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Research article Special Issues

Computational analysis of a collaboration network on human-computer interaction in Korea

  • Equally contributed first authors
  • Since information and communication technology (ICT) has become one of the leading and essential fields for allowing developing countries to have the major growth engines, the majority of the countries have promoted collaboration in every ICT-related topics. In this study, we performed the trend and collaboration network analysis (CNA) in Korea for 2010–2019 among researchers who are related to human–computer interaction, one of the hottest research areas in ICT. Publication data were collected from SciVal, and the collaboration network was determined using degree, closeness, betweenness centralities, and PageRank. Hence, key researchers were identified based on their centrality metrics. The dataset contained 7,155 publications, thus reflecting the contributions of a total of 243 authors. The results of our data analysis demonstrated that key researchers can be identified via CNA; this aspect was not evident from the results of the most productive researchers. Additionally, on the basis of the results, the implications and limitations of this study were analyzed.

    Citation: Seungpeel Lee, Jisu Kim, Eun Been Choi, Sojung Shin, Dogun Kim, HyeRim Yu, Seoyun Kim, Wongi S. Na, Eunil Park. Computational analysis of a collaboration network on human-computer interaction in Korea[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 13911-13927. doi: 10.3934/mbe.2022648

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  • Since information and communication technology (ICT) has become one of the leading and essential fields for allowing developing countries to have the major growth engines, the majority of the countries have promoted collaboration in every ICT-related topics. In this study, we performed the trend and collaboration network analysis (CNA) in Korea for 2010–2019 among researchers who are related to human–computer interaction, one of the hottest research areas in ICT. Publication data were collected from SciVal, and the collaboration network was determined using degree, closeness, betweenness centralities, and PageRank. Hence, key researchers were identified based on their centrality metrics. The dataset contained 7,155 publications, thus reflecting the contributions of a total of 243 authors. The results of our data analysis demonstrated that key researchers can be identified via CNA; this aspect was not evident from the results of the most productive researchers. Additionally, on the basis of the results, the implications and limitations of this study were analyzed.



    It is important to determine survivors, which ultimately shape an ecosystem. However, answering this fundamental question depends on what we consider in a food web. There are studies on ecosystems that incorporate a different concept, like epidemics [10,11] and allelopathy [8].

    Our work considers a microbial food web in the presence of parasitic fungi (e.g., chytrids). The importance of parasites in food webs has been emphasized in the literature; see [5,6,7]. According to a review paper [15] by Sommer et al, researchers have only recently considered parasites as one of the main drivers for phytoplankton succession. This review highlights the theory of mycoloop, a food chain conceived by Kagami and her team to explain the transfer of energy from large phytoplankton (Asterionella) to zooplankton (Daphnia) via parasitic fungi [1,2,3,4].

    In this paper, we consider the following chemostat model based on Figure 1, where =d/dt.

    Figure 1.  Food web of a microbial ecosystem with a mycoloop (red).
    {N(t)=[IqN(t)][aSPS(t)+aLPL(t)]N,N(0)>0,S(t)=[aSN(t)bZ(t)q]PS(t),PS(0)0,L(t)=[aLN(t)βF(t)q]PL(t),PL(0)0,F(t)=[fFβPL(t)γZ(t)q]F(t),F(0)0,Z(t)=[ePbPS(t)+eFγF(t)(q+mZ)]Z(t),Z(0)0. (1.1)

    The parameters of this model are described in Table 1.

    Table 1.  Model parameters.
    ParameterDescription
    IInput amount of phosphorus (nutrient level)
    N(0)Input concentration of nutrient
    qWashout rate
    q+mZZooplankton mortality rate, mZ is the additional death rate
    besides washout rate q
    aJNutrient affinity of phytoplankton PJ (J=S,L)
    βInfectivity constant of fungi
    bZooplankton clearance rate for small phytoplankton
    γZooplankton clearance rate for fungi
    eFGross growth efficiency (GGE) of zooplankton from fungi
    ePGGE of zooplankton from small phytoplankton
    fFGGE of fungi from its host PL

     | Show Table
    DownLoad: CSV

    Miki, Takimoto and Kagami formulated system (1.1) in their paper [9] and performed a local (steady-state) analysis to investigate the roles of parasitic fungi. We assume I=qN(0).

