Chapter 9 What Molecules Can't Tell Us About the Spread of Languages

What Molecules Can’t Tell Us

Chapter 9

What Molecules Can’t Tell Us about the Spread of Languages and the Neolithic

Hans-Jürgen Bandelt, Vincent Macaulay & Martin Richards

A wealth of molecular sequence data seems to have hapless hunter-gatherers as it flooded in. Curiously revolutionized our knowledge about the distant past, though, for us (being familiar with our Dennell, so what can molecular geneticists tell us about the Barker and Whittle), the initial interest lay in the spread of languages and the Neolithic? How does discovery that there were some Neolithic immigrants inference proceed — from data to tales? Is the ge- after all. It even seemed that an estimate of ~20 per netic approach the key to a new synthesis? We will cent Near Eastern Neolithic ancestry in Europe might retell some stories of past and recent publications, actually be taken as rather strongly confirming (at briefly comment on them and discuss the potential least one interpretation of) the Cavalli-Sforza et al. of the future and the limits of the genetic programme. picture, viz. the colonization model of farming ori- gins. The ‘archaeogenetic’ enterprise It soon became clear, though, that there might be more mileage in taking on the demic-diffusionists Following Amorim (1999), and echoing the ‘geneti- rather than the indigenists, especially as it seemed cal archaeology’ of von Haeseler et al. (1996), Ren- that a certain dogmatism had set in amongst the frew (2000) coined the term ‘archaeogenetics’ for ‘the former camp. But it was not clear that such a low newly-emerged discipline which applies molecular Neolithic input was incompatible with demic diffu- genetics to the study of the human past’. His earlier sion, or even a ‘wave of advance’. So it was not suggestion of ‘historical genetics’ (Renfrew 1992) simply ‘new data’ that moved this debate forward, never really gained currency amongst geneticists, but a consideration of those data in the context of, despite the elegant evocation of historical linguis- for example, arguments such as those of Zvelebil tics. To be awkward, we will adopt the latter expres- (1986) concerning the archaeological context, and a sion, which we have only just discovered; this can consideration of how seriously the more rigid inter- serve to remind us of the delay in communications pretations of the classical marker gradients needed that often hinders relations between archaeologists to be taken. Indeed, in 1996 the evidence for which and geneticists. mtDNAs arrived when was rather weak, because of It was in the late eighties that molecules such as the paucity of Near Eastern data. A minor subhaplo- mtDNA began to tell stories, with the ‘Out-of-Af- group (T1) was subsequently shunted into the rica’ narrative (Cann et al. 1987). This wave of ad- Neolithic component (Richards et al. 1998), and the vance soon reached Europe: Richards et al. (1996), distinction between ‘Palaeolithic’ and ‘Neolithic’ lin- putting molecules rather than populations into an eages eventually became somewhat fuzzier, as more archaeological context, saw the Neolithic arriving Near Eastern data were considered (Richards et al. with members of one major mitochondrial 2000), moving us away from a rather simplistic ‘one haplogroup (J). The context of discovery for most haplogroup–one migration’ model. geneticists at the time was, of course, the demic- In any case, more than three quarters of the diffusion model of Cavalli-Sforza and his colleagues mtDNA remain firmly rooted in the European (Menozzi et al. 1978). This had gradually been inter- Palaeolithic. But there is clearly some flexibility in preted as a tidal wave of farmers swelling into Eu- the way in which stories about such data can be told. rope from the Near East, engulfing small bands of Can we be sure that the ancestors of the ‘Neolithic’

