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Research article

The stability of bifurcating solutions for a prey-predator model with population flux by attractive transition

  • Received: 14 February 2021 Accepted: 13 April 2021 Published: 25 April 2021
  • MSC : 35B32, 35B35

  • This paper investigates the stability of bifurcating solutions for a prey-predator model with population flux by attractive transition. Applying spectral analysis and the principle of exchange of stability, we obtain that the bifurcating solutions are stable/unstable under some certain conditions.

    Citation: Qian Xu, Chunfeng Xing. The stability of bifurcating solutions for a prey-predator model with population flux by attractive transition[J]. AIMS Mathematics, 2021, 6(7): 6948-6960. doi: 10.3934/math.2021407

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  • This paper investigates the stability of bifurcating solutions for a prey-predator model with population flux by attractive transition. Applying spectral analysis and the principle of exchange of stability, we obtain that the bifurcating solutions are stable/unstable under some certain conditions.



    In this paper, we investigate the following Lotka-Volterra prey-predator model with population flux by attractive transition.

    {ut=d1Δu+u(m1ucv),xΩ,t(0,T),vt=[d2v+αu2(vu)]+v(m2+buv),xΩ,t(0,T),u=v=0,xΩ,t(0,T),u(,0)=u00,v(,0)=v00,xΩ. (1.1)

    Where u and v are the population densities of the prey and predator. d1 and d2 are the random diffusion coefficients. m1 represents the growth rate of the prey population. d1,d2 and m1 are positive constants and m2 is a real constant which can be negative. m2 represents the mortality rate while it is negative and it represents the increasing rate of the predator population while it is positive. b and c are positive constants which describe the rate of increase of the predator and the rate of decrease of the prey due to the predation respectively. J:=αu2(vu)=α(uv+vu) represents the population flux of the predator based on a biodiffusion in order that the transition probability of each individual of the predator depends on conditions at the point of arrival (see [2]). The nonnegative constant α is a magnitude of such a population flux by attractive transition.

    If α=0 holds, then system (1.1) is reduced to the classical Lotka-Volterra prey-predator model. Some prey-predator models with the linear diffusion terms have been extensively studied by many mathematicians, see [3,4,5,6,7]. Kadota and Kuto [8] investigated a prey-predator system with cross diffusion of quasilinear fractional type. They discussed the local and global bifurcation solutions and obtained a sufficient condition for the existence of positive steady state solutions. Xu and Guo [10] considered the same model as in [8]. They studied the bifurcation steady states which bifurcated from the semitrivial solution with different bifurcating parameter and they obtained the stability of the local bifurcating solutions. Kuto [11] investigated a Lotka-Volterra prey-predator system with cross-diffusion in a spatially heterogeneous environment. Kuto obtained the global bifurcation branch of positive stationary solutions and the bifurcation branch could form a bounded fish-hook curve. Djilali [12] studied the influence of the nonlocal interspecific competition of the prey population on the dynamics of the diffusive predator-prey model with prey social behavior (i.e. herd behavior). It was proved that the turning patterns occur in the presence of the nonlocal competition and can not be found in the original system. Djilali [13] investigated a predator-prey model with social behavior (i.e. herd behavior). The existence of Hopf bifurcation and Turing driven instability were proved. By calculating the normal form, on the center of the manifold associated to the Hopf bifurcation points, the stability of periodic solution was proved. Djilali and Bentout [14] studied the same model as in [13]. They proved the non-existence of a non-constant steady state solution for some values of the diffusion coefficients. They also proved the existence of the non-constant steady state solution under a suitable condision on the diffusion coefficients by applying the Leray-Schauder degree theory.