    We focus on global dynamics and the limiting behavior of the solution

    φ(t)=(N(t),PS(t),PL(t),F(t),Z(t))

    as t goes to infinity. In particular, we determine initial and parameter conditions for the solution to describe the survival and extinction of species PS, PL, F, and Z.

    Let

    X:={x=(N,PS,PL,F,Z):N>0,PS0,PL0,F0,Z0}.

    It is easy to show the state space X and its interior int(X) are positively invariant.

    The rest of this paper is organized as follows. In Section 2, we establish that our model is dissipative, from which the nutrient uniformly persists regardless of the input I. In Section 3, we study the boundary dynamics. Notably, we construct a Lyapunov function to determine the basin of attraction. An investigation on the local and global stability of boundary equilibrium points is presented in Section 4. In Section 5, we apply the uniform persistence theory [12,13,16] to prove the coexistence of species PS, PL, F, Z with FZ link, i.e., γ>0. Section 6 deals with the dynamics of system (1.1) with the presence of parasitic fungi, but no FZ link, i.e., γ=0. We conclude the paper with a discussion in Section 7.

    First we show that our model system (1.1) is dissipative, as stated in the following theorem.

    Theorem 2.1. Each solution of system (1.1) in X satisfies the following inequality:

    lim supt[N(t)+PS(t)+PL(t)+F(t)fF+Z(t)max{eP,fFeF}]N(0). (2.1)

    Proof. Let c=1max{eP,fFeF}. We introduce the variable

    u=N+PS+PL+FfF+cZ.

    It follows that uq[N(0)u]. Moreover,

    u(t)N(0)+[u(0)N(0)]exp(qt) (2.2)

    for all t0. Passing the limit supremum to inequality (2.2) as t, we get lim suptu(t)N(0), which is inequality (2.1).

    Theorem 2.2. The nutrient N uniformly persists in X.

    Proof. Observe that

    NIqNmax(aS,aL)(PS+PL)NIqNmax(aS,aL)(N(0)+ϵ)NI(q+max(aS,aL)(N(0)+ϵ))N,tTϵ.

    Hence N(t)δ>0 for tTϵ, where δ=Iq+max(aS,aL)(N(0)+ϵ). This proves the uniform persistence of N in X.

    The ecologically relevant equilibrium points lie in the state space X. For our analysis, we focus on boundary equilibrium points, which have at least one zero coordinate. To this end, we let

    λS=qaS,λL=qaL.

    From [14], our basic assumption is

    (H)0<λS<λL<Iq.

    That is, we assume phytoplankton species of small size, PS, is a strong competitor than that of large size, PL.

    When F0, system (1.1) becomes

    {dNdt=IqNaSPSNaLPLN,dPSdt=aSPSNbZPSqPS,dPLdt=aLNPLqPL,dZdt=ePbPSZ(q+mZ)Z (3.1)

    and its food web is shown in Figure 2.

    Figure 2.  The food web in the absence of parasitic fungi.

    The conditions for the global stability of the equilibrium points of system (3.1) are stated in the next theorem.

    Theorem 3.1. Let (H) hold and

    N=Iq+aSPS,PS=q+mZePb.

    For system (3.1), the following statements hold:

    (i) If N<λS, then EFS=(λS,N(0)λS,0,0) is globally asymptotically stable (G.A.S.).

    (ii) If λS<N<λL, then EFSZ=(N,PS,0,Z) is G.A.S., where Z=aSNqb.

    (iii) If λS<λL<N<Iq, then EFSLZ=(λL,PS,~PL,˜Z) exists and is G.A.S., where ˜Z=1b(aSλLq) and ~PL=IqλLaSaLPS.

    Proof. The statements are established as follows:

    (ⅰ) We introduce the Lyapunov function given by

    V=NλSξλSξdξ+c1PS^PSξ^PSξdξ+c2PL+c3Z,

    where ^PS=N(0)λS, and c1,c2,c3>0 are to be determined. Then

    V=(NλS)(INqaSPSaLPL)+c1(PS^PS)(aSNbZq)+c2PL(aLNq)+c3Z(ePbPS(q+mZ))=(NλS)(INIλSaS(PS^PS)aLPL)+c1(PS^PS)(aS(NλS)bZ)+c2PL(aL(NλS)aL(λLλS))+c3Z(ePb(PS^PS)((q+mZ)ePb^PS)).

