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Research article Special Issues

Discrete-time population dynamics on the state space of measures

  • If the individual state space of a structured population is given by a metric space S, measures μ on the σ-algebra of Borel subsets T of S offer a modeling tool with a natural interpretation: μ(T) is the number of individuals with structural characteristics in the set T. A discrete-time population model is given by a population turnover map F on the cone of finite nonnegative Borel measures that maps the structural population distribution of a given year to the one of the next year. Under suitable assumptions, F has a first order approximation at the zero measure (the extinction fixed point), which is a positive linear operator on the ordered vector space of real measures and can be interpreted as a basic population turnover operator. For a semelparous population, it can be identified with the next generation operator. A spectral radius can be defined by the usual Gelfand formula.We investigate in how far it serves as a threshold parameter between population extinction and population persistence. The variation norm on the space of measures is too strong to give the basic turnover operator enough compactness that its spectral radius is an eigenvalue associated with a positive eigenmeasure. A suitable alternative is the flat norm (also known as (dual) bounded Lipschitz norm), which, as a trade-off, makes the basic turnover operator only continuous on the cone of nonnegative measures but not on the whole space of real measures.

    Citation: Horst R. Thieme. Discrete-time population dynamics on the state space of measures[J]. Mathematical Biosciences and Engineering, 2020, 17(2): 1168-1217. doi: 10.3934/mbe.2020061

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  • If the individual state space of a structured population is given by a metric space S, measures μ on the σ-algebra of Borel subsets T of S offer a modeling tool with a natural interpretation: μ(T) is the number of individuals with structural characteristics in the set T. A discrete-time population model is given by a population turnover map F on the cone of finite nonnegative Borel measures that maps the structural population distribution of a given year to the one of the next year. Under suitable assumptions, F has a first order approximation at the zero measure (the extinction fixed point), which is a positive linear operator on the ordered vector space of real measures and can be interpreted as a basic population turnover operator. For a semelparous population, it can be identified with the next generation operator. A spectral radius can be defined by the usual Gelfand formula.We investigate in how far it serves as a threshold parameter between population extinction and population persistence. The variation norm on the space of measures is too strong to give the basic turnover operator enough compactness that its spectral radius is an eigenvalue associated with a positive eigenmeasure. A suitable alternative is the flat norm (also known as (dual) bounded Lipschitz norm), which, as a trade-off, makes the basic turnover operator only continuous on the cone of nonnegative measures but not on the whole space of real measures.


    In the most elementary population models, all members of a population are equal, except that they may live at different times. In reality, they are different in many aspects like spatial location, chronological age, development stage, size etc., and the distributions of their individual characteristics have a considerable influence on the population dynamics.

    Let the state space of individual characteristics [1] be described by a metric space (S,d), i.e., a nonempty set S with a metric d. A point in S gives an individual's characteristics, and d describes how close the characteristics of two different individuals are to each other. From a population point of view, S represents the population structure. To be specific, we consider the spatial structure of a population, but it could be any other structure given by one or several individual characteristics.

    A natural way to describe the structural distribution is to consider a collection B of subsets of S and set-functions μ:BR+ with the interpretation that μ(T) is the number of individuals located within the set TB. The total population size is given by μ(S). Here are a few properties of B and μ that are compatible with this interpretation:

    Definition 1.1B,μ()=0.

    ● If T1,T2B, then T1T2B and T1T2B and μ(T1T2)=μ(T1)+μ(T2)μ(T1T2).

    ● If TB, then STB and μ(ST)=μ(S)μ(T).

    ● If (Tn) is a sequence of sets in B and TnTn+1 for all nN, then T:=nNTnB and μ(T)=limnμ(Tn).

    If B and μ:BR+ have these properties, B is called a σ-algebra and μ is called a nonnegative measure; μ:BR with these properties is called a real measure.

    See any of the many books covering measure theory like [2,3,4,5,6] [7,App.B] and Section 6. The population state space is the set (actually cone) of nonnegative measures, denoted by M+(S).

    In spite of this natural setting, most of the time the special case is considered that μ(T)=Tf(x)ν(dx),TB, with some master measure ν and a density f. The dynamics of the population are then discussed in terms of densities [8,9], maybe because densities are perceived as mathematically easier than measures.

    Still, there is a substantial body of work in population or other dynamics in terms of measures. The reasons, among others, are the natural conceptuality of the setting [1,10,11,12,13] or the circumstance that, even if the initial conditions are given by densities, the solution can become measure-valued in finite time [14,15] [16,Sec.9], or in the limit as time tends to infinity [17,18,19,20], or via some other limiting procedure [21]. Another reason is the inclusion of random structural transitions ([22] and the references therein) like random mutations [17,23,24] or, in the case of this paper, yearly random spatial movements described by a measure kernel P:B×SR+ (Sections 9 and 10). Here, P(T,s) is the probability that an individual that is at sS at the beginning at the year is still alive at the end of the year and located within the set TB.

    Similarly as in [25,26], we want to consider M+(S) as the closed cone of the ordered normed vector space M(S) of real measures (Section 6) in order to take advantage of the rich mathematical theory that is available in this framework [27,28,29,30,31,33,34] (see Section 5), in particular the theory of homogeneous order-preserving operators and their spectral radius [35,36,37,38,39,40,41,43,44,45,46] (see Sections 4.2 and 7).

    Since S is a metric space, M(S) is an ordered normed vector space not only under the variation norm, but also under the weaker flat (aka dual bounded Lipschitz) norm (Section 6.1). The variation norm makes M(S) a Banach lattice which seems as good a framework as one can wish, except that compact sets are hard to come by. The flat norm provides applicable conditions for the compactness of subsets of M+(S) but has other challenges: M(S) is only complete if S is a uniformly discrete metric space and M+(S) is only complete if S is complete (Theorem 6.12, Theorem 6.14, [25,26,48]).

    Most of the population dynamics models with measure-valued solutions are formulated in continuous time. There, the flat norm offers the additional advantage that certain solutions depend continuously on time, while they do not do so with respect to the variation norm, and is also useful in obtaining approximation results ([26,Sec.7] and the references therein).

    Discrete-time models bypass the often quite difficult existence and uniqueness problems that continuous-time models with measures face [11,14,23,49,50,51] and, almost from the outset, are able to concentrate on threshold conditions for population extinction and survival, and more readily deal with the mathematical techniques that help with this fundamental issue (Sections 8 and 12, [16,36,47]). Biologically, discrete-time models are appropriate idealization for populations with very short reproductive seasons. Their essential ingredient is a population turnover map F:M+(S)M+(S) that maps the structural population distribution at the census period of a given year to that at the census period of the next year (Section 2). If the sequence (μn) in M+(S) is constituted by the structural distributions μn in the nth year, the population development is described by the difference equation

    μn=F(μn1),nN, (1.1)

    with a given initial distribution μ0.

    F has a linear first order approximation A at the origin, the order derivative (Section 8.1), which can be continuously extended to all of M(S) with respect to the variation norm. The map A is continuous on M+(S) with respect to the flat norm if and only if it is induced by a Feller kernel (Section 10); in general, it is not continuous with respect to the flat norm on all of M(S) (Example 10.14). The spectral radius of this basic population turnover operator A, denoted by r0 and called basic population turnover number, is the threshold parameter that decides about population extinction: If r0<1, the zero measure (the extinction state) is locally asymptotically stable; the extinction state is unstable and a nonzero equilibrium state exists if r0>1 (Sections 8 and 12). To establish the latter, one shows that r0 is an eigenvalue of the basic turnover operator (Sections 7 and 10).

    The dynamics of a structured population are governed by the processes of birth, death, and structural development, with the last being spatial movement in our case.

    We consider a population that reproduces during a very short annual reproduction season. For ease of exposition, we assume that the population is semelparous, i.e., individuals only reproduce once during their life-time, one year after birth, and die shortly thereafter. We count the years in such a way that the census period is just before reproductive season.

    Births and deaths can be affected by competition for resources. We restrict our exposition to the reduction of per capita birth rates by inner-species competition. If μ is the spatial distribution of the adult population, the competition level at the location s generated by μ, (Qμ)(s), is

    (Qμ)(s)=Sq(s,t)μ(dt),sS. (2.1)

    Here q:S2R+ is bounded and continuous and q(s,t) describes the competitive influence an individual at location t exerts on an individual at s. We call Qμ the competitive force generated by the population distribution μ.

    Let g:S×R+R+ be the per capita birth function, i.e., g(s,q) is the per capita number of offspring produced at sS by an adult located at s when the competition level for reproductive resources at s is qR+. The number of neonates in a set TB is then given by

    ν(T)=Tg(s,(Qμ)(s))μ(ds) (2.2)

    provided that μ is the spatial distribution of adults.

    Equation (2.2) defines a measure ν on B that represents the spatial distribution of neonates resulting from the distribution of adults, μ. The movement of individuals during a year is described by a measure kernel

    P:B×S[0,1] (2.3)

    where P(T,s) is the probability that a neonate born at sS at the beginning of the year is still alive at the end of the year and located at some point in the set T.

    Definition 2.1. P:B×SR+ is called a measure kernel, if

    ● for any sS, P(,s) is a measure on B and

    ● for any TB, P(T,) is a Borel measurable function on S.

    If the measure ν represents the spatial distribution of neonates shortly after the reproductive season at the beginning of the year,

    (˜Aν)(T)=SP(T,s)ν(ds),TB, (2.4)

    provides the resulting number of surviving adults at the end of the year that are located within the set T.

    If there were no deaths over the year, P(,s) would be a probability measure, P(S,s)=1, for all sS. In such a case, the measure kernel P is sometimes called a Markov kernel or a stochastic kernel [2,Def.19.11]. But we will not make this unrealistic assumption but rather assume that P(S,s)[0,1] for all sS. Such measure kernels are called transition kernels in [52].

    If the measure μ represents the spatial distribution of adults at the beginning of a given year, immediately before the reproductive season, the number of adults in a set TB at the end of the year (and the beginning of the next year), is given by

    SP(T,s)ν(ds)(2.2)=SP(T,s)g(s,(Qμ)(s))μ(ds)=:F(μ)(T). (2.5)

    Here Qμ is the competitive force due to the population distribution μ given by (2.1). (2.5) defines a map F from M+(S) to itself, called the yearly population turnover map. In (2.5), for μM+(S), the measure kernel κμ,

    κμ(T,s)=P(T,s)g(s,(Qμ)(s)),TB,sS, (2.6)

    does not necessarily satisfy κμ(T,s)[0,1] and is not a transition kernel in the sense of [52].

    Though individual development (here spatial movement) is modeled stochastically by the measure kernel P, population development is ultimately modeled deterministically by the map F.

    In the previous section, we have made some simplifying biological assumptions like semelparity of the population and competition that only affects reproduction. Instead, we may like to consider iteroparous populations and competitive effects on both reproduction and survival as we will do in a future publication. To have a general framework, let S be a non-empty set and B a σ-algebra of subsets of S.

    Definition 3.1. A map κ:B×SR+ is called a measure kernel if

    κ(,s)M+(S) for all sS and κ(T,)Mb+(S) for all TB.

    Here Mb(B) is the Banach space of bounded measurable functions with the supremum norm and Mb+(S) the cone on nonnegative function in Mb(S).

    We consider yearly turnover maps F of the following form,

    F(μ)(T)=Sκμ(T,s)μ(ds),μM+(S),TB, (3.1)

    where {κμ;μM+(S)} is a family of measure kernels κμ:B×SR+. Consider a typical individual that, in a given year, is located at the point sS, and let μM+(S) be the spatial distribution of the population in that year. Then, under the environment affected by the population distribution μ, κμ(T,s) is the probability that this individual is still alive and located within the set TB in the next year plus the amount of its offspring that is produced during this given year and is still alive and also located in T in the next year.

    In the special case of a semelparous population, where only the offspring is accounted for, κμ takes the form (2.6).

    If μ is the zero measure, we use the notation κo. κo is the basic population turnover kernel describing the yearly turnover in a competition-free environment. In the case of (2.6), κo(T,s)=P(T,s)g(s,0) for TB and sS.

    Assumption 3.2. For each μM+(S), κμ is a measure kernel and {κμ(S,t);μM+(S),tS} is a bounded subset of R.

    Standard measure-theoretic arguments imply the following result.

    Proposition 3.3. Let the Assumption 3.2 be satisfied. Then F maps M+(S) into itself.

    The convolution of two measure kernels κj:B×SR+, j=1,2, is defined by

    (κ1κ2)(T,s)=Sκ1(T,t)κ2(dt,s),TB,sS. (3.2)

    κ1κ2 is again a measure kernel.

    Definition 3.4. Let κ:B×SR+ be a measure kernel. We inductively define the multiple convolution kernels κn by κ1=κ and κ(n+1)=κnκ.

    The spectral radius of the kernel κ is defined by

    r(κ)=infnN(supsSκn(S,s))1/n. (3.3)

    We will see later that, in (3.3), infnN can be replaced by limn. See (9.7).

    Definition 3.5. The kernel family {κμ;μM+(S)} is called upper semicontinuous at the zero measure if for any ϵ(0,1) there is some δ>0 such that

    κμ(T,s)(1+ϵ)κo(T,s),TB,sS,

    for all μM+(S) with μ(S)δ.

    The kernel family {κμ;μM+(S)} is called lower semicontinuous at the zero measure if for any ϵ(0,1) there is some δ>0 such that

    κμ(T,s)(1ϵ)κo(T,s),TB,sS,

    for all μM+(S) with μ(S)δ.

    The kernel family {κμ;μM+(S)} is called continuous at the zero measure if for any ϵ(0,1) there is some δ>0 such that

    (1ϵ)κo(T,s)κμ(T,s)(1+ϵ)κo(T,s),TB,sS,

    for all μM+(S) with μ(S)δ.

