Inca and Spanish Empires had a profound impact on Peruvian demography


Open access Evolutionary genomic dynamics of Peruvians before, during, and after the Inca Empire by Harris et al., PNAS (2018) 201720798 (published ahead of print).

Abstract (emphasis mine):

Native Americans from the Amazon, Andes, and coastal geographic regions of South America have a rich cultural heritage but are genetically understudied, therefore leading to gaps in our knowledge of their genomic architecture and demographic history. In this study, we sequence 150 genomes to high coverage combined with an additional 130 genotype array samples from Native American and mestizo populations in Peru. The majority of our samples possess greater than 90% Native American ancestry, which makes this the most extensive Native American sequencing project to date. Demographic modeling reveals that the peopling of Peru began ∼12,000 y ago, consistent with the hypothesis of the rapid peopling of the Americas and Peruvian archeological data. We find that the Native American populations possess distinct ancestral divisions, whereas the mestizo groups were admixtures of multiple Native American communities that occurred before and during the Inca Empire and Spanish rule. In addition, the mestizo communities also show Spanish introgression largely following Peruvian Independence, nearly 300 y after Spain conquered Peru. Further, we estimate migration events between Peruvian populations from all three geographic regions with the majority of between-region migration moving from the high Andes to the low-altitude Amazon and coast. As such, we present a detailed model of the evolutionary dynamics which impacted the genomes of modern-day Peruvians and a Native American ancestry dataset that will serve as a beneficial resource to addressing the underrepresentation of Native American ancestry in sequencing studies.

Admixture among Peruvian populations. (A) Colors represent contributions from donor populations into the genomes of Peruvian mestizo groups, as estimated by CHROMOPAINTER and GLOBETROTTER. The label within parentheses for each Peruvian Native American source population corresponds to their geographic region where Ama, And, and Coa represent Amazon, Andes, and coast, respectively. (B) Admixture time and proportion for the best fit three-way ancestry (AP, Trujillo and Lima) and two-way ancestry (Iquitos, Cusco, and Puno) TRACT models [European, African, and Native American (NatAm) ancestries] for six mestizo populations. (C) Network of individuals from Peruvian Native American and mestizo groups according to their shared IBD length. Each node is an individual and the length of an edge equals to (1/total shared IBD). IBD segments with different lengths are summed according to different thresholds representing different times in the past (52), with 7.8 cM, 9.3 cM, and 21.8 cM roughly representing the start of the Inca Empire, the Spanish conquest and occupation, and Peruvian independence. IBD networks are generated by Cytoscape (98) and only the major clusters in the network are shown for different cutoffs of segment length. AP, Central Am, and Matsig are short for Afroperuvians, Central American, and Matsiguenka, respectively. The header of each IBD network specifies the length of IBD segments used in each network.

Interesting excerpts

The high frequency of Native American mitochondrial haplotypes suggests that European males were the primary source of European admixture with Native Americans, as previously found (23, 24, 41, 42). The only Peruvian populations that have a proportion of the Central American component are in the Amazon (Fig. 2A). This is supported by Homburger et al. (4), who also found Central American admixture in other Amazonian populations and could represent ancient shared ancestry or a recent migration between Central America and the Amazon.

Following the peopling of Peru, we find a complex history of admixture between Native American populations from multiple geographic regions (Figs. 2B and 3 A and C). This likely began before the Inca Empire due to Native American and mestizo groups sharing IBD segments that correspond to the time before the Inca Empire. However, the Inca Empire likely influenced this pattern due to their policy of forced migrations, known as “mitma” (mitmay in Quechua) (28, 31, 37), which moved large numbers of individuals to incorporate them into the Inca Empire. We can clearly see the influence of the Inca through IBD sharing where the center of dominance in Peru is in the Andes during the Inca Empire (Fig. 3C).

