- S. Shennan & K. Edinborough, Prehistoric population history: from the Late Glacial to the Late Neolithic in Central and Northern Europe (Journal of Archaeological Science, 2007).
- Mark Collard et al., Radiocarbon evidence indicates that migrants introduced farming to Britain. Antiquity, 2009.
Monthly Archives: June 2013
Revisiting the demographics of Northern and Central Europe in the Neolithic and Chalcolithic periods
(CC by Papa Lima Whiskey 2)
The origins of flora and fauna that are only found in Ireland and Iberia, but which are absent from intervening countries, is one of the enduring questions of biogeography. As Southern French, Iberian and Irish populations of the land snail Cepaea nemoralis sometimes have a similar shell character, we used mitochondrial phylogenies to begin to understand if there is a shared “Lusitanian” history. Although much of Europe contains snails with A and D lineages, by far the majority of Irish individuals have a lineage, C, that in mainland Europe was only found in a restricted region of the Eastern Pyrenees. A past extinction of lineage C in the rest of Europe cannot be ruled out, but as there is a more than 8000 year continuous record of Cepaea fossils in Ireland, the species has long been a food source in the Pyrenees, and the Garonne river that flanks the Pyrenees is an ancient human route to the Atlantic, then we suggest that the unusual distribution of the C lineage is most easily explained by the movements of Mesolithic humans. If other Irish species have a similarly cryptic Lusitanian element, then this raises the possibility of a more widespread and significant pattern.
The evidence gathered by this study is most readily visible in fig. 2:
- Hg D is found in Iberia, Ireland, small pockets in Britain and SW France but also in North and Central Europe.
- Hg F shows also disjunct presence in Cornwall, far away from the main cluster around the Bay of Biscay.
The recent genealogical history of human populations is a complex mosaic formed by individual migration, large-scale population movements, and other demographic events. Population genomics datasets can provide a window into this recent history, as rare traces of recent shared genetic ancestry are detectable due to long segments of shared genomic material. We make use of genomic data for 2,257 Europeans (in the Population Reference Sample [POPRES] dataset) to conduct one of the first surveys of recent genealogical ancestry over the past 3,000 years at a continental scale. We detected 1.9 million shared long genomic segments, and used the lengths of these to infer the distribution of shared ancestors across time and geography. We find that a pair of modern Europeans living in neighboring populations share around 2–12 genetic common ancestors from the last 1,500 years, and upwards of 100 genetic ancestors from the previous 1,000 years. These numbers drop off exponentially with geographic distance, but since these genetic ancestors are a tiny fraction of common genealogical ancestors, individuals from opposite ends of Europe are still expected to share millions of common genealogical ancestors over the last 1,000 years. There is also substantial regional variation in the number of shared genetic ancestors. For example, there are especially high numbers of common ancestors shared between many eastern populations that date roughly to the migration period (which includes the Slavic and Hunnic expansions into that region). Some of the lowest levels of common ancestry are seen in the Italian and Iberian peninsulas, which may indicate different effects of historical population expansions in these areas and/or more stably structured populations. Population genomic datasets have considerable power to uncover recent demographic history, and will allow a much fuller picture of the close genealogical kinship of individuals across the world.
- Very low (<1): Italy, France.
- Quite Low (1-1.4): Germany, UK, Belgium, England, Austria, French-Swiss,
- Somewhat low (1.5-1.9): Spain, German-Swiss, Greece, Portugal, Netherlands, Hungary.
- Somewhat high (2-2.9): Czech R., Romania, Scotland, Ireland, Serbia, Croatia,
- Quite high (3-3.9): Sweden, Poland
- Very high (4-5): Bosnia, Russia*
- Extremely high (>10): Albania
- I ignored strangely labeled samples like “Switzerland” and “Yugoslavia”, which seem to mean actually “other” within these labels. I retained the “United Kingdom” category for its large sample size, much larger than its obvious parts.
- The level of relatedness of Russians may be exaggerated by the small sample: n=6, still above my cautionary threshold.
