Research article

Generalized (αs,ξ,,τ)-Geraghty contractive mappings and common fixed point results in partial b-metric spaces

  • Received: 26 March 2023 Revised: 23 May 2024 Accepted: 27 May 2024 Published: 11 June 2024
  • MSC : 47H09, 47H10, 54H25

  • In this paper, we introduce two new classes of mixed (S,T)-α-admissible mappings and interspersed (S,g,T)-α-admissible mappings and study the sufficient conditions for the existence and uniqueness of a common fixed point of generalized (αs,ξ,,τ)-Geraghty contractive mapping in the framework of partial b-metric spaces. We also provide two examples to show the applicability and validity of our results. Moreover, we present an application to the existence of solutions to an integral equation by means of one of our results.

    Citation: Ying Chang, Hongyan Guan. Generalized (αs,ξ,,τ)-Geraghty contractive mappings and common fixed point results in partial b-metric spaces[J]. AIMS Mathematics, 2024, 9(7): 19299-19331. doi: 10.3934/math.2024940

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  • In this paper, we introduce two new classes of mixed (S,T)-α-admissible mappings and interspersed (S,g,T)-α-admissible mappings and study the sufficient conditions for the existence and uniqueness of a common fixed point of generalized (αs,ξ,,τ)-Geraghty contractive mapping in the framework of partial b-metric spaces. We also provide two examples to show the applicability and validity of our results. Moreover, we present an application to the existence of solutions to an integral equation by means of one of our results.



    The interactions between predators and their prey have always been and remain the main topics in both ecological and mathematical studies. Several researchers have established mathematical models that depend on the interactions between predators and prey. A famous model of predator-prey interactions was developed by Leslie [1,2]. In fact, in most ecosystems, there are many possible food chains that make up a food web, including a number of food chain systems consisting of three species [3]. Aziz-Alaoui therefore created a Leslie-Gower system for the tritrophic population to make the original model more realistic [4]. To improve the performance of the three-species system, Nindjin and Aziz-Alaoui introduced the Holling II functional response to the model [5]. Because biological population systems are inevitably subject to environmental disturbances, we need to introduce random noise to study them. Indeed, many studies have demonstrated that environmental noise causes a continuous variation of some of the key parameters of ecological dynamics, such as growth rates, around some average value [6,7,8,9].

    To simulate the parameters of a changing environment, there are two common methods. One method is to assume that the parameters can be well represented by a linear function of white noise (see example [10]). Gaussian white noise can also simulate complex stochastic dynamical behavior in nonlinear systems (see example [11]). Similarly, we can assume that the growth rate or mortality rate of a population is affected by the linear white noise. This can be written as follows:

    a(t)=ˉa+αdB(t)dt,

    where ˉa, which can be obtained by direct calculation, represents the average value of a over the long term. B(t) is the independent standard Brownian motion defined on a complete probability space {Ω,F,{Ft}t0,P} with the σ filtration {Ft}t0 satisfying the usual conditions [12], and α denotes the noise density of B(t).

    From a biological standpoint, this linear white noise is somewhat flawed. This flaw has been mildly criticized by Zhang et al. [13] and Cai et al. [14]. Next, we will explain this unreasonable part from both biological and mathematical perspectives. We assume that for any time interval [0,t], a(t) is the time average of a(t). According to direct calculation, we obtain:

    a(t):=1tt0a(s)ds=ˉa+αB(t)tN(ˉa,α2t), (1.1)

    where N(,) is a one-dimensional Gaussian distribution. It is evident that the average state a(t) on [0,t] has a variance of α2t, which approaches infinity as t0+. This leads to an unreasonable outcome where the stochastic fluctuation of the growth rate or mortality rate a(t) becomes very large for a small time interval.

    Another method is to assume that the parameters follow a mean-reverting stochastic process, i.e., each parameter obeys a certain stochastic differential equation (SDE). We suppose that the variable lna is affected by an Ornstein-Uhlenbeck process [15,16], and it is described by the following stochastic differential equation:

    d(lna)=α[lnˉalna(t)]dt+σdB(t), (1.2)

    where α is the speed of reversion and σ is the intensity of volatility, respectively. B(t) is the independent standard Brownian motion, which is the same as above. As stated by Mao [17], lna(t) has a unique explicit solution in the form below:

    lna(t)=lnˉa+[lna(0)lnˉa]eαt+σt0eα(ts)dB(s), (1.3)

    which shows that the variable lna(t)N(lnˉa+(lna(0)lnˉa)eαt,σ2(1e2αt)/(2α)). It is noted that

    limt0+E(lna(t))=lna(0),limt0+Var(lna(t))=0,limtE(lna(t))=lnˉa,limtVar(lna(t))=σ22α.

