MDIV

MDIV estimates divergence times and migration rates between two populations using Markov chain Monte Carlo under infinite-sites and Hasegawa-Kishino-Yano (HKY) finite-sites models to provide likelihood or Bayesian parameter inference.

Key Features:

• Simultaneous Estimation: Joint estimation of divergence time and migration rate between two populations using the same inference framework.
• Model Flexibility: Supports both the infinite-sites model and the HKY finite-sites substitution model for sequence data.
• Migration and Ancestry Testing: Tests hypotheses of ongoing migration versus recent shared ancestry and provides parameter estimates relevant to these hypotheses.
• Maximum Likelihood and Bayesian Inference: Generates maximum likelihood or Bayesian estimates of demographic parameters including population sizes and divergence times.
• MCMC Methodology: Employs Markov chain Monte Carlo to jointly estimate multiple demographic parameters.
• Application to Nonrecombining Loci: Demonstrates power to test migration hypotheses using single nonrecombining loci, such as mitochondrial DNA sequences.

Scientific Applications:

• Population Genetics and Evolutionary History: Estimating divergence times and gene flow to infer historical relationships between populations.
• Speciation and Adaptation Studies: Assessing migration and divergence patterns relevant to speciation and adaptive evolution.
• Conservation Genetics: Informing conservation strategies by quantifying population divergence and ongoing gene flow.
• Empirical Sequence Analysis: Applied to mitochondrial DNA data sets such as threespine stickleback (Gasterosteus aculeatus) to investigate population structure and history.

Methodology:

MDIV applies Markov chain Monte Carlo (MCMC) to jointly estimate divergence time, migration rate, and population sizes under either an infinite-sites model or the Hasegawa-Kishino-Yano (HKY) finite-sites model, producing maximum likelihood or Bayesian parameter estimates.