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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

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Simulations of Life History Variation for Demographic Inference From Population Genomic Data.

Rilquer Mascarenhas^1,2, Michael J Hickerson^1,2,3, Ana Carolina Carnaval^1,2

¹The City College of New York, New York City, New York, USA.

Molecular Ecology

|November 5, 2025

Summary

This summary is machine-generated.

Life history strategies significantly impact genetic diversity patterns, influencing demographic inference. Incorporating these traits improves population models for species with varying reproductive and survival strategies.

Keywords:

Amazon forest age‐structured populations comparative phylogeography nucleotide diversity polygamy

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Area of Science:

Evolutionary biology
Population genetics
Ecological genetics

Background:

Species' ecological differences, especially dispersal and life history, shape population responses to environmental shifts.
Genetic simulations enable modeling past demographic changes, but life history impacts are less studied than dispersal.

Purpose of the Study:

To investigate how life history traits (age at first reproduction, adult mortality, mating system) create predictable differences in genetic diversity.
To assess the utility of genetic summary statistics for inferring life history-driven demographic changes.
To estimate population parameters using empirical data from species with distinct life histories.

Main Methods:

Individual-based simulations of non-Wright-Fisher populations to model genetic diversity under varying life history traits.
Random Forest modeling to estimate population parameters (variance in reproductive success, generation time, effective population size) from genome-wide SNP data.
Application of methods to two bird species with known differences in life history strategies.

Main Results:

Life history variation predictably alters genetic diversity patterns: traits like polygamy, longevity, and late reproduction correlate with higher reproductive success variance, longer generation times, smaller effective population sizes, and lower genetic diversity.
Empirical data from bird species align with simulation results, showing expected demographic parameter differences based on life history.
Genetic summary statistics clearly reflect life history differences.

Conclusions:

Life history traits leave discernible signals in genetic diversity, crucial for demographic inference.
Integrating life history information into simulation- and model-based demographic inference will enhance multi-species analyses.
Understanding life history variation is key to accurately interpreting population genetic data and predicting responses to environmental change.