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Numerical analysis of variable-order fractional KdV-Burgers-Kuramoto equation


  • In this paper, a fully discrete local discontinuous Galerkin finite element method is proposed to solve the KdV-Burgers-Kuramoto equation with variable-order Riemann-Liouville time fractional derivative. The method proposed in this paper is based on the finite difference method in time and local discontinuous Galerkin method in space. For all ϵ(t)(0,1) with variable order, we prove the scheme is unconditional stable and convergent. Finally, numerical examples are provided to verify the theoretical analysis and the order of convergence for the proposed method.

    Citation: Leilei Wei, Xiaojing Wei, Bo Tang. Numerical analysis of variable-order fractional KdV-Burgers-Kuramoto equation[J]. Electronic Research Archive, 2022, 30(4): 1263-1281. doi: 10.3934/era.2022066

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  • In this paper, a fully discrete local discontinuous Galerkin finite element method is proposed to solve the KdV-Burgers-Kuramoto equation with variable-order Riemann-Liouville time fractional derivative. The method proposed in this paper is based on the finite difference method in time and local discontinuous Galerkin method in space. For all ϵ(t)(0,1) with variable order, we prove the scheme is unconditional stable and convergent. Finally, numerical examples are provided to verify the theoretical analysis and the order of convergence for the proposed method.



    Jellyfish plays an important role in the marine ecosystems as a keystone species and a potential resource for human consumption [1]. The amount of jellyfish has been significantly increasing in many waters since 1980s [2]. Jellyfish can be found in many regions worldwide such as Japan [3], the China Seas [4], the Mediterranean Sea [5], Taiwan [6], Southampton Water and Horsea Lake, England [7]. It can survive in a wide range of water temperatures (036C) and salinities (336%) [8,9].

    Jellyfish has a complex life history with several different phases: planula, polyp, strobila, ephyra and medusa [10]. The polyp and medusa are two main stages of the life cycle of jellyfish. Medusae are dioecious, the sperm combines with egg to form a planula, which normally settles to the bottom and then occur metamorphosis of planula into tentacles-ring polyp (or scyphistoma) [11]. For Aurelia aurita jellyfish, the scyphistoma produces external outgrowths asexually by budding, the vitally asexual reproduction of polyp (94%), stolon (5%) and podocysts (1%) [3]. The scyphistoma changes into strobila (strobilating polyp) through strobilation, which is asexual reproduction by division into segments developing into ephyra. After liberating from strobila, the ephyra becomes adult medusa. In addition, strobila reverts into the initial scyphistoma [11]. Since jellyfish has a distinct mobility patterns in different phases of its life history, it is interesting to take these facts into account for model formulation.

    Temperature has a great effect on variations of jellyfish populations [12] as the asexual reproduction rate and strobilation rate depend on the functions of temperatures [11]. Global warming has affected the increase of jellyfish populations because it might cause the distribution, growth, and ephyrae production of medusae [13]. The rapid strobilation might be proceeded at the warmer temperature, but the continuous high temperature results in the fewer budding and increased mortality [6]. Hence the population explosions of polyps and medusae might be caused at the appropriate increase of temperature, but rising temperatures lead to the decreased populations.

    Many approaches for jellyfish have been developed to discuss the nature of the correlations between environmental indices and population abundance [6,14]. In particular, mathematical modeling is one of the important tools in analyzing the dynamical properties in aquatic systems. In [15], Oguz et al. presented food web model with an anchovy population and bioenergetics-based weight growth model governed by system of differential equations. In [16], Melica et al. conducted that the dynamics of polyps population by the logistic model. In [11], Xie et al. proposed the following two-dimensional dynamic model of scyphozoan jellyfish:

    dPdt=α(T)P+s1γMd1Pd2Pb1P2,dMdt=s2β(T)nPd3Md4Mb2M2, (1.1)

    where P(t) and M(t) are the population sizes of polyps and medusae at time t, respectively. For system (1.1), by using the Bendixson-Dulac's negative criterion and Poincare-Bendixson Theorem, the conditions for the global asymptotical stability of the equilibria E0, E1 and E are given. The effects of temperature, substrate and predation on the population sizes of scyphozoan were investigated by numerical simulations.

    Although multiple progresses have been seen in the above work of [11], for system (1.1), it is assumed that each population preserves an equal density dependent rate and each individual has the same opportunity to compete for their common resources during the whole life history. Unfortunately, this is not realistic due to the life history of jellyfish which has a diverse mobility body structures in different stages. The immature stages of jellyfish are much weaker than the mature stages and so they cannot compete for their common resources. Jellyfish reaches maturity after surviving the immature stages. Therefore, it is realistic and interesting for us to construct the stage-structured jellyfish model that exhibits a diversity between these different stages.

