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Research article

Novel stability criteria on a patch structure Nicholson's blowflies model with multiple pairs of time-varying delays

  • Received: 07 August 2020 Accepted: 08 September 2020 Published: 21 September 2020
  • MSC : 34K13, 34C25

  • This paper investigates a patch structure Nicholson's blowflies model involving multiple pairs of different time-varying delays. Without assuming the uniform positiveness of the death rate and the boundedness of coefficients, we establish three novel criteria to check the global convergence, generalized exponential convergence and asymptotical stability on the zero equilibrium point of the addressed model, respectively. Our proofs make substantial use of differential inequality techniques and dynamical system approaches, and the obtained results improve and supplement some existing ones. Last but not least, a numerical example with its simulations is given to show the feasibility of the theoretical results.

    Citation: Xin Long. Novel stability criteria on a patch structure Nicholson's blowflies model with multiple pairs of time-varying delays[J]. AIMS Mathematics, 2020, 5(6): 7387-7401. doi: 10.3934/math.2020473

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  • This paper investigates a patch structure Nicholson's blowflies model involving multiple pairs of different time-varying delays. Without assuming the uniform positiveness of the death rate and the boundedness of coefficients, we establish three novel criteria to check the global convergence, generalized exponential convergence and asymptotical stability on the zero equilibrium point of the addressed model, respectively. Our proofs make substantial use of differential inequality techniques and dynamical system approaches, and the obtained results improve and supplement some existing ones. Last but not least, a numerical example with its simulations is given to show the feasibility of the theoretical results.


    As we all know, dynamic model analysis has developed in many disciplines such as biology, economic sciences, and engineer, etc [1,2,3,4,5,6,7,8,9,10]. Especially, using mathematical biological model to study the population dynamic behaviors plays an important role in maintaining the function of ecosystem [11,12,13], guiding production practice [14,15,16], designing artificial ecosystem [17,18] and promoting the development of information technology [19,20]. In order to better describe the influence of delays in mathematical biological model, the Functional Differential Equations (FDEs) have been applied extensively to the study of population dynamics [21], which can be traced back to the 1920s, when Volttera [22] studied the predator-prey model. In the past 100 years, the theory of population dynamics has made remarkable progress with main results scattered in numerous research papers [23,24,25,26,27,28,29,30,31,32,33,34,35,36]. In particular, there have been plenty of papers written about the dynamical characteristic analysis of FDEs, which include global stability [37,38,39,40], periodicity [19,20,27,32], almost periodicity [41,42,43], Hopf bifurcation [14], boundedness [16] and synchronization [17]. Just as pointed out by Berezansky and Braverman [18], the following delayed differential equation

    x(t)=mj=1Fj(t,x(tτ1(t)),,x(tτl(t)))G(t,x(t)), tt0, (1.1)

    can be used to characterise the dynamics of many population models. Here m and l are positive integers, G represents the instantaneous mortality rate, and each Fj describes the feedback control depending on the values of the stable variable with respective delays τ1(t),τ2(t),,τl(t). Obviously, (1.1) includes the modified Nicholson's blowflies equation [23,24]

    x(t)=α(t)x(t)+mj=1βj(t)x(tgj(t))eγj(t)x(thj(t)), tt0, (1.2)

    which in the case gj(t)hj(t) coincides with the classical models [25,26,27,28,29,30,31,32,33]. In (1.2), α(t),βj(t), gj(t),hj(t) and γj(t) are all continuous and nonnegative functions, α(t) is the death rate of the population which depends on time t and the current population level x(t), βj(t)x(tgj(t))eγj(t)x(thj(t)) is the time-dependent birth function which involves maturation delay gj(t) and incubation delay hj(t), and gets the reproduce at its maximum rate 1γj(t), and jI:={1,2,,m}.

    It should be noted that, in most of the aforementioned works [23,24,25,26,27,28,29,30,31,32,33], the per capita daily adult mortality term α(t) is uniformly positive (that means, there is a positive constant α such that α(t)α for all  tt0). However, throughout the time of some seasons, the death or harvest rate may be less or greater than the birth rate in nature [33,34,35], it is more reasonable to study Nicholson's blowflies models without restriction on uniform positiveness of the per capita death rate. On the other hand, it is of great practical significance to investigate the dynamic behaviors of the patch structure Nicholson's blowflies model [36,37,38]. In recent years, the dynamic behaviors of the following modified Nicholson's blowflies model with patch structure

