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Optimal harvesting of a competitive n-species stochastic model with delayed diffusions

  • In this study, we propose an n-species stochastic model which considers the influences of the competitions and delayed diffusions among populations on dynamics of species. We then investigate the stochastic dynamics of the model, such as the persistence in mean of the species, and the asymptotic stability in distribution of the model. Then, by using the Hessian matrix and theory of optimal harvesting, we investigate the optimal harvesting problem, obtaining the optimal harvesting effort and the maximum of expectation of sustainable yield (ESY). Finally, we numerically discuss some examples to illustrate our theoretical findings, and conclude our study by a brief discussion.

    Citation: Fangfang Zhu, Xinzhu Meng, Tonghua Zhang. Optimal harvesting of a competitive n-species stochastic model with delayed diffusions[J]. Mathematical Biosciences and Engineering, 2019, 16(3): 1554-1574. doi: 10.3934/mbe.2019074

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  • In this study, we propose an n-species stochastic model which considers the influences of the competitions and delayed diffusions among populations on dynamics of species. We then investigate the stochastic dynamics of the model, such as the persistence in mean of the species, and the asymptotic stability in distribution of the model. Then, by using the Hessian matrix and theory of optimal harvesting, we investigate the optimal harvesting problem, obtaining the optimal harvesting effort and the maximum of expectation of sustainable yield (ESY). Finally, we numerically discuss some examples to illustrate our theoretical findings, and conclude our study by a brief discussion.


    Cloaking using transformation optics (changes of variables) was introduced by Pendry, Schurig and Smith [30] for the Maxwell system and by Leonhardt [16] in the geometric optics setting. These authors used a singular change of variables, which blows up a point into a cloaked region. The same transformation had been used to establish (singular) non-uniqueness in Calderon's problem in [10]. To avoid using the singular structure, various regularized schemes have been proposed. One of them was suggested by Kohn, Shen, Vogelius and Weinstein [11], where instead of a point, a small ball of radius ε is blown up to the cloaked region. Approximate cloaking for acoustic waves has been studied in the quasistatic regime [11,26], the time harmonic regime [12,19,27,20], and the time regime [28,29], and approximate cloaking for electromagnetic waves has been studied in the time harmonic regime [4,14,24], see also the references therein. Finite energy solutions for the singular scheme have been studied extensively [9,32,33]. There are also other ways to achieve cloaking effects, such as the use of plasmonic coating [2], active exterior sources [31], complementary media [13,22], or via localized resonance [23] (see also [17,21]).

    The goal of this paper is to investigate approximate cloaking for the heat equation using transformation optics. Thermal cloaking via transformation optics was initiated by Guenneau, Amra and Venante [8]. Craster, Guenneau, Hutridurga and Pavliotis [6] investigate the approximate cloaking for the heat equation using the approximate scheme in the spirit of [11]. They show that for the time large enough, the largeness depends on ε, the degree of visibility is of the order εd (d=2,3) for sources that are independent of time. Their analysis is first based on the fact that as time goes to infinity, the solutions converge to the stationary states and then uses known results on approximate cloaking in the quasistatic regime [11,26].

    In this paper, we show that approximate cloaking is achieved at any positive time and established the degree of invisibility of order ε in three dimensions and |lnε|1 in two dimensions. Our results hold for a general source that depends on both time and space variables, and our estimates depend only on the range of the materials inside the cloaked region. The degree of visibility obtained herein is optimal due to the fact that a finite time interval is considered (compare with [6]). The analysis in this paper is of frequency type via Fourier transform with respect to time. This approach is robust and can be used in different context. A technical issue is on the blow up of the fundamental solution of the Helmholtz type equations in two dimensions in the low frequency regime. We emphasize that even though our setting is in a bounded domain, we employs Fourier transform in time instead of eigenmodes decomposition. This has the advantage that one can put the non-perturbed system and the cloaking system in the same context.