    In the following, we list the local bifurcation results and some preliminary results obtained in [1], which will be used in this paper. The corresponding steady state problem of (1.1) is as follows

    {d1Δu+u(m1ucv)=0,xΩ,[d2v+αu2(vu)]+v(m2+buv)=0,xΩ,u=v=0,xΩ. (1.2)

    It is easy to see that the second equation of (1.2) can be written as

    d2Δv+α(uΔvvΔu)+v(m2+buv)=0,xΩ. (1.3)

    Substituting the first equation of (1.2) into (1.3), we get

    Δv+vd2+αu(αud1(m1ucv)+m2+buv)=0,xΩ. (1.4)

    Together with (1.3) and (1.4), system (1.2) can be written as

    {d1Δu+u(m1ucv)=0,xΩ,Δv+vd2+αu(αud1(m1ucv)+m2+buv)=0,xΩ,u=v=0,xΩ. (1.5)

    For any fixed m1>d1λ1(which λ1 represents the least eigenvalue of Δ with the homogeneous Dirichlet boundary condition on Ω), system (1.5) has a couple of sets of semitrivial solutions with parameter m2 which can be denoted as follows

    Γu:={(θd1,m1,0,m2)X×R},Γv:={(0,θd2,m2,m2)X×(d2λ1,)}.

    For the following equation

    {Δϕ+q(x)ϕ=λϕ,xΩ,ϕ=0,xΩ. (1.6)

    Assume q(x)C(¯Ω) holds, let λ1(q) be the least eigenvalue of (1.6), then qλ1(q):C(¯Ω)R is increasing, i.e. if q1(x)q2(x) and q1(x)q2(x) in Ω, then λ1(q1)<λ1(q2).

    Define an operator F:X×RY by

    F(u,v,m2)=(d1Δu+u(m1ucv)Δv+vd2+αu(αud1(m1ucv)+m2+buv)). (1.7)

    Then solving system (1.2) is equivalent to solving the equation F(u,v,m2)=0.

    It is easy to compute that

    F(u,v)(θd1,m1,0,m2)(uv)=(d1Δu+(m12θd1,m1)ucθd1,m1vΔv+vd2+αθd1,m1(αd1θd1,m1(m1θd1,m1)+m2+bθd1,m1)). (1.8)

    According to the result obtained in [1], we have

    Ker{F(u,v)(θd1,m1,0,m2)}=span{ϕ,ψ}, (1.9)

    where ψ satisfies the following equation

    {Δψψd2+αθd1,m1(αd1θd1,m1(m1θd1,m1)+m2+bθd1,m1)=0,xΩ,ψ=0,xΩ, (1.10)

    with ψ=1, m2=f(m1),limm1d1λ1f(m1)=d2λ1,limm1f(m1)= and

    ϕ=[d1Δ+2θd1,m1m1]1(cθd1,m1ψ). (1.11)

    In the following, we restate the existence of bifurcating solutions which bifurcate from (θd1,m1,0,f(m1)) obtained in [1].

    Lemma 1.1 (Proposition 4.4 in [1]) Let m1(d1λ1,) be given arbitrarily. Positive solutions of (1.2) bifurcate from Γu as m2=f(m1). There exists a neighborhood N1 of (u,v,m2)=(θd1,m1,0,f(m1))X×R such that F1(0)N1 consists of the union of ΓuN1 and the local curve

    [uvm2](s)=[θd1,m10f(m1)]+[s(ϕ+˜u(s))s(ψ+˜v(s))μ(s)],s(δ,δ), (1.12)

    with some δ>0. Here (˜u,˜v,μ)(s)X×R is continuous differentiable for s(δ,δ) satisfying Ωψ˜v(s)=0 for all s(δ,δ) and (˜u,˜v,μ)(0)=(0,0,0) and

    μ(0)=Ω(ψ)2d2+αθd1,m1(α2θ2d1,m1+α{d1f(m1)d2(m12θd1,m1)}bd1d2d2+αθd1,m1ϕ+(αcθd1,m1+d1)ψ)d1Ω(ψ)2d2+αθd1,m1. (1.13)

    All positive solutions contained in F1(0)N1 can be expressed as

    C+u:={(u,v,m2)(s):0<s<δ}.

