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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
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Population Variability and Extinction Risk.

John A Vucetich1, Thomas A Waite2, Linda Qvarnemark2

  • 1School of Forestry, Michigan Technological University, Houghton, MI 49931, U.S.A.

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|June 15, 2022
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Summary
This summary is machine-generated.

Increased population variability (PV) is linked to higher extinction risk (ER). This study resolves conflicting data by showing that short-lived populations underestimate PV, obscuring the true positive relationship between PV and ER.

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Area of Science:

  • Ecology
  • Conservation Biology
  • Population Dynamics

Background:

  • Population models typically predict higher extinction risk (ER) with increased population variability (PV).
  • Empirical studies have yielded contradictory findings regarding the relationship between PV and ER.
  • Previous research has not adequately addressed statistical artifacts that may obscure this relationship.

Purpose of the Study:

  • To resolve the conflict in findings regarding the relationship between population variability and extinction risk.
  • To identify and correct for statistical biases that may lead to underestimation of PV in short-persistence populations.
  • To provide robust evidence for the link between PV and ER.

Main Methods:

  • Computer simulations to model the statistical artifact of underestimated PV.
  • Experimental analysis using laboratory populations of the bruchid beetle (Callosobruchus maculatus).
  • Reanalysis of published data on British island bird populations using Cox regression.
  • Analysis of time series data for naturally regulated animal populations.

Main Results:

  • Simulations confirmed that negative relationships between measured PV and ER are expected due to underestimation bias.
  • Experimental data supported the simulation findings.
  • Reanalysis of bird population data revealed a significant positive association between PV and ER (p = 0.03).
  • Analysis of animal population time series showed a positive association between PV and quasiextinction risk (p < 0.01).

Conclusions:

  • The study provides strong evidence that increased population variability leads to increased extinction risk.
  • Previous reports of negative or equivocal relationships are attributed to statistical artifacts.
  • Appropriate statistical methods are crucial for accurately assessing the relationship between PV and ER.