    In reality, many biological systems may have continuously changing environments due to the interaction of numerous variables. Consequently, mean-reverting processes may be more appropriate for modeling parameters. In most biological systems, both population growth and mortality rates are limited and tend to oscillate around an average value over time. Therefore, we use mean reversion processes to model stochastic perturbations to prevent unrealistic values of growth or mortality rates [15].

    In this paper, we are interested in a stochastic three-species food chain Leslie-Gower model with the standard white noise and the Holling-II functional response as

    {dx(t)=x(t)(a1b1x(t)v0y(t)x(t)+d0)dt,dy(t)=y(t)(a2+v1x(t)x(t)+d0v2z(t)y(t)+d2)dt,dz(t)=z(t)(a3v3z(t)y(t)+d3)dt. (1.4)

    All parameters a1,a2,a3,b1,v0,v1,v2,v3,d0,d2,d3 are positive. Toxin-producing phytoplankton (TPP) has a stabilizing role in aquatic systems and can be used as a biological control mechanism [18]. We consider a realistic three-species food chain model consisting of TPP-zooplankton-fish populations. Since the top predator z is assumed to be a sexually reproducing species, the growth of z has two phases: A linear phase and a quadratic phase [19]. In this specific case, the TPP population (prey) of size x serves as the only food for the predatory zooplankton population of size y. This zooplankton population, in turn, serves as a favorite food for the generalist vertebrate predator fish population of size z. The predatory population dies out exponentially in the absence of its prey. And the loss to a predatory population is proportional to the reciprocal of the per capita availability of its most favorite food. a1 and a3 are the intrinsic growth rates of prey population TPP and top predator fish populations, respectively, and a2 is the intrinsic mortality rate of zooplankton in the absence of the only food x. b1 measures the strength of intra-specific competition among the individuals of the prey species x(TPP). The parameters v0,v1,v2, and v3 are the maximum values that the per capita growth rate can attain. For example, the release of the TPP toxin reduces zooplankton growth and leads to massive zooplankton mortality, which is the biological significance of v1. d0 quantifies the extent to which the environment provides protection to the prey TPP and can be thought of as a refuge or a measure of the effectiveness of the prey in evading a predator's attack. d2 describes the value of zooplankton when the average mortality rate of y reaches v22. The mortality of the top predator fish population due to the scarcity of it is favorite food, zooplankton, is measured by d3. The third term on the right-hand side in the first equation of the system (1.4) is obtained by considering the probable effect of the density of the TPP's population on zooplankton's attack rate; the third term on the right-hand side in the second equation of the system (1.4) represents the per capita functional response of the vertebrate predator fish population [20]; the second term on the right-hand side in the third equation of the system (1.4) depicts the loss in the top predator (fish population).

    Based on the above literature, we want to make system (1.4) more realistic. Therefore, we use the Orenstein-Uhlenbeck process to simulate the stochastic dynamic process. We let

    d(lnai)=αi[lnˉailnai]dt+βidBi(t),i=1,2,3,

    where Bi(t) are three independent Brownian motions, αi>0 are the speed of reversion, and βi>0 are the intensity of volatility. We let ri=lnai, i = 1, 2, 3, and then we modify system (1.4) accordingly.

    {dx(t)=x(t)(er1(t)b1x(t)v0y(t)x(t)+d0)dt,dy(t)=y(t)(er2(t)+v1x(t)x(t)+d0v2z(t)y(t)+d2)dt,dz(t)=z(t)(er3(t)v3z(t)y(t)+d3)dt,dr1(t)=α1[ˉr1r1(t)]dt+β1dB1(t),dr2(t)=α2[ˉr2r2(t)]dt+β2dB2(t),dr3(t)=α3[ˉr3r3(t)]dt+β3dB3(t). (1.5)

    We begin by demonstrating that system (1.5) has a unique global solution in Section 3. Subsequently, the ultimate boundedness of the system (1.5) is given in Section 4. The existence of the stationary distribution is then shown in Section 5. Additionally, the extinction of populations is discussed in Section 6. Lastly, in Section 7, we carry out some computer numerical simulations to verify the above theoretical results.

    We define two necessary sets: Gn=(n,n)×(n,n)×(n,n) and Rn+={(x1,,xn)Rn|xk>0,0kn}, |||| is the Euclidean norm. Considering a stochastic differential equation in the following form

    dX(t)=ϕ(t,X(t))dt+nj=1σj(t,X(t))dBj(t). (2.1)

    The existence of a stationary solution to system (1.5) with any initial value is proved by the lemma of Khaminskii [21].