    We next describe the problem in more detail and state the main result. Our starting point is the regularization scheme [11] in which a transformation blows up a small ball Bε (0<ε<1/2) instead of a point into the cloaked region B1 in Rd (d=2,3). Here and in what follows, for r>0, Br denotes the ball centered at the origin and of radius r in Rd. Our assumption on the geometry of the cloaked region is mainly to simplify the notations. Concerning the transformation, we consider the map Fε:RdRd defined by

    Fε(x)={x in RdB2,(22ε2ε+|x|2ε)x|x| in B2Bε,xε in Bε. (1.1)

    In what follows, we use the standard notations

    FA(y)=F(x)A(x)FT(x)|detF(x)|,Fρ(y)=ρ(x)|detF(x)|,x=F1(y), (1.2)

    for the "pushforward" of a symmetric, matrix-valued function A, and a scalar function ρ, by the diffeomorphism F, and I denotes the identity matrix. The cloaking device in the region B2B1 constructed from the transformation technique is given by

    (FεI,Fε1) in B2B1, (1.3)

    a pair of a matrix-valued function and a function that characterize the material properties in B2B1. Physically, this is the pair of the thermal diffusivity and the mass density of the material.

    Let Ω with B2ΩRd (d=2,3)* be a bounded region for which the heat flow is considered. Suppose that the medium outside B2 (the cloaking device and the cloaked region) is homogeneous so that it is characterized by the pair (I,1), and the cloaked region B1 is characterized by a pair (aO,ρO) where aO is a matrix-valued function and ρO is a real function, both defined in B1. The medium in Ω is then given by

    *The notation DΩ means that the closure of D is a subset of Ω.

    (Ac,ρc)={(I,1) in ΩB2,(FεI,Fε1) in B2B1,(aO,ρO) in B1. (1.4)

    In what follows, we make the usual assumption that aO is symmetric and uniformly elliptic and ρO is a positive function bounded above and below by positive constants, i.e., for a.e. xB1,

    Λ1|ξ|2aO(x)ξ,ξΛ|ξ|2 for all ξRd, (1.5)

    and

    Λ1ρO(x)Λ, (1.6)

    for some Λ1. Given a function fL1((0,+),L2(Ω)) and an initial condition u0L2(Ω), in the medium characterzied by (Ac,ρc), one obtains a unique weak solution ucL2((0,);H1(Ω)) C([0,+);L2(Ω)) of the system

    {t(ρcuc)div(Acuc)=f in (0,+)×Ω,uc=0 on (0,+)×Ω,uc(t=0,)=u0 in Ω, (1.7)

    and in the homogeneneous medium characterized by (I,1), one gets a unique weak solution uL2((0,);H1(Ω))C([0,+);L2(Ω)) of the system

    {tuΔu=f in (0,+)×Ω,uc=0 on (0,+)×Ω,uc(t=0,)=u0 in Ω. (1.8)

    The approximate cloaking meaning of the scheme (1.4) is given in the following result:

    Theorem 1.1. Let u0L2(Ω) and fL1((0,+);L2(Ω)) be such that suppu0,suppf(t,)ΩB2 for t>0. Assume that uc and u are the solution of (1.7) and (1.8) respectively. Then, for 0<ε<1/2,

    uc(t,)u(t,)H1(ΩB2)Ce(ε,d)(fL1((0,+);L2(Ω))+u0L2(Ω)),

    for some positive constant C depending on Λ but independent of f, u0, and ε, where

    e(ε,d)={εif d=3,|lnε|1if d=2.

    As a consequence of Theorem 1.1, limε0uc(t,)=u(t,) in (0,+)×(ΩB2) for all f with compact support outside (0,+)×B2 and for all u0 with compact support outside B2. One therefore cannot detect the difference between (Ac,ρc) and (I,1) as ε0 by observation of uc outside B2: Cloaking is achieved for observers outside B2 in the limit as ε0.

    We now briefly describe the idea of the proof. The starting point of the analysis is the invariance of the heat equations under a change of variables which we now state.

    Lemma 1.1. Let d2, T>0, Ω be a bounded open subset of Rd of class C1, and let A be an elliptic symmetric matrix-valued function, and ρ be a bounded, measurable function defined on Ω bounded above and below by positive constants. Let F:ΩΩ be bijective such that F and F1 are Lipschitz, detF>c for a.e. xΩ for some c>0, and F(x)=x near Ω. Let fL1((0,T);L2(Ω)) and u0L2(Ω). Then uL2((0,T);H10(Ω))C([0,T);L2(Ω)) is the weak solution of

    {t(ρu)div(Au)=fin ΩT,u=0on (0,T)×Ω,u(0,)=u0in Ω, (1.9)

    if and only if v(t,):=u(t,)F1L2((0,T);H10(Ω))C([0,T);L2(Ω)) is the weak solution of

    {t(Fρv)div(FAv)=Ffin ΩT,u=0on (0,T)×Ω,v(0,)=u0F1in Ω. (1.10)

    Recall that F is defined in (1.2). In this paper, we use the following standard definition of weak solutions:

    Definition 1.1. Let d2 and T>0. We say a function

    uL2((0,T);H10(Ω))C([0,T);L2(Ω))

    is a weak solution to (1.9) if u(0,)=u0 in Ω and u satisfies

    ddtΩρu(t,)φ+ΩAu(t,)φ=Ωf(t,)φ in (0,T), (1.11)

    in the distributional sense for all φH10(Ω).

