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Research article

Bifurcation and optimal harvesting analysis of a discrete-time predator–prey model with fear and prey refuge effects

  • Received: 09 July 2024 Revised: 26 August 2024 Accepted: 04 September 2024 Published: 11 September 2024
  • MSC : 39A28, 39A30

  • In this contribution, the complicated dynamical behaviors and optimal harvesting policy of a discrete-time predator–prey model with fear and refuge effects are formulated. Both the fear and prey refuge effects refer to an interaction between predator and prey. In the first place, the existence and local stability of three fixed points of proposed model are investigated by virtue of our methodology, that is, the eigenvalues of the Jacobian matrix. One step further, it is worth mentioning that the model undergoes flip bifurcation (i.e., period–doubling bifurcation) and Neimark–Sacker bifurcation at the interior fixed point by the utilization of bifurcation theory and center manifold theory. Also, optimal harvesting strategy is investigated, and the expressions of optimal harvesting efforts are determined. Two examples, in the end, are put forward to prove that they are consistent with the previous theoretical results.

    Citation: Jie Liu, Qinglong Wang, Xuyang Cao, Ting Yu. Bifurcation and optimal harvesting analysis of a discrete-time predator–prey model with fear and prey refuge effects[J]. AIMS Mathematics, 2024, 9(10): 26283-26306. doi: 10.3934/math.20241281

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  • In this contribution, the complicated dynamical behaviors and optimal harvesting policy of a discrete-time predator–prey model with fear and refuge effects are formulated. Both the fear and prey refuge effects refer to an interaction between predator and prey. In the first place, the existence and local stability of three fixed points of proposed model are investigated by virtue of our methodology, that is, the eigenvalues of the Jacobian matrix. One step further, it is worth mentioning that the model undergoes flip bifurcation (i.e., period–doubling bifurcation) and Neimark–Sacker bifurcation at the interior fixed point by the utilization of bifurcation theory and center manifold theory. Also, optimal harvesting strategy is investigated, and the expressions of optimal harvesting efforts are determined. Two examples, in the end, are put forward to prove that they are consistent with the previous theoretical results.



    The use of mathematical models to study the interaction between different biological elements is an important area in ecosystems. The main purpose of the model is to explore the dynamical behaviors between them, such as predation, symbiosis, parasitism, competition, and so on [1,2,3]. Since Lotka [4] and Volterra [5] separately proposed the Lotka–Volterra model to describe the ecological interaction by differential equation model, many mathematicians and biologists have been deeply interested in studying such models, especially the predator–prey models. In biology and biomathematics, the predation model is one of the important research topics, and it is also an important model of ecosystems [6]. There is a large amount of literature on predator–prey models with different types of functional responses. Holling [7,8,9] came up with three types of functional response functions to describe the relationship between the quantity of prey captured by predators and the density of prey. Taking into account the living environment and situation of prey and predators in reality, the dwellings of many prey have a certain protective effect, and their ability to resist natural enemies will be enhanced with the increase of the density of prey, or prey will learn to hide themselves under the pursuit of natural enemies, which will reduce the predation ability of natural enemies. Therefore, Andrews [10] proposed the Holling–IV functional response function in 1968. Besides, Ajraldi [11] proposed a square–root functional response to explain the aggregation behavior that plankton exhibit.

    Recently, the discrete population models depicted by differential equations have been extensively studied [12,13,14]. Discrete-time systems, compared with continuous–time versions, have obvious advantages. On one side, data collection for biological samples is often on discrete time scales in reality (either in a week, a month, or another certain time period) rather than on a day. On the other side, assume that the population quantity of species does not overlap between successive generations; these could offer more efficient calculation results for numerical simulations [15,16]. Therefore, a lot of mathematicians have studied the dynamical behaviors of the discrete-time model corresponding to the continuous–time one. For instance, Agarwal [17] formulated bifurcation analysis of a discrete predator–prey model incorporating prey refuge. Ahmed et al. [18] explored a class of discrete prey predation models with fast–slow effects. Khan et al. [19] studied a class of discrete Rosenzweig–Macarthur predator–prey models and analysed their chaos.

    Since the fear effect was first put forward by Wang et al. [20], the research of models with the fear effect has been paid more attention by many scholars [21,22,23]. The fear effect mainly refers to the mutual relationship between prey and predators, which makes prey populations instinctively fear predators, thereby reducing the birth rate of prey and thus achieving the purpose of reducing prey capture. It is obvious that this is a kind of anti–predation behavior. In [20], the expression for the fear effect is F(x,y)=1/(1+ky), where the parameter k represents the level of the fear effect, and the prey population decreases as k goes up. Compared with the fear effect, the refuge effect is also an anti–predation behavior. It is important to note that the prey cost most of their lives nearby or hiding in shelters for avoiding predation, such as caves, crevices, dense vegetation, shells, or pipes. The notion of prey sanctuary has caught ecologists and mathematicians' attention since Gause et al. [24] and Smith [25] brought in the parameter refer to refuge. In the ecology, prey refuge could reduce the predation of the prey by the predator, thereby refraining from extinction of the prey population due to the predation factor; see [26,27,28,29,30] in detail. Many scholars studied a type of continuous predator–prey model with the fear effect or refuge effect [31,32,33,34,35,36]. For example, Pal et al. [32] added the fear effect, the Allee effect, and the influence of external disturbances such as refuges into a continuous predator–prey model. At the same time, the positivity, boundedness, stability of the equilibrium points, and bifurcation phenomena of the continuous predator–prey model were analyzed. Wang et al. [34] mainly considered the role of the fear effect and discussed the difference in stability with and without the fear effect; besides, they also studied some bifurcation phenomena. Consider that some populations without overlapping generations are not suitable for continuous models, so we can establish a type of discrete predator–prey model with a fear effect and a prey refuge effect. First of all, a continuous version is put forward as follows:

    {dxdt=rx1+ky(1xK)c(xR)y,dydt=ydy+e(xR). (1.1)

    The discretization system (1.1) is obtained

    {xn+1=xn+rxn1+kyn(1xnK)c(xnR)yn,yn+1=yndyn+e(xnR)yn. (1.2)

    Where xn and yn are the population densities at nth generation of prey and predator, respectively. r,k,K,c,d,e and R severally stand for the growth rate of prey, the level of the fear effect, the carrying capacity of prey, the capture efficiency of the predator of prey, the natural mortality rate of the predator, the rate of conversion of energy, and the quantity of prey using refuge. All of the above parameters are strictly positive.

