Research article Special Issues

A conceptual approach to integrate management of ecosystem service and disservice in coastal wetlands

  • Management of coastal wetlands is increasingly difficult because of increasing pressure arising from anthropogenic causes. These include sea level and climate change as well as coastline development caused by population growth and demographic shifts, for example, amenity migration where people move to coastal communities for lifestyle reasons. Management of mangroves and salt marshes is especially difficult because maintaining ecosystem values, including the goods and services provided, is countered by the potential of enhancing or even creating ecosystem disservices, such as unpleasant odour and mosquito hazards. Here we present, explain and apply a conceptual model aimed at improving understanding of management choices that primarily focus on mitigation of disservice while enabling improvement in ecosystem services. The model was developed after more than 30 years of habitat management following modification of a salt marsh to control mosquito production. We discuss the application of the model in a mangrove forest known to produce mosquitoes and outline the benefits arising from using the model.

    Citation: Jon Knight, Pat Dale, Patrick Dwyer, Sam Marx. A conceptual approach to integrate management of ecosystem service and disservice in coastal wetlands[J]. AIMS Environmental Science, 2017, 4(3): 431-442. doi: 10.3934/environsci.2017.3.431

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  • Management of coastal wetlands is increasingly difficult because of increasing pressure arising from anthropogenic causes. These include sea level and climate change as well as coastline development caused by population growth and demographic shifts, for example, amenity migration where people move to coastal communities for lifestyle reasons. Management of mangroves and salt marshes is especially difficult because maintaining ecosystem values, including the goods and services provided, is countered by the potential of enhancing or even creating ecosystem disservices, such as unpleasant odour and mosquito hazards. Here we present, explain and apply a conceptual model aimed at improving understanding of management choices that primarily focus on mitigation of disservice while enabling improvement in ecosystem services. The model was developed after more than 30 years of habitat management following modification of a salt marsh to control mosquito production. We discuss the application of the model in a mangrove forest known to produce mosquitoes and outline the benefits arising from using the model.


    Ecological stoichiometry is an important part of ecology that links the metabolic demands of organisms with the relative supply of elements in the environment [1]. It examines the balance between energy and chemical elements and the extent to which this balance plays a part in determining the organisms' growth and reproduction and their ecological interactions. Recognizing the importance of stoichiometric limitations between predator requirements and the nutrient composition of prey has greatly enhanced our appreciation of trophic relationships. Many studies have demonstrated that ecological stoichiometry is a highly suitable framework for explaining predator responses to prey food quality [1,2,3,4].

    ons among individual organisms of different species (notably competition, predation/parasitism, mutualism, commensalism, or amensalism). Intraguild predation (IGP) within the cotope group contains two interaction types: competition and predation, and usually, there is not only a predatory relationship but also a potential competition relationship between two predatory species in order to compete for limited resources. IGP distinguishes itself from traditional predation by the fact that the behavior reduces potential competition for exploitation. The direct energy gain of predators makes IGP different from the traditional competition [5]. Thus, its effect on population dynamics is more complex and worth studying. IGP is a significant factor in predicting the stability of food webs and the maintenance of biodiversity, in addition to being a taxonomically widespread interaction within communities that can occur at different trophic levels [6]. The development of the IGP model can be traced back to Holt and Polis [7], who developed a three-species Lotka–Volterra food web model to study the species coexistence of IGP and found that achieving a stable three-species steady state is challenging. Following that, a series of studies examined IGP models with different structures and forms such as the Lotka–Volterra type [8,9,10], special forms of the functional and numerical response [11,12,13], prey switching or adaptive prey behavior [14,15], generalist predators or time delays [16,17,18], stochastic approaches [19,20,21], and stoichiometry [22,23].

    Diehl [22] investigated a model of competition among predators to explore stoichiometry as an IGP-intraguild mechanism. Later, the dynamic resource model of Loladze et al. [24] assumed that omnivores were the inferior carbon competitors and showed that dynamic interaction between autotroph quality and quantity could facilitate coexistence when resource quality is low. The system investigated in [22] is based on a continuous time scale. However, many plants in the wild and agricultural contexts have non-overlapping generations, and many herbivores have distinct annual or seasonal dynamics. Therefore, the main purpose of this paper is to propose a discrete-time version of the stoichiometric IGP model based on the continuous stoichiometric IGP model [22] and compare the differences in the dynamic behavior of models under different time scales. In particular, we focus on the following four questions: 1) How do the stoichiometric effects of food quality affect IGP? 2) What is the coexistence interval of the population for both models when the environmental parameter changes? 3) Can chaotic dynamics emerge under a biologically plausible parameter set, and if so, do the parameter ranges of the discrete-time model admit chaos, as in the continuous-time model? 4) Does multistability exist in both models?

    We construct the discrete-time IGP model with stoichiometry following the discretization approach in [25] and theoretically analyze the dynamical behavior of this model. The dynamical behavior is compared between the discrete- and continuous-time models, which leads to our conclusions.

    We consider the IGP system in [22] in the aquatic ecosystem. Zooplankton competes with fish for algae and is consumed by fish, which constitutes an IGP system. We use A(t), Z(t), and F(t) to represent the population density of algae, zooplankton, and fish at time t, respectively. Then the continuous IGP model with stoichiometry takes the form [22]

    dAdt=rA(1Amin{K,(PTθZZθFF)/q})aAZ(A)ZaAF(A,Z)F,dZdt=eAZmin{1,(PTθZZθFF)/AθZ}aAZ(A)ZaZF(A,Z)FdZZ,dFdt=eAFmin{1,(PTθZZθFF)/AθF}aAF(A,Z)F+eZFmin{1,θZθF}aZF(A,Z)FdFF. (2.1)

    Here, r is the maximum growth rate of algae, K is the algae carrying capacity, and PT is the total Phosphorus in the system. θZ and θF are the constant P (Phosphorus): C (Carbon) ratios of the zooplankton and fish, respectively. q is the minimal algae P:C ratio. dZ and dF are the specific loss rates of the zooplankton and fish, respectively. eAZ denotes the conservation of algae into the zooplankton, eAF represents the conservation of algae into the fish, and eZF denotes the conservation of zooplankton into the fish. The functions aAZ(A), aAF(A,Z), and aZF(A,Z) are the functional responses of the zooplankton feeding on algae, the fish feeding on algae, and the fish feeding on zooplankton, respectively.