    Recently, population dynamic models with stage structure and time delays have attracted more attention from authors [17,18,19,20,21,22,23,24]. For instance, Aiello and Freedman proposed a stage-structured model of single species containing of the immature and mature stages and using a discrete time delay taken from birth to maturity [18]. Liu et al. showed that the global stability for the two competitive Lotka-Volterra system with time delay that denotes the time taken from birth to maturity. They proposed that the stage structure is one of the main reasons that cause permanence and extinction for the two competitive system [23]. There have been an increasing interest and progress in the study of the above stage-structured models which assume all individuals are in the same species that require the analogous amount of time to become mature at the same age. Unlike birds and mammals, jellyfish species have the different mobility shapes in the distinctive stages of its life cycle. Thus, the previous methods and techniques cannot be applied exactly to our system because we classify the single species jellyfish into two-stage structure. Mathematically, the proposed model has two delay terms and the equations are matched with each other, which is not similar with the previous models [18,23,24].

    In this paper, we will propose a time-delayed jellyfish model with stage structure and will investigate how the stage structure parameters and temperature affect the dynamical behaviors of system (2.2). Our main purpose is to study the population dynamics of jellyfish for the largest surviving probability as well as for final population numbers. To find the largest surviving probability, we will take the global asymptotical behaviors of the model by applying the monotone dynamical properties (for reference, see [25] and [26,p. 90]).

    This paper is organized as follows: in Section 2, we propose the model and show that the solutions are positiveness and ultimately bounded. The main results of this paper are presented in Section 3. In Section 4, we perform numerical simulations to explore the effects of two delays and temperature on the dynamics. Section 5 is the brief discussion of our results.

    The life history of jellyfish is divided into two main stages; polyp and medusa. The larval stage of polyp is planula and the elementary phase of medusa is ephyra. Let P(t) and M(t) be the population size or number of polyps and medusae at time t, respectively. The model is based on the following assumptions and the diagram in Figure 1:

    Figure 1.  Diagram of the model (2.1).

    (A1) τ1 is the length of the stage from the young polyp to the mature polyp. The immature polyp reproduces asexually at time tτ1 and surviving from time tτ1 to t is e(d1+d2)τ1.

    (A2) τ2 is the time lag taken from the developed polyp to ephyra (incipient medusa), i.e., the developed polyp reaches ephyra after surviving this stage. Denote τ=max{τ1,τ2}.

    (A3) Its maturity is denoted by τ=max{τ1,τ2},τ>0.

    (A4) Each population competes for their common resources.

    (A5) Each population has its own natural death rate, the mortality of polyp is varied by the factors of silt coverage or nudibranch consumption while that of medusa is because of different types of predators.

    By the preceding assumptions, we get the following polyp-medusa population with stage structure:

    dPdt=α(T)e(d1+d2)τ1P(tτ1)+s1γMd1Pd2Pb1P2,dMdt=s2β(T)ne(d3+d4)τ2P(tτ2)d3Md4Mb2M2. (2.1)

    As pointed out in [11], α(T) denotes the asexual reproduction rate affected by temperature, involving budding, stolon and podocyst et al., s1 is the survival and metamorphosis rate of planula, γ represents the sexual reproduction rate, b1 and b2 denote the density-dependent rates of polyps and medusae respectively, s2 is the survival and development rate of ephyra, β(T) is the strobilation rate affected by temperature and n is strobilation times. Assuming that the death rate of the immature population is proportional to the existing immature population with proportionality constants di>0, i = 1, 2, 3, 4. The loss of polyp is either due to natural death rate d1 or due to the factors of silt coverage d2 and τ1 is the time taken from the immature polyp to the mature one; thus e(d1+d2)τ1 is the survival probability of each immature polyp to reach the mature one. The death of medusa is either from natural fatality rate d3 or because of the predations d4 and τ2 is the time length between the developed polyp and ephyra (incipient medusa); thus e(d3+d4)τ2 is the survival rate of each developed polyp to reach the ephyra (incipient medusa) population. As it takes a few days in the larval stage of jellyfish life, the permanence and extinction criteria for the stage structured model are independent in this larval stage.

    For the goal of simplicity, we denote a=α(T), b=s1γ, d=d1+d2, c=s2β(T)n, d=d3+d4. Thus the following system can be obtained from system (2.1).

    dPdt=aeζ1P(tτ1)+bMdPb1P2,dMdt=ceζ2P(tτ2)dMb2M2, (2.2)

    where ζ1=dτ1 and ζ2=dτ2. Denote ζ1 and ζ2 the degrees of the stage structure.

    Let X=C([τ,0],R2) be the Banach space of all continuous function from [τ,0] to R2 equipped with the supremum norm, where τ=max{τ1,τ2}. By the standard theory of functional differential equations (see, for example, Hale and Verduyn Lunel [27]), for any ψC([τ,0],R2), there exists a unique solution Y(t,ψ)=(P(t,ψ),M(t,ψ)) of system (2.2); which satisfies Y0=ψ.

    For system (2.2), we consider the initial conditions to either the positive cone X+={ψX| ψi(θ)0 for all θ[τ,0]i=1,2 } or the subset of X+ of functions which are strictly positive at zero, X+0={ψX+|ψi(0)>0,i=1,2}.

    Lemma 2.1. For equation

    d¯Wdt=aeζ1¯W(tτ1)+bceζ2d¯W(tτ2)B2¯W2, (2.3)

    where a,b,c,d,B>0, τ=max{τ1,τ2},τ>0 and ¯W(0)>0 and ¯W(θ)0, θ[τ,0], we have: limt¯W(t)=2daeζ1+2bceζ2dB if daeζ1+bceζ2>0.

