+b27¯Pn3+c21¯Nn2δ+c22¯Nn¯Pnδ+c23¯Pn2δ+c24¯Nn3δ+c25¯Nn2¯Pnδ+c26¯Nn¯Pn2δ+c27¯Pn3δ
    a11=1h(λ2+1)[2m1θn1](λ21hϵ[2m(1+θ2m)n])n22f(λ2+1)a12=1h(λ2+1)(λ21hϵ[2m(1+θ2m)n])a21=1h(λ2+1)[2m1θn1](2+hϵ[2m(1+θ2m)n])+n22f(λ2+1)a22=1h(λ2+1)(2+hϵ[2m(1+θ2m)n])b11=1λ2+1[n2m(n1)](λ21hϵ[2m(1+θ2m)n])b1b12=1λ2+1[n2m(n1)](λ21hϵ[2m(1+θ2m)n])b2hλ2+1b13=1λ2+1[n2m(n1)](λ21hϵ[2m(1+θ2m)n])b3b17=1λ2+1[n2m(n1)](λ21hϵ[2m(1+θ2m)n])b7cij=hbij(i=1,2 ; j=1,2,,7)

    Using translation (¯Nn+1¯Pn+1)=T(xn+1yn+1), system (3.5) becomes

    (xn+1yn+1)=(xnλ2yn)+(f2(xn,yn,δ)g2(xn,yn,δ)) (3.6)

    where

    f2(xn,yn,δ)=d11xnδ+d12ynδ+e11x2n+e12xnyn+e13y2n+e14x3n+e15x2nyn+e16xny2n+e17y3n+f11x2nδ+f12xnynδ+f13y2nδ+f14x3nδ+f15x2nynδ+f16xny2nδ+f17y3nδ
    g2(xn,yn,δ)=d21xnδ+d22ynδ+e21x2n+e22xnyn+e23y2n+e24x3n+e25x2nyn+e26xny2n+e27y3n+f21x2nδ+f22xnynδ+f23y2nδ+f24x3nδ+f25x2nynδ+f26xny2nδ+f27y3nδ

    k11=k12=hϵ[2m(n1)n], k21=2hϵ[2m(1+θ2m)n], k22=λ21hϵ[2m(1+θ2m)n]

    d11=a11k11+a12k21,d12=a11k12+a12k22,e11=b11k211+b12k11k21+b13k221,e12=2b11k11k12+b12(k11k22+k12k21)+2b13k21k22,e13=b11k212+b12k12k22+b13k222,e14=b14k311+b15k211k21+b16k11k221+b17k321,e15=3b14k211k12+b15(k211k22+2k11k12k21)+b16(2k11k21k22+k12k221)+3b17k221k22,e16=3b14k11k212+b15(2k11k12k22+k212k21)+b16(k11k222+2k12k21k22)+3b17k21k222,e17=b14k312+b15k212k22+b16k12k222+b17k322,d21=a21k11+a22k21,d22=a21k12+a22k22,e21=b21k211+b22k11k21+b23k221,e22=2b21k11k12+b22(k11k22+k12k21)+2b23k21k22,e23=b21k212+b22k12k22+b23k222,e24=b24k311+b25k211k21+b26k11k221+b27k321,e25=3b24k211k12+b25(k211k22+2k11k12k21)+b26(2k11k21k22+k12k221)+3b27k221k22,e26=3b24k11k212+b25(2k11k12k22+k212k21)+b26(k11k222+2k12k21k22)+3b27k21k222,e27=b24k312+b25k212k22+b26k12k222+b27k322,fij=heij(i=1,2 ; j=1,2,,7).

    Next, by using the center manifold theorem and normal form theories, the direction of Flip bifurcation at E4 is given.

    α1=(2f˜xnδ+12fδ2f˜x2n)(0,0),α2=(163f˜x3n+(122f˜x2n)2)(0,0),

    where the coefficients of α1 and α2 are derived from (3.6).

    Theorem 3.1. If α10, α20, then the system undergoes Flip bifurcation at the immobile point (N0,P0), and if α2>0(<0), then the two-cycle point is stable (unstable).

