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A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
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Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System
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Constructing stage-structured matrix population models from life tables: comparison of methods.

Masami Fujiwara1, Jasmin Diaz-Lopez1

  • 1Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX, United States of America.

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|November 1, 2017
PubMed
Summary

Converting age-structured vital rates to stage-structured population models can introduce bias. Aggregating survival rates using a weighted mean is best for population growth rate (λ) estimation, but stage-structured models struggle with generation time.

Keywords:
Euler-Lotka equationFecundity scheduleLefkovitch matrixLeslie matrixLife historyLife table analysisMatrix population modelsPopulation matrixSurvivorship

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Area of Science:

  • Population Ecology
  • Mathematical Biology
  • Conservation Biology

Background:

  • Matrix population models are crucial for assessing species status, using vital rates to calculate population growth rate (λ) and generation time.
  • Stage-structured models are increasingly used, often with vital rates derived from life table analyses.
  • Potential biases in converting age-structured data to stage-structured models remain unevaluated.

Purpose of the Study:

  • To assess the performance of methods for converting age-structured vital rates to stage-structured population models.
  • To investigate biases introduced by different conversion techniques using simulated life histories.
  • To compare λ and generation time estimates from age-structured and stage-structured models.

Main Methods:

  • Simulated life histories with varying traits and population growth rates (λ).
  • Calculated λ and generation time using Euler-Lotka equation, age-structured matrices, and various stage-structured matrix conversion methods.
  • Evaluated bias by comparing estimates from different model types.

Main Results:

  • Discretizing age and assuming fixed maturation age introduced minimal bias in λ and generation time.
  • Aggregating age-specific survival rates into stage-specific rates caused substantial bias, dependent on life history and λ.
  • Weighted arithmetic mean (discounted by λ) was most robust for survival rate aggregation; matching transition proportions (discounted by λ) was best for stage-transition rates.

Conclusions:

  • Stage-structured models showed poor performance for generation time estimation across all methods.
  • Age-structured models or the Euler-Lotka equation are recommended for accurate λ and generation time when life table data are available.
  • Age-structured vital rates should be converted to stage-structured models for subsequent analyses after initial accurate estimation.