    The dramatic increase in people's demand for resources has led to biological resources being over-exploited. It needs us to maintain sustainable development and maximize economic benefits for the exploitation of ecological resources. Current research refers to the effect of harvesting and mainly focuses on constant harvesting [37], proportional harvesting [38], and nonlinear harvesting [39]. We introduce the type of proportional harvesting of prey and predator. Our system takes the following form:

    {xn+1=xn+rxn1+kyn(1xnK)c(xnR)ynq1E1xn,yn+1=yndyn+e(xnR)ynq2E2yn. (1.3)

    Here q1,q2 represent the catchability coefficients of the prey and predator, and E1,E2 mean the harvesting efforts of the prey and predator, respectively.

    The outline of the rest of this paper is in the following manner: Sections 2 and 3 explore the existence and stability of all possible fixed points of system (1.3), respectively. In Sections 4 and 5, we investigate in detail two types of bifurcation analysis (Flip bifurcation and Neimark–Sacker bifurcation) at the interior fixed point of system (1.3). Also, the optimal harvesting strategy is analysed in Section 6. Numerical simulations are applied in Section 7 to illustrate our theoretical results analysed above. Finally, Section 8 contains the conclusion and discussion of our manuscript.

    In this section, we are about to study the existence of possible fixed points, by solving the nonlinear system given by

    {xx+rx1+ky(1xK)c(xR)yq1E1x,yydy+e(xR)yq2E2y. (2.1)

    Theorem 2.1. Fixed points obtained are as follows:

    (1) A1(0,0) always exists.

    (2) A2(W,0) is feasible if r>q1E1, where W=K(rq1E1)r.

    (3) A3(Q,P) exists if S<0 is satisfied, where Q=d+q2E2+eRe, P=V+V24ZSZ and Z=ckQckR, V=cQcR+q1E1kQ, S=q1E1QrQ(1QK).

    Proof. Obviously, A1(0,0) always holds true. For A2(W,0), we just need to consider that W is positive (i.e., r>q1E1). For interior fixed point A3, through simple calculation for system (2.1), we obtain Eq (2.2)

    {Q=Q+rQ1+ky(1QK)c(QR)yq1E1Q,Q=d+q2E2+eRe. (2.2)

    Appropriate transformation of the first term in Eq (2.2) yields

    (ckQckR)y2+(cQcR+q1E1kQ)y+q1E1QrQ(1QK)=0. (2.3)

    Let's define

    F(y)=(ckQckR)y2+(cQcR+q1E1kQ)y+q1E1QrQ(1QK)=Zy2+Vy+S.

    And Z=ckQckR, V=cQcR+q1E1kQ, and S=q1E1QrQ(1QK), notice that

    (ⅰ) V24ZS>0 implies that there exist two roots for F(y)=0;

    (ⅱ) Since Q=d+q2E2+eRe>R, V=c(QR)+q1E1kQ>0 and Z=ck(QR)>0 are valid, it indicates the axis of symmetry ¯y=V2Z<0.

    Now we only need to judge the constant term of F(y) to know whether F(y) has positive roots. When S>0, there are two negative roots, whereas when S<0, there is a unique positive root

    P=V+V24ZSZ>0.

    At this point, we have completed the proof of Theorem 2.1.

    We investigate the local stability of the above fixed points of system (1.3). The Jacobian matrix of (2.1) at (x,y) is given by

    J(x,y)=(j1j2j3j4), (3.1)

    where

    j1=1+r(1+ky)2rxK(1+ky)cyq1E1,j2=krx(1+ky)2(1xK)c(xR),j3=ey,  j4=1d+e(xR)q2E2. (3.2)

    The characteristic equation of this Jacobian matrix (3.1) is

    F(λ)=λ2Tr(J)λ+Det(J)=0, (3.3)

    where

    Tr(J)=j1+j4,  Det(J)=j1j4j2j3.

    So as to the properties of fixed points of system (1.3), we provide the following lemma.

    Lemma 3.1. Assume that F(1)>0. Then

    (ⅰ) |λ1|<1 and |λ2|<1 if and only if F(1)>0 and Det(J)<1;

    (ⅱ) |λ1|>1 and |λ2|>1 if and only if F(1)>0 and Det(J)>1;

    (ⅲ) (|λ1|>1 and |λ2|<1) or (|λ1|<1 and |λ2|>1) if and only if F(1)<0;

    (ⅳ) |λ1|=1 and |λ2|1 if and only if F(1)=0 and Tr(J)0,2;

    (ⅴ) λ1 and λ2 are conjugate complex roots, and |λ1|=|λ2|=1 if and only if Tr(J)24Det(J)<0 and Det(J)=1.

    Proposition 3.1. The conclusions of fixed point A1(0,0) are as follows:

    (ⅰ) A1(0,0) is sink if 2<rq1E1<0 and 0<d+eR+q2E2<2;

    (ⅱ) A1(0,0) is source (unstable) if rq1E1<2 and d+eR+q2E2>2;

    (ⅲ) A1(0,0) is saddle if rq1E1<2, d+eR+q2E2<2 or 2<rq1E1<0 and d+eR+q2E2>2;

    (ⅳ) A1(0,0) is non–hyperbolic if rq1E1=2 and d+eR+q2E20 , 2.

    Proof. At fixed point A1(0,0), the Jacobian matrix is

    J1=(1+rq1E1cR01deRq2E2). (3.4)

    From the above matrix, we can calculate Tr(J1) and Det(J1) as follows:

    Tr(J1)=2+rq1E1q2E2deR,
    Det(J1)=1deRq2E2+rrderRrq2E2q1E1+dq1E1+eRq1E1+q1E1q2E2.

    At the same time, we can derive the eigenvalues

    λ1J1=1+rq1E1,  λ2J1=1deRq2E2.