    Similar to the functional response properties outlined in [26], it is assumed that aAZ(A), aAF(A,Z), and aZF(A,Z) are bounded smooth functions satisfying the following conditions:

    aAZ(0)=0,aAZ(A)>0,andaAZ(A)<0 for A>0.aAF(0,Z)=0,aAF(A,Z)A>0,2aAF(A,Z)2A<0,andaAF(A,Z)Z<0forA>0,Z>0.aZF(A,0)=0,aZF(A,Z)A<0,aZF(A,Z)Z>0,and2aZF(A,Z)2Z<0forA>0,Z>0.

    Based on the above model, the time is discretized, and a discrete stoichiometry model is established. There are several approaches to deriving discrete-time dynamical systems that correspond to continuous time [27,28]. We discretize Eq (2.1) using the method developed by Fan [25]. This method employs differential equations with piecewise constant arguments by assuming that the per capita growth rate remains constant on a given time interval [t,t+1]. Then Eq (2.1) turns to the following system:

    1A(t)dA(t)dt=r(1A[t]min{K,(PTθZZ[t]θFF[t])/q})aAZ(A[t])Z[t]A[t]aAF(A[t],Z[t])F[t]A[t],1Z(t)dZ(t)dt=eAZmin{1,PTθZZ[t]θFF[t]θZA[t]}aAZ(A[t])aZF(A[t],Z[t])F[t]Z[t]dZ,1F(t)dF(t)dt=eAFmin{1,PTθZZ[t]θFF[t]θFA[t]}aAF(A[t],Z[t])+eZFmin{1,θZθF}aZF(A[t],Z[t])dF,t0,1,2,... (2.2)

    where [t] denotes the integer part of t(0,+). Then, we integrate both sides of Eq (2.2) on any interval [n,n+1),n=0,1,2,..., and obtain, for nt<n+1, n=0,1,2,...,

    A(t)=A(n)exp{[r(1A(n)min{K,(PTθZZ(n)θFF(n))/q})aAZ(A(n))Z(n)A(n)aAF(A(n),Z(n))F(n)A(n)](tn)},Z(t)=Z(n)exp{[eAZmin{1,PTθZZ(n)θFF(n)θZA(n)}aAZ(A(n))aZF(A(n),Z(n))F(n)Z(n)dZ](tn)},F(t)=F(n)exp{[eAFmin{1,PTθZZ(n)θFF(n)θFA(n)}aAF(A(n),Z(n))+eZFmin{1,θZθF}aZF(A(n),Z(n))dF](tn)}.

    Let t tends to n+1, then we get the final discrete-time system

    A(n+1)=A(n)exp{r(1A(n)min{K,(PTθZZ(n)θFF(n))/q})aAZ(A(n))Z(n)A(n)aAF(A(n),Z(n))F(n)A(n)},Z(n+1)=Z(n)exp{eAZmin{1,PTθZZ(n)θFF(n)θZA(n)}aAZ(A(n))aZF(A(n),Z(n))F(n)Z(n)dZ},F(n+1)=F(n)exp{eAFmin{1,PTθZZ(n)θFF(n)θFA(n)}aAF(A(n),Z(n))+eZFmin{1,θZθF}aZF(A(n),Z(n))dF} (2.3)

    for nN.

    Initial values should be considered in the biologically meaningful regions. Thus, we assume that 0<A(0), 0<Z(0)<PT/θZ, and 0<F(0)<PT/θF. The meaning and values of most parameters are selected from [26] and [22], and are listed in Table 1.

    Table 1.  Parameters of Models (2.1) and (2.3).
    Parameter Description Values Unit
    PT Total phosphorus in system 0.33 mgPL1
    eAZ Conservation of algae to zooplankton 0.8
    eAF Conservation of algae to fish 0.6
    eZF Conservation of zooplankton to fish 0.6
    r Maximal growth rate of algae 0.58 day1
    dZ Loss rate of zooplankton 0.25 day1
    dF Loss rate of fish 0.15 day1
    θZ Constant P:C of zooplankton 0.03 mgP/mgC
    θF Constant P:C of fish 0.03 mgP/mgC
    q Minimal P:C of algae 0.003 mgP/mgC
    sAZ Search and attack rate of zooplankton on algae 0.085 day1(mgC/m3)1
    hAZ Handing time of zooplankton feeding on algae 0.16 day/(mgC/m3)
    sAF Search and attack rate of fish on algae 0.08 day1(mgC/m3)1
    hAF Handing time of fish feeding on algae 0.4 day/(mgC/m3)
    sZF Search and attack rate of fish on zooplankton 0.05 day1(mgC/m3)1
    hZF Handing time of fish feeding on zooplankton 0.5 day/(mgC/m3)
    K Carrying capacity of algae 0–120 mgCL1

     | Show Table
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    In this section, boundedness and positive invariance results are fully analyzed for Eq (2.3) using arguments similar to those in Theorems 3.1 and 3.2 of [25]. It is easy to show that solutions of Eq (2.3) remain nonnegative when they exist.