    Proof. By using the similar argument of Lu et al. [28,Proposition 1], we will prove that ¯W(t)>0, for all t0. Otherwise, there exists some constant ˊt0>0 such that min{¯W(ˊt0)}=0. Let t0=inf{ˊt0:¯W(ˊt0)=0}. Then we have that min{¯W(t0)=0} and min{¯W(t)}>0, t[0,t0). From system (2.3), we have

    ¯W(t)=¯W(0)et0B2¯W(ϑ)dϑ+aeζ1t0¯W(ωτ1)etωB2¯W(ϑ)dϑdω+bceζ2dt0¯W(ωτ2)etωB2¯W(ϑ)dϑdω. (2.4)

    Incorporation initial conditions and Eq (2.4), we get ¯W(t0)>0, contradicting min{¯W(t0)}=0. Consequently, ¯W(t)>0 for all t0.

    Let ¯W=2daeζ1+2bceζ2dB denotes the unique positive equilibrium of system (2.3). Denote u(t)=¯W(t)¯W, thus system (2.3) takes the form as

    dudt=aeζ1u(tτ1)+bceζ2du(tτ2)B2u2(t)B¯Wu(t). (2.5)

    Constructing the Lyapunov functional

    V(u,uτ)=12u2(t)+12aeζ1ttτ1u2(s)ds+12bceζ2dttτ2u2(s)ds,

    we have

    dVdt|(2.5)=aeζ1u(t)u(tτ1)+bceζ2du(t)u(tτ2)B2u3(t)B¯Wu2(t)+12aeζ1u2(t)12aeζ1u2(tτ1)+12bceζ2du2(t)12bceζ2du2(tτ2)12aeζ1u2(t)+12aeζ1u2(tτ1)+12bceζ2du2(t)+12bceζ2du2(tτ2)B2u3(t)B¯Wu2(t)+12aeζ1u2(t)12aeζ1u2(tτ1)+12bceζ2du2(t)12bceζ2du2(tτ2)=aeζ1u2(t)+bceζ2du2(t)B¯Wu2(t)B2u3(t)=(aeζ1+bceζ2dB2¯W)u2(t)(¯W+u(t))B2u2(t)=B2¯W(t)u2(t)0,

    which is negative definite and dVdt|(2.5)=0 if and only if u=0. By Lyapunov-LaSalle invariance principle ([29,Theorem 2.5.3]), we get limt¯W(t)=¯W=2daeζ1+2bceζ2dB, this proves Lemma 2.1.

    Lemma 2.2. Given system (2.2), then:

    (I) Under the initial conditions, all the solutions of system (2.2) are positive for all t0.

    (II) Solutions of system (2.2) are ultimately bounded.

    Proof. (I) We start with proving the positivity of solutions by using the similar argument of Lu et al. [28,Proposition 1]. We will prove that P(t)>0, M(t)>0 for t0. Otherwise, there exists some constant ˜t0>0 such that min{P(˜t0),M(˜t0)}=0. Let t0=inf{˜t0:P(˜t0)=0,M(˜t0)=0}. Then we have that min{P(t0),M(t0)}=0 and min{P(t),M(t)}>0, t[0,t0). From system (2.2), we have

    {P(t)=P(0)et0(d+b1P(η))dη+aeζ1t0P(κτ1)etκ(d+b1P(η))dηdκ+bt0M(κ)etκ(d+b1P(η))dηdκ,M(t)=M(0)et0(d+b2M(η))dη+ceζ2t0P(κτ2)etκ(d+b2M(η))dηdκ. (2.6)

    Incorporation initial conditions and Eq (2.6), we obtain P(t0)>0 and M(t0)>0, contradicting min{P(t0),M(t0)}=0. Consequently, P(t)>0, M(t)>0 for all t0.

    (II) We show that the boundedness of solutions as follows.

    Let W=dbP+M. By system (2.2), we have

    dWdt|(2.2)=aeζ1dbP(tτ1)+ceζ2P(tτ2)ddbPdb1bP2b2M2=aeζ1[dbP(tτ1)+M(tτ1)]aeζ1M(tτ1)+bceζ2d[dbP(tτ2)+M(tτ2)]bceζ2dM(tτ2)ddbPbb1d(dbP)2b2M2aeζ1W(tτ1)+bceζ2dW(tτ2)B[(dbP)2+M2],

    where B:=min{bb1d,b2}.

    dWdtaeζ1W(tτ1)+bceζ2dW(tτ2)B2W2,

    where 2((dbP)2+M2)(dbP+M)2.

    Consider the equation

    d¯Wdt=aeζ1¯W(tτ1)+bceζ2d¯W(tτ2)B2¯W2. (2.7)

    By using Lemma 2.1 and Comparison Theorem, we get

    lim suptW(t)2daeζ1+2bceζ2dB, which implies P(t) and M(t) are ultimately bounded. This completes the proof of Lemma 2.2.