    In the following, we rewrite the existence of bifurcating solutions which bifurcate from (0,θd2,g(m1),g(m1)) obtained in [1], first we give some preliminary results which has been obtained in [1].

    Let V=vθd2,m2,˜F(u,V,m2):=F(u,V+θd2,m2,m2), where F is defined by (1.7). Thus we have

    ˜F(u,V)(0,0,m2)(uV)=(d1Δu+(m1cθd2,m2)uΔV+d12{h21(x)u+(m22θd2,m2)V}), (1.14)

    where

    h21(x)=[α{m1d1m2d2(cd11d2)θd2,m2}+b]θd2,m2. (1.15)

    Ker(˜F(u,V)(0,0,m2))=span{¯ϕ,¯ψ}, where ¯ϕ satisfies

    {Δ¯ϕ+cd1θd2,m2¯ϕ=m1d1¯ϕ,xΩ,¯ϕ=0,xΩ, (1.16)

    with m1=d1λ1(cθd2,m2d1)(=:g1(m2)),¯ϕ=1, where g1(m2) is continuously differentiable and monotone increasing for m2>d2λ1 such that

    limm2d2λ1g1(m2)=d1λ1,limm2g1(m2)=. (1.17)

    For convenience, we denote the inverse function of m1=g1(m2) by m2=g(m1).

    ¯ψ=[d2Δ+2θd2,g(m1)g(m1)]1(h21¯ϕ). (1.18)

    Now we restate the existence of bifurcating solutions which bifurcate from

    (0,θd2,g(m1),g(m1)) obtained in [1].

    Lemma 1.2 (Proposition 4.6 in [1]) Let m1(d1λ1,) be given arbitrarily. Positive solutions of (1.2) bifurcate from Γv as m2=g(m1). There exists a neighborhood N2 of (u,v,m2)=(0,θd2,g(m1),g(m1)))X×R such that F1(0)N2 consists of the union of ΓvN2 and the local curve

    [uvm2](s)=[0θd2,g(m1)g(m1)]+[s(¯ϕ+˜u(s))s(¯ψ+˜v(s))μ(s)],s(δ,δ), (1.19)

    with some δ>0. Here (˜u,˜v,μ)(s)X×R is continuous differentiable for s(δ,δ) satisfying Ω¯ϕ˜u(s)=0 for all s(δ,δ) and (˜u,˜v,μ)(0)=(0,0,0) and

    μ(0)=Ω(¯ϕ+c¯ψ)¯ϕ2cΩζ(x)¯ϕ2,whereζ:=θd2,m2m2m2=g(m1)(>0). (1.20)

    All positive solutions contained in F1(0)N2 can be expressed as

    C+v:={(u,v,m2)(s):0<s<δ}.

    Oeda and Kuto [1] gives the asymptotic behavior of positive solutions of (1.2) as α which can be written as follows.

    Lemma 1.3 (Theorem 2.2 in [1]) Suppose that (m1,m2,d1,d2,b,c) satisfies

    m1>d1λ1,m2d2d1m1(d2d1+b)θd1,m122θd1,m11(=f(m1,d1,d2,b)),m2d2d1m1(d2d11c)θd1,m122θd1,m11(=h(m1,d1,d2,c)),m2g(m1,d1,d2,c). (1.21)

    Let {(un,vn)} be any sequence of positive solutions to (1.2) with α=αn. Then the following alternative holds true.

    (i) If {(αnun)} is unbounded, then

    f(m1,d1,d2,b)<m2<h(m1,d1,d2,c)

    and

    limn(un,vn)=(1s,sc)θd1,m1inC1(¯Ω)×C1(¯Ω),

    passing to a subsequence, where s(0,1) is defined by

    m2=(1s)f(m1,d1,d2,b)+sh(m1,d1,d2,c).

