Estimating Density Dependence, Environmental Variance, and Long-Term Selection on a Stage-Structured Life History

Area of Science:

• Ecology
• Population Biology
• Evolutionary Biology

Background:

• Analyzing long-term population data in stochastic environments is challenging.
• Comparing populations and species with diverse life histories requires robust methods.
• Density dependence and environmental stochasticity significantly impact population dynamics.

Purpose of the Study:

• To develop a method for analyzing density-dependent, stage-structured populations in stochastic environments.
• To facilitate comparisons across populations and species with different life histories.
• To estimate key demographic parameters and evaluate long-term selection gradients.

Main Methods:

• Approximating population dynamics as a univariate stochastic process.
• Modeling density dependence via a weighted sum of stage abundances (N).
• Estimating parameters: density-independent growth rate, net density dependence, and environmental variance.

Main Results:

• The method accurately predicts the mean, coefficient of variation, and fluctuation rate of population size (N).
• Key parameters governing population dynamics were successfully estimated.
• Long-term selection gradients on life history traits were evaluated using sensitivities.

Conclusions:

• The derived method provides a robust framework for analyzing complex population dynamics.
• It enables effective comparison of populations and species with varying life histories.
• The approach elucidates the interplay between life history, density dependence, and environmental stochasticity in evolution.