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Original Articles

Bifurcation and temporal periodic patterns in a plant–pollinator model with diffusion and time delay effects

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Pages 138-159 | Received 15 Oct 2015, Accepted 19 Apr 2016, Published online: 17 May 2016

ABSTRACT

This paper deals with a plant–pollinator model with diffusion and time delay effects. By considering the distribution of eigenvalues of the corresponding linearized equation, we first study stability of the positive constant steady-state and existence of spatially homogeneous and spatially inhomogeneous periodic solutions are investigated. We then derive an explicit formula for determining the direction and stability of the Hopf bifurcation by applying the normal form theory and the centre manifold reduction for partial functional differential equations. Finally, we present an example and numerical simulations to illustrate the obtained theoretical results.

AMS SUBJECT CLASSIFICATION:

1. Introduction

It is believed that the explosive diversification and present-day abundance of flowering plants is due to their co-evolution with animal pollinators, especially insects [Citation13]. The interactions between flowering plants and their insect pollinators remain an important ecological relationship crucial to the maintenance of both natural and agricultural ecosystems [Citation15]. Mathematical modeling plays a useful role in pollination research and various mathematical models have been proposed to study plant–pollinator population dynamics, see Soberon and Del Rio [Citation24], Lundberg and Ingvarsson [Citation19], Jang [Citation14], Neuhauser and Fargione [Citation20], Fishman and Hadany [Citation8], Wang et al. [Citation29], Wang [Citation26], and the references cited therein.

Consumer–resource systems model some biological phenomena and relationships between consumer and resource in the real world. A resource is considered to be a biotic population that helps to maintain the population growth of its consumers, whereas a consumer exploits a resource and then reduces its growth rate. Consumer–resource systems have been extensively studied by many researchers (see Chamberlain and Holland [Citation3], Holland and DeAngelis [Citation11], Li et al. [Citation17], Neuhauser and Fargione [Citation20], Wang and DeAngelis [Citation27], Wang et al. [Citation28]). Bi-directional consumer–resource interactions occur when each species acts as both a consumer and a resource of the other. Uni-directional consumer–resource interactions occur when one acts as a consumer and the other as a material and/or energy resource, but neither acts as both.

Recently, Wang, DeAngelis and Holland [Citation29] derived a plant–pollinator model based on unidirectional interactions between plants and pollinators [Citation11]. Pollinators travel from their nest to a foraging patch, collecting food, flying back to their nests, and unloading food. Interacting with flowers individually, the pollinators remove nectar, contact pollen, and provide pollination service. Therefore, the plants provide food, seeds, nectar, and other resources for the pollinators, while the pollinators have both positive and negative effects on the plants. Let N1 and N2 represent the population densities of plants and pollinators, respectively. The plant–pollinator model takes the following form: (1) dN1dt=r1N1+α12N1N21+aN1+bN2β1N1N2d1N12,dN2dt=α21N1N21+aN1+bN2d2N2.(1) where a,b,r1,β1,d1,d2,α12, and α21 are positive constants. The parameter r1 is the intrinsic growth rate of the plants and d1 the self-incompatible degree. Following Fishman and Hadany [Citation8], the positive effect of pollinators on plants is described by the Beddington–DeAngelis functional response aN1N2/(1+aN1+bN2), where the parameter a is the effective equilibrium constant for (undepleted) plant–pollinator interaction, which combines traveling and unloading times spent in central place pollinator foraging, with individual-level plant–pollinator interaction. b denotes the intensity of exploitation competition among pollinators (Pianka [Citation21]). Since a is fixed, the parameter α12 is regarded as the plants efficiency in translating plant–pollinator interactions into fitness (Beddington [Citation2], DeAngelis et al. [Citation6]) and α21 is the corresponding value for the pollinators. β1 denotes the per-capita negative effect of pollinators on plants. d2 is the per-capita mortality rate of pollinators. Wang et al. [Citation29] studied the globally asymptotically stability of the positive equilibria and demonstrated mechanisms by which interaction outcomes of this system vary with different conditions. In particular, it was shown in [Citation29] that system (Equation1) has no periodic orbits or cycle chains in the positive quadrant.

In order to reflect the dynamical behaviours of models depending on the history, it is necessary to incorporate time delay into the models. Following Adams et al. [Citation1], we assume that there is a time delay τ>0 in the process when the pollinators translate plant–pollinator interactions into the fitness. Also, as pollinators travel between their nests and foraging patches, we further introduce the spatial diffusion with zero-flux boundary conditions. Thus, the plant–pollinator model with diffusion and time delay effects is described by the following delayed reaction–diffusion system: (2) N1(t,x)t=N1(t,x)r1+α12N2(t,x)1+aN1(t,x)+bN2(t,x)β1N2(t,x)d1N1(t,x),xΩ,t>0,N2(t,x)t=D2ΔN2(t,x)+N2(t,x)α21N1(tτ,x)1+aN1(tτ,x)+bN2(tτ,x)d2,xΩ,t>0,N1(t,x)=φ(t,x)0,N2(t,x)=ψ(t,x)0,(t,x)[τ,0]×Ω¯,N1ν=N2ν=0,t0,x∂Ω,(2) where D2>0 denotes the diffusion coefficient of pollinators. Ω is a bounded open domain in Rn(n1) and its boundary ∂Ω is smooth, Δ=2/x12++2/xn2 is the Laplacian operator in Rn, ν is the outer normal direction on ∂Ω, and the homogeneous Neumann boundary conditions reflect the situation where the population cannot move across the boundary of the domain.

