Category Archives: demographics

Revisiting the demographics of Northern and Central Europe in the Neolithic and Chalcolithic periods

Stimulated by the discussion at another entry, yesterday I made a little graph, almost a mnemonic, on the demographics of Northern European Neolithic and Chalcolithic, based on academic data which I discussed back in 2009.
This is the result:

The very simplified graph is nothing but a version of another one, used in 2009 (and reproduced below), which in turn is an annotated and composite version extracted from two different studies (references also below).
For convenience I have marked the millennia marks at the bottom (meaning 5000, 4000, 3000 and 2000 BCE, from left to right) while the unmarked vertical scale ranks from 0 to 100 (marked by the lowest and highest dots, not the frame, which is actually outside of the graph itself). The dots mark population level at any time as proportion of the maximum (100) in discrete intervals rounded up/down to 10 ppts and taken at intervals of 250 years. Notice that I ignored monuments in the case of Britain, only considering the habitation and other productive sites.
Not sure if it will result useful to you but it did help me to visualize the demographics of Northern Europe in these four millennia of surely dramatic population changes. If you don’t like this version the more detailed original double graph is below, scroll down.
Something quite obvious is that while Danubian Neolithic first caused an important population expansion, it later declined to quite low population levels, maybe because of climatic cooling and the exhaustion of the lands because of poorly developed agricultural techniques. 
This late Danubian collapse lasted for about a millennium, when (1) Funnelbeaker (TRBK) in Denmark, (2) Megalithism in Britain and Denmark especially (later also in parts of Germany) and (3) Kurgan cultures in Poland (later also in Germany and Denmark) seem to have brought with them very notable demographic expansions.
But decline seems to set on again all around at the end of the Chalcolithic period, much more notably in the continent (in Poland the rate of archaeological findings decays to zero!) than in Britain and especially Denmark. 
And now indeed the original “verbose” graph:

And the sources:

Reconstructing human demographic history from IBS segments

Figure 1. An eight base-pair tract of identity by state (IBS).
Identity-by-state (IBS) segments are those located between any two SNPs (polymorphisms, letters that vary among individuals). According to this new paper, they seem to be evolutionarily neutral and therefore their length, modified by recombination events each new generation, is a good trail to reconstruct human demographic history.
Kelley Harris & Rasmus Nielsen, Inferring Demographic History from a Spectrum of Shared Haplotype Lengths. PLoS Genetics 2013. Open accessLINK [doi:10.1371/journal.pgen.1003521]


There has been much recent excitement about the use of genetics to elucidate ancestral history and demography. Whole genome data from humans and other species are revealing complex stories of divergence and admixture that were left undiscovered by previous smaller data sets. A central challenge is to estimate the timing of past admixture and divergence events, for example the time at which Neanderthals exchanged genetic material with humans and the time at which modern humans left Africa. Here, we present a method for using sequence data to jointly estimate the timing and magnitude of past admixture events, along with population divergence times and changes in effective population size. We infer demography from a collection of pairwise sequence alignments by summarizing their length distribution of tracts of identity by state (IBS) and maximizing an analytic composite likelihood derived from a Markovian coalescent approximation. Recent gene flow between populations leaves behind long tracts of identity by descent (IBD), and these tracts give our method power by influencing the distribution of shared IBS tracts. In simulated data, we accurately infer the timing and strength of admixture events, population size changes, and divergence times over a variety of ancient and recent time scales. Using the same technique, we analyze deeply sequenced trio parents from the 1000 Genomes project. The data show evidence of extensive gene flow between Africa and Europe after the time of divergence as well as substructure and gene flow among ancestral hominids. In particular, we infer that recent African-European gene flow and ancient ghost admixture into Europe are both necessary to explain the spectrum of IBS sharing in the trios, rejecting simpler models that contain less population structure.

The most interesting graph, synthesizing the result for standard HapMap European and African proxy samples is figure 7. However I have major issues with the age estimates, which seem to be half what is needed to be realistic according to archaeological and other genetic data (unlineal haplogroup history, for example). Therefore I have annotated it with a revised timeline, so it fits better with the objective data:

Figure 7. A history inferred from IBS sharing in Europeans and Yorubans.
This is the simplest history we found to satisfactorily explain IBS tract sharing in the 1000 Genomes trio data. It includes ancient ancestral population size changes, an out-of-African bottleneck in Europeans, ghost admixture into Europe from an ancestral hominid, and a long period of gene flow between the diverging populations.
(Right margin annotations by Maju).