    For sufficiently small neighborhoods of the parameter δ=0, there exists a central manifold at (0,0):

    Wc(0,0)={(˜x,˜y):˜yn+1=m1˜x2n+1+m2˜xn+1δ} (3.7)

    Substituting (3.7) into (3.6), the solution is given as

    m1=e211λ2=b21k211+b22k11k21+b23k2211λ2,m2=d211+λ2=(a21k11+a22k21) 1+λ2

    Restricting the system of equations on Wc(0,0), the results are as follows.

    ˜xn+1=˜xn+l1˜x2n+l2˜xnδ+l3˜x2nδ+l4˜xnδ2+l5˜x3n+O((|˜xn|+|δ|)4)

    where l1=e11=b11k211+b12k11k21+b13k221, l2=d11=a11k11+a12k21, l3=d12m1+f11+e12m2, l4=d12m2, l5=e12m1+e14.

    According to the normal form theories related to bifurcation analysis, we require the following quantity at (x,y,δ)=(0,0,0).

    α1=(2f˜xnδ+12fδ2f˜x2n)(0,0)=l2+l3,α2=(163f˜x3n+(122f˜x2n)2)(0,0)=l5+l21

    For numerical simulations, we choose the following parameters to prove our theoretical discussion. The unique positive equilibrium point (N0,P0)(0.89,0.0708765) of the model can be obtained by giving the parameters h=0.1, e=0.023933614, θ=0.2, f=1, m=0.89. By choosing the initial value of (0.9,0.07115), the result can be obtained by numerical simulation as Figure 1.

    Figure 1.  Flip bifurcation occurs at the equilibrium point (N0,P0)(0.89,0.0708765). (a) is the comparison of the trend of variables N and P with the change over time t. (b) and (c) mark the period two bifurcation cases for both variables N and P, respectively.

    It is obvious from the diagrams that variables N and P appear as two-point solutions with a period for a given parameter condition and gradually stabilize near the equilibrium point, which verifies Flip bifurcation occurs at this point.

    In this section, we still choose h as the bifurcation parameter to discuss Hopf bifurcation around its positive equilibrium (N0,P0). The necessary conditions for Hopf bifurcation to occur is given by the following curve:

    S={(m,n,θ,f,ϵ)R5+:h=h1=(2m1θ)[1+1+4f(1m)(mθ)]2(1m)(mθ)1+4f(1m)(mθ), |E|<2}

    where E=hϵ(m(1+θ2m)1+1+4f(1m)(mθ))+2.

    For emergence of Hopf bifurcation around positive equilibrium (N0,P0), two roots of the characteristic polynomial (2.2) must be complex conjugate with unit modulus. Therefore, it is easy to obtain the bifurcation parameter h1=(2mθ1)(1+1+4f(1m)(mθ))2(1m)(mθ)1+4f(1m)(mθ)>0, i.e., 0<m<θ or 1+θ2<m<1.

    We still consider parameter h with a small perturbation δ, and then characteristic equation (2.2) can be rewritten as:

    λ2+p(δ)λ+q(δ)=0,

    where

    p(δ)=[2+(h1+δ)ϵ[2m(1+θ2m)n]],
    q(δ)=1+(h1+δ)ϵ[2m(1+θ2m)n]+(h1+δ)2ϵ[m(n1)(n2)fn]

    The roots of the characteristic equation of J(N0,P0) are

    λ1=p(δ)+i4q(δ)p(δ)22     ,    λ2=p(δ)i4q(δ)p(δ)22

    Also,

    |λ1,2|=q(δ),d|λ1,2|dδδ=0=[14f(1+θ2m)2ϵn(n1)(n2)+2(2m1θ)ϵmn]12(1+θ2m)(m2)ϵn

    where d|λ1,2|dδδ=0>0 if and only if (1+θ2m)(m2)ϵn>0.

    If p(0)0,1, we have 4f(2m1θ)2ϵn(n1)(n2)2,3, which means λn1,21 n=1,2,3,4.

