
In this work, a Leslie-Gower model with a weak Allee effect on the prey and a fear effect on the predator is proposed. By using qualitative analyses, the local stability of the coexisting equilibrium and the existence of Turing instable are discussed. By analyzing the distribution of eigenvalues, the existence of a Hopf bifurcation is studied by using the gestation time delay as a bifurcation parameter. By utilizing the normal form method and the center manifold theorem, we calculate the direction of the Hopf bifurcation and the stability of bifurcating periodic solutions. We indicate that both the weak Allee effect on the prey and fear effect on the predator have an important impact on the dynamical behaviour of the new Leslie-Gower model. We also verify the obtained results by some numerical examples.
Citation: Fatao Wang, Ruizhi Yang, Yining Xie, Jing Zhao. Hopf bifurcation in a delayed reaction diffusion predator-prey model with weak Allee effect on prey and fear effect on predator[J]. AIMS Mathematics, 2023, 8(8): 17719-17743. doi: 10.3934/math.2023905
[1] | Yayun Fu, Mengyue Shi . A conservative exponential integrators method for fractional conservative differential equations. AIMS Mathematics, 2023, 8(8): 19067-19082. doi: 10.3934/math.2023973 |
[2] | Yong-Chao Zhang . Least energy solutions to a class of nonlocal Schrödinger equations. AIMS Mathematics, 2024, 9(8): 20763-20772. doi: 10.3934/math.20241009 |
[3] | Tingting Ma, Yuehua He . An efficient linearly-implicit energy-preserving scheme with fast solver for the fractional nonlinear wave equation. AIMS Mathematics, 2023, 8(11): 26574-26589. doi: 10.3934/math.20231358 |
[4] | Karmina K. Ali, Resat Yilmazer . Discrete fractional solutions to the effective mass Schrödinger equation by mean of nabla operator. AIMS Mathematics, 2020, 5(2): 894-903. doi: 10.3934/math.2020061 |
[5] | Erdal Bas, Ramazan Ozarslan . Theory of discrete fractional Sturm–Liouville equations and visual results. AIMS Mathematics, 2019, 4(3): 593-612. doi: 10.3934/math.2019.3.593 |
[6] | Dengfeng Lu, Shuwei Dai . On a class of three coupled fractional Schrödinger systems with general nonlinearities. AIMS Mathematics, 2023, 8(7): 17142-17153. doi: 10.3934/math.2023875 |
[7] | Mubashir Qayyum, Efaza Ahmad, Hijaz Ahmad, Bandar Almohsen . New solutions of time-space fractional coupled Schrödinger systems. AIMS Mathematics, 2023, 8(11): 27033-27051. doi: 10.3934/math.20231383 |
[8] | Xiaojun Zhou, Yue Dai . A spectral collocation method for the coupled system of nonlinear fractional differential equations. AIMS Mathematics, 2022, 7(4): 5670-5689. doi: 10.3934/math.2022314 |
[9] | Zunyuan Hu, Can Li, Shimin Guo . Fast finite difference/Legendre spectral collocation approximations for a tempered time-fractional diffusion equation. AIMS Mathematics, 2024, 9(12): 34647-34673. doi: 10.3934/math.20241650 |
[10] | Xiao-Yu Li, Yu-Lan Wang, Zhi-Yuan Li . Numerical simulation for the fractional-in-space Ginzburg-Landau equation using Fourier spectral method. AIMS Mathematics, 2023, 8(1): 2407-2418. doi: 10.3934/math.2023124 |
In this work, a Leslie-Gower model with a weak Allee effect on the prey and a fear effect on the predator is proposed. By using qualitative analyses, the local stability of the coexisting equilibrium and the existence of Turing instable are discussed. By analyzing the distribution of eigenvalues, the existence of a Hopf bifurcation is studied by using the gestation time delay as a bifurcation parameter. By utilizing the normal form method and the center manifold theorem, we calculate the direction of the Hopf bifurcation and the stability of bifurcating periodic solutions. We indicate that both the weak Allee effect on the prey and fear effect on the predator have an important impact on the dynamical behaviour of the new Leslie-Gower model. We also verify the obtained results by some numerical examples.
Fractional calculus is a popular subject because of having a lot of application areas of theoretical and applied sciences, like engineering, physics, biology, etc. Discrete fractional calculus is more recent area than fractional calculus and it was first defined by Diaz–Osler [1], Miller–Ross [2] and Gray–Zhang [3]. More recently, the theory of discrete fractional calculus have begun to develop rapidly with Goodrich–Peterson [4], Baleanu et al. [5,6], Ahrendt et al. [7], Atici–Eloe [8,9], Anastassiou [10], Abdeljawad et al. [11,12,13,14,15,16], Hein et al. [17] and Cheng et al. [18], Mozyrska [19] and so forth [20,21,22,23,24,25].
Fractional Sturm–Liouville differential operators have been studied by Bas et al. [26,27], Klimek et al.[28], Dehghan et al. [29]. Besides that, Sturm–Liouville differential and difference operators were studied by [30,31,32,33]. In this study, we define DFHA operators and prove the self–adjointness of DFHA operator, some spectral properties of the operator.
More recently, Almeida et al. [34] have studied discrete and continuous fractional Sturm–Liouville operators, Bas–Ozarslan [35] have shown the self–adjointness of discrete fractional Sturm–Liouville operators and proved some spectral properties of the problem.