    In a preview of results, we will showcase the spectral radius of the basic turnover kernel κo as a crucial threshold parameter between local stability and instability of the extinction state represented by the zero measure; r(κo) is called the basic population turnover number. For a semelparous population, the basic turnover number coincides with the basic reproduction number.

    The proofs can be found in Section 11.

    Theorem 3.6. Make Assumption 3.2. Let the kernel family {κμ;μM+(S) be upper semicontinuous at the zero measure.

    (a) If r=r(κo)<1, the extinction state is locally asymptotically stable in the following sense:

    For each α(r,1), there exist some δα>0 and Mα1 such that, for each solution (μn)nZ+ of the difference equation μn=F(μn1), nN,

    μn(S)Mααnμ0(S),nN,

    if μ0M+(S) with μ0(S)δα.

    (b) If r=r(κo)<1 and κμ(T,s)κo(T,s) for all TB, sS, the extinction state is globally stable in the following sense:

    For each α(r,1) there exists some Mα1 such that for each solution (μn)nZ+ of the difference equation μn=F(μn1), nN, μn(S)Mααnμ0(S) for all nN.

    Recall that μn(S) is the total population size in the nth year.

    Corollary 3.7. Let Assumption 3.2 be satisfied. Let the family of measure kernels {κμ;μM+(S)} be upper semicontinuous at the zero measure.

    Let S be the union of pairwise disjoint nonempty sets T1,,Tm in B and

    βjk=suptTjκo(Tk,t),j,k=1,,m,

    and assume that the matrix of size m with coefficients βjk is irreducible and has a spectral radius r<1.

    Then r(κo)r<1.

    (a) Further, the extinction state is locally asymptotically stable in the following sense:

    For each α(r,1), there exist some δα>0 and Mα1 such that, for each solution (μn)nZ+ of the difference equation μn=F(μn1), nN,

    μn(S)Mααnμ0(S),nN,

    if μ0M+(S) with μ0(S)δα.

    (b) If κμ(T,s)κo(T,s) for all TB, sS the extinction state is globally stable in the following sense:

    For each α(r,1) there exists some Mα1 such that for each solution (μn)nZ+ of the difference equation μn=F(μn1), nN, μn(S)Mααnμ0(S) for all nN.

    The next instability result only weakly highlights the threshold role of the spectral radius of κo, but may be more practical and is a counterpart to Corollary 3.7.

    Theorem 3.8. Let Assumption 3.2 be satisfied. Let the family of measure kernels {κμ;μM+(S)} be lower semicontinuous at the zero measure.

    Let T1,,Tm be pairwise disjoint nonempty sets in B and

    αjk=inftTjκo(Tk,t),j,k=1,,m,

    and assume that the matrix of size m with coefficients αjk has a spectral radius r>1.

    Then r(κo)r>1. Further, there is some δ0>0 such for that any solution (μn)Z+ of μn=F(μn1), nN, with μ0(Tk)>0, k=1,,m, there is some nZ+ with μn(S)δ0.

    Notice that there is some μ0M+(S) with μ0(Tk)>0 for k=1,,m, namely μ=mk=1δsk with suitable points sk from the nonempty sets Tk.

    Remark 3.9. Assume that S is the union of pairwise disjoint nonempty sets T1,,Tm, mN, such that, for j,k=1,,m, κo(Tk,) is constant on Tj,

    κo(Tk,s)=αjk,sTj.

    If the matrix of size m with coefficients αjk is irreducible, then its spectral radius, r0, equals r(κo) and is a threshold parameter:

    If r0<1, the zero measure is locally stable in the sense of Theorem 3.6; it is unstable in the sense of Theorem 3.8 if r0>1.

    Further r0=r(κo) is associated with an eigenfunction f in the interior of Mb+(S), r0f(s)=Sf(t)κo(dt,s) for all sS.

    In order to prove an instability result that shows that the stability result in Theorem 3.6 is almost sharp, we assume that S is a metric space and B the σ-algebra of Borel sets.

    We consider the following concepts [25,48].

    Definition 3.10. Consider a subset N of M+(S).

    N is called tight if for any ϵ>0 there exists a compact subset K of S such that μ(SK)<ϵ for all μN.

    ● A single measure μM+(S) is called tight, and we write μMt+(S), if {μ} is tight.

    N is called pre-tight if for any ϵ>0 there exists a closed totally bounded subset T of S such that μ(ST)<ϵ for all μN.

    ● A single measure μM+(S) is called separable, and we write μMs+(S), if there exists a countable subset T of S such that μ(SˉT)=0.

    ● A single measure μM(S) is called separable, and we write μMs(S), if its absolute value |μ| is separable.

    By definition, a subset T of S is totally bounded if for any ϵ>0 there exists a finite subset K of T such that TsKUϵ(s). Here Uϵ(s)={tS;d(t,s)<ϵ} is the open neighborhood with center s and radius ϵ. TS is compact if and only if T is totally bounded and complete [2,Sec.3.7].

    If S is a compact metric space, M+(S) is trivially tight. If S is a separable metric space, M+(S)=Ms+(S).

    Definition 3.11. A measure kernel κ is called a tight kernel if {κ(,s);sS} is a tight set of measures.

    A measure kernel κ is called a kernel of separable measures if all measures κ(,s), sS, are separable.

    Definition 3.12. κ:B×SR+ is called a Feller kernel if

    κ(,s)M+(S) for all sS and if κ has the Feller property

    Sf(y)κ(dy,)Cb(S) for any fCb(S).

    Cf. [2,Sec.19.3]. See Example 10.12. By Proposition 6.3, if κ is a Feller kernel, κ(U,) is a Borel measurable function on S for all open subsets U of S and thus for all Borel sets U in S.

    Theorem 3.13. Let Assumption 3.2 be satisfied. Let the kernel family {κμ;μM+(S)} be lower semicontinuous at the zero measure.

    Assume that κo=κ1+κ2 with two Feller kernels κj of separable measures and assume that κ1 is a tight kernel and r:=r(κo)>1r(κ2).

    Then there exists some eigenmeasure νMs+(S), ν(S)=1, such that

    rν(T)=Sκo(T,s)ν(ds),TB.

    Further, there is some δ0>0 such for that any solution (μn)Z+ of μn=F(μn1), nN, with ν-positive μ0M+(S) there is some nZ+ with μn(S)δ0.

    μ0M+(S) is called ν-positive if there exists some δ>0 such that μ(T)δν(T) for all TB.

    In an iteroparous population, the kernel κ1 may be associated with reproduction and first year development and the kernel κ2 with adult survival and adult development. If r(κ2)<1, κ2=n=1κn2 is a measure kernel, and the measure kernel

    κ1+κ1κ2=κ1+n=1κ1κn2

    can be interpreted as next generation kernel and its spectral radius as basic [53] (or inherent net [54,55]) reproduction number. We again like to think of κo=κ1+κ2 as basic population turnover kernel and its spectral radius as basic turnover number; this spectral radius has also been called inherent population growth rate [55].

    Remark 3.14. The following trichotomy holds (Theorem 7.16):

    r(κ1+κ1κ2)>1 and r(κ1+κ2)>1

    ● or

    r(κ1+κ1κ2)=1 and r(κ1+κ2)=1

    ● or

    r(κ1+κ1κ2)<1 and r(κ1+κ2)<1.

    Assumption 3.15. For all μMs+(S), the kernel κμ in (3.1) is a Feller kernel of separable measures.

    We will derive from the preceding assumption that F maps Ms+(S) into itself and from the next one that F is compact on Ms+(S) with respect to the flat norm.

    Assumption 3.16. If N is a bounded subset of Ms+(S), then the set of measures {κμ(,s);sS,μN} is tight and the set {κμ(S,s);sS,μN} is bounded in R.

    The next assumption looks rather technical, but is often satisfied; the technicality is the prize we pay for the generality of the framework. We will derive from it that F is continuous on Ms+(S) with respect to the flat norm.

    The Banach space of bounded continuous real-valued functions on S with the supremum norm is denoted by Cb(S) and the cone of nonnegative functions in it by Cb+(S). L denotes the following convex set of Lipschitz continuous functions,

    L={h[0,1]S;t,˜tS:|h(t)h(˜t)|d(t,˜t)}. (3.4)

    Recall that MS denotes the set of functions from S to a set M.

    Assumption 3.17. If μMs+(S) and (μn) is a sequence in Ms+(S) such that SfdμnSfdμ for all fCb+(S), then, for all hL,

    Sh(t)κμn(dt,s)Sh(t)κμ(dt,s) (3.5)

    uniformly for s in every closed totally bounded subset of S.

    Assumption 3.18. lim supμ(S)supsSκμ(S,s)<1.

    We will derive from this assumption that F and some perturbations of F map a bounded convex subset of Ms+(S) into itself. Applying the Schauder-Tychonov fixed point theorem [58,Thm.10.1] to perturbations of F, we will derive the following fixed-point result.

    Theorem 3.19. Let the Assumptions 3.2 and 3.15 – 3.18 be satisfied and r(κo)>1. Let the kernel family {κμ;μM+(S)} be lower semicontinuous at the zero measure.

    Then there exists a nonzero fixed point μMs+(S) of F, μ(T)=Sκμ(T,s)μ(ds), TB.

    To prove the results in Section 3, we want to use the rich theory of homogeneous operators and their spectral radius [35,36,37,38,39,40,41,43,44,45,46].

    Given a yearly population turnover map F:XFXF like in (2.5) or (3.1) on a nonempty set XF0 of a real vector space X, the dynamics of the population can be studied by a difference equation

    xn=F(xn1),nN,x0XF, (4.1)

    with the population structure being encoded in the set XF [47]. The vector xn describes the structural distribution of the population in year n while F:XFXF formulates the rule how the structural distribution in a given year follows from the structural distribution of the previous year. If X is a normed vector space, the norm xn is some measure of the population size in year n.

    A fundamental question (Sections 8 and 12) is as to whether or not the population dies out, xn0 as n.

    In order to motivate the framework in which we will address this question, we assume that XF is a (positively) homogeneous subset of X:

    If xXF and αR+, then αxXF.

    Let us assume for the moment that F is Gateaux-differentiable at 0, i.e., that all directional derivatives

    B(x)=F(0,x)=limR+b01bF(bx),xXF, (4.2)

    exist. Then B:XFXF is (positively) homogeneous (of degree one) [36,Thm.3.1]:

    If xXF and αR+, then B(αx)=αB(x).

    Since we rarely consider homogeneity in a different sense, an operator B with this property is simply called homogeneous. B is a first order approximation of F in a weak sense, and we will need B to be a first order approximation in a stronger sense. In Section 3, XF=M+(S) and B is given by

    (Bμ)(T)=Sκo(T,s)μ(ds),μM+(S),TB,

    with Definition 3.5 describing in what sense B is a first order approximation of F. We call B the basic population turnover operator because it approximates the turnover operator at low population densities.

    Lemma 4.1. Assume that there are δ>0 and c>0 such F:XFXF satisfies F(x)cx for all xXF with xδ. Then the Gateaux derivative B of F at 0 is bounded: Bxcx for all xXF.

    Let B:XBXB be a homogeneous operator: XB is a subset of a real normed vector space X and

    xXB,αR+αxXB,B(αx)=αB(x). (4.3)

    The operator norm of the homogenous operator B is defined as

    B:=sup{B(x);xXB,x1}, (4.4)

    and B is called bounded if this supremum exists. By the homogeneity,

    B(x)Bx,xXB, (4.5)

    when B is bounded. The powers (iterates) Bn:XBXB of a bounded homogeneous B are also bounded homogeneous operators and

    Bn+mBnBm,n,mN. (4.6)

    The spectral radius of a bounded homogeneous B:XBXB is defined by the Gelfand formula [56]

    r(B)=infnNBn1/n=limnBn1/n. (4.7)

    The last equality is shown in the same well-known way as for a bounded linear everywhere-defined map (Theorem 3 in [57,Sec.Ⅷ.2]). The name "spectral radius" is motivated by the following fact. Let B be a bounded linear map on X. If XC is the complexification of X and BC the extension of B to XC, then

    r(B)=sup{|λ|;λσ(BC)}, (4.8)

    where σ(BC)C denotes the spectrum of B [40,56] [58,Sec.9.8].

    Equation (4.8) is partially preserved for bounded homogeneous B. Recall that, for linear bounded everywhere-defined B, eigenvalues are special elements of the spectrum of B.

    For homogeneous B:XBXB, we call λ(0,) an eigenvalue of B and vXB an associated eigenvector of B if B(v)=λv0.

    Proposition 4.2. Let B:XBXB be homogeneous and bounded, λ(0,), 0vXB and B(v)=λv. \quad Then λr(B).

    Proof. Since B is homogeneous, we can assume that v=1. By induction, λnv=Bn(v) for all nN. Since Bn is homogeneous and bounded, λnBnvBn and λBn1/n for all nN. The assertion now follows from (4.7).

    Mallet-Paret and Nussbaum [38,39] suggest an alternative definition of a spectral radius for homogeneous (not necessarily bounded) maps B:XBXB. First, define asymptotic least upper bounds for the geometric growth factors of B-orbits,

    γ(x,B):=γB(x):=γx(B):=lim supnBn(x)1/n,xXB, (4.9)

    and then the orbital spectral radius

    ro(B)=supxXBγB(x). (4.10)

    Here γB(x):= if the sequence (Bn(x)1/n) is unbounded and ro(B)= if γB(x)= for some xXB or the set {γB(x);xXB} is unbounded. Since Bn(x)Bnx by (4.6),

    γ(x,B)r(B),xXB,ro(B)r(B). (4.11)

    If XB is a closed cone in X, the number r(B) has been called partial spectral radius by Bonsall [35], XB spectral radius by Schaefer [31,33], and cone spectral radius by Nussbaum [41]. Mallet-Paret and Nussbaum [38,39] call r(B) the Bonsall cone spectral radius and ro(B) the cone spectral radius. For xXB, the number γB(x) has been called local spectral radius of B at x by Förster and Nagy [59].

