title: Using phylochronology to reveal cryptic population histories: review and synthesis of 29 ancient DNA studies.
abstract: The evolutionary history of a population involves changes in size, movements and selection pressures through time. Reconstruction of population history based on modern genetic data tends to be averaged over time or to be biased by generally reflecting only recent or extreme events, leaving many population historic processes undetected. Temporal genetic data present opportunities to reveal more complex population histories and provide important insights into what processes have influenced modern genetic diversity. Here we provide a synopsis of methods available for the analysis of ancient genetic data. We review 29 ancient DNA studies, summarizing the analytical methods and general conclusions for each study. Using the serial coalescent and a model-testing approach, we then re-analyse data from two species represented by these data sets in a common interpretive framework. Our analyses show that phylochronologic data can reveal more about population history than modern data alone, thus revealing 'cryptic' population processes, and enable us to determine whether simple or complex models best explain the data. Our re-analyses point to the need for novel methods that consider gene flow, multiple populations and population size in reconstruction of population history. We conclude that population genetic samples over large temporal and geographical scales, when analysed using more complex models and the serial coalescent, are critical to understand past population dynamics and provide important tools for reconstructing the evolutionary process.
authors.affiliation: National Centre for Biological Sciences, TIFR, GKVK Campus, Bangalore, India.
authors.forename: Uma
authors.initials: U
authors.lastname: Ramakrishnan
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Hadly
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D019143:Evolution, Molecular; D005580:Fossils; D051456:Gene Flow; D014644:Genetic Variation; D005828:Genetics, Population; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D011156:Population Density; D011157:Population Dynamics; D017422:Sequence Analysis, DNA / Q000379:methods*