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mtDNAs were carried into Europe along with the Since the methodology of this classic series of very first cereals and goats? Hardly, although ex- papers on Saami/Finnish mtDNA still lives on, one actly this inference was the fallback position at the needs to take a closer look at how data were turned time, when the predominant view among geneticists into tales in this case. The HVS-I data were proc- was that of Neolithic replacement or waves of ad- essed by Sajantila et al. (1996) in a quite peculiar vance, so that any molecules which could be tied to way, discarding most of the polymorphic sites as the spread of the Neolithic were greeted with enthu- recently mutated. The ‘rationale’ for this was an er- siasm. A molecule entering in the Early Neolithic roneously-presumed statistical sorting, in that slow could well have had a Mesolithic carrier — it de- sites preferably changed in the distant past while pends on the archaeological reconstruction whether fast sites did so relatively recently. For the distinc- Neolithic or Mesolithic affiliation is more likely. Once tion of slow vs fast, they appealed to Hasegawa et al. the Neolithic became established, exchange networks (1993), who reconstructed multiple hits at the HVS-I broadened, allowing for introgression of molecules sites for the worldwide mtDNA data set available at during subsequent stages. Perhaps a number of mi- the time. Sajantila et al. (1996) regarded those sites tochondria rolled along with the secondary-prod- listed with exactly one hit as the potentially most ucts revolution (Sherratt 1981)? conservative sites — thus ignoring the most con- Molecular genetics, however, is conventionally servative candidates, viz. the sites unvaried (i.e. zero seen to fuel a ‘new synthesis’ centred on ‘population hits) in the test data of Hasegawa et al. (1993). Het- history’ (Renfrew 1999b). A prime example of a (far erozygosity values calculated with respect to those from grand) synthesis attempted by human geneti- ‘one-hit’ sites then were apparently much smaller cists in this spirit is laid out next. for the Finnish sample compared to the samples from most other European sources. This effect, however, The Finnish Adam and Eve story completely disappears when the zero-hit sites are also taken into consideration. Why are Finns so alike physically? A commentary in Now comes the dating of the perceived mito- th journal ‘Science’ (Holden 1996) revealed the excit- chondrial bottleneck that should account for the low- ing reasons uncovered by molecular geneticists: the seeming diversity of the Finnish mtDNA pool. The Finns descended from a small band of people who average nucleotide difference in the 360-bp segment settled in what is now Finland some 4000 years ago of HVS-I was calculated as 3.9 substitutions (all sites (Sajantila et al. 1996). Their language was originally having been put back for this exercise). One unit of Indo-European but later these people switched to pairwise difference scaled with the divergence rate Finnish, which they learnt from their Saami neigh- of Ward et al. (1991) would then correspond to 8,300 bours (Sajantila & Pääbo 1995). The Saami, on the years, but Sajantila et al. (1995, 1996) enigmatically other hand, appear to have been separated from the cited this as 13,000 years. On top of this, they con- other European populations for tens of thousands of founded divergence rate (the rate at which two line- years (Sajantila et al. 1995). The data on which these ages diverge from a common ancestor) with sweeping claims were based were a meagre handful substitution rate (the rate at which a lineage diverges of Y-chromosome microsatellites and the mitochon- from an ancestor), so that they effectively turned the drial hypervariable segment I (HVS-I). false 13,000 years into 6500 years (a lab-specific blun- None of those assertions has had a long half- der; see Bandelt & Forster 1997). Even then the per- life as more data from Finland accumulated; cf. Kittles ceived bottleneck came out too old for the story to be et al. (1999), Peltonen et al. (2000, Box 2), and Finnilä told. So, a substitution rate faster by more than an et al. (2001). As to the question of potential language order of magnitude was invoked (gleaned from ma- replacement in Scandinavia, most scholars prefer to ternal pedigree studies; cf. Pääbo 1996): this gave an believe that Proto-Finnic, ancestral to the present- age reported as 3900 years, which again has to be day languages Saami, Finnish, Estonian etc., spread halved (now, however, turning out to be too young) into the eastern Baltic area during the Eastern Bronze because of the substitution/divergence confusion. Age, thus eventually leading to a complete replace- The slightly earlier published story about the ment of the languages spoken earlier in the area of Saami (Sajantila et al. 1995) uses the conventional Finland. In particular, Kallio (2000) suggests (in modi- mutation rate and genetic distances in the form of fying Posti’s theory) that the Proto-Baltic and Proto- net nucleotide differences, which are the averaged Germanic speakers in the area became Finnic nucleotide differences between two populations mi- speakers (Finnic notabene, not Finnish). nus the average of the averaged nucleotide differ-