    Choose c1=1, c2=1, c3=1eP. Since

    q+mZePb=PS>^PS=IqλSaSλSIqλS<aSλSPSN<λS,

    it follows that

    V=I(NλS)2NλSPLaL(λLλS)1eP((q+mZ)ePb^PS)Z0.

    Therefore, LaSalle's invariance principle implies that EFS=(λS,^PS,0,0) is G.A.S.

    (ⅱ)Note that N>λS implies that Z>0. Define the Lyapunov function

    V=NNξNξdξ+PSPSξPSξdξ+PL+1ePZZξZξdξ.

    We obtain

    V=(NN)[ININaS(PSPS)aLPL]+(PSPS)[aS(NN)b(ZZ)]+PL(aL(NN)(qaLN))+1eP(ZZ)(ePb(PSPS))=I(NN)2NNPL(qaLN)0,

    by using the equivalent expression

    λL>NqaLN>0.

    Hence, by LaSalle's invariance principle, EFSZ=(N,PS,0,Z) is G.A.S.

    (ⅲ) We construct the following Lyapunov function:

    V=NNCξNCξdξ+PSPCSξPCSξdξ+PLPCLξPCLξdξ+1ePZZCξZCξdξ,

    where

    NC=λL,PCS=q+mZePb=PSPCL=1aL(INCqaSPS)ZC=1b(aSNCq).

    From the assumptions that NC=λL>λS=qaS and N>λL, we see that PCL>0 and ZC>0. Thus

    V=(NNC)[ININCaS(PSPCS)aL(PLPCL)]+(PSPCS)[aS(NNC)b(ZZC)]+(PLPCL)(aL(NNC))+1eP(ZZC)(ePb(PSPCS))=I(NNC)2NNC0.

    We conclude from the invariance principle that EFSLZ is G.A.S.

    As a consequence of the above theorem, we obtain the following equivalent expressions:

    N<λSI<λS(q+aSPS)=q(λS+PS)λS<N<λLλS(q+aSPS)<I<λL(q+aSPS)q(λS+PS)<I<q(λL+λLλSPS)λL<N<IqλL(q+aSPS)<Iq(λL+λLλSPS)<I

    Letting IF1=q(λS+PS) and IF2=q(λL+λLλSPS) with IF1<IF2, we conclude that

    (ⅰ) if 0<I<IF1, then EFS=(λS,^PS,0,0) is G.A.S.

    (ⅱ) if IF1<I<IF2, then EFSZ=(N,PS,0,Z) is G.A.S.

    (ⅲ) if IF2<I, then EFSLZ(λL,PS,~PL,˜Z) is G.A.S.

    The global stability of equilibrium points of system (3.1) is depicted in Figure 3.

    Figure 3.  Operation diagram of system (3.1).

    We consider the case that Z0. Then system (1.1) becomes

    {N=IqNaSPSNaLPLN,S=[aSNq]PS,L=(aLNβFq)PL,F=(fFβPLq)F,N(0)>0,PS(0)>0,PL(0)>0,F(0)>0 (3.2)

    and its food web is presented in Figure 4.

    Figure 4.  The food web in the absence of zooplankton.

    From hypothesis (H), we see that PS is a better competitor for nutrient than PL. Obviously from the fact that parasitic fungi F only consume PL, it follows that EZS=(λS,^PS,0,0) with ^PS=N(0)λS is G.A.S. Below we state the result without proof.

    Theorem 3.2. Under assumption (H), EZS=(λS,^PS,0,0) is G.A.S. for system (3.2).

    For the case when PS0, system (1.1) becomes

    {dNdt=IqNaLPLN,dPLdt=aLNPLβFPLqPL,dFdt=fFβPLFγZFqF,dZdt=eFγFZ(q+mZ)F,N(0)>0,PL(0)>0,F(0)>0,Z(0)>0 (3.3)

    with the corresponding food web provided in Figure 5.

    Figure 5.  The food web in the absence of phytoplankton species.

    Using the same Lyapunov functions V as defined in Theorem 3.1, we can prove Theorem 3.3 stated below. We thus omit the proof.

    Theorem 3.3. Let hypothesis (H):0<λS<λL<Iq hold. Then the solution of system (3.3) satisfies the following statements:

    (i) If 0<I<λLq(1+aLfFβ), then EPSL=(λL,N(0)λL,0,0) is G.A.S.