    Lemma 1. (Khasminskii). Let the vectors ψ(s,x),σ1(s,x),,σl(s,x) (s[t0,T],xRm) be continuous functions of (s,x), such that, for some constants M, the following conditions hold in the entire domain of definition:

    |ψ(s,x)ψ(s,y)|+mj=1|σj(s,x)σj(s,y)|M|xy|,|ψ(s,x)|+mj=1|σj(s,x)|M(1+|x|). (2.2)

    Further, there exists a non-negative function V(x) such that

    LV(x)1,xRmH, (2.3)

    where H is a compact subset defined on Rm. Then the Markov process (2.1) has at least one stationary solution X(t), which has a stationary distribution ω() on Rm.

    Theorem 1. For any initial value, (x0,y0,z0,ri0)R3+×R3, there exists a unique solution (x(t),y(t),z(t),ri(t)) of the system (1.5) on t>0, where i=1,2,3, and it will remain in R3+×R3 with probability one.

    Proof. It is easy to verify that Eq (1.5) satisfies the linear growth condition and the local Lipschitz condition. So there exists a locally unique solution (x(t),y(t),z(t),ri(t)) defined on t[0,τe) (see [17]). Where i=1,2,3 and τe is the explosion time, we only need to verify τe=. We let n be sufficiently large so that x0, y0 and z0 are in the interval [n,n], defining the stopping time as

    τn=inf{t[0,τe]:lnx(t)(n,n) or lny(t)(n,n) or lnz(t)(n,n) or ri(t)(n,n)},

    where i=1,2,3. Obviously, τn is monotonically increasing as n. Set τ=limn+τn, obviously, ττe. So, it is only necessary to prove that τ=. If not, then there exists a constant T>0 and ε(0,1) such that P{τT}>ε. Therefore, there exists an integer n1>n0, such that

    P{τnT}ε,nn1. (3.1)

    To simplify the proof, we omit all bracketed t. For any tτn, a non-negative Lyapunov function V0(x,y,z,ri) is constructed as follows:

    V0(x,y,z,ri)=[x+y+z3lnxlnylnz]+r414+r424+r434. (3.2)

    Applying Ito's formula, we have

    LV=(x1)(er1b1xv0yx+d0)+(y1)(er2+v1xx+d0v2zy+d2)+(z1)(er3v3zy+d3)+3i=1(αir3iˉri+32r2iβ2iαir4i)er2+(er1+b1)x+er3zb1x2(v0v1)xyx+d0v2z(y1)y+d2(v1xv0y)x+d0er2y(v3z2v3z)y+d3+3i=1(αir3iˉri+32r2iβ2iαir4i)Π0<,

    where

    Π0=sup(x,y,z,ri)R3+×R3{er2+(er1+b1)x+er3zb1x2(v0v1)xyx+d0v2z(y1)y+d2(v1xv0y)x+d0er2y(v3z2v3z)y+d3+3i=1(αir3iˉri+32r2iβ2iαir4i)}.i=1,2,3.

    After the routine calculation, it implies:

    dVΠ0+β1r31dB1(t)+β2r32dB2(t)+β3r33dB3(t). (3.3)

    Integrating both sides of inequality (3.3) from 0 to τnT and taking expectations, we get

    E[V(x(τnT),y(τnT),z(τnT),ri(τnT))]V(x(0),y(0),z(0),ri(0))+Π0T. (3.4)

    When τnT, let Ωn=τnT. Then P(Ωn)>ε holds inequality (3.1). Notice that for any ωΩn, there exists i such that x(τn,ω),y(τn,ω),z(τn,ω)=n or n.

    According to Eq (3.4), it can be deduced that

    V(x(0),y(0),z(0),ri(0))+Π0TE[1Ωn(ω)V(x(τn,ω),y(τn,ω),z(τn,ω),ri(τn,ω))]εmin{en1n,en1+n,n44},i=1,2,3,

    as n, we have

    >V(x(0),y(0),z(0),ri(0))+Π0T=,i=1,2,3, (3.5)

    and Theorem 1 is proved.

    Ecosystems can only support a limited number of individuals due to finite resources. When a population exceeds this limit, the environment cannot provide enough sources for its survival. This means that population density has an upper bound and will stabilize over time. To show this theoretically, we need to define stochastically ultimate boundedness [22] and apply it to the system (1.5).