    For convenience, we assume that aAZ(A)=AfAZ(A), aAF(A,Z)=AfAF(A,Z), and aZF(A,Z)=ZfZF(A,Z). From [29], we have

    limA0fAZ(A)=aAZ(0)<,andfAZ(A)<0forA>0.limA0fAF(A,Z)=aAF(0,Z)A<,fAF(A,Z)A<0andfAF(A,Z)Z<0forA>0,Z>0.limZ0fZF(A,Z)=aZF(A,0)Z<,fZF(A,Z)A<0andfZF(A,Z)Z<0forA>0,Z>0.

    Theorem 3.1. For System (2.3), the following statements hold for all n>0;

    A(n)max{A(0),K/rexp(r1)}S,Z(n)max{Z(0),g}exp(2eAZaAZ(S)2dZ)G,F(n)max{F(0),h}exp(2eAFaAF(S,0)+2eZFaZF(0,G)2dF)H,

    where g and h satisfy

    eAZaAZ(exp(rfAZ(S)g))<dZ and eAFaAF(Sexp(rfAZ(S)gfAF(S,G)h),0)+eZFaZF(0,Gexp(eAZaAZ(S)fZF(S,G)hdZ))<dF.

    Proof. Note that all components of the solution to Eq (2.3) are non-negative when they exist. It is easy to find that R(A)=Aexpr(1A/K) attains its maximum value at A=K/r. We obtain

    A(n+1)<A(n)exp{rrA(n)K}Krexp(r1)s.

    Then

    A(n)max{A(0),s}S

    for all non-negative integers n.

    If eAZaAZ(S)dZ, it is easy to show that Z(n)Z(0) for all n>0. Thus, it is assumed that eAZaAZ(S)>dZ. Let g be sufficiently large such that

    eAZaAZ(exp(rfAZ(S)g))<dZ.

    We claim that

    Z(n)max{Z(0),g}exp(2eAZaAZ(S)2dZ)G

    for all non-negative integers n. This holds for n=1,2. Let us assume that this claim is false. We consider two cases.

    (Ⅰ) Z(0)g. Then for some n1>2, g<Z(n12)G, g<Z(n11)G, and Z(n1)>G. In this case, we get

    A(n11)A(n12)exp(rfAZ(A(n12))Z(n12)fAF(A(n12),Z(n12))F(n12))A(n12)exp(rfAZ(A(n12))Z(n12))Sexp(rfAZ(S)g).

    This implies that

    Z(n1)<Z(n11)exp{eAZaAZ(Sexp(rfAZ(S)g))dZ}<Z(n11)<G,

    which is a contradiction, implying that the claim is true for this case.

    (Ⅱ) Z(0)>g. In this case, we have

    A(1)<Sexp(rfAZ(S)g),

    which indicates that

    Z(2)<Z(1).

    That is, as long as Z(n)>g, then Z(n+2)<Z(n+1). Then, there exists two cases: (ⅰ) Z(n)g for some n>0 or (ⅱ) Z(n)>g for all n>0. In case (ⅱ), we have that Z(n) is strictly decreasing for n>1, and the claim is obviously true. In case (ⅰ), from the proof of case (Ⅰ), we find that Z(n)<G for n>n, and hence the claim is again true.

    If eAFaAF(S,0)+eZFaZF(0,G)dF, then it is clear that, for all n>0, we have F(n)F(0). Thus, we assume that eAFaAF(S,0)+eZFaZF(0,G)>dF. Let g, h be sufficiently large such that

    eAFaAF(Sexp(rfAZ(S)gfAF(S,G)h),0)+eZFaZF(0,Gexp(eAZfZF(S,G)hdZ))<dF.

    We conclude that, for all non-negative integers n,

    F(n)max{F(0),h}exp(2eAFaAF(S,0)+2eZFaZF(0,G)2dF)H.

    This holds for n=1,2. Let us assume that this claim is not true. We consider two cases

    (Ⅰ) F(0)h. Then, for some n1>2, h<F(n12)H, h<F(n11)H, and F(n1)>H. In this case, we have

    F(n1)<F(n11)exp{eAFaAF(Sexp(rfAZ(S)gfAF(S,G)h),0)+eZFaZF(0,Gexp(eAZaAZ(S)fZF(S,G)hdZ))dF}<F(n11)<H,

    which is a contradiction, implying that the claim is true for this case.

    (Ⅱ) F(0)>h. In this case, we have

    A(1)<Sexp(rfAZ(S)gfAF(S,G)h),Z(1)<Gexp(eAZaAZ(S)fZF(S,G)hdZ),

    which implies that

    F(2)<F(1).

    In other words, as long as F(n)>h, then F(n+2)<F(n+1). Hence, there are two possibilities: (i) F(n)h for some n>0 or (ii) F(n)>h for all n>0. In case (ii), we have that F(n) is strictly decreasing for n>1, and the claim is obviously true. In case (i), from the proof of case (I), we see that F(n)<H for n>n, and then the claim is again true.

    From the above theorem, we obtain the conclusion that

    △={(A,Z,F):0<A<Krexp(r1),0<Z<g,0<F<h}

    is positively invariant. Indeed, we have the following theorem.

    Theorem 3.2. is globally attractive for System (2.3) with initial values satisfying A(0)>0, 0<Z(0)<PT/θZ and 0<F(0)<PT/θF.

    Proof. From the above theorem, it is apparent that, for large values of n, 0<A(n)<(K/r)exp(r1). Note that if Z(n)>g for n0, then Z(n) tends to Zg. Hence, for n 0, we obtain

    Z(n)<Z(n1)exp{eAZaAZ(Sexp(rfAZ(S)g))dZ}.

    Letting n yields

    ZZexp{eAZaAZ(Sexp(rfAZ(S)g))dZ}<Z.