Throughout this paper, without of loss of generality, we consider the domain Ω=(0,π). Thus, Δ=2/x2. We also assume that (φ,ψ)C:=C([τ,0],X) and X is a suitable Hilbert space. For example, we can take X=(N1,N2):N1,N2W2,2(0,π):N1(t,x)x=N2(t,x)x=0,x=0,π with the inner product ,.

The rest of the paper is organized as follows. In Section 2, we consider the corresponding characteristic equation of system (Equation2) and give conditions on the stability of the positive constant steady-state and the existence of Hopf bifurcation. In Section 3, by applying the normal form theory and centre manifold reduction of partial functional differential equations (PFDEs) (Wu [Citation30], Faria [Citation7]), an explicit algorithm for determining the direction and stability of the Hopf bifurcation is given. Finally, some numerical simulations are included to support our theoretical predictions in Section 4 and a brief discussion is given in Section 5.

2. Stability and Hopf bifurcation

In this section, we consider the local stability of the positive constant steady-state and the Hopf bifurcation of system (Equation2) by regarding the time delay τ as the bifurcation parameter. We assume that

  • (A1) α21>ad2,a1<0,a124a0a2=0;

  • (A2) α21>ad2,4a0a2<0.

where a0=bβ1α21ad2+d1d2b2(α21ad2)2,a1=β1br1α21ad2+2d1d2b(α21ad2)2α12α21,a2=r1α21ad2+d1d2(α21ad2)2.

We can prove that, if (A1) or (A2) hold, then system (Equation2) has two boundary equilibria E0(0,0), E1(r1/d1,0), and a unique positive constant steady-state E(N1,N2), where N1=2a0d2a1bd2+bd2a124a0a22a0(α21ad2),N2=a1+a124a0a22a0.

Let u=N1N1,v=N2N2. Then system (Equation2) can be rewritten as (3) u(t,x)t=(u+N1)r1+α12(v+N2)1+a(u+N1)+b(v+N2)β1(v+N2)d1(u+N1),v(t,x)t=D22v(t,x)x2+(v+N2)α21(u(tτ,x)+N1)1+a(u(tτ,x)+N1)+b(v(tτ,x)+N2)d2,N1ν=N2ν=0,t0,x∂Ω,u(t,x)=φ(t,x)N1,v(t,x)=ψ(t,x)N2,t[τ,0],xΩ¯.(3) The positive equilibrium E(N1,N2) of system (Equation2) is transformed into the zero equilibrium of system (Equation3).

Let f(1)(u,v)=(u+N1)r1+α12(v+N2)1+a(u+N1)+b(v+N2)β1(v+N2)d1(u+N1),f(2)(u,v,w)=(w+N2)α21(u+N1)1+a(u+N1)+b(v+N2)d2. By the definition of the above functions, for i,j,lN0={0,1,2}, define fij(1)(i+j1) and fijl(2)(i+j+l1) as follow: fij(1)=i+jf(1)(0,0)uivj,fijl(2)=i+j+lf(2)(0,0,0)uivjwl, in particularly α1:=f10(1)=d1N1α12aN1N2(1+aN1+bN2)2<0,α2:=f01(1)=α12N1(1+aN1)(1+aN1+bN2)2β1N1,γ1:=f100(2)=α21N2(1+bN2)(1+aN1+bN2)2>0,γ2:=f010(2)=bα21N1N2(1+aN1+bN2)2<0. Obviously, we have α1+γ2<0. By Taylor expansion, Equation (Equation3) becomes (4) u(t,x)t=α1u(t,x)+α2v(t,x)+i+j21i!j!fij(1)ui(t,x)vj(t,x),v(t,x)t=D22v(t,x)x2+γ1u(tτ,x)+γ2v(tτ,x)+i+j+l21i!j!l!fijl(2)ui(tτ,x)vj(tτ,x)vl(t,x).(4) Let u1(t)=u(t,),u2(t)=v(t,) and U=(u1,u2)T. Then system (Equation4) can be rewritten as an abstract differential equation in the phase space C:=C([τ,0],X), (5) U(t)=DΔU(t)+L(Ut)+F(Ut),(5) where D=000D2,Δ=x200x2, Ut(θ)=U(t+θ),τθ0,L:CX and F:CX are defined by L(ϕ)=α1ϕ1(0)+α2ϕ2(0)γ1ϕ1(τ)+γ2ϕ2(τ) and F(ϕ)=i+j21i!j!fij(1)ϕ1i(0)ϕ2j(0)i+j+l21i!j!l!fijl(2)ϕ1i(τ)ϕ2j(τ)ϕ2l(0), respectively, for ϕ=(ϕ1,ϕ2)TC. The linearized system of system (Equation5) at (0,0) has the form: (6) U(t)=DΔU(t)+L(Ut),(6) and its characteristic equation is (7) λyDΔyL(eλy)=0,(7) where ydom(Δ){0} and dom(Δ)X. It is well known that the Laplacian operator Δ on X has eigenvalues k2,k=0,1,2,, with corresponding eigenfunctions βk1=coskx0,βk2=0coskx. Clearly, (βk1,βk2)k=0 form a basis of X. Thus, any yX can be expanded as Fourier series in the following form: y=k=0Ykβk1βk2andYk=(y,βk1,y,βk2). Therefore, (Equation7) is equivalent to k=0(y,βk1,y,βk2)λα1α2γ1eλτλ+D2k2γ2eλτβk1βk2=0, k=0,1,2,.