    Since r(B) makes sense as long as the domain of definition XB is homogeneous (including the familiar special case XB=X) and the domain of definition is part of the concept of an operator, we simply call r(B) the spectral radius of B and ro(B) the orbital spectral radius. If a name were to be attached to r(B), Gelfand spectral radius might be as appropriate as Bonsall cone spectral radius.

    The spectral radius and orbital spectral radius coincide for many applications [43]. For the purposes of this paper, the following condition for ro(B)=r(B) is the most relevant.

    Theorem 4.3. Let XB be a closed homogeneous set in the normed vector space X and B:XBXB be continuous, compact and homogeneous. Then ro(B)=r(B).

    This result is proved in [38,Thm.2.3] under the overall assumptions that X is a Banach space and XB a closed cone in X (see Section 5), but the proof also works for the weaker assumptions in Theorem 4.3.

    Various other conditions for equality are given in [38,43]. But they require more mathematical structure than homogeneity, namely also order and monotonicity (Section 5). Gripenberg [60], gives an example for ro(B)<r(B).

    There are at least three motivations to consider the more general situation of a bounded homogenous order-preserving operator rather than of a bounded linear positive operator. The first is of mathematical nature, namely that the Gateaux derivative (4.2) is homogeneous but not linear and that homogenous operators are not Frechet differentiable at 0 unless they are linear [36,Sec.3].

    The second, biological, motivation are two-sex population models [61,Ch.4] which often use homogeneous mating functions resulting in homogeneous first order approximations of the population turnover operator [36,44,45,46,62,63].

    The third motivation comes from the population turnover map F in (2.5) that has the first order approximation

    (Bμ)(T)=SP(T,s)g(s,0)μ(ds),TB,sS,μM+(S). (4.12)

    B can readily be extended to a linear map A on M(S). If M(S) is endowed with the variation norm (Section 6.1), A is a bounded linear operator but B lacks needed compactness properties. If S is a metric space, M(S) can alternatively be endowed with the flat norm which makes B continuous on M+(S) (Theorem 10.4 (e)) with useful compactness properties (Proposition 10.10), but the extension A of B to M(S) is not bounded in general (Example 10.14).

    Let X be a vector space over R. A nonempty subset W of X is called homogeneous, if αxW for all αR+ and xW.

    Any homogeneous subset contains the vector 0. If W is a homogeneous subset of X, we use the notation

    ˙W=W{0}. (5.1)

    X and {0} are obvious homogeneous subsets of X.

    A subset W of X is called convex, if αx+(1α)yW for all x,yW and α(0,1).

    Geometrically, convexity of W means that a line segment is contained in W whenever its endpoints are elements of W.

    W is called additive if x+yW for all x,yW. A homogeneous subset of X is convex if and only if it is additive.

    WX is called a wedge if it is convex and homogeneous. A wedge W is called a cone if W(W)={0}, i.e., x=0 is the only vector xX such that x and x are elements in W.

    X is a wedge and {0} is a cone. If xX, {αx;αR+} is a cone.

    If X is a normed vector space over R and a cone (wedge) W is a closed subset of X, W is called a closed cone (wedge).

    If W is a cone, the definition

    xyyxW (5.2)

    provides a partial order on X. We also write yx for xy. This order is compatible with addition and with the multiplication by positive real numbers. We recover the cone from the order by

    W={xX;x0}. (5.3)

    A real vector space X has many cones. When we talk about an ordered vector space we typically have a specific cone in mind which induces the order. This cone is denoted by X+, and we will talk about it as the order cone in order to single it out from the other cones contained in X.

    X is called an ordered Banach space if X is an ordered normed vector space which is complete. If X is a vector space of real-valued functions, X+ typically is the cone of nonnegative functions.

    Let X be an ordered vector space with order cone X+ and SX.

    S is called an inf-semilattice [64] (or minihedral [27]) if xy=inf{x,y} exist and are elements of S for all x,yS.

    S is called a sup-semilattice if xy=sup{x,y} exist and are elements of S for all x,yS. S is called a lattice if xy and xy exist and are elements of S for all x,yS.

    X is a lattice if xy exist for all x,yX. Since xy=((x)(y)), also xy exist for all x,y in a lattice X.

    Equivalently, X is a lattice if for any xX the absolute value

    |x|=x(x)=sup{x,x} (5.4)

    exists. The following connections hold if X is a lattice:

    xy=(1/2)(x+y+|xy|),xy=(1/2)(x+y|xy|). (5.5)

    An ordered normed vector space X that is a lattice is called a normed lattice if

    x,yX,|x||y|xy. (5.6)

    In a normed lattice X,

    |x||y|xy,x,yX. (5.7)

    A normed lattice that is complete is called a Banach lattice [2,34].

    The following result for normed vector spaces is well-known [27,Sec.1.2].

    Theorem 5.1. Let X be a normed vector space with wedge W. Then the following three properties are equivalent:

    (i) There exists some δ>0 such that x+zδ whenever xW, zW and x=1=z.

    (ii) The norm is semi-monotonic: There exists some M0 such that xMx+z for all x,zW.

    (iii) There exists some ˜M0 such that x˜My whenever xX, yW, and y+x and yx are elements in W.

    A wedge W in a normed vector space X is called

    normal if it satisfies one (and then all) of (ⅰ), (ⅱ), (ⅲ) in Theorem 5.1.

    Remark 5.2. By (ⅰ), any normal wedge is a cone. We have formulated these equivalences for wedges to make clear that any of the properties (ⅰ), (ⅱ), (ⅲ) makes a wedge a cone. In a normed lattice, the order cone X+ is normal (see (5.6).

    If X is a real vector space and W a wedge in X, then

    WW={wv;v,wW} (5.8)

    is a linear subspace of X.

    W is called generating if X=WW.

    Now let W be a wedge in a normed vector space X with norm .

    W is called total if WW is dense in X.

    W is called solid if W contains an interior point.

    W is called serially complete if every series n=1xn with xnW and n=1xn converging in R converges in W.

    W is called non-flat [28,Sec.1.8] if there exists some c1 such that for any xWW there exist v,wW such that x=vw and v+wcx.

    W is called flat if there is a bounded sequence (xn) in WW such that, for all sequences (vn) and (wn) in W with xn=vnwn for all nN, at least one of the sequences (vn) or (wn) is unbounded.

    Bonsall [35] says that WW has the strict bounded decomposition property if W is non-flat.

    One easily checks the following relation between the last two concepts.

    Proposition 5.3. Let W be a wedge in a normed vector space X. Then W is non-flat if and only if it is not flat.

    Let X and Y be vector spaces and B:XBY with XBX.

    Definition 5.4. B is called additive if B(x+z)=B(x)+B(z) for all x,zXB with x+zXB.

    Definition 5.5. Let Y be an ordered vector space with order cone Y+.

    B is called superadditive if B(x+z)B(x)+B(z) for all x,zXB with x+zXB.

    B is called subadditive if B(x+z)B(x)+B(z) for all x,zXB with x+zXB.

    Let XB be a convex subset of X.

    B is called a convex operator if B(αx+(1α)z)αB(x)+(1α)B(z) for all x,zXB and α(0,1).

    B is called a concave operator if B(αx+(1α)z)αB(x)+(1α)B(z) for all x,zXB and α(0,1).

    Remark 5.6. If XB is convex and B is homogeneous, superadditivity (subadditivity) of B is equivalent to concavity (convexity) of B.

    Definition 5.7. Let X and Y be ordered normed vector spaces with order cones X+ and Y+. We use the same symbols and for the norms on X and Y and for the orders induced by X+ on X and by Y+ on Y, respectively.

    B is called a positive operator if B(XBX+)Y+.

    B is called order-preserving if B(x)B(z) for all x,zXB with xz.

    B is called order-preserving on ZXB if B(x)B(z) for all x,zZ with xz.

    Example 5.8. Let X and Y be as above and B:XBY with X+XBX. If B is positive and superadditive, B is order-preserving.

    Proof. Let x,zXB and xz. By (5.2), zxX+XB. Since B is positive, B(zx)Y+ and, since B is superadditive, B(x)B(x)+B(zx)B(x+(zx))=B(z).

    Let S be a nonempty set and B a σ-algebra on S.

    Let M(S) denote the set of real measures on B (Definition 1.1).

    M(S) becomes a real vector space by the definitions (μ+ν)(T)=μ(T)+ν(T) and (αμ)(T)=αμ(T) where TB and αR and μ,νM(S).

    M(S) has the cone of all nonnegative measures, M+(S). M(S) is a vector lattice which is even order-complete: Each subset N of M(S) which has an lower (upper) bound has an infimum (supremum). For this, it is enough to provide an infimum (largest lower bound) to any subset N of M+(S),

    μ(T)=inf{mj=1μj(Tj)},TB, (6.1)

    where the infimum is taken over all mN, all subsets {μ1,,μm} of N and all subsets {T1,,Tm} of B such that T is their disjoint union [5,Ⅲ.7.5]. The same construction works for any subset N with lower bound. If N has an upper bound, the infimum in (6.1) is replaced by the supremum.

    The absolute value |μ| of a measure (in this context also called the variation of the measure) is given by |μ|=sup{μ,μ},

    |μ|(T)=sup{μ(U)μ(TU);BUT}=sup{|μ(U)|+|μ(TU)|;BUT}=sup{nj=1|μ(Tj)|}, (6.2)

    where the supremum is taken over all nN and subsets {T1,,Tn} of B such that T is its disjoint union [2,Cor.10.54 & Thm.10.56].

    The following is a consequence of the Vitali-Hahn-Saks Theorem [5,Ⅲ.7] [57,Ⅱ.2], but can also be proved by more elementary means.

    Corollary 6.1. Let (μn) be an increasing sequence in M+(S) such that (μn(S)) is a bounded sequence in R. Then μ(T)=limnμn(T) exists for every TB and provides a measure μM+(S).

    The variation norm (also called total variation) on M(S) is defined by

    μ=|μ|(S),μM(S), (6.3)

    where |μ| is the absolute value of μ defined by (6.2).

    If μM+(S), μ=μ(S). So the variation norm is additive and order-preserving on M+(S), and M+(S) is a normal cone. The variation norm makes M(S) a Banach lattice (Section 5.3); in particular, M+(S) is a non-flat generating cone: Every real-valued measure μ can be written as the difference of its positive and negative variation, μ=μ+μ, and μ±μ. By Corollary 6.1, the cone M+(S) is serially complete (Section 5.5). The variation norm is equivalent to the supremum norm μ=supTB|μ(T)|, and the two norms are equal on M+(S).

    Let (S,d) be a metric space. B now denotes the Borel σ-algebra of S which is the smallest σ-algebra that contains all open and closed sets. The sets in the Borel σ-algebra are called Borel sets. In a metric space, the Borel σ-algebra is also the smallest σ-algebra for which all (bounded) continuous functions are measurable [4,Thm.7.1.1]. This second σ-algebra is often [4] but not always [2] called the Baire-σ-algebra.

    The following is a summary of results needed later. For more details, we refer to [26]. Many of the results can already been found in [4,48]. See also [22,25].

    The subsequent lemma guarantees that the two σ-algebras mentioned above coincide in a metric space for all common definitions of Baire sets. Recall L in (3.4).

    Lemma 6.2. B is the smallest σ-algebra that contains all sets f1({0}), fL.

    Proposition 6.3. Let T be a closed subset of S. Then there exists a decreasing sequence of Lipschitz continuous functions (fn) with values between 0 and 1 such that fnχT pointwise.

    Let T be a open subset of S. Then there exists an increasing sequence of Lipschitz continuous functions (fn) with values between 0 and 1 such that fnχT pointwise.

    We introduce the following functional on M(S),

    μ=supfL|Sfdμ|, (6.4)

    with L in (3.4). is a norm on M(S) [26], which we call the flat norm, and

    μμ,μM(S). (6.5)

    In the literature, definitions different from (6.4) are used [4,50,51,66] that lead to equivalent norms. For instance, [0,1]S is replaced by [1,1]S. Also different names are used for the flat norm or its equivalent definitions. For details see [26].

    All the definitions have in common that

    μ=μ(S)=μ,μM+(S). (6.6)

    This implies that the flat norm is additive and order-preserving on M+(S).

    In the following, all topological notions concerning M(S) and M+(S) are meant with respect to the flat norm unless it is explicitly said otherwise.

    Theorem 6.4. M+(S) is a generating, normal, closed, and serially complete cone.

    Serial completeness (Section 5.5) follows from Corollary 6.1.

    Lemma 6.5. Let (S,d) be a metric space. For xS, let δx denote the Dirac measure at x. Then 1=δx and, for y,xS,

    δxδy=min{1,d(x,y)}.

    Recall Definition 3.10.

    Theorem 6.6. Let (μn) in M+(S) and μMs+(S). Equivalent are

    (ⅰ) μnμ0,

    (ⅱ) Sfd(μnμ)0 for all continuous functions fCb(S),

    (ⅲ) Sfd(μnμ)0 for all Lipschitz continuous functions f:S[0,1].

    Cf. [4,Thm.11.3.3].

    Corollary 6.7. Ms+(S) is a closed cone of M(S).

    The next result follows from [25,Thm.3.9] and [26,Thm.4.19] and its proof.

    Theorem 6.8. The set of measures nj=1qjδsj with nN, qjQ+ and sjS is dense in Ms+(S).

    The set of measures nj=1qjδsj with nN, qjQ and sjS is dense in Ms(S). If (S,d) is separable, then M(S) is separable.