    The transversal condition at (N0,P0) is given by

    d|λ1|2dδδ=0=(λ1dλ2dδ+λ2dλ1dδ)δ=0=m(1+θ2m)ϵn[3+2mh1(1+θ2m)ϵn]+mh1(n1)(n2)ϵfn

    If 1+θ2<m<1, we have d|λ1|2dδδ=0>0. Then, Hopf bifurcation will occur at (N0,P0).

    Now, let

    α=1+h1ϵ[m(1+θ2m)n],β=h1m(n1)(n2)ϵfnm2(1+θ2m)2ϵ2n2

    The invertible matrix T is given by

    T=( hϵ[2m(n1)n]0  α1hϵ[2m(1+θ2m)n]β )

    Using the following translation

    (¯Nn+1¯Pn+1)=T(¯xn+1¯yn+1)

    Then, the Eq (3.4) transforms to

    (¯xn+1¯yn+1)=(αββα)(ˉxnˉyn)+(f(ˉxn,ˉyn)g(ˉxn,ˉyn))

    where

    f(ˉxn,ˉyn)=l11ˉx2n+l12ˉxnˉyn+l13ˉy2n+l14ˉx3n+l15ˉxn2ˉyn+l16ˉxnˉy2n+l17ˉy3n,g(ˉxn,ˉyn)=l21ˉx2n+l22ˉxnˉyn+l23ˉy2n+l24ˉx3n+l25ˉxn2ˉyn+l26ˉxnˉy2n+l27ˉy3n,l11=b1α3+b2α2β+αβ2+b3αβ2,l12=2b1α2β+b2α3+βα2b2αβ2β3+2b3α2β,l13=b1αβ2b2α2βαβ2+b3α3,l14=b4α4+b5α3β+b6α2β2+b7αβ3,l15=3b4α3β+b5α42b5α2β2+2b6α3βb6αβ3+3b7α2β2,l16=3b4α2β2+b5αβ32b5α3β+b6α42b6α2β2+3b7α3β,l17=b4αβ3+b5α2β2b6α3β+b7α4,l21=b1α2β+b2αβ2α2β+b3β3,l22=2b1αβ2+b2α2βb2β3α3+αβ2+2b3αβ2,l23=b1β3b2αβ2+α2β+b3α2β,l24=b4α3β+b5α2β2+b6αβ3+b7β4,l25=3b4α2β2+b5α3β2b5αβ3+2b6α2β2b6β4+3b7αβ3,l26=3b4αβ3+b5β42b5α2β2+b6α3β2b6αβ3+3b7α2β2,l27=b4β4+b5αβ3b6α2β2+b7α3β

    Next, by using the center manifold theorem and normal form theories, the direction of Hopf bifurcation at E4 is given. According to the normal form theories related to bifurcation analysis, we require the following quantity at (x,y,δ)=(0,0,0).

    L=Re[(12λ)ˉλ21λξ11ξ20]12|ξ11|2|ξ02|2+Re(ˉλξ21)

    where

    ξ20=18[fˉxˉx+fˉyˉy+2gˉxˉy+i(gˉxˉxgˉyˉy2fˉxˉy)],ξ11=14[fˉxˉx+fˉyˉy+i(gˉxˉx+gˉyˉy)],ξ02=18[fˉxˉxfˉyˉy+2gˉxˉy+i(gˉxˉxgˉxˉy+2fˉxˉy)],ξ21=116[fˉxˉxˉx+fˉxˉyˉy+gˉxˉxˉy+gˉyˉyˉy+i(gˉxˉxˉx+gˉxˉyˉyfˉxˉxˉyfˉyˉyˉy)],fˉxˉx=2l11 , fˉxˉy=l12 , fˉxˉxˉx=6l14 , fˉxˉxˉy=2l15 , fˉxˉyˉy=2l16 , fˉyˉy=2l13 , fˉyˉyˉy=6l17,gˉxˉx=2l21 , gˉxˉy=l22 , gˉxˉxˉx=6l24 , gˉxˉxˉy=2l25 , gˉxˉyˉy=2l26 , gˉyˉy=2l23 , gˉyˉyˉy=6l27 . 