ASPCA of combined Peruvian Genome Project with the HGDP genotyped on the Human Origins Array. A.) European ancestry. B.) African ancestry. Samples are filtered by their corresponding ancestral proportion: European ≥ 30% (panel A) and African ≥ 10% (panel B). The two plots in each panel are identical except for the color scheme: reference populations are colored on the left and Peruvian populations are colored on the right. Each point is one haplotype. In the African ASPCA we note three outliers among our samples, two from Trujillo and one from Iquitos, that cluster closer to the Luhya and Luo populations, though not directly. It is likely that these individuals share ancestry with other regions of Africa in addition to western Africa, but we cannot test this hypothesis explicitly as we have too few samples.

A similar policy of large-scale consolidation of multiple Native American populations was continued during Spanish rule through their program of reducciones, or reductions (31, 32), which is consistent with the hypothesis that the Inca and Spanish had a profound impact on Peruvian demography (25). The result of these movements of people created early New World cosmopolitan communities with genetic diversity from the Andes, Amazon, and coast regions as is evidenced by mestizo populations’ ancestry proportions (Fig. 3A). Following Peruvian independence, these cosmopolitan populations were those same ones that predominantly admixed with the Spanish (Fig. 3B). Therefore, this supports our model that the Inca Empire and Spanish colonial rule created these diverse populations as a result of admixture between multiple Native American ancestries, which would then go on to become the modern mestizo populations by admixing with the Spanish after Peruvian independence.

Further, it is interesting that this admixture began before the urbanization of Peru (26) because others suspected the urbanization process would greatly impact the ancestry patterns in these urban centers (25). (…)


Improving environmental conditions favoured higher local population density, which favoured domestication


New paper (behind paywall) Hindcasting global population densities reveals forces enabling the origin of agriculture, by Kavanagh et al., Nature Human Behaviour (2018)

Abstract (emphasis mine):

The development and spread of agriculture changed fundamental characteristics of human societies1,2,3. However, the degree to which environmental and social conditions enabled the origins of agriculture remains contested4,5,6. We test three hypothesized links between the environment, population density and the origins of plant and animal domestication, a prerequisite for agriculture: (1) domestication arose as environmental conditions improved and population densities increased7 (surplus hypothesis); (2) populations needed domestication to overcome deteriorating environmental conditions (necessity hypothesis)8,9; (3) factors promoting domestication were distinct in each location10 (regional uniqueness hypothesis). We overcome previous data limitations with a statistical model, in which environmental, geographic and cultural variables capture 77% of the variation in population density among 220 foraging societies worldwide. We use this model to hindcast potential population densities across the globe from 21,000 to 4,000 years before present. Despite the timing of domestication varying by thousands of years, we show that improving environmental conditions favoured higher local population densities during periods when domestication arose in every known agricultural origin centre. Our results uncover a common, global factor that facilitated one of humanity’s most significant innovations and demonstrate that modelling ancestral demographic changes can illuminate major events deep in human history.

Path diagram for piecewise-SEM exploring the effects of environmental and cultural variables on population densities of foraging societies. Measured variables are represented by the large boxes and R2 GLMM values (see Methods) are provided for response variables. n = 220. Red arrows depict negative relationships among variables, black arrows positive relationships, and dashed grey arrows depict non-significant paths (P ≥ 0.05). Standardized coefficients are presented for all paths (small boxes) and arrow widths are scaled to reflect the magnitude of path coefficients.

Interesting excerpts:

(…) our results are consistent with the surplus hypothesis, which suggests that improving environmental conditions and the potential for increased population density may have facilitated the domestication of plants and animals in agricultural origin centres4,7 (Fig. 3). Several factors may explain the links between environmental conditions, potential population density and the origin of domestication. For one, rates of innovation may scale positively with the number of potential innovators13,14. In turn, the likelihood of domestication innovations may have increased in environments that could support increasingly higher densities of foraging people.

