The Tollense Valley battlefield: the North European ‘Trojan war’ that hints to western Balto-Slavic origins

bronze-age-tollense-battle

It was reported long ago that genetic studies were being made on remains of a surprisingly big battle that happened in the Tollense valley in north-eastern Germany, at the confluence between Nordic, Tumulus/Urnfield, and Proto-Lusatian/Lusatian territories, ca. 1200 BC.

At least 130 bodies and 5 horses have been identified from the bones found. Taking into account that this is a small percentage of the potential battlefield, around 750 bodies are expected to be buried in the riverbank, so an estimated 4,000-strong army fought there, accounting for one in five participants killed and left on the battlefield.

Tollense riverbank
The river Tollense near the village Weltzin in the district Demmin (Mecklenburg-Vorpommern, Germany). From Wikipedia

Body armour, shields, helmet, and corselet used may have needed training and specialised groups of warriors, with their organisation being a display of military force. According to Kristiansen , this battle is therefore unlike any other known conflict of this period north of the Alps – circumscribed to raids by small groups of young men –, and may have heralded a radical change in the north, from individual farmsteads and a low population density to heavily fortified settlements.

The Urnfield culture (ca. 1300-750 BC) is associated with the rise of a new warrior elite, and the formation of new farming settlements and their urnfields. In some areas there is continuity from Tumulus to Urnfield culture, with narrowing and concentration of settlements along the river valleys, but there is also wide-ranging migrations. These migrations are similar to those seen later in the La Tène culture. This period is also coincident with the time of the mythical battle of Troy, with the collapse of the Mycenaean civilisation, and with the raids of Sea People in Egypt, and the marauders of the Hittites.

bronze-age-tollense
Diachronic map of migrations in Europe ca. 1250-750 BC, with the site of the Tollense valley marked.

Chemical traces already suggested that warriors fighting in Tollense came from far away, with only a few showing values typical of the northern European plain. A recently published PhD dissertation, Addressing challenges of ancient DNA sequence data obtained with next generation methods, by Christian Sell (2017) has not confirmed this:

The majority of sampled individuals fall within the variation of contemporary northern central European samples (including Nordic Late Neolithic and Bronze Age and Únětice samples); however, there are also some outliers closer to Neolithic LBK and modern Basques, suggesting that central and western European cultures were still at that time closely interconnected, continuing thus the connections created during the Bell Beaker expansion a thousand years earlier. The genetic similarity of most samples to modern western Slavic populations (as well as Austrians and Scots) gives support to the origin of Balto-Slavic in Bronze Age north-central Europe, and more specifically in the Lusatian culture.

tollense-welzin
PCA of samples from Tollense Valley battlefield. Welzin samples cluster closely to East German and Polish samples.

The Indo-European demic diffusion model supports the origin of Pre-Balto-Slavic in north-central Europe, with Únětice and Mierzanowice/Nitra groups as its potential homeland, from a common North-West Indo-European parent language (expanded through East Bell Beaker). Proto-Lusatian is therefore the best candidate for its initial development, and Lusatian for its eastern expansion, before its separation into its two main dialects (or maybe three, if Baltic is to be divded in two branches).

In fact, scarce aDNA from late Urnfield populations from its north-eastern territories, in Saxony – near the Lusatian culture –, already show a mixture of lineages, which suggest genetic continuity with older cultures (or more likely a resurge) after the Bell Beaker expansions: R1a1a1b1a-Z282 lineage was found in Halberstadt (ca. 1085 BC), and of the eight males studied from the Lichtenstein cave (ca. 1000 BC), five were of haplogroup I2a2b-L38, two of haplogroup R1a1-M459, and one of haplogroup R1b-M343.

Regarding modern populations, the eastern and western peaks in R1a1a1b1a1-M458 lineages might support a west-east migration, as well as an east-west migration, and indeed both in different periods, which is expected to be found if Lusatian is linked to the initial eastward expansion of Balto-Slavic during the Bronze and Iron Ages, and later younger subclades are linked to the West Slavic expansion to the west during Antiquity.

R-M458_frequency_distribution
Map rendered in pseudocolours for R-M458 frequencies, data derived from Underhill et al. (2014). Positions of boundaries (NE,NW,C,etc) are approximate. Variation of N and S. Caucasus region of Russia rendered as stripes showing range of variation in the region. From Wikipedia.