Indeed the simplest revision of the time-scale was to double it. I guess it can be refined a bit more than that, maybe pushing it a bit further into the past, but the alternative time-scale I propose fits closely enough with known archaeological data like the time of the OoA to Arabia and Palestine or the spread of Acheulean (and therefore H. ergaster, common ancestor of Neanderthals and H. sapiens) out of Africa c. 1 Ma ago to illustrate that the reconstruction seems pretty much correct overall but fails when estimating the dates (because of scholastic-autistic academic biases that are too common in the field of human population genetics).

Update: even Dienekes agrees, on his own well documented reasoning, with a x2 mutation rate being necessary for the above graph.


How hunter-gatherers slowed farmers’ advance.

A very technical paper yet one that addresses how farmers could not advance so fast in Europe:

We can see from equation (9) that if the Mesolithic (indigenous) population density M increases along the direction y, then the probability of Neolithic invaders to jump forward (θ = π/2) is minimum and the probability to jump backwards (θ = 3π/2) is maximum.

Where y is the main vector of Neolithic expansion, drawn along the Morava-Danube axis in SE-NW direction.
So there is a forager population density value for which farmers are more likely to fall back than continue advancing… interesting.

From equation (22), we see that if the Mesolithic population density increases with y, then the front speed decreases because of two effects: (i) the higher the gradient of the reduced Mesolithic density m, the higher the correction on the front speed; (ii) the speed also changes if there is less available space for the Neolithic population, i.e. for lower values of s = (1–m(y)) (if s = 1, this second effect disappears).

And there is an interesting issue of what happens if Epipaleolithic (“Mesolithic”) population density increases through the same vector as that of Neolithic expansion (as it was the case in Europe without doubt)?

Fig. 3 (legend below)
Figure 3. Curves: relative Neolithic front speed predicted by a model with the dispersion and growth processes dependent on the presence of Mesolithic populations, equation (25). Symbols: observed front speeds calculated from archaeological data [9].
Interesting: archaeology seems to support m4 of all estimated curves.
The value of m4 is described earlier:


A1 = 0.999/1300, B1 = 0; A2 = –0.1 = –B2 = A3 = B3, τ2 = –ln(10.99)/1300 = –τ3; A4 = 0.99, B4 = 42, τ4 = 1/0.007.

Just for the record.
They conclude:

Comparing the results from equation (25) to those from archaeological data in figure 3, we see that, even though none of the four test functions reproduce exactly the behavior of the archaeological data (which is not surprising for such a complex phenomenon), they do give a good approximation to the general behavior (especially m4). Thus, a simple physical model can explain qualitatively the decrease in the front speed during the Neolithic expansion range in Europe. Therefore, physical models are useful to explain not only the average Neolithic front speed [8] , but also its gradual slowdown in space.

Press release: Eurekalert (found at AiE)

Revisiting Bocquet-Appel 2005: the population of Europe in the Paleolithic.

Something I want to do in this new blog is to revisit some areas that I have explored in the past, hopefully with an improved approach. One of the key elements in understanding European Prehistory as a whole is the demographics of Paleolithic Europe.  Nothing better surely than this excellent survey of archaeological density and corresponding population estimates:
The paper evidences the demographic growth, specially after the Last Glacial Maximum (LGM), when it really explodes (from 5900 to 28,700 – average figures). 
But maybe even more interesting is the demographic tendencies of the various regions. Most outstandingly, the Franco-Cantabrian region comprises almost half of the all Europeans early on, reaching to 2/3 in the LGM.
Second is the Danube region, with 20-25% in the early period and a marked decrease in the LGM. Third is the related Rhine region with 6-9%, sharply declining to near zero in the LGM. 
These three regions are fused in the latest map (Late UP, Magdalenian), including together 95% of all Europeans some 15-10,000 years ago. These would be then something between 11,000 and 73,000 individuals, average: 29,000 (more than 27,000 in the Magdalenian area). 
Fig. 5 (2nd part). Population estimates for LGM (top) and Late UP (bottom)

Less important regions are East Europe (4% in the Gravettian era, declining after that) and Iberia (7% in the LGM, much less in the next period). Italy is not mentioned but does indeed show some sparse continuity (Gravettian/Epigravettian).
You can easily compare these maps and those of the patterns of R1b1b2a1a2; it seems clear to me that the best possible explanation for its subclades’ dispersion patterns is at the post-LGM stage.