In addition, foraging societies may have become more sedentary to take advantage of locally abundant resources, some of which were later domesticated35. Our results indicate that residential mobility scales negatively with population density in foraging societies (Fig. 1). Therefore, increasingly sedentary lifestyles may have contributed further to increases in population density and the potential for innovation. Increases in the productivity of wild progenitors of important domesticates may have also facilitated growing population densities and the viability of cultivation for food production15,16.

Predictions of potential population density for foragers. a–c, Predicted population densities at 4,000 (a), 10,000 (b) and 21,000 (c) YBP. Blue hues depict potential population densities below the median population density of observed foraging societies, and red hues depict potential population densities above the median. The second red hue and above are greater than the mean population density of observed foraging societies. Note the increase in area, through time, with potential population densities greater than the mean of observed foraging societies (number of 0.5° × 0.5° cells: 21,000 YBP = 3,027; 4,000 YBP = 4,673). For example, a notable increase in the number of red cells in the Sudanic savannah and Ganges of East India (Northeast India) between panels c and a.

It is also possible that improving environmental conditions may have resulted in a situation where necessity drove the origins of domestication. For example, population densities may have increased in foraging societies that occupied productive, coastal areas, causing an outflow of groups into regions with less ideal conditions where the cultivation of plants and animals was required to secure adequate food resources6,17,18. Our results cannot support, or refute, the possible influence the outflow of people from hospitable locations to less ideal environments may have played. A detailed understanding of the movements of ancient populations is required for more rigorous testing of the role that forced habitation of marginal environments may have played in the origins of domestication at particular sites.

See also:

Bayesian estimation of partial population continuity by using ancient DNA and spatially explicit simulations


Open access Bayesian estimation of partial population continuity by using ancient DNA and spatially explicit simulations, by Silva et al., Evolutionary Applications (2018).

Abstract (emphasis mine):

The retrieval of ancient DNA from osteological material provides direct evidence of human genetic diversity in the past. Ancient DNA samples are often used to investigate whether there was population continuity in the settlement history of an area. Methods based on the serial coalescent algorithm have been developed to test whether the population continuity hypothesis can be statistically rejected by analysing DNA samples from the same region but of different ages. Rejection of this hypothesis is indicative of a large genetic shift, possibly due to immigration occurring between two sampling times. However, this approach is only able to reject a model of full continuity model (a total absence of genetic input from outside), but admixture between local and immigrant populations may lead to partial continuity. We have recently developed a method to test for population continuity that explicitly considers the spatial and temporal dynamics of populations. Here we extended this approach to estimate the proportion of genetic continuity between two populations, by using ancient genetic samples. We applied our original approach to the question of the Neolithic transition in Central Europe. Our results confirmed the rejection of full continuity, but our approach represents an important step forward by estimating the relative contribution of immigrant farmers and of local hunter‐gatherers to the final Central European Neolithic genetic pool. Furthermore, we show that a substantial proportion of genes brought by the farmers in this region were assimilated from other hunter‐gatherer populations along the way from Anatolia, which was not detectable by previous continuity tests. Our approach is also able to jointly estimate demographic parameters, as we show here by finding both low density and low migration rate for pre‐Neolithic hunter‐gatherers. It provides a useful tool for the analysis of the numerous aDNA datasets that are currently being produced for many different species.

A) Different zones defined for computing proportions of ancestry in Central Europeans 4,500 BP. B) Schematic representation of various population contributions. C) Mean proportions of ancestry from the various PHG zones (A+B+C+D) in Central European populations from zone A at the end of the Neolithic transition 4,500 BP, computed for autosomal and mitochondrial markers.