    Theorem 4.1. If the above conditions hold, and L0, the Hopf bifurcation occurs at the point (N0,P0). When δ>0, if L<0, then it attracts at that point; when δ<0, if L>0, then it repels at that point.

    To verify whether the condition is correct, we select the following parameters for numerical simulations and obtain the following conclusions. By selecting the first set of parameters h0.062163, e=1, θ=0.1, f=1, m=0.554, the unique positive equilibrium point (N0,P0)(0.554,0.004932) of the model can be obtained. By choosing the initial value K1=(0.5,0.005), the following results can be obtained by numerical simulation.

    Figure 2 reflects that variable N gradually tends to oscillate steadily at the equilibrium point N=0.554 with the increase of the number of iterations n, while variable P also gradually oscillates steadily with the increase of the number of iterations N, but the equilibrium point is attracted to P0.157.

    Figure 2.  Hopf bifurcation occurs at the equilibrium point (N0,P0)(0.554,0.004932). (a) and (b) are the variations of N, P with the number of iterations n, respectively.

    By choosing the second set of parameters h1.120901, e=1, θ=0.61, f=1, m=0.823 the only internal equilibrium point (N0,P0)(0.823,0.020442) of the model can be obtained. Choosing the initial value K2=(0.8,0.02), the following results can be obtained by numerical simulation.

    Figure 3 shows that as the number of iterations n increases, eventually, the variable P will also be attracted to stabilize at P0.035.

    Figure 3.  Hopf bifurcation occurs at the equilibrium point (N0,P0)(0.823,0.020442). (a) and (b) are the variations of N, P with the number of iterations n, respectively.

    In this section, chaotic cases at bifurcating points are analyzed using numerical simulations. We give maximum Lyapunov exponents and phase diagrams for different perturbation parameters to prove our results. The chaos theory analysis of Flip bifurcation and Hopf bifurcation is given as follows.

    Definition 5.1. [27] The formula for the maximum Lyapunov index is given by

    λ=limn1nn1n=0ln|df(xn,μ)dx|

    Theorem 5.1. [27] If λ<0, the neighboring points eventually come together and merge into a single point, which corresponds to stable immobile points and periodic motion. If λ>0, the neighboring points eventually separate, which corresponds to local instability of the orbit generating chaotic situations.

    In the following, we choose two different equilibria to consider the Flip bifurcation with chaotic cases. Initially, we select the parameters h=0.95, e0.1841359, θ=0.5, f=1, m=0.961, and obviously the model comes to

    {Nn+1=Nn+0.95{10.1841359[Nn(1Nn)(Nn0.5)11+PnNnPn]}Pn+1=Pn+0.95[NnPn0.961Pn]

    where the internal equilibrium point is (N1,P1)(0.961,0.01807).

    After selecting the perturbation δ(0,0.2) and analyzing the trend of N with δ by numerical simulation, we obtain the chaotic bifurcating cases (see Figure 4).

    Figure 4.  δ(0,0.2)-bifurcation diagrams at point (N1,P1)(0.961,0.01807) compared with maximum Lyapunov exponent. (a) indication that in the range of δ(0,0.2), the bifurcation diagram corresponds to the maximum Lyapunov exponent. The accuracy of the conclusions can be seen by comparing bifurcation amplifications and the maximum Lyapunov exponent amplifications in the same range of δ from (b) to (k).

    Next, choosing the parameters h=0.95, e0.184136, θ=0.5, f=1, m=0.9601, we obtain the model

    {Nn+1=Nn+0.95{10.184136[Nn(1Nn)(Nn0.5)11+PnNnPn]}Pn+1=Pn+0.95[NnPn0.9601Pn]

    Obviously, the internal equilibrium point (N2,P2)(0.9601,0.0180734). We still choose the perturbation δ(0,0.2) to analyze the trend of N with δ. The chaotic bifurcating case is obtained (see Figure 5). By calculating the bifurcating diagrams and the maximum Lyapunov exponent for two bifurcating points with small differences, it can be shown that the small change of the initial value near the equilibrium point has a great influence on the stability of the initial state. Although they are different perturbed bifurcating cases, the two cases tend to go the same during the process of δ gradually increasing.