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ences within the respective two populations. Al- theoretically convenient (it means that each gene though no attempt was made to scale these genetic copy throughout the history of the sample is a part distances to absolute time (using the assumed muta- of a population of the same size), it could not be said tion rate), Sajantila et al. (1995) tacitly assumed that to be useful. Even under (marginally) more realistic the ages of typical population splits in Europe were scenarios of different population sizes in the three of the order of a few thousand years, so that the populations, linearity in the time of the split is lost, genetic distances of the Saami to the other European unless the daughter populations have been going populations would correspond to tens of thousands their separate ways for a very long time. For real of years. Note that the authors regard the Saami as a data, which typically show imprints of founder/ex- constant-size population whereas all other European pansion events, this distance measure is therefore populations are seen as having expanded in size. rather meaningless. As an illustration, consider the This, however, clashes with the theory behind the Basque sample (of size 156; Richards et al. 2000) and distance measure employed (see below). the Korean sample (of size 124; Horai et al. 1996; The contrast between the mtDNA pools of Saami Pfeiffer et al. 1998): then the net nucleotide difference and Finns was interpreted by von Haeseler et al. (when scoring only transitions in 16090 to 16365, (1996) in the same way (under the roof of coalescent without reconstruction of recurrent events) equals theory) as the typical genetic contrast between 5.094 – (2.769 + 5.670)/2 = 0.874, which corresponds hunter-gatherers and agriculturalists. What was evi- to a time of 8800 years (Forster et al. 1996). This may dently overlooked is that both mtDNA samples share seduce the working historical geneticist to conclude some basal branches of the Eurasian mtDNA phylo- that Koreans and Basques originally spoke a Proto- geny and coalesce on the same Eurasian founder Basque–Korean language and arrived with the haplotype. This ancestral type cannot at the same Neolithic from the Fertile Crescent. time be tens of thousands of years old and four (or In order to address phenomena of historical or two) thousand years old. The message for archaeolo- recent prehistorical times, one may study the number gists and linguists interested in what geneticists have of matching haplotypes across regional or ethnic to say about Finno-Ugric prehistory must be: don’t groups. For reflecting ancient events dating to the believe everything you read in the papers. Palaeolithic, however, matching coefficients would be so small that subsequent fluctuations in popula- Split the difference tion sizes and gene flow would likely destroy any potentially meaningful relationship to real time. This Genetic distances (viz. net nucleotide differences) did not hinder Barbujani & Bertorelle (2001) from between several European populations, Turkey, and taking matching coefficients as time estimators for the Near East based on mitochondrial HVS-I se- putative population splits back to 40,000 years. When quences were listed in table 2 of Richards et al. (1996) comparing European and Near Eastern populations, for the sake of demonstrating their futility. The dis- they observed two major modes (at coefficient val- tances between the Near East and the other ues of 3 per cent and 0.6 per cent, respectively) in the populations would be scaled to times within the pairwise population comparisons, which they inter- range of 1000–4000 years, whereas all other distances preted as pointing to splits at 4000 years ago (re- fall into the range of 0–1000 years. Curiously, the ferred to as ‘Neolithic divergence’, although this date former values have been taken seriously as evidence sits rather in the Bronze Age) and at 40,000 years ago for ‘Neolithic’ population splits by Barbujani et al. (equated with ‘Palaeolithic divergence’, although the (1998). But what about the latter values? For instance, Palaeolithic ended only about 12,000 years ago). the split between Bavaria and Turkey receives an Before jumping on any desired interpretation, entertaining age of under 100 years. It seems wise at one should first consider the numerical cause for this point to consult the theoretical basis of the dis- having greatly differing matching coefficients. In the tance measure employed. European mtDNA pool, one HVS-I haplotype is ab- The net nucleotide difference distance (Nei & solutely predominant, viz. the Cambridge reference Li 1979), when scaled by twice the mutation rate, is sequence (CRS), which accounts for 20 per cent and an unbiased estimator of the time of a clean popula- more of the regional mtDNA pools in western Eu- tion split - but only under very limiting circum- rope. The matching between such pools is thus over- stances. In particular, the (constant) population sizes whelmed by shared CRS haplotypes, which essentially of both the ancestral and each of the two daughter determine the matching coefficient. The pattern of populations are required to be equal. While this is CRS sharing runs counter to any ‘Neolithic’ inter-