Relevant excerpts:

Our results are in general accordance with two distinct ancestry components that have previously been detected at the continental scale by Lazaridis, Patterson et al. (2014): the “early European farmer” (EEF), which corresponds here to the NFA from Anatolia (zone C in Figure 3), and the “West European hunter-gatherer” (WHG), which corresponds here to the PHG from zones A and B in Figure 3. Notably, the contribution of an Ancient North Eurasians (ANE) component is not included in our model as we did not consider potential post-Neolithic immigration waves, which could have contributed to the modern European genetic pool, such as the wave that came from the Pontic steppes and was associated with the Yamnaya culture (Haak, Lazaridis et al. 2015). Without considering the ANE ancestry component, our estimate of the autosomal genetic contribution of Early farmers to the gene pool of Central European populations (25%) tends to be lower than the EEF ancestry estimated in most modern Western European populations, but is of the same order than the estimations in modern Estonians and in the ancient Late Neolithic genome “Karsdorf” from Germany (Lazaridis, Patterson et al. 2014, Haak, Lazaridis et al. 2015). Note that the contribution of hunter-gatherers to Neolithic communities appears to be variable in different regions of Europe (Skoglund, Malmstrom et al. 2012, Brandt, Haak et al. 2013, Lazaridis, Patterson et al. 2014), while we computed an average value for Central Europe. Moreover, we computed the ancestry of the two groups at the end of the Neolithic period while previous studies estimated it in modern times. Finally, previous studies used molecular information to directly estimate admixture proportions, while we use molecular information to estimate the model parameters and, then, we computed the expected genetic contributions of both groups using the best parameters, without using molecular information during this second step. Model assumptions may thus influence the inferences on the relative genetic contribution of both groups. In particular, we made the assumption of a uniform expansion of NFA with constant and similar assimilation of PHG over the whole continent but spatio-temporally heterogeneous environment, variable assimilation rate and long distance dispersal may have played an important role. The effects of those factors should be investigated in future studies.

Population structure in Argentina shows most European sources of South European origin


Open access Population structure in Argentina, by Muzzio et al., PLOS One (2018).

Abstract (emphasis mine):

We analyzed 391 samples from 12 Argentinian populations from the Center-West, East and North-West regions with the Illumina Human Exome Beadchip v1.0 (HumanExome-12v1-A). We did Principal Components analysis to infer patterns of populational divergence and migrations. We identified proportions and patterns of European, African and Native American ancestry and found a correlation between distance to Buenos Aires and proportion of Native American ancestry, where the highest proportion corresponds to the Northernmost populations, which is also the furthest from the Argentinian capital. Most of the European sources are from a South European origin, matching historical records, and we see two different Native American components, one that spreads all over Argentina and another specifically Andean. The highest percentages of African ancestry were in the Center West of Argentina, where the old trade routes took the slaves from Buenos Aires to Chile and Peru. Subcontinentaly, sources of this African component are represented by both West Africa and groups influenced by the Bantu expansion, the second slightly higher than the first, unlike North America and the Caribbean, where the main source is West Africa. This is reasonable, considering that a large proportion of the ships arriving at the Southern Hemisphere came from Mozambique, Loango and Angola.

Principal component analysis.
On the x axis is PC 1 while PC2 is the y axis. Plus symbols represent Argentinian samples and circles are for reference panels. Fig 2a (left) Argentinians with YRI and LWK for African references (“African”), IBS and TSI for European references (“European”) and the PEL, MXL, PUR and CLM as a Latin American references. Fig 2b (right) samples from Argentina with IBS, MXL, CLM and PEL.


Population turnover in Remote Oceania shortly after initial settlement


Open Access article Population Turnover in Remote Oceania Shortly after Initial Settlement, by Lipson, Skoglund, Spriggs, et al. (2018), based on the recent preprint at bioRxiv.


