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Resolvent Positive Linear Operators Exhibit the Reduction Phenomenon

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Resolvent Positive Linear Operators Exhibit the Reduction Phenomenon Resolvent positive linear operators exhibit the reduction phenomenon Lee Altenberg1 Associate Editor, BioSystems, 2605 Lioholo Place, Kihei, HI 96753-7118 Edited by Joel E. Cohen, The Rockefeller University, New York, NY, and approved December 6, 2011 (received for review August 23, 2011) The spectral bound, sðαA þ βVÞ, of a combination of a resolvent xðt þ 1Þ¼½ð1 − αÞI þ αPDxðtÞ; [1] positive linear operator A and an operator of multiplication V, was shown by Kato to be convex in β ∈ R. Kato's result is shown where xðtÞ is a vector of the rare allele’s frequency at time t among here to imply, through an elementary “dual convexity” lemma, that different population subdivisions, α is the rate of dispersal be- sðαA þ βVÞ is also convex in α > 0, and notably, ∂sðαA þ βVÞ∕ tween subdivisions, P is the stochastic matrix representing the ∂α ≤ sðAÞ. Diffusions typically have sðAÞ ≤ 0, so that for diffusions pattern of dispersal, and D is a diagonal matrix of the growth rates with spatially heterogeneous growth or decay rates, greater mix- of the allele in each subdivision. The allele is protected from ing reduces growth. Models of the evolution of dispersal in parti- extinction if its asymptotic growth rate when rare is greater than cular have found this result when A is a Laplacian or second-order 1. This asymptotic growth rate is the spectral radius, elliptic operator, or a nonlocal diffusion operator, implying selec- tion for reduced dispersal. These cases are shown here to be part rðAÞ≔ supfjλj: λ ∈ σðAÞg; [2] of a single, broadly general, “reduction” phenomenon. where σðAÞ is the set of eigenvalues of matrix A. perturbation theory ∣ positive semigroup ∣ reduction principle ∣ Karlin discovered that for MðαÞ≔½ð1 − αÞI þ αP, the spectral non-self-adjoint ∣ Schrödinger operator radius, rðMðαÞDÞ, is a decreasing function of the dispersal rate α, for arbitrary strongly connected dispersal pattern: he main result to be shown here is that the growth bound, TωðαA þ VÞ, of a positive semigroup generated by αA þ V Theorem 1. (Karlin's Theorem 5.2) [(16), pp. 194–196] Let P be changes with positive scalar α at a rate less than or equal to ωðAÞ, an arbitrary nonnegative irreducible stochastic matrix. Consider where A is also a generator, and V is an operator of multiplica- the family of matrices tion. Movement of a reactant in a heterogeneous environment is M α 1 − α I αP; 0 < α < 1. often of this form, where V represents the local growth or decay ð Þ¼ð Þ þ α rate, and represents the rate of mixing. Lossless mixing means Then for any diagonal matrix D with positive terms on the diagonal, ω 0 ω 0 ðAÞ¼ , while lossy mixing means ðAÞ < , so this result im- the spectral radius plies that greater mixing reduces the reactant’s asymptotic growth rate, or increases its asymptotic decay rate. Decreased growth or rðαÞ¼rðMðαÞDÞ increased decay are familiar results when A is a diffusion opera- α D ≠ I tor, so what is new here is the generality shown for this phenom- is decreasing as increases (strictly provided d ). ’ enon. At the root of this result is a theorem by Kingman on the Karlin s Theorem 5.2 means that greater mixing between sub- M α D “superconvexity” of the spectral radius of nonnegative matrices divisions produces lower rð ð Þ Þ, and if it goes below 1, the (1). The logical route progresses from Kingman through Cohen allele will go extinct. While this theorem was motivated by the (2) to Kato (3). The historical route begins in population genetics. issue of genetic diversity in a subdivided population, its form ap- In early theoretical work to understand the evolution of genet- plies generally to situations where differential growth is combined D ic systems, Feldman, colleagues, and others kept finding a com- with mixing. could just as well represent the investment returns P D mon result from each model they examined (4–14)—be they on different assets and a pattern of portfolio rebalancing. Or models for the evolution of recombination, or of mutation, or could represent the decay rates of reactant in different parts of a P of dispersal. Evolution favored reduced levels of these processes reactor, and a pattern of stirring within the reactor. In a very in populations near equilibrium under constant environments, general interpretation, Theorem 5.2 means that greater mixing and this result was called the Reduction Principle (11). reduces growth and hastens decay. α MATHEMATICS These results were found for finite-dimensional models. But If the dispersal rate is not an extrinsic parameter, but is a the same reduction result has also been found in models for variable which is itself controlled by a gene, then a gene which α the evolution of unconditional dispersal