    The equilibria (P,M) of system (2.2) satisfies the following system

    aeζ1P+bMdPb1P2=0,ceζ2PdMb2M2=0. (2.8)

    System (2.2) has the equilibria E0=(0,0) for all parameter values and E1=(aeζ1db1,0) if aeζ1d>0 and c=0.

    Since Eq (2.8) can be rewritten as

    (aeζ1db1P)P=bM,  ceζ2P=(d+b2M)M. (2.9)

    When PM0, from Eq (2.9) it follows that

    aeζ1db1Pceζ2+bd+b2M=0. (2.10)

    Further, substituting P=(d+b2M)Mceζ2 into Eq (2.10) and we get

    aeζ1db1(d+b2M)Mceζ2ceζ2+bd+b2M=0.

    Set

    F(M):=aeζ1db1(d+b2M)Mceζ2ceζ2+bd+b2M,

    thus F(M) is a decreasing function of M for any M>0.

    Noting that the continuity and monotonicity of F(M) and that F(+)<0, furthermore since one can get F(0)>0 provided that

    (aeζ1d)d+bceζ2>0,  c0 (2.11)

    hold true, therefore we conclude that system (2.2) admits a unique positive equilibrium given Eq (2.11) is satisfied.

    The purpose of this section is to study the global stability of system (2.2).

    Now we consider the local stability of the equilibria. The characteristic equation of system (2.2) takes the form as follows;

    det(λIGH1eλτ1H2eλτ2)=0,

    where

    G=(d2b1Pb0d2b2M),H1=(aeζ1000),H2=(00ceζ20).

    Lemma 3.1. Suppose that (aeζ1d)d+bceζ2<0, then the equilibrium E0=(0,0) of system (2.2) is locally asymptotically stable.

    Proof. The characteristic equation of system (2.2) at the equilibrium E0 is as follows:

    C(λ):=(λ+d)(λ+daeζ1λτ1)bceζ2λτ2=0. (3.1)

    To show that it is asymptotically stable under assumption (aeζ1d)d+bceζ2<0, we just need to prove that the solutions of the characteristic equation C(λ)=0 must have negative real parts. Let λ=u+iv, where u and v are real numbers. Denote

    A1=u+d,B1=v,A2=u+daeζ1uτ1cos(vτ1),B2=v+aeζ1uτ1sin(vτ1),C1=bceζ2uτ2cos(vτ2),C2=bceζ2uτ2sin(vτ2).

    Substituting λ by u+iv into Eq (3.1)

    A1A2B1B2=C1,A1B2+A2B1=C2.

    Then

    (A1A2)2+(B1B2)2+(A1B2)2+(A2B1)2=(C1)2+(C2)2. (3.2)

    Assume that u0, then we get

    A1d>0,A2daeζ1>0.

    Hence

    (A1A2)2>((daeζ1)d)2. (3.3)
    (A1A2)2+(B1B2)2+(A1B2)2+(A2B1)2(A1A2)2>((daeζ1)d)2.

    From Eq (3.2), we obtain

    (C1)2+(C2)2(A1A2)2>((daeζ1)d)2(bceζ2)2(cos2(vτ2)+sin2(vτ2))(A1A2)2>((daeζ1)d)2(bceζ2)2(A1A2)2>((daeζ1)d)2.

    Hence Eq (3.3) contradicts to the assumption (aeζ1d)d+bceζ2<0, thus u<0, which means λ must have negative real parts. This proves Lemma 3.1.

    Lemma 3.2. Suppose that aeζ1d>0 and c=0, then the equilibrium E1=(aeζ1db1,0) of system (2.2) is locally asymptotically stable.

    Proof. The characteristic equation of system (2.2) at the equilibrium E1 is

    X(λ):=(λ+d)(λd+2aeζ1aeζ1λτ1)=0. (3.4)

    Then λ=d is a negative root of the equation X(λ)=0. Let λd+2aeζ1aeζ1λτ1=0; then if the root is λ=u+iv, we have u+2aeζ1daeζ1uτ1cos(vτ1)=0. Assume that u0, then u+2aeζ1daeζ1uτ1cos(vτ1)aeζ1d>0 is a contradiction, hence u<0. This shows that all the roots of X(λ)=0 must have negative real parts, and therefore E1 is locally asymptotically stable. This proves Lemma 3.2.

    Lemma 3.3. Suppose that (aeζ1d)d+bceζ2>0 and c0, then the equilibrium E=(P,M) of system (2.2) is locally asymptotically stable.

    Proof. The characteristic equation of system (2.2) at the equilibrium E is

    (λ+d+2b1Paeζ1λτ1)(λ+d+2b2M)bceζ2λτ2=0.

    To show that it is asymptotically stable under (aeζ1d)d+bceζ2>0, we just need to prove that the solutions of the characteristic equation must have negative real parts. Let λ=u+iv where u and v are real numbers. Denote

    D1=u+d+2b1Paeζ1uτ1cos(vτ1),E1=v+aeζ1uτ1sin(vτ1),D2=u+d+2b2M,E2=v,F1=bceζ2uτ2cos(vτ2),F2=bceζ2uτ2sin(vτ2).