Hence, we conclude that the characteristic equation (Equation7) is equivalent to the following sequence of characteristic equations: (8) λ2+(D2k2α1)λα1D2k2+(γ2λ+α1γ2α2γ1)eλτ=0,k=0,1,2,.(8) Set (9) Δk(λ,τ):=λ2+(D2k2α1)λα1D2k2+(γ2λ+α1γ2α2γ1)eλτ,k=0,1,2,.(9) Notice that (Equation8) with τ=0 is the characteristic equation of the linearization of (Equation2) without delay at the positive equilibrium. Because D2k2α1γ2>0, so the characteristic equation (Equation8) with τ=0 does not have a pair of purely imaginary roots for any kN0 with N0:={0,1,2,}. According to the Hopf bifurcation theorem, we obtain the following result.

Theorem 2.1.

Assume that (A1) or (A2) hold. Then system (Equation2) without delay cannot undergo a Hopf bifurcation at the positive constant steady-state E(N1,N2).

Lemma 2.2.

Assume that (A1) or (A2) hold. Assume further that α1γ2α2γ1>0. Then λ=0 is not a root of Equation (Equation8) for any kN0 with N0:={0,1,2,}.

Proof.

From Equation (Equation9), we have Δk(0,τ)=α1D2k2+α1γ2α2γ1. Since α1<0,D2>0 and α1γ2α2γ1>0, we obtain Δk(0,τ)>0 for any kN0, which implies that λ=0 is not a root of Equation (Equation8) for any kN0.

Lemma 2.3.

Assume that (A1) or (A2) hold. Assume further that α1γ2α2γ1>0. Then all roots of Equation (Equation8) with τ=0 have negative real parts for all kN0={0,1,2,} and the positive constant steady-state E(N1,N2) of Equation (Equation2) with τ=0 is locally asymptotically stable.

Proof.

When τ=0, Equation (Equation9) is equivalent to the following equation: Δk(λ,0)=λ2+(D2k2α1γ2)λα1D2k2+α1γ2α2γ1,kN0. Let λ1 and λ2 be two roots of the above equation. Then λ1+λ2=α1+γ2D2k2,λ1λ2=α1D2k2+α1γ2α2γ1. Since λ1+λ2<0 and λ1λ2>0, all roots of Equation (Equation8) with τ=0 have negative real parts.

Let λ=iω(ω>0) be a purely imaginary root of Equation (Equation8) for kN0 with N0:={0,1,2,}. Then we have ω2+i(D2k2α1)ωα1D2k2+(α1γ2α2γ1)eiωτiγ2ωeiωτ=0. Separating the real and imaginary parts in the above equation, we obtain (10) ω2α1D2k2=γ2ωsin(ωτ)(α1γ2α2γ1)cos(ωτ),(D2k2α1)ω=(α1γ2α2γ1)sin(ωτ)+γ2ωcos(ωτ),(10) which imply that (11) (α1D2k2ω2)2+(D2k2α1)2ω2=(α1γ2α2γ1)2+γ22ω2,(11) i.e. (12) ω4+(D22k4+α12γ22)ω2+(α1D2k2)2(α1γ2α2γ1)2=0.(12) Set z=ω2, (Equation12) is transformed into (13) z2+(D22k4+α12γ22)z+(α1D2k2)2(α1γ2α2γ1)2=0.(13) If α1D2k2+α1γ2α2γ1>0, then Equation (Equation13) has only one positive root which is denoted by zk. Hence Equation (Equation12) has only one positive root w+k=zk. From Equation (Equation10), we know that Equation (Equation8) with kN0 has a pair of purely imaginary roots ±iw+k when τ=τjk, j=0,1,2,, where (14) (w+k)2=D22k4+α12γ222+(γ22α12D22k4)24[(α1D2k2)2(α1γ2α2γ1)2]2,τjk=1w+k(arccosE(w+k)+2jπ),if F(w+k)0,1w+k(2πarccosE(w+k)+2jπ),if F(w+k)<0(14) with (15) F(w+k):=sin(w+kτ)=γ2w+k((w+k)2+α1D2k2)+w+k(D2k2α1)(α1γ2α2γ1)γ22(w+k)2+(α1γ2α2γ1)2,E(w+k):=cos(w+kτ)=α1D2k2(α1γ2α2γ1)+γ2D2(w+k)2k2α2γ1(w+k)2γ22(w+k)2+(α1γ2α2γ1)2.(15)

Lemma 2.4.

Assume that (A1) or (A2) hold. Assume further that α1D2k2+α1γ2α2γ1>0. Then dΔk(λ,τ)dλλ=iw+k0. Therefore, λ=iw+k is a simple root of (Equation8) for kN0.

