Citation: Yan Wang, Xianning Liu, Yangjiang Wei. A consumer-resource competition model with a state-dependent delay and stage-structured consumer species[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 6064-6084. doi: 10.3934/mbe.2020322
[1] | Robert Stephen Cantrell, Chris Cosner, William F. Fagan . Edge-linked dynamics and the scale-dependence of competitive. Mathematical Biosciences and Engineering, 2005, 2(4): 833-868. doi: 10.3934/mbe.2005.2.833 |
[2] | Jessica L. Hite, André M. de Roos . Pathogens stabilize or destabilize depending on host stage structure. Mathematical Biosciences and Engineering, 2023, 20(12): 20378-20404. doi: 10.3934/mbe.2023901 |
[3] | Tyler Cassidy, Morgan Craig, Antony R. Humphries . Equivalences between age structured models and state dependent distributed delay differential equations. Mathematical Biosciences and Engineering, 2019, 16(5): 5419-5450. doi: 10.3934/mbe.2019270 |
[4] | Nancy Azer, P. van den Driessche . Competition and Dispersal Delays in Patchy Environments. Mathematical Biosciences and Engineering, 2006, 3(2): 283-296. doi: 10.3934/mbe.2006.3.283 |
[5] | Gonzalo Robledo . Feedback stabilization for a chemostat with delayed output. Mathematical Biosciences and Engineering, 2009, 6(3): 629-647. doi: 10.3934/mbe.2009.6.629 |
[6] | Lizhong Qiang, Ren-Hu Wang, Ruofan An, Zhi-Cheng Wang . A stage-structured SEIR model with time-dependent delays in an almost periodic environment. Mathematical Biosciences and Engineering, 2020, 17(6): 7732-7750. doi: 10.3934/mbe.2020393 |
[7] | Nahla Abdellatif, Radhouane Fekih-Salem, Tewfik Sari . Competition for a single resource and coexistence of several species in the chemostat. Mathematical Biosciences and Engineering, 2016, 13(4): 631-652. doi: 10.3934/mbe.2016012 |
[8] | Paolo Fergola, Marianna Cerasuolo, Edoardo Beretta . An allelopathic competition model with quorum sensing and delayed toxicant production. Mathematical Biosciences and Engineering, 2006, 3(1): 37-50. doi: 10.3934/mbe.2006.3.37 |
[9] | Jacques A. L. Silva, Flávia T. Giordani . Density-dependent dispersal in multiple species metapopulations. Mathematical Biosciences and Engineering, 2008, 5(4): 843-857. doi: 10.3934/mbe.2008.5.843 |
[10] | Qingwen Hu . A model of regulatory dynamics with threshold-type state-dependent delay. Mathematical Biosciences and Engineering, 2018, 15(4): 863-882. doi: 10.3934/mbe.2018039 |
In the evolutionary history of natural communities, ecological competition has justifiably been a significant force, which consists of exploitation and interference [1,2,3]. In consequence, there has been increasing studies for consumer-resource competitive models by dynamical systems theory [4,5,6,7]. Mathematically, Gopalsamy [8] developed the following resource-based competition model.
{dxdt=x(t)[b−ax(t)−a1z1(t)−a2z2(t)],dz1dt=z1(t)[b1x(t)−β1z1(t)−μ1z2(t)],dz2dt=z2(t)[b2x(t)−β2z2(t)−μ2z1(t)], | (1.1) |
where x(t) represents the density of a logistically self-renewing resource at time t, z1(t) and z2(t) represent the densities at time t of two species which feed on this resource alone.
For a population individual, its whole life history generally includes two or more stages, particularly, mammals show two different stages of immaturity and maturity [9,10,11]. Moreover, it takes some time from birth to adulthood which is considered as the maturity time delay. Thus, the competition models and stage structure population models with constant maturity delays have been studied. For example, the two-species competition model and stage-structured single population model established in [12,13] respectively have a constant maturity delay.
Later, people noticed the biological fact about the maturity time of Antarctic whales and seals around the Second World War. Subsequent to the introduction of factory ships and with it a depletion of the large whale population, there has been a substantial increase in krill for seals and whales. It was then noted that seals took 3 to 4 years to mature and small whales only took 5 years [14]. Hence, their maturation time varies with the number of krill available, which implies that the maturity time delay is state-dependent, not a constant.
Due to this, Aiello and Freedman [15] formulated the following stage-structured state-dependent delay model, in which the delay τ(z(t)) is a bounded increasing function of the total population z(t)=x(t)+y(t).
{dx(t)dt=αy(t)−γx(t)−αy(t−τ(z(t)))e−γτ(z(t)),dy(t)dt=αy(t−τ(z(t)))e−γτ(z(t))−βy2(t). |
Subsequently, the stage-structured and competitive models with state-dependent delays were developed [16,17,18,19,20,21,22,23,24,25,26]. In 2017, Lv and Yuan [27] proposed the competitive model with state-dependent delays:
{dz1dt=b1z1(t−τ(z1))e−γ1τ(z1)−β1z21(t)−μ1z1(t)z2(t),dz2dt=b2z2(t−τ(z2))e−γ2τ(z2)−β2z22(t)−μ2z1(t)z2(t), |
where the delays τ(zi(t)) are bounded increasing functions of the populations zi(t), i=1,2, respectively.
It is obvious that the above-mentioned models with state-dependent delays straight change the constant delays into the state-dependent delays, which is not appropriate to population modeling. Therefore, Wang, Liu and Wei [28] proposed the following new single population model with a state-dependent delay and a correction factor.
{dx(t)dt=αy(t)−γx(t)−α[1−τ′(z(t))˙z(t)]y(t−τ(z(t)))e−γτ(z(t)),dy(t)dt=α[1−τ′(z(t))˙z(t)]y(t−τ(z(t)))e−γτ(z(t))−βy2(t), |
where the delay τ(z(t)) is a function of the total population z(t)=x(t)+y(t).
However, the aforementioned state-dependent delays are functions of populations, not resources. A worthwhile thought is how to reflect the direct relationship between the resources and maturity time? In view of the biological background above, the time varies with the resources available, that is, the species must spend enough time in the immature stage to accumulate a certain amount of food to reach maturity. To address the question raised above, we consider a stage-structured consumer-resource competition model with a state-dependent maturity time delay, in which the delay involves a correction term, 1−τ′(x)x′(t), related to resource changes.
The paper is organized as follows. In section 2, we formulate a resource-based competition model with a state-dependent maturity delay. In section 3, we study the well-posedness properties for the model and prove the existence and uniqueness of all equilibria. In section 4, we analyze the local stabilities of equilibria. In section 5, we discuss the global behaviors of the coexistence equilibrium. Finally, section 6 gives the conclusions of the paper.
Based on model (1.1), we will formulate the competition model with a state-dependent maturity delay. For each i=1,2, let yi(t) and zi(t) be the densities at time t of the immature and mature consumer species, respectively. Let x(t) be the density at time t of a logistically self-renewing resource which is necessary for two consumer species. Motivated by [28,29,30,31], we introduce a threshold age τ(x(t)) to distinguish the immature and mature individuals, which depends on the density of the same resource. Suppose that ρi(t,a) is the population density of age a at time t, then the densities of yi(t) and zi(t), respectively, are given by
yi(t)=∫τ(x(t))0ρi(t,a)daandzi(t)=∫∞τ(x(t))ρi(t,a)da. |
By virtue of [32,33], we have the following age structure partial differential equations to represent the development of the consumer species.