in continuous space, in decreases will have a growth advantage over its competitor which matrices are replaced by linear operators. This finding alleles. The action of such modifier genes produces a process raises the questions of whether this common result, discovered that will reduce the rates of dispersal in a population. Therefore, in such a diversity of models, reflects a single mathematical phe- Theorem 5.2 also means that differential growth selects for reduced nomenon. Here, the question is answered affirmatively. mixing. The mathematical underpinnings of the reduction principle In the evolutionary context, the generality of the mixing pat- P ’ for finite-dimensional models were discovered by Sam Karlin tern in Karlin s Theorem 5.2 makes it applicable to other kinds POPULATION BIOLOGY (15, 16) [although he did not realize it, and he had earlier pro- posed an alternate to the reduction principle—the mean fitness Dedicated to Sir John F. C. Kingman on the fiftieth anniversary of his theorem on the principle (17), which was found to have counterexamples (18)]. “superconvexity” of the spectral radius (1), which is at the root of the results presented Karlin wanted to understand the effect of population subdivision here. on the maintenance of genetic variation. Genetic variation is Author contributions: L.A. designed research, performed research, and wrote the paper. preserved if an allele has a positive growth rate when it is rare, The author declares no conflict of interest. protecting it from extinction. The dynamics of a rare allele are This article is a PNAS Direct Submission. approximately linear, and of the form 1E-mail: [email protected]. www.pnas.org/cgi/doi/10.1073/pnas.1113833109 PNAS ∣ March 6, 2012 ∣ vol. 109 ∣ no. 10 ∣ 3705–3710 Downloaded by guest on September 27, 2021 of “mixing” besides dispersal. The pattern matrix P can just as BðXÞ represents the set of all bounded linear operators well refer to the pattern of mutations between genotypes, and A: X → X. α P ⊂ then refers to the mutation rate. Or can represent the pattern A is a positive operator if AXþ X þ. of transmission when two loci recombine, and then α represents The resolvent of A is Rðξ;AÞ≔ðξ − AÞ−1, the operator inverse of the recombination rate. The early models for the evolution of ξ − A, ξ ∈ C. recombination and mutation that exhibited the reduction princi- The resolvent set ρðAÞ ⊂ C are those values of ξ for which ple in fact had the same form as Eq. 1 for the dynamics of a rare ξ − A is invertible. modifier allele. Once this commonality of form was recognized The spectrum of A ∈ BðXÞ, σðAÞ, is the complement of the (19–21), it was clear that Karlin’s theorem explained the repeated resolvent set, ρðAÞ. appearance of the reduction result in the different contexts, and The spectral bound of closed linear operator A, not necessarily generalized the result to a whole class of genetic transmission bounded, is patterns beyond the special cases that had been analyzed. The dynamics of movement in space have been long modeled supfReðλÞ: λ ∈ σðAÞg if σðAÞ ≠ ∅ sðAÞ≔ : by infinite-dimensional models, where space is continuous and −∞ if σðAÞ¼∅ the concentrations of a quantity at each point are represented as a function. The dynamics of change in the concentration are The type (growth bound) of an infinitesimal generator, A,ofa modeled as diffusions, where the Laplacian or elliptic differential tA 0 operator or nonlocal integral operator takes the place of the strongly continuous (C0) semigroup, fe : t> g,is matrix P in the finite-dimensional case. When the substance 1 grows or decays at rates that are a function of its location, the ωðAÞ≔lim log ∥etA∥ ¼ log rðeAÞ: t→∞ t system is often referred to as a reaction diffusion. In reaction- diffusion models for the evolution of dispersal, the reduction Generally, −∞ ≤ sðAÞ ≤ ωðAÞ < ∞, but conditions for principle again makes its appearance (22) (23, Lemma 5.2) (24, sðAÞ¼ωðAÞ or sðAÞ < ωðAÞ are part of a more involved theory Lemma 2.1) (25). In nonlocal diffusion models, again the reduc- for the asymptotic growth of semigroups (see refs. 36–38). tion principle appears (26). This repeated occurrence points to the possibility of an underlying mathematical unity. Here, a broad characterization of this “reduction phenomen- Definition 1: Operator A is resolvent positive if there is ξ0 such that on” is established by generalizing Karlin’s theorem to linear ðξ0;∞Þ ⊂ ρðAÞ and Rðξ;AÞ is positive for all ξ > ξ0 (39). operators. The reduction results previously found for various The relationship of the resolvent positive property to other linear operators are, therefore, seen to be special cases of a familiar operator properties includes the following list of key general phenomenon. This result is actually implicit in Kato’s generalization (3) of results: Cohen’s theorem (2) on the convexity of the spectral bound of ≥ 1.
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