Studies of genetic data from old specimens – ancient DNA – offer a unique possibility to explore evolutionary history. Ancient DNA allows examination of past populations, extinct prehistoric species, and even large-scale analyses of past ecosystems. Since the beginnings of ancient DNA research in the mid 1980s, the field has undergone major technological and methodological transitions. However, investigations of the theory and methods used to analyse ancient DNA sequences have not kept up with the rapid pace of data generation. This thesis investigates a number of issues associated with the evolutionary analysis of ancient DNA. First, I built a model of post-mortem DNA degradation and examined its potential effects on the resulting sequence data (Chapter 2). Second, I conducted a large-scale study of time-dependent rates in order to look into their causes, characteristics, and ubiquity (Chapter 3). Third, I explored the impact of incorporating uncertainties associated with sample age into phylogenetic analysis (Chapters 4 and 5). Fourth, I evaluated the performance of Bayesian skyline plots in detecting recent population bottlenecks (Chapter 6). Finally, I combined several of these developments in a biogeographical analysis of the extinct cave bear (Chapter 7). My thesis shows that many of the common practices in ancient DNA studies, such as employing sequence-authentication criteria or ignoring age uncertainty in phylogenetic analyses, appear to be effective and to produce reliable results. However, there are areas in which more caution is needed when interpreting the results of DNA analyses. These include estimations of evolutionary rates, which are highly sensitive to the calibration points that are used; and the poor performance of Bayesian skyline reconstructions of recent population-size changes. The results of my studies improve our understanding of ancient DNA research and will serve as a useful guide for future evolutionary analyses of ancient DNA sequences