∂ρ1(t,a)∂t+∂ρ1(t,a)∂a=−γ1ρ1(t,a),ifa≤τ(x(t)),∂ρ1(t,a)∂t+∂ρ1(t,a)∂a=−(β1z1(t)+μ1z2(t))ρ1(t,a),ifa>τ(x(t)),∂ρ2(t,a)∂t+∂ρ2(t,a)∂a=−γ2ρ2(t,a),ifa≤τ(x(t)),∂ρ2(t,a)∂t+∂ρ2(t,a)∂a=−(β2z2(t)+μ2z1(t))ρ2(t,a),ifa>τ(x(t)). | (2.1) |
We suppose that y1(t) and y2(t) die at the constant rate γi (i=1,2). The parameters βi (i=1,2) represent the mature natural death and overcrowding rate for zi(t), respectively. The parameters μi (i=1,2) represent interspecific competition for zi(t), respectively.
Taking the derivatives of yi(t) and zi(t), respectively, and combining system (2.1), it then follows that
dy1(t)dt=ρ1(t,0)−γ1y1(t)−[1−τ′(x)x′(t)]ρ1(t,τ(x(t))),dz1(t)dt=[1−τ′(x)x′(t)]ρ1(t,τ(x(t)))−ρ1(t,∞)−β1z21(t)−μ1z1(t)z2(t),dy2(t)dt=ρ2(t,0)−γ2y2(t)−[1−τ′(x)x′(t)]ρ2(t,τ(x(t))),dz2(t)dt=[1−τ′(x)x′(t)]ρ2(t,τ(x(t)))−ρ2(t,∞)−β2z22(t)−μ2z1(t)z2(t). |
Note that the two primes refer to differentiation with respect to x and time t, respectively, namely, ˙τ(x(t))=dτ(x(t))/dt=τ′(x)x′(t).
Because of the finitude of individual life span, ρi(t,∞) is considered as zero. Assume that the immature consumers' functional response is Holling type I, that is, bix(t), then the term ρi(t,0)=bix(t)zi(t) represents the number of immature individuals born at time t. Therefore, for t≥˜τ=max{τ(x(t))}, we have
ρi(t,τ(x(t)))=ρi(t−τ(x(t)),0)=bix(t−τ(x(t)))zi(t−τ(x(t)))e−γiτ(x(t)),i=1,2. |
Consequently, we obtain the following stage-structured consumer-resource competition model with a state-dependent delay.
{dxdt=rx(t)(1−x(t)K)−a1x(t)z1(t)−a2x(t)z2(t),dy1dt=b1x(t)z1(t)−b1[1−τ′(x)x′(t)]x(t−τ(x))z1(t−τ(x))e−γ1τ(x)−γ1y1(t),dz1dt=b1[1−τ′(x)x′(t)]x(t−τ(x))z1(t−τ(x))e−γ1τ(x)−β1z21(t)−μ1z1(t)z2(t),dy2dt=b2x(t)z2(t)−b2[1−τ′(x)x′(t)]x(t−τ(x))z2(t−τ(x))e−γ2τ(x)−γ2y2(t),dz2dt=b2[1−τ′(x)x′(t)]x(t−τ(x))z2(t−τ(x))e−γ2τ(x)−β2z22(t)−μ2z1(t)z2(t), | (2.2) |
where r and K represent the growth rate of the resource and its carrying capacity, respectively. The parameters a1 and a2 represent the capture rate of z1 and z2, respectively. By [28], the inequality, 1−τ′(x)x′(t)>0, holds true, which implies that t−τ(x(t))) is a strictly increasing function in t. This shows that mature individuals become immature only by birth.
For system (2.2), there are the following basic hypotheses:
(A1) The constant parameters r, K, a1, a2, b1, b2, γ1, γ2, β1, β2, μ1, μ2 are all positive;
(A2) The state-dependent time delay τ(x) is a decreasing differentiable bounded function of the resource x, where τ′(x)≤0, and 0≤τm≤τ(x)≤τM with τ(+∞)=τm, τ(0)=τM.
To simplify system (2.2), we can readily scale off r and K by proper rescaling of t and x. Accordingly, system (2.2) becomes
{dxdt=x(t)(1−x(t)−a1z1(t)−a2z2(t)),dy1dt=b1x(t)z1(t)−b1[1−τ′(x)x′(t)]x(t−τ(x))z1(t−τ(x))e−γ1τ(x)−γ1y1(t),dz1dt=b1[1−τ′(x)x′(t)]x(t−τ(x))z1(t−τ(x))e−γ1τ(x)−β1z21(t)−μ1z1(t)z2(t),dy2dt=b2x(t)z2(t)−b2[1−τ′(x)x′(t)]x(t−τ(x))z2(t−τ(x))e−γ2τ(x)−γ2y2(t),dz2dt=b2[1−τ′(x)x′(t)]x(t−τ(x))z2(t−τ(x))e−γ2τ(x)−β2z22(t)−μ2z1(t)z2(t). | (2.3) |
In this paper, we will study the dynamics of system (2.3). The initial data for system (2.3) are
x(s)=Υ(s)≥0,z1(s)=Φ1(s)≥0,y1(s)=Ψ1(s)≥0for all s∈[−τM,0],z2(s)=Φ2(s)≥0,y2(s)=Ψ2(s)≥0for all s∈[−τM,0], |
with
Ψ1(0)=∫0−τ(x(0))b1x(s)z1(s)eγ1sdsandΨ2(0)=∫0−τ(x(0))b2x(s)z2(s)eγ2sds, |
which represent the number of immature consumers who survived to time t=0.
In this section, we first study the well-posedness properties of the solution for system (2.3) and then prove the existence and uniqueness of all equilibria.
Theorem 3.1. Let Υ(t)≥0, Φ1(t)≥0 and Φ2(t)≥0 for −τM≤t≤0, then the solution (x(t),y1(t),z1(t),y2(t),z2(t)) of system (2.3) is nonnegative and uniformly ultimate bounded for all t≥0.
Proof. Consider the first equation in system (2.3).
dxdt=x(t)(1−x(t)−a1z1(t)−a2z2(t)), |
with x(0)=Υ(0)≥0. Then
x(t)=x(0)exp∫t0[1−x(s)−a1z1(s)−a2z2(s)]ds≥0,t≥0. |
Suppose that there exists t>0 such that z1(t)=0. Let t∗=inf{t:t>0,z1(t)=0}, then
z′1(t∗)=b1[1−τ′(x(t∗))x′(t∗)]x(t−τ(x(t∗)))z1(t∗−τ(x(t∗)))e−γ1τ(x(t∗)). |
Since τ(x)>0, 1−τ′(x(t∗))x′(t∗)>0, t∗−τ(x(t∗))<t∗, it follows from the definition of t∗ that z1(t∗−τ(x(t∗)))>0, which implies that z′1(t∗)>0 and is a contradiction. Therefore, there does not exist t∗ and z1(t)>0 for all t>0.
Integrating the second equation of system (2.3), we have the following integral expression for y1(t).
y1(t)=e−γ1t(Φ1(0)+∫t0b1x(s)z1(s)eγ1sds−∫t−τ(x(t))−τ(x(0))b1x(s)z1(s)eγ1sds)=∫tt−τ(x(t))b1x(s)z1(s)e−γ1(t−s)ds. |
It is easy to see that y1(t)≥0 by the nonnegativity x(t), z1(t) and τ(x). In a similar way, we can prove the nonnegativity of z2(t) and y2(t). Hence, the solution (x(t),y1(t),z1(t),y2(t),z2(t)) of system (2.3) is nonnegative. Especially, when Υ(0)>0,Ψ1(0)>0,Φ1(0)>0, the solution of system (2.3) is positive.
Now we prove uniform ultimate boundedness of the solution. Define the following Lyapunov functional.