    Substituting λ by u+iv into the above equation.

    D1D2E1E2=F1,D1E2+D2E1=F2.

    Then

    (D1D2)2+(E1E2)2+(D1E2)2+(D2E1)2=(F1)2+(F2)2. (3.5)

    Assume that u0, then we get

    D1d+2b1Paeζ1>d+2b1aeζ1db1aeζ1=aeζ1d>0,D2d+2b2M=d+2b2b1(P)2(aeζ1d)Pb1>d+2b2b1(aeζ1db1)2(aeζ1d)(aeζ1db1)b1=d>0.

    Hence

    (D1D2)2>((aeζ1d)d)2.
    (D1D2)2+(E1E2)2+(D1E2)2+(D2E1)2(D1D2)2>((aeζ1d)d)2.

    By Eq (3.5), we get

    (F1)2+(F2)2(D1D2)2>((aeζ1d)d)2(bceζ2)2(cos2(vτ2)+sin2(vτ2))(D1D2)2>((aeζ1d)d)2(bceζ2)2(D1D2)2>((aeζ1d)d)2.

    By assumption (aeζ1d)d+bceζ2>0, which is a contradiction, thus u must be negative real parts. This completes the proof of Lemma 3.3.

    Before the details, we will present the notion from the literature [25]. We define

    xtC([τ,0],R2),

    by xt(θ)=x(t+θ),θ[τ,0]. Consider a delay system

    x(t)=f(xt), (3.6)

    for which uniqueness of solutions is assumed, x(t,ψ) designates the solution of Eq (3.6) with initial condition x0=ψ (ψC).

    A non-negative equilibrium v=(vp,vm)R2 of system (2.2) is said to be globally attractive if Y(t)v as t, for all admissible solutions Y(t) of system (2.2). We say that v is globally asymptotically stable if it is stable and globally attractive.

    System (2.2) is written as Eq (3.6),

    f1(ψ)=ψ1(0)[db1ψ1(0)]+aeζ1ψ1(τ1)+bψ2(0),f2(ψ)=ψ2(0)[db2ψ2(0)]+ceζ2ψ1(τ2). (3.7)

    Observe that system (2.2) is cooperative, i.e., Dfi(ψ)φ0, for all ψ,φX+ with φi(0)=0,i=1,2. This implies that f satisfies quasi-monotonicity condition [26,p. 78]. Typically, in population dynamics the stability of equilibria is closely related to the algebraic properties of some kinds of competition matrix of the community. Denote

    A=(aeζ1d00d),D=(0bceζ20).

    For convenience, we shall refer to N=A+D as the (linear) community matrix:

    N=(aeζ1dbceζ2d). (3.8)

    Since D0, the matrix N in Eq (3.8) is called cooperative. If D is irreducible, then the matrix N in Eq (3.8) is also irreducible; in this case, system (2.2) is called an irreducible system [26,p. 88], and the semiflow ψYt(ψ) is eventually strongly monotone. f=(f1,f2)T:R2R2 is strictly sublinear, i.e., for any P0,M0 and any α(0,1),

    f1(αP,αM)=αP[db1αP]+aeζ1αP(tτ1)+bαM>α[P(db1P)+aeζ1P(tτ1)+bM]=αf1(P,M),f2(αP,αM)=αM[db2αM]+ceζ2αP(tτ2)>α[M(db2M)+ceζ2P(tτ2)]=αf2(P,M).

    Cooperative DDEs satisfying these sublinearity conditions have significant properties [30,Proposition 4.3].

    Recall that the stability modulus of square matrix N in Eq (3.8), denoted by s(N), is defined by s(N)=max{Reλ: λ is an eigenvalue of N }. If the matrix N in Eq (3.8) has nonnegative off diagonal elements and is irreducible, then s(N) is a simple eigenvalue of the matrix N with a (componentwise) positive eigenvector (see, e.g., [31,Theorem A.5]).

    The matrix N in Eq (3.8) is

    N=(aeζ1dbceζ2d),

    then we can easily get the following:

    s(N)>0 if and only if (aeζ1d)d+bceζ2>0 and s(N)<0 if and only if (aeζ1d)d+bceζ2<0.

    Definition 3.4. [32] A square matrix A=[aij] with non-positive off diagonal entries, i.e., aij0 for all ij, is said to be an M-matrix if all the eigenvalues of A have a non-negative real part, or equivalently, if all its principal minors are non-negative, and A is said to be a non singular M-matrix if all the eigenvalues of A have positive real part, or, equivalently, if all its principal minors are positive.

    Theorem 3.5. Suppose that (aeζ1d)d+bceζ2<0, then the equilibrium E0 of system (2.2) is globally asymptotically stable.

