# Estimating the evolution of human life history traits in age-structured populations

## TL;DR

This paper introduces a new method to estimate how life history traits like reproduction and survival evolve in age-structured populations.

## Contribution

The novelty is a Bayesian MCMC method that estimates selection responses of all vital rates using genetic and relatedness data.

## Key findings

- The method was demonstrated on a simulated model with two age classes.
- It can estimate traits such as age at first reproduction and total fertility.
- The approach links empirical analysis to dynamic models of natural selection.

## Abstract

I propose a method that estimates the selection response of all vital rates in an age-structured population. I assume that vital rates are determined by the additive genetic contributions of many loci. The method uses all relatedness information in the sample to inform its estimates of genetic parameters, via an MCMC Bayesian framework. One can use the results to estimate the selection response of any life history trait that is a function of the vital rates, including the age at first reproduction, total lifetime fertility, survival to adulthood, and others. This method closely ties the empirical analysis of life history evolution to dynamically complete models of natural selection, and therefore enjoys some theoretical advantages over other methods. I demonstrate the method on a simulated model of evolution with two age classes. Finally I discuss how the method can be extended to more complicated cases.