V=(b1a1+b2a2)x(t)+z1(t)+z2(t)+∫tt−τ(x(t))b1x(s)z1(s)e−γ1(t−s)ds+∫tt−τ(x(t))b2x(s)z2(s)e−γ2(t−s)ds. |
Calculating the time derivative of V(t) along the solutions of system (2.3), we have
V′(t)=(b1a1+b2a2)x(t)[1−x(t)−a1z1(t)−a2z2(t)]+b1x(t)z1(t)+b2x(t)z2(t)−γ1∫tt−τ(x(t))b1x(s)z1(s)e−γ1(t−s)ds−β1z21(t)−μ1z1(t)z2(t)−γ2∫tt−τ(x(t))b2x(s)z2(s)e−γ2(t−s)ds−β2z22(t)−μ2z1(t)z2(t)<−γV+(b1a1+b2a2)[(1+γ)x(t)−x2(t)]+γz1(t)−β1z21(t)+γz2(t)−β2z22(t)≤−γV+M1+M2+M3, |
where γ=min{γ1,γ2}, M1, M2 and M3 are the maximum values of quadratic function (b1/a1+b2/a2)[(1+γ)x(t)−x2(t)], γz1(t)−β1z21(t) and γz2(t)−β2z22(t), respectively. Obviously, M1, M2 and M3 are positive. Thus, lim supt→∞V(t)≤(M1+M2+M3)/γ and the solution of system (3.1) is uniformly ultimate bounded. It then follows that the solution of system (2.3) is uniformly ultimate bounded.
Since variables y1(t) and y2(t) of system (2.3) are decoupled from the other equations, we study the following reduced system:
{dxdt=x(t)(1−x(t))−a1x(t)z1(t)−a2x(t)z2(t),dz1dt=b1[1−τ′(x)x′(t)]x(t−τ(x))z1(t−τ(x))e−γ1τ(x)−β1z21(t)−μ1z1(t)z2(t),dz2dt=b2[1−τ′(x)x′(t)]x(t−τ(x))z2(t−τ(x))e−γ2τ(x)−β2z22(t)−μ2z1(t)z2(t). | (3.1) |
In the rest of this section, we will discuss the existence and patterns of equilibria (x,z1,z2) of system (3.1). The equilibria satisfy the following equations:
{x(1−x−a1z1−a2z2)=0,b1xz1e−γ1τ(x)−β1z21−μ1z1z2=0,b2xz2e−γ2τ(x)−β2z22−μ2z1z2=0. | (3.2) |
It is easy to see that system (3.1) has one trivial equilibrium E0=(0,0,0) and one semitrivial equilibrium E1=(1,0,0). And we obtain the following result for nontrivial equilibria.
Theorem 3.2. System (3.1) has exactly two semitrivial equilibria E2=(ˆx,^z1,0) and E3=(˜x,0,~z2). Assume that
min{β1μ2,μ1β2}<b1e−γ1τMb2e−γ2τm<b1e−γ1τmb2e−γ2τM<max{β1μ2,μ1β2};min{β1μ1,μ2β2}<a1a2<max{β1μ1,μ2β2}. | (3.3) |
Then there exists unique coexistence equilibrium E∗=(x∗,z∗1,z∗2).
Proof. There are three cases to prove the existence of nontrivial equilibria.
Case 1 If z2=0, then Eq (3.2) reduces to
{x(1−x−a1z1)=0,b1xz1e−γ1τ(x)−β1z21=0. |
The inequality, z1<b1/(a1b1+β1), is valid to make sure x=1−a1z1>0, y1=γ−11z1(b1−a1b1z1−β1z1)>0. Thus, we will investigate the existence and uniqueness of nontrivial equilibrium in Λ={z1∈R∣0<z1<b1/(a1b1+β1)}⊂R. Define f:¯Λ→R be a continuous mapping by
f(z1)=b1(1−a1z1)e−γ1τ(1−a1z1)−β1z1. |
Clearly, f(0)=b1xe−γ1τ(1)>0, f(b1/(a1b1+β1))=b1β1/(a1b1+β1)[e−γ1τ(β1/(a1b1+β1))−1]<0, which implies that f(z1) has at least one positive zero point ^z1.
Note that f′(^z1)=−a1b1e−γ1τ(1−a1^z1)+a1b1γ1(1−a1^z1)e−γ1τ(1−a1^z1)τ′(1−a1^z1)−β1<0, it then follows that f(z1) has a unique positive root in the interval (0,b1/(a1b1+β1)). Hence, system (3.1) has a unique boundary equilibrium E2=(ˆx,^z1,0).
Case 2 If z1=0, then system (3.1) has a unique boundary equilibrium E3=(˜x,0,~z2) by similar arguments in the case 1.
Case 3 If z1≠0 and z2≠0, then the coexistence equilibrium E∗=(x∗,z∗1,z∗2) satisfies the following equations:
{1−x−a1z1−a2z2=0,b1xe−γ1τ(x)−β1z1−μ1z2=0,b2xe−γ2τ(x)−β2z2−μ2z1=0. | (3.4) |
Solving Eq (3.4), we obtain that
{x∗=11+a1Γ1+a2Γ2;z∗1=x∗Γ1,Γ1=β2b1e−γ1τ(x∗)−μ1b2e−γ2τ(x∗)β1β2−μ1μ2;z∗2=x∗Γ2,Γ2=β1b2e−γ2τ(x∗)−μ2b1e−γ1τ(x∗)β1β2−μ1μ2. | (3.5) |
In order to make sure the positivity of x∗, z∗1 and z∗2, we get
min{β1μ2,μ1β2}<b1e−γ1τMb2e−γ2τm<b1e−γ1τ(x∗)b2e−γ2τ(x∗)<b1e−γ1τmb2e−γ2τM<max{β1μ2,μ1β2}. |
Now we prove the uniqueness of E∗. Suppose that f(x)=1−x−a1z1−a2z2, combining Eq (3.5), we have
f(x)=1−x−a1z1−a2z2=1−x−xb1e−γ1τ(x)a1β2−a2μ2β1β2−μ1μ2−xb2e−γ2τ(x)a2β1−a1μ1β1β2−μ1μ2=1−x−xf1(x)−xf2(x), |
where f1(x)=b1e−γ1τ(x)(a1β2−a2μ2)/(β1β2−μ1μ2), f2(x)=b2e−γ2τ(x)(a2β1−a1μ1)/(β1β2−μ1μ2).
Obviously, f(0)=1>0 and
f(1)=−a1(β2b1e−γ1τ(1)−μ1b2e−γ2τ(1))−a2(β1b2e−γ2τ(1)−μ2b1e−γ1τ(1))β1β2−μ1μ2<0, |
which implies that f(x) has at least one positive zero point x∗.
It follows from f1(x∗)+f2(x∗)=1/x∗−1 that
f′(x∗)=−1−[f1(x∗)+f2(x∗)]−x∗[f′1(x∗)+f′2(x∗)]=−1x∗−x∗[f′1(x∗)+f′2(x∗)]. |
If min{β1/μ1,μ2/β2}<a1/a2<max{β1/μ1,μ2/β2}, then f′(x∗)<0. Thus, f(x) has a unique positive root x∗ in the interval (0,1), and system (3.1) has a unique coexistence equilibrium E∗=(x∗,z∗1,z∗2). This completes the proof.
In this section, the linearized stability of equilibria will be studied. We utilize the method proposed by Cooke [34] to linearize system (3.1).