    Definition 1. System (1.5) is said to be stochastically ultimately bounded if for any ϵ(0,1), there is a positive constant χ=χ(ω), such that for any initial value (x0,y0,z0,ri0), where i=1,2,3, and the solution of system (1.5) has the property that

    lim suptP{x2+y2+z2>χ}<ε. (4.1)

    Lemma 2. Let θ(0,1), then there is a positive constant M=M(θ), which is independent of the initial value (x0,y0,z0,ri0), where i=1,2,3, such that the solution of system (1.5) has the property that

    lim suptE{|(x,y,z)|θ}<M. (4.2)

    Proof. Define the Lyapunov function W0:R3+×R3R

    W0=xθθ+yθθ+zθθ+3i=1r2θ+2i2θ+2.

    Applying the generalized Ito's formula and mathematical expectation to eλtW0, we obtain

    E[eλtW0(x,y,z,ri)]=E[W0(x(0),y(0),z(0),ri(0))]+t0E{L[eλsW0(x,y,z,ri)]}ds,i=1,2,3, (4.3)

    where λ=θmin{α1,α2,α3}. Note that

    LW0=eλ[λθxθ+λθyθ+λθzθ+λ2θ+23i=1r2θ+2i+xθ(er1b1xv0yx+d0)+yθ(er2+v1xx+d0v2zy+d2)+zθ(er3v3zy+d3)+3i=1(αir2θ+1iˉriαir2θ+2i+2θ+12r2θiβ2i)]eλ{(α1+er1)xθ+(α3+er3)zθ(er2α2)yθb1xθ+1xy(v0xθ1v1yθ1)x+d0v2zyθy+d2v3zθ+1y+d3+3i=1[αir2θ+1iˉriαi2r2θ+2i+(θ+1)r2θiβ2i]}eλκ(θ),

    where

    κ(θ):=sup(x,y,z,ri)R3+×R3{(α1+er1)xθ+(α3+er3)zθ(er2α2)yθb1xθ+1xy(v0xθ1v1yθ1)x+d0v2zyθy+d2v3zθ+1y+d3+3i=1[αir2θ+1iˉriαi2r2θ+2i+(θ+1)r2θiβ2i]}.i=1,2,3.

    Thus, we can obtain

    E[eλtW0(x,y,z,ri)]E[W0(x(0),y(0),z(0),ri(0))]+κ(θ)(eλt1)λ.

    Then we have

    lim suptE[|(x,y,z)|θ]3θ2θlim suptE[W0(x,y,z,ri)]3θ2θlimteλtE[W0(x(0),y(0),z(0),ri(0))]+3θ2θlimtκ(θ)(eλt1)λeλt3θ2θκ(θ)λ, (4.4)

    where i=1,2,3, and then the result (4.2) holds by setting M(θ)=3θ2θκ(θ)λ.

    Theorem 2. The equation of system (1.5) is stochastically ultimately bounded.

    Proof. By Lemma 2, there exists M such that

    lim suptE|(x,y,z)|<M.

    Now, for any ϵ>0, let χ=3κ(0.5)24ϵ2λ2. Then, using Chebyshev's inequality, we can conclude that

    P(|(x,y,z)|>χ)E[|(x,y,z)|]χ.

    Hence,

    lim suptP(|(x,y,z)|>χ)MMϵ=ϵ.

    In biology, one of the primary goals is to comprehend the long-term behavior of systems. In this part of the paper, we demonstrate that system (1.5) possesses a stationary distribution that can be used to make long-term predictions for the system under the influence of stochastic perturbations. We also derive the sufficient conditions that guarantee the existence of such a stationary distribution for the system (1.5).

    Definition 2. Define M0 to be a natural number that satisfies the following conditions

    M0(max{2d20b1d20b21+v21+2d0b1v1,2+Π13i=1¯ri},min{d04v0,14(v2d2+v3d3),2d20b1d20b21+v21+2d0b1v1}),

    where

    Π1=sup(x,y,z,ri)R3+×R3{|er1|x+|er3|z+y2+z2b1x22+|er2|y+(er1+er2er3+3i=1ri)M0(v0v1)xyx+d0v2zyy+d2v3z2y+d3+3i=1(αiˉrir3i+32r2iβ2i12αir4i)},

    where i=1,2,3.

    Theorem 3. For any initial value (x(0),y(0),z(0),ri(0)), where i=1,2,3, the system (1.5) has a stationary distribution with the definition of M0.

    Proof. We divide the relevant proof into the following two steps.