    For a function f:SR that is bounded below define [2,Thm.3.13]

    [f]k(s)=inf{f(t)+kd(s,t);tS},sS,kN. (6.7)

    Then inff(S)[f]k(s)[f]k+1(s)f(s) for all kN, and [f]k is Lipschitz continuous with k being a Lipschitz constant. If f is lower semicontinuous on S, [f]kf pointwise on S; if f is uniformly continuous, [f]kf uniformly on S.

    Lemma 6.9. Let F be an equicontinuous family of functions f:SR+ [6,Def.8.3]. For each fF, let ([f]k) be the approximation (6.7). Then, for each sS for which {f(s);fF} is bounded, supfF|f(s)[f]k(s)|0 as k.

    Proof. Let sS and {f(s);fF} be bounded. Suppose that supfF|f(s)[f]k(s)|0 as k does not hold. Since f(s)[f]k(s), there exists some ϵ>0 and a sequence (kn) in N such that kn as n and supfF(f(s)[f]kn(s))>ϵ for all nN. Then there exist fnF such that

    fn(s)>[fn]kn(s)+ϵ,nN.

    By (6.7), there exists a sequence (tn)S such that

    fn(s)>fn(tn)+knd(s,tn)+ϵ,nN.

    Since kn and {fn(s);nN} is bounded, d(s,tn)0 as n. Since {fn;nN}F is equicontinuous, fn(s)fn(tn)0 as n which implies 0ϵ, a contradiction.

    Proposition 6.10. Let F be an equicontinuous family of functions f:SR+ such that {f(s);fF,sS} is bounded. Let μM(S) and (μn) be a sequence in M+(S) such that μnμ0 as n. Then SfdμnSfdμ as n uniformly for fF.

    Proof. We can assume that 0f(s)1 for all fF and sS. For fF, let ([f]k) be the approximation in (6.7). Then

    0[f]k(s)f(s)1,kN,fF,sS. (6.8)

    By Lemma 6.9,

    hk(s):=supfF|f(s)[f]k(s)|0,k,sS.

    Since {f[f]k;fF} is equicontinuous, hk is continuous and thus integrable. By (6.8), hk(s)supfFf(s)1. By the dominated convergence theorem,

    supfF(SfdμS[f]kdμ)=supfFS|f[f]k|dμShkdμ0,k.

    Let ϵ>0. Then there exists kN such that

    0SfdμS[f]kdμϵ4,fF.

    For all nN, ,

    SfdμS[f]kdμ+ϵ4=S[f]kd(μμn)+S[f]kdμn+ϵ4.

    Since k is a Lipschitz constant for [f]k and [f]kf,

    Sfdμkμμn+Sfdμn+ϵ4.

    Since μμn0 as n, there exists some N=NkN such that μμnϵ/(4k) for all nNk and so

    SfdμSfdμn+ϵ2,nNk,fF. (6.9)

    Define gf(s)=1f(s) for all sS and all fF. Then {gf;fF} is an equicontinuous family in Cb+(S) and, by (6.8), gf(s)[0,1] for all fF, sS. By (6.9), applied to {gf;fF} instead of F, there exists some NN such that

    μ(S)Sfdμ=SgfdμSgfdμn+ϵ2μn(S)Sfdμn+ϵ2,nN,

    for all fF. We rearrange

    SfdμSfdμnϵ2+μ(S)μn(S),nN,fF.

    Since μn(S)μ(S) as n, there is some ˜NN such that

    SfdμSfdμnϵ,n˜N,fF.

    In combination with (6.9), SfdμnSfdμ uniformly for fF.

    Theorem 6.11. Let (μn) be a tight sequence in M+(S) such that (μn(S)) is bounded. Then (μn) has a converging subsequence (with the limit measure being tight as well).

    Theorem 6.12. If S is not uniformly discrete (for any ϵ>0 there exist s,tS with 0<d(s,t)<ϵ), then the ordered normed vector space M(S) is not complete.

    Proposition 6.13. Let NMs+(S) be a totally bounded set of separable measures. Then N is pre-tight and, if S is complete, tight.

    Theorem 6.14. ([25,Thm.3.8]). Ms+(S) is complete if and only if S is complete.

    In the following, let X be an ordered normed vector space with closed order cone X+.

    In this subsection, let B:XBXB be a homogeneous operator on a homogeneous set XB contained in X+.

    Lemma 7.1 (Bonsall [35]). Let (an) be an unbounded sequence in R+. Then there exists a sequence (nj) in N such that nj and

    anj,j,akanj,k=1,,nj,jN.

    Proof. For each jN, choose njj such that anj=max{a1,,aj}. Since (an) is unbounded, nj and anj as j. By construction, akanj for k=1,,jnj.

    Lemma 7.2. Let X be an ordered normed vector space with closed order cone X+. Let B:XBXB be a homogeneous operator on a homogeneous set XB contained in X+ and let some power of B be compact.

    Further let xXB and (yn) be a bounded sequence in X+. Let mN, and Bn(x)yn for all nN, nm.

    Then the sequence (Bn(x)) is bounded.

    Proof. Assume that xXB and (Bn(x)) is unbounded. Set an=Bn(x). By Lemma 7.1, there exists a sequence (nj) in N such that

    anj,j,akanj,k=1,,nj,jN.

    Set wj=Bnj(x)anj. Then wjXB and wj=1. Choose N such that B is compact. Since B is homogeneous, wj=B(vj) with vj=Bnj(x)anjXB. By the properties of the (an), vj1. After choosing a subsequence, wjw with wX+, w=1. By assumption, after choosing subsequences again, wjynjanj for jN. Since (ynj) is bounded and anj, ynjanj0. Since X+ is closed, w=limjwj0. Since wX+ and X+ is a cone, w=0 contradicting w=1.

    Recall the large-time bound of the geometric growth factor for initial value uXB,

    γB(u):=lim supnBn(u)1/n. (7.1)

    Lemma 7.3. Let X be an ordered normed vector space with closed order cone X+. Let B:XBXB be a homogeneous operator on a homogeneous set XB contained in X+ and let some power of B be compact. Further let (xn) be a bounded sequence in X+, x a point in XB, m,kN, λ0, and Bn+m(x)λn+kxn for all nN, nm. Then γB(x)λ.

    Proof. If λ=0, then Bn+m(x)=0 for all nN and γB(x)=0.

    So we can assume that λ>0. Set C(x)=λ1B(x). Then C is homogeneous, and some power of C is compact. Further,

    Cn+m(x)=λ(n+m)Bn+m(x)λkmxn,nN,

    with the right hand side of this inequality forming a bounded sequence. By Lemma 7.2, applied to C, γC(x)1. By definition of the growth bounds, (4.9), and the homogeneity of B, γB(x)λ.

    Theorem 7.4. Let X be an ordered normed vector space with closed order cone X+ and Z be an ordered normed vector space with a normal order cone Z+. Let XHX+ be a serially complete cone and H be a family of homogeneous order-preserving operators H:XHZ+.

    If {H(x);HH} is bounded for each xXH, then {H;HH} is bounded.

    Proof. Assume that {H(x);HH} is bounded for each xXH, but {H;HH} is not bounded. Then, for every nN, there exists some HnH such that Hn>n4 and, by (4.4), some xnXH with xn1 such that Hn(xn)>n4. Since XH is serially complete, x=n=1n2xn converges and is an element in XHX+ by the definitions in Section 5.5. Since X+ is closed, xn2xn for all nN. Since each Hn is order-preserving and homogeneous,

    Hn(x)n2Hn(xn),nN.

    Since Z+ is normal, there exists some c>0 such that

    cHn(x)n2Hn(xn)n2,

    a contradiction.

    We give a characterization of Datko/Pazy type. The proof uses an adaption of the one of [65,Prop.9.4] in combination with Theorem 7.4. See [65] for bibliographic notes.

    Theorem 7.5. Let X be an ordered normed vector space with normal order cone X+. Let B be a homogeneous order-preserving operator B:XBXB on a serially complete cone XBX+. Then the following are equivalent for λ>0 and p>0:

    (i) r(B)<λ. (ⅱ) n=1λpnBnp<.

    (iii) n=1λpnBn(x)p<,xXB.

    Proof. Since r(B)=λ if and only if r((1/λ)B)=1 by (4.7), it is sufficient to prove the statement for λ=1.

    (ⅰ) (ⅱ). Let r(B)<1. Choose some r(r(B),1). Then there exists some c>1 such that Bncrn for all nN and (ii) follows.

    (ⅱ) (ⅲ) is obvious.

    (ⅲ) (ⅰ). (ⅲ) implies that Bn(x)0 for each xXB. By the Uniform Boundedness Theorem 7.4, there is some M>0 such that BnM for all nN. For all nN and xXB,

    nBn(x)p=nk=1Bn(x)p=nk=1Bnk(Bk(x))p.

    By (4.5),

    nBn(x)pnk=1BnkpBk(x)pMpk=1Bk(x)p<.

    So, for each xXB, {n1/pBn(x),nN} is bounded. Again by Theorem 7.4, {n1/pBn,nN} is bounded. So there exists some nN such that Bn<1. By (4.7), r(B)=infnNBn1/n<1.

    Corollary 7.6. Let X be an ordered normed vector space with normal order cone X+. Let B be a homogeneous order-preserving operator B:XBXB on a serially complete cone XBX+. Then, for all p>0,

    r(B)=inf{λ>0;n=1λpnBn(x)p<,xXB}=inf{λ>0;n=1λpnBnp<}.

    Corollary 7.7. Let X be an ordered normed vector space with normal closed order cone X+. Let B be a homogeneous order-preserving operator B:XBXB on a serially complete cone XBX+. Let r=r(B)>0. Then there exists some xXB such that n=1rnBn(x)=.

    In the following, let X be an ordered normed vector space with closed order cone X+ and B:XBXB be a homogeneous, compact and order-preserving operator on a closed cone XB contained in X+.

    Theorem 7.8. Assume that B:XBXB is compact and continuous and that r=r(B)>0. Then there exists xXB, x=1, such that B(x)=rx.

    Notice that this theorem does not assume that X+ is complete and can be applied to Ms(S) with the flat norm even if the metric space S and so Ms+(S) is not complete (Theorem 6.14). If X+ is complete, the compactness assumption for B can considerably be relaxed [39].

    Theorem 7.8 is proved in [42] under the additional assumption that X+ is normal. The normality assumption can be avoided by using Lemma 7.3.

    To set the stage for the proof of Theorem 7.8, we recall (7.1).

    Proposition 7.9. Let B:XBXB be compact and continuous and let u˙XB. Then there exist xXB, x=1, and λγu(B) such that B(x)+u=λx.

    The idea of perturbing B and using one of the classical fixed point theorems seems as old as the theory of positive operators [27,Sec.2.2] [30,Sec.3] [67,Thm.3.6].

    Proof. For u˙XB, define

    Bu(x)=B(x)+u,xXB.

    Then Bu(x)u and {Bu(x);xXB} is bounded away from 0 because X+ is closed. We define

    K(x)=Bu(x)Bu(x),xXB.

    Since B is continuous on XB, K is continuous on XB. K maps the set C=XBˉU1, ˉU1={xX;x1}, into itself. C is a closed convex subset of X and K(C) has compact closure. By Tychonov's fixed point theorem [58,Thm.10.1], K has a fixed point vXB, v=1,

    B(v)+u=λv,λ=B(v)+u>0. (7.2)

    Since B(v)X+, uλv. Since uX+, B(v)λv. Since B is order preserving and homogeneous, Bn(v)λnv for all nN. Hence

    Bn(u)λnλv,nN. (7.3)

    Since B is compact, γB(u)λ by Lemma 7.3.

    Recall that the orbital spectral radius is defined by ro(B)=supxXBγB(x).

    Proof of Theorem 7.8. Since B is compact, ro(B)=r(B)>0 by Theorem 4.3.

    Since γB(αu)=γB(u) for all uXB, α>0, we can choose a sequence (un) in ˙XB such that un0 and γB(un)ro(B)>0. By Proposition 7.9, for each nN, there exist xnXB such that xn=1 and λnγB(un) such that

    λnxn=B(xn)+un. (7.4)

    Since B is compact, after choosing a subsequence, B(xn) converges to some yXB. The corresponding subsequence of (λn) is bounded and λnλro(B)>0 after choosing another subsequence. Since un0, by (7.4) xnxXB with x=1 and, since B is continuous at x, λx=B(x). By Proposition 4.2, λ=ro(B)=r(B).

    Corollary 7.10. Assume that B:XBXB is additive and continuous and some power of B is compact and r=r(B)>0.

    Then there exists some xXB, x=1, such that B(x)=rx.

    Proof. Let mN such that Bm is compact. Bm is also continuous, homogeneous and order-preserving and r(Bm)=(r(B))m=rm>0. By Theorem 7.8, there exists some y˙XB such that Bmy=rmy. Set x=m1j=0rjBjy [28,Thm.9.3] [41,Thm.2.2]. Since B is additive,

    B(x)=m1j=0rjBj+1(y)=mj=1r1jBj(y)+ry=rx.

    For a moment, we consider the more general situation that X is a normed vector space and W is a wedge in X. Then ˜X=WW is a linear subspace of X. The following construction is well-known from the literature [32,40] and will be put to new use.

    Define a seminorm on ˜X by

    x=inf{v+w;x=vw,v,wW}. (7.5)

    We have

    xx,x˜X,x=x,xW. (7.6)

    The inequality shows that is a norm and follows from vwv+w. For xW, x=x0 and so x=x. The inequality also shows that W is closed under if it is closed under the original norm. The equality of the norms on W shows that W is normal under if it is normal under .

    Remark 7.11. W is a generating non-flat cone of ˜X under .

    Proposition 7.12. If W is a serially complete wedge in X under , then ˜X is a Banach space under .