    By Lemma 3.1, A1(0,0) is sink if λ1J1,2J2∣<1 (2<rq1E1<0 and 0<d+eR+q2E2<2). Similarly, A1(0,0) is source if rq1E1<2 and d+eR+q2E2>2, saddle if rq1E1<2, d+eR+q2E2<2 or 2<rq1E1<0 and d+eR+q2E2>2 and non–hyperbolic if rq1E1=2 and d+eR+q2E20,2.

    Proposition 3.2. The conclusions of fixed point A2(W,0) are as follows:

    (ⅰ) A2(W,0) is sink if 2<q1E1r<0 and 2+d+eR+q2E2<eK(rq1E1)r<d+eR+q2E2;

    (ⅱ) A2(W,0) is source (unstable) if q1E1r<2 and eK(rq1E1)r<2+d+eR+q2E2 (or eK(rq1E1)r>d+eR+q2E2);

    (ⅲ) A2(W,0) is saddle if q1E1r<2 and 2+d+eR+q2E2<eK(rq1E1)r<d+eR+q2E2 or 2<q1E1r<0 and eK(rq1E1)r>d+eR+q2E2 or eK(rq1E1)r<2+d+eR+q2E2;

    (ⅳ) A2(W,0) is non–hyperbolic if q1E1r=2 and eK(rq1E1)r2+d+eR+q2E2 and eK(rq1E1)rd+eR+q2E2.

    Proof. Bring A2(K(rq1E1)r,0) into (3.1), then we obtain

    J2=(1r+q1E1kK(rq1E1)(1+ky)2(1rq1E1r)c(K(rq1E1)rR)01d+eK(rq1E1)reRq2E2). (3.5)

    At the same time, we can derive the eigenvalues λ1J2 and λ2J2 from the above matrix

    λ1J2=1r+q1E1,λ2J2=1+eK(rq1E1)rdeRq2E2.

    By Lemma 3.1, if λ1J2 = 1r+q1E1∣<1 and λ2J2 = 1+eK(rq1E1)rdeRq2E2∣<1, i.e., 2<q1E1r<0 and 2+d+eR+q2E2<eK(rq1E1)r<d+eR+q2E2, then A2(W,0) is sink. Analogously, A2(W,0) is source if q1E1r<2 and eK(rq1E1)r<2+d+eR+q2E2 (or eK(rq1E1)r>d+eR+q2E2), saddle if q1E1r<2 and 2+d+eR+q2E2<eK(rq1E1)r<d+eR+q2E2 or 2<q1E1r<0 and eK(rq1E1)r>d+eR+q2E2 or eK(rq1E1)r<2+d+eR+q2E2 and non–hyperbolic if q1E1r=2 and eK(rq1E1)r2+d+eR+q2E2 and eK(rq1E1)rd+eR+q2E2.

    Proposition 3.3. The conclusions of fixed point A3(Q,P) are as follows:

    (ⅰ) A3(Q,P) is sink if 1+Tr(J3)+Det(J3)>0 and Det(J3)<1;

    (ⅱ) A3(Q,P) is source (unstable) if 1+Tr(J3)+Det(J3)>0 and Det(J3)>1;

    (ⅲ) A3(Q,P) is saddle if 1+Tr(J3)+Det(J3)<0;

    (ⅳ) A3(Q,P) is non–hyperbolic if 1+Tr(J3)+Det(J3)=0 and Tr(J3)0,2 (or Det(J3)=1 and |Tr(J3)|<2).

    Proof. We put A3(d+q2E2+eRe,(cxcR+q1E1kx)+(cxcR+q1E1kx)24ck(xR)[q1E1xrx(1xR)]2ck(xR)) into (3.1), and if q1E1QrQ(1QK)<0 and 4ac<0 are satisfied, then it yields that

    J3=(1+r(1+kP)22rQK(1+kP)cPq1E1krQ(1+kP)2(1QK)c(QR))eP1d+e(QR)q2E2). (3.6)

    From the above matrix, we can calculate its Tr(J3) and Det(J3):

    Tr(J3)=2+r1+kP2rQK(1+kP)cPq1E1dq2E2+e(QR),Det(J3)=1+[r(2QK)K(1+kP)+q1E11][d+q2E2e(QR)](cP+q1E1)+r(K2Q)K(1+kP) +cP(d+q2E2)+kreQP(1+kP)2(1QK).

    The characteristic equation can be written as follows:

    F(λ)=λ2Tr(J3)λ+Det(J3)=0. (3.7)

    By Lemma 3.1, A3(Q,P) is sink if F(1)=1+Tr(J3)+Det(J3)>0 and Det(J3)<0. In the same way, A3(Q,P) is source if 1+Tr(J3)+Det(J3)>0 and Det(J3)>1, saddle if 1+Tr(J3)+Det(J3)<0 and non–hyperbolic if 1+Tr(J3)+Det(J3)=0 and Tr(J3)0,2 (or Det(J3)=1 and |Tr(J3)|<2).

    It is well known that flip bifurcation may occur when A3(Q,P) is non–hyperbolic. If (r,k,K,c,R,q1,E1,d,q2,E2,e)FB, where FB={r,k,K,c,R,q1,E1,d,q2,E2,e>0;4+kreQP(1+kP)2(1QK)+2r(KQ)K(1+kP)+cP(d+q2E2) = [2q1E1r(2QK)K(1+kP)][d+q2E2e(QR)];r(K2Q)K(1+kP)(cP+q1E1)+[d+q2E2e(QR)], (cP+q1E1)+[d+q2E2e(QR)]2}.

    In this section we consider the bifurcation case at A3. It is not difficult to find that system (1.3) experiences flip bifurcation at A3 if e changes in a small range of e=ˆe. Giving ¯e (where ¯e1) of the parameter e in a small range of e=ˆe to system (1.3), it yields that

    {xx+rx1+ky(1xK)c(xR)yq1E1x,yydy+(ˆe+¯e)(xR)yq2E2y. (4.1)

    In order to translate A3 to the origin, we take the transformation u=xQ and v=yP, then the system (4.1) transforms into the following form:

    {uu+r(u+Q)1+k(v+P)(1(u+Q)K)c[(u+Q)R](v+P)q1E1(u+Q),vvd(v+P)+(ˆe+¯e)[(u+Q)R](v+P)q2E2(v+P). (4.2)

    Taylor expansion of system (4.2) at (u,v,¯e)=(0,0,0)

    {ua11u+a12v+a13u2+a14uv+o(u,v)3,va21u+a22v+a23u2+a24uv+a25¯e+a26v¯e+a27u¯e. (4.3)

    Further, we can obtain

    a11=1+r1+ky2rxK(1+ky)cyq1E1,a12=krx(1+ky)2(1xK),a13=rK(1+ky),a14=kr2(1+ky)2+rxkK[K(1+ky)]2c2,a21=ey,a22=1d+e(xR)q2E2,a23=0,a24=e2,a25=(xR)y,a26=(xR)2,a27=Ry2.