Sturm–Liouville equation having hydrogen atom potential is defined as follows
d2Rdr2+ardRdr−ℓ(ℓ+1)r2R+(E+ar)R=0(0<r<∞). |
In quantum mechanics, the study of the energy levels of the hydrogen atom leads to this equation. Where R is the distance from the mass center to the origin, ℓ is a positive integer, a is real number E is energy constant and r is the distance between the nucleus and the electron.
The hydrogen atom is a two–particle system and it composes of an electron and a proton. Interior motion of two particles around the center of mass corresponds to the movement of a single particle by a reduced mass. The distance between the proton and the electron is identified r and r is given by the orientation of the vector pointing from the proton to the electron. Hydrogen atom equation is defined as Schrödinger equation in spherical coordinates and in consequence of some transformations, this equation is defined as
y′′+(λ−l(l+1)x2+2x−q(x))y=0. |
Spectral theory of hydrogen atom equation is studied by [39,40,41]. Besides that, we can observe that hydrogen atom differential equation has series solution as follows ([39], p.268)
y(x)=a0xl+1{1−k−l−11!(2l+2).2xk+(k−l−1)(k−l−2)2!(2l+2)(2l+3)(2xk)2+…+(−1)n(k−l−1)(k−l−2)…3.2.1(k−1)!(2l+2)(2l+3)…(2l+n)(2xk)n},k=1,2,… | (1.1) |
Recently, Bohner and Cuchta [36,37] studied some special integer order discrete functions, like Laguerre, Hermite, Bessel and especially Cuchta mentioned the difficulty in obtaining series solution of discrete special functions in his dissertation ([38], p.100). In this regard, finding series solution of DFHA equations is an open problem and has some difficulties in the current situation. For this reason, we study to obtain solutions of DFHA eq.s in a different way with representation of solutions.
In this study, we investigate DFHA equation in Riemann–Liouville and Grü nwald–Letnikov sense. The aim of this study is to contribute to the spectral theory of DFHA operator and behaviors of eigenfunctions and also to obtain the solution of DFHA equation.
We investigate DFHA equation in three different ways;
i) (nabla left and right) Riemann–Liouville (R–L)sense,
L1x(t)=∇μa(b∇μx(t))+(l(l+1)t2−2t+q(t))x(t)=λx(t), 0<μ<1, |
ii) (delta left and right) Grünwald–Letnikov (G–L) sense,
L2x(t)=Δμ−(Δμ+x(t))+(l(l+1)t2−2t+q(t))x(t)=λx(t), 0<μ<1, |
iii) (nabla left) Riemann–Liouville (R–L)sense,
L3x(t)=∇μa(∇μax(t))+(l(l+1)t2−2t+q(t))x(t)=λx(t), 0<μ<1. |
Definition 2.1. [42] Falling and rising factorial functions are defined as follows respectively
tα_=Γ(t+1)Γ(t−α+1), | (2.1) |
t¯α=Γ(t+α)Γ(t), | (2.2) |
where Γ is the gamma function, α∈R.
Remark 2.1. Delta and nabla operators hold the following properties
Δtα_=αtα−1_,∇t¯α=αt¯α−1. | (2.3) |
Definition 2.2. [2,8,11] Nabla fractional sum operators are given as below,
(i) The left fractional sum of order μ>0 is defined by
∇−μax(t)=1Γ(μ)t∑s=a+1(t−ρ(s))¯μ−1x(s), t∈Na+1, | (2.4) |
(ii) The right fractional sum of order μ>0 is defined by
b∇−μx(t)=1Γ(μ)b−1∑s=t(s−ρ(t))¯μ−1x(s), t∈ b−1N, | (2.5) |
where ρ(t)=t−1 is called backward jump operators, Na={a,a+1,...}, bN={b,b−1,...}.
Definition 2.3. [12,14] Nabla fractional difference operators are as follows,
(i) The left fractional difference of order μ>0 is defined by
∇μax(t)=∇n∇−(n−μ)ax(t)=∇nΓ(n−μ)t∑s=a+1(t−ρ(s))¯n−μ−1x(s), t∈Na+1, | (2.6) |
(ii) The right fractional difference of order μ>0 is defined by
b∇μx(t)=(−1)n∇n∇−(n−μ)ax(t)=(−1)nΔnΓ(n−μ)b−1∑s=t(s−ρ(t))¯n−μ−1x(s), t∈ b−1N. | (2.7) |
Fractional differences in (2.6−2.7) are called the Riemann–Liouville (R–L) definition of the μ-th order nabla fractional difference.
Definition 2.4. [1,18] Fractional difference operators are given as follows
(i) The delta left fractional difference of order μ, 0<μ≤1, is defined by
Δμ−x(t)=1hμt∑s=0(−1)sμ(μ−1)...(μ−s+1)s!x(t−s), t=1,...,N. | (2.8) |
(ii) The delta right fractional difference of order μ, 0<μ≤1, is defined by
Δμ+x(t)=1hμN−t∑s=0(−1)sμ(μ−1)...(μ−s+1)s!x(t+s), t=0,..,N−1, | (2.9) |
fractional differences in (2.8−2.9) are called the Grünwald–Letnikov (G–L) definition of the μ-th order delta fractional difference.
Definition 2.5 [14] Integration by parts formula for R–L nabla fractional difference operator is defined by, u is defined on bN and v is defined on Na,
b−1∑s=a+1u(s)∇μav(s)=b−1∑s=a+1v(s)b∇μu(s). | (2.10) |
Definition 2.6. [34] Integration by parts formula for G–L delta fractional difference operator is defined by, u, v is defined on {0,1,...,n}, then
n∑s=0u(s)Δμ−v(s)=n∑s=0v(s)Δμ+u(s). | (2.11) |
Definition 2.7. [17] f:Na→R, s∈ℜ, Laplace transform is defined as follows,
La{f}(s)=∞∑k=1(1−s)k−1f(a+k), |
where ℜ=C∖{1} and ℜ is called the set of regressive (complex) functions.