    Step 1. Define the Lyapunouv Function V1:R3+×R3R

    V1=M0[lnxlnylnz3i=1riαi]+x+y+z+3i=1r4i4. (5.1)

    Applying Ito's formula to the definition of M0, we obtain

    LV1=(xM0)(er1b1xv0yx+d0)+(yM0)(er2+v1xx+d0v2zy+d2)+(zM0)(er3v3zy+d3)+3i=1(M0ˉri+M0ri+αiˉrir3i+32r2iβ2iαir4i)M03i=1ˉri+|er1|x+|er3|z+y2+z2b1x22+|er2|y+(er1+er2er3+3i=1ri)M0(v0v1)xyx+d0v2zyy+d2v3z2y+d3+3i=1(αiˉrir3i+32r2iβ2i12αir4i)+M0(b1x+v1xx+d0+v2zy+d2+v3zy+d3+v0yx+d0)b1x22y2z23i=112αir4i2+M0(b1x+v1xx+d0+v2zy+d2+v3zy+d3+v0yx+d0)b1x22y2z23i=112αir4i.

    Noting that the function V1 tends to as x, y, or z approaches the boundary of R+ or as ||(x,y,z,ri)||. Thus, there exists a point (x0,y0,z0,r0i) in the interior of R3+×R3, at which the value of the function will be minimized. A non-negative function V2(x,y,z,ri) can be constructed as

    V2(x,y,z,ri)=V1(x,y,z,ri)V1(x0,y0,z0,r0i).i=1,2,3.

    According to Ito's formula, we have

    LV22+M0(b1x+v1xx+d0+v2zy+d2+v3zy+d3+v0yx+d0)b1x22y2z23i=112αir4i. (5.2)

    Step 2. Considering a closed set Hε in the form

    Hε={(x,y,z,ri)R3+×R3x[ε2,1ε2],y[ε4,1ε4],z[ε4,1ε4],ri[1ε,1ε]},

    and we define

    Π2=sup(x,y,z,ri)R3+×R3{2+M0(b1x+v1xx+d0+v2zy+d2+v3zy+d3+v0yx+d0)b1x24y4z43i=114αir4i}.

    Let ε(0,1) be a sufficiently small number, such that the following inequalities hold:

    2+Π2min{b,1,αi}4(1ε)41,i=1,2,3. (5.3)
    2+(M0b1+v1M0d0)ε21. (5.4)
    2+12M20b1+v21M202d20b1+v1M20d0+M0v0d0ε41. (5.5)
    2+12M20b1+v21M202d20b1+v1M20d20+M0(v2d2+v3d3)ε41. (5.6)

    We verify LV2(x,y,z,ri)1 for any (x,y,z,ri)(R3+×R3)Hε. Noting that (R3+×R3)Hε=9k=1Hck,ε, where

    Hc1,ε={(x,y,z,ri)R3+×R3x(1ε2,)},Hc2,ε={(x,y,z,ri)R3+×R3y(1ε4,)},Hc3,ε={(x,y,z,ri)R3+×R3z(1ε4,)},Hcj,ε={(x,y,z,ri)R3+×R3|ri|(1ε,)},j=4,5,6; i=1,2,3,Hc7,ε={(x,y,z,ri)R3+×R3x(0,ε2)},Hc8,ε={(x,y,z,ri)R3+×R3y(0,ε4)},Hc9,ε={(x,y,z,ri)R3+×R3z(0,ε4)}.

    Case 1. If (x,y,z,ri) is located in the set defined by Hc1,ε, then one can obtain the corresponding results by combining Eqs (5.2) and (5.3).

    LV22+Π2b1x242+Π2b14(1ε)41.

    Case 2. If (x,y,z,ri) is located in the set defined by Hc2,ε and Hc3,ε, consequently, from (5.2) and (5.3), we can obtain the relevant result.

    LV22+Π2min{y,z}42+Π214(1ε)41.

    Case 3. For any (x,y,z,ri), which is located in the set defined in Hcj,ε, according to Eqs (5.2) and (5.3), we obtain

    LV22+Π2αir4i42+Π2αi4(1ε)41,j=4,5,6 and i=1,2,3.

    Case 4. If (x,y,z,ri)Hc7,ε, it follows from (5.2) and (5.4) that

    LV22+M0b1x+v1M0d0x(14M0v0d0)y(14v2M0d2v3M0d3)z2+M0b1x+v1M0d0x2+(M0b1+v1M0d0)ε21.

    Case 5. If (x,y,z,ri) lie within the set demarcated by Hc8,ε, the relevant conclusion is deduced through (5.2) and (5.5).

    LV22+12M20b1+v21M202d20b1+v1M20d0+M0v0d0y(14v2M0d2v3M2d3)z2+12M20b1+v21M202d20b1+v1M20d0+M0v0d0ε41.