    The existence and uniqueness of weak solutions are standard, see, e.g., [1] (in fact, in [1], f is assumed in L2((0,T);L2(Ω)), however, the conclusion holds also for fL1((0,T);L2(Ω)) with a similar proof, see, e.g., [25]). The proof of Lemma 1.1 is similar to that of the Helmholtz equation, see, e.g., [12] (see also [6] for a parabolic version).

    We now return to the idea of the proof of Theorem 1.1. Set

    uε(t,)=uc(t,)F1ε for t(0,+).

    Then uε is the unique solution of the system

    {t(ρεuε)div(Aεuε)=f in (0,+)×Ω,uε=0 on (0,+)×Ω,uε(t=0,)=u0 in Ω, (1.12)

    where

    (Aε,ρε)={(I,1) in ΩBε,(ε2daO(/ε),εdρO(/ε)) in Bε. (1.13)

    Moreover,

    ucu=uεu in (0,+)×(ΩB2).

    In comparing the coefficients of the systems verified by u and uε, the analysis can be derived from the study of the effect of a small inclusion Bε. The case in which finite isotropic materials contain inside the small inclusion was investigated in [3] (see also [5] for a related context). The analysis in [3] partly involved the polarization tensor information and took the advantage of the fact that the coefficients inside the small inclusion are finite. In the cloaking context, Craster et al. [6] derived an estimate of the order εd for a time larger than a threshold one. Their analysis is based on long time behavior of solutions to parabolic equations and estimates for the degree of visibility of the conducting problem, see [11,26], hence the threshold time goes to infinity as ε0.

    In this paper, to overcome the blow up of the coefficients inside the small inclusion and to achieve the cloaking effect at any positive time, we follow the approach of Nguyen and Vogelius in [28]. The idea is to derive appropriate estimates for the effect of small inclusions in the time domain from the ones in the frequency domain using the Fourier transform with respect to time. Due to the dissipative nature of the heat equation, the problem in the frequency for the heat equation is more stable than the one corresponding to the acoustic waves, see, e.g., [27,28], and the analysis is somehow easier to handle in the high frequency regime. After using a standard blow-up argument, a technical point in the analysis is to obtain an estimate for the solutions of the equation Δv+iωε2v=0 in RdB1 (ω>0) at the distance of the order 1/ε in which the dependence on ε and ω are explicit (see Lemma 2.2). Due to the blow up of the fundamental solution in two dimensions, the analysis requires new ideas. We emphasize that even though our setting is in a bounded domain with zero Dirichlet boundary condition, we employs Fourier transform in time instead of eigenmodes decomposition as in [6] to put both systems of uε and u in the same context.

    To implement the analysis in the frequency domain, let us introduce the Fourier transform with respect to time t:

    ˆφ(k,x)=Rφ(t,x)eiktdt for kR, (2.1)

    for φL2((,+),L2(Rd)). Extending u,uc, uρ and f by 0 for t<0, and considering the Fourier with respect to time at the frequency ω>0, we obtain

    Δˆu+iωˆu=(ˆf+u0) in Ω,

    and

    div(Aεˆuε)+iωρεˆuε=(ˆf+u0) in Ω,

    where

    (Aε,ρε)={(I,1) in ΩBε,(ε2daO(/ε),εdρO(/ε)) in Bε.

    The main ingredient in the proof of Theorem 1.1 is the following:

    Proposition 2.1. Let ω>0, 0<ε<1/2, and let gL2(Ω) with suppgΩB2. Assume that v,vεH1(Ω) are respectively the unique solution of the systems

    {Δv+iωv=gin Ω,v=0on Ω,

    and

    {div(Aεvε)+iωρεvε=gin Ω,vε=0on Ω.