Definition 2.8. [17] Let f,g:Na→R, all t∈Na+1, convolution of f and g is defined as follows
(f∗g)(t)=t∑s=a+1f(t−ρ(s)+a)g(s), |
where ρ(s) is the backward jump function defined in [42] as
ρ(s)=s−1. |
Theorem 2.1. [17] f,g:Na→R, convolution theorem is expressed as follows,
La{f∗g}(s)=La{f}La{g}(s). |
Lemma 2.1. [17] f:Na→R, the following property is valid,
La+1{f}(s)=11−sLa{f}(s)−11−sf(a+1). |
Theorem 2.2. [17] f:Na→R, 0<μ<1, Laplace transform of nabla fractional difference
La+1{∇μaf}(s)=sμLa+1{f}(s)−1−sμ1−sf(a+1),t∈Na+1. |
Definition 2.9. [17] For |p|<1, α>0, β∈R and t∈Na, Mittag–Leffler function is defined by
Ep,α,β(t,a)=∞∑k=0pk(t−a)¯αk+βΓ(αk+β+1). |
Theorem 2.3. [17] For |p|<1, α>0, β∈R, |1−s|<1 and |s|α>p, Laplace transform of Mittag–Leffler function is as follows,
La+1{Ep,α,β(.,a)}(s)=sα−β−1sα−p. |
Let us consider equations in three different forms;
i) L1 DFHA operator L1 is defined in (nabla left and right) R–L sense,
L1x(t)=∇μa(p(t)b∇μx(t))+(l(l+1)t2−2t+q(t))x(t)=λx(t), 0<μ<1, | (3.1) |
where l is a positive integer or zero, q(t)+2t−l(l+1)t2 are named potential function., λ is the spectral parameter, t∈[a+1,b−1], x(t)∈l2[a+1,b−1], a>0.
ii) L2 DFHA operator L2 is defined in (delta left and right) G–L sense,
L2x(t)=Δμ−(p(t)Δμ+x(t))+(l(l+1)t2−2t+q(t))x(t)=λx(t), 0<μ<1, | (3.2) |
where p,q,l,λ is as defined above, t∈[1,n], x(t)∈l2[0,n].
iii) L3 DFHA operator L3 is defined in (nabla left) R–L sense,
L3x(t)=∇μa(∇μax(t))+(l(l+1)t2−2t+q(t))x(t)=λx(t), 0<μ<1, | (3.3) |
p,q,l,λ is as defined above, t∈[a+1,b−1], a>0.
Theorem 3.1. DFHA operator L1 is self–adjoint.
Proof.
u(t)L1v(t)=u(t)∇μa(p(t)b∇μv(t))+u(t)(l(l+1)t2−2t+q(t))v(t), | (3.4) |
v(t)L1u(t)=v(t)∇μa(p(t)b∇μu(t))+v(t)(l(l+1)t2−2t+q(t))u(t). | (3.5) |
Subtracting (16−17) from each other
u(t)L1v(t)−v(t)L1u(t)=u(t)∇μa(p(t)b∇μv(t))−v(t)∇μa(p(t)b∇μu(t)) |
and applying definite sum operator to both side of the last equality, we have
b−1∑s=a+1(u(s)L1v(s)−v(s)L1u(s))=b−1∑s=a+1u(s)∇μa(p(s)b∇μv(s))−b−1∑s=a+1v(s)∇μa(p(s)b∇μu(s)). | (3.6) |
Applying the integration by parts formula (2.10) to right hand side of (18), we have
b−1∑s=a+1(u(s)L1v(s)−v(s)L1u(s))=b−1∑s=a+1p(s)b∇μv(s)b∇μu(s)−b−1∑s=a+1p(s)b∇μu(s)b∇μv(s)=0, |
⟨L1u,v⟩=⟨u,L1v⟩. |
The proof completes.
Theorem 3.2. Eigenfunctions, corresponding to distinct eigenvalues, of the equation (3.2) are orthogonal.
Proof. Assume that λα and λβ are two different eigenvalues corresponds to eigenfunctions u(n) and v(n) respectively for the equation (3.1),
∇μa(p(t)b∇μu(t))+(l(l+1)t2−2t+q(t))u(t)−λαu(t)=0,∇μa(p(t)b∇μv(t))+(l(l+1)t2−2t+q(t))v(t)−λβv(t)=0, |
Multiplying last two equations to v(n) and u(n) respectively, subtracting from each other and applying sum operator, since the self–adjointness of the operator L1, we get
(λα−λβ)b−1∑s=a+1r(s)u(s)v(s)=0, |
since λα≠λβ,
b−1∑s=a+1r(s)u(s)v(s)=0,⟨u(t),v(t)⟩=0, |
and the proof completes.
Theorem 3.3. All eigenvalues of the equation (3.1) are real.
Proof. Assume λ=α+iβ, since the self–adjointness of the operator L1, we have
⟨L1u,u⟩=⟨u,L1u⟩,⟨λu,u⟩=⟨u,λu⟩, |
(λ−¯λ)⟨u,u⟩=0 |
Since ⟨u,u⟩r≠0,
λ=¯λ |
and hence β=0. So, the proof is completed.