    Subsequently, give an invertible matrix T as follows:

    T=(a12a121a121a12).

    Then using the following conversion

    (uv)=T(xy), (4.4)

    system (4.3) becomes

    (xy)=(100λ2)(uv)+(f(u,v,¯e)g(u,v,¯e)),

    where

    f(u,v,¯e)=1Det(T){[(λ2a11)a13a12a13]u2+[(λ2a11)a14a12a14]uva12a25¯ea12a26v¯e                    a12a27u¯e+o(u,v,¯e)3},
    g(u,v,¯e)=1Det(T){[(1+a11)a13a12a13]u2+[(1+a11)a13+a12a14]uv+a12a25¯e+a12a26v¯e                  +a12a27u¯e+o(u,v,¯e)3}.

    According to the theoretical knowledge related to the center manifold theorem [40,41], in a small range ¯e=0, it exists a center manifold Wc(0,0) at the fixed point (0,0) of system (1.3) of the form

    Wc(0,0)={(x,y):y=a1¯e+a2x2+a3x¯e+a4¯e2+o((|x|,|¯e|)3)}. (4.5)

    With some simple transformations, we obtain

    {u=a12(x+y),v=(1+a11)x+(λ2a11)y, (4.6)

    with uv=a12(1+a11)x2+a12(λ22a111)xy+a12(λ2a11)y2 and u2=a212(x2+2xy+y2). By combining the above (4.5) and (4.6), we can get the corresponding coefficients of (4.5)

    a1=a251λ22,a2=1(1λ22{[(1+a11)a13+a12a23]a12(1+a11)[(λ2a11)a14a12a24]},a3=2a2a25+(1+a11)a26a12a27(1+λ2)2,a4=a3a25(1λ2)2a2a225(1λ2)(1+λ2)2.

    Limiting the center manifold Wc(0,0) to the map G

    G(x,¯e)=x+f(x,y,¯e)=x+h0¯e+h1x2+h2x¯e+h3¯e2+h4x2¯e+h5x¯e2                 +h6x3+h7¯e3+O(|x|+|¯e|)3. (4.7)

    The coefficients of the above system are as follows:

    h0=a25(1+λ2),h1=1a12(1+λ2){[(λ2a11)a13a12a13]a212[(λ2a11)a14a12a14]a12(1+a11)},h2=1a12(1+λ2){2[(λ2a11)a13a12a13]a212a1[(λ2a11)a14a12a14]a12(λ22a111)a1+         a12a26(1+a11)a212a27},h3=1a12(1+λ2){[(λ2a11)a13a12a13]a212a21+[(λ2a11)a14a12a14]a12(λ2a11)a21         a12a26(λ2a11)a1a212a27a1},h4=1a12(1+λ2){[(λ2a11)a13a12a13]a2122(a3+a1a2)+[(λ2a11)a14a12a24]         [a12(λ22a111)a3+a12(λ2a11)2a1a2]},h5=1a12(1+λ2){[(λ2a11)a13a12a13]a2122a1a3+[(λ2a11)a14a12a14]a12(λ2a11)2a1a3},h6=1a12(1+λ2){[(λ2a11)a13a12a13]a2122a2+[(λ2a11)a14a12a14]a12(λ22a111)2a2},h7=1a12(1+λ2){[(λ2a11)a13a12a13]a2122a2+[(λ2a11)a14a12a14]a12(λ22a111)2a2         a12a26(λ2a11)a4a212a27a4}.

    In order to make system (1.3) generate flip bifurcation, it is sufficient to ensure that both discriminatory quantities Υ1 and Υ2 are not equal to 0, i.e.,

    {Υ1=(22Gx¯e+G¯e×2Gx2)|(0,0)=2(h2+h0h1)0,Υ2=(12(2Gx2)2+13(3Gx3))|(0,0)=2(h6+h21)0. (4.8)

    Theorem 4.1. System (1.3) exists flip bifurcation at A3 when e changes in a small range of ˆe and Υ10 and Υ20 are satisfied. Moreover, if Υ2>0 (or Υ2<0), then the period–2 point of the bifurcation from A3 is stable (or unstable).

    By a similar method as above, k can be selected as the bifurcation parameter, and we can limit the system (1.3) to

    σ(x)=x+ϵ1k+ϵ2x2+ϵ3k2+ϵ4x2k+ϵ5xk2+ϵ6x3+ϵ7k2+o{(|x|,|k|)4}. (4.9)

    Similarly, to ensure that flip bifurcation occurs in system (1.3), we set the two discriminants Ψ1 and Ψ2 to be non-zero

    {Ψ1=(22σxk+σk×2σx2)|(0,0)=2(ϵ2+ϵ0h1)0,Ψ1=(12(2σx2)2+13(3σx3))|(0,0)=2(ϵ6+ϵ21)0. (4.10)

    The computational processes of Ψ1 and Ψ1 are shown in Appendix A.

    Here we focus on the Neimark–Sacker bifurcation of system (1.3) at A3. System (1.3) exists Neimark–Sacker bifurcation at A3 if and only if all parameters belong to the set NSB={r,k,K,c,R,q1,E1,d,q2,E2, e>0 | e=(r(2QK)K(1+kP)+q1E11)(d+q1E1)(cP+q1E1)+r(K2Q)K(1+kP)+cP(d+q2E2)(QR)(r(2QK)K(1+kP)+q1E11)krQP(1+kP)2(1QK), |2+r1+kP2rQK(1+kP)cPq1E1dq2E2+e(QR)|<2; q1E1r(1Qk)<0 and r,k,K,c,R,q1,E1,d,q2,E2>0}.