    We have

    vεvH1(ΩB2)Ce(ε,ω,d)(1+ω1/2)gL2(Ω), (2.2)

    for some positive constant C independent of ε, ω and g. Here

    e(ε,ω,3)=εeω1/2/4, (2.3)

    and

    e(ε,ω,2)={eω1/2/4/|lnε|if ω1/2,lnω/ln(ωε)if 0<ω<1/2. (2.4)

    The rest of this section is divided into three subsections. In the first subsection, we present several lemmas used in the proof of Proposition 2.1. The proofs of Proposition 2.1 and Theorem 1.1 are then given in the second and the third subsections, respectively.

    In this subsection, we state and prove several useful lemmas used in the proof of Proposition 2.1. Throughout, DB1 denotes a smooth, bounded, open subset of Rd such that RdD is connected, and ν denotes the unit normal vector field on D, directed into RdD.

    The first result is the following simple one:

    Lemma 2.1. Let d=2,3, k>0, and let vH1(RdD) be such that Δv+ikv=0 in RdD. We have, for R>2,

    vH1(BRD)CR(1+k)vH1/2(D), (2.5)

    for some positive constants CR independent of k and v.

    Proof. Multiplying the equation by ˉv (the conjugate of v) and integrating by parts, we have

    RdD|v|2ikRdD|v|2=Dνvˉv.

    This implies

    RdD|v|2+kRdD|v|2CνvH1/2(D)vH1/2(D). (2.6)

    Here and in what follows, C denotes a positive constant independent of v and k. Since Δv=ikv in B2D, by the trace theory, see, e.g., [7,Theorem 2.5], we have

    νvH1/2(D)C(vL2(B2D)+ΔvL2(B2D))C(vL2(B2D)+kvL2(B2D)). (2.7)

    Combining (2.6) and (2.7) yields

    RdD|v|2+kRdD|v|2C(1+k)v2H1/2(D). (2.8)

    The conclusion follows when k1.

    Next, consider the case 0<k<1. In the case where d=3, the conclusion is a direct consequence of (2.8) and the Hardy inequality (see, e.g., [18,Lemma 2.5.7]):

    R3D|v|2|x|2CR3D|v|2. (2.9)

    We next consider the case where d=2. One just needs to show

    BRD|v|2Cv2H1/2(D). (2.10)

    By the Hardy inequality (see, e.g., [18,Lemma 2.5.7]),

    R2D|v|2|x|2ln(2+|x|)2C(R2D|v|2+B2D|v|2), (2.11)

    it suffices to prove (2.10) for R=2 by contradiction. Suppose that there exists a sequence (kn)0 and a sequence (vn)H1(R2D) such that

    Δvn+iknvn=0 in R2D,vnL2(B2D)=1, and limn+vnH1/2(D)=0.

    Denote

    W1(R2D)={uL1loc(R2D);u(x)ln(2+|x|)1+|x|2L2(R2D) and uL2(R2D)}.

    By (2.8) and (2.11), one might assume that vn converges to v weakly in H1loc(R2D) and strongly in L2(B2D). Moreover, vW1(R2D) and v satisfies

    Δv=0 in R2D,v=0 on D, (2.12)

    and

    vL2(B2D)=1. (2.13)

    From (2.12), we have v=0 in R2D (see, e.g., [18]) which contradicts (2.13). The proof is complete.

    We also have

    Lemma 2.2. Let d=2,3, ω>0, 0<ε<1/2, and let vH1(RdD) be a solution of Δv+iωε2v=0 in RdD. We have, for 3/2<|x|<R,

    |v(x/ε)|Ce(ε,ω,d)vH1/2(D), (2.14)

    for some positive constant C=CR independent of ε, ω and v.

    Recall that e(ε,ω,d) is given in (2.3) and (2.4).

    Proof. By the trace theory and the regularity theory of elliptic equations, we have

    vL2(B2)+vL2(B2)CvH2(B5/2B3/2)C(1+ω1/2ε)vH1(B3B1). (2.15)

    It follows from Lemma 2.1 that

    vL2(B2)+vL2(B2)C(1+ω3/2)vH1/2(D). (2.16)

    Here and in what follows in this proof, C denotes a positive constant depending only on R and D.

    The representation formula gives

    v(x)=B2(G(x,y)rv(y)ryG(x,y)v(y))dy for xRdˉB2, (2.17)

    where =eiπ/4εω1/2, and, for xy,

    G(x,y)=ei|xy|4π|xy| if d=3 and G(x,y)=i4H(1)0(|xy|) if d=2.