    This contradicts Z>g>0. Then, for n0, 0<Z(n)<g. Note that if F(n)>h for any n>0, then F(n) tends to Fh. Hence, for n0, we obtain

    F(n)<F(n1)exp{eAFaAF(Sexp(rfAZ(S)gfAF(S,G)h),0)+eZFaZF(0,Gexp(eAZfZF(S,G)hdZ))dF}.

    Letting n yields

    FFexp{eAFaAF(Sexp(rfAZ(S)gfAF(S,G)h),0)+eZFaZF(0,Gexp(eAZfZF(S,G)hdZ))dF}<F.

    This contradicts F>h>0, proving the theorem.

    For convenience, we rewrite Eqs (2.1) and (2.3) as

    dAdt=AL(A,Z,F),dZdt=ZM(A,Z,F),dFdt=FN(A,Z,F),

    and

    A(n+1)=A(n)exp{L(A(n),Z(n),F(n))},Z(n+1)=Z(n)exp{M(A(n),Z(n),F(n))},F(n+1)=F(n)exp{N(A(n),Z(n),F(n))},

    respectively, where

    L(A,Z,F)=r(1Amin{K,(PTθZZθFF)/q})fAZ(A)ZfAF(A,Z)F,M(A,Z,F)=eAZmin(1,PTθZZθFFθZA)aAZ(A)fZF(A,Z)FdZ,N(A,Z,F)=eAFmin{1,PTθZZθFFθFA}aAF(A,Z)+eZFmin{1,θZθF}aZF(A,Z)dF.

    To find equilibria of Eqs (2.1) and (2.3), we solve the following equations

    AL(A,Z,F)=0,ZM(A,Z,F)=0,FN(A,Z,F)=0, (4.1)

    and

    A[1exp{L(A,Z,F)}]=0,Z[1exp{M(A,Z,F)}]=0,F[1exp{N(A,Z,F)}]=0, (4.2)

    respectively. (4.1) and (4.2) both lead to

    A=0 or L(A,Z,F)=0,Z=0 or M(A,Z,F)=0,F=0 or N(A,Z,F)=0,

    which shows that Systems (2.3) and (2.1) share exactly the same equilibria.

    After some straightforward algebraic calculations, the variational matrix of Systems (2.1) and (2.3) lead

    J(A,Z,F)=(L+ALAALZALFZMAM+ZMZZMFFNAFNZN+FNF)

    and

    J(A,Z,F)=(eL+AeLLAAeLLZAeLLFZeMMAeM+ZeMMZZeMMFFeNNAFeNNZeN+FeNNF),

    respectively, where

    LA=LA=rmin{K,(PTθZZθFF)/q}fAZ(A)ZfAF(A,Z)AF.LZ=LZ={fAZ(A)fAF(A,Z)ZF,qK+θZZ+θFF<PT,rqθZA(PTθZZθFF)2fAZ(A)fAF(A,Z)ZF,qK+θZZ+θFF>PT.LF=LF={fAF(A,Z)<0,qK+θZZ+θFF<PT,rqθFA(PTθZZθFF)2fAF(A,Z)<0,qK+θZZ+θFF>PT.MA=MA={eAZaAZ(A)fZF(A,Z)AF>0,θZA+θZZ+θFF<PT,eAZPTθZZθFFθZfAZ(A)fZF(A,Z)AF,θZA+θZZ+θFF>PT.MZ=MZ={fZF(A,Z)ZF>0,θZA+θZZ+θFF<PT,eAZfAZ(A)fZF(A,Z)ZF,θZA+θZZ+θFF>PT.MF=MF={fZF(A,Z)<0,θZA+θZZ+θFF<PT,eAZθFθZfAZ(A)fZF(A,Z)<0,θZA+θZZ+θFF>PT.NA=NA={eAFaAF(A,Z)A+eZFmin{1,θZθF}aZF(A,Z)A,θFA+θZZ+θFF<PT,eAFPTθZZθFFθFfAF(A,Z)A+eZFmin{1,θZθF}aZF(A,Z)A<0,θFA+θZZ+θFF>PT.NZ=NZ={eAFaAF(A,Z)Z+eZFmin{1,θZθF}aZF(A,Z)Z,θFA+θZZ+θFF<PT,eAFfAF(A,Z)θZθF+eAFPTθZZθFFθFfAF(A,Z)Z+eZFmin{1,θZθF}aZF(A,Z)Z,θFA+θZZ+θFF>PT.NF=NF={0,θFA+θZZ+θFF<PT,eAFfAF(A,Z)<0,θFA+θZZ+θFF>PT.

    System (2.3) has four possible boundary equilibria: E0(0,0,0), E1(k,0,0), E2(ˉA,ˉZ,0), and E3(˜A,0,˜F), where k=min(K,PT/q). The equilibrium E0(0,0,0) represents the extinction of all species; The equilibrium E1(k,0,0) denotes the extinction of zooplankton and fish and the persistence of algae; The equilibrium E2(ˉA,ˉZ,0) represents the extinction of fish and persistence of algae and zooplankton; The equilibrium E3(˜A,0,˜F) denotes the extinction of zooplankton and persistence of algae and fish.

    Theorem 4.1. For System (2.3), E0 is always unstable. E1 is locally asymptotically stable if

    0<r<2,eAZmin{1,PTkθZ}aAZ(k)<dZ and ,eAFmin{1,PTkθF}aAF(k,0)<dF;

    E1 is unstable if

    r>2 or eAZmin{1,PTkθZ}aAZ(k)>dZ or eAFmin{1,PTkθF}aAF(k,0)>dF.

    Proof. At the extinction equilibrium E0(0,0,0), the form of Jacobian matrix is as follows

    J(E0)=(er000edZ000edF).

    Obviously, the characteristic roots edZ and edF are less than 1, while the other characteristic root er is greater than 1. Therefore, the extinction equilibrium E0 is always unstable.