    xi(t)=αii(t)xi(t)+nj=1,jiαij(t)xj(t)+mj=1βij(t)xi(tgij(t))eγij(t)xi(thij(t)), tt0, iQ:={1,2,,n}, (1.3)

    have aroused the current research interest, and some useful results have been obtained in the existing papers [38,39,40,41,42]. In particular, without restriction on uniform positiveness of αii(t)(iQ), Xu, Cao and Guo [39] established some new criteria for the global attractivity of model (1.3) with the following admissible initial conditions:

    xi(t0+θ)=φi(θ), θ[ri,0], φC0+={φC+|φi(0)>0,iQ}, (1.4)

    where  ri=maxjI{supt[t0,+) gij(t),supt[t0,+)hij(t)},  C+=ni=1C([ri,0],[0,+)). Moreover, the authors in [39] obtained the main result as follows.

    Theorem 1.1. Suppose that

    (W1) limt+t0α(s)ds=+, where α(t)=miniQ{αii(t)},

    and

    (W2) 1>lim supt+[nj=1,jiαij(t)αii(t)+mj=1βij(t)αii(t)], iQ,

    are satisfied. Then the zero equilibrium point in system (1.3) is globally generalized exponentially stable, i.e., there exist two constants T>max{t0,0} and M(φ)>0 such that every solution x(t)=x(t;t0,φ) of (1.3) and (1.4) agrees with that

    xi(t)M(φ)eλtTα(s)ds  for all t>T and iQ.

    Unfortunately, there exist two mistakes in the proof of the main results in [39]. The first mistake is on lines 1-2 of page 3. More precisely, let xi(t)=xi(t;t0,φ),  yi(t)=maxt0ristxi(s)  and y(t)=maxiQyi(t), the inequality

    xi(t)N(t,i):=∥φ+tt0[nj=1,jiαij(v)+mj=1βij(v)]y(v)dv, (1.5)

    can not lead to

    y(t)=maxiQ{maxt0ristxi(s)}N(t,i), (1.6)

    where t[t0ri, η(φ)), iQ and φ∥=maxiQ{maxt[ri,0]|φi(t)|}. The same mistake also appeared in [40] [see, p. 4], [41] [see, p. 5] and [43] [see, p. 4]. The logical error for the above mistake is that the left side of (1.6) is the whole maximum value for all i, but the right side of (1.6) is only for a fixed i. Fortunately, this error will be corrected in the Lemma 2.1 of this present paper. The second mistake in literature [39] is on line 26 of page 3 that the inequality

    y(t)y(t0)ett0α(v)dv+(1ξ)y(t),    0<ξ<1,

    which is absurd. Actually, by taking φ∥=100 and letting t+, one can find that

    100y(t)(1ξ)y(t)<y(t),

    is a contradiction.

    Furthermore, we consider the following concrete example

    {x1(t)=tx1(t)+t3x2(t)          +t4x1(t50|sint|)e(1+cos2t)x1(t46|sint|)          +t5x1(t39|sint|)e(1+sin2t)x1(t46|sint|), x2(t)=(t+1)x2(t)+(t4+14)x1(t)          +(t5+15)x2(t50|cost|)e(1+sint)x2(t49|cost|)          +(t6+16)x2(t38|cost|)e(1+cost)x2(t39|cost|),  (1.7)

    and its numerical simulations in Figure 1 to illustrate the above mistakes. Obviously, the assumptions (W1) and (W2) are satisfied in (1.7). Therefore, from Theorem 1.1, we have that the zero equilibrium point of system (1.7) is globally generalized exponentially stable. However, the numerical solutions with three different initial values shown in Figure 1 reveal that three numerical trajectories of (1.7) are convergent to 0 as t+, but they are not generalized exponential convergence to 0. This makes us doubt whether the conclusions of Theorem 1.1 are correct.

    Figure 1.  Numerical solutions of (1.7) for differential initial values.

    Based on the above theoretical analysis and numerical simulations, two problems naturally arise. One is whether the assumptions (W1) and (W2) can guarantee that every solution of (1.3) and (1.4) is convergent to 0 as t+. The other is what kind of conditions can ensure the global generalized exponential convergence of the zero equilibrium point of (1.3).

    Regarding the above discussions, in this manuscript, we first establish the global convergence of model (1.3) under the original conditions (W1) and (W2). It is worth noting that, two or more delays appearing in the same time-dependent birth function will cause complex dynamic behaviors, even chaotic oscillations. It is impossible to establish the global exponential convergence of (1.1) without appropriate restrictions on the distinctive delays and some corresponding examples are also given in [18]. Therefore, we add a new delay-dependent assumption to obtain some new criteria for the generalized exponential convergence and global exponential asymptotic stability on the zero equilibrium point of (1.3). In a nutshell, our results not only correct the errors in the existing literature [39,40,41,43], but also improve and complement the existing conclusions in the recent publications [23,24,26,32,39], and the effectiveness is demonstrated by a numerical example.