- I suspect that the extreme disparity of sample sizes may influence the results to some extent.
|Figure 3. Geographic decay of recent relatedness.
In all figures, colors give categories based on the regional groupings of Table 1. (A–F) The area of the circle located on a particular population is proportional to the mean number of IBD blocks of length at least 1 cM shared between random individuals chosen from that population and the population named in the label (also marked with a star). Both regional variation of overall IBD rates and gradual geographic decay are apparent. (G–I) Mean number of IBD blocks of lengths 1–3 cM (oldest), 3–5 cM, and >5 cM (youngest), respectively, shared by a pair of individuals across all pairs of populations; the area of the point is proportional to sample size (number of distinct pairs), capped at a reasonable value; and lines show an exponential decay fit to each category (using a Poisson GLM weighted by sample size). Comparisons with no shared IBD are used in the fit but not shown in the figure (due to the log scale). “E–E,” “N–N,” and “W–W” denote any two populations both in the E, N, or W grouping, respectively; “TC-any” denotes any population paired with Turkey or Cyprus; “I-(I,E,N,W)” denotes Italy, Spain, or Portugal paired with any population except Turkey or Cyprus; and “between E,N,W” denotes the remaining pairs (when both populations are in E, N, or W, but the two are in different groups). The exponential fit for the N–N points is not shown due to the very small sample size. See Figure S8 for an SVG version of these plots where it is possible to identify individual points.
|Figure 2. Substructure in (A) Italian and (B) U.K. samples.
The leftmost plots of (A) show histograms of the numbers of IBD blocks that each Italian sample shares with any French-speaking Swiss (top) and anyone from the United Kingdom (bottom), overlaid with the expected distribution (Poisson) if there was no dependence between blocks. Next is shown a scatterplot of numbers of blocks shared with French-speaking Swiss and U.K. samples, for all samples from France, Italy, Greece, Turkey, and Cyprus. We see that the numbers of recent ancestors each Italian shares with the French-speaking Swiss and with the United Kingdom are both bimodal, and that these two are positively correlated, ranging continuously between values typical for Turkey/Cyprus and for France. Figure (B) is similar, showing that the substructure within the United Kingdom is part of a continuous trend ranging from Germany to Ireland. The outliers visible in the scatterplot of Figure 2B are easily explained as individuals with immigrant recent ancestors—the three outlying U.K. individuals in the lower left share many more blocks with Italians than all other U.K. samples, and the individual labeled “SK” is a clear outlier for the number of blocks shared with the Slovakian sample.
|Figure 4. Estimated average number of most recent genetic common ancestors per generation back through time.
Estimated average number of most recent genetic common ancestors per generation back through time shared by (A) pairs of individuals from “the Balkans” (former Yugoslavia, Bulgaria, Romania, Croatia, Bosnia, Montenegro, Macedonia, Serbia, and Slovenia, excluding Albanian speakers) and shared by one individual from the Balkans with one individual from (B) Albanian-speaking populations, (C) Italy, or (D) France. The black distribution is the maximum likelihood fit; shown in red is smoothest solution that still fits the data, as described in the Materials and Methods. (E) shows the observed IBD length distribution for pairs of individuals from the Balkans (red curve), along with the distribution predicted by the smooth (red) distribution in (A), as a stacked area plot partitioned by time period in which the common ancestor lived. The partitions with significant contribution are labeled on the left vertical axis (in generations ago), and the legend in (J) gives the same partitions, in years ago; the vertical scale is given on the right vertical axis. The second column of figures (F–J) is similar, except that comparisons are relative to samples from the United Kingdom.
In most cases, only pairs within the same population are likely to share genetic common ancestors within the last 500 years [i.e.: ~1100 years]. Exceptions are generally neighboring populations (e.g., United Kingdom and Ireland). During the period 500–1,500 ya [i.e. ~1100-3300 years ago: most of the Metal Ages], individuals typically share tens to hundreds of genetic common ancestors with others in the same or nearby populations, although some distant populations have very low rates. Longer ago than 1,500 ya [i.e. before ~3300 years ago: before the Late Bronze Age crisis], pairs of individuals from any part of Europe share hundreds of genetic ancestors in common, and some share significantly more.