    (ii) If λLq(1+aLfFβ)<I<λL(1+aLfFβ)(q+βePγ(q+mZ)), then EPSLF=(ˉN,ˉL,ˉF,0) is G.A.S. Here ˉL=qfFβ, ˉN=Iq+aLˉL, ˉF=aLˉNqβ>0.

    (iii) If I>λL(1+aLfFβ)(q+βeFγ(q+mZ)), then the positive equilibrium G.A.S. He EPSLFZ=(ˆN,^PL,ˆF,ˆZ) exists and is G.A.S. Here ˆF=q+mZγeF, ˆN=βˆF+qaL, ˆZ=fFβ^PLqγ>0, ^PL=IqˆNaLˆN>0.

    Taking IPS1=λLq(1+aLfFβ) and IPS2=λL(1+aLfFβ)(q+βeFγ(q+mZ)), the global stability of equilibrium points of system (3.3) is provided in Figure 6.

    Figure 6.  Operation diagram of system (3.3).

    From Section 3, there are seven distinct boundary equilibrium points of system (1.1) listed below :

    E0=(N(0),0,0,0,0),ES=(λS,N(0)λS,0,0,0),N(0)>λS,ESZ=(N,PS,0,0,Z),N(0)>λS+PS,ESLZ=(λL,PS,~PL,0,˜Z),N(0)>λL+λLλSPS,EL=(λL,0,N(0)λL,0,0),N(0)>λL,ELF=(ˉN,0,ˉL,ˉF,0),N(0)>λL(1+aLfFβ),ELFZ=(ˆN,0,^PL,ˆF,ˆZ),N(0)>λL(1+aLfFβ)(1+βeFγ(1+mZq)), (4.1)

    where

    N=Iq+aSPS,PS=q+mZePb,Z=aSNqb>0,~PL=IqλLaSaLPS>0,˜Z=1b(aSλLq)>0,ˉN=Iq+aLˉL,ˉL=qfFβ,ˉF=aLˉNqβ>0,ˆF=q+mZγeF,ˆN=βˆF+qaL,ˆZ=fFβ^PLqγ>0,^PL=IqˆNaLˆN>0. (4.2)

    Next we discuss the local asymptotic stability of the boundary equilibrium points in (4.1) with respect to system (1.1). Obviously E0 is unstable under hypothesis (H).

    For the stability of ES, let N(0)<λS+q+mZePb. Then

    1FF(t)|ES=fFβPLγZq=q<0,1ZZ(t)|ES=ePb(N(0)λS)(q+mZ)<0,1PLL(t)|ES=aLλSq<0 (4.3)

    and all of the eigenvalues of the Jacobian matrix of system (1.1) at ES are negative. Hence ES is asymptotically stable if N(0)<λS+q+mZePb.

    For the stability of ESZ, if N(0)<λL+λLλSPS, then

    1PLL|ESZ=aLNq<0,1FF|ESZ=γZq<0. (4.4)

    Thus, if N(0)<λL+λLλSPS, then ESZ is asymptotically stable.

    For the stability of ESLZ, consider

    1FF|ESLZ=fˉFβ~PLγ˜Zq<0. (4.5)

    Therefore, ESLZ is asymptotically stable if (4.5) holds.

    For the stability of EL, let N(0)<λL+qfFβ. Then

    1PSS|EL=aSλLq>0,1FF|EL=fFβ(N(0)λL)q<01ZZ|EL=(q+mZ)<0. (4.6)

    We conclude that EL is unstable in the PS direction and stable in F and Z directions.

    For the stability of ELF, the assumption and ˉF>q+mZeFγ imply that

    1PSS|ELF=aSˉNq>0,1ZZ|ELF=eFγˉF(q+mZ)>0

    For the stability of ELFZ, consider

    1PSS|ELFZ=aSˆNbˆZq. (4.7)

    Hence ELFZ is asymptotically stable if aSˆNbˆZq<0.

    A summary of the results on the asymptotic stability of boundary equilibrium points of system (1.1) is provided in Table 2.