Proof.

Firstly, we have dΔk(λ,τ)dλλ=iw+k=i2w+k+(D2k2α1)γ2eiw+kτjkα1γ2α2γ1τjkeiw+kτjk+iγ2w+kτjkeiw+kτjk. Then, from Δk(λ,τ)=0, we obtain that [2λ+(D2k2α1)γ2eλττ(α1γ2α2γ1γ2λ)eλτ]dλ(τ)dτ=λ(α1γ2α2γ1γ2λ)eλτ. Thus, if dΔk(λ,τ)/dλ|λ=iw+k=0, then iw+k(α1γ2α2γ1γ2iw+k)eiw+kτjk=0. Since w+k>0, we have α1γ2α2γ1γ2iw+k=0 which implies that α1γ2α2γ1=γ2=0. However, γ2>0. Hence, we have dΔk(λ,τ)dλλ=iw+k0. This completes the proof.

Lemma 2.5.

Assume that (A1) or (A2) hold. Assume further that α1D2k2+α1γ2α2γ1>0. Let λ(τ)=μ(τ)+iw(τ) be the root of Equation (Equation8) for kN0 satisfying μ(τjk)=0,w(τjk)=w+k, jN0. Then λ(τ) satisfies the following transversality condition: signRedλdττ=τjk>0.

Proof.

Differentiating both sides of Equation (Equation8) with respect to τ yields dλdτ1=2λeλτ(α1D2k2)eλτγ2(α1γ2α2γ1)λγ2λ2τλ. From Equation (Equation10), we have signRedλdτ1τ=τjk=signRe2λeλτ(α1D2k2)eλτγ2(α1γ2α2γ1)λγ2λ2τλτ=τjk=sign2(w+k)2γ22+(α1D2k2)2+2α1D2k2(α1γ2α2γ1)2+γ22(w+k)2. By inserting the expression of (w+k)2 into the last expression, we obtain that signRedλdτ1τ=τjk>0. The proof is complete.

Notice that Equation (Equation8) with k=0 is the characteristic equation of the linearization of (Equation2) without diffusion at the positive equilibrium. By Rouché theorem and Lemmas Equation5– Equation7, we have the following results [Citation22,Citation23] :

Theorem 2.6.

Assume that (A1) or (A2) hold. Assume further that α1γ2α2γ1>0. The following statements hold:

  1. If τ[0,τ00), then all roots of Equation (Equation8) with k=0 have negative real parts;

  2. If τ>τ00, then system (Equation8) with k=0 has at least one root with positive real part;

  3. If τ=τ00, then system (Equation8) with k=0 has a pair of simple purely imaginary roots ±iw+0, and all roots of (Equation8) with k=0, except ±iw+0, have negative real parts.

Furthermore, we can obtain the following results:

Theorem 2.7.

Assume that (A1) or (A2) hold. Assume further that α1γ2α2γ1>0, α1(D2+γ2)α2γ1<0 and D22+α12γ22>0. Then Equation (Equation8) with τ=τj0 (j=0,1,2,) has a pair of simple purely imaginary roots ±iw+0, and all roots of Equation (Equation8) for any kN0, except ±iw+0, have no zero real parts. Moreover, for τ=τ00, all roots of Equation (Equation8) for any kN0, except ±iw+0, have negative real parts.

Theorem 2.8.

Assume that (A1) or (A2) hold. Assume further that α1γ2α2γ1>0, α1(D2+γ2)α2γ1<0 and D22+α12γ22>0. The following statements hold:

  1. If τ[0,τ00), then the positive constant steady-state E(N1,N2) is asymptotically stable;

  2. If τ>τ00, then the positive constant steady-state E(N1,N2) is unstable;

  3. τ=τj0(j=0,1,2,) are Hopf bifurcation values of system (Equation2) and these Hopf bifurcations are all spatially homogeneous.

Denote N~=|α1γ2α2γ1|α1D2andN1=N~1,N~N.[N~],N~N.

From Equation (Equation14), we have τjk<τj+1k for any 0kN1,jN0. In the rest of this paper, we assume that F(w+k)0 and have the following lemma. The case for F(w+k)<0 can be discussed in a similar way.

Lemma 2.9.

Let τjk be defined as Equation (Equation14). Assume that (A1) or (A2) hold. Assume further that α1D2N12+α1γ2α2γ1>0,α1<γ2,γ2D2N12α2γ1>0 and α1D2(α1γ2α2γ1)+γ2D2(w+1)2α2γ1(w+1)2<0. Then for any 1kN1,jN0, τjk<τjk+1.

Proof.