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pretation as the CRS frequency cline runs opposite no limit to the diachronic perspective, as long as the to a putative Neolithic cline from the Near East, genetic polymorphisms under study have existed from ~20 per cent in the west to ~12–16 per cent in for long enough. In the case of a uniparental marker central and eastern Europe, through ~10 per cent in such as mtDNA the limit is the coalescence time of the southeast and ~6 per cent in the Near East the sample. Coalescent theory allows exercises such (Richards et al. 2000). Relatively low CRS frequency, as the ones enjoyed by Weiss & von Haeseler (1998), though, is not always a ‘Neolithic marker’, but may who discovered that ‘the population of the Basques be due to founder events in historical time (Saami, has expanded, whereas that of the Biaka pygmies is Icelanders) or simply due to poor sequencing (Ladins) most likely decreasing. The Nuu-Chah-Nulth data that carried phantom mutations into innocent CRS are consistent with a model of constant population’. (and other) sequences (see Bandelt et al. 2001; 2002). In these cases, the populations are considered as Note that the European story told by Barbujani viable isolated genetic units, well-delineated from & Bertorelle (2001) requires the assumption of expo- the rest of the world since between 50,000 and 150,000 nential growth after population splitting in order to years ago. This clearly does not sit easily with the make the dates fit. In contrast, the above stories told results of modern ethnographic research (e.g. Hodder by Barbujani et al. (1998) from net nucleotide differ- 1982). ences require constant sizes throughout. One cannot Cutting down the information contained in have both demographic scenarios at the same time, mtDNA, the Y chromosome, or autosomal genes to unless one adheres to research programmes such as the population level did not prove conclusive even those described in the next two sections. in the (outdated) debate of multiregionalism versus Out-of-Africa. It is instructive and discouraging to Populations as æther see that even with the wealth of new data hardly any progress could be achieved in discriminating clearly The desire to date population splits closely follows between the two alternative models when the popu- the paradigm set forth by the mentor of human popu- lation remains the target of investigation; see lation geneticists, Cavalli-Sforza (1998), who empha- Harpending & Rogers (2000) and Relethford (2001). sized that ‘it is the date of population splits, not the Concepts that were introduced in the early de- birth of individuals carrying the first mutation, that velopment of a discipline where they actually may is of interest for comparison with archaeological have played a useful role may cease to be useful, just dates, which usually refer to the early settlement of as did the concept of the æther in physics. Even the new areas’. The relationship of dating with popula- constant elaboration of models of the æther could tion models has become so intimate that there is a not reconcile the observations with the theory (Ein- widespread belief that any rate of accumulation of stein 1905); so too with the theoretical unit at the genetic changes is based on the assumption of de- heart of classical population genetics, the popula- mographically stable populations, so that ‘the dat- tion. To dismiss the concept of a population does ing of genetic changes have very broad confidence not, of course, mean that one should, for example, limits and may be in error altogether’ (Zvelebil 2000). turn mtDNA molecules into fictive Ur-mothers, com- Although age estimation for a mutational event does plete with names and absurd birthplaces in space rely on a molecular clock, it does not have to be and time, not to mention a mystical bond with their channelled through doubtful a priori assumptions of descendants (Sykes 2001). demographic scenarios. The concept of the population, even at the Alchemy synchronic level, is absolutely vague, although it appears that most geneticists think they have an In a way similar to the tale about Finnish and Saami intuitive (nationally inspired?) feeling of what a origins, contrasting so-called hunter-gatherers to ag- population really is. The list of European populations riculturalists, mtDNA data have been (ab)used to in the compendium of Cavalli-Sforza et al. (1994) adduce the demographic impact of food-production apparently equates populations with nation states in Africa (Watson et al. 1996). Bandelt & Forster (1997) except for a few ‘tribes’ that are delineated as differ- have rejected ‘the superficial assertion that the adop- ent, such as the Basques, Saami, and Sardinians. The tion of agriculture is the key to mismatch distribu- history of the thus (un)defined populations then be- tions, without reference to a particular cultural/ comes the centre of interest to the population geneti- ethnic population with an identifiable mixture of cist (Barbujani & Bertorelle 2001). There seems to be genotypes with a defined statistical shape and the