    The characteristic equation (3.3) associated with (3.1) at A3 is given by

    F(λ)=λ2Tr(J3)λ+Det(J3)=0.

    There exists a pair of conjugate complex roots λ1,λ2 of the above characteristic equation at A3, i.e.,

    λ1,2=Tr(J3)±i4Det(J3)(Tr(J3))22.

    Given a parameter e (where e1) and e is located in a range of e=¯e in (3.1), we have

    xx+rx1+ky(1xK)c(xR)yq1E1x,yydy+(ˆe+e)(xR)yq2E2y. (5.1)

    Moving A3 to the original point, we take the transformations γ=xQ and δ=yP, system (5.1) transforms into the following form:

    γγ+r(γ+Q)1+k(δ+P)(1(γ+Q)K)c[(γ+Q)R](δ+P)q1E1(γ+Q),δδd(δ+P)+(ˆe+e)[(γ+Q)R](δ+P)q2E2(δ+P). (5.2)

    Taylor expansion of the above system (5.2)

    γc11γ+c12δ+c13γ2+c14γδ+o(γ,δ)3,δc21γ+c22δ+c23γ2+c24γδ+c25e+c26δe+c27γe+o(γ,δ)3. (5.3)

    Parameters of (5.3) are as follows:

    c11=1+r1+ky2rxK(1+ky)cyq1E1,c12=krx(1+ky)2(1xK),c13=rK(1+ky),c14=kr2(1+ky)2+rxkK(1+ky)2c2,c21=ey,c22=1d+e(xR)q2E2,c23=0,c24=e2,c25=(xR)y,c26=(xR)2,c27=Ry2.

    When (γ,δ)=(0,0), the characteristic roots of (5.3) above are as follows:

    λ1,2=TrJ(e)±i4DetJ(e)(TrJ(e))22.

    So we have |λ1,2(e)|=[DetJ(e)]12. When e=0, then

     θ1=d|λ1,2|de|e=e0. (5.4)

    Set α1=Re(λ1,2) and β1=Im(λ1,2). We obtain an invertible matrix N

    N=(c120α1c11β1).

    Then use the following conversion:

    (γδ)=N(xy). (5.5)

    System (5.3) becomes

    (xy)=(α1β1βα1)(γδ)+(F(γ,δ,¯e)G(γ,δ,¯e)),

    where

    F(γ,δ,e)=1c12[c12γ2+c14γδ+O(γ,δ)3],G(γ,δ,e)=1c12β1{(c11α1)c13γ2+(c11α1)c14γδ+c12c23γ2+c12c24γδ+c12c25e                    +c12c26δe+c12c27γe+O(γ,δ)3}.

    We can know from the transformation (5.5) that γ=c12x and δ=(α1c11)xβ1y, therefore

    F(x,y,e)=1c12{c12(c12x)2+c14c12x[(α1c11)xβ1y]+O(γ,δ)3},G(x,y,e)=1c12β1{[(c11α1)c13+c12c23](c12x)2+[(c11α1)c14+c12c24](c12x)[(α1c11)xβ1y]                     +c12c25e+c12c26[(α1c11)xβ1y]e+c12c27(c12x)e+O(γ,δ)3}.

    The Neimark–Sacker bifurcation occurs on system (1.3) if the following condition is met:

    l1=Re{(12¯λ)¯λ21λξ11ξ20}12|ξ11|2|ξ02|2+Re(¯λξ21)0, (5.6)

    where

    ξ11=14[Fxx+Fyy+i(Gxx+Gyy)],ξ20=18[Fxx+Fyy+2Gxy+i(GxxGYY2Fxy)],ξ02=18[Fxx+Fyy2Gxy+i(GxxGyy+2Fxy)],ξ21=116[Fxxx+Fxyy+Gyyy+Gxxy+i(Gxxx+GxyyFxxyFyyy)].

    By calculation we have

    Fxx=2[c12c13+c13(α1c11)], Fyy=0, Fxy=c13β1, Fxxx=Fxyy=Fxxy=Fyyy=0,Gxx=2c12β1{[(c11α1)c13+c12c23]c212[(c11α1)c14+c12c24]c12(α1c11)}, Gyy=0,Gxy=(c11α1)c14+c12c24, Gxxx=Gxyy=Gxxy=Gyyy=0.

    Theorem 5.1. If l1 defined is nonzero, then system (1.3) exists Neimark–Sacker bifurcation at A3 provided that e changes in a small range of e=e. Moreover, if l1<1 (or l1>1), then an attractively invariant closed curve bifurcates at A3.

    Similarly, consider k as a bifurcation parameter, it can be obtained that

    τ1=Re{(12¯λ)¯λ21λς11ς20}12|ς11|2|ς02|2+Re(¯λς21)0, (B.3)

    Neimark–Sacker bifurcation occurs in system (1.3). The detailed calculation process of τ1 is shown in Appendix B.

    Our aim is to maximize net returns and maintain ecological balance. Assign the net income function as M=exp(δt){(p1q1xh1)E1(t)+(p2q2xh2)E2(t)}, where δ expresses the discount rate. Thus

    maxkn=1exp(δn){(p1q1xh1)E1(n)+(p2q2xh2)E2(n)},

    such that

    {xn+1=xn+rxn1+kyn(1xnK)c(xnR)yq1E1xn,yn+1=yndyn+(ˆe+¯e)(xnR)ynq2E2yn,x1=x0,y1=y00E1(t)maxE1,0E2(t)maxE2. (6.1)

    The Hamiltonian function could be written in the following form:

    Hn=exp(δn){(p1q1xnh1)E1(n)+(p2q2xnh2)E2(n)}+λ1(n+1)[xn+rxn1+kyn(1xnK)         c(xnR)ynq1E1xn]+λ2(n+1)[yndyn+(ˆe+¯e)(xnR)ynq2E2yn].

    According to the Pontryagin maximum principle, we have

    HHi=0 (i=1,2);  dλ1(n+1)dt=Hdx  and  dλ2(n+1)dt=Hdy.