    (ii) If {(αnun)} is bounded, then there exists (w,v)C2(¯Ω)×C2(¯Ω) such that

    limn(αnun,vn)=(w,v)inC1(¯Ω)×C1(¯Ω),

    passing to a subsequence, and moreover, (w,v) is a positive solution to

    {d1Δu+u(m1cv)=0,xΩ,[d2v+w2(vw)]+v(m2v)=0,xΩ,u=v=0,xΩ. (1.22)

    According to the first type (ⅰ), the coexistence steady state (u,v) of prey and predator can be approximated by a coexistence steady state of the competition model with equal conditions. In the second type (ⅱ), the component of prey shrinks with order O(1α) when α is sufficiently large. The bifurcation structure of positive solutions of (1.22) will be discussed in a forthcoming paper [9].

    In this paper, we study the stability of bifurcating solutions obtained in [1]. Applying spectral analysis and the principle of exchange of stability, we obtain that the bifurcating solutions are stable/unstable under some certain conditions. The plan of this paper is as follows. In section 2, we prove that the bifurcating solutions near (θd1,m1,0,f(m1)) are locally asymptotically stable/unstable under some certain conditions. In section 3, we prove that the bifurcating steady states near (0,θd2,g(m1),g(m1)) are locally asymptotically stable/unstable under some certain conditions. A conclusion section ends the paper.

    We consider the stability of bifurcating solutions near (0,θd2,g(m1),g(m1)) of the following system.

    {ut=d1Δu+u(m1ucv),(x,t)Ω×(0,+),vt=Δv+vd2+αu(αud1(m1ucv)+m2+buv),(x,t)Ω×(0,+),u=v=0,xΩ. (2.1)

    Denote

    ˜f(u(s),v(s),m2)=d2αm1v(s)d1(d2+αu(s))22d2αu(s)v(s)+α2u2(s)v(s)d1(d2+αu(s))2αd2cv2(s)d1(d2+αu(s))2αm2v(s)(d2+αu(s))2+bd2v(s)(d2+αu(s))2+αv2(s)(d2+αu(s))2,˜g(u(s),v(s),m2)=αm1u(s)d1(d2+αu(s))αu2(s)d1(d2+αu(s))2αcu(s)v(s)d1(d2+αu(s))+m2d2+αu(s)+bu(s)d2+αu(s)2v(s)d2+αu(s) (2.2)

    Linearizing (2.1) at (u(s),v(s)) defined by (1.19) and investigating the following eigenvalue problem, by (2.2), we have

    {d1Δu+m1u2u(s)ucv(s)ucu(s)v=λu,xΩ,Δv+˜f(u(s),v(s),m2)u+˜g(u(s),v(s),m2)v=λv,xΩ,u=v=0,xΩ. (2.3)

    According to (1.7), (2.3) can be rewritten as follows

    F(u,v)(u(s),v(s),m2)(uv)=(λuλv), (2.4)
    F(u,v)(0,θd2,g(m1),g(m1))(uv)=(d1Δu+m1ucθd2,g(m1)uΔv+˜f(0,θd2,g(m1),g(m1))u+˜g(0,θd2,g(m1),g(m1))v)=(d1Δu+m1ucθd2,g(m1)uΔv+d12{h21(x)u+(m22θd2,g(m1))v},), (2.5)

    where h21(x) is defined by (1.15).

    It is easy to see that

    Ker(F(u,v)(0,θd2,g(m1),g(m1)))=span{¯ϕ,¯ψ}, (2.6)

    where ¯ϕ and ¯ψ are defined by (1.16) and (1.18).

    According to Shi [16] (Theorem 2.1 and (4.5)), we can define the functional

    l1:XRby<[f,g],l1>:=Ωf¯ϕdx. (2.7)

    Theorem 2.1. For any fixed m1(d1λ1,), the bifurcating steady state (u(s),v(s)) defined by (1.19) of system (1.2) is locally asymptotically stable when μ(0)<0 defined by (1.20); the bifurcating steady state (u(s),v(s)) defined by (1.19) of system (1.2) is locally asymptotically unstable when μ(0)>0 defined by (1.20).

    Proof. First we show that 0 is the first eigenvalue of F(u,v)(0,θd2,g(m1),g(m1)).