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archaeological/ethnographic data that addresses simplistic model and an upper limit distilled from a when that particular population may be inferred to hand-waving contrast with Africa — were effectively have settled in a region and farmed in a way distinc- plucked from the air. This sort of post hoc accommo- tive enough (specific stylized tool kit) to be associ- dation is set up just in order to make the ages of ated with that particular group. In effect, Watson et founder haplogroups match the archaeological dates. al. (1996) provided no internal calibration to their Data are often regarded as ‘storytellers’ (Bertran- system. This lack of calibration leads to a confusion petit 2000). ‘This reflects the culture history view between population and lineage identity. Many Af- that new data, rather than new frameworks, are the rican populations actually coalesce at the deepest most important aspect in the development of histori- nodes of the global human tree, if one is paying cal genetics. The framework allows the data to ‘speak attention to tribal/linguistic affiliations. Languages for themselves’ and what they tell the ‘listening’ change, food preferences change, but genotypes per- geneticist is when change took place and from what sist, sometimes, under different population struc- direction it came. Very often any further interpreta- tures. How long they persist is a matter of drift and tion is regarded as speculation and if it occurs it will effective population size. The persistence of the same only be found hidden away in the closing remarks mtDNA allelic lineages in hunter-gatherer popu- rather than the body of the report’ (cited from Gam- lations and farming populations implies that mis- ble 2001 by substituting ‘archaeology’ and ‘archae- match distributions are insensitive to very recent ologist’ by the genetic counterparts). This, nonetheless, demographic expansion, as argued by Rogers (1995). does not exclude a situation where it may be fairly How then can the effects of agriculture, as opposed pedestrian, with appropriate genetic data in hand, to to infectious disease, be seen with any accuracy? It reject simple tales (such as the speedboat-to-Poly- seems like alchemy’ (edited from an anonymous ref- nesia story of Diamond 1998; Oppenheimer & eree report, January 1997). Richards this volume). To build up reasonable mod- The analogy to alchemy suggests yet another els that explain the past with some guidance from feature in common: you make grand claims to the genetic data is quite another matter. funding body — then, it was king; now, the national Historical genetics thus appears to be ‘pre- science foundation. One of the main problems using processual’ when compared to archaeology. It lacks genetics to answer prehistoric questions is that the a nuanced theoretical base — or, to put it more dras- geneticist is expected to get a grant to do something tically, it has no theory at all which would take into stupendous, and then quickly solve it and move on account the specific prehistoric issues. The few for- to do something else. Thus, there has been an over- malized migration models or coalescent models that emphasis on superficial population-genetics formali- come along with some cute mathematics are as ‘ap- zations and insufficient attention to the resources of plicable’ to humans as they are to lizards. But would other disciplines. we really envision a prehistory of lizards?

The tale goes on Mind the gap

Coalescence times are still being calculated using the The predominant desire in historical genetics thus (theoretically convenient) model of random-mating seems to be a craving for black boxes (genetic dis- populations of constant sizes. For instance, Su et al. tance measures, mismatch distributions, principal (1999) used this model for estimating coalescence components, population trees, synthetic maps, times of Y-chromosome haplogroups in eastern Asia. autocorrelograms, constant-size coalescent models) They explicitly claim that the estimation is robust into which data are poured (Bertranpetit 2000) and even under a ‘strong bottleneck event followed by a whence fundamental truths emerge. They are per- rapid population expansion’. This issue, however, is ceived as adding a patina of rigorous model-testing not really relevant here since the major departure to the discourse. However, in most cases the surface from the model is the presence of geographic sub- is easily scratched away to reveal an interpretative structure — random mating was never possible in lacuna. A common approach is to construct a null an area as huge as China — and several phases of hypothesis of almost no interest to the historical ge- considerable expansion, leading to potentially dra- netic programme and to reject it if possible; within matic miscalculations of coalescence times. Bounds an orthodox statistical framework, this is well and on the effective population size — a spurious lower good. However, the miracle then happens when you limit that is clearly not robust to departures from the are left to commune with the statistics you have