    Case 6. In the event that (x,y,z,ri) is situated within the set defined by Hc9,ε, the associated findings can be calculated by (5.2) and (5.6).

    LV22+12M20b1+v21M202d20b1+v1M20d0+M0(v2d2+v3d3)z(14M0v0d0)y2+12M20b1+v21M202d20b1+v1M20d0+M0(v2d2+v3d3)ε41.

    Defining

    A=12M20b1+v21M202d20b1+v1M20d0,

    In summary, we can obtain that there exists a sufficiently small constant ε such that LV2(x,y,z,ri)1 for any (x,y,z,ri)(R3+×R3)Hε, where ε satisfies that

    εmin{1,1M0b1+v1M0d0,4(1A)d0M0v0,41AM0(v2d2+v3d3)},

    for any Π2<1. And for any Π2>1, we set

    εmin{1,4min{1,b1,αi}4(Π21)},i=1,2,3.

    Theorem 4. We define

    φi(t)=aiβ2i4αi+β2i4αie2αit,ˉφi=limt+t1t0φi(s)ds=aiβ2i4αi,i=1,2,3.

    When φ1<0,φ2<0,φ3<0, then x(t),y(t),z(t) are extinct.

    Proof. From Eq (1.1) and the definition of the Ornstein-Uhlenbeck process, we have

    ai(t)=ˉai+[ai(0)ˉai]eαit+βit0eαi(ts)dBi(s),i=1,2,3. (6.1)

    Equation (6.1) clearly indicates that a(t) follows the Gaussian distribution N(E[ai(t)],VAR[ai(t)]) on [0,t]. It is easy to derive that E[ai(t)]=ˉai+[ai(0)ˉai]eαit and VAR[ai(t)]=β2i2αi(1e2αit). Therefore, we find that the term βit0eαi(ts)dBi(s) follows the normal distribution N(0,β2i2αi(1e2αit)). Then, it is equivalent to βi2αi1e2αitdBi(t)dt. Let σi(t)=βi2αi1e2αit, where Bi(t) stands for a standard Brownian motion. Thus, (6.1) can be almost surely written as follows: [23]

    ai(t)=ai+(ai(0)ai)eαit+σi(t)dBi(t)dt,i=1,2,3. (6.2)

    And then we modify system (1.5) accordingly.

    {dx(t)=x(t)(a1+[a1(0)a1]eα1tb1x(t)v0y(t)x(t)+d0)dt+σ1x(t)dB1(t),dy(t)=y(t)(a2+[a2(0)a2]eα2t+v1x(t)x(t)+d0v2z(t)y(t)+d2)dt+σ2y(t)dB2(t),dz(t)=z(t)(a3+[a3(0)a3]eα3tv3z(t)y(t)+d3)dt+σ3z(t)dB3(t). (6.3)

    Applying Itô formula to lnx(t),lny(t),lnz(t), we can get

    dlnx(t)=(φ1+[a1(0)a1]eα1tb1x(t)v0y(t)x(t)+d0)dt+σ1dB1(t),dlny(t)=(φ2+[a2(0)a2]eα2t+v1x(t)x(t)+d0v2z(t)y(t)+d2)dt+σ2dB2(t),dlnz(t)=(φ3+[a3(0)a3]eα3tv3z(t)y(t)+d3)dt+σ3dB3(t).

    Integrating from 0 to t, we have

    lnx(t)=lnx(0)+t0φ1(s)dsa1(0)a1α1(eα1t1)b1t0x(s)dsv0t0y(s)x(s)+d0ds+t0σ1(s)dB1(s), (6.4)
    lny(t)=lny(0)+t0φ2(s)dsa2(0)a2α2(eα2t1)+v1t0x(s)x(s)+d0dsv2t0z(s)y(s)+d2ds+t0σ2(s)dB2(s), (6.5)
    lnz(t)=lnz(0)+t0φ3(s)dsa3(0)a3α3(eα3t1)+v3t0z(s)y(s)+d3ds+t0σ3(s)dB3(s). (6.6)

    From Eq (6.4), we obtain

    t1lnx(t)x(0)ˉφ1+ε1+t1t0σ1(s)dB1(s)a1(0)a1tα1(eα1t1). (6.7)

    Then, according to the strong law of large numbers and the definition of the Ornstein-Uhlenbeck process, if ˉφ1+ε1<0, we have limt+x(t)=0. Similarly, when ˉφ2<0 and ˉφ3<0,limt+y(t)=0 and limt+z(t)=0. Theorem 4 is proved.