Self–adjointness of L2 DFHA operator G–L sense, reality of eigenvalues and orthogonality of eigenfunctions of the equation 3.2 can be proven in a similar way to the Theorem 3.1–3.2–3.3 by means of Definition 2.5.
Theorem 3.4.
L3x(t)=∇μa(∇μax(t))+(l(l+1)t2−2t+q(t))x(t)=λx(t),0<μ<1, | (3.7) |
x(a+1)=c1,∇μax(a+1)=c2, | (3.8) |
where p(t)>0, r(t)>0, q(t) is defined and real valued, λ is the spectral parameter. The sum representation of solution of the problem (3.7)−(3.8) is given as follows,
x(t)=c1((1+l(l+1)(a+1)2−2a+1+q(a+1))Eλ,2μ,μ−1(t,a)−λEλ,2μ,2μ−1(t,a))+c2(Eλ,2μ,2μ−1(t,a)−Eλ,2μ,μ−1(t,a))−t∑s=a+1Eλ,2μ,2μ−1(t−ρ(s)+a)(l(l+1)s2−2s+q(s))x(s). | (3.9) |
Proof. Taking Laplace transform of the equation (3.7) by Theorem 2.2 and take (l(l+1)t2−2t+q(t))x(t)=g(t),
La+1{∇μa(∇μax)}(s)+La+1{g}(s)=λLa+1{x}(s),=sμLa+1{∇μax}(s)−1−sμ1−sc2=λLa+1{x}(s)−La+1{g}(s),=sμ(sμLa+1{x}(s)−1−sμ1−sc1)−1−sμ1−sc2=λLa+1{x}(s)−La+1{g}(s), |
=La+1{x}(s)=1−sμ1−s1s2μ−λ(sμc1+c2)−1s2μ−λLa+1{g}(s). |
Using Lemma 2.1, we have
La{x}(s)=c1(sμ−λs2μ−λ)−1−ss2μ−λ(11−sLa{g}(s)−11−sg(a+1))+c2(1−sμs2μ−λ). | (3.10) |
Now, taking inverse Laplace transform of the equation (3.10) and applying convolution theorem, then we have the representation of solution of the problem (3.7)−(3.8), |λ|<1, |1−s|<1 and |s|α>λ from Theorem 2.3., i.e.
L−1a{sμs2μ−λ}=Eλ,2μ,μ−1(t,a),L−1a{1s2μ−λ}=Eλ,2μ,2μ−1(t,a), |
L−1a{1s2μ−λLa{q(s)x(s)}}=t∑s=a+1Eλ,2μ,2μ−1(t−ρ(s)+a)q(s)x(s). |
Consequently, we have sum representation of solution for DFHA problem 3.7–3.8
x(t)=c1((1+l(l+1)(a+1)2−2a+1+q(a+1))Eλ,2μ,μ−1(t,a)−λEλ,2μ,2μ−1(t,a))+c2(Eλ,2μ,2μ−1(t,a)−Eλ,2μ,μ−1(t,a))−t∑s=a+1Eλ,2μ,2μ−1(t−ρ(s)+a)(l(l+1)s2−2s+q(s))x(s). |
Presume that c1=1,c2=0,a=0 in the representation of solution (3.9) and hence we may observe the behaviors of solutions in following figures (Figures 1–7) and tables (Tables 1–3);
x(t) | μ=0.3 | μ=0.35 | μ=0.4 | μ=0.45 | μ=0.5 |
x(1) | 1 | 1 | 1 | 1 | 1 |
x(2) | 0.612 | 0.714 | 1.123 | 0.918 | 1.020 |
x(3) | 0.700 | 0.900 | 1.515 | 1.370 | 1.641 |
x(5) | 0.881 | 1.336 | 2.402 | 2.747 | 3.773 |
x(7) | 1.009 | 1.740 | 3.352 | 4.566 | 7.031 |
x(9) | 1.099 | 2.100 | 4.332 | 6.749 | 11.461 |
x(12) | 1.190 | 2.570 | 5.745 | 10.623 | 20.450 |
x(15) | 1.249 | 2.975 | 6.739 | 15.149 | 32.472 |
x(16) | 1.264 | 3.098 | 7.235 | 16.793 | 37.198 |
x(18) | 1.289 | 3.330 | 8.233 | 20.279 | 47.789 |
x(20) | 1.309 | 3.544 | 9.229 | 24.021 | 59.967 |
x(t) | q(t)=1 | q(t)=t | q(t)=√t |
x(1) | 1 | 1 | 1 |
x(2) | 7.37∗10−17 | 4.41∗10−17 | 5.77∗10−17 |
x(3) | −0.131 | −0.057 | −0.088 |
x(5) | −0.123 | −0.018 | −0.049 |
x(7) | −0.080 | −0.006 | −0.021 |
x(9) | −0.050 | −0.003 | −0.011 |
x(12) | −0.028 | −0.001 | −0.005 |
x(15) | −0.017 | −0.0008 | −0.003 |
x(16) | −0.015 | −0.0006 | −0.0006 |
x(18) | −0.012 | −0.0005 | −0.002 |
x(20) | −0.010 | −0.0003 | −0.001 |
x(t) | λ=0.1 | λ=0.11 | λ=0.12 |
x(1) | 1 | 1 | 1 |
x(2) | 1 | 1.025 | 1.052 |
x(3) | 1.668 | 1.751 | 1.841 |
x(5) | 3.876 | 4.216 | 4.595 |
x(7) | 7.243 | 8.107 | 9.095 |
x(9) | 11.941 | 13.707 | 12.130 |
x(12) | 22.045 | 26.197 | 25.237 |
x(15) | 36.831 | 45.198 | 46.330 |
x(16) | 43.042 | 53.369 | 55.687 |
x(18) | 57.766 | 73.092 | 78.795 |
x(20) | 76.055 | 98.154 | 127.306 |
We have analyzed DFHA equation in Riemann–Liouville and Grü nwald–Letnikov sense. Self–adjointness of the DFHA operator is presented and also, we have proved some significant spectral properties for instance, orthogonality of distinct eigenfunctions, reality of eigenvalues. Moreover, we give sum representation of the solutions for DFHA problem and find the solutions of the problem. We have carried out simulation analysis with graphics and tables. The aim of this paper is to contribute to the theory of hydrogen atom fractional difference operator.