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evaluated and to weave them into an interpretation. tation at a single locus is rather meaningless on its The interpretations of correlograms within spatial own since this was just a random event and only the autocorrelation analysis or of the pattern of statistics aggregation of the variation observed at the genome evaluated within nested cladistic analysis should be would allow any inference about prehistory. This, seen in this light. In both cases, the rejection of the however, is only an issue when the fictive populations trivial null of no geographical structure leaves one at are the sole target of investigation. Every single mu- the mercy of some interpretative key, in the latter tational event has its specific space-time coordinates, case made painfully explicit (Templeton 1997). which may be difficult to reconstruct exactly, al- Granted, you can disguise the void between the train though broad regions and time intervals of origin and the platform with a dose of simulation studies can potentially be estimated within an archaeologi- to see how your statistics behave under alternatives cal/ecological model (Richards et al. 2000). Group- to the null. But without explicit models and at least ing numerous mutations with similar space-time an approximate estimate of the probability of the coordinates could then help to postulate trajectories data given each of those models, the interpretation of contacts, which in turn may lead to a more refined should be received with the same scepticism as a modelling. story one might tell about a synthetic map (Richards et al. this volume). The grand synthesis

Aggregation of genetic information In order to arrive at some sort of synthesis of the results derived from different disciplines, it is man- Before thinking of a grand synthesis, which genetic datory that the analyses of the separate disciplines data should be considered for historical genetics and be carried out with an adequate level of proficiency how should they (if at all) be aggregated? Classical (Renfrew 1999a). We have seen above that as far as markers, large in number but each one typically mea- genetics is concerned neither the sequencing (Bandelt gre in information, bear only indirect evidence of et al. 2001) nor the data handling and analysis (Torroni genetic polymorphisms: only few alleles can be dis- et al. 2000) always meet minimal quality criteria, cerned, which in many cases were spread across the thus paving the way for phantasms to appear. Weak continents, albeit at different frequencies. By han- and highly controversial linguistic evidence (such as dling a large number of these markers as vectors of for the Nostratic hypothesis; cf. Trask 1999; Campbell combined allele frequencies for perceived popu- 1999) would not be improved by tagging molecules lations, Cavalli-Sforza et al. (1994) were able to visu- onto it, which would suggest the desired timing for alize the dominant features of these allele distributions the spread quite wonderfully. In this sense, there is via Principal Component Analysis (PCA) and subse- no application of genetics to historical linguistics quent synthetic mapping. This has hence become the (pace Renfrew 2000) — at least none in a direct way stance of aggregation of genetic data: compile and (Renfrew 1999b). For example, the fact that some feed them into PCA or subject them as genetic dis- mtDNAs may have been carried from the Near East tances to cluster analysis. to Europe with the early Neolithic (Richards et al. The assertion that ‘HVR-1 data . . . can and must 2000) makes it plausible that some new languages be treated like any other set of genetic data and be were transported into Europe. Whether or not these analyzed by the standard population-genetics meth- included Proto-Indo-European is, however, far from ods’ (Simoni et al. 2000) reflects this classical think- clear, especially since most linguists would prefer a ing. It ignores the fact that no other genetic system later date for the expansion of the Indo-European analyzed thus far bears the same fine-grained, deep family. Again, although we could not find major hierarchical structure as mitochondrial DNA (al- traces of mtDNA input to Europe during the though Y-chromosome information is starting to Mesolithic, a minor introgression of Proto-Indo-Eu- catch up), which allows one to tap into the phylo- ropean speakers into the Balkans with the onset of genetic information. Furthermore, as a technical the Holocene (Adams & Otte 1999) is not excluded point, the resolution offered by the first hypervariable by the mitochondrial record. segment (‘HVR-1’) alone does not suffice for one to The (early and later) Neolithic may well have be able to identify all of the important (monophyletic) brought more than one language family to Europe clades of the matrilineal genealogy, even if one were (Sheratt & Sheratt 1988). Any leap-frog migration or able to reconstruct all recurrent mutational events. small-scale movements must have brought people It has been claimed repeatedly, too, that a mu- with (not necessarily related) languages into new