    Proof. We use the characterization of a Banach space in terms of series: A normed vector space is a Banach space if and only if any absolutely convergent series converges in the space. [6,Prop.9.3].

    Let (xk) be a sequence in ˜X such that n=1xn converges in R. For any nN, by (7.5), there exist vn,wnW such that xn=vnwn and vn+wn<xn+2n. Since W is serially complete, v=n=1vn and w=n=1wn converge in W with respect to . Since and coincide on W, these series also converge with respect to . So n=1xn=n=1(vnwn) converges with respect to .

    Corollary 7.13. ([28,Thm.1.5]). Let X be an ordered normed Banach space and the order cone X+ be closed and generating. Then X+ is non-flat. In particular, there exists some c>0 such that for all xX there exist v,wX+ such that x=vw and v+wcx.

    Proof. Since W=X+ is generating, X=˜X as sets. By (7.6), the identity is continuous from X with to X with . Since X is a Banach space under both norms (Proposition 7.12), the identity is continuous from X with to X with by the open mapping theorem. By Remark 7.11, X+ is a non-flat cone with respect to .

    Example 7.14. Let S be a non-empty set and B a σ- algebra of subsets of S. Let be a norm on M(S) such that μ=μ(S) for all μM+(S). Then is the variation norm on M(S).

    Let B:WW be a homogeneous additive operator on W. We define an extension A:˜X˜X by

    Ax=BvBw,x=vw˜X,v,wW.

    Since B is additive, the definition does not depend on the choice of v and w. Indeed, if vw=˜v˜w for v,˜v,w,˜wW, then v+˜w=˜v+wW and B(v)+B(˜w)=B(˜v)+B(w) and B(v)B(w)=B(˜v)B(˜w). It is obvious from the definition that A:˜X˜X.

    One readily checks that A is additive and (positively) homogeneous on ˜X. Let α<0. Then

    A(αx)=A(αvαw)=A((α)w(α)v)=B((αw)B((α)v)=αB(w)+αB(v)=αAx.

    This shows that A is linear. The linear extension A is uniquely determined by B.

    Proposition 7.15. Let B:WW be a bounded homogeneous additive operator on W. Then the linear extension A:˜X˜X is bounded with respect to and A=B for the respective operator norms. This equality translates to the respective spectral radii.

    Proof. We can assume that B is not the zero operator on W. Otherwise, A is the zero operator on ˜X and the assertion is valid.

    Let x˜X and x=vw with v,wW. Then Ax=B(v)B(w) with

    B(v)+B(w)B(v+w).

    Since B(v),B(w)W, by (7.5),

    A(x)B(v+w).

    Now B>0 and so

    v+wAx/B.

    Since x=vw, again by (7.5), xAx/B. This implies that A is bounded with respect to and AB. The oppositive inequality is obvious because A extends B, and we obtain equality.

    The next result is an extension of [68,Thm.3.10] which, in turn, is a generalization of [54,Thm.3] to infinite dimensions.

    Theorem 7.16. Let X be an ordered normed vector space and let its order cone X+ be closed, normal and serially complete. Let Q and B be bounded homogeneous additive operators on X+, r(Q)<1. Then r(Q+B)1 and r(Bn=0Qn)1 have the same sign.

    Proof of Theorem 7.16. By Proposition 7.12, Remark 7.11 and the preceding remarks, ˜X under is an ordered Banach space and W=X+ is a normal generating closed cone of ˜X with . Let ˜Q be the bounded linear extension of Q to ˜X and A the bounded linear extension of B to ˜X with . Then ˜Q+A is the extension of Q+B to ˜X and A(I˜Q)1 the extension of Bn=0Qn to ˜X. By Proposition 7.15, the respective spectral radii of the extensions are equal to those of the original operators. By [68,Thm.3.10], r(˜Q+A)1=r(A(I˜Q)1)1 have the same sign, which implies the assertion.

    Theorem 7.17. Let X be an ordered normed vector space and let its order cone X+ be closed, normal and serially complete. Let Q and B be continuous homogeneous additive operators on X+, r(Q)<r(B+Q)=:λ. Assume that B2 and at least one of BQ or QB are compact on X+.

    Then there exists some w˙X+ such that (B+Q)(w)=λw.

    Proof. We first assume that r(Q+B)=:λ=1. So r(Q)<1.

    By Theorem 7.16, r(BR)=1 with

    R=n=0Qn=I+QR=I+RQ. (7.7)

    Since B is additive,

    BR=B+BQR=B+BRQ.

    Since all ingredients are additive and QR=RQ by (7.7),

    (BR)2=B2R+BQR(BR)=B2R+BR(QB)R.

    Since by assumption, B2 and at least one of BQ or QB are compact on X+, (BR)2 is compact (and continuous) on X+. By Corollary 7.10, there exists some v˙X+ such that v=BRv. Set w=Rv. By (7.7), w=v+Qw and wQw=v=Bw, i.e., w=(B+Q)(w).

    We return to the general case λ=r(Q+B)>r(Q). Set Bλ=1λB and Qλ=1λQ. Then

    r(Qλ+Bλ)=1λr(Q+B)=1>1λr(Q)=r(Qλ).

    By our previous consideration, there exists some w˙X+ such that w=(Qλ+Bλ)w=1λ(Q+B)w.

    Let X+ be the closed order cone of an ordered normed vector space X and F:X+X+, F(0)=0.

    B:X+X+ is called an order derivative of F:X+X+ at 0 if

    foranyϵ(0,1)thereissomeδ>0suchthat(1ϵ)B(x)F(x)(1+ϵ)B(x)forallxX+withxδ. (8.1)

    The following is shown in [36], Prop.3.3, Lemma 3.6 and 3.7.

    Lemma 8.1. The order derivative B satisfies B(0)=0. B is uniquely determined if it is homogeneous. If X+ is normal and B is homogeneous, B is the Gateaux-derivative of F at 0.

    A homogeneous B:X+X+ is called a lower order derivative of F at 0 if one part of (8.1) holds:

    Foranyϵ(0,1)thereissome δ>0suchthatF(x)(1ϵ)B(x)forallxX+withxδ. (8.2)

    B is called an upper order derivative if the other part of (8.1) holds:

    Foranyϵ(0,1)thereissome δ>0suchthatF(x)(1+ϵ)B(x)forallxX+withxδ. (8.3)

    Theorem 8.2. Let F,B:X+X+ and let B be homogeneous and order-preserving and a lower order-derivative of F at 0 as in (8.2).

    Let r>1 and let θ:X+R+ be a homogeneous bounded order-preserving functional with θ(B(x))rθ(x) for all xX+.

    Then there exists some δ0>0 such that

    supnZ+Fn(x)δ0  for  all  xX+  with  θ(x)>0.

    In particular, 0 is an unstable equilibrium of F.

    Proof. Choose some ϵ(0,1) such that 1<(1ϵ)r=:s. Then choose some δ>0 such that F(x)(1ϵ)B(x) for all xX+ with xδ.

    Suppose that the statement is false. By contraposition, there exists some xX+ such that θ(x)>0 and supnZ+Fn(x)<δ.

    Since θ is bounded, this implies that (θ(Fn(x))) is a bounded sequence.

    We will show by induction that θ(Fn(x))snθ(x) for all nZ+, which is a contradiction because s>1.

    This statement holds for n=0. Let nZ+ and θ(Fm+n(x))snθ(Fm(x)). Then

    Fn+1(x)=F(Fn(x))(1ϵ)B(Fn(x)).

    Since B and θ are order-preserving and homogeneous,

    θ(Fn+1(x))(1ϵ)θ(B(Fn(x)))(1ϵ)rθ(Fn(x))ssnθ(x)=sn+1θ(x).

    If x,vX+, x is called v-positive

     if there exists some δ>0 such that xδv. (8.4)

    Theorem 8.3. Let B be a lower order derivative of F at 0, B order-preserving, and let r>1 and v˙X+ such that B(v)rv.

    Then there exists some δ0>0 such that for any v-positive xX+ with xδ0 there is some nN such that Fn(x)>δ0.

    In particular, 0 is unstable.

    Proof. Let r>1 and v˙X+ such that B(v)rv. We define [44,(2.14)]

    θ(x)=sup{βR+;βvx}=:[x]v.

    By [44,Rem.3.4], θ(B(x))rθ(x) for all xX+. Apply Theorem 8.2.

    For discrete time-dynamics, 'persistence at equilibrium'

    comes in the form of a fixed point of the population turnover map.

    Theorem 8.4. Let B be a lower order derivative of F at 0, B order-preserving, and let F be continuous and compact on ˙X+. Assume that there is some continuous homogeneous subadditive functional θ:X+R+ and the following hold:

    (i) There is some ζ(0,1) such that θ(x)ζx for all xX+.

    (ii) lim supxθ(F(x))/θ(x)<1.

    (iii) There is some v˙X+ and r>1 such that B(v)rv.

    Then there exists some x˙X+ such that F(x)=x.

    We emphasize that X+ does not need to be complete.

    Proof. For λ(0,1), we define Fλ:X+X+ by

    Fλ(x)=F(x+λv)+λv,xX+, (8.5)

    where v is chosen according to assumption (ⅲ).

    We claim: There exists some R>0 such that θ(Fλ(x))R for all λ(0,1] and all xX+ with θ(x)R.

    Suppose not. Then, for any nN, there exist xnX+ and λn(0,1] such that θ(xn)n and θ(Fλn(xn))n. Since θ is subadditive and homogeneous, for all nN,

    nθ(F(xn+λnv)+λnv)θ(F(xn+λnv))+θ(v).

    Since F is compact on ˙X+ and θ bounded on X+, (xn) is unbounded. After choosing subsequences, xn as n. By assumption (ⅱ), there exists some α(0,1) and β>0 such that

    θ(F(x))αθ(x),xX+,xβ.

    So, for large enough n, nαθ(xn+λnv)+θ(v)αn+2θ(v), a contradiction because α(0,1).

    Thus, we have shown that there is an R>0 such that, for any λ(0,1], Fλ maps the convex closed bounded set {xX+;θ(x)R} into itself. Since Fλ is compact and continuous on X+, by Tychonov's fixed point theorem [58,Thm.10.1], there exists xλX+ with θ(xλ)R and Fλ(xλ)=xλ.

    Now, choose a sequence (λn) in (0,1] such that λn0 for n. By assumption (i) and (8.5), for each nN, there exists some xn such that

    F(xn+λnv)+λnv=xn,0<xnR/ζ. (8.6)

    We choose ϵ(0,1) such that (1ϵ)r>1 with the number r from assumption (iii). Since B is a lower order derivative of F, by (8.2) we choose δ>0 such that F(x)(1ϵ)B(x) for all x˙X+ with x2δ. We claim that

    lim infnxnδ. (8.7)

    Suppose not. Then, for some large nN, xnδ and λnvδ and so xn+λnv2δ and

    xn(1ϵ)B(xn+λnv)+λnv.

    Since vX+ and B is order-preserving, xn(1ϵ)B(xn). Since B is homogeneous and order-preserving, xn(1ϵ)kBk(xn), kN. We also have that xnλnv and B(v)rv. So

    xn(1ϵ)kBk(λnv)[(1ϵ)r]kλnv,kN.

    Since X+ is a closed cone and (1ϵ)r>1, this implies that v=0, a contradiction. So (8.7) holds.

    Since F is compact on ˙X+ and the sequence (xn+λnv) in ˙X+ is bounded and λn0, F(xn+λnv)yX+ after choosing a subsequence. By (8.6) and (8.7), xny as n with yX+ and δy. Since F is continuous on ˙X+, F(y)=y.

    By Theorem 7.8, there is the following consequence.

    Corollary 8.5. Let F:X+X+ be continuous and compact on ˙X+. Assume that lim supxF(x)/x<1. Let B be a lower order derivative of F and let B:X+X+ be compact, continuous and order-preserving, and r(B)>1. Then there exists some x˙X+ such that F(x)=x.

    Again, we emphasize that X+ does not need to be complete.

    Let (S,B) and (˜S,˜B) be measurable spaces with B and ˜B σ-algebras of subsets of S and ˜S, respectively.

    Definition 9.1. A function κ:BטSR+ is called a measure kernel if

    κ(,s)M+(S) for all s˜S and κ(T,)Mb(˜S) for all TB.

    Here M(˜S) denotes the vector space of measurable functions from ˜S to R and Mb(˜S) the Banach space of bounded measurable functions from ˜S to R with the supremum norm. Cf. [2,Sec.19.2] [3,Sec.10.3][69]. Measure kernels are simply called measurable maps in [70,Sec.5.3]. See also [22].

    The measure kernel κ induces a linear bounded map A from Mb(S) to Mb(˜S) with the supremum norm and a linear bounded map A from M(˜S) to M(S) with the variation norm by

    (Af)(s)=Sf(t)κ(dt,s),s˜S,fMb(S), (9.1)
    (Aμ)(T)=˜Sκ(T,s)μ(ds),TB,μM(˜S). (9.2)

    The following duality relation holds,

    ˜S(Af)dμ=Sfd(Aμ),fMb(S),μM(˜S). (9.3)

    This formula is first proved for simple measurable functions, then for nonnegative bounded measurable functions, which are uniform limits of increasing sequences of simple functions, and finally for bounded measurable functions, which are differences of nonnegative measurable bounded functions.

    There is equality of the operator norms,

    A=A=sups˜Sκ(S,s). (9.4)

    Let S=˜S and B=˜B. The convolution of two measure kernels κj, j=1,2, is defined by (3.2). κ1κ2 is again a measure kernel,

    Sf(z)(κ1κ2)(dz,s)=S(Sf(z)κ1(dz,t))κ2(dt,s),fMb(S),sS. (9.5)

    This follows from (9.3) with μ=κ2(,s), sS. (9.5) implies that this convolution is associative, i.e., for three measure kernels κj, j=1,2,3,

    κ1(κ2κ3)=(κ1κ2)κ3. (9.6)

    Lemma 9.2. For j=1,2, let κj be measure kernels and Aj and Aj be the linear bounded operators on Mb(S) and M(S), respectively. Then A2A1 and A1A2 are induced by κ1κ2 via (9.1) and (9.2).