Now, if this is so, then we have to accept that these territories of north-central Europe (between East Germany and Poland), occupied earlier by Corded Ware cultures, adopted Balto-Slavic only after the Bell Beaker expansion; therefore, models arguing for Balto-Slavic origins in east European late Corded Ware groups (or heir cultures), like Trzciniec, Chornoles, Bilozerska, or Milograd (see e.g. the article on Wikipedia) have to be rejected. We also know that Pre-Germanic could have only formed in the Nordic Late Neolithic, after the cultural unification of the Dagger Period, heraled by the arrival of Bell Beakers; and that Indo-Iranian was the language of the Sintashta-Petrovka culture, which had absorbed the previous (Yamna-related) Poltavka culture.

chalcolithic-bell-beaker-europe
Diachronic map of Late Copper Age migrations including Classical Bell Beaker (east group) expansion from central Europe ca. 2600-2250 BC

But, if Indo-European was only spoken at both ends of territories previously occupied by Corded Ware cultures – stretching from Scandinavia to the Urals, including the Baltic region… what language did Corded Ware peoples actually speak? The most likely one? Uralic, indeed.

Related:

Forces driving grammatical change are different to those driving lexical change

Grammar change

A new paper at PNAS, Evolutionary dynamics of language systems, by Greenhill et al. (2017).

Significance

Do different aspects of language evolve in different ways? Here, we infer the rates of change in lexical and grammatical data from 81 languages of the Pacific. We show that, in general, grammatical features tend to change faster and have higher amounts of conflicting signal than basic vocabulary. We suggest that subsystems of language show differing patterns of dynamics and propose that modeling this rate variation may allow us to extract more signal, and thus trace language history deeper than has been previously possible.

Abstract

Understanding how and why language subsystems differ in their evolutionary dynamics is a fundamental question for historical and comparative linguistics. One key dynamic is the rate of language change. While it is commonly thought that the rapid rate of change hampers the reconstruction of deep language relationships beyond 6,000–10,000 y, there are suggestions that grammatical structures might retain more signal over time than other subsystems, such as basic vocabulary. In this study, we use a Dirichlet process mixture model to infer the rates of change in lexical and grammatical data from 81 Austronesian languages. We show that, on average, most grammatical features actually change faster than items of basic vocabulary. The grammatical data show less schismogenesis, higher rates of homoplasy, and more bursts of contact-induced change than the basic vocabulary data. However, there is a core of grammatical and lexical features that are highly stable. These findings suggest that different subsystems of language have differing dynamics and that careful, nuanced models of language change will be needed to extract deeper signal from the noise of parallel evolution, areal readaptation, and contact.

This is in line with the studies by Bendt, like Adaptive Communication: Languages with More Non-Native Speakers Tend to Have Fewer Word Forms, which suggest a simplification of grammar with language contact.

It might then give further support to my proposal of Uralic as the Corded Ware substrate – common to Balto-Slavic and Indo-Iranian -, since they are the only Late Indo-European branches that clearly retain the grammatical complexity in word forms, which – together with their shared phonetic isoglosses (also present partially between Balto-Slavic and Germanic) -, put them nearer to a complex, potentially related Uralic (or other Indo-Uralic) branch.

On the other hand, the finding of a greater stability of lexicon gives further support to the concept of a North-West Indo-European group, since one of its foundations (the main one originally) is the shared vocabulary between Italo-Celtic, Germanic, and Balto-Slavic.

Featured image: from the article (copyrighted), “Map showing locations of languages in this study. The phylogenies show the maximum clade credibility tree of the Austronesian languages in our sample. Each phylogeny is colored by the average rate of change, with branches showing more change colored redder, while bluer branches show reductions in rate. Branches with significant shifts are annotated with an asterisk, and the languages showing significantly different rates of change in their grammatical data are located on the map”.