    Figure 5.  δ(0,0.2)-bifurcation diagrams at point (N2,P2)(0.9601,0.0180734) compared with maximum Lyapunov exponent. (a) indication that in the range of δ(0,0.2), the bifurcation diagram corresponds to the maximum Lyapunov exponent. The accuracy of the conclusions can be seen by comparing bifurcation amplifications and the maximum Lyapunov exponent amplifications in the same range of δ from (b) to (k).

    In the following, we choose two different equilibria to consider Hopf bifurcation with chaotic cases. By picking the first set of parameters h0.062163, e=1, θ=0.1, f=1, m=0.554, the unique internal equilibrium point (N3,P3)(0.554,0.004932) of the model can be obtained.

    After choosing perturbation δ(0,0.5), we obtain phase diagrams (see Figure 6).

    Figure 6.  Phase diagrams under different perturbations at the equilibrium point (N3,P3)(0.554,0.004932). (a) indicates that N will slowly stabilize without perturbation. (b), (c) and (d) show that the rate of convergence to stability will become faster with increasing δ. No chaotic situations occur.

    The analysis mentioned above shows that with the perturbation δ increasing, the asymptotic stability of variable N gradually becomes weaker, and the range of periodic solutions gradually becomes larger, but the convergence speed comes faster increases (see Figure 6).

    By choosing the second set of parameters h1.120901, e=1, θ=0.61, f=1, m=0.823, we can obtain the unique internal equilibrium point (N4,P4)(0.823,0.020442) of the model.

    After choosing perturbation δ(1,1), we obtain phase diagrams (see Figure 7).

    Figure 7.  Phase diagrams under different perturbations at the equilibrium point (N4,P4)(0.823,0.020442). (a) and (b) tend to stabilize at a center point. However, (c), (d), and(e) are stable in a orbit. No chaotic situations occur.

    Our work deals with the study of local dynamical properties of a predator-prey model with discrete time (1.2), Flip bifurcation and Hopf bifurcation associated with the periodic solution, as well as their chaotic cases. We prove that the system (1.2) has four unique equilibria. In addition, their stability and instability conditions are given, which mark the final equilibrium state of both groups as fear, climate and other factors change. We focus on the unique positive equilibrium point E4=(m,1+1+4f(1m)(mθ)2f), with stability condition:

    {1+hϵ[m(1+θ2m)1+1+4f(1m)(mθ)]+h2ϵ[m(1m)(mθ)1+4f(1m)(mθ)[1+1+4f(1m)(mθ)]2]>0                      m(1m)(mθ)1+4f(1m)(mθ)>01+θ2m1+1+4f(1m)(mθ)+h[2(1m)(mθ)1+4f(1m)(mθ)[1+1+4f(1m)(mθ)]2]<0

    Significantly, after analyzing the formation conditions of Flip bifurcation and Hopf bifurcation at (N0,P0), we give their chaotic scenarios from both theoretical and numerical simulations. Moreover, it is found that Flip bifurcation is more prone to chaos scenarios, and Hopf bifurcation is closely related to periodic solutions.

    Biologically, the occurrence of Flip bifurcation implies the number of predators and prey will alternate around a value instead of converging to a fixed value, eventually. The corresponding chaotic scenario suggests that small perturbations in the variable parameters will eventually have a significant impact on the population, ultimately leading to chaos. The appearance of Hopf bifurcation points indicates that the equilibrium point of the system loses its attraction and eventually produces a closed orbit, which implies the existence of periodic oscillations of predators and prey. Therefore, we can precisely change the biological density of predators and prey to achieve the desired goal by regulating the number of bifurcation parameters h, for the fact that the chaotic scenario does not exist. All the conclusions illustrate the effect of fear on populations. Finally, the accuracy of the theory is demonstrated using the maximum Lyapunov exponent and phase diagrams.

    In future work, the model can be investigated using different discrete methods. In addition, to obtain specific biological properties, new bifurcation parameters can be selected for the study to obtain the desired conclusions.

    The authors declare that they have no competing interests.



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