Ancient DNA from Vanuatu and Tonga dating to about 2,900–2,600 years ago (before present, BP) has revealed that the “First Remote Oceanians” associated with the Lapita archaeological culture were directly descended from the population that, beginning around 5000 BP, spread Austronesian languages from Taiwan to the Philippines, western Melanesia, and eventually Remote Oceania. Thus, ancestors of the First Remote Oceanians must have passed by the Papuan-ancestry populations they encountered in New Guinea, the Bismarck Archipelago, and the Solomon Islands with minimal admixture [ 1 ]. However, all present-day populations in Near and Remote Oceania harbor >25% Papuan ancestry, implying that additional eastward migration must have occurred. We generated genome-wide data for 14 ancient individuals from Efate and Epi Islands in Vanuatu from 2900–150 BP, as well as 185 present-day individuals from 18 islands. We find that people of almost entirely Papuan ancestry arrived in Vanuatu by around 2300 BP, most likely reflecting migrations a few hundred years earlier at the end of the Lapita period, when there is also evidence of changes in skeletal morphology and cessation of long-distance trade between Near and Remote Oceania [ 2, 3 ]. Papuan ancestry was subsequently diluted through admixture but remains at least 80%–90% in most islands. Through a fine-grained analysis of ancestry profiles, we show that the Papuan ancestry in Vanuatu derives from the Bismarck Archipelago rather than the geographically closer Solomon Islands. However, the Papuan ancestry in Polynesia—the most remote Pacific islands—derives from different sources, documenting a third stream of migration from Near to Remote Oceania

The population of Vanuatu in the Pacific was largely replaced2,900–2,300 years ago
This second wave of migrants came from New Britain, east of New Guinea
A third wave spread different ancestry to the far-flung islands of Polynesia

See also:

Integrative studies of cultural evolution: crossing disciplinary boundaries to produce new insights

Interesting open access article Integrative studies of cultural evolution: crossing disciplinary boundaries to produce new insights, Oren Kolodny, Marcus W. Feldman, Nicole Creanza, Philos. Trans. Royal Soc. B (2018).


Culture evolves according to dynamics on multiple temporal scales, from individuals’ minute-by-minute behaviour to millennia of cultural accumulation that give rise to population-level differences. These dynamics act on a range of entities—including behavioural sequences, ideas and artefacts as well as individuals, populations and whole species—and involve mechanisms at multiple levels, from neurons in brains to inter-population interactions. Studying such complex phenomena requires an integration of perspectives from a diverse array of fields, as well as bridging gaps between traditionally disparate areas of study. In this article, which also serves as an introduction to the current special issue, we highlight some specific respects in which the study of cultural evolution has benefited and should continue to benefit from an integrative approach. We showcase a number of pioneering studies of cultural evolution that bring together numerous disciplines. These studies illustrate the value of perspectives from different fields for understanding cultural evolution, such as cognitive science and neuroanatomy, behavioural ecology, population dynamics, and evolutionary genetics. They also underscore the importance of understanding cultural processes when interpreting research about human genetics, neuroscience, behaviour and evolution.


The arrival of haplogroup R1a-M417 in Eastern Europe, and the east-west diffusion of pottery through North Eurasia


Henny Piezonka recently uploaded an old chapter, Die frühe Keramik Eurasiens: Aktuelle Forschungsfragen und methodische Ansätze, in Multidisciplinary approach to archaeology: Recent achievements and prospects. Proceedings of the International Symposium “Multidisciplinary approach to archaeology: Recent achievements and prospects”, June 22-26, 2015, Novosibirsk, Eds. V. I. Molodin, S. Hansen.

Abstract (in German):

Die älteste bisher bekannte Gefäßkeramik der Welt wurde in Südostchina von spätglazialen Jäger-Sammlern wahrscheinlich schon um 18.000 cal BC hergestellt. In den folgenden Jahrtausenden verbreitete sich die neue Technik bei Wildbeutergemeinschaften in der russischen Amur-Region, in Japan, Korea und Transbaikalien bekannt, bevor sie im frühen und mittleren Holozän das Uralgebiet und Ost- und Nordeuropa erreichte. Entgegen verbreiteter Forschungsmeinungen zur Keramikgeschichte, die frühe Gefaßkeramik als Bestandteil des „neolithischen Bündes” der frühen Bauernkulturen sehen, stellt die eurasische Jäger-Sammler-Keramiktradition eine Innovation dar, die sich offenbar völlig unabhängig von anderen neolithischen Kulturerscheinungen wie Ackerbau, Viehzucht und sesshafre Lebensweise entwickelt hat Im vorliegenden Beitrag wird die chronologische Abfolge des ersten Auftretens von Tongefäßen in nordeurasischen Jäger-Sammler-Gemeinschaften anahnd von 14C-Datierungen Pazifik bis ins Baltikum nachvollzogen. Gleichzeitig werden vielversprechende methodische Ansätze vorgestellet, die derzeit ein Rolle bei der Erforschung dieses viel diskutierten Themas spielen.