    Table 2.  Conditions for existence and local asymptotic stability of boundary equilibrium points of system (1.1).
    ExistenceLocally asymptotically stable if
    E0always
    ESunder (H) N(0)<λS+PS
    ESZ λS+PS<N(0) N(0)<λL+λLλSPS
    ESLZ λL+λLλSPS<N(0) fFβ~PLγ˜Zq<0
    ELunder (H)
    ELF N(0)>λL(1+aLfFβ)
    ELFZ N(0)>λL(1+aLfFβ)(1+βeFγ(1+mZq)) aSˆNbˆZq<0

     | Show Table
    DownLoad: CSV

    Now we present some extinction results in the next theorem.

    Theorem 4.1. Suppose fFePeF holds. Then the following statements hold.

    (i) If N(0)<λS+q+mZePb, then ES=(λS,N(0)λS,0,0,0) attracts each point (N,PS,PL,F,Z)R5+.

    (ii) If λS+PS<N(0)<λL+λLλSPS, then ESZ=(N,PS,0,0,Z) attracts each point (N,PS,PL,F,Z)R5+.

    Proof. (ⅰ) Introduce the Lyapunov function

    V=NλSξλSξdξ+c1PSN(0)λSξ(N(0)λS)ξdξ+c2PL+c3F+c4Z.

    Choose c1=c2=1, c3=eFeP, and c4=1eP. Then

    V=(NλS)(INIλSaS(PS(N(0)λS))aLPL)+(PS(N(0)λS))(aS(NλS)bZ)+PL(aL(NλS)βF+(aLλSq))+eFePF(fPβPLγZq)+1ePZ(ePb(PS(N(0)λS))+eFγF+(ePb(N(0)λS)(q+mZ)))=(NλS)(INIλS)+PL(aLλSq)eFePqF+PLF(β+fFβeFeP)+Z(ePb(N(0)λS)(q+mZ))0.

    It folows from the invariance principle that ES is a global attractor.

    (ⅱ) Define the Lyapunov function by

    V=NNξNξdξ+c1PSPSξPSξdξ+c2PL+c3F+c4ZZξZξdξ.

    Let c1=1, c2=1, c3=eFeP, and c4=1eP. Then

    V=(NN)(ININaS(PSPS)aLPL)+(PSPS)(aS(NN)b(ZZ))+(aL(NN)βF+(aLNq))PL+eFeP(fFβPLγ(ZZ)+(γZq))F+1eP(ZZ)(ePb(PSPS)+eFγF+ePbPS(q+mZ))=(NN)(ININ)+FPL(βc2+fFβeFeP)+(aLNq)PL+eFeP(γZq)F0.

    By invariance principle, ESZ is a global attractor.

    Remark 4.2: From our numerical simulation results, we conjecture that the equilibria ES and ESZ are G.A.S. even when fF>ePeF.

    In this section, we determine conditions for the species in system (1.1) to coexist by applying the theory of uniform persistence of Butler, Freedman and Waltman [12,13,16]. Since the boundary dynamics for F0, Z0, and PS0 are discussed in Section 3 and the acyclic conditions are easy to verify, it remains only to verify that WS(Mi)Int_(R5+)=ϕ for each boundary equilibrium Mi.

    Consider the operation diagram in Figure 3 and the case that I>IF2=λL(q+aLPS). From the equation for F in system (1.1), the invasion condition for the boundary equilibrium ESLZ=(λL,PS,~PL,0,˜Z) is FF>0. That is,

    fFβ~PLγ˜Zq>0, (5.1)

    where ~PL=IqλLaSaLPS>0, ˜Z=1b(aSλLq)>0, and PS=q+mZePb (See 4.2).

    Lemma 5.1 below shows that inequality (5.1) is equivalent to

    I>λL(q+aLPS)+aLfFβλL(γaSb(λLλS)+q)=I2. (5.2)

    Next, we consider the operation diagram in Figure 6 and the case that

    I>IPS2=λL(1+aSfFβ)(q+βePγ(q+mZ)).

    Similarly, the equation for PS in system (1.1) provides the following invasion condition for the boundary equilibrium ELFZ=(ˆN,0,^PL,ˆF,ˆZ) :

    aSˆNbˆZq>0, (5.3)

    where ˆF=q+mZγeF, ^PL=IqˆNaLˆN>0, ˆZ=fFβ^PLqγ, and ˆN=βˆF+qaL (See (4.2)).