    From the above, we get that 0 is the eigenvalue of F(u,v)(0,θd2,g(m1),g(m1)). Therefore we will show that 0 is the first eigenvalue of F(u,v)(θd1,m1,0,f(m1)). Otherwise, there exists a positive eigenvalue ˜λ1 of F(u,v)(0,θd2,g(m1),g(m1)) with the corresponding eigenfunction (u1v1)X such that

    F(u,v)(0,θd2,g(m1),g(m1))(u1v1)=(˜λ1u1˜λv1), (2.8)

    that is

    {d1Δu1+m1u1cθd2,g(m1)u1=˜λ1u1,Δv1+˜f(0,θd2,g(m1),g(m1))u1+˜g(0,θd2,g(m1),g(m1))v1=˜λ1v1, (2.9)

    where ˜f and ˜g are defined by (2.2)

    Assume u1=0 and v10 hold, from the second equation of (2.9), we obtain

    d2Δv1+2θd2,g(m1)v1g(m1)v1=˜λ1d2v1. (2.10)

    Because of ˜λ1d2>0 and in [BK, 17] (Lemma 2.1), it was proved that all the eigenvalues of the operator (d2Δ+2θd2,g(m1)g(m1)) are strictly positive, which is in contradiction with (2.10), therefore we have u10.

    By virtue of (1.16) and the scalar elliptic equation theorem, 0 is the first eigenvalue of the first equation of (2.9), which contradicts ˜λ1. Therefore we obtain that 0 is the first eigenvalue of F(u,v)(0,θd2,g(m1),g(m1)) and the other eigenvalues are negative.

    For small 0<s<δ, by Proposition I.7.2 in [15], there exist perturbed eigenvalue λ(s) and continuous differential functions φ1(s),φ2(s)XRange(Fu,v(0,θd2,g(m1),g(m1))) satisfying

    F(u(s),v(s),m2(s))(¯ϕ+φ1(s)¯ψ+φ2(s))=λ(s)(¯ϕ+φ1(s)¯ψ+φ2(s)), (2.11)

    with λ(0)=φ1(0)=φ2(0)=0.

    Similarly, there exist perturbed eigenvalue λ(m2) and continuous differential functions φ1(m2),φ2(m2)XRange(F(u,v)(0,θd2,g(m1),g(m1))) satisfying

    F(u,v)(0,θd2,g(m1),g(m1))(¯ϕ+φ1(m2)¯ψ+φ2(m2))=λ(m2)(¯ϕ+φ1(m2)¯ψ+φ2(m2)), (2.12)

    with λ(m2)=φ1(m2)=φ2(m2)=0.

    Differentiating (2.12) with respect to m2 at m2=g(m1) and together with λ(m2)=φ1(m2)=φ2(m2)=0, we obtain

    ddm2F(u,v)(0,θd2,g(m1),g(m1))(¯ϕ¯ψ)+F(u,v)(0,θd2,g(m1),g(m1))(φ1(g(m1))φ2(g(m1)))=λ(g(m1))(¯ϕ¯ψ), (2.13)

    where λ(g(m1))=ddm2λ(m2)|m2=g(m1).

    According to (2.7) and (2.13), we have

    ddm2F(u,v)(0,θd2,g(m1),g(m1))(¯ϕ¯ψ),l1=λ(g(m1))¯ϕ2L2(Ω). (2.14)

    In virtue of (2.5), we have

    ddm2F(u,v)(0,θd2,g(m1),g(m1))(¯ϕ¯ψ)=(cθd2,m2m2m2=g(m1)αθd2,m2d22¯ϕ+1d2¯ψ+1d2¯ϕh21(x)θd2,m2θd2,m2m2|m2=g(m1)2d2¯ψθd2,m2m2|m2=g(m1)). (2.15)

    According to (2.7), (2.14) and (2.15), we get

    λ(g(m1))¯ϕ2L2(Ω)=Ωcθd2,m2m2m2=g(m1)¯ϕdx<0.(θd2,m2m2m2=g(m1)>0,c>0,¯ϕ>0). (2.16)

    Applying the formula I.7.40 in [15], we have

    ˙λ(0)=˙m2(0)λ(g(m1)), (2.17)

    where ˙λ(s)=ddsλ(s).