From Equation (Equation12), we have (w+k)2=2Yk2+4Wk+Yk, where Yk=D22k4+α12γ22(α1γ2α2γ1)2(α1D2k2)2,Wk=(α1γ2α2γ1)2(α1D2k2)2. Simple computation shows that dw+kdYk=(1+YkYk2+4Wk)2(Yk2+4Wk+Yk)3/2<0,dYkdk=4D22k3[(α1γ2α2γ1)2+α12(α12γ22)][(α1γ2α2γ1)2(α1D2k2)2]2>0. Notice that Wk is strictly decreasing in k for 0kN1. Then we obtain that w+k is strictly decreasing in k for 0kN1. From Equation (Equation15), we have E(w+k)=α1D2k2(α1γ2α2γ1)+γ2D2(w+k)2k2α2γ1(w+k)2γ22(w+k)2+(α1γ2α2γ1)2. By direct computation, we have dE(w+k)dk=[2α1D2k(α1γ2α2γ1)+2kγ2D2(w+k)2][γ22(w+k)2+(α1γ2α2γ1)2][γ22(w+k)2+(α1γ2α2γ1)2]2+2γ2D2k2w+kdw+kdk2α2γ1w+kdw+kdk[γ22(w+k)2+(α1γ2α2γ1)2][γ22(w+k)2+(α1γ2α2γ1)2]22γ22w+kdw+kdk[α1D2k2(α1γ2α2γ1)+γ2D2(w+k)2k2α2γ1(w+k)2][γ22(w+k)2+(α1γ2α2γ1)2]2. Since dw+kdk<0, by the fact that α1D2(α1γ2α2γ1)+γ2D2(w+1)2α2γ1(w+1)2<0 and γ2D2N12α2γ1>0, we obtain dE(w+k)dk<0. That is, E(w+k) is strictly decreasing in k for 1kN1. So arccos(E(w+k)) is strictly increasing in k for 1kN1. From Equation (Equation14), if F(w+k)0, then τjk is strictly increasing in k for 1kN1.

From the above lemma, we have τ0k<τ1k<τ2k<<τjk< for any 0kN1 and τj1<τj2<τj3<<τjn<<τjN1,jN0. Denote F:={τjk:τjkτmn,τjkτl0,1n<kN1,j<m or 1k<nN1,j>m,j,m,lN0}.

From the above analysis, we have the following conclusion.

Theorem 2.10.

Assume that (A1) or (A2) hold. Assume further that α1D2N12+α1γ2α2γ1>0,α1<γ2,γ2D2N12α2γ1>0 and α1D2(α1γ2α2γ1)+γ2D2(w+1)2α2γ1(w+1)2<0. The following statements are true:

  1. If τ[0,min{τ00,τ01}), then the positive constant steady-state E(N1,N2) is asymptotically stable;

  2. If τ>min{τ00,τ01}, then the positive constant steady-state E(N1,N2) is unstable;

  3. τF is a Hopf bifurcation value of system (Equation2) and these Hopf bifurcations are all spatially inhomogeneous.

3. Properties of Hopf bifurcations

In this section, we shall study the direction, stability and the period of bifurcating periodic solution by applying the normal form theory and the centre manifold theorem presented in [Citation7,Citation10,Citation30]. Let τjkF{τj0,jN0}. Normalizing the delay τ in system (Equation4) by the time-scaling tt/τ, Equation (Equation4) is transformed into (16) u(t,x)t=τα1u(t,x)+α2v(t,x)+i+j21i!j!fij(1)ui(t,x)vj(t,x),v(t,x)t=τD22v(t,x)x2+γ1u(t1,x)+γ2v(t1,x)i+j+l21i!j!l!fijl(2)+i+j+l21i!j!l!fijl(2)ui(t1,x)vj(t1,x)vl(t,x).(16) Let τ=τjk+α,αR,u1(t)=u(t,),u2(t)=v(t,), and U=(u1,u2)T. Then system (Equation16) can be rewritten in the abstract form in the space C:=C([1,0],X) as (17) ddtU(t)=τjkDΔU(t)+L(τjk)(Ut)+F(Ut,α),(17) where L(α)():CX and F:C×RX are defined by L(α)(ϕ)=αα1ϕ1(0)+α2ϕ2(0)γ1ϕ1(1)+γ2ϕ2(1),F(ϕ,α)=αDΔϕ(0)+L(α)(ϕ)+f(ϕ,α), respectively, for ϕ=(ϕ1,ϕ2)TC:=C([1,0],X), with (18) f(ϕ,α)=(τjk+α)i+j21i!j!fij(1)ϕ1i(0)ϕ2j(0)i+j+l21i!j!l!fijl(2)ϕ1i(1)ϕ2j(1)ϕ2l(0).(18)

Consider the linear equation (19) ddtU(t)=τjkDΔU(t)+L(τjk)(Ut).(19) According to results in Section 3, we know that the origin (0,0) is an equilibrium of Equation (Equation16), and under some conditions, the characteristic equation of (Equation19) has a pair of simple purely imaginary eigenvalues Λk={iw+kτjk,iw+kτjk}.

We now consider the ordinary functional differential equation: (20) X(t)=τjkDk2X(t)+L(τjk)(Xt).(20) By the Riesz representation theorem, there exists a 2×2 matrix function η(θ,τjk),θ[1,0], whose entries are of bounded variation such that (21) τjkDk2φ(0)+L(τjk)(φ)=10d[η(θ,τjk)]φ(θ)(21) for φC([1,0],R2). In fact, we can choose (22) η(θ,τjk)=τjkα1α20D2k2,θ=0,0,θ(1,0),τjk00γ1γ2,θ=1.(22) Let A(τjk) denote the infinitesimal generator of the semigroup induced by the solutions of system (Equation20) and A be the formal adjoint of A(τjk) under the bilinear pairing (23) ψ,φ=ψ(0)φ(0)+τjk10ψ(ξ+1)00γ1γ2φ(ξ)dξ(23) for ψC([0,1],R2),φC([1,0],R2). From the previous section, we know that A(τjk) has a pair of simple purely imaginary eigenvalues ±iw+kτjk. Because A(τjk) and A are a pair of adjoint operators (see Hale [Citation9]), ±iw+kτjk are also eigenvalues of A. Let P and P be the centre subspace, that is, the generalized eigenspace of A(τjk) and A associated with Λk  respectively. Then P is the adjoint space of P and dimP=dimP=2.