    In Lemma 5.1, we also prove that inequality (5.3) is equivalent to

    I<I3=λL(q+βˆF)(1+aLfFβ)+(βˆF+q)1fFβ((λLλS1)q+λLλSβˆF)γb. (5.4)

    We state the lemma below.

    (i) Inequalities (5.1) and (5.2) are equivalent.

    (ii) Inequalities (5.3) and (5.4) are equivalent.

    (iii) If ePeF>fF, then I3>I2.

    Proof. (ⅰ) Equivalence is established by substituting ~PL=IqλLaSaLPS and ˜Z=1b(aSλLq) into inequality (5.1). We have

    1FdFdt|ESLZ=fFβ~PLγ˜Zq>0~PL>γfFβ˜Z+qfFβ1aL(IλLqaSPS)>γfFβ˜Z+qfFβI>λL(q+aSPS+aL(γfFβ˜Z+qfFβ))I>λL(q+aSPS)+aLfFβλL(γaSb(λLλS)+q)=I2.

    This proves (ⅰ).

    (ⅱ) First note that ˆN=βaLˆF+λL>λS implies aSˆNq>0. We have

    1PSdPSdt|ELFZ=aSˆNbˆZq>0aSˆNq>bˆZ=bfFβ^PLqγγbfFβ(aSˆNq+bqγ)>^PL=IaLˆNλLλL+γbfFβ(aSˆNq+bqγ)>IaLˆNI<aLˆN(λL+γbfFβ(aSˆNq+bqγ))=I3.

    Using the equalities

    aLˆN=βˆF+qandaSˆNq=aS(ˆNλS)=aS((λLλS)+βˆFaL),

    we express I3 as

    I3=(βˆF+q)(λL+γbfFβ(aS(λLλS)+aSβˆFaL+bqγ)).

    Next, by the equivalence

    q=λLaL<γb(aS(λLλS)+aSaLβˆF+bqγ)1+aLfFβ<1+γbfFβ(aS(1λSλL)+aSaL1λLβˆF+bqγ1λL),

    it follows that IPS2=λL(1+aLfFβ)(q+βˆF)<I3. Hence (ii) is established.

    (ⅲ) Expanding I2 and I3, we have

    I2=λLq+λLaSq+mZePbqfFβγaSb(λLλS)+q2fFβ,

    and

    I3=βq+mZγeFλL+βq+mZγeFγbfFβaS(λLλS)+βq+mZγeFγbfFβasaLβq+mZγeF+βq+mZγeFqfFβ+qλL+γbfFβqaS(λLλS)+qaSβaLq+mZγeF+bγq2γbfFβ.

    If eFfF<eP, then aSλLbfFeF>aSλLePb. Thus, it is easy to verify that I3I2>0.

    We establish the coexistence of species in the next theorem wherein the proof follows directly from the above lemma.

    Theorem 5.2. The following statements hold:

    (i) If I2<I<I3, then system (1.1) is uniformly persistent and the positive equilibrium is globally asymptotically stable.

    (ii) If I>I3, then PS(t)0 as t.

    Using the parameter values q=1, aS=0.8, aL=0.5, λs=1.25, λL=2, b=1, eP=0.5, eF=0.4, β=1, γ=1, fF=0.6, and mZ=0.5, we have I2=11.35 and I3=66.92. By setting I=20, we obtain a numerical simulation of statement (ⅰ) as depicted in Figure 7. Letting I=80, a numerical simulation for statement (ⅱ) is shown in Figure 8.

    Figure 7.  Numerical solution of Model (1.1) exhibiting uniform persistence with parameter values q=1, aS=0.8, aL=0.5, λs=1.25, λL=2, b=1, eP=0.5, eF=0.4, β=1, γ=1, fF=0.6, mZ=0.5, I2=11.35, I3=66.92, and I=20.
    Figure 8.  Numerical solution of system (1.1) exhibiting the extinction of PS(t) with parameter values q=1, aS=0.8, aL=0.5, λs=1.25, λL=2, b=1, eP=0.5, eF=0.4, β=1, γ=1, fF=0.6, mZ=0.5, I2=11.35, I3=66.92, and I=80.