Let ˜E=(˜x,~z1,~z2) be an arbitrary equilibrium. Then the linearization of system (3.1) is
{x′(t)=Ax(t)−a1˜xz1(t)−a2˜xz2(t),z′1(t)=A1x+b1e−γ1τ(˜x)~z1x(t−τ(˜x))+b1e−γ1τ(˜x)˜xz1(t−τ(˜x))+B1z1+C1z2,z′2(t)=A2x+b2e−γ2τ(˜x)~z2x(t−τ(˜x))+b2e−γ2τ(˜x)˜xz2(t−τ(˜x))+B2z1+C2z2, | (4.1) |
where
A=1−2˜x−a1~z1−a2~z2,A1=−b1˜x~z1τ′(˜x)e−γ1τ(˜x)(γ1−A),A2=−b2˜x~z2τ′(˜x)e−γ2τ(˜x)(γ2−A),B1=a1b1(˜x)2~z1τ′(˜x)e−γ1τ(˜x)−2β1~z1−μ1~z2,B2=a1b2(˜x)2~z2τ′(˜x)e−γ2τ(˜x)−μ2~z2,C1=a2b1(˜x)2~z2τ′(˜x)e−γ1τ(˜x)−μ1~z1,C2=a2b2(˜x)2~z2τ′(˜x)e−γ2τ(˜x)−2β2~z2−μ2~z1. |
The corresponding characteristic equation of system (4.1) is as follows:
|λ−Aa1˜xa2˜x−A1−b1~z1e−(γ1+λ)τ(˜x)λ−B1−b1˜xe−(γ1+λ)τ(˜x)−C1−A2−b2~z2e−(γ2+λ)τ(˜x)−B2λ−C2−b2˜xe−(γ2+λ)τ(˜x)|=0. |
For the extinction equilibrium E0=(0,0,0), it follows that A=1, A1=0, B1=0, C1=0, A2=0, B2=0, C2=0, then (λ−1)λ2=0, and λ=1>0 is one of these eigenvalues. Thus, we have the following result.
Theorem 4.1. The extinction equilibrium E0=(0,0,0) is unstable.
For the trivial equilibrium E1=(1,0,0), the characteristic equation is as follows.
(λ+1)(λ−b1e−(γ1+λ)τ(1))(λ−b2e−(γ2+λ)τ(1))=0. |
Obviously, λ=−1<0 is one of these eigenvalues. All the other eigenvalues λ satisfy the equations λeτ(1)(γ1+λ)=b1>0 and λeτ(1)(γ2+λ)=b2>0, which always have real, positive solutions. Hence, the conclusion about the linearized stability of E1 is as follows.
Theorem 4.2. The trivial equilibrium E1=(1,0,0) is a saddle point and unstable.
In order to obtain the linearized stability of E2 and E3, we first give the results on the real positive roots of a quartic equation. Suppose that
v4+K1v3+K2v2+K3v+K4=0. | (4.2) |
Let
M=12K2−316K21,N=132K31−18K1K2+K3,Δ=(N2)3+(M2)3,σ=−1+√3i2,y1=3√−N2+√Δ+3√−N2−√Δ,y2=σ3√−N2+√Δ+σ23√−N2−√Δ,y3=σ23√−N2+√Δ+σ3√−N2−√Δ,zi=yi−3K14,i=1,2,3. |
By virtue of [35], we have the following lemma.
Lemma 4.3. The following statements hold true for Eq (4.2):
(i) If K4<0, then Eq (4.2) has at least one positive root;
(ii) If K4≥0 and Δ≥0, then Eq (4.2) has positive roots if and only if z1>0 and h(z1)<0;
(iii) If K4≥0 and Δ<0, then Eq (4.2) has positive roots if and only if there exists at least one z∗∈{z1,z2,z3} such that z∗>0 and Q(z∗)≤0, where Q(z)=z4+K1z3+K2z2+K3z+K4.
We are now in a position to prove the linearized stability of E2.
Theorem 4.4. If ^z1<3/(4a1) and b1μ2e−γ1τ(ˆx)>b2β1e−γ2τ(ˆx), then the boundary equilibrium E2=(ˆx,^z1,0) is locally asymptotically stable.
Proof. The characteristic equation for the boundary equilibrium E2 is as follows:
(λ−C2−b2ˆxe−(γ2+λ)τ(ˆx))[(λ−A)(λ−B1−b1ˆxe−(γ1+λ)τ(ˆx))+a1ˆx(A1+b1^z1e−(γ1+λ)τ(ˆx))]=0. | (4.3) |
It is easy to see that some of the eigenvalues satisfy the following equation:
λ+μ2^z1−b2ˆxe−(γ2+λ)τ(ˆx)=0. | (4.4) |
Claim. If b1μ2e−γ1τ(ˆx)>b2β1e−γ2τ(ˆx), that is, ^z1>μ−12b2ˆxe−γ2τ(ˆx), then all eigenvalues of Eq (4.4) have negative real parts.
Obviously, if τ(ˆx)=0, then Eq (4.4) has a unique negative real root λ=b2ˆx−μ2^z1<0. In order to study whether any roots cross the imaginary axis, let λ=iω with ω>0 and substitute it into Eq (4.4), it follows from ^z1>μ−12b2ˆxe−γ2τ(ˆx) that
ω2=(b2ˆxe−γ2τ(ˆx))2−(μ2^z1)2<0. |
Therefore, Eq (4.4) has no purely imaginary roots and each root has a negative real part.
Other roots are given by equation:
G(λ)=(λ−A)(λ−B1−b1ˆxe−(γ1+λ)τ(ˆx))+a1ˆx(A1+b1^z1e−(γ1+λ)τ(ˆx))=λ2+H1λ+H2+(N1λ+N2)e−λτ(ˆx)=0, | (4.5) |
where H1=−A−B1, H2=AB1+a1A1ˆx, N1=−β1^z1, N2=Aβ1^z1+a1β1^z12.
Since τ′(x)≤0, we have G(0)=β1ˆx^z1−a1β1γ1ˆx(^z1)2τ′(ˆx)+a1β1(^z1)2>0, and thus Eq (4.5) has no zero roots.
Next, we prove that Eq (4.5) has no purely imaginary roots.
Assume, by contradiction, that Eq (4.5) has a purely imaginary root λ=iv, where v>0. Substituting it into Eq (4.5) and separating the real and imaginary parts, we have
{v2−H2=N1vsin(τ(ˆx)v)+N2cos(τ(ˆx)v),−H1v=N1vcos(τ(ˆx)v)−N2sin(τ(ˆx)v). |
Considering sin(τ(ˆx)v)2+cos(τ(ˆx)v)2=1, it follows that
v4+D1v2+D2=0, | (4.6) |
where D1=H21−2H2−N21, D2=H22−N22.
In view of τ′(ˆx)≤0, there are the following two cases.
Case (1). If τ′(ˆx)=0, then τ(ˆx)≥0, since ˆx>0, it follows from the hypothesis (A2) that τ(ˆx)≠0 and τ(ˆx)>0. Therefore, D1=A2+3β21^z12>0, D2=β21^z12(4ˆx−1)=β21^z12(3−4a1^z1)>0, which implies that Eq (4.5) has no purely imaginary roots and each root of characteristic equation has a negative real part.
Case (2). If τ′(ˆx)<0, according to Lemma 4.3, we have K1=0,K2=D1,K3=0,K4=D2=β21^z12(3−4a1^z1)−4a1β21γ1ˆx2^z13τ′(ˆx)+(a1β1γ1ˆx^z12τ′(ˆx))2>0 providing ^z1<3/(4a1). Hence, Eq (4.6) has positive roots if the equation satisfies the case (ⅰ) or (ⅱ) in Lemma 4.3. However, Zi=Yi−3K1/4=Yi=0, i=1,2,3. Then neither case (ⅰ) nor case (ⅱ) in Lemma 4.3 holds true, Eq (4.6) has no positive roots, that is, Eq (4.5) has no purely imaginary roots. Therefore, all eigenvalues of Eq (4.3) have negative real parts, and E2 is locally asymptotically stable. This completes the proof.