We observe the behaviors of solutions by changing the order of the derivative μ in Figure 1 and Figure 5, by changing the potential function q(t) in Figure 2, we compare solutions under different λ eigenvalues in Figure 3, and Figure 7, also we observe the solutions by changing μ with a specific eigenvalue in Figure 4 and by changing l values in Figure 6.
We have shown the solutions by changing the order of the derivative μ in Table 1, by changing the potential function q(t) and λ eigenvalues in Table 2, Table 3.
The authors would like to thank the editor and anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.
The authors declare no conflict of interest.
[1] |
T. Faria, L. T. Magalhaes, Normal forms for retarded functional differential equations with parameters and applications to Hopf bifurcation, J. Differ. Equations, 122 (1995), 181–200. https://doi.org/10.1006/JDEQ.1995.1144 doi: 10.1006/JDEQ.1995.1144
![]() |
[2] |
T. Faria, Normal forms and Hopf bifurcation for partial differential equations with delays, Trans. Amer. Math. Soc., 352 (2000), 2217–2238. https://doi.org/10.1090/S0002-9947-00-02280-7 doi: 10.1090/S0002-9947-00-02280-7
![]() |
[3] |
F. Yi, J. Wei, J. Shi, Bifurcation and spatiotemporal patterns in a homogeneous diffusive predator-prey system, J. Differ. Equations, 246 (2009), 1944–1977. https://doi.org/10.1016/J.JDE.2008.10.024 doi: 10.1016/J.JDE.2008.10.024
![]() |
[4] |
B. Messaoud, M. B. Almatrafi, Bifurcation and stability of two-dimensional activator-inhibitor model with fractional-order derivative, Fractal Fract., 7 (2023), 344. https://doi.org/10.3390/fractalfract7050344 doi: 10.3390/fractalfract7050344
![]() |
[5] |
A. Q. Khan, S. A. H. Bukhari, M. B. Almatrafi, Global dynamics, Neimark-Sacker bifurcation and hybrid control in a Leslie's prey-predator model, Alexandria Eng. J., 61 (2022), 11391–11404. https://doi.org/10.1016/j.aej.2022.04.042 doi: 10.1016/j.aej.2022.04.042
![]() |
[6] |
A. Q. Khan, F. Nazir, M. B. Almatrafi, Bifurcation analysis of a discrete Phytoplankton-Zooplankton model with linear predational response function and toxic substance distribution, Int. J. Biomath., 16 (2022), 2250095. https://doi.org/10.1142/s1793524522500954 doi: 10.1142/s1793524522500954
![]() |
[7] |
A. Q. Khan, M. Tasneem, M. B. Almatrafi, Discrete-time COVID-19 epidemic model with bifurcation and control, Math. Biosci. Eng., 19 (2022), 1944–1969. https://doi.org/10.3934/mbe.2022092 doi: 10.3934/mbe.2022092
![]() |
[8] |
J. Li, Y. Song, Spatially inhomogeneous periodic patterns induced by distributed memory in the memory-based single population model, Appl. Math. Lett., 137 (2023), 108490. https://doi.org/10.1016/j.aml.2022.108490 doi: 10.1016/j.aml.2022.108490
![]() |
[9] |
H. Shen, Y. Song, H. Wang, Bifurcations in a diffusive resource-consumer model with distributed memory, J. Differ. Equations, 347 (2023), 170–211. https://doi.org/10.1016/j.jde.2022.11.044 doi: 10.1016/j.jde.2022.11.044
![]() |
[10] |
S. Pal, S. Majhi, S. Mandal, N. Pal, Role of fear in a predator-prey model with Beddington-DeAngelis functional response, Z. Nat. A, 74 (2019), 581–595. https://doi.org/10.1515/ZNA-2018-0449 doi: 10.1515/ZNA-2018-0449
![]() |
[11] |
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. https://doi.org/10.1371/journal.pone.0002465 doi: 10.1371/journal.pone.0002465
![]() |
[12] |
S. Creel, D. Christianson, Relationships between direct predation and risk effects, Trends Ecol. Evol., 23 (2008), 194–201. https://doi.org/10.1016/j.tree.2007.12.004 doi: 10.1016/j.tree.2007.12.004
![]() |
[13] |
R. Yang, Q. Song, Y. An, Spatiotemporal dynamics in a predator-prey model with functional response increasing in both predator and prey densities, Mathematics, 10 (2022), 17. https://doi.org/10.3390/math10010017 doi: 10.3390/math10010017
![]() |
[14] |
