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- Jagers, Peter, 1941, et al.
(författare)
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Amendment to: populations in environments with a soft carrying capacity are eventually extinct
- 2021
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Ingår i: Journal of Mathematical Biology. - : Springer Science and Business Media LLC. - 0303-6812 .- 1432-1416. ; 83:1
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Tidskriftsartikel (refereegranskat)abstract
- This sharpens the result in the paperJagers and Zuyev (J Math Biol 81:845-851, 2020): consider a population changing at discrete (but arbitrary and possibly random) time points, the conditional expected change, given the complete past population history being negative, whenever population size exceeds a carrying capacity. Further assume that there is an epsilon > 0 such that the conditional probability of a population decrease at the next step, given the past, always exceeds epsilon if the population is not extinct but smaller than the carrying capacity. Then the population must die out.
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| 2. |
- Jagers, Peter, 1941, et al.
(författare)
-
Populations in environments with a soft carrying capacity are eventually extinct
- 2020
-
Ingår i: Journal of Mathematical Biology. - : Springer Science and Business Media LLC. - 0303-6812 .- 1432-1416. ; 81:3, s. 845 -851
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Tidskriftsartikel (refereegranskat)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 C-n, n = 1, 2, .... Population sizes hence develop successively as Z(1) = Z(0) + C-1, Z(2) = Z(1)+ C-2 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 C-n 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.
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