    Further we can obtain

    HH1=0exp(δt)(p1q1xnh1)q1xnλ1(n+1)=0λ1=exp(δt)(p1h1q1xn),HH2=0exp(δt)(p2q2xnh2)q2xnλ2(n+1)=0λ2=exp(δt)(p2h2q2xn),λ1(n+1)=Hdx=exp(δt)p1q1E1λ1(n+1)[1+r1+ky2rxK(1+ky)cyq1E1]λ2(n+1)ey,λ2(n+1)=Hdy=exp(δt)p2q2E2λ1(n+1)[krx(1+ky)2(1xK)c(xR)y]               λ2(n+1)[1d+e(xR)q2E2].

    By combining the above equations that

    E1=xh1{(p1h1q1x)[2+r1+ky2rxK(1+ky)cy]+(p2h2q2y)ey},E2=yh1{(p1h1q1x)[krx(1+ky)2(1xK)c(xR)y]+(p2h2q2y)[1d+e(xR)]}.

    Example 7.1. To verify the theoretical results numerically. First, we value all parameters in turn r=2.001, k=0.57, K=4.078, c=0.743, R=0.829, d=0.8, q1=0.467, E1=0.201, q2=0.42, E2=0.435, and e(0,3.5) with initial conditions (x0,y0)=(1,1.5). By simple calculation, we can know that λ1,2∣=1, Neimark–Sacker bifurcation may appear at (2.29572, 1.65075). We can see from Figure 1 that Neimark–Sacker bifurcation occurs when e=0.67. It follows that λ1∣=1 and λ2∣≠1, flip bifurcation may appear at (1.214, 1.8965). We can see from Figure 1 that flip bifurcation occurs when e=2.55.

    Figure 1.  (a) Bifurcation diagram of system (1.3) in the (e,x)–plane for r=2.001, k=0.57, K=4.078, c=0.743, R=0.829, d=0.8, q1=0.467, E1=0.201, q2=0.42, E2=0.435, and e(0,3.5); (b) Bifurcation diagram of system (1.3) in the (e,y)–plane for r=2.001, k=0.57, K=4.078, c=0.743, R=0.829, d=0.8, q1=0.467, E1=0.201, q2=0.42, E2=0.435, and e(0,3.5).

    From Figure 2(a) and (d), we can know that Neimark–Sacker bifurcation will appear when e=0.67, and when e belongs to (0, 1) and is greater than 0.67, system (1.3) is stable at the internal fixed point (2.29572,1.65075), whereas when the bifurcation parameter e is less than 0.67, the system will lose stability at the internal fixed point (2.29572,1.65075). In view of the ecological perspective, when e<0.67, a fluctuation will occur, which means that the number of prey and predator populations will continue to fluctuate periodically around a central value, rather than reaching a fixed point. Such fluctuation may reflect seasonal changes in resources or other cyclical environmental factors in the ecosystem. According to Figure 2(b) and (c), with the change of bifurcation parameters e, flip bifurcation will be generated in system (1.3) when its value reaches 2.55. When the bifurcation parameter e belongs to (1, 3.5) and is less than 2.55, system (1.3) is stable; conversely, when e is greater than 2.55, the system will lose stability. Biologically speaking, when e>2.55, the number of prey and predators is no longer maintained at a constant level, whereas it presents a state of periodic fluctuation. Such fluctuation could have an impact on the stability of ecosystems and lead to a large fluctuation in the number of prey and predator populations, which can affect the overall ecosystem's sustainability.

    Figure 2.  (a) Neimark–Sacker bifurcation diagram of system (1.3) in the (e,y)–plane for e(0,0.67); (b) Flip bifurcation diagram of system (1.3) in the (e,y)–plane for e(0.67,3.5); (c) Flip bifurcation diagram of system (1.3) in the (e,x)–plane for e(0.67,3.5); (d) Neimark–Sacker bifurcation diagram of system (1.3) in the (e,x)–plane for e(0,0.67).

    In the process from Figure 3(a) to (f), it is clearly shown that the stability of the system (1.3) varies with the bifurcation parameter e when it belongs to the range of (1,3.5).

    Figure 3.  Phase portraits of system (1.3) with different e.

    Example 7.2. We take the parameters r=3.5,K=8, c=0.69,k=3.9,d=3.6,e=0.77,q1=0.36,q2=0.21, p1=0.75,p2=3,h1=1.41,h2=1.8, and assign the initial value (x0,y0,E10,E20) = (1.32, 3, 0.05, 0.37). Through Figure 4, with the increase of the refuge coefficient R, harvesting effort E1 for prey will increase rapidly, when the refuge coefficient R=0.03, the harvesting of the prey will tend to a stable state. Due to the value of E1 for the prey population changing too rapidly, as the refuge coefficient R increases, the prey population will show a downward trend; until R=0.03, the number of the prey population will tend to a stable state. For the predator population, the harvesting E2 will also increase rapidly with increasing R. Compared with the prey population, the number of predator populations is inherently smaller. Due to the rapid increase in the refuge coefficient R and the harvesting E2, the predator population will be in a state of extinction. In general, whether it is for predators or prey, the harvesting of both of them should be moderate, so as to better promote the harmonious coexistence between humans and nature and ensure the maximum harvesting.

    Figure 4.  Optimal harvesting with respect to refuge coefficient R.

    In this article, we mainly study the bifurcation behavior and optimal harvesting strategy of a discrete predator–prey model in possession of fear and refuge effects. Firstly, the existence and stability of three fixed points of system (1.3) are researched, respectively. At the same time, the flip bifurcation and Neimark–Sacker bifurcation are analysed at the internal fixed point in detail. Together with Example 7.1, when the bifurcation parameter e=0.67, Neimark–Sacker bifurcation will occur, and when e=2.55, flip bifurcation will appear. It is easy to observe that relying on the increasing conversion rate of e, the unstable system turns stable, and then it becomes unstable again. Besides, the predator populations gradually accumulate for the value of the conversion rate at a high level. Finally, we analyze an optimal harvesting problem and theoretically get the value of the optimal harvesting effort, which implies there exists a value of the harvesting effort guaranteeing maximization of the net revenue. From Example 7.2, with the increase of the refuge coefficient R, xoptimal,yoptimal,Eoptimal1 and Eoptimal2 change in the range.