    At E1(k,0,0) the Jacobian is

    J(E1)=(1rkLZ(k,0,0)kLF(k,0,0)0exp{M(k,0,0)}000exp{N(k,0,0)}),

    where

    M(k,0,0)=eAZmin{1,PTkθZaAZ(k)}dZ,N(k,0,0)=eAFmin{1,PTkθFaAF(k,0)}dF.

    Let λ1, λ2 and λ3 denote the characteristic roots of J(E1), then the following inequalities

    0<r<2,eAZmin{1,PTkθZ}aAZ(k)<dZ and ,eAFmin{1,PTkθF}aAF(k,0)<dF

    ensure that |λi|<1, i=1,2,3. While the following inequalities

    r>2 or eAZmin{1,PTkθZ}aAZ(k)>dZ or eAFmin{1,PTkθF}aAF(k,0)>dF

    imply λi>1 for some i.

    The equilibrium E2(ˉA,ˉZ,0) represents the extinction of fish. So, E2 is the internal equilibrium of a two-dimensional system in which only algae and zooplankton exist. E2(ˉA,ˉZ,0) can be explored in the AZ phase plane. Since both Eqs (2.1) and (2.3) share the same equilibria, from [29], we can conclude that E2 in Eq (2.3) possibly contains multiple equilibria.

    Theorem 4.2. If N(ˉA,ˉZ,0)<0, then the following hold.

    In region i (i.e., ˉZ<PT/θZˉA), if the algae's nullcline is decreasing at E2(i.e.,LA<0), and

    12ˉAˉZLZMA2<ˉALA<ˉAˉZLZMA, (4.3)

    then E2 is locally asymptotically stable.

    In region ii (i.e., ˉZ>PT/θZˉA), if the slope of the zooplankton's nullcline at E2 is greater than the algae's (i.e.,MA/MZ>LA/LZ), and

    12ˉAˉZ[LZMALAMZ]2<ˉALA+ˉZMZ<ˉAˉZ[LZMALAMZ], (4.4)

    then E2 is locally asymptotically stable.

    Proof. In order to study local stability of E2, the Jacobian at E2(ˉA,ˉZ,0) can be described as

    J(E2)=(Jsub(E2)J10exp{N(ˉA,ˉZ,0)})

    where

    Jsub(E2)=(1+ˉALAˉALZˉZMA1+ˉZMZ) and J1=(ˉALFˉZMF).

    N(ˉA,ˉZ,0)<0 ensures that one characteristic root has a magnitude less than 1. Then we focus on the characteristic roots of Jsub(E2). The trace and determinant of Jsub(E2) are

    Tr(Jsub(E2))=2+ˉALA+ˉZMZ

    and

    Det(Jsub(E2))=Tr(Jsub(E2))1+ˉAˉZ[LAMZLZMA].

    We discuss the stability of E2 in region i and region ii, respectively.

    Suppose E2 lies in region i. Then LZ<0, MA>0, MZ=0. Note that,

    Tr(Jsub(E2))=2+ˉALA=2+A(LALZ)(LZ),

    where (LA/LZ) is the slope of the algae's nullcline. Assume that the algae's nullcline is increasing at E2, i.e., LA>0. Then Tr(Jsub(E2))>2 and E2 is unstable. If the algae's nullcline is decreasing at E2, i.e., LA<0, and Eq (4.3) holds, then one can prove that 2>1+Det(Jsub(E2))>|Tr(Jsub(E2))|. From Jury Test in [25], E2 is stable. On the contrary, if Eq (4.3) does not hold, then E2 is unstable.

    Suppose E2 is in region ii. Then at E2, LZ<0, MA<0, MZ<0. Note that

    Det(Jsub(E2))=1+ˉALA+ˉZMZ+ˉAˉZLZMZ[(MAMZ)(LALZ)].

    If the slope of the algae's nullcline at E2 is greater than the zooplankton's, i.e., MA/MZ<LA/LZ, then 1+Det(Jsub(E2))<|Tr(Jsub(E2))|, which means that E2 is unstable. If the slope of the zooplankton's nullcline at E2 is greater than the algae's, then LA/LZ<MA/MZ<0. If Eq (4.4) holds, then 2>1+Det(Jsub(E2))>|Tr(Jsub(E2))| holds and E2 is stable. On the contrary, if Eq (4.4) does not hold, then E2 is unstable.

    Similarly, the boundary equilibrium E3(˜A,0,˜F) indicates the extinction of zooplankton. E3(˜A,0,˜F) can be considered the internal equilibrium of a simple two-dimensional system with only fish and algae. E3(˜A,0,˜F) can be explored in the AF phase plane. The Jacobian at E3(˜A,0,˜F) can be described as

    J(E3)=(Jsub(E3)J20exp{M(˜A,0,˜F)})

    where

    Jsub(E3)=(1+˜ALA˜ALF˜FNA1+˜FNF) and J2=(˜ALZ˜FNZ)

    The trace and determinant of Jsub(E3) are

    Tr(Jsub(E3))=2+˜ALA+˜FNF

    and

    Det(Jsub(E3))=Tr(Jsub(E3))1+˜A˜F[LANFLFNA].

    We find that the Jacobian matrixes of E2(ˉA,ˉZ,0) and E3(˜A,0,˜F) take similar forms. The locally asymptotic stabilities of E2 and E3 can both be studied with the help of the stability of internal equilibria of a stoichiometric predator-prey system in discrete time scale [25]. Due to the similarity between the stability conditions of E2 and E3 and the similar argument in [25], we only provide the conclusion on the stability of E3. Therefore, details of proofs will be skipped here.

    Theorem 4.3. If M(˜A,0,˜F)<0, then the following are true.

    In region i (i.e., ˜F<PT/θF˜A), if algae's nullcline is decreasing at E3 (i.e., LA>0), and

    12˜A˜FLFNA2<˜ALA<˜A˜FLFNA, (4.5)

    then E3 is locally asymptotically stable.