    Here H(1)0 is the Hankel function of the first kind of order 0. Recall, see, e.g., [15,Chapter 5], that

    H(1)0(z)=2iπln|z|2+1+2iγπ+O(|z|2log|z|) as z0,z(,0], (2.18)

    and

    H(1)0(z)=2πzei(z+π4)(1+O(|z|1))z,z(,0]. (2.19)

    We now consider the case d=3. We have, for 3/2<|x|<R and yB2,

    |ei|x/εy||e22ω1/2|xεy|eω1/2|x|/3.

    It follows that, for 3/2<|x|<R and yB2,

    |G(x/ε,y)|Cεe3ω1/2/10. (2.20)

    Similarly, one has, for 3/2<|x|<R and yB2,

    |ryG(x/ε,y)|C(ε2ω1/2|x|+ε2|x|2)eω1/2|x|/3Cεe3ω1/2/10. (2.21)

    Combining (2.17), (2.20) and (2.21) yields

    |v(x/ε)|Cεe3ω1/2/10(vL2(B2)+vL2(B2)) for 3/2<|x|<R.

    We derive from (2.16) that

    |v(x/ε)|Cεeω1/2/4vH1/2(D) for 3/2<|x|<R;

    which is the conclusion in the case d=3.

    We next deal with the case where d=2 and ω>ε2/4, which is equivalent to ||>1/2. From (2.19), we derive that, for 3/2<|x|<R and yB2,

    |G(x/ε,y)|Cω1/4e3ω1/2/10 and |ryG(x/ε,y)|Cεω1/4e3ω1/2/10. (2.22)

    Using (2.16) and combining (2.17) and (2.22), we obtain, since ω>ε2/4,

    |v(x/ε)|Cεeω1/2/4vH1/2(D) for 3/2<|x|<R,

    which gives the conclusion in this case.

    We finally deal with the case where d=2 and 0<ω<ε2/4, which is equivalent to ||<1/2. From (2.17), we obtain, for xB4,

    v(x)=B2([G(x,y)G(x,0)]rv(y)ryG(x,y)v(y))dy+B2G(x,0)rv(y)dy. (2.23)

    Since d=2, we have

    vL(B5B3)CvH2(B5B3)CvH2(B5B2)C(1+ω1/2)vH1(B6B1).

    It follows from Lemma 2.1 and the trace theory that

    vL(B5B3)+vL2(B2)+vL2(B2)C(1+ω3/2)vH1/2(D). (2.24)

    Since, by (2.18),

    |yG(x,y)|C for xB4 and yB2

    and

    |G(x,0)|C|ln||| for xB4,

    we derive from (2.23) and (2.24) that

    |B2rv(y)dy|C(1+ω3/2)|ln|||vH1/2(D). (2.25)

    Again using (2.17), we get, for 3/2<|x|<R,

    v(x/ε)=B2([G(x/ε,y)G(x/ε,0)]rv(y)ryG(x/ε,y)v(y))dy+B2G(x/ε,0)rv(y)dy. (2.26)

    Since, by (2.18), for 0<ω<1/2,

    |G(x/ε,0)|C|lnω| and |yG(x/ε,y)|Cε for 3/2<|x|<R,yB2,

    and, by (2.19), for 1/2<ω<ε2/4,

    |G(x/ε,0)|Cω1/4e3ω1/2/10 and |yG(x/ε,y)|Cεω1/4e3ω1/2/10 for 3/2<|x|<R,yB2,

    we derive from (2.24), (2.25) and (2.26) that, for 3/2<|x|<R,

    |v(x/ε)|{C|lnω||ln|||vH1/2(D) if 0<ω<1/2,Cω3/2e3ω1/2/10|ln|||vH1/2(D) if 1/2<ω<ε2/4,

    which yields the conclusion in the case 0<ω<ε2/4. The proof is complete.

    In this proof, C denotes a positive constant depending only on Ω and Λ. Multiplying the equation of vε by ˉvε and integrating in Ω, we derive that

    ΩAεvε,vε+ωΩρε|vε|2Cg2L2(Ω). (2.27)

    Here we used Poincaré's inequality

    vεL2(Ω)CvεL2(Ω).