    Generally speaking, comparing the continuous–time predator–prey models with the corresponding discrete versions, the conclusions on the existence and stability of equilibrium points (or fixed points) can be studied. Also, bifurcation analysis of the models can be discussed. Relative to bifurcation analysis, several bifurcation phenomena, such as Hopf bifurcation, saddle–node bifurcation, and transcritical bifurcation, are mainly investigated in the continuous system. Otherwise, fold bifurcation, flip bifurcation, and Neimark–Sacker bifurcation can be discussed in the discrete system. All in all, whether discrete or continuous system, by studying these characteristics for the system, we can better understand external disturbances, thereby achieving a better role in maintaining ecological balance.

    Jie Liu: Conceptualization, Investigation, Methodology, Validation, Writing-original draft, Formal analysis, Software; Qinglong Wang: Conceptualization, Methodology, Formal analysis, Writing-review and editing, Supervision; Xuyang Cao: Validation, Visualization, Data curation; Ting Yu: Validation, Visualisation, Inspection.

    The authors thank the editor and referees for their careful reading and valuable comments.

    The work is supported by the Natural Science Foundation of Hubei Province (No.2023AFB1095) and the National Natural Science Foundation of China (No.12101211) and the Program for Innovative Research Team of the Higher Education Institution of Hubei Province (No.T201812) and the Teaching Research Project of Education Department of Hubei Province (No.2022367) and the Graduate Education Innovation Project of Hubei Minzu University (Nos.MYK2024071, MYK2023042).

    The authors declare that there are no conflicts of interest regarding the publication of this paper.