Related:

New preprint papers on Finland’s population history and disease, skin pigmentation in Africa, and genetic variation in Thailand hunter-gatherers

finland-genetics

New and interesting research these days in BioRxiv:

Haplotype sharing provides insights into fine-scale population history and disease in Finland, by Martín et al. (2017):

Finland provides unique opportunities to investigate population and medical genomics because of its adoption of unified national electronic health records, detailed historical and birth records, and serial population bottlenecks. We assemble a comprehensive view of recent population history (≤100 generations), the timespan during which most rare disease-causing alleles arose, by comparing pairwise haplotype sharing from 43,254 Finns to geographically and linguistically adjacent countries with different population histories, including 16,060 Swedes, Estonians, Russians, and Hungarians. We find much more extensive sharing in Finns, with at least one ≥ 5 cM tract on average between pairs of unrelated individuals. By coupling haplotype sharing with fine-scale birth records from over 25,000 individuals, we find that while haplotype sharing broadly decays with geographical distance, there are pockets of excess haplotype sharing; individuals from northeast Finland share several-fold more of their genome in identity-by-descent (IBD) segments than individuals from southwest regions containing the major cities of Helsinki and Turku. We estimate recent effective population size changes over time across regions of Finland and find significant differences between the Early and Late Settlement Regions as expected; however, our results indicate more continuous gene flow than previously indicated as Finns migrated towards the northernmost Lapland region. Lastly, we show that haplotype sharing is locally enriched among pairs of individuals sharing rare alleles by an order of magnitude, especially among pairs sharing rare disease causing variants. Our work provides a general framework for using haplotype sharing to reconstruct an integrative view of recent population history and gain insight into the evolutionary origins of rare variants contributing to disease.

finland-migration-haplotype
Migration rates and haplotype sharing within Finland and between neighboring countries. A) Map of regional Finnish, Swedish, and Estonian birthplaces Purple triangle indicates St. Petersburg, Russia. Hungary not shown. 1 Finnish, Swedish, and Estonian region labels are shown in Table S3. B) Principal components analysis (PCA) of unrelated individuals, colored by birth region as shown in A) if available or country otherwise. C-D) Migration rates inferred with EEMS. Values and colors indicate inferred rates, for example with +1 (shades of blue) indicating an order of magnitude more migration at a given point on average, and shades of orange indicating migration barriers. C) Migration rates among municipalities in Finland. D) Migration rates within and between Finland, Sweden, Estonia, and St. Petersburg, Russia. Available under a CC-BY 4.0 International license.

Interesting to understand this paper is the whole research published by the Institute for Molecular Medicine Finland (FIMM): their website contains detailed research on Finland’s recent genetic history.

NOTE: The featured image of this article contains three figures from the FIMM (License CC-BY 4.0). Left: Position of the points represents the locations of 1042 Finnish individuals. By clustering the individuals into two groups based on genome data we see a split between eastern (blue) and western (red) parts. Individuals who show considerable relatedness to both groups have been colored with cyan. Both parents of each individual were born close to each other and based on the parents’ birth years we can infer that we are looking at the genetic structure present in Finland before 1950s. Center: An estimated borderline of the Treaty of Nöteborg on top of the map from the left. The border line is drawn between Jääski (28.92 N, 61.04 E) and Pyhäjoki (24.26 N, 64.46 E). Right: The settlement border divides Finland into the early settlement region (to west and south of the border) and the late settlement region (to east and north of the border) (Jutikkala 1933, s. 91). We see that Southern Savo (in south-eastern part of the early settlement) is among the only parts of the early settlement region that is dominated by the eastern genetic group. Information from Matti Pirinen and Sini Kerminen, 24.5.2017.

An Unexpectedly Complex Architecture for Skin Pigmentation in Africans, by Martin et al (2017):

Fewer than 15 genes have been directly associated with skin pigmentation variation in humans, leading to its characterization as a relatively simple trait. However, by assembling a global survey of quantitative skin pigmentation phenotypes, we demonstrate that pigmentation is more complex than previously assumed with genetic architecture varying by latitude. We investigate polygenicity in the Khoe and the San, populations indigenous to southern Africa, who have considerably lighter skin than equatorial Africans. We demonstrate that skin pigmentation is highly heritable, but that known pigmentation loci explain only a small fraction of the variance. Rather, baseline skin pigmentation is a complex, polygenic trait in the KhoeSan. Despite this, we identify canonical and non-canonical skin pigmentation loci, including near SLC24A5, TYRP1, SMARCA2/VLDLR, and SNX13 using a genome-wide association approach complemented by targeted resequencing. By considering diverse, under-studied African populations, we show how the architecture of skin pigmentation can vary across humans subject to different local evolutionary pressures.