    Hereafter, for iQ,jI, we assume that αii,γijC([t0, +),(0,+)) and αij(ij),βij,gij,hijC([t0,+),[0,+)). Label x(t;t0,φ) for a solution of (1.3) associating to (1.4), and [t0, η(φ)) be the maximal existence right-interval.

    Lemma 2.1. Every solution x(t)=x(t;t0,φ) is positive on [t0,+).

    Proof. According to (1.3), (1.4) and Theorem 5.2.1 in [44] [see p. 81], one can find that xi(t)0 for all t[t0,η(φ)) and iQ. Now, we reveal that η(φ)=+. For all t[t0,η(φ)) and iQ, define yi(t)=maxt0ristxi(s) and y(t)=maxiQyi(t), we gain

    xi(s)nj=1,jiαij(s)xj(s)+mj=1βij(s)xi(sgij(s))[nj=1,jiαij(s)+mj=1βij(s)]y(s)

    and

    xi(s)≤∥φ+st0[nj=1,jiαij(v)+mj=1βij(v)]y(v)dv≤∥φ+tt0[nj=1,jiαij(v)+mj=1βij(v)]y(v)dv ≤∥φ+tt0maxiQ[nj=1,jiαij(v)+mj=1βij(v)]y(v)dv,

    where s[t0, t] and iQ. On combining this with the fact that

    xi(s)≤∥φ  for all  s[t0ri,t0] and iQ,

    we deduce that

    y(t)≤∥φ+tt0maxiQ[nj=1,jiαij(v)+mj=1βij(v)]y(v)dv for all t[t0, η(φ)).

    Hence, by the Gronwall-Bellman inequality, we obtain

    xi(t)yi(t)y(t)≤∥φett0maxiQ[nj=1,jiαij(v)+mj=1βij(v)]dv for all   t[t0, η(φ)) and iQ.

    This and [45] [see Theorem 2.3.1] suggest that η(φ)=+, and xi(t)0 for all t[t0,+) and iQ. Note that xi(t0)=φi(0)>0, we get

    xi(t)=xi(t0)ett0αii(v)dv+tt0etsαii(v)dv[nj=1,jiαij(s)xj(s)+mj=1βij(s)xi(sgij(s))eγij(s)xi(shij(s))]ds>0   for all t[t0, +) and iQ,

    which evidences Lemma 2.1.

    Theorem 3.1. Under (W1) and (W2), every solution x(t)=x(t;t0,φ) is convergent to 0 as t+.

    Proof. First, we prove that every solution x(t)=x(t;t0,φ) is bounded on [t0,+). By (W2), we have

    σ:=lim supt+(nj=1,jiαij(t)αii(t)+mj=1βij(t)αii(t))<1,

    and hence, 1σ2>0. Therefore, there exists T>t0ri such that

    nj=1,jiαij(t)αii(t)+mj=1βij(t)αii(t)<σ+1σ2=1+σ2<1,  where t[T,+) and iQ. (3.1)

    Clearly,

    xi(t)=xi(t;t0,φ)<M(φ):=ni=1maxt[t0ri,T]xi(t)+1   forall t[t0ri,T] and  iQ. (3.2)

    Furthermore, we state that

    xi(t)<M(φ)   forall t(T,+) and iQ.

    Otherwise, there exist iQ and T>T such that

    xi(T)=M(φ),  xj(t)<M(φ)   forall t[t0rj, T) and  jQ.

    Then

    0xi(T)=αii(T)xi(T)+nj=1,jiαij(T)xj(T) +mj=1βij(T)xi(Tgij(T))eγij(T)xi(Thij(T))M(φ)αii(T)[1+nj=1,jiαij(T)αii(T)+mj=1βij(T)αii(T)]<0,

    which is a contradiction and proves that x(t) is bounded on [t0,+).

    Now, it is sufficient to show that u=maxiQlim supt+xi(t)=0.

    For any ε>0, one can choose Λ>T such that

    xi(t)<u+ε   forall t[Λri,  +) and  iQ.