    It follows from (2.27) that

    vε(ε)2H1/2(B1)Cvε(ε)2H1(B1)CBε1εd2|vε|2+1εd|vε|2C(1+ω1)g2L2(Ω). (2.28)

    Similarly, using the equation for v and Poincaré's inequality, we get

    vH1(Ω)CgL2(Ω). (2.29)

    Since Δv+iωv=0 in B2, using Caccioppolli's inequality, we have

    vH3(B1)CvH2(B3/2)CvH1(B2)CgL2(Ω). (2.30)

    By Sobolev embedding, as d3,

    vW1,(B1)CvH3(B1). (2.31)

    It follows that

    v(ε)H1/2(B1)Cv(ε)H1(B1)CvW1,(B1)CgL2(Ω). (2.32)

    Set

    wε=vεv in ΩBε.

    Then wεH1(ΩBε) and satisfies

    {Δwε+iωwε=0 in ΩBε,wε=vεv on Bε,wε=0 on Ω. (2.33)

    Let ˜wεH1(RdBε) be the unique solution of the system

    {Δ˜wε+iω˜wε=0 in RdBε,˜wε=wε on Bε, (2.34)

    and set

    ˜Wε=˜wε(ε) in RdB1.

    Then ˜WεH1(RdB1) is the unique solution of the system

    {Δ˜Wε+iωε2˜Wε=0 in RdB1,˜Wε=wε(ε) on B1. (2.35)

    Fix r0>2 such that ΩBr0. By Lemma 2.2, we have, for 1|x|<r0, that

    |˜Wε(x/ε)|Ce(ε,ω,d)wε(ε)H1/2(B1),

    which yields, for xBr0B1, that

    |˜wε(x)|Ce(ε,ω,d)wε(ε)H1/2(B1).

    Since Δ˜wε+iω˜wε=0 in Br0B1, it follows from Caccioppoli's inequality that

    ˜wεH1(B2B3/2)Ce(ε,ω,d)wε(ε)H1/2(B1). (2.36)

    Fix φC2(Rd) such that φ=1 in B3/2 and φ=0 in RdB2, and set

    χε=wεφ˜wε in ΩBε.

    Then χεH10(ΩBε) and satisfies

    Δχε+iωχε=Δφ˜wε2φ˜wε in ΩBε.

    Multiplying the equation of χε by ˉχε and integrating by parts, we obtain, by Poincaré's inequality,

    χεH1(ΩBε)C˜wεH1(B2B3/2). (2.37)

    Combining (2.36) and (2.37) yields

    wεH1(ΩB2)Ce(ε,ω,d)wε(ε)H1/2(B1). (2.38)

    The conclusion now follows from (2.28) and (2.32).

    Let vε=uεu. Using the fact that vε is real, by the inversion theorem and Minkowski's inequality, we have, for t>0,

    vε(t,)L2(ΩB2)C0ˆvε(ω,)L2(ΩB2)dω. (2.39)

    Using Proposition 2.1, we get

    0ˆvε(ω,)L2(ΩB2)dωC0(1+ω1/2)e(ε,ω,d)ˆf(ω)+u0L2(ΩB2)dωCesssupω>0ˆf(ω)+u0L2(ΩB2)0(1+ω1/2)e(ε,ω,d)dωCe(ε,d)(fL1((0,+);L2(Ω))+u0L2(Ω)).

    It follows from (2.39) that, for t>0,

    vε(t,)L2(ΩB2)Ce(ε,d)(fL1((0,+);L2(Ω))+u0L2(Ω)).

    Similarly, we have, for t>0,

    vε(t,)L2(ΩB2)Ce(ε,d)(fL1((0,+);L2(Ω))+u0L2(Ω)).

    The conclusion follows.

    The second author is funded by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 101.02-2015.21.

    The authors declare no conflict of interest in this paper.