Sites named in the text with earlier ceramic pottery in Eurasia up to the Urals.

If you have followed the updates to the Indo-European demic diffusion model, my proposal of a potential late arrival of haplogroup R1a-M417 during the Mesolithic did not change by the potential earlier arrival of EHG ancestry and haplogroup R1a in the North Pontic steppe, after the findings in Mathieson et al. (2017).

That is so because of the anthropological models of migration – or, lacking them, archaeological models of cultural expansion – that we have to date.

If I had followed a simplistic autochthonous continuity view, I would have thought that R1a-M417 was autochthonous to Eastern Europe, because an older subclade is found in the North Pontic steppe during the Mesolithic, akin to how some people want to believe that R1b-M269 shows autochthonous continuity in or around Central Europe, because of the Villabruna sample and later R1b-L23 subclades found there.

However, it is difficult to assert today that the population movement involving a community of mostly haplogroup R1a-M417 happened from west to east:

  1. If you follow Piezonka’s work, who did her Ph.D. dissertation in Eastern European Mesolithic (you can buy a more readable version), and has dedicated a great amount of time and effort to the research of cultural connections between Eastern Europe and Eurasia during the Mesolithic;
  2. taking into account the potential migration waves behind the increase in EHG ancestry in Eastern Europe in these periods, and this ancestral component’s speculative connection with ANE ancestry;
  3. and if we accept the TMRCA of R1a-M417 based on modern samples, dated ca. 6500 BC, and the appearance of the first samples at a similar time in Eastern Europe and in Baikalic cultures.

NOTE. More and more findings of Eastern Europe are showing how the sample of haplogroup N1c found in Eastern Europe and dated ca. 2500 BC is probably wrong, either in its haplogroup or in the radiocarbon date: after all, the lab has published just one study. The study of Baikalic samples, on the other hand, seems to have been corroborated by a more recent study.

Another interesting sample is that of Afontova Gora, whose community may have actually been mostly of haplogroup R1a (based on its position in PCA and relation to ANE ancestry), and thus the regional distribution of this haplogroup could have been quite large in North Eurasia during the Palaeolithic-Mesolithic transition, although this is highly speculative, like the connection WHG:ANE for EHG.

Early radiocarbon-dated complexes with pottery in different regions of North Eurasia

It is obvious that we cannot know what happened during these millennia without more samples, and indeed I don’t see anything a priori wrong with having an origin of R1a-M417 (and thus some sort of continuity) in Eastern Europe during the Mesolithic and Neolithic; just as I don’t see any problem with the continuity of other European haplogroups. Or with their discontinuity, mind you. That would not change the Proto-Indo-European homeland, or the complexity of language and ethnicity in Eastern Europe in the millennia following the expansion of Late Indo-European.

It just amazes me again and again how otherwise serious and capable people are often blinded by the desire to have their direct paternal line (some ancestors among an infinite number of them, probably representing for them genetically much less than other ancestral lines) stem from the own region and have the same ethnolinguistic affiliation since time immemorial, instead of betting for sounder migration models supported by anthropological data…


Demographic research of Neolithic, Chalcolithic, and Bronze Age Europe


I mentioned in the Indo-European demic diffusion model the need to assess absolute and relative population growth – as well as other demographic changes – to interpret genomic data from the different European regions studied.