Similarly, we have the following observation about the boundary equilibrium E3.
Theorem 4.5. If ~z2<3/(4a2) and b2μ1e−γ2τ(˜x)>b1β2e−γ1τ(˜x), then the boundary equilibrium E3=(˜x,0,~z2) is locally asymptotically stable.
In this section, we investigate the global attractivity of the coexistence equilibrium E∗ by a method of asymptotic estimates.
Theorem 5.1. Let condition (3.3) hold, and assume that:
1>a1b1β1e−γ1τ(1)+a2b2β2e−γ2τ(1); | (5.1) |
b1(1−a1b1β1e−γ1τ(1)−a2b2β2e−γ2τ(1))e−γ1τ(0)>μ1b2β2e−γ2τ(1); | (5.2) |
b2(1−a1b1β1e−γ1τ(1)−a2b2β2e−γ2τ(1))e−γ2τ(0)>μ2b1β1e−γ1τ(1); | (5.3) |
(a1b1β2+a2b1μ2)e−γ1τ(0)+(a1b2μ1+a2b2β1)e−γ2τ(0)>β1β2−μ1μ2. | (5.4) |
Then the coexistence equilibrium E∗ is globally attractive.
Proof. Since e−γτ(x) is increase with respect to x, then e−γτ(1)>e−γτ(0). It follows from inequality (5.2) that
b1e−γ1τ(1)>b1e−γ1τ(0)>b1(1−a1b1β1e−γ1τ(1)−a2b2β2e−γ2τ(1))e−γ1τ(0)>μ1b2β2e−γ2τ(1). |
Similarly, we have
b2e−γ2τ(1)>b2e−γ2τ(0)>b2(1−a1b1β1e−γ1τ(1)−a2b2β2e−γ2τ(1))e−γ2τ(0)>μ2b1β1e−γ1τ(1). |
Namely,
μ1β2<b1e−γ1τMb2e−γ2τm<b1e−γ1τ(1)b2e−γ2τ(1)<b1e−γ1τmb2e−γ2τM<β1μ2, |
which implies that both the boundary equilibria E2 and E3 are unstable.
For the system
{dm1dt=m1(t)(1−m1(t)),dm(1)1dt=b1[1−τ′(m1)m′1(t)]m1(t−τ(m1))m(1)1(t−τ(m1))e−γ1τ(m1)−β1(m(1)1(t))2,dm(2)1dt=b2[1−τ′(m1)m′1(t)]m1(t−τ(m1))m(2)1(t−τ(m1))e−γ2τ(m1)−β2(m(2)1(t))2, |
we have the unique positive equilibrium m∗=(1,b1/β1e−γ1τ(1),b2/β2e−γ2τ(1)).
Clearly, for system (3.1), we obtain that
{dxdt<x(t)(1−x(t)),dz1dt<b1[1−τ′(x)x′(t)]x(t−τ(x(t)))z1(t−τ(x(t)))e−γ1τ(x(t))−β1z21(t),dz2dt<b2[1−τ′(x)x′(t)]x(t−τ(x(t)))z2(t−τ(x(t)))e−γ2τ(x(t))−β2z22(t). | (5.5) |
By Theorem 3.1, for any ϵ1>0, we can prove that there exists a t1>0 such that the following inequalities hold true for all t≥t1.
{x(t)<M1=1+ϵ1,z1(t)<M(1)1=b1β1e−γ1τ(M1)+ϵ12,z2(t)<M(2)1=b2β2e−γ2τ(M1)+ϵ12. | (5.6) |
We first select ϵ1>0 and the corresponding t1>0 such that
{1−a1M(1)1−a2M(2)1>0,b1(1−a1M(1)1−a2M(2)1)e−γ1τ(M1)>μ1M(2)1,b2(1−a1M(1)1−a2M(2)1)e−γ2τ(M1)>μ2M(1)1. | (5.7) |
Conditions (5.1)-(5.3) can guarantee the existence of ϵ1 to satisfy conditions (5.6) and (5.7). Based on ϵ1>0,t1>0, we choose sufficiently small ϵ2>0 to make that ϵ2<min{1/2,ϵ1}, and
{1−a1M(1)1−a2M(2)1−ϵ2>0,[b1(1−a1M(1)1−a2M(2)1)e−γ1τ(M1)−μ1M(2)1]1β1−ϵ22>0,[b2(1−a1M(1)1−a2M(2)1)e−γ2τ(M1)−μ2M(1)1]1β1−ϵ22>0. | (5.8) |
In view of inequality (5.7), it is possible to choose positive number ϵ2 satisfying inequality (5.8). Combining with inequalities (5.6) and (5.8), we obtain that
{dxdt>x(t)[(1−a1M(1)1−a2M(2)1)−x(t)],dz1dt>b1[1−τ′(x)x′(t)]x(t−τ(x(t)))z1(t−τ(x(t)))e−γ1τ(x(t))−β1z21(t)−μ1M(2)1z1(t),dz2dt>b2[1−τ′(x)x′(t)]x(t−τ(x(t)))z2(t−τ(x(t)))e−γ2τ(x(t))−β2z22(t)−μ2M(1)1z2(t). |
It follows that there exists a t2>t1 to make that
{x(t)>N1=1−a1M(1)1−a2M(2)1−ϵ2,z1(t)>N(1)1=(b1N1e−γ1τ(N1)−μ1M(2)1)1β1−ϵ22,z2(t)>N(2)1=(b2N1e−γ2τ(N1)−μ2M(1)1)1β2−ϵ22. | (5.9) |
By system (3.1) and inequality (5.9), we have for t>t2,
{dxdt<x(t)[(1−a1N(1)1−a2N(2)1)−x(t)],dz1dt<b1[1−τ′(x)x′(t)]x(t−τ(x))z1(t−τ(x))e−γ1τ(x)−β1z21(t)−μ1N(2)1z1(t),dz2dt<b2[1−τ′(x)x′(t)]x(t−τ(x))z2(t−τ(x))e−γ2τ(x)−β2z22(t)−μ2N(1)1z2(t). | (5.10) |
With the first inequality of (5.7), we see that
1−a1N(1)1−a2N(2)1=1−a1[(b1N1e−γ1τ(N1)−μ1M(2)1)1β1−ϵ22]−a2[(b2N1e−γ2τ(N1)−μ2M(1)1)1β2−ϵ22]>1−a1b1β1N1e−γ1τ(N1)−a2b2β2N1e−γ2τ(N1)>1−a1b1β1M1e−γ1τ(M1)−a2b2β2M1e−γ2τ(M1)>0. | (5.11) |
Then from inequalities (5.10) and (5.11), there exists a t3>t2 and 0<ϵ3<min{1/3,ϵ2} such that, for t>t3,
{x(t)<M2=1−a1N(1)1−a2N(2)1+ϵ3,z1(t)<M(1)2=(b1M2e−γ1τ(M2)−μ1N(2)1)1β1+ϵ32,z2(t)<M(2)2=(b2M2e−γ2τ(M2)−μ2N(1)1)1β2+ϵ32. | (5.12) |
We next prove that the estimates M(1)2 and M(2)2 are positive.
b1M2e−γ1τ(M2)−μ1N(2)1=b1(1−a1N(1)1−a2N(2)1+ϵ3)e−γ1τ(M2)−μ1[(b2N1e−γ2τ(N1)−μ2M(1)1)1β2−ϵ22]>b1(1−a1N(1)1−a2N(2)1)e−γ1τ(M2)−μ1b2β2N1e−γ2τ(N1)>b1(1−a1M(1)1−a2M(2)1)e−γ1τ(0)−μ1b2β2M1e−γ2τ(M1)>0, | (5.13) |
and
b2M2e−γ2τ(M2)−μ2N(1)1=b2(1−a1N(1)1−a2N(2)1+ϵ3)e−γ2τ(M2)−μ2[(b1N1e−γ1τ(N1)−μ1M(2)1)1β1−ϵ22]>b2(1−a1N(1)1−a2N(2)1)e−γ2τ(M2)−μ2b1β1N1e−γ1τ(N1)>b2(1−a1M(1)1−a2M(2)1)e−γ2τ(0)−μ2b1β1M1e−γ1τ(M1)>0. | (5.14) |
In view of the upper estimates in equality (5.12), we will get a lower set of estimates. Since ϵ3<ϵ1, inequalities (5.13) and (5.14), the following result is valid.
