The past hidden in our genes : Combining archaeological and genetic methodology : Prehistoric population bottlenecks in Finland - Sundell, Tarja (Helsingin yliopisto, 2014)
This doctoral dissertation combines archaeological and genetic methodology to find evidence of prehistoric population bottleneck(s) in Finland. This was performed by applying forward-time population genetic simulations with archaeologically justified development of population size. We ran different simulation scenarios to evaluate genetic effects of past demographic events and the results were compared with the known population genetic features of the current gene pool. In this thesis the focus in the simulations was on uniparental markers, the mitochondrion and the Y chromosome. The simulations were carried out in the forward time population genetic simulation environment simuPOP. The prehistoric Finnish population has not been previously simulated to this extent with population genetic simulations. In addition to genetic simulations, we performed analyses of the Stone and Ceramic Artefact Database as well as the radiocarbon date database to find out temporal differences in the intensity of the archaeological signal. We found similar evidence for a marked increase in the archaeological signal 4000-3500 cal BC followed by a distinct weakening. Previously, the Stone Artefact Database has been used to study singular artefacts and a few artefact types exclusively. This is the first time the Database including thousands of objects belonging to several Mesolithic and Neolithic time periods has been data mined to a large extent. Furthermore, we carried out a Bayesian spatial analysis of radiocarbon datings and archaeological finds from Finland and ceded Karelia, to better understand the overall geographical distribution of human activity through time. The analyses were carried out within the framework of Bayesian spatial modelling, using the Besag-York-Mollie (BYM) model to build spatial distributions of various archaeological datasets in Finland. This model is based on image analysis and assumes similarity of neighbouring areas in geospatial applications. The methodology presented here is one of the first efforts of ...