    [1] Z. Lu and Y. Takeuchi, Global asymptotic behavior in single-species discrete diffusion systems, J. Math. Biol., 32 (1993), 67–77.
    [2] E. Beretta and Y. Takeuchi, Global stability of single-species diffusion Volterra models with continuous time delays, Bull. Math. Biol. 49 (1987), 431–448.
    [3] D. Li, J. Cui and G. Song, Permanence and extinction for a single-species system with jump diffusion, J. Math. Anal. Appl., 430 (2015), 438–464.
    [4] L. J. Allen, Persistence and extinction in single-species reaction-diffusion models, Bull. Math. Biol. 45.2 (1983), 209–227.
    [5] W. Wang and T. Zhang, Caspase-1-Mediated Pyroptosis of the Predominance for Driving CD4+ T Cells Death: A Nonlocal Spatial Mathematical Model, B. Math. Biol., 80 (2018), 540--582.
    [6] Y. Cai andW.Wang, Fish-hook bifurcation branch in a spatial heterogeneous epidemic model with cross-diffusion, Nonlinear Anal. Real World Appl., 30 (2016), 99–125.
    [7] T. Zhang, X. Liu, X. Meng and T. Zhang, Spatio-temporal dynamics near the steady state of a planktonic system, Comput. Math. Appl., 75 (2018), 4490–4504.
    [8] T. Zhang, T. Zhang, X. Meng, Stability analysis of a chemostat model with maintenance energy, Appl. Math. Lett., 68 (2017), 1–7.
    [9] J. Zhou, C. Sang, X. Li, M. Fang and Z. Wang, H1 consensus for nonlinear stochastic multi-agent systems with time delay, Appl. Math. Comput., 325 (2018), 41–58.
    [10] K. Gopalsamy, Stability and oscillations in delay differential equations of population dynamics, Kluwer Academic, Dorecht. 1992.
    [11] Y. Kuang, Delay differential equations with applications in population dynamics, Academic Press, 1993.
    [12] X. Meng, F. Li and S. Gao, Global analysis and numerical simulations of a novel stochastic ecoepidemiological model with time delay, Appl. Math. Comput., 339 (2018), 701–726.
    [13] T. Zhang, W. Ma and X. Meng, Global dynamics of a delayed chemostat model with harvest by impulsive flocculant input, Adv. Difference Equ., 2017 (2017), 115.
    [14] G. Liu, X. Wang, X. Meng and S. Gao, Extinction and persistence in mean of a novel delay impulsive stochastic infected predator-prey system with jumps, Complexity, 2017 (2017), 15.
    [15] Y. Tan, S. Tang, J. Yang and Z. Liu, Robust stability analysis of impulsive complex-valued neural networks with time delays and parameter uncertainties, J. Inequal. Appl., 2017 (2017), 215.
    [16] T. Zhang and H. Zang, Delay-induced Turing instability in reaction-diffusion equations, Phys. Rev. E, 90 (2014) 052908.
    [17] R. M. May, Stability and Complexity in Model Ecosystems, Princeton Univ. Press, NewYork, 2001.
    [18] X. Yu, S. Yuan and T. Zhang, Survival and ergodicity of a stochastic phytoplankton-zooplankton model with toxin producing phytoplankton in an impulsive polluted environment, Appl. Math. Comput., 347 (2019), 249-264.
    [19] H. Qi, X. Leng, X. Meng and T. Zhang, Periodic Solution and Ergodic Stationary Distribution of SEIS Dynamical Systems with Active and Latent Patients, Qual. Theory Dyn. Syst., 18 (2019).
    [20] S. Zhang, X. Meng, T. Feng and T. Zhang, Dynamics analysis and numerical simulations of a stochastic non-autonomous predator-prey system with impulsive effects, Nonlinear Anal. Hybrid Syst., 26 (2017), 19–37.
    [21] M. Liu, H. Qiu and K. Wang, A remark on a stochastic predator-prey system with time delays, Appl. Math. Lett., 26 (2013), 318-323.
    [22] X. Meng and L. Zhang, Evolutionary dynamics in a Lotka-Volterra competition model with impulsive periodic disturbance, Math. Method. Appl. Sci., 39 (2016), 177–188.
    [23] L. Zhu and H. Hu, A stochastic SIR epidemic model with density dependent birth rate, Adv. Difference Equ., 2015 (2015), 1.
    [24] X. Meng, S. Zhao, T. Feng and T. Zhang, Dynamics of a novel nonlinear stochastic SIS epidemic model with double epidemic hypothesis, J. Math. Anal. Appl., 433 (2016), 227–242.
    [25] M. Liu and C. Bai, A remark on a stochastic logistic model with diffusion, Appl. Math. Lett., 228 (2014), 141–146.
    [26] F. Bian, W. Zhao, Y. Song and R. Yue, Dynamical analysis of a class of prey-predator model with Beddington-Deangelis functional response, stochastic perturbation, and impulsive toxicant input, Complexity, 2017 (2017) Article ID 3742197.