    Using Lemma 1.2 and (2.17), we have

    sgn(˙λ(0))=sgn(˙m2(0))=sgn(μ(0)), (2.18)

    where μ(0) is defined by (1.20).

    By (2.18), when μ(0)<0 holds, then λ(s)<0 for small s>0, the bifurcating solution (u(s),v(s)) defined by (1.19) of system (1.2) is locally asymptotically stable.When μ(0)>0 holds, then λ(s)>0 for small s>0, the bifurcating solution (u(s),v(s)) defined by (1.19) of system (1.2) is locally asymptotically unstable.

    For system (2.1), let α=100,b=2,c=10,m1=300,m2=20,Ω=(0,1),t=1000 and (u0,v0)=(0.01sin2(πx),0.01sin(πx)) hold, which guarantee μ(0)<0. We give the following simulation results which verify the stability of locally bifurcating steady states near (0,θd2,g(m1),g(m1)), see Figure 1.

    Figure 1.  The stability of locally bifurcating steady states near (0,θd2,g(m1),g(m1)).

    In this section, we use the similar method in section 2 in order to investigate the stability of positive solutions bifurcating from (θd1,m1,0,f(m1)).

    We linearize (1.2) at (u(s),v(s)) defined by (1.12) and study the following eigenvalue problem

    {d1Δu+m1u2u(s)ucv(s)ucu(s)v=σu,xΩ,Δv+˜f(u(s),v(s),m2)u+˜g(u(s),v(s),m2)v=σv,xΩ,u=v=0,xΩ.u(x,0)=u0(x)0,v(x,0)=v0(x)0, (3.1)

    where ˜f and ˜g are defined by (2.2).

    By (1.7), (3.1) can be rewritten by

    F(u,v)(u(s),v(s),m2)(uv)=(σuσv), (3.2)
    F(u,v)(θd1,m1,0,f(m1))(uv)=(d1Δu+m1u2θd1,m1ucθd1,m1vΔv+˜g(θd1,m1,0,f(m1))v), (3.3)

    where ˜g is defined by (2.2).

    Obviously, we have

    N(F(u,v)(θd1,m1,0,f(m1)))=span{ϕ,ψ}, (3.4)

    where ϕ and ψ are defined by (1.10) and (1.11).

    According to Shi [16] (Theorem 2.1 and (4.5)), we can define the functional

    l2:XRby<[f,g],l2>:=Ωgψdx. (3.5)

    Theorem 3.2. The bifurcating solution (u(s),v(s)) defined by (1.12) of system (1.2) is locally asymptotically stable when μ(0)>0 holds defined by (1.13); the bifurcating steady state (u(s),v(s)) defined by (1.12) of system (1.2) is locally asymptotically unstable when μ(0)<0 holds defined by (1.13).

    Proof. We first prove that 0 is the first eigenvalue of F(u,v)(θd1,m1,0,f(m1)).

    From the above, we obtain that 0 is the eigenvalue of F(u,v)(θd1,m1,0,f(m1)). Then we will prove that 0 is the first eigenvalue of F(u,v)(θd1,m1,0,f(m1)). Otherwise, there exists a positive eigenvalue σ1 of F(u,v)(θd1,m1,0,f(m1)) with the corresponding eigenfunction (˜u1˜v1)X such that

    F(u,v)(θd1,m1,0,f(m1))(˜u1˜v1)=(σ1˜u1σ1˜v1), (3.6)

    that is

    {d1Δ˜u1+m1˜u12θd1,m1˜u1cθd1,m1˜v1=σ1˜u1,xΩ,Δ˜v1+˜g(θd1,m1,0,f(m1))˜v1=σ1˜v1,xΩ,˜u1=˜v1=0,xΩ, (3.7)

    where ˜g is defined by (2.2).