M. Clinchy, M. J. Sheriff, L. Y. Zanette, Predator-induced stress and the ecology of fear, Funct. Ecol., 27 (2013), 56–65. https://doi.org/10.1111/1365-2435.12007 doi: 10.1111/1365-2435.12007
![]() |
[15] |
Y. Song, Y. Peng, T. Zhang, The spatially inhomogeneous Hopf bifurcation induced by memory delay in a memory-based diffusion system, J. Differ. Equations, 300 (2021), 597–624. https://doi.org/10.1016/J.JDE.2021.08.010 doi: 10.1016/J.JDE.2021.08.010
![]() |
[16] |
X. Wang, L. Y. Zanette, X. Zou, Modelling the fear effect in predator-prey interactions, J. Math. Biol., 73 (2016), 1179–1204. https://doi.org/10.1007/S00285-016-0989-1 doi: 10.1007/S00285-016-0989-1
![]() |
[17] |
R. Pringle, T. Kartzinel, T. Palmer, T. J. Thurman, K. Fox-Dobbs, C. C. Y. Xu, et al., Predator-induced collapse of niche structure and species coexistence, Nature, 570 (2019), 58–64. https://doi.org/10.1038/s41586-019-1264-6 doi: 10.1038/s41586-019-1264-6
![]() |
[18] |
P. Pandy, N. Pal, S. Samanta, J. Chattopadhyay, A three species food chain model with fear induced trophic cascade, Int. J. Appl. Comput. Math., 5 (2019), 100. https://doi.org/10.1007/s40819-019-0688-x doi: 10.1007/s40819-019-0688-x
![]() |
[19] |
J. P. Suraci, M. Clinchy, L. M. Dill, D. Roberts, L. Y. Zanette, Fear of large carnivores causes a trophic cascade, Nat. Commun., 7 (2016), 10698. https://doi.org/10.1038/ncomms10698 doi: 10.1038/ncomms10698
![]() |
[20] | W. C. Allee, A. Aggregations, A study in general sociology, University of Chicago Press, 1931. https://doi.org/10.2307/2961735 |
[21] |
T. Liu, L. Chen, F. Chen, Z. Li, Dynamics of a Leslie-Gower model with weak Allee effect on prey and fear effect on predator, Int. J. Bifurcation Chaos, 33 (2023), 2350008. https://doi.org/10.1142/s0218127423500086 doi: 10.1142/s0218127423500086
![]() |
[22] |
J. Jiao, C. Chen, Bogdanov-Takens bifurcation analysis of a delayed predator-prey system with double Allee effect, Nonlinear Dyn., 104 (2021), 1697–1707. https://doi.org/10.1007/s11071-021-06338-x doi: 10.1007/s11071-021-06338-x
![]() |
[23] |
P. Aguirre, A general class of predation models with multiplicative Allee effect, Nonlinear Dyn., 78 (2014), 629–648. https://doi.org/10.1007/S11071-014-1465-3 doi: 10.1007/S11071-014-1465-3
![]() |
[24] |
F. Courchamp, T. Clutton-Brock, B. Grenfell, F. Courchamp T. Clutton-Brock, B. Grenfell, et al., Inverse density dependence and the Allee effect, Trends Ecol. Evol., 14 (1999), 405–410. https://doi.org/10.1016/S0169-5347(99)01683-3 doi: 10.1016/S0169-5347(99)01683-3
![]() |
[25] |
P. Feng, Y. Kang, Dynamics of a modified Leslie-Gower model with double Allee effects, Nonlinear Dyn., 80 (2015), 1051–1062. https://doi.org/10.1007/S11071-015-1927-2 doi: 10.1007/S11071-015-1927-2
![]() |
[26] |
N. Iqbal, R. Wu, Turing patterns induced by cross-diffusion in a 2D domain with strong Allee effect, C. R. Math., 357 (2019), 863–877. https://doi.org/10.1016/j.crma.2019.10.011 doi: 10.1016/j.crma.2019.10.011
![]() |
[27] |
D. S. Boukal, L. Berec, Modelling mate-finding Allee effects and populations dynamics, with applications in pest control, Popul. Ecol., 51 (2009), 445–458. https://doi.org/10.1007/s10144-009-0154-4 doi: 10.1007/s10144-009-0154-4
![]() |
[28] |
M. H. Wang, M. Kot, Speeds of invasion in a model with strong or weak Allee effects, Math. Biosci., 171 (2001), 83–97. https://doi.org/10.1016/S0025-5564(01)00048-7 doi: 10.1016/S0025-5564(01)00048-7
![]() |
[29] |
T. Liu, L. Chen, F. Chen, Z. Li, Stability analysis of a Leslie-Gower model with strong Allee effect on prey and fear effect on predator, Int. J. Bifurcation Chaos, 32 (2022), 2250082. https://doi.org/10.1142/S0218127422500821 doi: 10.1142/S0218127422500821
![]() |
[30] |
K. Fang, Z. L. Zhu, F. D. Chen, Z. Li, Qualitative and bifurcation analysis in a Leslie-Gower model with Allee effect, Qual. Theory Dyn. Syst., 21 (2022), 86. https://doi.org/10.1007/s12346-022-00591-0 doi: 10.1007/s12346-022-00591-0
![]() |
[31] |