    Proof. Let P(t,l), M(t,k) be the solutions of system (2.2) with P(0+θ,l)=l, M(0+θ,k)=k for θ[τ,0]. Note that f1(l)=l[d+b+aeζ1b1l]<0 for l>0 sufficiently large and f2(k)=k[d+ceζ2b2k]<0 for k>0 sufficiently large. Hence we can easily conclude that all admissible solutions of system (2.2) are bounded [26,Corollary 5.2.2]. We have s(N)<0 if and only if (aeζ1d)d+bceζ2<0. By the assumption (aeζ1d)d+bceζ2<0, we observed that it is equivalent to having N a non singular M-matrix. Since matrix N is a non singular M-matrix, there exists the equilibrium v=(vp,vm)R2,v>0, such that Nv<0, hence we get

    aeζ1vpdvp+bvm<0,ceζ2vpdvm<0. (3.9)

    Let P(t)0, M(t)0 be solutions of system (2.2). Denote yp(t)=P(t)vp and ym(t)=M(t)vm, thus system (2.2) takes the form as

    yp(t)=yp(t)[db1yp(t)vp]+aeζ1yp(tτ1)+bvmvpym(t),ym(t)=ym(t)[db2ym(t)vm]+ceζ2vpvmyp(tτ2). (3.10)

    It suffices to prove that (Lp,Lm):=lim supt(yp(t),ym(t))=(0,0). Let Lp:=lim sup{yp(t)}, Lm:=lim sup{ym(t)}, ˜L:=max{Lp,Lm} and suppose that ˜L>0. From Eq (3.9), we can choose ε>0 such that

    ˜L[db1˜Lvp+aeζ1+bvmvp]+ε[aeζ1+bvmvp]=:γp<0,˜L[db2˜Lvm+ceζ2vpvm]+ε[ceζ2vpvm]=:γm<0.

    Let T>0 be such that yp(t)˜L+ε, ym(t)˜L+ε for all t>Tτ and the cases of yp(t) and ym(t) are separated as eventually monotone and not eventually monotone. By [26,Proposition 5.4.2], if yp(t) and ym(t) are eventually monotone, then yp(t)˜L and ym(t)˜L as t for tT and we obtain

    yp(t)yp(t)[db1yp(t)vp]+aeζ1(˜L+ε)+(˜L+ε)bvmvpγp,ym(t)ym(t)[db2ym(t)vm]+ceζ2vpvm(˜L+ε)γm as t. (3.11)

    Since γp<0 and γm<0, these imply that limt(yp(t),ym(t))=, which is impossible. By using the similar argument of Aiello and Freedman [18,Theorem 2], if yp(t) and ym(t) are not eventually monotone, there is a sequence tn such that yp(tn)˜L, yp(tn)=0 and ym(tn)˜L, ym(tn)=0. We obtain (3.11) with t replaced by tn, again a contradiction. This proves limt(yp(t),ym(t))=(0,0). Using Lemma 3.1, we complete the proof of Theorem 3.5.

    Theorem 3.6. Suppose that aeζ1d>0 and c=0, then the equilibrium E1 of system (2.2) is globally asymptotically stable.

    Proof. If c=0, the second equation of system (2.2) becomes

    M(t)=dMb2M2, (3.12)

    For the independent subsystem (3.12), it is obvious that limtM(t)=0.

    Then the first equation of system (2.2) becomes

    P(t)=aeζ1P(tτ1)dP(t)b1P2(t). (3.13)

    Let ε>0 be sufficiently small and L>0 be sufficiently large such that εP(t)L, t[τ,0], and

    aeζ1εdεb1ε2>0,aeζ1LdLb1L2<0.

    Let Pε(t) and PL(t) be the solutions of Eq (3.13) with Pε(t)=ε and PL(t)=L for t[τ,0]. From the monotone properties of the equation [26], the function Pε(t) is increasing and PL(t) is decreasing for t0 and

    Pε(t)P(t)PL(t),t0.

    It therefore follows that

    aeζ1db1=limtP(t)limtPL(t)=aeζ1db1

    because the only equilibrium of the equation between ε and L is aeζ1db1. Using Lemma 3.2, we complete the proof of Theorem 3.6.

    Lemma 3.7. Suppose there is a positive equilibrium (P,M) of system (2.2), and that (aeζ1d)d+bceζ2>0 and c0. Then all solutions P(t,ψ1), M(t,ψ2) of system (2.2) with ψiX+0,i=1,2 satisfy lim inft(P(t,ψ1),M(t,ψ2))(P,M).

    Proof. For (P,M) an equilibrium of system (2.2), we have

    aeζ1d+bMP=b1P>0,d+ceζ2PM=b2M>0. (3.14)

    Denote ˉP(t)=P(t)P and ˉM(t)=M(t)M in system (2.2), and dropping the bar for simplicity, we get

    P(t)=P(t)[db1P(t)P]+aeζ1P(tτ1)+bMPM(t),M(t)=M(t)[db2M(t)M]+ceζ2PMP(tτ2). (3.15)

    For the solutions P(t)=P(t,ψ1) and M(t)=M(t,ψ2) of Eq (3.15) with ψiX+0 for i=1,2, we first claim that (lp,lm):=lim inft(P(t),M(t))>(0,0). Otherwise, there exist δ(0,1) and t0>τ such that ˜l=min{(P(t),M(t)):t[0,t0]} and ˜l<δ. By using Eq (3.14),

    P(t0)=P(t0)[db1P(t0)P]+aeζ1P(t0τ1)+bMPM(t0)˜l[db1˜lP]+aeζ1˜l+bMP˜l=˜l(b1Pb1˜lP)=˜lb1P(1˜l)>0,M(t0)=M(t0)[db2M(t0)M]+ceζ2PMP(t0τ2)˜l[db2˜lM]+ceζ2PM˜l=˜l(b2Mb2˜lM)=˜lb2M(1˜l)>0.