    In region ii (i.e., ˜F>PT/θF˜A), if the slope of the fish's nullcline at E3 is greater than algae's (i.e.,NA/NF>LA/LF), and

    12˜A˜F[LFNALANF]2<˜ALA+˜FNF<˜A˜F[LFNALANF], (4.6)

    then E3 is locally asymptotically stable.

    Through theoretical analysis, we present Table 2 to better summarize stability conditions of boundary equilibria between continuous model and discrete model.

    Table 2.  Comparison of stability conditions of boundary equilibria.
    Equilibria Discrete model System (2.3) Continuous model System (2.1) Results
    E0(0,0,0) Always unstable. Always unstable. Same
    E1(k,0,0) M(k,0,0)<0, N(k,0,0)<0, M(k,0,0)<0, N(k,0,0)<0. Eq (2.3) Stronger
    0<r<2.
    E2(ˉA,ˉZ,0) N(ˉA,ˉZ,0)<0. N(ˉA,ˉZ,0)<0. Eq (2.3) Stronger
    Region ⅰ: LA<0 and Eq (4.3); Region ⅰ: LA<0;
    Region ⅱ: MA/MZ>LA/LZ and Eq (4.4). Region ⅱ: MA/MZ>LA/LZ.
    E3(˜A,0,˜F) M(˜A,0,˜F)<0. M(˜A,0,˜F)<0. Eq (2.3) Stronger
    Region ⅰ: LA<0 and Eq (4.5); Region ⅰ: LA<0;
    Region ⅱ: NA/NF>LA/LF and Eq (4.6). Region ⅱ: NA/NF>LA/LF.

     | Show Table
    DownLoad: CSV

    The coexistence equilibrium E(A,Z,F) can be solved by

    L(A,Z,F)=0,M(A,Z,F)=0 and N(A,Z,F)=0.

    To determine the species interactions of intraguild predation, it is necessary to explore the Jacobian matrix of continuous model (System (2.1)) and discrete model (System (2.3)) at E, which present as follows,

    J(A,Z,F)|(2.1)=(ALAALZALFZMAZMZZMFFNAFNZFNF)=(A000Z000F)(LALZLFMAMZMFNANZNF)

    and

    J(A,Z,F)|(2.3)=(1+ALAALZALFZMA1+ZMZZMFFNAFNZ1+FNF)=(100010001)+(A000Z000F)(LALZLFMAMZMFNANZNF).

    Define

    M(A,Z,F)=(LALZLFMAMZMFNANZNF)

    as the ecosystem matrix of Systems (2.1) and (2.3) at E.

    The ecological matrix of the continuous model and discrete model take the same form. We can use the ecosystem matrix to study the interactions between species in intraguild predations. In this matrix, the ij-th term measures the effect of the j-th species on the i-th species' per capita growth rate [30]. That is, the positive sign in the ecological matrix means that the j-th species is favorable to the growth of the i-th species, and the negative sign means that the j-th species is adverse to the growth of the i-th species. According to the relationship between the P:C ratio in the three species, the symbol of the ecological matrix is determined. The differences in P:C ratio of the three species affect the system. We study the matrix in two cases: θZ>θF and θZ<θF. The different cases on the ecological matrix can be shown in Table 3.

    Table 3.  The ecosystem matrix of model (2.3).
    Conditions θZ>θF θZ<θF
    Q=(PTθZZθFF)/A>max{θZ,θF} (+/+/+++/+/0) (+/+/+++/+/0)
    min{θZ,θF}<Q<max{θZ,θF} (+/+/+/+/+/+/0) (+/+/+++/)
    Q<min{θZ,θF} (+/+/+/+/+/) (+/+/+/+/+/)

     | Show Table
    DownLoad: CSV

    When the P:C ratio in algae is bigger than those in zooplankton and fish, the algae quality is good for both zooplankton and fish. The ecological matrices for both cases take the same form. The relationship between algae and zooplankton is (+,+/). The increase in algae is certainly positive for zooplankton. Due to the existence of fish, the effect of zooplankton on algae is unconcern. The interactions of algae–fish and zooplankton–fish take the form (+/,). The effect of algae and zooplankton on fish is unconcern. When the P:C ratio in algae changes between the P:C ratio of zooplankton (θZ) and the P:C ratio of fish (θF), something shows different. 1) When θZ>θF, the quality of the algae is good for the fish but bad for the zooplankton. The interaction between algae and zooplankton has changed from (+,+/) to (+/,+/). The increase of algae is uncertain for zooplankton due to the decline in food quality. 2) When θZ<θF, the quality of the algae is good for the zooplankton but bad for the fish. The increase of algae is positive for zooplankton. The relationship between algae and fish has changed from traditional (+/,) to (,). The increase in algae is certainly negative for fish due to poor food quality. When the P:C ratio in algae is smaller than those in zooplankton and fish, the algae quality is bad for both zooplankton and fish. The ecological matrices for both cases take the same form. The algae-zooplankton relationship is (+/,+/). And algae-fish interactions take the form (,). The symbols of ecological matrixes reveal that the increase in algae has an uncertain effect on zooplankton, but can have a certain negative impact on fish.

    In summary, with the decline of algae food quality, the relationship between algae and fish changes from positive correlation (+,+/) to uncertainty (+/,+/). The relationship between algae and fish changes from uncertainty (+/,) to negative correlation (,). The interaction of zooplankton – fish takes (+/,) form. The impact of the increase of zooplankton on fish is uncertain. The interaction between fish and zooplankton is not only predation but also competition. Predation in accompanies by competition jointly affects the interaction between fish and zooplankton. In general, the decline in food quality has a negative impact on the flow of energy.