    So far, we have proved that there is a unique global solution for the system (1.5), the solution to the system (1.5) is stochastically ultimately bounded, the system (1.5) has a stationary distribution, and the system (1.5) has an extinction. Next, we will perform some computer simulations to verify our conclusions.

    In this section, we will use numerical simulations to verify our conclusions. Using the Milstein higher order method, we obtain the discretization equation for system (1.5). The corresponding discretization equation is as follows:

    {xj+1=xj+xj(aj1b1xjv0yjxj+d0)Δt,yj+1=yj+yj(aj2+v1xjxj+d0v2zjyj+d2)Δt,zj+1=zj+zj(aj3v3zjyj+d3)Δt,aj+11=aj1+α1(ˉa1aj1)Δt+β1Δtηj,aj+12=aj2+α2(ˉa2aj2)Δt+β2Δtξj,aj+13=aj3+α3(ˉa3aj3)Δt+β3Δtζj, (7.1)

    where Δt>0 denotes the time increment, and ηj, ξj, and ζj are three independent stochastic variables that follow the standard Gaussian distribution N(0,1). Besides, (xj,yj,zj,aji) is the corresponding value of the jth iteration of the discretization Eq (7.1), where i=1,2,3 and j=1,2,. We will use some different combinations of biological parameters in Table 1.

    Table 1.  List of biological parameters in the system (1.5).
    Parameter Description
    ˉa1 Average growth rate of Prey
    ˉa2 Average mortality of Predator in the lack of Prey
    ˉa3 Average growth rate of Top Predator
    b1 Intraspecific competition coefficient of prey
    v0 Maximum average rate of Prey reduction
    v1 Maximum average rate of Predator reduction with the effect of Prey
    v2 Maximum average rate of Predator reduction with the effect of Top Predator
    v3 Maximum average rate of Top Predator reduction
    d0 A factor measuring the degree of protection to Prey and Predator
    d2 The value of Predator when the average mortality rate of Predator reaches v22
    d3 Mortality of Top Predator due to scarcity of its favorite food, Predator
    α1 The reversion speed of a1
    α2 The reversion speed of a2
    α3 The reversion speed of a3
    β1 The intensity of volatility of a1
    β2 The intensity of volatility of a2
    β3 The intensity of volatility of a3

     | Show Table
    DownLoad: CSV

    Based on the real datasets [4,18,24,25,26,27], we use the TPP-zooplankton-fish population food chain system as an example. After determining the range of values for each parameter of the system (1.5) (e.g., the values of the intensity of volatility βi(i = 1, 2, 3) belong to [0, 5] [13,14,28]), we fixed some of the parameter values by making reasonable assumptions in combination in Table 2.

    Table 2.  Several combinations of biological parameters of the system (1.5) in Table 1.
    Parameter Description
    (A1) ˉa1=0.1, ˉa2=0.01, ˉa3=0.25, b1=0.2, v0=1, v1=3, v2=2, v3=1, d0=2
    d2=1, d3=1, α1=0.3, α2=0.2, α3=0.4, β1=0.01, β2=0.01, β3=0.01
    (A2) ˉa1=0.42, ˉa2=0.04, ˉa3=0.10, b1=2, v0=1, v1=3, v2=2, v3=1, d0=2
    d2=1, d3=1, α1=0.3, α2=0.2, α3=0.4, β1=0.01, β2=0.01, β3=0.01
    (A3) ˉa1=0.5, ˉa2=0.05, ˉa3=0.12, b1=2, v0=1, v1=3, v2=2, v3=1, d0=2
    d2=1, d3=1, α1=0.3, α2=0.2, α3=0.4, β1=0.02, β2=0.01, β3=0.03
    (A4) φ1=0.01, φ2=0.01, φ3=0.1, b1=2, v0=1, v1=3, v2=2, v3=1, d0=2
    d2=1, d3=1, α1=0.3, α2=0.2, α3=0.4, β1=0.02, β2=0.01, β3=0.03
    (A5) φ1=0.05, φ2=0.05, φ3=0.12, b1=2, v0=1, v1=3, v2=2, v3=1, d0=2
    d2=1, d3=1, α1=0.3, α2=0.2, α3=0.4, β1=0.02, β2=0.01, β3=0.03
    (A6) φ1=0.1, φ2=0.02, φ3=0.25, b1=0.5, v0=1, v1=0.5, v2=0.5, v3=1, d0=3
    d2=1, d3=1, α1=0.3, α2=0.2, α3=0.4, β1=0.03, β2=0.01, β3=0.01

     | Show Table
    DownLoad: CSV

    Example 1. In Table 1, we choose the combination A1-A3 as the value of biological parameters of the system (1.5). Clearly, it follows from Theorem 1 that the system (1.5) is stochastically ultimately bounded. By choosing the total number of iterations, Tmax=2000. Then, we obtain Figure 1.