    But these are not possible. Since the definition of t0, P(t0)0 and M(t0)0.

    Next we prove that (lp,lm)(1,1). Choose ˜l=min{lp,lm} and suppose that ˜l<1. Let T>0 and ε>0 be chosen so that P(t)˜lε and M(t)˜lε for all t>Tτ.

    ˜lb1P(1˜l)ε[aeζ1˜l+bMP]=:np>0,˜lb2M(1˜l)ε[ceζ2PM]=:nm>0.

    By [26,Proposition 5.4.2], if P(t) and M(t) are eventually monotone, then P(t)˜l and M(t)˜l and for tT, we have

    P(t)P(t)[db1P(t)P]+aeζ1(˜lε)+(˜lε)bMPnp,M(t)M(t)[db2M(t)M]+(˜lε)ceζ2PMnm as t,

    leading to P(t) and M(t) as t, contradicting ˜l<1. By using the similar argument of Aiello and Freedman [18,Theorem 2], if P(t) and M(t) are not eventually monotone, there is a sequence tn such that P(tn)˜l, P(tn)=0 and M(tn)˜l, M(tn)=0. For tnT, we obtain the above inequalities tn instead of t, which yield that 0=P(tn)np and 0=M(tn)nm, again contradicting ˜l<1. This proves that ˜l1.

    Theorem 3.8. Suppose that (aeζ1d)d+bceζ2>0 and c0, then the equilibrium E of system (2.2) is globally asymptotically stable.

    Proof. For (P,M) of system (2.2), after the changes P(t)P(t)P and M(t)M(t)M, consider system (3.15) with positive equilibrium (1,1)R2. In view of Lemmas 3.3 and 3.7, we only need to prove that (Lp,Lm):=lim supt(P(t),M(t))(1,1) and any positive solution P(t), M(t) of Eq (3.15).

    For the sake of contradiction, suppose that ˜L=max{Lp,Lm}>1. Choose ε>0 and t>τ, such that P(t)˜L+ε and M(t)˜L+ε for all t>Tτ and

    ˜Lb1P(1˜L)+ε[aeζ1˜L+bMP]=:Np<0,˜Lb2M(1˜L)+ε[ceζ2PM]=:Nm<0.

    Separating the cases of P(t) and M(t) eventually monotone and not eventually monotone, and reasoning as in the proofs of Theorem 3.5 and Lemma 3.7, we obtain a contradiction, thus ˜L1. Finally we get limt(P(t),M(t))=(P,M). Using Lemma 3.3, we complete the proof of Theorem 3.8.

    Remark 1. Note that when τ1=τ2=0, system (2.2) becomes system (1.1). Theorems 4–6 in [11] are the corresponding results of Theorems 3.5, 3.6 and 3.8 for system (2.2), respectively. Our main results not only extend the results in [11] but also generalize the related results into the stage-structured system with two delays. But the proof methods of our results are quite different to those in [11].

    In this section, we numerically simulate the dynamics of system (2.2) for a range of parameters which are the same as those in [11]. In this paper, we add the values of two delays τ1 and τ2 from [10,33]. The parameters are given in Table 1.

    Table 1.  Two sets of parameter values used in numerical simulations.
    Parameter Ranges Ref. Unit data 1 data 2
    α(T) 0.030.15a [14] ind d1 P1 0.12 0.15
    β(T) 0.0650.139a [14] ind d1time1 P1 0.108 0.122
    γ 19178a [33,34] ind d1 M1 100 170
    s1 0.0010.3b [34,35] no unit 0.008 0.01
    s2 0.010.8b [34] no unit 0.2 0.8
    n 12 [14] times 1 1
    d1 00.028a,b [6,14] d1 0.0001 0.0001
    d2 0.00010.3b [34] d1 0.0001 0.0001
    d3 0.0040.02a [34,36] d1 0.006 0.004
    d4 0.00010.8b [1] d1 0.0001 0.0001
    b1 0.000010.1b [3,37] d1ind1 0.0012 0.0001
    b2 00.1b d1ind1 0.0001 0.0001
    τ1 30120b [10,33] d 120 120
    τ2 60300b [10,33] d 90 150

     | Show Table
    DownLoad: CSV

    Values signatured by a are from experimental data with unit innovation and those signatured by b are estimated from references.

    The left figure of Figure 2 shows that the positive equilibrium E of system (2.2) is globally asymptotically stable under different initial values. The left figure and right figure of Figure 2 take the parameters data 1 and data 2, respectively. Figure 2 shows that the population sizes change with respect to environmental indices but do not depend on the initial values. The population explosion occurs even though the initial values (P=0,M=2) are small (see the right figure of Figure 2). The numbers of two stages in the right figure of Figure 2 are larger than those in the left figure of Figure 2 because the reproduction is high while the destructions and competitions are low in the right figure of Figure 2. The trajectories of the right figure of Figure 2 finally tend towards a higher population level up to 10–15 times than the trajectories in the left figure of Figure 2 (in the corresponding Figure 3 of [11], the populations of Figure 3(b) is higher 30–50 times than Figure 3(a)) although the initial values (0,2) are of equal values.