    Multiple convolutions κn of a measure kernel κ:B×SR+ are defined in Definition 3.4.

    By Lemma 9.2, κn induces An on Mb(S) and An on M(S). Guided by (9.4) and (4.7), the spectral radius of the kernel κ, r(κ) is defined by (3.3), and we have

    r(κ)=r(A)=r(A)=limn(supsSκn(S,s))1/n. (9.7)

    Since M(S) with the variation norm is an ordered Banach space with the closed generating order cone M+(S), by Proposition 7.15 and Example 7.14,

    r(κ)=r(A)=r(A+), (9.8)

    where A+=B is the restriction of the bounded linear map A in (9.2) to M+(S). By [43,Thm.1.7],

    r(κ)=r(A+)supμ˙M+(S)inf{Sκ(T,s)μ(T)μ(ds);TB,μ(T)>0}. (9.9)

    Further, Mb(S) with the supremum norm is an ordered Banach space with the closed generating order cone Mb+(S), which is non-flat, and

    r(κ)=r(A)=r(A+), (9.10)

    where A+ is the restriction of the bounded linear map A in (9.1) to Mb+(S). By [43,Thm.1.7],

    r(κ)=r(A+)supf˙Mb+(S)inf{Sf(s)f(t)κ(ds,t);tS,f(t)>0}. (9.11)

    Mb+(S) is a solid cone and f˘Mb+(S), the open interior of Mb+(S), if and only if fM(S) and 0<inff(S)supf(S)<. By [43,Thm.1.15],

    r(κ)=r(A+)inff˘Mb+(S)sup{Sf(s)f(t)κ(ds,t);tS}. (9.12)

    Since μ=μ(S) for μM+(S) in the variation norm, the series characterization of the spectral radius in Corollary 7.6, with p=1, translates to

    r(κ)=inf{λ>0;μM+(S):n=1λnSκn(S,t)μ(dt)<}=inf{λ>0;n=1λnsupsSκn(S,s)<}=inf{λ>0;supsSn=1λnκn(S,s)<}. (9.13)

    The third equality follows from the first two. The formulas are reminiscent of what has been called the Perron root of κ by Shurenkov ("On the relationship between spectral radii and Perron roots", preprint) [69],

    r(κ)=inf{λ>0;sS:n=1λnκn(,s) is σ-finite}. (9.14)

    Obviously, r(κ)r(κ) and equality does not always hold (even for Feller kernels, Examples 10.12 and 10.13). Conditions for equality appear to involve irreducibility conditions (Shurenkov) or communication conditions [69] that are beyond the scope of this paper. Actually, in view of the fact that the equalities in (9.13) hold without any such conditions, the question whether or under which conditions r(κ) equals

    inf{λ>0;sS:n=1λnκn(S,s)<}

    may be more compelling. Corollary 7.7 implies the following for measure kernels.

    Remark 9.3. Let r=r(κ)>0. Then there exists some μM+(S) such that n=1rnSκn(S,t)μ(dt)=.

    Let S and ˜S be metrizable topological spaces and B and ˜B the respective Borel σ-algebras.

    Definition 10.1. A function κ:BטSR+ is called a Feller kernel if

    κ(,s)M+(S) for all s˜S and if κ has the Feller property

    Sf(y)κ(dy,)Cb(˜S) for any fCb(S).

    A Feller kernel κ is called a Feller kernel of separable measures if

    κ(,s)Ms+(S) for all s˜S.

    Cf. [2,Sec.19.3]. See Example 10.12. By Proposition 6.3, if κ is a Feller kernel, κ(U,) is a Borel measurable function on ˜S for all open subsets U of S and thus for all Borel sets U in ˜S. This implies that every Feller kernel is a measure kernel and induces the maps A:M(˜S)M(S) and A:Mb(S)Mb(˜S). Since κ is a Feller kernel, A maps Cb(S) to Cb(˜S).

    Theorem 10.2. Let κ:BטSR+ be a Feller kernel. Let μM+(˜S) and A(μ)Ms+(S). Then A is continuous at μ with respect to the flat norm.

    Proof. Let (μn) be a sequence in M+(˜S) with μnμ0. Let fLCb(S). By our Feller type property, the definition g(s)=Sf(t)κ(dt,s)=(Af)(s), s˜S, provides a function gCb(˜S). By (9.3) and Theorem 6.6,

    Sf(t)(Aμn)(dt)=SAfdμnSAfdμ=Sf(t)(Aμ)(dt).

    By Proposition 6.6, AμnAμ0.

    Proposition 10.3. Let A:Ms+(˜S)Ms+(S) be additive, homogeneous and continuous with respect to the flat norm. Then A is induced by a Feller kernel of separable measures via (9.2).

    Proof. Set κ(T,s)=(Aδs)(T) for s˜S and TB. Since δsMs+(S), κ(,s)Ms+(S) for any s˜S. Let (sn) be a sequence in ˜S and sns˜S. By Lemma 6.5, δsnδs0 and AδsnAδs0 because A is continuous. By Theorem 6.6, κ has the Feller property. Let B be the additive homogenous operator induced by κ. Then Bδs=Aδs for all s˜S. Let N be the set of measures kj=1qjδsj with sj˜S and qjQ+. By Theorem 6.8, N is dense in Ms+(˜S) and Bν=Aν for all νN. Let μMs+(˜S). Choose a sequence (μn) in N such that μnμ0 as n. Let fCb(S). Since κ has the Feller property, gCb(˜S) where g(s)=Sf(t)κ(dt,s) for s˜S. By Theorem 6.6,

    Sfd(Bμ)=Sgdμ=limnSgdμn=limnSfd(Bμn)=limnSfd(Aμn).

    Since A is continuous by assumption, Sfd(Bμ)=Sfd(Aμ). By Theorem 6.3, (Bμ)(T)=(Aμ)(T) for all open subsets T of S and so for all TB.

    Theorem 10.4. Let κ:BטSR+ be a Feller kernel of separable measures. Then the following hold:

    (a) The map ˜Ssκ(,s) from ˜S to Ms+(S) is continuous with respect to the flat norm.

    (b) For any compact subset K of ˜S, the set of measures {κ(,s);sK} in Ms+(S) is pre-tight and, if S is complete, tight.

    (c) If N is a tight bounded subset of M+(˜S), then A(N) is a pre-tight set of measures in M+(S) and, if S is complete, tight.

    (d) For any separable set ˜T˜B, there exists a separable closed subset T of S such that κ(ST,s)=0 for all s˜T.

    (e) A maps Ms+(˜S) continuously into Ms+(S).

    (f) A maps Ms(˜S) into Ms(S).

    Proof. (a) Since κ has the Feller property, the map ˜Sxκ(,x)Ms+(S) is continuous with respect to the flat norm by Theorem 6.6.

    (b) If K is a compact subset of ˜S, the set ΓK={κ(,s),sK} is the continuous image of a compact set by (a) and thus a compact set in Ms+(S) with the flat norm. By Proposition 6.13, it is a pre-tight set and, if S is complete, a tight set.

    (c) Let N a tight bounded subset of M+(˜S) and ϵ>0. Since κ has the Feller property, there exists some c1>0 such that κ(S,x)c1 for all x˜S. Since N is a bounded subset of M+(˜S), there exists some c2>0 such that μ(S)c2 for all μN.

    Since N is tight, there exists a compact set K such that μ(SK)<ϵ2c1 for all μN. Further, there exists a closed totally bounded set T in S such that κ(ST,x)<ϵ2c2 for all xK. Then, for all μN,

    (Aμ)(ST)=Kκ(ST,x)μ(dx)+˜SKκ(ST,x)μ(dx)supxKκ(ST,x)c2+c1μ(˜SK)ϵ.

    So the set of measures A(N) is pre-tight and, if S is complete, tight.

    (d) Let ˜T˜B be separable and D be a dense countable subset of ˜T. For any tD, there exists a separable subset TtB such that κ(STt,t)=0. Let T be the closure of tDTt. Since D is countable, T is a closed separable subset of S. Further, for all tD, κ(ST,t)κ(STt,t)=0.

    Let s˜T. Then there exists a sequence (tj) in D such that tjs. By (a), κ(,tj)κ(,t)0 as j. Since ST is open,

    κ(ST,s)lim infjκ(ST,tj)=0

    by Theorem [26,Thm.4.10].

    (e) Let μMs+(˜S). By definition, there exists a separable set ˜T˜B such that μ(S˜T)=0. Choose T according to (d). Then

    (Aμ)(ST)=Tκ(ST,s)μ(ds)=0.

    This shows that Aμ is separable. By Theorem 10.2, A is continuous at μ.

    (f) Let μMs(˜S). By Definition 3.10, |μ|Ms+(˜S) and μ±=12(|μ|±μ)Ms+(˜S). Since A is linear, by (e), Aμ=Aμ+AμMs(S).

    Theorem 10.5. Let d be a metric that induces the topology of S. Let κ:BטSR+ be a measure kernel of separable measures. Assume that Sf(y)κ(dy,)Cb(˜S) for any f:S[0,1] with |f(y)f(z)|d(y,z) for all y,zS. Then κ has the Feller property.

    Further, the map sκ(,s) from (˜S,d) to Ms+(S) endowed with the associated flat norm is continuous.

    Proof. Let (sn) be a sequence in ˜S and s˜S and sns. By assumption, Sf(y)κ(dt,sn)Sf(y)κ(dt,s) for all f:S[0,1] with |f(y)f(z)|d(y,z) for all y,zS. Since κ(,s) is separable, by Proposition 6.6, Sg(y)κ(dy,sn)Sg(y)κ(dy,s) for all gCb(S).

    The last statement follows from Theorem 10.4 (a).

    Remark 10.6. Let κ be a Feller kernel of separable measures and As denote the restriction of A from Ms(˜S) to Ms(S) and As+ the restriction of A from Ms+(˜S) to Ms+(S). Since the Dirac measures are separable, we still have for the operator norms that As=As+=sups˜Sκ(S,s), (see (9.4)). If S=˜S and B=˜B, by (3.3),

    r(As)=r(A)=r(As+)=r(κ).

    Remark 10.7. The map A induced by a measure kernel via (9.2) is continuous from M+(˜S) to M+(S) with respect to the variation norms even without the Feller type property. But it seems difficult to come up with conditions for A to be compact with respect to the variation norm.

    Definition 10.8. A measure kernel κ:BטSR+ is called a tight measure kernel if the set of measures {κ(,x);x˜S} is tight.

    A measure kernel κ is called a pre-tight measure kernel if set of measures {κ(,x); x˜S} is pre-tight.

    Proposition 10.9. Let κ:BטSR+ be a Feller kernel of separable measures. Then κ is a pre-tight Feller kernel (tight Feller kernel if S is complete) if for any ϵ>0 there is a compact subset K of ˜S such that κ(S,t)<ϵ for all t˜SK.

    Proof. Let ϵ>0. By assumption, there is a compact subset K of ˜S such that κ(S,t)<ϵ for all t˜SK. By Theorem 10.4 (b), the set of measures {κ(,s);sK} in Ms+(S) is pre-tight and, if S is complete, tight. So there exists some totally bounded closed (compact if S is complete) subset T of S such the κ(ST,t)<ϵ for all tK. But also κ(ST,t)κ(S,t)<ϵ for all t˜SK.

    Proposition 10.10. Let κ:BטSR+ be a tight measure kernel. Then A is continuous and compact from M+(˜S) to M+(S) with respect to the flat norm and maps M+(˜S) into Mt+(S).

    Proof. To show that A is compact, let (μn) be a bounded sequence in M+(˜S) with the flat norm.

    Since {κ(,t);t˜S} is tight, for any ϵ>0 there exists some compact set K in S such that κ(SK;t)<ϵ for all t˜S. By (9.2),

    (Aμn)(SK)=Sκ(SK,t)μn(dt)ϵμn(S),nN.

    Since (μn(˜S)) is a bounded sequence, the sequence (Aμn) is tight.

    Finally, (Aμn)(S)supt˜Sκ(S,t)μn(S) and the set {(Aμn)(S);nN} is boun\-ded in R. By Proposition 6.11, (Aμn) has a convergent subsequence.

    By the same arguments as above, A maps M+(S) into Mt+(S) and is continuous with respect to the flat norm by Theorem 10.2.

    Proposition 10.11. Let P:BטSR+ be a tight Feller kernel and gCb+(˜S×S). Then ˜κ:BטSR+,

    ˜κ(T,s)=Tg(s,t)P(dt,s),s˜S,TB, (10.1)

    is a tight Feller kernel. In particular, ˜κ(S,)Cb(˜S).

    Proof. ˜κ inherits tightness from P via the boundedness of g.

    Let fCb(S). Set h(s)=Sf(t)˜κ(dt,s), s˜S. Then

    h(s)=Sf(t)g(s,t)P(dt,s),s˜S.

    Our task is to show that hCb(˜S). Since g and f are bounded and P(S,) is bounded, h is bounded.

    To demonstrate the continuity of h on ˜S, let s˜S and (sn) a sequence in ˜S with sns. To show that h(sn)h(s), let ϵ>0. Since P is tight, there exists a compact subset K of S such that P(SK,s)ϵ for all s˜S. By the triangle inequality,

    |h(sn)h(s)|S|f(t)||g(sn,t)g(s,t)|P(dt,sn)+|Sf(t)g(s,t)P(dt,sn)Sf(t)g(s,t)P(dt,s)|.

