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Simulation of Scenarios of a Deep Population Crisis in a Rapidly Growing Population

Abstract—This article focuses on the modeling of crisis and threshold development of the population process during the formation of a new population in a competitive environment. As a population spreads, a deep population crisis may arise as a result an abrupt triggering of biotic countermeasures be...

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Autor principal: Perevaryukha, A. Yu.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Pleiades Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831010/
https://www.ncbi.nlm.nih.gov/pubmed/35194226
http://dx.doi.org/10.1134/S0006350921060130
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author Perevaryukha, A. Yu.
author_facet Perevaryukha, A. Yu.
author_sort Perevaryukha, A. Yu.
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description Abstract—This article focuses on the modeling of crisis and threshold development of the population process during the formation of a new population in a competitive environment. As a population spreads, a deep population crisis may arise as a result an abrupt triggering of biotic countermeasures before resources for a further increase in population size are exhausted. A bottleneck occurred in the history of many populations, including humans at the time of the Neolithic crash in Europe. Invaders with high reproductive potential often exert deleterious effects on biosystems. The emergence of efficient competition can not only cause classical cyclical fluctuations, but also lead to a complete extinction of the population after a series of high peaks in its abundance. Two alternative scenarios provide classical examples of induced population crises. One was observed in Gause’s experiments where an introduction of a predatory ciliate drove another ciliate species to extinction. The other scenario was observed in a series of experiments where bacteriophages were introduced into colonies of actively dividing bacteria that had a dynamically adapting antiviral mechanism. In this work, modifications to the model were proposed to describe the actual scenarios of crisis effects in population dynamics. Equations with deviating arguments in the time variable allowed a threshold effect of conditions on reproduction of the invasive species and an aggregated nature of the lagging regulation with two time factors. The computational scenarios described both completion of the process after a repeated outbreak and successful elimination of the population crisis via rapid adaptation. Deep crisis phenomena are characteristic of local population dynamics when organisms interact with viruses that are new to them.
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spelling pubmed-88310102022-02-18 Simulation of Scenarios of a Deep Population Crisis in a Rapidly Growing Population Perevaryukha, A. Yu. Biophysics (Oxf) Cell Biophysics Abstract—This article focuses on the modeling of crisis and threshold development of the population process during the formation of a new population in a competitive environment. As a population spreads, a deep population crisis may arise as a result an abrupt triggering of biotic countermeasures before resources for a further increase in population size are exhausted. A bottleneck occurred in the history of many populations, including humans at the time of the Neolithic crash in Europe. Invaders with high reproductive potential often exert deleterious effects on biosystems. The emergence of efficient competition can not only cause classical cyclical fluctuations, but also lead to a complete extinction of the population after a series of high peaks in its abundance. Two alternative scenarios provide classical examples of induced population crises. One was observed in Gause’s experiments where an introduction of a predatory ciliate drove another ciliate species to extinction. The other scenario was observed in a series of experiments where bacteriophages were introduced into colonies of actively dividing bacteria that had a dynamically adapting antiviral mechanism. In this work, modifications to the model were proposed to describe the actual scenarios of crisis effects in population dynamics. Equations with deviating arguments in the time variable allowed a threshold effect of conditions on reproduction of the invasive species and an aggregated nature of the lagging regulation with two time factors. The computational scenarios described both completion of the process after a repeated outbreak and successful elimination of the population crisis via rapid adaptation. Deep crisis phenomena are characteristic of local population dynamics when organisms interact with viruses that are new to them. Pleiades Publishing 2022-02-10 2021 /pmc/articles/PMC8831010/ /pubmed/35194226 http://dx.doi.org/10.1134/S0006350921060130 Text en © Pleiades Publishing, Inc. 2021, ISSN 0006-3509, Biophysics, 2021, Vol. 66, No. 6, pp. 974–991. © Pleiades Publishing, Inc., 2021.Russian Text © The Author(s), 2021, published in Biofizika, 2021, Vol. 66, No. 6, pp. 1144–1163. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Cell Biophysics
Perevaryukha, A. Yu.
Simulation of Scenarios of a Deep Population Crisis in a Rapidly Growing Population
title Simulation of Scenarios of a Deep Population Crisis in a Rapidly Growing Population
title_full Simulation of Scenarios of a Deep Population Crisis in a Rapidly Growing Population
title_fullStr Simulation of Scenarios of a Deep Population Crisis in a Rapidly Growing Population
title_full_unstemmed Simulation of Scenarios of a Deep Population Crisis in a Rapidly Growing Population
title_short Simulation of Scenarios of a Deep Population Crisis in a Rapidly Growing Population
title_sort simulation of scenarios of a deep population crisis in a rapidly growing population
topic Cell Biophysics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831010/
https://www.ncbi.nlm.nih.gov/pubmed/35194226
http://dx.doi.org/10.1134/S0006350921060130
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