    When γ0, system (1.1) becomes

    {dNdt=IqNaSPSNaLPLN,dPSdt=aSNPSbZPSqPS,dPLdt=aLNPLβFPLqPL,dFdt=fFβPLFqF,dZdt=ePbPSZ(q+mZ)Z (6.1)

    and its food web is shown in Figure 9.

    Figure 9.  The food web of system (6.1).

    Theorem 6.1. Let (H):0<λS<λL<Iq hold.

    (i) If PS>^PS, then ˜ES=(λS,^PS,0,0,0) is G.A.S., where ^PS=IqλSaSλS and PS=q+mZePb.

    (ii) If PS<^PS and λS<N<λL, then ˜ESZ=(N,PS,0,0,Z) is G.A.S., where N=Iq+aSPS and Z=aSNqb.

    (iii) If PS<^PS and NC<λL<N, then ˜ESLZ=(λL,PS,PL,0,ˆZ) is G.A.S., where PL=IqλLaSPSλLaLλL.

    (iv) If PS<^PS, N>λL, and NC>λL, then ˜EC=(NC,PCS,PCL,FC,ZC) is G.A.S., where PCL=qfFβ, PCS=q+mZePb=PS, NC=Iq+aSPCS+aLPCL, ZC=aSNCqb, and FC=aLNCqβ.

    Let ˜I1=qλS+aSPCSλS, ˜I2=qλL+aSPCSλL and ˜I3=qλL+aSPCSλL+aLPCLλL. Then ˜I1<˜I2<˜I3. As a consequence of Theorem 6.1, the following statements hold.

    (ⅰ) If 0<I<˜I1, then ˜ES is G.A.S.

    (ⅱ) If ˜I1<I<˜I2, then ˜ESZ is G.A.S.

    (ⅲ) If ˜I2<I<˜I3, then ˜ESLZ is G.A.S.

    (ⅳ) If I>˜I3, then ˜EC is G.A.S.

    The global stability of system (6.1) is given in Figure 10.

    Figure 10.  Operation diagram of system.

    Proof. (ⅰ) Note that ^PS>PSN>λSZ>0. We introduce the Lyapunov function

    V=NλSξλSξdξ+PSPSξPSξdξ+PL+1fFF+1ePZ.

    Then

    V=(NλS)[INIλSaS(PSPS)aLPL]+(PSPS)[aS(NλS)bZ]+PL[aL(NλS)(qaLλS)βF]+1fFF(fFβPLq)+1ePZ[ePb(PSPS)[(q+mZ)ePbPS]]=(NλS)2NλSqfFF((q+mZ)ePbPS)1ePZ(qaLλS)PL0,

    from the assumption PS>^PS. Thus ˜ES is G.A.S. by invariance principle.

    (ⅱ) Note that ^PS>PS ensures that Z>0. Introduce the Lyapunov function

    V=NNξNξdξ+PSPCSξPSξdξ+PL+1fPF+1ePZZξZξdξ.

    Then, by the assumptions PS<^PS and N<λL, we have

    V=(NN)[ININaS(PSPS)aLPL]+(PSPS)[aS(NN)b(ZZ)]+PL(aL(NN)βF(qaLN))+1fFF(fFβPLq)+1eP(ZZ)[ePb(PSPS)[(q+mZ)ePbPS]]=(NN)2NN(qaLN)PLqfFF0.

    Therefore ˜ESZ is G.A.S. by invariance principle.

    (ⅲ) Define the Lyapunov function

    V=NλLξλLξdξ+PSPSξPSξdξ+PLPLξPLξdξ+1fFF+1ePZˆZξˆZξdξ.

    Then

    V=(NλL)[INIλLaS(PSPS)aL(PLPL)]+(PSPS)[aS(NλL)b(ZˆZ)]+(PLPL)(aL(NλL)βF)+1fFF(fFβ(PLPL)(qfFβPL))+1eP(ZˆZ)(ePb(PSPS))=I(NλL)2NλL1fF(qfFβPL).

    Using the assumptions PS<^PS and NC<λL<N, and the equivalence below

    qfFβPL>0qfFβ=PCL>PLPCL>IqλLaSPSλLaLλL(q+aSPS+aLPCL)λL>IλL>NC=Iq+aSPS+aLPCL,

    we have V0. Hence ˜ESLS is G.A.S. by invariance principle.