    If ˜v1=0 and ˜u10 hold, the first equation of (3.7) implies

    d1Δ˜u1+2θd1,m1˜u1m1˜u1=σ1˜u1, (3.8)

    In [17] (Lemma 2.1), it was proved that all the eigenvalues of the operator (d1Δ+2θd1,m1m1) are strictly positive, which is in contradiction with (3.8), then ˜v10.

    According to (1.10) and the scalar elliptic equation theorem, 0 is the first eigenvalue of the second equation of (3.7), which contradicts σ1. Then we have proved that 0 is the first eigenvalue of F(u,v)(θd1,m1,0,f(m1)) and the other eigenvalues are negative.

    For small 0<s<δ, by Proposition I.7.2 in [15], there exist perturbed eigenvalue σ(s) and continuous differential functions ω1(s),ω2(s)XRange(F(u,v)(θd1,m1,0,f(m1))) satisfying

    F(u(s),v(s),m2(s))(ϕ+ω1(s)ψ+ω2(s))=σ(s)(ϕ+ω1(s)ψ+ω2(s)), (3.9)

    with σ(0)=ω1(0)=ω2(0)=0.

    Similarly, there exist perturbed eigenvalue σ(m2) and continuous differential functions ω1(m2),ω2(m2)XRange(F(u,v)(θd1,m1,0,f(m1))) satisfying

    F(u,v)(θd1,m1,0,f(m1))(ϕ+ω1(m2)ψ+ω2(m2))=σ(m2)(ϕ+ω1(m2)ψ+ω2(m2)), (3.10)

    with σ(m2)=ω1(m2)=ω2(m2)=0.

    Differentiating (3.10) with respect to m2 at m2=f(m1) and together with σ(m2)=ω1(m2)=ω2(m2)=0, we have

    ddm2F(u,v)(θd1,m1,0,f(m1))(ϕψ)+F(u,v)(θd1,m1,0,f(m1))(ω1(f(m1))ω2(f(m1)))=σ(f(m1))(ϕψ), (3.11)

    where σ(f(m1))=ddm2σ(m2)|m2=f(m1).

    Together with (3.5) and (3.11), we obtain

    ddm2F(u,v)(θd1,m1,0,f(m1))(ϕψ),l2=σ(f(m1))ψ2L2(Ω). (3.12)

    According to (1.8), we have

    ddm2Fu,v(θd1,m1,0,f(m1))(ϕψ)=(0ψd2+αθd1,m1). (3.13)

    Using (3.5), (3.12) and (3.13), we obtain

    σ(f(m1))ψ2L2(Ω)=Ω(ψ)2d2+αθd1,m1dx>0. (3.14)

    It follows from the formula I.7.40 in [15] that

    ˙σ(0)=˙m2(0)σ(f(m1)), (3.15)

    where ˙σ(s)=ddsσ(s).

    Together with Lemma 1.1 and (2.16), we obtain

    sgn(˙σ(0))=sgn(˙m2(0))=sgn(μ(0)), (3.16)

    where μ(0) is defined by (1.13). According to (3.16), when μ(0)>0 holds, then λ(s)<0 for small s>0, the bifurcating solution (u(s),v(s)) defined by (1.12) of system (1.2) is locally asymptotically stable. When μ(0)<0 holds, then λ(s)>0 for small s>0, the bifurcating solution (u(s),v(s)) defined by (1.12) of system (1.2) is locally asymptotically unstable.

    In this paper, we have investigated the local stability of bifurcation steady states obtained in [1] for a prey-predator model with population flux by attractive transition. By applying spectral analysis and the principle of exchange of stability, we show the stability/unstability of the bifurcating solutions under some certain conditions. We give numerical simulation result (which satisfies μ(0)<0) in order to verify the local stability of bifurcation solutions near the bifurcating point (0,θd2,g(m1),g(m1)).