Contrasting maternal and paternal genetic variation of hunter-gatherer groups in Thailand, by Kutanan et al. (2017):

The Maniq and Mlabri are the only recorded nomadic hunter-gatherer groups in Thailand. Here, we sequenced complete mitochondrial (mt) DNA genomes and ~2.364 Mbp of non-recombining Y chromosome (NRY) to learn more about the origins of these two enigmatic populations. Both groups exhibited low genetic diversity compared to other Thai populations, and contrasting patterns of mtDNA and NRY diversity: there was greater mtDNA diversity in the Maniq than in the Mlabri, while the converse was true for the NRY. We found basal uniparental lineages in the Maniq, namely mtDNA haplogroups M21a, R21 and M17a, and NRY haplogroup K. Overall, the Maniq are genetically similar to other negrito groups in Southeast Asia. By contrast, the Mlabri haplogroups (B5a1b1 for mtDNA and O1b1a1a1b and O1b1a1a1b1a1 for the NRY) are common lineages in Southeast Asian non-negrito groups, and overall the Mlabri are genetically similar to their linguistic relatives (Htin and Khmu) and other groups from northeastern Thailand. In agreement with previous studies of the Mlabri, our results indicate that the Malbri do not directly descend from the indigenous negritos. Instead, they likely have a recent origin (within the past 1,000 years) by an extreme founder event (involving just one maternal and two paternal lineages) from an agricultural group, most likely the Htin or a closely-related group.

Related:

Human ancestry solves language questions? New admixture citebait

human_ancestry

A paper at Scientific Reports, Human ancestry correlates with language and reveals that race is not an objective genomic classifier, by Baker, Rotimi, and Shriner (2017).

Abstract (emphasis mine):

Genetic and archaeological studies have established a sub-Saharan African origin for anatomically modern humans with subsequent migrations out of Africa. Using the largest multi-locus data set known to date, we investigated genetic differentiation of early modern humans, human admixture and migration events, and relationships among ancestries and language groups. We compiled publicly available genome-wide genotype data on 5,966 individuals from 282 global samples, representing 30 primary language families. The best evidence supports 21 ancestries that delineate genetic structure of present-day human populations. Independent of self-identified ethno-linguistic labels, the vast majority (97.3%) of individuals have mixed ancestry, with evidence of multiple ancestries in 96.8% of samples and on all continents. The data indicate that continents, ethno-linguistic groups, races, ethnicities, and individuals all show substantial ancestral heterogeneity. We estimated correlation coefficients ranging from 0.522 to 0.962 between ancestries and language families or branches. Ancestry data support the grouping of Kwadi-Khoe, Kx’a, and Tuu languages, support the exclusion of Omotic languages from the Afroasiatic language family, and do not support the proposed Dené-Yeniseian language family as a genetically valid grouping. Ancestry data yield insight into a deeper past than linguistic data can, while linguistic data provide clarity to ancestry data.

Regarding European ancestry:

Southern European ancestry correlates with both Italic and Basque speakers (r = 0.764, p = 6.34 × 10−49). Northern European ancestry correlates with Germanic and Balto-Slavic branches of the Indo-European language family as well as Finno-Ugric and Mordvinic languages of the Uralic family (r = 0.672, p = 4.67 × 10−34). Italic, Germanic, and Balto-Slavic are all branches of the Indo-European language family, while the correlation with languages of the Uralic family is consistent with an ancient migration event from Northern Asia into Northern Europe. Kalash ancestry is widely spread but is the majority ancestry only in the Kalash people (Table S3). The Kalasha language is classified within the Indo-Iranian branch of the Indo-European language family.

Sure, admixture analysis came to save the day. Yet again. Now it’s not just Archaeology related to language anymore, it’s Linguistics; all modern languages and their classification, no less. Because why the hell not? Why would anyone study languages, history, archaeology, etc. when you can run certain algorithms on free datasets of modern populations to explain everything?

What I am criticising here, as always, is not the study per se, its methods (PCA, the use of Admixture or any other tools), or its results, which might be quite interesting – even regarding the origin or position of certain languages (or more precisely their speakers) within their linguistic groups; it’s the many broad, unsupported, striking conclusions (read the article if you want to see more wishful thinking).