1−a1M(1)2−a2M(2)2=1−a1[(b1M2e−γ1τ(M2)−μ1N(2)1)1β1+ϵ32]−a2[(b2M2e−γ2τ(M2)−μ2N(1)1)1β2+ϵ32]>1−a1(b1β1M2e−γ1τ(M2)+ϵ32)−a2(b2β2M2e−γ2τ(M2)+ϵ32)>1−a1(b1β1M1e−γ1τ(M1)+ϵ32)−a2(b2β2M1e−γ2τ(M1)+ϵ32)>0. | (5.15) |
Denote n2, n(1)2, n(2)2 as follows:
{n2=1−a1M(1)2−a2M(2)2,n(1)2=b1n2e−γ1τ(n2)−μ1M(2)2,n(2)2=b2n2e−γ2τ(n2)−μ2M(1)2. | (5.16) |
By inequalities (5.2) and (5.15) and the second inequality of (5.7), we get that
n(1)2=b1(1−a1M(1)2−a2M(2)2)e−γ1τ(n2)−μ1M(2)2>b1(1−a1M(1)2−a2M(2)2)e−γ1τ(0)−μ1M(2)1>0. | (5.17) |
It follows from inequalities (5.3) and (5.15) and the third inequality of (5.7) that
n(2)2=b2(1−a1M(1)2−a2M(2)2)e−γ2τ(n2)−μ2M(1)2>b2(1−a1M(1)2−a2M(2)2)e−γ2τ(0)−μ2M(1)1>0. | (5.18) |
Combining with inequalities (5.15)–(5.18), there exists a 0<ϵ4<min{1/4,ϵ3} satisfying
{n2−ϵ4>0,(b1n2e−γ1τ(n2)−μ1M(2)2)1β1−ϵ42>0,(b2n2e−γ2τ(n2)−μ2M(1)2)1β2−ϵ42>0. | (5.19) |
With the upper estimates in inequality (5.12), we obtain the following comparative system for all t>t3.
{dxdt>x(t)[(1−a1M(1)2−a2M(2)2)−x(t)],dz1dt>b1[1−τ′(x)x′(t)]x(t−τ(x))z1(t−τ(x))e−γ1τ(x)−β1z21(t)−μ1M(2)2z1(t),dz2dt>b2[1−τ′(x)x′(t)]x(t−τ(x))z2(t−τ(x))e−γ2τ(x)−β2z22(t)−μ2M(1)2z2(t). |
Using inequality (5.19), there exists a t4>t3 such that
{x(t)>N2=1−a1M(1)2−a2M(2)2−ϵ4,z1(t)>N(1)2=(b1N2e−γ1τ(N2)−μ1M(2)2)1β1−ϵ42,z2(t)>N(2)2=(b2N2e−γ2τ(N2)−μ2M(1)2)1β2−ϵ42. |
The estimates N2, N(1)2 and N(2)2 are all positive by inequality (5.19). Therefore, we obtain that, for t>t2,
N1<x(t)<M1,N(1)1<z1(t)<M(1)1,N(2)1<z2(t)<M(2)1, |
and for t>t4,
N2<x(t)<M2,N(1)2<z1(t)<M(1)2,N(2)2<z2(t)<M(2)2. |
Now we compare the obtained estimates, respectively:
M2−M1=1−a1N(1)1−a2N(2)1+ϵ3−(1−ϵ1)<ϵ3−ϵ1<0,M(1)2−M(1)1=(b1M2e−γ1τ(M2)−μ1N(2)1)1β1+ϵ32−b1β1e−γ1τ(M1)−ϵ12<b1β1M2e−γ1τ(M2)−b1β1M1e−γ1τ(M1)+12(ϵ3−ϵ1)<b1β1e−γ1τ(M1)(M2−M1)+12(ϵ3−ϵ1)<0. |
Similarly, M(2)2−M(2)1<0.
Moreover,
N2−N1=1−a1M(1)2−a2M(2)2−ϵ4−(1−a1M(1)1−a2M(2)1)+ϵ2=−a1(M(1)2−M(1)1)−a2(M(2)2−M(2)1)+(ϵ2−ϵ4)>0, |
and
N(1)2−N(1)1=(b1N2e−γ1τ(N2)−μ1M(2)2)1β1−ϵ42−(b1N1e−γ1τ(N1)−μ1M(2)1)1β1+ϵ22>b1β1e−γ1τ(N1)(N2−N1)−μ1β1(M(2)2−M(2)1)+12(ϵ2−ϵ4)>0. |
Similarly, N(2)2−N(2)1>0.
Hence, from the above arguments, we have for t>t4
N1<N2<x(t)<M2<M1,N(1)1<N(1)2<z1(t)<M(1)2<M(1)1,N(2)1<N(2)2<z2(t)<M(2)2<M(2)1. |
By extending the above step, it then follows that
N1<N2<N3<⋯<Nn<x(t)<Mn<⋯<M3<M2<M1,N(1)1<N(1)2<N(1)3<⋯<N(1)n<z1(t)<M(1)n<⋯<M(1)3<M(1)2<M(1)1,N(2)1<N(2)2<N(2)3<⋯<N(2)n<z2(t)<M(2)n<⋯<M(2)3<M(2)2<M(2)1, |
where
Mn=1−a1N(1)n−1−a2N(2)n−1+ϵ2n−1,M(1)n=(b1Mne−γ1τ(Mn)−μ1N(2)n−1)1β1+ϵ2n−12,M(2)n=(b2Mne−γ2τ(Mn)−μ2N(2)n−1)1β2+ϵ2n−12, | (5.20) |
with n=2,3,4,⋯, and
Nn=1−a1M(1)n−a2M(2)n−ϵ2n,N(1)n=(b1Nne−γ1τ(Nn)−μ1M(2)n)1β1−ϵ2n2,N(2)n=(b2Nne−γ2τ(Nn)−μ2M(2)n)1β2−ϵ2n−12, | (5.21) |
with n=1,2,3,⋯.
Since ϵn<1/n, we have ϵn→0 as n→∞. And the monotone sequences Nn, N(1)n, N(2)n, Mn, M(1)n, and M(2)n converge to positive limits as n→∞. Let
M∗=limn→∞MnandN∗=limn→∞Nn,M(1)∗=limn→∞M(1)nandN(1)∗=limn→∞N(1)n,M(2)∗=limn→∞M(2)nandN(2)∗=limn→∞N(2)n. | (5.22) |
By virtue of Eqs (5.20)–(5.22) and limn→∞ϵn=0, it follows that
M∗=1−a1N(1)∗−a2N(2)∗,N∗=1−a1M(1)∗−a2M(2)∗, | (5.23) |
and
β1M(1)∗=b1M∗e−γ1τ(M∗)−μ1N(2)∗,β1N(1)∗=b1N∗e−γ1τ(N∗)−μ1M(2)∗,β2M(2)∗=b2M∗e−γ2τ(M∗)−μ2N(1)∗,β2N(2)∗=b2N∗e−γ2τ(N∗)−μ2M(1)∗. | (5.24) |
From Eq (5.24), we have
M(1)∗−N(1)∗=b1β2B1+b2μ1B2β1β2−μ1μ2,M(2)∗−N(2)∗=b1μ2B1+b2β1B2β1β2−μ1μ2, | (5.25) |
where B1=(M∗e−γ1τ(M∗)−N∗e−γ1τ(N∗)), B2=(M∗e−γ2τ(M∗)−N∗e−γ2τ(N∗)).
Combining Eqs (5.23) and (5.25), we see that
M∗−N∗=a1(M(1)∗−N(1)∗)−a2(M(2)∗−N(2)∗)=(a1b1β2+a2b1μ2)B1+(a1b2μ1+a2b2β1)B2β1β2−μ1μ2. |
Let Q1=(a1b1β2+a2b1μ2)/(β1β2−μ1μ2), Q2=(a1b2μ1+a2b2β1)/(β1β2−μ1μ2), then
M∗(1−Q1e−γ1τ(M∗)−Q2e−γ2τ(M∗))=N∗(1−Q1e−γ1τ(N∗)−Q2e−γ2τ(N∗)). | (5.26) |
Denote h(x)=x(1−Q1e−γ1τ(x)−Q2e−γ2τ(x)), for all x≥0. From (5.4) and τ′(x)≤0, we have
h′(x)=1−Q1e−γ1τ(x)−Q2e−γ2τ(x)+x[Q1γ1τ′(x)e−γ1τ(x)+Q2γ2τ′(x)e−γ2τ(x)]<1−Q1e−γ1τ(x)−Q2e−γ2τ(x)<1−Q1e−γ1τ(0)−Q2e−γ2τ(0)<0, |
and thus, h(x) is a monotone decrease function with respect to x. It then follows from Eq (5.26) that M∗=N∗. Therefore, M(1)∗=N(1)∗ and M(2)∗=N(2)∗. Further, the relations in Eqs (5.23) and (5.24) are also true for E∗=(x∗,z∗1,z∗2). Then by the uniqueness of the coexistence equilibrium of system (3.1), we know that (M∗,M(1)∗,M(2)∗) and (N∗,N(1)∗,N(2)∗) are the coexistence equilibrium of system (3.1), and hence M∗=N∗=x∗, M(1)∗=M(1)∗=z∗1, M(2)∗=M(2)∗=z∗2. Accordingly, x∗=limt→∞x(t), z∗1=limt→∞z1(t) and z∗2=limt→∞z2(t). The proof is complete.
Next we deduce the global attractivity for the other variables y1 and y2 in system (2.3). By using the integral form for y1 and y2, we obtain that
y∗i=limt→∞yi(t)=∫tt−τ(x∗)bix∗z∗ie−γi(t−s)ds,i=1,2. |
Therefore, we have the following result.
Theorem 5.2. Let condition (3.3) hold. Assume that conditions (5.1)-(5.4) hold true, then the coexistence equilibrium E∗=(x∗,y∗1,z∗1,y∗2,z∗2) is globally attractive for system (2.3).
At the end of this section, we give the dynamical results of two boundary equilibria and coexistence equilibrium for the original system (2.2). The reduced system of model (2.2) is as follows.
{dxdt=rx(t)(1−x(t)K)−a1x(t)z1(t)−a2x(t)z2(t),dz1dt=b1[1−τ′(x)x′(t)]x(t−τ(x))z1(t−τ(x))e−γ1τ(x)−β1z21(t)−μ1z1(t)z2(t),dz2dt=b2[1−τ′(x)x′(t)]x(t−τ(x))z2(t−τ(x))e−γ2τ(x)−β2z22(t)−μ2z1(t)z2(t). | (5.27) |
Obviously, system (5.27) has exactly one trivial equilibrium E0=(0,0,0), one semitrivial equilibrium E1=(K,0,0), two boundary equilibria E2=(ˆx,^z1,0) and E3=(˜x,0,~z2). And condition (3.3) holds true, then there exists a unique coexistence equilibrium E∗=(x∗,z∗1,z∗2).
Theorem 5.3. If ^z1<3r/(4a1) and b1μ2e−γ1τ(ˆx)>b2β1e−γ2τ(ˆx), then the boundary equilibrium E2=(ˆx,^z1,0) of system (5.27) is locally asymptotically stable.
Theorem 5.4. If ~z2<3r/(4a2) and b2μ1e−γ2τ(˜x)>b1β2e−γ1τ(˜x), then the boundary equilibrium E3=(˜x,0,~z2) of system (5.27) is locally asymptotically stable.
Theorem 5.5. Let condition (3.3) hold, and assume that:
r>a1b1β1Ke−γ1τ(K)+a2b2β2Ke−γ2τ(K); | (5.28) |
b1(r−a1Kb1β1e−γ1τ(K)−a2Kb2β2e−γ2τ(K))e−γ1τ(0)1r>μ1b2β2e−γ2τ(K); | (5.29) |
b2(r−a1Kb1β1e−γ1τ(K)−a2Kb2β2e−γ2τ(K))e−γ2τ(0)1r>μ2b1β1e−γ1τ(K); | (5.30) |
Kr[(a1b1β2+a2b1μ2)e−γ1τ(0)+(a1b2μ1+a2b2β1)e−γ2τ(0)]>β1β2−μ1μ2. | (5.31) |
Then the coexistence equilibrium E∗ of system (5.27) is globally attractive.
In this paper, given that the maturity time of Antarctic whales and seals varies with the number of krill available around the Second World War, we formulated a consumer-resource competition model that, for the first time, incorporates a state-dependent maturity time delay associated with resource changes and structured consumer species. The main difference from the state-dependent delay equations previously studied is that model (2.3) directly manifests the relationship between resources and maturity time of consumers through the correction term, 1−τ′(x(t))x′(t).
Besides, the state-dependent delay τ(x(t)) reflects exploitation and interference competition effects of two consumer species. On one hand, the exploitative ability of the mature individual is stronger, the juvenile will get more resources, which leads to a shorter maturation time. On the other hand, because of the limited resource, the consumer species spend more time and energy to obtain resource for keeping themselves alive. Unfortunately, what often happens is that there is not enough food for their children and so they take longer time to mature. Further, it is clear that the greater the ability of an adult to intervene is, the shorter the maturity time is.
From mathematical point of view, firstly, we study the well-posedness of the solution for model (2.3) and the existence and uniqueness of all equilibria. We then show the linear stability of equilibria. The trivial equilibria E0=(0,0,0) and E1=(1,0,0) are always unstable, which can be explained by the fact that since the resource is self-renewing with a logistic growth and two competitive species depend entirely on it, the resource are not used up or maximized. For the linear stability of boundary equilibrium E2=(ˆx,^z1,0), according to Theorem 4.4, the first sufficient condition ^z1<3/(4a1) is equivalent to ˆx>1/4, which means the resource must be the specific level to make sure that one of the consumer species survives. For the original system (2.2), in Theorem 5.3, the condition is changed to ^z1<3r/(4a1), which is equivalent to ˆx>K−3K/(4r) and implies that the resource must be above a certain value of the interaction between the growth rate of resource and its carrying capacity. And another sufficient condition b1μ2e−γ1τ(ˆx)>b2β1e−γ2τ(ˆx) implies (b1ˆxe−γ1ˆx)/β1>(b2ˆxe−γ2ˆx)/μ2, on the basis of the threshold resource ˆx, when the maximum growth of the first species is large than the interspecific competitive effect on the second species, the second species has a negative growth effect, which illustrates the first species will win the competition. There is a similar biological explanation for the linear stability of E3=(˜x,0,~z2). Finally, we discuss the global properties of E∗. Theorem 5.1 shows the sufficient conditions for E∗ to be globally attractive. The condition (5.1) implies that the number of the two consumers are at their potential maximum, and the maximum self-updating value of the resource is large than the exploitation effects; conditions (5.2) and (5.3) imply that when the resource is at its minimum, the number of the consumers who were born at time t−τ(x(t)) and still alive now are enough to make up for the possible interspecific competition effects; condition (5.4) is a technical assumption and has no practical biological significance. For the global properties of E∗ in original system (2.2), the condition (5.28) implies that when the maximum carrying capacity K of the resource is reached, the two consumers are at their potential maximum, and the growth rate r of the resource is larger than the exploitation effect. Conditions (5.29) and (5.30) have biological explanations similar to conditions (5.2) and (5.3).