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territory, where the new languages — at a later stage genetic database, left notoriously poorly analyzed — would come into competition with the already by the geneticist, and to explore its impact. A syn- resident ones in the course of the formation of a thesis, however, would be going full circle — back to unified regional culture. There is no compelling rea- culture history — if it were to aim at a fallacious son to believe that the Fertile Crescent itself, as a congruity of material culture, language and genes core area for Neolithic beginnings, was monolin- (Zvelebil 1995; Sims-Williams 1998). We should ex- gual. One could therefore assume, by default, that pect to see these spheres somehow inter-related (trivi- (for example) the early spread of Neolithic cultures ally or accidentally) but more often ‘out of step’ (as into the Balkans and along the Mediterranean was in Beringia: Fortescue 1998). It is then an under- accompanied by quite different language families in standing of the specific dynamics of these spheres each case. The western and central Mediterranean and their disharmony that enables us to transcend regions in fact do not show any signs of early Indo- syntheses framed as neat ‘origins-of’ packages, such European speech. On the other hand, the ancient as the speedboat-to-Polynesia or the three-wave sce- languages of the Iberian and Italian peninsulas need nario for American beginnings. not all be regarded as Palaeolithic leftovers (as is presumably the case with Basque). For instance, the Acknowledgements related languages Raetic and Etruscan, which were replaced by Latin vernaculars, were in turn related This work has been supported by a travel grant from to a language once spoken in the Aegean island of the DAAD (Deutscher Akademischer Austausch- Lemnos (Rix 1998). dienst) to H-J. Bandelt and a Wellcome Trust Re- To stretch the origin of language families to the search Career Development fellowship to V. Fertile Crescent or nearby regions (Barbujani et al. Macaulay. 1993; 1994; Renfrew 1999a) may not explain the real processes, which could actually have run in the op- References posite direction or have involved other centres of origin. For example, the languages of the Altaic type Adams, J. & M. Otte, 1999. Did Indo-European languages most likely originated from a Manchurian spread spread before farming? Current Anthropology 40, 73– zone, as was convincingly argued by Janhunen (1996). 7. The steppes have repeatedly been zones of rapid Amorim, A., 1999. Archaeogenetics. Journal of Iberian Ar- chaeology 1, 15–25. spread in either direction (east or west) at climati- Bandelt, H.-J. & P. Forster, 1997. The myth of bumpy cally favourable periods. Further, in view of the grow- hunter-gatherer mismatch distributions. American ing evidence for an indigenous Neolithic in South Journal of Human Genetics 61, 980–83. Asia and the pre-agricultural subsistence of Proto- Bandelt, H.-J., P. Lahermo, M. Richards & V. Macaulay, Dravidians (Fuller, this volume), there is no need to 2001. Detecting errors in mtDNA data by phylo- posit that the Dravidian language family was intru- genetic analysis. International Journal of Legal Medi- sive to India (whether or not Elamo-Dravidian is cine 115, 64–9. accepted). Bandelt, H.-J., L. Quintana-Murci, A. Salas & V. Macaulay, A trans-disciplinary approach to prehistory, for 2002. The fingerprint of phantom mutations in mi- tochondrial DNA data. American Journal of Human which a synthesis — grand or not — may strive, Genetics 71, in press. would take account of the multi-facetted past by Barbujani, G., G. Bertorelle & L. Chikhi, 1998. Evidence engendering a nuanced modelling process. It would for Paleolithic and Neolithic gene flow in Europe. tell the human geneticist that there exists a well es- American Journal of Human Genetics 62, 488–91. tablished discipline, prehistoric archaeology, from Barbujani, G. & G. Bertorelle, 2001. Genetics and the popu- which he or she could learn, forestalling the testing lation history of Europe. Proceedings of the National of irrelevant hypotheses with dubious methods and Academy of Sciences of the United States of America 98, inappropriate data (Zvelebil 2000). It would stimu- 22–5. late the linguist to develop more complex models for Barbujani, G.A. & A. Pilastro, 1993. Genetic evidence on origin and dispersal of human populations speak- the origin and spread of a specific language family ing languages of the Nostratic macrofamily. Pro- and to investigate ancient sprachbund and other con- ceedings of the National Academy of Sciences of the USA tact phenomena that could point to language meshes 90, 4670–73. of the early Holocene or the late Upper Palaeolithic Barbujani, G.A., A. Pilastro, S. de Domenico & C. Ren- (Janhunen 1996; Dixon 1997; Fortescue 1998). It would frew, 1994. Genetic variation in North Africa and encourage the archaeologist to embrace the human Eurasia: Neolithic demic diffusion vs paleolithic colo-

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