This is obviously simplistic citebait – that benefits only journals and authors, and it is therefore tacitly encouraged -, but not knowledge, because it is not supported by any linguistic or archaeological data or expertise.

Is anyone with a minimum knowledge of languages, or general anthropology, actually reviewing these articles?

Related:

Featured image: Ancestry analysis of the global data set, from the article.

Another hint at the role of Corded Ware peoples in spreading Uralic languages into north-eastern Europe, found in mtDNA analysis of the Finnish population

corded-ware-migration-yamna

Open article at Scientific Reports (Nature): Identification and analysis of mtDNA genomes attributed to Finns reveal long-stagnant demographic trends obscured in the total diversity, by Översti et al. (2017).

Of special interest is its depiction of Finland’s past as including the expansion of Corded Ware population of mtDNA U5b1b2 (and probably Y-DNA R1a-M417 subclades), most likely Uralic speakers of the Forest Zone, to the north of the Yamna culture (where Late Proto-Indo-European was spoken).

A later expansion of other subclades – particularly Y-DNA N1c -, was probably associated with the later western expansion of the Eurasian Seima-Turbino phenomenon, and its current prevalence in Finnish Y-DNA haplogroups might have been the consequence of the population decline ca. 1500 BC, and later Iron Age population bottleneck (with the population peak ca. 500 AD) described in the article.

That would more naturally explain the ‘cultural diffusion’ of Finnic languages into invading eastern N1c lineages, a diffusion which would have been in fact a long-term, quite gradual replacement of previously prevalent Y-DNA R1a subclades in the region, as supported by the prevalent “steppe” component in genome-wide ancestry of Finns.

Therefore, there were probably no sudden, strong population (and thus cultural) changes associated with the arrival of N1c lineages, like the ones seen with R1a (Corded Ware / Uralic) and R1b (Yamna / Proto-Indo-European) expansions in Europe.

How the Saami fit into this scheme is not yet obvious, though.

Abstract:

In Europe, modern mitochondrial diversity is relatively homogeneous and suggests an ubiquitous rapid population growth since the Neolithic revolution. Similar patterns also have been observed in mitochondrial control region data in Finland, which contrasts with the distinctive autosomal and Y-chromosomal diversity among Finns. A different picture emerges from the 843 whole mitochondrial genomes from modern Finns analyzed here. Up to one third of the subhaplogroups can be considered as Finn-characteristic, i.e. rather common in Finland but virtually absent or rare elsewhere in Europe. Bayesian phylogenetic analyses suggest that most of these attributed Finnish lineages date back to around 3,000–5,000 years, coinciding with the arrival of Corded Ware culture and agriculture into Finland. Bayesian estimation of past effective population sizes reveals two differing demographic histories: 1) the ‘local’ Finnish mtDNA haplotypes yielding small and dwindling size estimates for most of the past; and 2) the ‘immigrant’ haplotypes showing growth typical of most European populations. The results based on the local diversity are more in line with that known about Finns from other studies, e.g., Y-chromosome analyses and archaeology findings. The mitochondrial gene pool thus may contain signals of local population history that cannot be readily deduced from the total diversity.

From its results:

In general, there appears to be two loose and largely overlapping clusters among the Finn-characteristic haplogroups: the first between 1,000–2,000 ybp and the second around 3,300–5,500 ybp. The age of the older cluster coincides temporally with the arrival of the Corded-Ware culture and, notably, the spread of agriculture in Finland. The arrival and spread of agriculture, temporally corresponding with the age estimates for most of the haplogroups characteristic of Finns, might be a sign of population size increase enabled by the new mode of subsistence, resulting in reduced drift and accumulation of genetic diversity in the population.

(…)

Another insight in the past population sizes in Finland is based on radiocarbon-dated archaeological findings in different time periods. These analyses suggest two prehistoric population peaks in Finland, the Stone Age peak (c. 5,500 ybp) and the Metal Age peak (~1,500 ybp). Both of these peaks were followed by a population decline, which appears to have reached its ebb around 3,500 ybp. These developments are not distinguishable in the BSPs. However, these ages correspond well to the two haplogroup age clusters described above. The presumably less severe Iron Age population bottleneck seen in the archaeological data, 1,500–1,300 ybp, temporally coincides with the population size reduction visible for the Finn-characteristic subhaplogroups.

Related:

Discovered via Eurogenes.