Populations in environments with a soft carrying capacity are eventually extinct
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Mathematical Biology
Populations in environments with a soft carrying capacity are eventually extinct Peter Jagers1
· Sergei Zuyev1
Received: 2 May 2020 / Revised: 3 August 2020 © The Author(s) 2020
Abstract Consider a population whose size changes stepwise by its members reproducing or dying (disappearing), but is otherwise quite general. Denote the initial (non-random) size by Z 0 and the size of the nth change by Cn , n = 1, 2, . . .. Population sizes hence develop successively as Z 1 = Z 0 + C1 , Z 2 = Z 1 + C2 and so on, indefinitely or until there are no further size changes, due to extinction. Extinction is thus assumed final, so that Z n = 0 implies that Z n+1 = 0, without there being any other finite absorbing class of population sizes. We make no assumptions about the time durations between the successive changes. In the real world, or more specific models, those may be of varying length, depending upon individual life span distributions and their interdependencies, the age-distribution at hand and intervening circumstances. We could consider toy models of Galton–Watson type generation counting or of the birth-and-death type, with one individual acting per change, until extinction, or the most general multitype CMJ branching processes with, say, population size dependence of reproduction. Changes may have quite varying distributions. The basic assumption is that there is a carrying capacity, i.e. a non-negative number K such that the conditional expectation of the change, given the complete past history, is non-positive whenever the population exceeds the carrying capacity. Further, to avoid unnecessary technicalities, we assume that the change Cn equals -1 (one individual dying) with a conditional (given the past) probability uniformly bounded away from 0. It is a simple and not very restrictive way to avoid parity phenomena, it is related to irreducibility in Markov settings. The straightforward, but in contents and implications far-reaching, consequence is that all such populations must die out. Mathematically, it follows by a supermartingale convergence property and positive probability of reaching the absorbing extinction state. Keywords Population dynamics · Extinction · Martingales · Stochastic stability
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Peter Jagers [email protected]
Extended author information available on the last page of the article
123
P. Jagers, S. Zuyev
Mathematics Subject Classification 92D25 · 60G42 · 60K40
1 “All surnames tend to be lost” Almost a century and a half have passed since Galton (1873) and Galton and Watson (1875) introduced their famous simple branching process followed by the infamous conclusion that all families (“surnames”) must die out: “All surnames tend to extinction […] and this result might have been anticipated, for a surname lost can never be recovered.” Since long it is textbook knowledge, that the extinction probability of supercritical Galton–Watson (and more general) branching processes is less than one, the alternative to extinction being unbounded exponential growth. For a loose
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