    Since f(t)g(s,t) is a continuous bounded function of tS and P is a Feller kernel, the second expression on the right hand side of this inequality converges to 0 as n and

    lim supn|h(sn)h(s)|lim supnS|f(t)||g(sn,t)g(s,t)|P(dt,sn)2sup|f|sup|g|ϵ+sup|f|lim supnsuptK|g(sn,t)g(s,t)|P(S,sn).

    Since g is uniformly continuous on the compact set ({sn;nN}{s})×K,

    lim supnsuptK|g(sn,t)g(s,t)|=0 and so 
    lim supn|h(sn)h(s)|2sup|f|sup|g|ϵ.

    Since this holds for any ϵ>0, |h(sn)h(s)|0 as n.

    Example 10.12 (Survival and deterministic movement (development)). Let

    κ(T,s)=χT(ξ(s))g(s),sS,TB,

    where ξ:SS is continuous, gCb+(S). Notice that

    fCb(S)Sf(t)κ(dt,)=f(ξ())g()Cb(S).

    For each sS, κ(,s)=g(s)δξ(s) is a tight measure and κ is a Feller kernel of separable measures. By Theorem 10.4, the map A induced by κ on M(S) maps Ms(S) into itself and maps Ms+(S) continuously into itself with respect to the flat norm. One readily derives from (6.4) that A maps M(S) continuously into itself if ξ and g are both Lipschitz continuous.

    κ is tight if and only if for any ϵ>0 there is a compact subset T of S such that g(s)<ϵ if sS and ξ(s)T.

    An is associated with the kernel χT(ξn(s))n1j=0g(ξj(s)),

    r(A)=limn(supsSn1i=0g(ξj(s)))1/n. (10.2)

    There may or may not be an eigenvector of A associated with r(A).

    Suppose that sS and ξ(s)=s and g(s)g(s) for all sS. Then g(s)=r(A) and Aδs=g(s)δs.

    Now assume that for any compact subset K of S there is some nN such that ξn(s)SK for all sS. We claim that there is no r>0 and μMt+(S), μ0, such that Aμ=rμ. Suppose that such r>0 and μ exist. Then μ=rnAnμ for all nN and

    μ(T)=rnSχT(ξn(s))n1j=0g(ξj(s))μ(ds),nN,TB. (10.3)

    By our assumption, μ(T)=0 for any compact subset T of S. Since μMt+(S), μ=0.

    Let us assume that for any compact subset T of S and any sS, ξn(s)T for all but finitely many nN and g(s)=1 for all sS. Then χT(ξn(s))0 as n and the right hand side of (10.3) converges to 0 by the dominated convergence theorem and μ(T)=0. So μ is the zero measure if μ is tight.

    Let us finally assume in addition that S is σ-compact, i.e., K is a countable union of compact sets. Then any μM+(S) is tight. So, the spectral radius is not associated with an eigenmeasure; however it is associated with all constant positive functions as eigenfunctions.

    Further, for any compact set T of S and any sS, κn(T,s)=χT(ξn(s))=0 for all but finitely many nN. This implies that n=1λnκn(T,s)R+ for all λ(0,) and the so-called Perron root of κ in (9.14) equals r(κ)=0 while r(κ)=1.

    Example 10.13 (Bonsall's kernel). A Feller kernel on the σ-compact metric space S=(0,1] that has got some attention without the Feller kernel perspective is κ(,s)=δs/2, s(0,1] [35,44,71]. Obviously, r(κ)=1, and according to our considerations in Example 10.12, r(κ)=0. There are no eigenmeasures of κ associated with positive eigenvalues, but plenty of eigenfunctions, f(s)=sα, α0, associated with the eigenvalues (1/2)α. For α>0, these eigenfunctions are also in C0(0,1], the space of continuous functions f on (0,1] with f(s)0 as s0, which is a Banach space under the supremum norm and has M((0,1]) as dual space. A maps C0(0,1] into itself, and the spectral radius of its restriction A0 to C0(0,1] still equals 1. Let Ccon(0,1] be the closed cone of nonnegative convex function in C0(0,1]. Ccon(0,1] is a total but not generating cone of C0(0,1] that is invariant under A0 and the restriction of A0 to Ccon(0,1], Acon, is compact and r(Acon)=1/2 [35,71].

    Example 10.14 (Multiplication operator). Consider the special case ξ(s)=s for all sS in Example 10.12 with gCb+(S). The induced map A on M(S) takes the form of a multiplication operator (Aμ)(T)=Tg(s)μ(ds) for TB. A maps Ms(S) into itself. A is a bounded linear map on M(S) with respect to the variation norm. A maps Ms+(S) into itself continuously with respect to the flat norm.

    A is continuous on Ms(S) with respect to the flat norm if and only if g is Lipschitz continuous.

    Proof. One readily derives from (6.4) that A maps M(S) continuously into itself if g is Lipschitz continuous. Now let A be continuous and thus a bounded linear operator on Ms(S) with the respect to the flat norm. Let s,tS. Since δs=1,

    |g(s)g(t)|=(g(s)g(t))δsg(s)δsg(t)δt+g(t)(δtδs).

    Since Aδs=g(s)δs, |g(s)g(t)|=AδsAδt+g(t)δtδs. Since A is a bounded linear operator on Ms(S) with respect to the flat norm and δsMs(S), by Lemma 6.5,

    |g(s)g(t)|(A+supS|g|)δtδs=(A+supg(S))min{1,d(t,s)}.

    So g is Lipschitz continuous with a Lipschitz constant A+supg(S).

    We return to general Feller kernels κ and the maps A induced by them via (9.2). A is a bounded linear map on M(S) with the variation-norm. Let A+ denote the restriction of A to M+(S). Then A=A+=A+ and r(A)=r(A+). Since the variation norm and the flat norm coincide on M+(S), this means that the spectral radius of A with respect to the variation norm coincides with the spectral radius of A+ with respect to the flat norm and with the spectral radius of κ. Recall (3.3), (9.8), (9.11) and (9.12).

    Theorem 10.15. Assume that κ is a tight Feller kernel and r=r(A)=r(κ)>0. Then there exists some tight μM+(S), μ0, such that Sκ(,s)μ(ds)=rμ.

    This result follows from Theorem 7.8 and Proposition 10.10. It does not follow from the Krein-Rutman theorem [72] because A is only compact and continuous on M+(S) and not necessarily on M(S). It would follow from [35] if S were complete, but not if S (and then M+(S)) is not complete (or cannot be made complete by transition to a topologically equivalent metric, Theorem 6.14). See [26] for details and further remarks.

    Theorem 10.16. Let S be a metric space. Let κj:B×SR+, j=1,2, be Feller kernels of separable measures and κ=κ1+κ2. Assume that κ1 is a tight measure kernel and r:=r(κ)>r(κ2). Then there exists some νMs+(S) with ν(S)=1 and Sκ(T,s)ν(ds)=rν(T),TB. Further,

    r(κ)=supμ˙M+(S)inf{1μ(T)Sκ(T,s)μ(ds);TB,μ(T)>0}.

    This result would follow from [39] if S were complete or completely remetrizable because completeness of S is equivalent to the completeness of Ms+(S) with respect to the flat norm (Theorem 6.14).

    Proof. We apply Theorem 7.17 with X=Ms(S) and X+=Ms+(S), endowed with the flat norm. X+ is a closed, normal and serially complete cone in X with respect to flat norm (Corollary 6.7). Let Bj:Ms+(S)M(S) be given by

    (Bjμ)(T)=Sκj(T,s)μ(ds),μMs+(S),TB.

    By Theorem 10.4, for j=1,2, Bj maps Ms+(S) into itself and is a continuous homogeneous additive map on Ms+(S). By Proposition 10.10, B1 is compact on X+. By Theorem 7.17, there exists μ˙Ms+(S) such that (B1+B2)(μ)=rμ.

    The formula for r(κ) follows from the inequality (9.9) which is turned into an equality by the existence of the eigenmeasure.

    Proof of Theorem 3.6. Apply [36,Thm.4.2] or [63,Thm.4.1] where B is the homogeneous additive operator on M+(S) given by

    (Bμ)(T)=Sκo(T,s)μ(ds),TB,μM+(S),

    that is continuous with respect to the variation norm. Then r(B)=r(κo)<1 and also all other assumptions are satisfied.

    Proof of Corollary 3.7. By Theorem 3.6 it is sufficient to show that r(κo)r.

    Let S be the union of pairwise disjoint nonempty sets T1,,Tm in B and

    βjk=suptTjκo(Tk,t)

    and assume that the matrix of size m with coefficients βjk is irreducible and has a spectral radius r<1.

    By the Perron-Frobenius theorem, there exists a vector w(0,)m such that

    mk=1βjkwk=rwj,j=1,,m. (11.1)

    Define f:SR+ by

    f=mk=1wkχTk.

    Then f is in the interior of Mb+(S). For j{1,,m} and tTj,

    Sf(s)κo(ds,t)mk=1βjkwk=rwj=rf(t).

    By (9.12), r(κo)r.

    Proof of Theorem 3.8. Let T1,,Tm, mN, be pairwise disjoint nonempty sets in B and

    αjk=inftTjκo(Tk,t)

    and assume that the matrix of size m with coefficients αjk has a spectral radius r>1. Then there exists an eigenvector vRm+ such that

    mk=1αjkvk=rvj,j=1,,m.

    Set f=mk=1vkχTk. Since v is not the zero vector, f˙Mb+(S). Similarly as in the proof before, one shows that Sf(s)κo(ds,t) rf(t). By (9.11), r(κo)r. We define the linear nonnegative functional θ:M(S)R by

    θ(μ)=Sfdμ=mk=1vkμ(Tk).

    Let A and A be the maps on M(S) and Mb(S) that are induced by κo. Then Afrf and, by (9.3),

    θ(Aμ)=SAfdμSrfdμ=rθ(μ).

    The statement now follows from Theorem 8.2.

    Proof of Theorem 3.13. Combine Theorem 8.3 and Theorem 10.16.

    Proof of Remark 3.14. Combine Theorem 7.16 with Section 9.

    Proposition 11.1. Let the Assumption 3.2 be satisfied. Then F maps Ms+(S) into itself.

    Proof. Theorem 10.4 (b).

    Lemma 11.2. Let (˜fn) be a bounded sequence in Cb(S) and (μn) be a bounded pre-tight sequence in M+(S). Then

    S˜fndμnn0 if ˜fnn0

    uniformly on every totally bounded subset of S.

    Proof. Let ϵ>0. Since {μn;nN} is pre-tight, there exists a closed totally bounded subset T of S such that μn(ST)<ϵ for all nN. For all nN,

    |S˜fndμn|T|˜fn|dμn+ST|˜fn|dμnsupT|˜fn|supkNμk(S)+supkNsupS|˜fk|μn(ST).

    Since ˜fn0 uniformly on T, the last but one expression converges to 0 as n and

    lim supn|S˜fndμn|supkNsupS|˜fk|ϵ.

    Since this holds for arbitrary ϵ>0, the limit superior is zero and we have proved the assertion.

    Proposition 11.3. Let the family of Feller kernels {κμ);μMs+(S)} satisfy the Assumptions 3.2 and 3.17. Then F:Ms+(S)Ms+(S) is continuous with respect to the flat norm.

    Proof. Let μMs+(S) and (μn) be a sequence in Ms(S) such that μnμ0. By Theorem 6.6,

    S˜fdμnS˜fdμ,˜fCb+(S). (11.2)

    Then {μn;nN} is a compact subset of M+(S) with respect to the flat norm and pre-tight by Proposition 6.13 and a bounded subset of M+(S).

    Let fF. By (3.1),

    |SfdF(μn)SfdF(μ)|=|SfndμnS˜fdμ| (11.3)

    with

    fn(s)=Sf(t)κμn(dt,s),˜f(s)=Sf(t)κμ(dt,s).

    By Theorem 6.6, it is sufficient that the expression on the right hand side of the equation converges to 0 as n.

    By the triangle inequality and (11.3),

    |SfdF(μn)SfdF(μ)||S(fn˜f)dμn|+|S˜fdμnS˜fdμ|.

    Since κμ is a Feller kernel, ˜fCb+(S) and the second term on the right hand side of the last inequality converges to 0 as n by (11.2). As for the first term, by Assumption 3.17, for any closed totally bounded subset T of S,

    fn(s)˜f(s)0,n, uniformly for sT. (11.4)

    Further, by Assumption 3.2, (fn˜f) is a bounded sequence in Cb(S). Now the first term of the last inequality converges to 0 by Lemma 11.2.

    Proposition 11.4. Under the Assumptions 3.2 and 3.16, the yearly population turnover map F:Ms+(S)Ms+(S) is compact; for any bounded subset N of Ms+(S), F(N) is a tight bounded subset of Ms+(S).

    Proof. By Proposition 11.1, F maps Ms+(S) into itself. Let N be a bounded subset of Ms+(S). For any set TB and μN,

    F(μ)(ST)=Sκμ(ST,s)μ(ds)supsSκμ(ST,s)μ(S). (11.5)

    For T=, we obtain that {F(μ)(S);μN} is bounded in R by Assumption 3.16.

    Let ϵ>0. By Assumption 3.16, there exists some compact set T in S such that

    κμ(ST,s)ϵ(1+supμNμ(S))1.

    By (11.5), F(μ)(ST)ϵ for all μN. By Definition 3.10, F(N) is a tight subset of Ms+(S).

    By Theorem 6.11, F(N) has compact closure in Ms+(S).

    Proposition 11.5. Let the Assumptions 3.2 and 3.18 be satisfied. Then

    lim supμ(S)F(μ)(S)μ(S)<1.

    Proof. For all μMs+(S),

    F(μ)(S)=Sκμ(S,s)μ(ds)supsSκμ(S,s)μ(S).

    This implies the assertion.