    Figure 1.  Computer simulations of the change rate and the population of prey, predator, and top predator of system (1.5). It can be intuitively seen that the images confirm Theorem 1: System (1.5) has a unique global solution. The relevant parameters are determined by the combination (A1)(A3).

    Remark 1. It can be seen from Figure 1 that the growth rates are disturbed around the given mean value, which reflects the characteristics of the mean regression of the Ornstein-Uhlenbeck process. At the same time, it can be seen that different coefficient combinations have different solutions, and the solutions are all existing and unique. Thus, the conclusion of Theorem 1 can be verified.

    Example 2. In Table 1, we choose the combination A1-A3 as the value of the biological parameters of the system (1.5). Obviously, it follows from Lemma 2 that the θth order moments of the solutions of the system (1.5) are bounded, and the solutions of the system (1.5) are ultimately bounded. By choosing the total number of iterations, Tmax=2000. Then, we obtain Figure 2.

    Figure 2.  Computer simulations the θth order of solutions of system (1.5), then found an upper bound M=0.67. The system (1.5) is ultimately bounded, and the probability is less than 1. The relevant parameters are determined by the combination (A1)(A3).

    Remark 2. It can be seen from Figure 2 that the expected value of the above three coefficient combinations (A1,A2,andA3) is less than an upper bound. With the increase in time t, the probability P is gradually stable and greater than a constant, which means lim suptP(|(x,y,z)|>χ)ϵ. which verifies the conclusion of Theorem 2.

    Example 3. Based on Table 1, we select the A3 combination as the biological parameters of the system (1.5). By Theorem 3, we know that the distribution of the system (1.5) exists. We set the number of iterations to T=2000. From Figure 3, we can see the system (1.5) has a stationary distribution.

    Figure 3.  Computer simulations of the stationary system (1.5). The relevant parameters are determined by the combination (A3).

    Example 4. In Table 1, we choose the combination A4A6 as the value of the biological parameters of the system (1.5). As proven in Theorem 4, the system (1.5) possesses extinction. By choosing the total number of iterations, Tmax=2000. Then, we obtain Figure 4.

    Figure 4.  The computer simulated the extinction of the system (1.5). When φ1<0, the prey goes extinct; when φ2<0, the predator goes extinct; and when φ3<0, the top predator goes extinct. All simulation parameters were selected from combinations (A4)(A6).

    This paper focuses on the mathematical properties of the three-species food chain stochastic Leslie-Gower model with the Ornstein-Uhlenbeck process. On the basis of previous research on food web models, we consider that the real dynamic environment may be affected by random factors such as seasonal changes and natural disasters, and then random noise is introduced. Compared to using standard white noise methods, simulations with Ornstein-Uhlenbeck processes are more realistic and reliable. Therefore, we demonstrate the feasibility of studying a more complex Leslie-Gower model with the Ornstein-Uhlenbeck process. The main contributions of this paper are as follows:

    We investigate the good properties of stochastic mathematical models with the Ornstein-Uhlenbeck process, providing a new approach to studying the stochastic properties of the Leslie-Gower systems. We derive the conditions for the existence and uniqueness of the global solution under stochastic disturbance, as well as the conditions for the stochastic ultimate boundedness, the existence of a stationary distribution, and the extinction.

    However, there are still many open questions that warrant further research. In this paper, we only study the effect of stochastic noise driven by the Ornstein-Uhlenbeck process on the three-species food chain Leslie-Gower model, while the study of Markov transformation and Lévy jump is still scarce, which is one of the future directions. Most existing studies assume that the system parameters are constant or time-invariant, or they rarely consider the case of periodic coefficients. However, in reality, the ecological environment that supports populations often exhibits cyclical patterns. Consequently, it would be more realistic to study stochastic population systems with periodic coefficients.

    Ruyue Hu and Xiaohui Ai: Conceptualization, Writing-original draft; Yifan Wu: Software, Visualization, Writing-original draft; Chi Han: Software, Visualization. All authors have read and approved the final version of the manuscript for publication.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    Thanks to all anonymous reviewers for their valuable comments and helpful suggestions that made the article better.

    This study was supported by the National Natural Science Foundation of China under Grant (No.11401085), the Central University Basic Research Grant(2572021DJ04), the Heilongjiang Postdoctoral Grant(LBH-Q21059), and the Innovation and Entrepreneurship Training Program for University Students(202210225154).

    The authors declare there is no conflicts of interest.



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