    [27] W. Wang, Y. Cai, J. Li and Z. Gui, Periodic behavior in a FIV model with seasonality as well as environment fluctuations, J. Franklin Inst., 354 (2017), 7410–7428.
    [28] X. Song and L. Chen, Optimal harvesting and stability for a two-species competitive system with stage structure, Math. Biosci., 170 (2001), 173–186.
    [29] J. Liang, S. Tang and R. A. Cheke, Pure Bt-crop and mixed seed sowing strategies for optimal economic profit in the face of pest resistance to pesticides and Bt-corn, Appl. Math. Comput., 283 (2016), 6–21.
    [30] S. Sharma and G. P. Samanta, Optimal harvesting of a two species competition model with imprecise biological parameters, Nonlinear Dynam., 77 (2014), 1101–1119.
    [31] J. Xia, Z. Liu and R. Yuan, The effects of harvesting and time delay on predator-prey systems with Holling type II functional response, SIAM J. Appl. Math., 70 (2009), 1178–1200.
    [32] W. Li, K. Wang, H. Su, Optimal harvesting policy for stochastic logistic population model, Appl. Math. Comput., 218 (2011), 157–162.
    [33] X. Zou,W. Li and K.Wang, Ergodic method on optimal harvesting for a stochastic Gompertz-type diffusion process, Appl. Math. Lett., 26 (2013), 170–174.
    [34] Q. Song, R. H. Stockbridge and C. Zhu, On optimal harvesting problems in random environments, J. Soc. Ind. Appl. Math., 49 (2011), 859–889.
    [35] G. Zeng, F. Wangand J. J. Nieto, Complexity of a delayed predator-prey model with impulsive harvesting and Holling type II functional response, Adv. Complex Syst., 11 (2008), 77–97.
    [36] X. Zou, W. Li and K. Wang, Ergodic method on optimal harvesting for a stochastic Gom-pertztype diffusion process, Appl. Math. Lett., 26 (2013), 170–174.
    [37] C. Ji, D. Jiang and N. Shi, Analysis of a predator-prey model with modified Leslie-Gower and Holling-type II schemes with stochastic perturbation, J. Math. Anal. Appl., 359 (2009), 482–498.
    [38] J. Bao and C. Yuan, Comparison theorem for stochastic differential delay equations with jumps, Acta Appl. Math., 116 (2011), 119.
    [39] D. Jiang and N. Shi, A note on nonautonomous logistic equation with random perturbation, J. Math. Anal. Appl., 303 (2005), 164–172.
    [40] D. Jiang, C. Ji, X. Li and D. O'Regan, Analysis of autonomous Lotka-Volterra competition systems with random perturbation, J. Math. Anal. Appl., 390 (2012), 582–595.
    [41] I. Barbalat, Systems d'equations differentielles d'osci d'oscillations nonlineaires, Rev. Roumaine Math. Pures Appl. 1959.
    [42] X. Leng, T. Feng and X. Meng, Stochastic inequalities and applications to dynamics analysis of a novel SIVS epidemic model with jumps, J. Inequel. Appl., 2017 (2017), 138.
    [43] J. Prato and J. Zabczyk, Ergodicity for infinite dimensional systems, Cambridge Univ. Press, Cambridge, (1996), 229.
    [44] Y. Zhao, S. Yuan and I. Barbalat, Systems dequations differentielles doscillations nonlineaires, Phys. A., 477 (2017), 20–33.
    [45] L. Liu and X. Meng, Optimal harvesting control and dynamics of two species stochastic model with delays, Adv. Differ Equat., 2017 (2017), 1–18.
    [46] H. Qi, L. Liu and X. Meng, Dynamics of a non-autonomous stochastic SIS epidemic model with double epidemic hypothesis, Complexity, 2017 (2017), 14.
    [47] D. Higham, An algorithmic introduction to numerical simulation of stochastic differential equations, SIAM Rev., 43 (2001), 525–546.
    [48] N. Bruti-Liberati and E. Platen, Monte Carlo simulation for stochastic differential equations, Encyclopedia of Quantitative Finance, 10 (2010), 23–37.
    [49] M. Liu and C. Bai, Optimal harvesting of a stochastic delay competitive model, Discrete Contin. Dyn. Syst. Ser. B., 22 (2017), 1493–1508.
    [50] H. Ma and Y. Jia, Stability Analysis For Stochastic Differential EquationsWith Infinite Markovian Switchings, J. Math. Anal. Appl., 435 (2016), 593–605.
    [51] M. Liu, X. He and J. Yu, Dynamics of a stochastic regime-switching predator-prey model with harvesting and distributed delays, Nonlinear Anal. Hybrid Syst., 28 (2018), 87–104.
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