    This work is supported by the National Natural Science Foundation of China (No.11871048), Premium Funding Project for Academic Human Resources Development in Beijing Union University (BPHR2019CZ07, BPHR2020EZ01) and Scientific Research Program of Beijing Municipal Education Commission (KM202011417010).

    The authors declare no conflicts of interest.



    [1] K. Oeda, K. Kuto, Positive steady states for a prey-predator model with population flux by attractive transition, Nonlinear Anal.: Real World Appl., 44 (2018), 589–615. doi: 10.1016/j.nonrwa.2018.06.006
    [2] A. Okubo, S. A. Levin, Diffusion and Ecological Problems: Modern Perspectives, New York: Springer-Verlag, 2001.
    [3] L. Li, Coexistence theorems of steady states for predator-prey interacting system, Trans. Am. Math. Soc., 305 (1988), 143–166. doi: 10.1090/S0002-9947-1988-0920151-1
    [4] L. Li, On positive solutions of a nonlinear equilibrium boundary value problem, J. Math. Anal. Appl., 138 (1989), 537–549. doi: 10.1016/0022-247X(89)90308-9
    [5] J. López-Gómez, Nonlinear eigenvalues and global bifurcation to the search of positive solutions for general Lotka-Volterra reaction diffusion systems with two species, Differ. Integr. Equations, 7 (1994), 1427–1452.
    [6] J. López-Gómez, R. Pardo, Coexistence regions in Lotka-Volterra models with diffusion, Nonlinear Anal., 19 (1992), 11–28. doi: 10.1016/0362-546X(92)90027-C
    [7] Y. Yamada, Stability of steady states for prey-predator diffusion equations with homogeneous Dirichlet conditions, SIAM J. Math. Anal., 21 (1990), 327–345. doi: 10.1137/0521018
    [8] T. Kadota, K. Kuto, Positive steady-states for a prey-predator model with some nonlinear diffusion terms, J. Math. Anal. Appl., 323 (2006), 1387–1401. doi: 10.1016/j.jmaa.2005.11.065
    [9] K. Oeda, K. Kuto, Characterization of coexistence states for a prey-predator model with large population flux by attractive transition, preprint.
    [10] Q. Xu, Y. Guo, The existence and stability of steady states for a prey-predator system with cross diffusion of quasilinear fractional type, Acta Math. Appl. Sin. Engl. Ser., 30 (2014), 257–270. doi: 10.1007/s10255-014-0281-3
    [11] K. Kuto, Bifurcation branch of stationary solutions for a Lotka-Volterra cross-diffusion system in a spatially heterogeneous environment, Nonlinear Anal.: Real World Appl., 10 (2009), 943–965. doi: 10.1016/j.nonrwa.2007.11.015
    [12] S. Djilali, Pattern formation of a diffusive predator-prey model with herd behavior and nonlocal prey competition, Math. Methods Appl. Sci., 43 (2020), 2233–2250. doi: 10.1002/mma.6036
    [13] S. Djilali, Herd behavior in a predator-prey model with spatial diffusion: Bifurcation analysis and Turing instability, J. Appl. Math. Comput., 58 (2018), 125–149. doi: 10.1007/s12190-017-1137-9
    [14] S. Djilali, S. Bentout, Spatiotemporal patterns in a diffusive predator-prey model with prey social behavior, Acta Appl. Math., 169 (2020), 125–143. doi: 10.1007/s10440-019-00291-z
    [15] H. Kielhöfer, Bifurcation Theory: An Introduction with Applications to PDEs, Springer, 2004.
    [16] J. P. Shi, Persistence and bifurcation of degenerate solutions, J. Funct. Anal., 169 (1999), 494–531. doi: 10.1006/jfan.1999.3483
    [17] J. Blat, K. J. Brown, Global bifurcation of positive solutions in some systems of elliptic equations, SIAM J. Math. Anal., 17 (1986), 1339–1353. doi: 10.1137/0517094
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