    Numerical simulations on the continuous model and discrete model can be conducted to verify and deepen the results of our analysis. We will compare the two models with intraguild structure by choosing Holling type Ⅱ functional response, which has been used in [22]. Let

    aAZ(A)=sAZA1+sAZhAZA,aAF(A,Z)=sAFA1+sAFhAFA+sZFhZFZ,aZF(A,Z)=sZFZ1+sAFhAFA+sZFhZFZ,

    where the meanings of the related parameters have been listed in Table 1. The three functional response functions mean that the predations in the intraguild structure are saturated. We numerically study the following models:

    A(n+1)=A(n)exp{r(1A(n)min{K,(PTθZZ(n)θFF(n))/q})sAZZ(n)1+sAZhAZA(n)sAFF(n)1+sAFhAFA(n)+sZFhZFZ(n)},Z(n+1)=Z(n)exp{eAZmin{1,PTθZZ(n)θFF(n)θZA(n)}sAZA(n)1+sAZhAZA(n)sZFF(n)1+sAFhAFA(n)+sZFhZFZ(n)dZ},F(n+1)=F(n)exp{eAFmin{1,PTθZZ(n)θFF(n)θFA(n)}sAFA(n)1+sAFhAFA(n)+sZFhZFZ(n)+eZFmin(1,θZθF)sZFZ(n)1+sAFhAFA(n)+sZFhZFZ(n)dF}, (5.1)

    and

    dAdt=rA(1Amin{K,(PTθZZθFF)/q})sAZAZ1+sAZhAZAsAFAF1+sAFhAFA+sZFhZFZ,dZdt=eAZmin(1,PTθZZθFFθZA)sAZAZ1+sAZhAZAsZFZF1+sAFhAFA+sZFhZFZdZZ,dFdt=eAFmin{1,PTθZZθFFθFA}sAFAF1+sAFhAFA+sZFhZFZ+eZFmin{1,θZθF}sZFZF1+sAFhAFA+sZFhZFZdFF. (5.2)

    The main parameters are selected from [26] and [22] for numerical simulation of the two models at different initial conditions, as shown in Table 1. For convenience, we record four typical initial conditions. 1st initial conditions is (A(0),Z(0),F(0))=(58.5,1.07,1.6); 2nd initial conditions is (A(0),Z(0),F(0))=(0.5,0.5,0.5); 3rd initial conditions is (A(0),Z(0),F(0))=(5,5,5); and 4th initial conditions is (A(0),Z(0),F(0))=(0.1,0.05,0.01). The carrying capacity of the primary producer, K, is an indirect index to evaluate the light intensity or energy input of the system. Bifurcation diagrams can intuitively show the equilibrium of a dynamical model changing with a parameter. The bifurcation diagrams in Figure 1 reveal how the dynamics of the populations in Systems (5.1) and (5.2) change with K. To facilitate intuitive analysis, Figure 2 fully reflects the dynamic behavior differences between the discrete model and the continuous model along the gradient of light levels K from 0 to 120 mgCL1 with different initial values.

    Figure 1.  The bifurcation curves versus carrying capacity for the discrete model (ai, i=1,2,3) and continuous model (bi, i=1,2,3). The red, blue, and black lines represent the bifurcation diagrams of algae, zooplankton, and fish, respectively.
    Figure 2.  Phase plot for discrete model (ai, i=1,2,...,6) and continuous model (bi, i=1,2,...,6). The red points and cycles denote the stable equilibria and limit cycle, respectively.

    When the carrying capacity varies at the low level (e.g., 0<K<18.2 in Figure 1), both discrete model and continuous model show similar dynamic behaviors. When the carrying capacity is extremely low (e.g., 0<K<3.4 in Figure 1 and K=3 in Figure 2a1, b1), the zooplankton and the fish of discrete model and continuous model cannot be sustained due to starvation and the equilibrium E1(k,0,0) is stable. When carrying capacity further increases (e.g., 3.4<K<18.2 in Figure 1), the algae and fish persist but the lack of food and the pressure of predator still can't maintain the survival of fish, which implies that E3(˜A,0,˜F) is stable. Figure 2a2, Figure 2b2 show the typical case with K=10. Space trajectories with different initial values tend to steady state.

    When K varies in the intermediate level, a clear difference between the continuous and discrete models appears for the first time, as shown in 18.2<K<55.6 in Figure 1. When K=18.2, Hopf bifurcation appears in the discrete model, and a limit cycle is generated. But the continuous model is always in a stable state (e.g., K=20 in Figure 2a3, Figure 2b3). This region shows that, compared with the continuous model, the discrete model is more prone to oscillations. We notice that when 37.2<K<42.2, different types of bistability appeared in the discrete model and continuous model. For the discrete model, one stable state maintains the previous periodic oscillation. Another stable state ends the oscillation, and the system is in a stable equilibrium state (e.g., K=40 in Figure 2a4); For the continuous model, two local stable steady states coexist (e.g., K=40 in Figure 2b4). With the further increase of K (42.2<K<55.6), the continuous model still admits two local stable steady states. While the discrete model shows one global stable state. As K increases in this region, the density of algae increases while the density of fish decreases due to poor food quality. The stoichiometric effect can be found in this region.

    When carrying capacity varies at the high level (e.g., 55.6<K<120 in Figure 1), models with different initial values show different dynamic behaviors. Some differences in dynamical behaviors between the continuous model and discrete model can also be observed in this region. During this interval, different from the previous, due to the abundance of food and the reduction of predation pressure, the zooplankton can persist. Three species can coexist in this interval. That is the equilibrium E(A,Z,F) is stable. Especially, for the discrete model, E(A,Z,F) is globally asymptotically stable (e.g., 55.6<K<97.5 in Figure 1); for the continuous model, the model exhibits bistable states (e.g., 55.6<K<101.5 in Figure 1). E(A,Z,F) together with E3(˜A,0,˜F) are both locally asymptotically stable. The survival of the zooplankton in the continuous model will be affected by different initial values, while the survival of the zooplankton in the discrete model will not be affected by the initial values. Figure 2a5, Figure 2b5 show the typical cases with K=80.