    Figure 2.  Global stability of E under different initial values and the population sizes for data 1 and data 2, respectively.
    Figure 3.  The effects of delays on the population sizes for data 2.

    Based on data 2, Figure 3 illustrates how time delays affect the population dynamics. In the left figure of Figure 3, we fix the delay τ2 as the best fit value and increase the delay τ1[30,120]. We find that the populations are slightly fallen over the longer period τ1 (see the left figure of Figure 3). This is because of the lack of needed temperature and resources and so the asexual reproduction period is long, and the results of the population are low. When we fix the delay τ1 and change the delay τ2 from 60 to 300, the populations are significantly decreased over the longer period τ2 (see the right figure of Figure 3). Overall, Figure 3 can be seen that the peaks of population abundance occur at the small τ1 and τ2 while the longer maturation periods may be responsible for the lower populations.

    Figure 4 depicts the effect of temperature T[7,36] on the populations. Temperature is the impact factor that affects the asexual reproduction and strobilation of the jellyfish. In [11], Xie et al. presented that

    α(T)=1.9272T330.3904T2+294.7234T871.29+0.0378,
    β(T)=0.1430 exp {(T16.810810.5302)2}.
    Figure 4.  The effects of temperature on the population sizes for data 1 and data 2, respectively.

    In Figure 4, the numbers of polyp reach a peak at 12.5C, which correlates with the maximum budding rate of experimental data [14] and then gradually declined over the high temperature. From 12.5C to 16.8C is the maximum level of the number of medusae which is different from the experimental result 15C [14]. Figure 4 reveals that an appropriate increase of temperature might cause a large increase in the number of populations but the rise of temperatures would result in the fewer populations. Comparing the corresponding Figure (4d) in [11] with the right figure of Figure 4 in this paper, we find out that we can exactly see the peak populations due to the stage structure and can exactly know the effects of temperature on the population dynamics because the temperature is considered up to 36C in this paper.

    In this paper, we propose and analyze a delayed jellyfish model with stage structure, which is an extension of ODE model studied by Xie et al. in [11]. We have investigated how the phenomena of budding and strobilation influence the population dynamics of the jellyfish population. τ1 stands the time needed from the stage of the young polyp to the developed polyp and τ2 stands the time taken from the mature polyp to ephyra (incipient medusa). We have developed the systematic analysis of the model in both theoretical and numerical ways.

    We have proved the global stability of the equilibria under suitable conditions. Our results not only extend but also improve some related results of literature [11]. Our Theorems 3.5, 3.6 and 3.8 straightly extend the corresponding Theorems 4–6 in [11], respectively. Comparing the corresponding Theorems 4–6 in [11] for the ODE system (1.1) with Theorem 3.5, 3.6 and 3.8 for system (2.2), we find out that there are two extra terms edτ1 and edτ2 in our permanence and extinction criteria, i.e., the surviving probability of each immature population to develop into mature, which obtains due to the stage structure. From our results, we find that the jellyfish population will be extinct in the large immature mortality rate d,d or the long maturation τ1,τ2. Thus we may suggest that the proper increases of dτ1 and dτ2 have a negative effect of jellyfish population.

    Biologically, our results suggest that (i) jellyfish species go extinct if the survival rate of polyp during cloning and the survival rate of the incipient medusa during strobilation are less than their death rates; (ii) polyps will continue and there is no complement from polyp to medusa if the survival rate of polyp during cloning is larger than its death rate and the temperature is not enough to strobilate; (iii) both polyp and medusa will survive in a certain ideal environment and our result converges to the positive constant when the survival rate of polyp during cloning and the survival rate of the incipient medusa during strobilation are larger than their death rates.

    Besides the above systematic theoretical results, we have performed the numerical simulations to support the theoretical results. Our numerical results suggest that the positive equilibrium is globally asymptotically stable under distinctive initial values and the population sizes don't deal with the initial values but they change with respect to environmental factors. In Figures 3 and 4, our results suggest that the abundance in population occurs at the smaller periods τ1 and τ2 whereas the longer periods τ1 and τ2 will lower the peak population of polyp and medusa. In addition to the problem due to increasing τ1 and τ2, the increase of temperatures might cause the outburst of the population dynamics. If there is much higher temperature, the population rate leads to decline. Since temperature has a great impact on jellyfish population, it is interesting for one to consider the populations under the relevance to temperature. We leave this interesting problem as our future work.

    We would like to take this chance to thank the editor and the anonymous referees for their very valuable comments, which led to a significant improvement of our previous versions. The authors would like to thank Dr. Zhanwen Yang for his warm help on the numeric simulations. Z. Win, B. Tian and S. Liu are supported by the Natural Science Foundation of China (NSFC) (No. 11871179, 11771374, 91646106).

    All authors declare no conflicts of interest in this paper.



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