    Proof of Theorem 3.19. We apply Corollary 8.5 with

    (Bμ)(T)=Sκo(T,s)μ(ds),TB,μM+(S).

    Then B:Ms+(S)M+(S) is homogeneous, additive, continuous and compact by Proposition 10.10, and r(B)=r(κo)>1. Further, F is continuous and compact by Propositions 11.3 and 11.4. Since {κμ;μM+(S)} is lower semicontinuous at measure zero, B is the lower order derivative of F. Further use Proposition 11.5. Then the assumptions of Corollary 8.5 are satisfies from which the existence of a non-zero fixed point follows.

    The metric space S with the σ-algebra B of Borel sets represents the habitat of a spatially distributed population. We consider a difference equation on M+(S), the cone of nonnegative finite measures on B,

    μn(T)=F(μn1)(T),nN,TB, (12.1)

    with μ0M+(S) and F:M+(S)M+(S) given by

    F(μ)(T)=Sκμ(T,s)μ(ds),κμ(T,s)=P(T,s)g(s,Sq(s,t)μ(dt))}{μM+(S),TB. (12.2)

    The difference equation describes the year-to-year development of a spatially distributed population with a short reproductive season. μn is the spatial distribution at the beginning of the nth year. See Section 2 for the interpretation of the measure kernel P, and the functions g:S×R+R+ and q:S2R+.

    Assumption 12.1. For the per capita reproduction function g,

    (g1) g:S×R+R+ is continuous and bounded. (g2) g(s,0)>0 for all sS and g(s,u)g(s,0)1 as u0 uniformly for sS.

    For the competitive influence function q,

    (q1) q:S2R+ is continuous and bounded.

    For the survival/migration kernel P,

    (P1) P:B×SR+ is a measure kernel (Definition 9.1).

    (P2) 0P(S,s)1 for all sS.

    Proposition 12.2. Assume Assumptions 12.1. Then Assumption 3.2 is satisfied.

    Further, the kernel family {κμ;μM+(S)} is continuous at the zero measure (Definition 3.5).

    If sS and g(s,w)g(s,0) for all wR+, κμ(T,s)κo(T,s) for all TB.

    Proof. Let μM+(S). Set

    v(s)=Sq(s,t)μ(dt),w(s)=g(s,v(s)),sS. (12.3)

    By the Assumption 12.1 (q1) and the dominated convergence theorem, v:SR+ is continuous and bounded, and so is w:SR+ by Assumption 12.1 (g1). This implies that

    κμ(T,s)=P(T,s)w(s),TB,sS, (12.4)

    is a measure kernel and κμ(S,s)supg for all μM+(S), sS.

    Let ϵ(0,1). By Assumption 12.1 (g2), there exists some ˜δ>0 such that (1ϵ)g(s,0)g(s,u)(1+ϵ)g(s,0) for all u[0,˜δ] and sS. Since q is bounded by Assumption 12.1 (q1), there exist some δ>0 such that q(s,t)δ˜δ for all s,tS. Let μM+(S) and μ(S)δ. Then Sq(s,t)μ(dt)suptSq(s,t)δ˜δ for all sS. This implies that

    (1ϵ)κo(T,s)κμ(T,s)(1+ϵ)κo(T,s),TB,sS,

    and the kernel family {κμ;μM+(S)} is continuous at the zero measure.

    Theorem 12.3. Assume Assumptions 12.1 and r=r(κo)<1.

    Then the zero measure (extinction fixed point) is locally asymptotically stable in the sense of Theorem 3.6 (a).

    If, in addition, g(s,u)g(s,0) for all u0, sS, then the origin is globally stable in the sense of Theorem 3.6 (b).

    Proof. Combine Theorem 3.6 and Proposition 12.2.

    Remark 12.4. Notice that we have only used the variation norm so far. Therefore, the previous results remain valid if S is a measurable space with σ-algebra B and the assumptions are slightly modified.

    Instead of Assumption 12.1 (g1) we would assume

    (g1)' g:S×R+R+ is bounded and measurable where S×R+ is equipped with the appropriate product σ-algebra,

    and instead of Assumption 12.1 (q1) we would assume

    (q1)' q:S2R+ is bounded and measurable where S2 is equipped with the appropriate product σ-algebra.

    Notice that continuity of q on S2 does not imply that q is measurable with respect to the product σ-algebra without additional assumptions (like S being separable [2,Sec.4.10]).

    Proposition 12.5. Assume Assumptions 12.1.

    (a) If P is a Feller kernel of separable measures, κo is a Feller kernel of separable measures.

    (b) If P is a tight measure kernel, then the set of measures {κμ(,s);μM+(S), sS} is tight and the set {κμ(S,s);μM+(S), sS} is bounded in R.

    Proof. (a) κo inherits these properties from P via (12.4); recall that g(s,0) is a continuous function of sS.

    (b) Let P be a tight measure kernel. Let KB and μM+(S). Since g is bounded by Assumption 12.1 (g1),

    κμ(SK,s)(supg)P(SK,s),μM+(S).

    With K=, we see that κμ(S,s)supg for all sS, μM+(S).

    Let ϵ>0. Since P is a tight kernel, there exists a compact subset K of S such that P(SK,s)<δ with δsupg<ϵ and κμ(SK,s)<ϵ for all μM+(S) and sS. This implies that the set of measures {κμ(,s);μM+(S),sS} is tight.

    Theorem 12.6. Make Assumption 12.1.

    Assume that P=P1+P2 with Feller kernels P1 and P2 of separable measures where P1 is a tight Feller kernel. Let κ1 and κ2 and κ be related to P1, P2 and P by (12.4), respectively, and r=r(κ)>1 and r>r(κ2).

    Then there exists some νMs+(S) such that Sκ(,s)ν(ds)=rν, and there is some δ0>0 such that for any ν-positive μ0M+(S) and any solutions (μn) in M+(S) of μn=F(μn1), nN, there is some nZ+ with μn(S)>δ0. In particular, the zero measure is unstable.

    By (8.4), μ is ν-positive if there is some δ>0 such that μ(T)δν(T) for all TB.

    Proof. The measure kernels κj related to Pj by (12.4) are also Feller kernels of separable measures because w is continuous, and κ1 inherits tightness from P1.

    Combine Proposition 12.2 with Theorem 3.13.

    Assumption 12.7. Assume:

    (ⅰ) For any closed totally bounded subset T of S, the set of functions {q(s,);sT} is equicontinuous on S: For any t0S and ϵ>0, there exists δ>0 such that |q(s,t)q(s,t0)|<ϵ whenever s,tS and d(t,t0)<δ.

    (ⅱ) For any closed totally bounded subset T of S, the set of functions {g(s,);sS} is uniformly equicontinuous on bounded subsets of R+: For any c(0,) and ϵ>0, there exists δ>0 such that |g(s,w)g(s,v)|<ϵ whenever sS and w,v[0,c] and |wv|<δ.

    (ⅲ) P:B×SR+ is a Feller kernel of separable measures.

    Lemma 12.8. Assumption 12.7 (i), (ii) are satisfied if S is completely metrizable, and Assumption 12.1 holds.

    Proof. Let T be a closed totally bounded subset of S, which is completely metrizable. Then T is compact.

    (ⅱ) Let c>0. Then the set T×[0,c] is compact. Since g is continuous on S×R+, g is uniformly continuous on T×[0,c]. This implies (ⅱ)

    (ⅰ) Suppose that Assumption 12.7 (ⅰ) is false. Then there is some ˜sS such that {q(s,);sT} is not equicontinuous at ˜s.

    Then there exists some ϵ>0 and a sequence (sn) in T and a sequence (˜sn) in S such that ˜sn˜s as n and

    |q(sn,˜sn)q(sn,˜s)|>ϵ,nN.

    Since T×({˜sn;nN}{˜s}) is a compact subset of S2, q is uniformly continuous on this set, a contradiction.

    Lemma 12.9. Let the Assumptions 12.1 and 12.7 (i) be satisfied. Further let μMs+(S) and (μn) be a sequence in Ms+(S), μnμ0 as n.

    Then, (Qμn)(s)(Qμ)(s) as n uniformly for s in any closed totally bounded subset of S. Further Qμn and Qμ are bounded functions.

    Proof. The convergence statement follows from Proposition 6.10. The boundedness statements are immediate.

    Lemma 12.10. Let the Assumptions 12.1 and 12.7 (ii) be satisfied. Let T be a closed totally bounded subset of S and fn:TR+, nN, and f:TR+ be bounded functions such that fnf uniformly on T. Let g:T×R+R+ be continuous. Then g(s,fn(s))g(s,f(s)) as n uniformly for sT.

    Proof. There exists some c(0,) such that fn(s),f(s)c for all nN, sT. Since {g(s,);sT} is uniformly equicontinuous on [0,c], the assertion follows.

    Proposition 12.11. Make the Assumptions 12.1 and Assumptions 12.7.

    Then Assumption 3.17 is satisfied for {κμ;μM+(S)} given by (12.2).

    Proof. Let fCb+(S). For sS,

    Sf(t)κμn(dt,s)Sf(t)κμ(dt,s)=Sf(t)P(dt,s)[g(s,(Qμn)(s))g(s,(Qμ)(s))].

    Since Sf(t)P(dt,s)supf(S), it is sufficient to show that

    g(s,(Qμn)(s))g(s,(Qμ)(s)),n,

    uniformly for s in every closed totally bounded subset T of S. But this follows by combining Lemma 12.9 and Lemma 12.10.

    Assumption 12.12. We assume the following for the competitive influence function q and the per capita birth rate g:

    (ⅰ) infq(S2)>0; (ⅱ) g(s,u)0 as u uniformly for sS.

    Proposition 12.13. Make the Assumptions 12.1 and Assumptions 12.12.

    Then Assumption 3.18 is satisfied.

    Proof. For μ˙M+(S), by Assumption 12.1 (g1) and (12.2),

    κμ(S,s)supsSg(s,Sq(s,t)μ(dt)).

    Further, Sq(s,t)μ(dt)infq(S2)μ(S). Since infq(S2)>0 by Assumption 12.12 (i), as μ(S), Sq(s,t)μ(dt) uniformly in sS. By Assumption 12.12 (ii), κμ(S,s)0 as μ(S) uniformly for sS.

    Theorem 12.14. Make the Assumptions 12.1, 12.7, and 12.12. Assume that the Feller kernel P is tight and that r(κ)>1 for the Feller kernel κ given by κ(T,s)=P(T,s)g(s,0). Then F has a nonzero fixed point in Ms+(S).

    Proof. Combine Theorem 3.19 with Propositions 12.13, 12.11, 12.5, 12.2.

    This paper establishes the spectral radius r0 of a suitable Feller kernel as threshold parameter that decides about the extinction of the population the dynamics of which are modeled in the cone of nonnegative measures. This Feller kernel induces the order derivative of the yearly population turnover map F at the zero measure.

    If r0<1, the zero measure (the extinction state) is locally asymptotically stable and, under some extra conditions, even globally asymptotically stable (Theorem 12.3). If r0>1, the zero measure is unstable (Theorem 12.6) and there exists a nonzero equilibrium measure (Theorem 12.14). While the first result is quite satisfactory, it is desirable to replace the instability of the extinction state by a persistence result analogously to population models in other state spaces [36] [73,Ch.7]: If r0>1, then there exists some ϵ>0 such that lim infnμn(S)ϵ for all solutions (μn) of μn=F(μn1), nN, with μ0˙M+(S). Even more desirable is the existence of a compact persistence attractor [73,Sec.5.2]. The underlying technical problem consists in finding an eigenfunction f of the Feller kernel associated with r0, r0f(s)=Sf(t)κ(dt,s),sS. While we have found an eigenmeasure under acceptable assumptions (Section 10.2) and an eigenfunction in a special case (Remark 3.9), general assumptions for the existence of an eigenfunction we have found so far are quite restrictive and will be presented elsewhere.

    The threshold role of r0 makes estimates of this spectral radius desirable. Such estimates seem hard to come by, except in special cases [47,Sec.7] (Remark 3.9). Theoretically, formulas (9.11) and (9.9) provide lower estimates of the spectral radius and formula (9.12) provides upper estimates, but they may not be of very practical use. The inequality in (9.9) becomes an equality if there is an eigenmeasure associated with the spectral radius (Theorem 10.16). The inequality in (9.11) becomes an equality if there is a a bounded strictly positive eigenfunction associated with the spectral radius, and the one in (9.12) if the eigenfunction is also bounded away from zero. If the Feller kernel is the sum of two other Feller kernels, Remark 3.14 can be helpful provided that one of the kernels is of simple form.

    We have strived for results that do not require that the individual state space S is separable or is completely metrizable (i.e., becomes complete after switching to a topologically equivalent metric). Depending on the application, individual state spaces could become quite intricate.

    By results by Alexandrov and Mazurkiewicz, a subset of a complete metric space S is completely metrizable itself if and only if it is a countable intersection of open subsets (i.e., a so-called Gδ subset) of S [2,Sec.3.7] [4,Thm.2.5.4]. In particular, the set of rational numbers with the standard topology is not completely metrizable.

    If S is the Banach space S=Cb(K) of bounded continuous real-valued functions on a metric space K with the supremum norm, then S is separable if and only if K is compact [5,Ⅳ.13.16]. In particular, the Banach space of bounded real-valued sequences with the supremum norm is not separable.

    The results in Section 3 and Section 12 appear to be new even if the individual state space S is separable or completely metrizable; some complicated looking assumptions like Assumption 12.7 (ⅰ) and (ⅱ) are satisfied if S is completely metrizable.

    The author thanks Azmy Ackleh for useful discussions and hints and Odo Diekmann and Eugenia Franco and one anonymous referee for helpful suggestions and corrections that improved the presentation of the paper.

    The author declares no conflict of interest.



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