One article I referred to was Demographic traces of technological innovation, social change and mobility: from 1 to 8 million Europeans (6000–2000 BCE), by Johannes Müller.

Excerpts (emphasis mine):

  • The neolithization of Northern and Northwestern Europe (probably with new forms of slash-and-burn agriculture; Feeser et al. 2012; Schier 2009) was also one of the causes for the population increase observed.
  • The introduction of the plough and developing technologies (e.g. the introduction of the wheel) (cf. Mischka 2011) might also be causes of rising population figures from ca. 3500–3000 BC.
  • The establishment of subcontinental value systems, such as the Corded Ware and Bell-Beaker phenomena (Czebreszuk/Szmyt 2003; Furholt 2004), in contrast to regional identities, might have triggered different reactions in different areas, leading to fluctuating population levels.
  • The introduction of Bronze Age ideologies, including bronze as a technology, triggered the spread of Neolithic and Bronze Age societies to vast areas of Europe (e.g. Earle/Kristiansen 2010).A major population increase is observed in both the areas already settled as well as in new areas of interest.
Absolute population values in Europe and the Near East from 6500–1500 BCE (interpolation line: spline).

In our population estimations for Central Europe and Scandinavia, population increases are associated with the periods from 5500–5000 BCE (LBK) and 3500–3000 BCE (middle and late Funnelbeaker Culture), but not for the period from 2500–2000 BCE (Bell Beaker). Consequently, this would possibly indicate forms of immigration for the first two periods and a form of interregional networking (e.g. through marriage) for the latter. But as also for the first cases on the supra-regional level (which our enquiries investigated), no other area with a significant population decrease could be observed, therefore “proof” for larger population displacement is not given. For such inquiries, studies on a more regional level are probably necessary. Nevertheless, for the Bell Beaker period I would like to exclude the possibility of large population influxes at least to Central Europe and South Scandinavia as the population values show no indication of such an event (cf. Fig. 10). In consequence, supra-regional networks or population-exchanges between smaller regions might be responsible for the isotope values.

One could interpret from the graphics, including known anthropological and genomic data, that:

  • The population growth corresponding to the Corded Ware expansion from ca. 3300 into Central Europe was seen initially with the introduction of new technology, but then stalled – probably with population replacement during the A-horizon of the Corded Ware culture.
  • The Yamna expansion into South-East Europe must have included some population replacement, i.e. influx into progressively deserted areas (such as that of the Cucuteni-Trypillia culture), since it did not leave traces of population growth.
  • The impact of the expansion of East Bell Beakers from ca. 2500 BC is clear in South-East Europe, and especially in Western Europe – taking into account the whole population growth in Europe. In Central Europe and Scandinavia the overall impact of BB migration was more limited, which suggests some degree of population replacement.

Also important to interpret genomic data are the actual economic and social differences in the different periods and cultures – usually growing after the introduction of farming. A good example is the scarce data from Khvalynsk, where the sample of haplogroup R1b (most likely of subclade M269) shows – apart from a closer position in PCA to Yamna – a a high-status burial, similar to high-status individuals buried under kurgans in later Yamna graves. This man was therefore probably a founder of an elite group of patrilineally-related families, which dominated in the following Yamna culture, which explains the clear expansion of this haplogroup’s subclades from this region.

Figures from Rebellion and Inequality in Archaeology (2017), by Johannes Müller

Other interesting papers on European demographics by Johannes Müller include:

Check out also works by Marko Porčić (such as Radiocarbon test for demographic events in written and oral history) or Stephen Shennan.

EDIT (17 Feb 2018): For how variation in the effective population size governs genetic diversity, see:

Featured image, from the main article: “The distribution of agrarian regions in Europe and the Near East in relation to the supra-regions as defined in this study: Near East (NE) about 2.400.000 km2; South East Europe (SEE) about 1.087500 km2; Central Europe and South Scandinavia (CE/SSc) about 1.613.000 km2. Europe includes 10.050.000 km2 (without Iceland)”.

See also: