Literature DB >> 17405862

On the path to extinction.

Peter Jagers1, Fima C Klebaner, Serik Sagitov.   

Abstract

Populations can die out in many ways. We investigate one basic form of extinction, stable or intrinsic extinction, caused by individuals on the average not being able to replace themselves through reproduction. The archetypical such population is a subcritical branching process, i.e., a population of independent, asexually reproducing individuals, for which the expected number of progeny per individual is less than one. The main purpose is to uncover a fundamental pattern of nature. Mathematically, this emerges in large systems, in our case subcritical populations, starting from a large number, x, of individuals. First we describe the behavior of the time to extinction T: as x grows to infinity, it behaves like the logarithm of x, divided by r, where r is the absolute value of the Malthusian parameter. We give a more precise description in terms of extreme value distributions. Then we study population size partway (or u-way) to extinction, i.e., at times uT, for 0 < u < 1, e.g., u = 1/2 gives halfway to extinction. (Note that mathematically this is no stopping time.) If the population starts from x individuals, then for large x, the proper scaling for the population size at time uT is x into the power u - 1. Normed by this factor, the population u-way to extinction approaches a process, which involves constants that are determined by life span and reproduction distributions, and a random variable that follows the classical Gumbel distribution in the continuous time case. In the Markov case, where an explicit representation can be deduced, we also find a description of the behavior immediately before extinction.

Mesh:

Year:  2007        PMID: 17405862      PMCID: PMC1847507          DOI: 10.1073/pnas.0610816104

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  4 in total

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Journal:  Math Biosci       Date:  2011-10-06       Impact factor: 2.144

2.  A plea for stochastic population dynamics.

Authors:  Peter Jagers
Journal:  J Math Biol       Date:  2010-03-06       Impact factor: 2.259

3.  Predicting colorectal cancer risk from adenoma detection via a two-type branching process model.

Authors:  Brian M Lang; Jack Kuipers; Benjamin Misselwitz; Niko Beerenwinkel
Journal:  PLoS Comput Biol       Date:  2020-02-05       Impact factor: 4.475

4.  Generalized drivers in the mammalian endangerment process.

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Journal:  PLoS One       Date:  2014-02-26       Impact factor: 3.240

  4 in total

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