    (ⅳ) Observe that the assumptions PS<^PS, N>λL, and NC>λL imply ^PS>0, PL>0, and FC>0, respectively. By introducing the Lyapunov function

    V=NNCξNCξdξ+PSPCSξPCSξdξ+PLPCLξPCLξdξ+1fFFFCξFCξdξ+1ePZZCξZCξdξ,

    we obtain

    V=(NNC)[ININCaS(PSPCS)aL(PLPCL)]+(PSPCS)[aS(NNC)b(ZZC)]+(PLPCL)(aL(NNC)β(FFC))+1fF(FFC)(fFβ(PLPCL))+1eP(ZZC)(ePb(PSPCS))=I(NNC)2NNC0.

    Therefore it follows from invariance principle that ˜EC is G.A.S.

    In this paper, we study an aquatic ecosystem with five species : a single nutrient resource N (Phosphorus), the small phytoplankton PS, the large phytoplankton PL, the zooplankton Z, and the parasitic fungi F. Both PS and PL consume N. In the food web (see Figure 1), the zooplankton Z only consumes the small phytoplankton PS but the large phytoplankton PL is inedible to the zooplankton. In the absence of zooplankton, we assume that the small phytoplankton PS is a better competitor than the large PL in the exploitative competition for nutrient. With the presence of parasitic fungi F, we consider two cases : the food web with an FZ link and that without an FZ link. In Section 3, we first study the boundary dynamics of the food web, i.e., the population dynamics under the assumptions that F0, Z0 and PS0. We then employ Lyapunov functions to establish the results of global stability as the nutrient input I varies. Section 4 deals with the determination of conditions for the local stability of boundary equilibrium points of system (1.1) and the proofs of several partial results on the extinction of species. In Section 5, with the well understood information of boundary dynamics proven in Section 3, we establish the uniform persistence of the food web. Section 6 is devoted to determine conditions for the global stability of species in the system of food web without an FZ link.

    Now we discuss the role played by parasitic fungi in the coexistence of species in the food web. Recall that, in the absence of parasitic fungi, from Figure 3, coexistence of species occurs when I>IF2. When parasitic fungi are present in the food web, we consider two cases: one with an FZ link and another without an FZ link. In the case without an FZ link, coexistence of species occurs when I>˜I3 (see Theorem 6.1), whereas in the case with an FZ link, coexistence of species occurs when I2<I<I3 (see Theorem 5.2). A comparison of the three quantities IF2, ˜I3, I2 shows that

    IF2<˜I3<I2<I3,

    where

    IF2=q(λL+λLλSPS),PS=q+mZePb˜I3=λLq+aSPSλL+aLqfFβλL=IF2+aLqfFβλLI2=λL(q+aSPS)+aLfFβλL(q+γaSb(λLλS))I3=˜I3+aLfFβλLγaSb(λLλS).

    In view of the above, the best case for the coexistence of species of the food web is when I>IF2. That is, coexistence of species occurs if there is no parasitic fungus. In the case that parasitic fungus is present and there is no FZ link, coexistence of species occurs if I>˜I3. With the presence of parasitic fungi and an FZ link, we have coexistence in the parameter region I2<I<I3. From our numerical simulation (see Figure 8), we observe that, if I>I3, then the small phytoplankton PS goes to extinction.

    Finally, we note that in [9] the authors discuss the role of parasitic fungi in zooplankton biomass at steady states. Their conclusion is that the presence of an FZ link can benefits large phytoplankton and strengthens competition between small and large phytoplankton reducing material transfer from smaller phytoplankton to zooplankton. While our analysis shows that without FZ link if IF1<I<IF2 then zooplankton, fungi, small phytoplankton coexist; if I>IF2, the zooplankton, fungi, small and large phytoplankton coexist (see Figure 3). However, with FZ link, the system (1.1) is uniformly persistent if I2<I<I3 (Theorem 5.2). From the inequality IF2<I2<I3, it is easier to obtain coexistence when there is no FZ link.

    The first author would like to acknowledge the financial support of the Department of Science and Technology-Science Education Institute (DOST-SEI), through the Accelerated Science and Technology Human Resource Development Program-National Science Consortium (ASTHRDP-NSC). The second author acknowledges the support of Ministry of Science and Technology (MOST), Taiwan and National Center of Theoretical Science, Taiwan.

    The authors declare there is no conflict of interest.



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