    When carrying capacity varies at an extremely high level, rich dynamic behaviors show in this region. The discrete model exhibits bistable states (e.g., 97.5<K<120 in Figure 1), while the continuous model shows tristable states (e.g., 101.5<K<120 in Figure 1). For the discrete model, the limit set can be a stable equilibrium and a strange attractor (e.g., K=120 in Figure 2a6); For the continuous model, the limit set can be two local stable equilibria and a strange attractor (e.g., K=120 in Figure 2b6).

    The continuous stoichiometric model with intraguild structure can be used to explore interactions of the species with overlapping generations. However, when facing the situation of non-overlapping generations of herbivores, annuals, and regularly collected experimental data, we need to consider the impact of time discretization on the model. In this paper, discrete intraguild predation models with biological stoichiometry are developed and analyzed. The continuous model and discrete model share the same ecological matrix, which helps us study the interspecific relationship under different food qualities. In comparing the dynamic behaviors of the continuous model and discrete model, we conduct numerical simulations and mainly focus on the following aspects: the stoichiometric effects of food quality, multistability, coexistence, and chaos.

    Nutrient recycling rates have a significant impact on ecosystem-level nutrient availability. Figure 4 shows the density of algae, zooplankton, and fish with respect to food quality (P:C). When the carrying capacity is low, the transfer efficiency of zooplankton and fish can reach their maximum value. As K increases, the food quality of algae shows a decreasing trend (Figure 4a). When the food quality varies in (q,θF), the density of algae increases, but the density of fish decreases (Figure 4b). Poor food quality causes negative effects on fish. Due to the combined effects of food quality and high trophic level predation, the impact of increased algae on zooplankton is uncertain. Due to the combined effects of interspecific competition and high trophic level predation, the impact of increased zooplankton on fish is also uncertain. This phenomenon is consistent with the results of the ecological matrix.

    In some parameter intervals, the attractors of continuous and discrete models differ. When the carrying capacity varies at an intermediate level, the steady state of the continuous model is a stable equilibrium, and the steady state of the discrete model is a stable limit cycle. When the carrying capacity is at a high level, there exist three and two stable states in the continuous model and discrete model, respectively. The reason why the steady state of the continuous model is more than that of the discrete model is that the stability condition of the equilibrium point of the discrete model is stronger than that of the continuous model [31].

    The coexistence regions of three species in continuous and discrete models are the same, which both persist when the carrying capacity is high. When the carrying capacity is high, both types of models exhibit chaos. Figure 3 shows the spectrum of the maximum Lyapunov exponent (MLE) for K, revealing the presence of chaotic behavior for both discrete and continuous models. The difference point is that the interval of existence of chaotic behavior of the continuous model is smaller than that of the discrete model.

    Figure 3.  Spectrum of the maximum Lyapunov exponent (λ) with respect to carrying capacity (K) for the discrete-time model (a) and continuous-time model (b).
    Figure 4.  Bifurcation diagram of the densities of algae, zooplankton, and fish plotted against food quality for Model (2.3). The red curve, the blue curve, and the black curve represent the density of algae, zooplankton, and fish with respect to food quality, respectively. The red vertical dashed lines represent the algae minimal P:C (q). The blue vertical dashed line represents the zooplankton constant P:C (θZ) and fish constant P:C (θF), here θZ=θF=0.03.

    In comparison, the continuous model has a high level of robustness to time discretization. The population changes of continuous and discrete models show similar trends as parameters are changed. Some interesting dynamical features can be simulated in both continuous and discrete models, according to the numerical simulations. There are also distinctions to be made between continuous and discrete models. The results show that we should pay close attention to the selection of time scale in the research of intraguild system model prediction.

    In summary, our analytical and numerical analysis suggest the following similarities and differences between continuous model and discrete model, which have been listed in Table 4.

    Table 4.  Comparisons between continuous model (2.1) and discrete model (2.3).
    Similarities Differences
    1) The equilibria and the ecological matrix take the same forms. A negative effect of poor food quality on top predator can be observed. 1) The stability conditions of equilibria in the discrete model are stronger than those in the continuous model.
    2) The coexistence regions of populations in continuous and discrete models are the same and occur under high carrying capacity conditions. 2) The coexistence of populations in continuous models depends on initial values, while in discrete models, the coexistence of populations does not depend on initial values.
    3) Chaos phenomena can be observed in both types of model dynamics. 3) The chaos interval of the discrete model is larger than that of the continuous model.
    4) Multistability can be observed in both types of model dynamics. 4) There are more types of multistabilities in the continuous model than in the discrete model.

     | Show Table
    DownLoad: CSV

    Models on algae-zooplankton-fish have been widely studied from many perspectives, like bifurcation [32,33], chaos [34], toxicity [35], and fear effect [33,34]. The effect of discretization on the dynamic behavior of these models is worth studying. Because of the difference in dynamic behavior between continuous and discrete models, we can try to fit the predictions of the two types of models with the collected experimental data. The comparisons can be used to test which model is more suitable for prediction. This question can be used as a subject for future research.

    This research has been supported by the Natural Science Foundation of China (No. 11801052, No. 12271144) and the Fundamental Research Funds for the Central Universities (No. 3132023207).

    The authors declare there is no conflict of interest.

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    3. Debjit Pal, Dipak Kesh, Debasis Mukherjee, Study of a diffusive intraguild predation model, 2025, 2, 2997-6006, 10.20935/AcadEnvSci7508
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