L. M. Zhang, Y. K. Xu, G. Y. Liao, Codimension-two bifurcations and bifurcation controls in a discrete biological system with weak Allee effect, Int. J. Bifurcation Chaos, 32 (2022), 2250036. https://doi.org/10.1142/s0218127422500365 doi: 10.1142/s0218127422500365
![]() |
[32] |
L. Zhao, J. H. Shen, Relaxation oscillations in a slow-fast predator-prey model with weak Allee effect and Holling-Ⅳ functional response, Commun. Nonlin. Sci. Numer. Simul., 112 (2022), 106517. https://doi.org/10.1016/j.cnsns.2022.106517 doi: 10.1016/j.cnsns.2022.106517
![]() |
[33] |
R. Yang, X. Zhao, Y. An, Dynamical analysis of a delayed diffusive predator-prey model with additional food provided and anti-predator behavior, Mathematics, 10 (2022), 469. https://doi.org/10.3390/math10030469 doi: 10.3390/math10030469
![]() |
[34] |
W. Zuo, J. Wei, Stability and Hopf bifurcation in a diffusive predator-prey system with delay effect, Nonlinear Anal.: Real World Appl., 12 (2011), 1998–2011. https://doi.org/10.1016/J.NONRWA.2010.12.016 doi: 10.1016/J.NONRWA.2010.12.016
![]() |
[35] |
R. Yang, D. Jin, W. Wang, A diffusive predator-prey model with generalist predator and time delay, AIMS Math., 7 (2022), 4574–4591. https://doi.org/10.3934/math.2022255 doi: 10.3934/math.2022255
![]() |
[36] |
J. F. Zhang, X. P. Yan, Effects of delay and diffusion on the dynamics of a Leslie-Gower type predator-prey model, Int. J. Bifurcation Chaos, 24 (2014), 1450043. https://doi.org/10.1142/S0218127414500436 doi: 10.1142/S0218127414500436
![]() |
[37] |
Y. Song, Y. Peng, T. Zhang, Double Hopf bifurcation analysis in the memory-based diffusion system, J. Dyn. Differ. Equ., 2022. https://doi.org/10.1007/s10884-022-10180-z doi: 10.1007/s10884-022-10180-z
![]() |
[38] |
M. U. Akhmet, M. Beklioglu, T. Ergenc, V. I. Tkachenko, An impulsive ratio-dependent predator-prey system with diffusion, Nonlinear Anal.: Real World Appl., 7 (2006), 1255–1267. https://doi.org/10.1016/j.nonrwa.2005.11.007 doi: 10.1016/j.nonrwa.2005.11.007
![]() |
[39] |
Y. Liu, J. Wei, Double Hopf bifurcation of a diffusive predator-prey system with strong Allee effect and two delays, Nonlinear Anal.: Model. Control, 26 (2021), 72–92. https://doi.org/10.15388/namc.2021.26.20561 doi: 10.15388/namc.2021.26.20561
![]() |
[40] |
Y. Liu, D. Duan, B. Niu, Spatiotemporal dynamics in a diffusive predator-prey model with group defense and nonlocal competition, Appl. Math. Lett., 103 (2019), 106175. https://doi.org/10.1016/j.aml.2019.106175 doi: 10.1016/j.aml.2019.106175
![]() |
[41] |
R. Yang, F. Wang, D. Jin, Spatially inhomogeneous bifurcating periodic solutions induced by nonlocal competition in a predator-prey system with additional food, Math. Methods Appl. Sci., 45 (2022), 9967–9978. https://doi.org/10.1002/mma.8349 doi: 10.1002/mma.8349
![]() |
[42] |
S. Chen, J. Yu, Stability and bifurcation on predator-prey systems with nonlocal prey competition, Discrete Contin. Dyn. Syst., 38 (2018), 43–62. https://doi.org/10.3934/DCDS.2018002 doi: 10.3934/DCDS.2018002
![]() |
[43] |
R. Yang, C. Nie, D. Jin, Spatiotemporal dynamics induced by nonlocal competition in a diffusive predator-prey system with habitat complexity, Nonlinear Dyn., 110 (2022), 879–900. https://doi.org/10.1007/s11071-022-07625-x doi: 10.1007/s11071-022-07625-x
![]() |
[44] |
D. Geng, W. Jiang, Y. Lou, H. Wang, Spatiotemporal patterns in a diffusive predator-prey system with nonlocal intraspecific prey competition, Stud. Appl. Math., 148 (2021), 396–432. https://doi.org/10.1111/sapm.12444 doi: 10.1111/sapm.12444
![]() |
[45] |
M. G. Clerc, D. Escaff, V. M. Kenkre, Analytical studies of fronts, colonies, and patterns: combination of the Allee effect and nonlocal competition interactions, Phys. Rev. E, 82 (2010), 036210. https://doi.org/10.1103/PHYSREVE.82.036210 doi: 10.1103/PHYSREVE.82.036210
![]() |
[46] |
