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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
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Population switching under a time-varying environment.

Tom Israeli1, Michael Assaf1

  • 1Racah Institute of Physics, Hebrew University of Jerusalem, Jerusalem 91904, Israel.

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|March 15, 2020
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Summary
This summary is machine-generated.

Environmental variability impacts population establishment dynamics. This study quantifies changes in establishment probability and time due to environmental fluctuations, offering insights for ecological and genetic systems.

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Area of Science:

  • Ecology
  • Theoretical Biology
  • Biophysics

Background:

  • Population establishment is crucial in ecology and influenced by demographic and environmental variations.
  • Deterministic models show a critical population size for establishment.
  • Stochastic dynamics under time-varying environments are not fully understood.

Purpose of the Study:

  • To investigate the switching dynamics of stochastic populations in deterministically time-varying environments.
  • To quantify the impact of environmental variability on population establishment probability and mean establishment time.
  • To provide a theoretical framework applicable to ecological and gene regulatory network dynamics.

Main Methods:

  • Semiclassical approximation for computing establishment probability and mean establishment time.
  • Analysis of two environmental scenarios: temporary and periodic changes.
  • Verification using a modified Gillespie algorithm with time-dependent rates.

Main Results:

  • Environmental variability significantly alters population establishment probability and mean establishment time.
  • Analytical results provide exponential accuracy for changes induced by environmental fluctuations.
  • The modified Gillespie algorithm confirms the theoretical predictions.

Conclusions:

  • The study provides a robust theoretical framework for analyzing population dynamics under environmental variability.
  • The findings are relevant for understanding ecological population establishment and switching dynamics in gene regulatory networks.
  • The approach offers a method to predict system behavior under external environmental variations.