There is relatively little common ancestry shared between the Italian peninsula and other locations, and what there is seems to derive mostly from longer ago than 2,500 ya [i.e. ~5500 y.a.: Megalithic era onwards]. An exception is that Italy and the neighboring Balkan populations share small but significant numbers of common ancestors in the last 1,500 years [i.e. after 3750 years: since the Mycenaean period] …
Patterns for the Iberian peninsula are similar, with both Spain and Portugal showing very few common ancestors with other populations over the last 2,500 years [i.e. 5500 years: Megalithic era onwards]. However, the rate of IBD sharing within the peninsula is much higher than within Italy…
The maximum likelihood history (grey) and smoothest consistent history (red) for all pairs of population groupings of Figure S12 (including those of Figure 5). Each panel is analogous to a panel of Figure 4; time scale is given by vertical grey lines every 500 years. For these plots on a larger scale, see Figure S17.
Update (Jun 23): on IBD-based molecular-clock-o-logy:
I have now and then found strange insistence on IBD-based chronological estimates being almost beyond reasonable doubt. I admittedly don’t know a great deal on the matter, so when Davidski (see comments) insisted again on that, I asked him for a reference, so I could learn something. He kindly suggested me to read Gusev et al. 2011, The Architecture of Long-Range Haplotypes Shared within and across Populations, which is indeed a good paper. However I could not find the clearly explained basis for the chronological estimates in general, probably buried deep in the bibliography. What I found instead was a clear example of these being short from historical reality by a lot.
This example corresponds to one of the best documented populations to have suffered a “recent” bottleneck event: Ashkenazi Jews (AJ). According to Gusev et al., these would have suffered a bottleneck (founder effect of some 400 nuclear families followed by expansion) around 20 generations ago (~600 years = 1400 CE) or, a few lines later more specifically: 23 generations ago (~1320 CE). So here we do have a clear case study.
When we look at historical reality however, it is just impossible that AJ would have their founder effect bottleneck so late. Historical records document them often already in the Frankish period and they were definitely a vibrant expanding community by the time of the founding of Prague and Krakov c. 900 CE. A historical reasonable estimate for the AJ founder effect should be instead c. 700 CE, when they begin to appear in historical records, or maybe even a bit earlier, because of the lack of documentation in the Dark Ages.
That is not at all a mere 20-23 generations ago but almost double (counting generation time = 30 years, if gen-time would be 27 years, for example, the difference between estimates and reality would be even greater). Assuming a very reasonable AJ founder effect at 700 CE, then:
- For gen-time = 30 years → 43 generations till now → 43/23 = 1.9 times for realistic correction
- For gen-time = 27 years → 48 generations → 48/23 = 2.1 times for realistic correction
- For gen-time = 25 years → 52 generations → 52/23 = 2.3 times for realistc correction
While it has become nowadays standard issue to assimilate generation time to 30 years, this is not any absolute measure because the actually observed generation time (i.e. the age difference between parental and child generations on average) varies in real life depending on cultural factors (such as marriage age), gender (female generation time is almost invariably shorter than male), life expectancy (mothers dead at birth at young age, for example, don’t have any more children), etc. So it is in the fine detail a somewhat blurry issue, with some significant variability among cultures and surely also through time.
Another issue is if this “short term” estimate correction is stable along time or does in fact vary somewhat. I can’t say.
Whatever the case, the approximate x2 correction proposed above, seems to stand in general terms.
|Jwalapuram industries (from Petraglia 2007)|
PS- Petraglia himself finds Mellar’s alternative model untenable. From ABC Science (emphasis mine):
… Professor Michael Petraglia, an archaeologist from the University of Oxford disputes Richards’ and Mellars’ argument.
Petraglia says there’s not enough evidence to rule out an earlier colonisation before the eruption of Mount Toba.
“The research reported by Mellars and colleagues is riddled with problems,” he says.
Petraglia says that the similarity between tools used in Africa
60,000 years ago and those from Asia dating to around 35,000 years ago
is not a consequence of direct migration.