Y. E. Maruvka, T. Kalisky, N. M. Shnerb, Nonlocal competition and the speciation transition on random networks, Phys. Rev. E, 78 (2008), 031920. https://doi.org/10.1103/PHYSREVE.78.031920 doi: 10.1103/PHYSREVE.78.031920
![]() |
[47] |
N. F. Britton, Aggregation and the competitive exclusion principle, J. Theor. Biol., 136 (1989), 57–66. https://doi.org/10.1016/S0022-5193(89)80189-4 doi: 10.1016/S0022-5193(89)80189-4
![]() |
[48] |
J. Furter, M. Grinfeld, Local vs. non-local interactions in population dynamics, J. Math. Biol., 27 (1989), 65–80. https://doi.org/10.1007/BF00276081 doi: 10.1007/BF00276081
![]() |
1. | Erdal Bas, Funda Metin Turk, Ramazan Ozarslan, Ahu Ercan, Spectral data of conformable Sturm–Liouville direct problems, 2021, 11, 1664-2368, 10.1007/s13324-020-00428-6 | |
2. | Tom Cuchta, Dallas Freeman, Discrete Polylogarithm Functions, 2023, 84, 1338-9750, 19, 10.2478/tmmp-2023-0012 | |
3. | B. Shiri, Y. Guang, D. Baleanu, Inverse problems for discrete Hermite nabla difference equation, 2025, 33, 2769-0911, 10.1080/27690911.2024.2431000 | |
4. | Muhammad Sulthan Zacky, Heru Sukamto, Lila Yuwana, Agus Purwanto, Eny Latifah, The performance of space-fractional quantum carnot engine, 2025, 100, 0031-8949, 025306, 10.1088/1402-4896/ada9de |
x(t) | μ=0.3 | μ=0.35 | μ=0.4 | μ=0.45 | μ=0.5 |
x(1) | 1 | 1 | 1 | 1 | 1 |
x(2) | 0.612 | 0.714 | 1.123 | 0.918 | 1.020 |
x(3) | 0.700 | 0.900 | 1.515 | 1.370 | 1.641 |
x(5) | 0.881 | 1.336 | 2.402 | 2.747 | 3.773 |
x(7) | 1.009 | 1.740 | 3.352 | 4.566 | 7.031 |
x(9) | 1.099 | 2.100 | 4.332 | 6.749 | 11.461 |
x(12) | 1.190 | 2.570 | 5.745 | 10.623 | 20.450 |
x(15) | 1.249 | 2.975 | 6.739 | 15.149 | 32.472 |
x(16) | 1.264 | 3.098 | 7.235 | 16.793 | 37.198 |
x(18) | 1.289 | 3.330 | 8.233 | 20.279 | 47.789 |
x(20) | 1.309 | 3.544 | 9.229 | 24.021 | 59.967 |
x(t) | q(t)=1 | q(t)=t | q(t)=√t |
x(1) | 1 | 1 | 1 |
x(2) | 7.37∗10−17 | 4.41∗10−17 | 5.77∗10−17 |
x(3) | −0.131 | −0.057 | −0.088 |
x(5) | −0.123 | −0.018 | −0.049 |
x(7) | −0.080 | −0.006 | −0.021 |
x(9) | −0.050 | −0.003 | −0.011 |
x(12) | −0.028 | −0.001 | −0.005 |
x(15) | −0.017 | −0.0008 | −0.003 |
x(16) | −0.015 | −0.0006 | −0.0006 |
x(18) | −0.012 | −0.0005 | −0.002 |
x(20) | −0.010 | −0.0003 | −0.001 |
x(t) | λ=0.1 | λ=0.11 | λ=0.12 |
x(1) | 1 | 1 | 1 |
x(2) | 1 | 1.025 | 1.052 |
x(3) | 1.668 | 1.751 | 1.841 |
x(5) | 3.876 | 4.216 | 4.595 |
x(7) | 7.243 | 8.107 | 9.095 |
x(9) | 11.941 | 13.707 | 12.130 |
x(12) | 22.045 | 26.197 | 25.237 |
x(15) | 36.831 | 45.198 | 46.330 |
x(16) | 43.042 | 53.369 | 55.687 |
x(18) | 57.766 | 73.092 | 78.795 |
x(20) | 76.055 | 98.154 | 127.306 |
x(t) | μ=0.3 | μ=0.35 | μ=0.4 | μ=0.45 | μ=0.5 |
x(1) | 1 | 1 | 1 | 1 | 1 |
x(2) | 0.612 | 0.714 | 1.123 | 0.918 | 1.020 |
x(3) | 0.700 | 0.900 | 1.515 | 1.370 | 1.641 |
x(5) | 0.881 | 1.336 | 2.402 | 2.747 | 3.773 |
x(7) | 1.009 | 1.740 | 3.352 | 4.566 | 7.031 |
x(9) | 1.099 | 2.100 | 4.332 | 6.749 | 11.461 |
x(12) | 1.190 | 2.570 | 5.745 | 10.623 | 20.450 |
x(15) | 1.249 | 2.975 | 6.739 | 15.149 | 32.472 |
x(16) | 1.264 | 3.098 | 7.235 | 16.793 | 37.198 |
x(18) | 1.289 | 3.330 | 8.233 | 20.279 | 47.789 |
x(20) | 1.309 | 3.544 | 9.229 | 24.021 | 59.967 |
x(t) | q(t)=1 | q(t)=t | q(t)=√t |
x(1) | 1 | 1 | 1 |
x(2) | 7.37∗10−17 | 4.41∗10−17 | 5.77∗10−17 |
x(3) | −0.131 | −0.057 | −0.088 |
x(5) | −0.123 | −0.018 | −0.049 |
x(7) | −0.080 | −0.006 | −0.021 |
x(9) | −0.050 | −0.003 | −0.011 |
x(12) | −0.028 | −0.001 | −0.005 |
x(15) | −0.017 | −0.0008 | −0.003 |
x(16) | −0.015 | −0.0006 | −0.0006 |
x(18) | −0.012 | −0.0005 | −0.002 |
x(20) | −0.010 | −0.0003 | −0.001 |
x(t) | λ=0.1 | λ=0.11 | λ=0.12 |
x(1) | 1 | 1 | 1 |
x(2) | 1 | 1.025 | 1.052 |
x(3) | 1.668 | 1.751 | 1.841 |
x(5) | 3.876 | 4.216 | 4.595 |
x(7) | 7.243 | 8.107 | 9.095 |
x(9) | 11.941 | 13.707 | 12.130 |
x(12) | 22.045 | 26.197 | 25.237 |
x(15) | 36.831 | 45.198 | 46.330 |
x(16) | 43.042 | 53.369 | 55.687 |
x(18) | 57.766 | 73.092 | 78.795 |
x(20) | 76.055 | 98.154 | 127.306 |