    Thus,

    xi(t)=xi(t0)ett0αii(v)dv+tt0etsαii(v)dv[nj=1,jiαij(s)xj(s)+mj=1βij(s)xi(sgij(s))eγij(s)xi(shij(s))]ds<xi(t0)ett0αii(v)dv+(u+ε)tt0etsαii(v)dv[nj=1,jiαij(s)+mj=1βij(s)]ds<xi(t0)ett0αii(v)dv+(u+ε)1+σ2tt0etsαii(v)dvαii(s)ds<xi(t0)ett0αii(v)dv+(u+ε)1+σ2    forall t[Λ, +) and  iQ.

    By taking the upper limits, (W1) leads to

    0u(u+ε)1+σ2,

    which, together with the arbitrariness of ε, implies that u=0. This finishes the proof of Theorem 3.1.

    We now make the following assumptions:

    (W2) 1>suptt0[nj=1,jiαij(t)αii(t)+mj=1βij(t)αii(t)] forall iQ.

    (W3) There exists a positive constant m[α]>0 such that

    suptgij(t)0ttgij(t)α(s)dsm[α]    forall iQ  and jI.

    Theorem 3.2. Under assumptions (W1), (W2) and (W3), all positive solutions of system (1.3) are generalized exponentially convergent to the zero equilibrium point. More precisely, there exist positive constants κ,λ,M(φ) such that every solution x(t)=x(t;t0,φ) of (1.3) and (1.4) agrees with that

    xi(t)κM(φ)eλtt0α(s)ds, iQ.

    Proof. From (3.1) and (W3), one can see that

    supt[T,+)[nj=1,jiαij(t)+mj=1βij(t)αii(t)]<0 forall iQ, (3.3)

    and there exists λ>0 such that

    λαii(t)+nj=1,jiαij(t)+mj=1βij(t)eλttgij(t)α(s)ds<αii(t), (3.4)

    where  t[T,+) and  iQ.

    Let us consider the following function:

    v(t)=κM(φ)eλtt0α(s)ds, 

    where  tt0ri, κ=eλTt0α(s)ds, and M(φ) is defined in (3.2). In view of (3.4) and the fact that

    v(tgij(t))=eλttgij(t)α(s)dsv(t),

    we obtain

    v(t)=κM(φ)eλtt0α(s)ds[λα(t)]=[λα(t)]v(t)[λαii(t)]v(t)[nj=1,jiαij(t)+mj=1βij(t)eλttgij(t)α(s)dsαii(t)]v(t)=[nj=1,jiαij(t)+mj=1βij(t)v(tgij(t))v(t)αii(t)]v(t)=αii(t)v(t)+nj=1,jiαij(t)v(t)+mj=1βij(t)v(tgij(t)) (3.5)

    where  t[T,+) and  iQ.

    Hereafter, we show that

    xi(t)<v(t)=κM(φ)eλtt0α(s)ds   for all tt0ri and iQ. (3.6)

    It is easy to see that

    v(t)=κM(φ)eλtt0α(s)ds=M(φ)eλTt0α(s)dseλtt0α(s)ds=M(φ)eλTtα(s)dsM(φ)>xi(t)  for all t[t0ri,T].

    Next, we prove that

    xi(t)<v(t),  t(T,+). (3.7)

    Suppose, for the sake of contradiction, there exist ˜t>T and i0Q satisfying that

    xi0(˜t)=v(˜t),xj(t)<v(t)  for all t[t0rj, ˜t)  and  jQ.

    Then, xi0(t)v(t) for all t[t0ri,˜t]. From (3.5), we have

    v(t)αi0i0(t)v(t)+nj=1,ji0αi0j(t)v(t)+mj=1βi0j(t)v(tgi0j(t)),

    which together with (1.3) and (3.3), we gain

    0xi0(˜t)v(˜t)=αi0i0(˜t)xi0(˜t)+nj=1,ji0αi0j(˜t)xj(˜t)+mj=1βi0j(˜t)xi0(˜tgi0j(˜t))eγi0j(˜t)xi0(˜thi0j(˜t))v(˜t)αi0i0(˜t)xi0(˜t)+mj=1βi0j(˜t)xi0(˜tgi0j(˜t))+nj=1,ji0αi0j(˜t)xj(˜t)+αi0i0(˜t)v(˜t)mj=1βi0j(˜t)v(˜tgi0j(˜t))nj=1,ji0αi0j(˜t)v(˜t)<αi0i0(˜t)v(˜t)+mj=1βi0j(˜t)v(˜tgi0j(˜t))+nj=1,ji0αi0j(˜t)v(˜t)+αi0i0(˜t)v(˜t)mj=1βi0j(˜t)v(˜tgi0j(˜t))nj=1,ji0αi0j(˜t)v(˜t)=0,

    which yields a contradiction and shows (3.7) holds. Consequently, (3.6) holds, and the proof of Theorem 3.2 is now completed.