Direct computations yield the following results.

Lemma 3.1.

Let (24) ξ=iw+kα1α2,η=iw+kα1γ1eiw+kτjk.(24) Then p1(θ)=eiw+kτjkθ(1,ξ)T,p2(θ)=p1(θ)¯,1θ0 form a basis of P with Λk and q1(s)=eiw+kτjks(1,η),q2(s)=q1(s)¯,0s1 form a basis of P with Λk.

Let Φ=(Φ1,Φ2) and Ψ=(Ψ1,Ψ2)T with Φ1(θ)=p1(θ)+p2(θ)2=cosw+kτjkθα1α2cosw+kτjkθw+kα2sinw+kτjkθ,Φ2(θ)=p1(θ)p2(θ)2i=sinw+kτjkθα1α2sinw+kτjkθ+w+kα2cosw+kτjkθ for θ[1,0], and Ψ1(s)=q1(s)+q2(s)2=cosw+kτjksα1γ1cosw+kτjkw+kγ1sinw+kτjkcosw+kτjks+α1γ1sinw+kτjk+w+kγ1cosw+kτjksinw+kτjksT,Ψ2(s)=q1(s)q2(s)2i=sinw+kτjksα1γ1cosw+kτjk+w+kγ1sinw+kτjkcosw+kτjksα1γ1sinw+kτjkw+kγ1cosw+kτjksinw+kτjksT for s[0,1]. Now we define (Ψ,Φ)=(Ψj,Φk)=(Ψ1,Φ1)(Ψ1,Φ2)(Ψ2,Φ1)(Ψ2,Φ2) and construct a new basis Ψ for P by Ψ=(Ψ1,Ψ2)T=(Ψ,Φ)1Ψ. Then (Ψ,Φ)=I2, where I2 is the identity matrix. In addition, fk:=(βk1,βk2), where βk1=coskx0,βk2=0coskx. Let cfk be defined by cfk=c1βk1+c2βk2 for c=(c1,c2)TC([1,0],X). Then the centre subspace of linear equation  (Equation19) is given by PCNC, where (25) PCNC(ϕ)=Φ(Ψ,ϕ,fk)fk,ϕC,(25) and we can decompose C([1,0],X) as C=PCNCPSC, in which PSC denotes the complement subspace of PCNC in C.

Let Aτjk be the infinitesimal generator induced by the linear system (Equation19), and Equation (Equation17) can be rewritten as the following abstract form: (26) Ut=AτjkUt+X0F(Ut,α),(26) where X0(θ)=0,θ[1,0),I,θ=0. By the decomposition of C, the solution of Equation (Equation17) can be written as (27) Ut=Φx1(t)x2(t)fk+h(x1,x2,α),(27) where (x1(t),x2(t))T=(Ψ,Ut,fk), and h(x1,x2,α)PSC,h(0,0,0)=0,Dh(0,0,0)=0. In particular, the solution of (Equation17) on the centre manifold is given by (28) Ut=Φx1(t)x2(t)fk+h(x1,x2,0).(28) Let Ψ(0)=(Ψ1(0),Ψ2(0))T,z=x1ix2, and p1=Φ1+iΦ2. Then we obtain Φx1(t)x2(t)fk=12(p1z+p¯1z¯)fk. Hence, Equation (Equation28) can be transformed into (29) Ut=12(p1z+p¯1z¯)fk+W(z,z¯),(29) where W(z,z¯)=hz+z¯2,zz¯2i,0. From Wu [Citation30], z satisfies (30) z=iw+kτjkz+g(z,z¯),(30) where (31) g(z,z¯)=(Ψ1(0)iΨ2(0))f(Ut,0),fk.(31) Let (32) W(z,z¯)=W20z22+W11zz¯+W02z¯22+W21z2z¯2+,(32) (33) g(z,z¯)=g20z22+g11zz¯+g02z¯22+g21z2z¯2+.(33) Notice that 0πcos3kxdx=0,kN={1,2,}. Let (ψ1,ψ2)=Ψ1(0)iΨ2(0). Then by computation, we obtain the following quantities: g20=0,kN,τjk2ξf11(1)+12f20(1)+12ξ2f02(1)ψ1+e2iw+kτjkξf110(2)+12f200(2)+12ξ2f020(2)+eiw+kτjkξf101(2)+eiw+kτjkξ2f011(2)ψ2,k=0,g11=0,kN,τjk4((ξ¯+ξ)f11(1)+f20(1)+ξ¯ξf02(1))ψ1+(ξ¯+ξ)f110(2)+eiw+kτjkξ¯(f101(2)+ξf011(2))+eiw+kτjkξ(f101(2)+ξ¯f011(2))+f200(2)+ξ¯ξf020(2)ψ2,k=0,g02=g20¯, g21=τjk<f11(1)W11(2)(0)+12W20(2)(0)+W11(1)(0)ξ+12W20(1)(0)ξ¯coskx,coskx>+f20(1)W11(1)(0)+12W20(1)(0)coskx,coskx+f02(1)W11(2)(0)ξ+12W20(2)(0)ξ¯coskx,coskxψ1+τjkf110(2)eiw+kτjkW11(2)(1)+W11(1)(1)ξ+e2iw+kτjk12W20(2)(1)+e2iw+kτjk12W20(2)(1)ξ¯coskx,coskx+f101(2)eiw+kτjkW11(2)(0)+eiw+kτjk12W20(2)(0)+W11(1)(1)ξ+12W20(1)(1)ξ¯coskx,coskx+f011(2)eiw+kτjkW11(2)(0)ξ+eiw+kτjk12W20(2)(0)ξ¯+W11(2)(1)ξ+12W20(2)(1)ξ¯coskx,coskx+12f200(2)2eiw+kτjkW11(1)(1)+eiw+kτjkW20(1)(1)coskx,coskx+12f020(2)2eiw+kτjkW11(2)(1)ξ+eiw+kτjkW20(2)(1)ξ¯coskx,coskxψ2. where (34) W20(θ)=i2g20w+kτjkp1(θ)+g02¯3w+kτjkp2(θ)fk+Ee2iw+kτjkθ,(34) with (35) E=W20(0),kN,W20(0)i2g20w+0τj0p1(0)+g02¯3w+0τj0p2(0)f0,k=0.(35) (36) W11(θ)=i2w+kτjk[p2(θ)g11¯p1(θ)g11]+E,(36) with (37) E=14E2(ξ¯+ξ)f11(1)+f20(1)+ξ¯ξf02(1)(ξ¯+ξ)f110(2)+eiw+kτjkξ¯(f101(2)+ξf011(2))+eiw+kτjkξ(f101(2)+ξ¯f011(2))+f200(2)+ξ¯ξf020(2)cos2kx,(37) and E2=α1α2γ1D2k2γ21.

