Population Dynamics

The Yamnaya archaeological complex appeared around 3300 BC across the steppes north of the Black and Caspian Seas, and by 3000 BC it reached its maximal extent, ranging from Hungary in the west to Kazakhstan in the east. To localize Yamnaya origins among the preceding Eneolithic people, we assembled ancient DNA from 435 individuals, demonstrating three genetic clines. A Caucasus-lower Volga (CLV) cline suffused with Caucasus hunter-gatherer1 ancestry extended between a Caucasus Neolithic southern end and a northern end at Berezhnovka along the lower Volga river. Bidirectional gene flow created intermediate populations, such as the north Caucasus Maikop people, and those at Remontnoye on the steppe. The Volga cline was formed as CLV people mixed with upriver populations of Eastern hunter-gatherer2 ancestry, creating hypervariable groups, including one at Khvalynsk. The Dnipro cline was formed when CLV people moved west, mixing with people with Ukraine Neolithic hunter-gatherer ancestry3 along the Dnipro and Don rivers to establish Serednii Stih groups, from whom Yamnaya ancestors formed around 4000 BC and grew rapidly after 3750-3350 BC. The CLV people contributed around four-fifths of the ancestry of the Yamnaya and, entering Anatolia, probably from the east, at least one-tenth of the ancestry of Bronze Age central Anatolians, who spoke Hittite4,5. We therefore propose that the final unity of the speakers of 'proto-Indo-Anatolian', the language ancestral to both Anatolian and Indo-European people, occurred in CLV people some time between 4400 BC and 4000 BC.

After a long-distance migration, Avars with Eastern Asian ancestry arrived in Eastern Central Europe in 567 to 568 CE and encountered groups with very different European ancestry1,2. We used ancient genome-wide data of 722 individuals and fine-grained interdisciplinary analysis of large seventh- to eighth-century CE neighbouring cemeteries south of Vienna (Austria) to address the centuries-long impact of this encounter1,2. We found that even 200 years after immigration, the ancestry at one site (Leobersdorf) remained dominantly East Asian-like, whereas the other site (Mödling) shows local, European-like ancestry. These two nearby sites show little biological relatedness, despite sharing a distinctive late-Avar culture3,4. We reconstructed six-generation pedigrees at both sites including up to 450 closely related individuals, allowing per-generation demographic profiling of the communities. Despite different ancestry, these pedigrees together with large networks of distant relatedness show absence of consanguinity, patrilineal pattern with female exogamy, multiple reproductive partnerships (for example, levirate) and direct correlation of biological connectivity with archaeological markers of social status. The generation-long genetic barrier was maintained by systematically choosing partners with similar ancestry from other sites in the Avar realm. Leobersdorf had more biological connections with the Avar heartlands than with Mödling, which is instead linked to another site from the Vienna Basin with European-like ancestry. Mobility between sites was mostly due to female exogamy pointing to different marriage networks as the main driver of the maintenance of the genetic barrier.

Roman writers found the relative empowerment of Celtic women remarkable1. In southern Britain, the Late Iron Age Durotriges tribe often buried women with substantial grave goods2. Here we analyse 57 ancient genomes from Durotrigian burial sites and find an extended kin group centred around a single maternal lineage, with unrelated (presumably inward migrating) burials being predominantly male. Such a matrilocal pattern is undescribed in European prehistory, but when we compare mitochondrial haplotype variation among European archaeological sites spanning six millennia, British Iron Age cemeteries stand out as having marked reductions in diversity driven by the presence of dominant matrilines. Patterns of haplotype sharing reveal that British Iron Age populations form fine-grained geographical clusters with southern links extending across the channel to the continent. Indeed, whereas most of Britain shows majority genomic continuity from the Early Bronze Age to the Iron Age, this is markedly reduced in a southern coastal core region with persistent cross-channel cultural exchange3. This southern core has evidence of population influx in the Middle Bronze Age but also during the Iron Age. This is asynchronous with the rest of the island and points towards a staged, geographically granular absorption of continental influence, possibly including the acquisition of Celtic languages.

People at different ages and with different educational background have different migration patterns. However, international migration flows by age and educational attainment are scarce and not always comparable. We use a super learning algorithm with multiple initial models to estimate age and education composition of male and female immigrants for 199 countries over six five-year periods from 1990-1995 to 2015-2020. We access the performance of our approach through cross-validation and comparing initial learners with the super learner algorithm using multiple evaluation metrics. We also compare our age composition estimates against equivalent measures from Eurostat. The estimates indicate that while the proportion of immigration flows with higher education increase in all regions, Europe has the highest immigration flow with secondary or higher education.