“These toolkits are separated in time by more than 20,000 years and distances exceeding several thousand miles.”
He questions the evidence supporting a migration along the coast. He
says that surveys of ancient shorelines have not revealed any evidence
for human settlements anywhere along the Indian Ocean shore between
55,000 and 50,000 years ago.
He also says genetic dating should be treated cautiously.
“Most geneticists will admit that genetic dating of the out-of-Africa
event is tenuous, at best. Published genetic ages for out-of-Africa
range anywhere between 45,000 to 130,000 years ago.
says his team is currently conducting archaeological fieldwork in
Arabia, India and Sri Lanka they expect will show that the story of
human dispersal from Africa is complex.
“What we can agree on is that little research in these key geographic
regions has been conducted and much more evidence needs to be collected
to support or refute the different theories,” says Petraglia.
Update (Jun 18): complementary review of the full paper now available here.
It has been argued recently that the initial dispersal of anatomically modern humans from Africa to southern Asia occurred before the volcanic “supereruption” of the Mount Toba volcano (Sumatra) at ∼74,000 y before present (B.P.)—possibly as early as 120,000 y B.P. We show here that this “pre-Toba” dispersal model is in serious conflict with both the most recent genetic evidence from both Africa and Asia and the archaeological evidence from South Asian sites. We present an alternative model based on a combination of genetic analyses and recent archaeological evidence from South Asia and Africa. These data support a coastally oriented dispersal of modern humans from eastern Africa to southern Asia ∼60–50 thousand years ago (ka). This was associated with distinctively African microlithic and “backed-segment” technologies analogous to the African “Howiesons Poort” and related technologies, together with a range of distinctively “modern” cultural and symbolic features (highly shaped bone tools, personal ornaments, abstract artistic motifs, microblade technology, etc.), similar to those that accompanied the replacement of “archaic” Neanderthal by anatomically modern human populations in other regions of western Eurasia at a broadly similar date.
|South Asian artifacts from ~30-50 Ka BP.|
By “genetic evidence” they obviously mean “molecular clock” nonsense, so it is not evidence at all but mere speculation. However I am indeed very interested in knowing in detail what they mean by “archaeological evidence”, because they seem to get into direct confrontation with much accumulated evidence, first and foremost all of Petraglia’s research in both India and Arabia but also with the quite strong evidence for pre-60 Ka human presence in Australia and growing evidence for pre-60 Ka modern humans in SE Asia (in some cases even as old as 100 Ka).
- They dedicate much text to attempt to justify a particular version of mainstream “molecular clock” hypothesis, which are clearly broke in my understanding. The kind of arguments “rebated” are more or less what I have been putting forward since many years ago. Ironically their “molecular clock” estimates make N and R much older than M, what I absolutely oppose (just count mutations downstream of the L3 node).
- No real attention is given instead to the geographical structure/distribution of major mtDNA haplogroups, only mentioned in relation to “molecular clock” speculations.
- The criticism of the African affinity of the Jwalapuram (Jurreru Valley) cores (Petraglia 2007) focuses on dismissal of any possibility of comparison, rather than on alternative comparisons.
- Another “criticism” is that there is no apparent connection between Jwalapuram and the Nubian Complex (why there should be any?, it is not the only East African techno-culture, nor the only group that shows indications of traveling to Arabia in the Abbassia Pluvial).
- Also it is “criticized” that the most comparable African culture, Howiesons Poort) is not recorded before c. 71 Ka BP (what IMO may indicate late cultural dispersals to Southern Africa from East Africa, for example, but, hey!, Mellars is fencing off balls like crazy at his conservative goal).
- They find clear similitudes between Indian and African microlithic industries (apparently related to the development of “mode 4” in both areas, as well as in West Eurasia). Indian industries are dated to c. 38-40 Ka BP, while African ones are dated to c. 49 Ka BP (Kenya) or later. However West Eurasian ones have dates as old as 55 Ka BP (not for Mellars, who remains stuck in older date references which he describes as ∼40–45 ka [calibrated (cal.) before present (B.P.)]), what really suggest that we are talking here not of the “out of Africa” but of the West Eurasian colonization process (necessarily from further into Asia, genetic phylo-geographic structure demands) with offshoots to the nearby regions.