So far, we have obtained W20(θ) and W11(θ) which can be expressed by the parameters of system (Equation2). Hence, we can compute the following quantities: c1(0)=i2w+kτjkg20g112|g11|213|g02|2+12g21,μ2=Re(c1(0))Re(λ(τjk)),σ2=2Re(c1(0)),T2=Im(c1(0))+μ2Im(λ(τjk))w+kτjk. Thus, we obtain the following results:

Theorem 3.2.

For any critical value τjk, we have

  1. μ2 determines the direction of the Hopf bifurcation: if μ2>0 then the Hopf bifurcation is forward, and if μ2<0 then the Hopf bifurcation is backward;

  2. σ2 determines the stability of the bifurcated periodic solutions on the centre manifold: if σ2<0 then the bifurcated periodic solutions are asymptotically stable, and if σ2>0 then the bifurcated periodic solutions are unstable;

  3. T2 determines the period of the bifurcated periodic solutions: if T2<0 then the period decreases, and if T2>0 then the period increases.

4. Numerical simulations

In this section, we present some numerical simulations to illustrate the theoretical analysis for the system (Equation2).

Choose the parameter values as follows so that the conditions in Theorem 2.8 are satisfied: D2=2.735375,a=0.391625,b=0.391625,d1=0.001,d2=0.391625,r1=0.001,β1=0.001,α12=0.001,α21=1.5635. The initial conditions are taken as φ(t,x)=0.427839×(1+2sin(3.732x)+0.13sin(1.4142x0.6)),ψ(t,x)=1.380211×(1+2sin(2.732x)+0.13sin(0.74142x+0.5)). Then system (Equation2) becomes (38) N1(t,x)t=0.001N1(t,x)+0.001N1(t,x)N2(t,x)1+0.391625N1(t,x)+0.391625N2(t,x)0.001N1(t,x)N2(t,x)0.001N12(t,x),N2(t,x)t=2.7353752N2(t,x)x2+1.5635N1(tτ,x)N2(t,x)1+0.391625N1(tτ,x)+0.391625N2(tτ,x)0.391625N2(t,x),N1(t,x)=0.427839×(1+2sin(3.732x)+0.13sin(1.4142x0.6)),N2(t,x)=1.380211×(1+2sin(2.732x)+0.13sin(0.74142x+0.5)),N1ν=N2ν=0,t0,x∂Ω.(38)

By computation, we have E(N1,N2)=(0.427839,1.380211),w0+=0.123963, τ00=12.518011. First we choose τ=10<τ00 and plot the solutions N1(t,x) and N2(t,x) by using the software Matlab in Figure . From the numerical simulations we can see that the solutions of system (Equation38) with τ=10 tend asymptotically to the positive equilibrium E(N1,N2)=(0.427839,1.380211). Under the same initial values, now we choose τ=20>τ00 and plot the graphs of N1(t,x) and N2(t,x) in Figure . From Figure , we see that there exists a family temporal periodic solutions, which implies that Hopf bifurcation occurs for system (Equation38) at τ00.

Figure 1. The positive equilibrium E(0.427839,1.380211) is asymptotically stable when τ=10<τ00=12.518011.