    Theorem 3.3. Let (W1), (W2) and (W3) be satisfied, the zero equilibrium point in (1.3) is globally asymptotically stable.

    Proof. According to Theorem 3.2, it follows from (W1), (W2) and (W3) that the zero equilibrium point in system (1.3) is globally attractive. Now, we only need to show the local stability on the zero equilibrium point in system (1.3). By (W2), we gain

    αii(t)+nj=1,jiαij(t)+mj=1βij(t)<0  forall t[t0, +)  and  iQ. (3.8)

    For ε>0, we choose δ(0, ε). Next, we claim that, for φ<δ,

    xi(t)=xi(t;t0,φ)<ε  forall t[t0,+) and iQ. (3.9)

    In the contrary case, there exist t(t0,+) and iQ such that

    xi(t)=ε,  xj(t)<ε  forall t[t0rj, t) and jQ. (3.10)

    Consequently, (1.3), (3.8) and (3.10) involve that

    0xi(t)[αii(t)+nj=1,jiαij(t)+mj=1βij(t)]ε<0,

    which is absurd and proves (3.9). Therefore, the zero equilibrium point is globally asymptotically stable.

    Remark 3.1. One can easily see that all convergence results in references [23,24,26,32] are special ones of Theorems 3.1, 3.2 and 3.3 with n=1 and gij(t)hij(t). In addition, the errors in the existing literatures [39,40,41,43] have been corrected in the proof of Lemma 2.1 and Theorem 3.2. Specially, we add a new delay-dependent condition W3 to ensure the correctness of the main conclusions Theorems 3.1 and 3.2 in [39]. Therefore, the obtained results of this present paper improve and complement the above mentioned references [23,24,26,39,40,41,43].

    In this section, we present an example to check the validity of the main results obtained in Section 3.

    Example 4.1. Consider the following patch structure Nicholson's blowflies model with multiple pairs of time-varying delays:

    {x1(t)=112t+3x1(t)+12t+3x2(t)          +62t+3x1(t|sint|)e(1+cos2t)x1(t12|sint|)          +32t+3x1(t23|sint|)e(1+sin2t)x1(t35|sint|), x2(t)=132t+2x2(t)+32t+2x1(t)          +52t+2x2(t34|cost|)e(1+sint)x2(t|cost|)          +12t+2x2(t16|cost|)e(1+cost)x2(t13|cost|).  (4.1)

    One can see that (4.1) satisfies all the conditions in Theorem 3.3. It follows that the zero equilibrium point of system (4.1) is globally asymptotically stable on C+={φC([1, 0],[0, +))×C([1, 0],[0, +))}. The numerical trajectories with different initial values are shown in Figure 2, which reveal the above conclusions.

    Figure 2.  Numerical solutions of (4.1) for differential initial values.

    Remark 4.1. It should be pointed out that the per capita daily adult mortality terms of (4.1) are not uniformly positive mortality terms since

    limt+α11(t)=limt+112t+3=limt+α22(t)=limt+132t+2=0.

    In addition, the global stability of the patch structure Nicholson's blowflies model with multiple pairs of time-varying delays has not been touched in [46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66]. This implies that all the results in [23,24,25,26,27,28,29,30,31,32,33] and [46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66] cannot be used to show the global convergence and stability on system (3.1) where τij(t)hij(t)(iQ,jI). Moreover, the proposed techniques could be taken into consideration in the dynamics research on other patch structure population models involving two or more delays in the same time-dependent birth function.

    In the present manuscript, we investigate the asymptotic behavior for a patch structure Nicholson's blowflies model involving multiple pairs of different time-varying delays. Here, we obtain some novel results about the asymptotic behavior on the zero equilibrium point of the addressed model without assuming the uniform positiveness of the death rate and the boundedness of coefficients, which complement some earlier publications to some extent. In addition, the method used in this paper provides a possible method for studying the global asymptotic stability of other patch structure population dynamic models with multiple pairs of different time-varying delays.

    I would like to thank the anonymous referees and the editor for very helpful suggestions and comments which led to improvements of my original paper. This work was supported by the Postgraduate Scientific Research Innovation Project of Hunan Province (No. CX20200892) and "Double first class" construction project of CSUST in 2020 ESI construction discipline Grant No. 23/03.

    We confirm that we have no conflict of interest.



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