- Another element of late Africa-India “similitude” they find is “the remarkable, double bounded criss-cross design incised on ostrich eggshell”, dated in India (Patne) to at least ∼30 ka (cal. B.P.), much earlier in South Africa. For Mellars this is beyond the range of either pure coincidence or entirely independent and remarkably convergent cultural evolutionary processes. Hmmm, really? Or are we before a clear case of wishful thinking as happens with the Solutrean-Clovis relationship hypothesis? Isn’t it 30 Ka BP anyhow well beyond any reasonable expectations for the OoA time frame, including Mellar’s own conjectures?
- Mellars accepts the paradox that the geographical limits of these highly distinctive microblade and geometric microlithic technologies are confined to the Indian subcontinent, with no currently documented traces of these technologies in regions farther to the east. And then makes up excuses for it, such as biological and cultural bottlenecks caused by “founder effects”, mysteriously leading to a loss or simplification of cultural and technological know-how, as well as fininding new and contrasting environments (in the same latitudes?!)
- Even in the case of Arabian colonization, Mellars shows to be in a very defensive attitude, admitting only to the reality of the Palestinian sites with clearly modern skulls, as well as to the area of Nubian Complex colonization (on whose peculiarities he insists a lot, as if it would be the only expression of the wider MSA techno-complex), disdaining all the other MSA colonization areas and, often ill-defined, variants.
|Primate phylogenetic tree (from Wikipedia) with the aprox. placement of Archicebus|
evolution. Nature. Vol. 498, June 6, 2013, p. 60.
Update (Jun 25): map:
Includes also Kurdish data from Palisto’s update.
The two Balochi samples are pooled in one (same weight for each), instead the two Italian samples were retained separated and assumed to be from South and North Italy respectively (not sure but makes sense).
Causes of skin and hair color variance in Europeans remain undetermined (includes data for eye color)
Update (Jun 27): Kurdish DNA just published the HERC2 data a much wider sample of populations from all Eurasia and not anymore focusing only on the blue eye haplotypes but all them instead.
It is very interesting that ht3, ancestral to blue eyes’ haplotypes ht1 and, through this one, also ht2 , is widespread through the continent with very few exceptions: Russians, Belorussians, Lithuanians and a Mordvin tribe in Europe, as well as the Kurmi, Nihali, Chenchu and Puliyar in India.
Ht5 and ht6 are also very common in Eurasia, ht7 is rare in most groups but dominant in a few (Kurmi, Melanesians) while ht4 (ancestral to ht3) is rather rare as well (highest in South and Central Asia, as well as Lebanon). Other (undetermined) haplotypes are also concentrated in some populations like the Chenchu and have some importance across Asia.
The research found that by age 2, babies who had been breastfed
exclusively for at least three months had enhanced development in key
parts of the brain compared to children who were fed formula exclusively
or who were fed a combination of formula and breastmilk. The extra
growth was most pronounced in parts of the brain associated with
language, emotional function, and cognition, the research showed.
The study showed that the exclusively breastfed group had the fastest
growth in myelinated white matter of the three groups, with the increase
in white matter volume becoming substantial by age 2. The group fed
both breastmilk and formula had more growth than the exclusively
formula-fed group, but less than the breastmilk-only group.
“We’re finding the difference [in white matter growth] is on the order
of 20 to 30 percent, comparing the breastfed and the non-breastfed
kids,” said [lead researcher Sean] Deoni. “I think it’s astounding that you could have that
much difference so early.”
Ref. (pay per view): Sean C.L. Deoni, Douglas C. Dean, Irene Piryatinksy, Jonathan
O’Muircheartaigh, Nicole Waskiewicz, Katie Lehman, Michelle Han, Holly
Dirks. Breastfeeding and early white matter development: A cross-sectional study. NeuroImage, 2013; DOI: 10.1016/j.neuroimage.2013.05.090