Figure 1. The positive equilibrium E∗(0.427839,1.380211) is asymptotically stable when τ=10<τ00=12.518011.

Figure 2. The temporal periodic solutions bifurcated from the equilibrium are stable, where τ=20>τ00=12.518011.

Figure 2. The temporal periodic solutions bifurcated from the equilibrium are stable, where τ=20>τ00=12.518011.

5. Discussion

Various mathematical models have been proposed to study plant–pollinator population dynamics, see Soberon and Del Rio [Citation24], Lundberg and Ingvarsson [Citation19], Jang [Citation14], Neuhauser and Fargione [Citation20], Fishman and Hadany [Citation8], Wang et al. [Citation29], and Wang [Citation26]. Most of these models are described by ordinary differential equations. Since pollinators travel between their nests and foraging patches, we believe that reaction–diffusion equations are more suitable to model the interactions between the plants and pollinators. We also assumed that there is a time delay in the process when the pollinators translate plant–pollinator interactions into the fitness and considered a plant–pollinator model with diffusion and time delay effects. As far as we know, there are no results for system (Equation2) with diffusion and time delay.

Firstly, by considering the distribution of eigenvalues of the corresponding linearized equation, stability of the positive constant steady-state and existence of spatially homogeneous and spatially inhomogeneous periodic solutions were studied. Secondly, by applying the normal form theory and the centre manifold reduction for partial functional differential equations, an explicit formula for determining the direction and stability of the Hopf bifurcation was given. Finally, to explain the obtained results, numerical simulations were presented.

Our results showed that if α21>ad2 and either (A1) a1<0,a124a0a2=0 or (A2) 4a0a2<0 holds, where a0=bβ1α21ad2+d1d2b2(α21ad2)2,a1=β1br1α21ad2+2d1d2b(α21ad2)2α12α21,a2=r1α21ad2+d1d2(α21ad2)2, then system (Equation2) has a unique positive constant steady-state E(N1,N2), in which N1=2a0d2a1bd2+bd2a124a0a22a0(α21ad2),N2=a1+a124a0a22a0. The first inequality α21>ad2 ensures the existence of a0,a1,a2, and N1. Recall that α21 is regarded as the pollinators efficiency in translating plant–pollinator interactions into fitness, a is the effective constant for plant–pollinator interaction, and d2 is the per-capita mortality rate of pollinators. This inequality means that the efficiency in translating plant–pollinator interactions into fitness of the pollinators must be greater than their mortality rate; otherwise the pollinators even cannot survive.

The inequality a1<0 in (A1) is equivalent to β1br1α21ad2+2d1d2b(α21ad2)2<α12α21, which indicates that the ratio of the efficiencies in translating plant–pollinator interactions into fitness of the plants and pollinators is greater than a certain value. In this case, an additional condition a124a0a2=0 is needed to ensure the existence of E(N1,N2). Under the assumption (A2), it requires that 4a0a2<0. Note that now a0>0, so the condition is equivalent to a2<0, which, in turn, is equivalent to r1>d1d2α21ad2. The last inequality means that the intrinsic growth rate r1 of the plants must be large enough compared to the death rates of the plants and pollinators.

We were interested in not only the effect of diffusion but also the effect of delay [Citation4,Citation12,Citation31]. We found that system (Equation2) without delay cannot undergo Hopf bifurcations at the positive constant steady-state. But, under certain conditions, system (Equation2) undergoes Hopf bifurcations at the positive constant steady-state under the effect of delay. Recall that α1=d1N1α12aN1N2(1+aN1+bN2)2<0,α2=α12N1(1+aN1)(1+aN1+bN2)2β1N1,γ1=α21N2(1+bN2)(1+aN1+bN2)2>0,γ2=bα21N1N2(1+aN1+bN2)2<0. Our results demonstrated that if α1γ2α2γ1>0,α1(D2+γ2)α2γ1<0,D22+α12γ22>0 then the positive equilibrium E is locally asymptotically stable if the time delay is less than a critical value τ<τ0, unstable when τ>τ0, and a family of periodic solutions bifurcates from E when τ passes through τ0 via Hopf bifurcation. Moreover, the direction, stability and period of the bifurcating periodic solutions can be determined analytically. Notice that Wang et al. [Citation29] showed that the ODE model (Equation2) does not have periodic solutions and Wang et al. [Citation25] proved that the unique positive steady-state solution of a reaction–diffusion plant–pollinator model is a global attractor. Our results thus indicate that the time delay causes bifurcations and induces temporal periodic patterns in the diffusive plant–pollinator model. Such properties have been observed in many delay differential equation models [Citation5,Citation16]. This is similar to the observation in our other work [Citation18] that oscillations occur in age-structured resource–consumer (plant–pollinator) models.

Wang et al. [Citation29] and Wang [Citation26] indeed investigated three species plant–pollinator–robber models. Since the movement of the nectar robbers plays an important role in their invasibility and coexistence of all species, it will be very interesting to study the population dynamics of the three species diffusive plant–pollinator–robber models. We leave this for future consideration.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was partially supported by NSFC (No. 11471044 and No. 11371058), the Fundamental Research Funds for the Central Universities, and NSF (DMS-1412454).

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