    [1] G.-Q. Sun, J. Zhang, L.-P. Song, Z. Jin, B.-L.-Li, Pattern formation of a spatial predator–prey system, Appl. Math. Comput., 218 (2012), 11151–11162. http://doi.org/10.1016/j.amc.2012.04.071 doi: 10.1016/j.amc.2012.04.071
    [2] B. Dubey, B. Das, J. Hussain, A predator–prey interaction model with self and cross-diffusion, Ecol. Model., 141 (2001), 67–76. http://doi.org/10.1016/S0304-3800(01)00255-1 doi: 10.1016/S0304-3800(01)00255-1
    [3] G.-Q. Sun, Z. Jin, L. Li, M. Haque, B.-L.-Li, Spatial patterns of a predator–prey model with cross diffusion, Nonlinear Dyn., 69 (2012), 1631–1638. http://doi.org/10.1007/s11071-012-0374-6 doi: 10.1007/s11071-012-0374-6
    [4] A. J. Lotka, Elements of physical biology, Baltimore: Williams and Wilkins, 1925.
    [5] V. Volterra, Variations and fluctuations of the number of individuals in animal species living together, ICES J. Mar. Sci., 3 (1928), 3–51. http://doi.org/10.1093/icesjms/3.1.3 doi: 10.1093/icesjms/3.1.3
    [6] E. Diz-Pita, M. V. Otero-Espinar, Predator–prey models: a review of some recent advances, Mathematics, 9 (2021), 1783. http://doi.org/10.3390/math9151783 doi: 10.3390/math9151783
    [7] C. S. Holling, The components of predation as revealed by a study of small-mammal predation of the european pine sawfly, Can. Entomol., 91 (1959), 293–320. http://doi.org/10.4039/Ent91293-5 doi: 10.4039/Ent91293-5
    [8] C. S. Holling, The functional response of predators to prey density and its role in mimicry and population regulation, The Memoirs of the Entomological Society of Canada, 97 (1965), 5–60. http://doi.org/10.4039/entm9745fv doi: 10.4039/entm9745fv
    [9] C. S. Holling, The functional response of invertebrate predators to prey density, The Memoirs of the Entomological Society of Canada, 98 (1966), 5–86. https://doi.org/10.4039/entm9848fv doi: 10.4039/entm9848fv
    [10] J. F. Andrews, A mathematical model for the continuous culture of microorganisms utilizing inhibitory substrates, Biotechnol. Bioeng., 10 (1968), 707–723. http://doi.org/10.1002/bit.260100602 doi: 10.1002/bit.260100602
    [11] P. A. Braza, Predator–prey dynamics with square root functional responses, Nonlinear Anal. Real World Appl., 13 (2012), 1837–1843. http://doi.org/10.1016/j.nonrwa.2011.12.014 doi: 10.1016/j.nonrwa.2011.12.014
    [12] P. Chakraborty, U. Ghosh, S. Sarkar, Stability and bifurcation analysis of a discrete prey–predator model with square-root functional response and optimal harvesting, J. Biol. Syst., 28 (2020), 91–110. http://doi.org/10.1142/S0218339020500047 doi: 10.1142/S0218339020500047
    [13] X. Li, X. Shao, Flip bifurcation and Neimark–Sacker bifurcation in a discrete predator–prey model with Michaelis–Menten functional response, Electron. Res. Arch., 31 (2023), 37–57. http://doi.org/10.3934/era.2023003 doi: 10.3934/era.2023003
    [14] X. Liu, D. Xiao, Complex dynamic behaviors of a discrete-time predator–prey system, Chaos Soliton. Fract., 32 (2007), 80–94. http://doi.org/10.1016/j.chaos.2005.10.081 doi: 10.1016/j.chaos.2005.10.081
    [15] J. D. Murray, Mathematical biology: II: spatial models and biomedical applications, New York: Springer, 2003. https://doi.org/10.1007/b98869
    [16] R. P. Agarwal, P. J. Y. Wong, Advanced topics in difference equations, Dordrecht: Springer, 1997. http://doi.org/10.1007/978-94-015-8899-7
    [17] A. Q. Khan, I. M. Alsulami, Complicate dynamical analysis of a discrete predator–prey model with a prey refuge, AIMS Math., 8 (2023), 15035–15057. http://doi.org/10.3934/math.2023768 doi: 10.3934/math.2023768
    [18] R. Ahmed, N. Tahir, N. A. Shah, An analysis of the stability and bifurcation of a discrete-time predator–prey model with the slow–fast effect on the predator, Chaos, 34 (2024), 033127. https://doi.org/10.1063/5.0185809 doi: 10.1063/5.0185809
    [19] A. Q. Khan, A. Maqbool, T. D. Alharbi, Bifurcations and chaos control in a discrete rosenzweig–macarthur prey–predator model, Chaos, 34 (2024), 033111. https://doi.org/10.1063/5.0165828 doi: 10.1063/5.0165828
    [20] X. Wang, L. Zanette, X. Zou, Modelling the fear effect in predator–prey interactions, J. Math. Biol., 73 (2016), 1179–1204. http://doi.org/10.1007/s00285-016-0989-1 doi: 10.1007/s00285-016-0989-1
    [21] A. Das, G. P. Samanta, Stochastic prey–predator model with additional food for predator, Physica A, 512 (2018), 121–141. https://doi.org/10.1016/j.physa.2018.08.138 doi: 10.1016/j.physa.2018.08.138
    [22] N. Pettorelli, T. Coulson, S. M. Durant, J.-M. Gaillard, Predation, individual variability and vertebrate population dynamics, Oecologia, 167 (2011), 305–314. https://doi.org/10.1007/s00442-011-2069-y doi: 10.1007/s00442-011-2069-y
    [23] E. L. Preisser, D. I. Bolnick, The many faces of fear: comparing the pathways and impacts of nonconsumptive predator effects on prey populations, PLOS ONE, 3 (2008), e2465. http://doi.org/10.1371/journal.pone.0002465 doi: 10.1371/journal.pone.0002465
    [24] G. F. Gause, N. P. Smaragdova, A. A. Witt, Further studies of interaction between predators and prey, J. Anim. Ecol., 5 (1936), 1–18. https://doi.org/10.2307/1087 doi: 10.2307/1087
    [25] J. M. Smith, Models in ecology, Cambridge: Cambridge University Press, 1974.
    [26] A. Sih, Prey refuges and predator–prey stability, Theor. Popul. Biol., 31 (1987), 1–12. http://doi.org/10.1016/0040-5809(87)90019-0 doi: 10.1016/0040-5809(87)90019-0
    [27] A. R. Ives, A. P. Dobson, Antipredator behavior and the population dynamics of simple predator–prey systems, Am. Nat., 130 (1987), 431–447. https://doi.org/10.1086/284719 doi: 10.1086/284719
    [28] G. D. Ruxton, Short term refuge use and stability of predator–prey models, Theor. Popul. Biol., 47 (1995), 1–17. http://doi.org/10.1006/tpbi.1995.1001 doi: 10.1006/tpbi.1995.1001
    [29] M. E. Hochberg, R. D. Holt, Refuge evolution and the population dynamics of coupled host–parasitoid associations, Evol. Ecol., 9 (1995), 633–661. http://doi.org/10.1007/BF01237660 doi: 10.1007/BF01237660
    [30] E. G. Olivares, R. Ramos-Jiliberto, Dynamic consequences of prey refuges in a simple model system: more prey, fewer predators and enhanced stability, Ecol. Model., 166 (2003), 135–146. http://doi.org/10.1016/S0304-3800(03)00131-5 doi: 10.1016/S0304-3800(03)00131-5
    [31] S. A. A. Hamdallah, A. A. Arafa, Stability analysis of filippov prey–predator model with fear effect and prey refuge, J. Appl. Math. Comput., 70 (2024), 73–102. http://doi.org/10.1007/s12190-023-01934-z doi: 10.1007/s12190-023-01934-z
    [32] S. Pal, P. Panday, N. Pal, A. K. Misra, J. Chattopadhyay, Dynamical behaviors of a constant prey refuge ratio-dependent prey–predator model with allee and fear effects, Int. J. Biomath., 17 (2024), 2350010. http://doi.org/10.1142/S1793524523500109 doi: 10.1142/S1793524523500109
    [33] J.-G. Wang, X.-Y. Meng, L. Lv, J. Li, Stability and bifurcation analysis of a Beddington–DeAngelis prey–predator model with fear effect, prey refuge and harvesting, Int. J. Bifurcat. Chaos, 33 (2023), 2350013. http://doi.org/10.1142/S021812742350013X doi: 10.1142/S021812742350013X
    [34] J. Wang, Y. Cai, S. Fu, W. Wang, The effect of the fear factor on the dynamics of a predator–prey model incorporating the prey refuge, Chaos, 29 (2019), 083109. http://doi.org/ 10.1063/1.5111121 doi: 10.1063/1.5111121
    [35] N. Sk, P. K. Tiwari, S. Pal, M. Martcheva, A delay non-autonomous model for the combined effects of fear, prey refuge and additional food for predator, J. Biol. Dynam., 15 (2021), 588–622. http://doi.org/10.1080/17513758.2021.2001583 doi: 10.1080/17513758.2021.2001583
    [36] F. Rao, Y. Kang, Dynamics of a stochastic prey–predator system with prey refuge, predation fear and its carry-over effects, Chaos Soliton. Fract., 175 (2023), 113935. https://doi.org/10.1016/j.chaos.2023.113935 doi: 10.1016/j.chaos.2023.113935
    [37] B. Sahoo, S. Poria, Diseased prey predator model with general holling type interactions, Appl. Math. Comput., 236 (2014), 83–100. http://doi.org/10.1016/j.amc.2013.10.013 doi: 10.1016/j.amc.2013.10.013
    [38] A. Das, M. Pal, Theoretical analysis of an imprecise prey–predator model with harvesting and optimal control, J. Optim., 2019 (2019), 9512879. http://doi.org/10.1155/2019/9512879 doi: 10.1155/2019/9512879
    [39] G. Lan, Y. Fu, C. Wei, S. Zhang, Dynamical analysis of a ratio-dependent predator–prey model with holling Ⅲ type functional response and nonlinear harvesting in a random environment, Adv. Differ. Equ., 2018 (2018), 198. http://doi.org/10.1186/s13662-018-1625-8 doi: 10.1186/s13662-018-1625-8
    [40] J. Guckenheimer, P. Holmes, K. K. Lee, Nonlinear oscillations, dynamical systems, and bifurcations of vector fields, Phys. Today, 38 (1985), 102–105. https://doi.org/10.1063/1.2814774 doi: 10.1063/1.2814774
    [41] H. Shu, J. Wei, Bifurcation analysis in a discrete BAM network model with delays, J. Differ. Equ. Appl., 17 (2011), 69–84. https://doi.org/10.1080/10236190902953771 doi: 10.1080/10236190902953771
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