This paper only considers the case of one resource. And since the fitting of state-dependent delay τ(x(t)) is very challenging, we are unable to validate theoretical results by numerical simulations. We would like to leave this problem and propose a competition model with state-dependent delay between two species for two resources in the further research.
This work is supported by the National Natural Science Foundation of China (Grant Nos. 11671327, 11901326). We are grateful to the editor and reviewers for their valuable comments and suggestions that greatly improved the presentation of this paper.
The authors declared that they have no conflicts of interest to this work.
[1] | J. H. Connell, The influence of interspecific competition and other factors on the distribution of the barnacle chthamalus stellatus, Ecology, 42 (1961), 710-723. |
[2] | P. K. Dayton, Competition, disturbance and community organisation; the provision and subsequent utilization of space in a rocky intertidal community, Ecol. Monogr., 41 (1971), 351-389. |
[3] | R. S. Miller, Pattern and process in competition, Adv. Ecol. Res., 4 (1967), 1-74. |
[4] | F. G. Bader, J. S. Meyer, A. G. Fredrickson, H. M. Tsuchiya, Comments on microbial growth rate, Biotechnol. Bioeng., 17 (1975), 279-283. |
[5] | A. M. De Roos, J. A. J. Metz, L. Persson, Ontogenetic symmetry and asymmetry in energetics, J. Math. Biol., 66 (2013), 889-914. |
[6] | L. Persson, A. M. de Roos, Symmetry breaking in ecological systems through different energy efficiencies of juveniles and adults, Ecology, 94 (2013), 1487-1498. |
[7] | D. Tilman, Resource competition and community structure, Monogr. Popul. Biol., 17 (1982), 1-296. |
[8] | K. Gopalsamy, Convergence in a resource-based competition system, Bull. Math. Biol., 48 (1986), 681-699. |
[9] | H. D. Landahl, B. D. Hansen, A three stage population model with cannibalism, Bull. Math. Biol., 37 (1975), 11-17. |
[10] | P. J. Wangersky, W. J. Cunningham, On time lags in equations of growth, Proc. Natl. Acad. Sci. USA, 42 (1956), 699-702. |
[11] | P. J. Wangersky, W. J. Cunningham, Time lag in prey-predator population models, Ecology, 38 (1957), 136-139. |
[12] | W. G. Aiello, H. I. Freedman, A time-delay model of single-species growth with stage structure, Math. Biosci., 101 (1990), 139-153. |
[13] | K. Gopalsamy, Time lags and global stability in two-species competition, Bull. Math. Biol., 42 (1980), 729-737. |
[14] | R. Gambell, Birds and mammals-antarctic whales, in Antarctica (eds. W. Bonner and D. Walton), Pergamon Press, NewYork, (1985), 223-241. |
[15] | W. G. Aiello, H. I. Freedman, J. Wu, Analysis of a model representing stage-structured population growth with state-dependent time delay, SIAM J. Appl. Math., 52 (1992), 855-869. |
[16] | J. F. M. Al-Omari, S. A. Gourley, Stability and traveling fronts in Lotka-Volterra competition models with stage structure, SIAM J. Appl. Math., 63 (2003), 2063-2086. |
[17] | J. F. M. Al-Omari, The effect of state dependent delay and harvesting on a stage-structured predator-prey model, Appl. Math. Comput., 271 (2015), 142-153. |
[18] | T. Cassidy, M. Craig, A. R. Humphries, Equivalences between age structured models and state dependent distributed delay differential equations, Math. Biosci. Eng., 16 (2019), 5419-5450. |
[19] | F. Chen, D. Sun, J. Shi, Periodicity in a food-limited population model with toxicants and state dependent delays, J. Math. Anal. Appl., 288 (2003), 136-146. |
[20] | Q. Hu, X.-Q. Zhao, Global dynamics of a state-dependent delay model with unimodal feedback, J. Math. Anal. Appl., 399 (2013), 133-146. |
[21] | M. Kloosterman, S. A. Campbell, F. J. Poulin, An NPZ model with state-dependent delay due to size-structure in juvenile zooplankton, SIAM J. Appl. Math., 76 (2016), 551-577. |
[22] | Y. Lv, Y. Pei, R. Yuan, Modeling and analysis of a predator-prey model with state-dependent delay, Int. J. Biomath., 11 (2018), 1850026(1-22). |
[23] | F. M. G. Magpantay, N. Kosovalic, An age-structured population model with state-dependent delay: derivation and numerical integration, SIAM J. Numer. Anal., 52 (2014), 735-756. |
[24] | A. Rezounenko, Partial differential equations with discrete and distributed state-dependent delays, J. Math. Anal. Appl., 326 (2007), 1031-1045. |
[25] | L. Zhang, S. Guo, Slowly oscillating periodic solutions for the Nicholson's blowflies equation with state-dependent delay, Math. Methods Appl. Sci., 145 (2017), 4893-4903. |
[26] | Z. Zhou, Z. Yang, Periodic solutions in higher-dimensional Lotka-Volterra neutral competition systems with state-dependent delays, Appl. Math. Comput., 189 (2007), 986-995. |
[27] | Y. Lv, R. Yuan, Y. Pei, T. Li, Global stability of a competitive model with state-dependent delay, J. Dyn. Differ. Equ., 29 (2017), 501-521. |
[28] | Y. Wang, X. Liu, Y. Wei, Dynamics of a stage-structured single population model with state-dependent delay, Adv. Differ. Equ., 2018 (2018), 364. |
[29] | M. V. Barbarossa, K. P. Hadeler, C. Kuttler, State-dependent neutral delay equations from population dynamics, J. Math. Biol., 69 (2014), 1027-1056. |
[30] | Y. Lou, X.-Q. Zhao, A theoretical approach to understanding population dynamics with seasonal developmental durations, J. Nonlinear Sci., 27 (2017), 573-603. |
[31] | N. Macdonald, Biological Delay Systems: Linear Stability Theory, Cambridge University Press, New York, 1989. |
[32] | J. M. Cushing, An Introduction to Structured Population Dynamics, Society for Industrial and Applied Mathematics, Philadephia, 1998. |
[33] | G. F. Webb, Theory of Nonlinear Age-dependent Population Dynamics, CRC Press, 1985. |
[34] | K. L. Cooke, W. Z. Huang, On the problem of linearization for state-dependent delay differential equations, Proc. Amer. Math. Soc., 124 (1996), 1417-1426. |
[35] | X. Li, J. Wei, On the zeros of a fourth degree exponential polynomial with applications to a neural network model with delays, Chaos Soliton. Fract., 26 (2005), 519-526. |
1. | Eduardo Hernandez, Shashank Pandey, Dwijendra N. Pandey, Abstract integro‐differential equations with state‐dependent integration intervals: Existence, uniqueness, and local well‐posedness, 2024, 0025-584X, 10.1002/mana.202400126 |