A geo-referenced radiocarbon database for Early Iron Age …tcrnjst/RussellSteele2009.pdf ·...

327

Southern African Humanities Vol. 21 Pages 327–344 Pietermaritzburg December, 2009

http://www.sahumanities.org.za

A geo-referenced radiocarbon database for Early Iron Age sites in sub-Saharan Africa: initial analysis

1Thembi Russell and 2James Steele1 Corresponding author: School of Geography, Archaeology & Environmental

Studies, University of the Witwatersrand, Wits, 2050 South Africa; [email protected]

2 AHRC Centre for the Evolution of Cultural Diversity, Institute of Archaeology, University College London, 31–34 Gordon Square, London WC1H 0PY, UK;

[email protected]

ABSTRACTWe report on the compilation of a new geo-referenced database of Early Iron Age dates for the regions associated with the expansion of Bantu-language speaking peoples in sub-Saharan Africa. We explore the database as a source of coarse-grained evidence of ecological constraints on site location, and as a source of evidence for large-scale spatial trends in dates for first arrival and subsequent in-filling. It is evident even at this very coarse spatial scale that Early Iron Age sites are not typically found in areas with very low or very high annual rainfall. Four hundred millimetres of annual rainfall seems to be an effective minimum for Early Iron Age sites, although the database contains a few outliers located in areas where present-day rainfall is slightly below that threshold level. It is also evident that Early Iron Age sites are preferentially found in locations that provide suitable habitat for growing seed crops of tropical origin such as sorghum. It is not clear that there is any temporal delay differentiating the arrival of the Early Iron Age in the 10°S–20°S band from its arrival in latitudes further to the south, with visibility increasing markedly in both latitudinal bands at about AD 200.KEY WORDS: 14C, radiocarbon, sorghum, rainfall, demic expansion, African Early Iron Age.

In Africa today there are some 600 Bantu languages that are spoken by 200 million people who live in an area covering nine million square miles (Phillipson 2005; Robertson & Bradley 2000; Vansina 1995; see Fig. 1). This pattern derives from a dispersal that started in the region of Cameroon at approximately 2000 BC and reached South Africa by AD 350 (Phillipson 2005; Vansina 1994–95). This dispersal is archaeologically manifest in the spread of the Iron Age. By AD 600, Bantu-speaking peoples were living in much of southern Africa. These were people who made iron, grew crops such as sorghum and millet, lived in villages, kept cattle, sheep and goats and made ceramics (Huffman 1970, 2005, 2007, Phillipson 1985). They were distinct from hunter-gatherers, who at the time of contact with the expanding Bantu-speaking people, spoke languages of the Khoisan family (Cavalli-Sforza 2001). Also present on the southern African landscape at this time are a pastoralist people, whose presence postdates that of hunter-gatherers but might have slightly preceded that of farmers (Sadr 2008). Pastoralists made ceramics, and kept cattle, sheep and goats, and probably spoke languages of the Khoisan group.

Parallels can be drawn between the African Early Iron Age (EIA) and the spread of the Neolithic in Europe (e.g. Diamond & Bellwood 2003; Gronenborn 2004; Mitchell 2004). Whereas in Europe the debate has revolved around the degree to which cultural innovations diffused by adoption amongst pre-existing hunter-gatherer populations, as opposed to by the invasive spread of growing and fissioning farmer groups, in Africa

328 SOUTHERN AFRICAN HUMANITIES, VOL. 21, 2009

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RUSSELL & STEELE: RADIOCARBON DATABASE FOR EARLY IRON AGE SITES 329

there has been little opposition to the idea that this was wholly a process of demic expansion. Whilst we accept this consensual view, we see the potential of the database reported here to provide us with an opportunity to unravel the Bantu-speaker package so as to explore its complexity (see Chami 2001; Ehret 2001; Robertson & Bradley 2000; Phillipson 1969; Vansina 1994–95 who question, to different degrees, the extent to which an exotic people, language and culture can be wrapped into a moving package).

In the case of Neolithic Europe there is now a substantial literature evaluating radiocarbon evidence for the rate of spatial spread of the Neolithic transition (e.g. Pinhasi et al. 2005; Russell 2004); for coastal and riverine corridors of unusually rapid dispersal (Davison et al. 2006); and for multiple origins for different elements of the Neolithic package (Davison et al. 2009). The average rate of spread of the main European Neolithic package has been modelled in terms of an underlying demographic process in which incoming populations grow until they reach a carrying capacity imposed by the environment and by their technological and social strategies, and simultaneously fission and disperse into neighbouring territory (where new settlements are formed, and the process begins again from this new location). The basic dynamics are modelled analogously to those studied by invasion biologists (who deal with the modern spread of introduced or invasive species). Two-population models allow for interactions with pre-existing local hunter-gatherer populations, which may have provided a source of additional recruits to the farming way of life (e.g. Ackland et al. 2007; overview in Steele 2009). One virtue of such models is that they can be used to predict the speed of spread of the invading farmers’ population front, which can be compared with the observed rate of spread in the archaeological record.

This positioning paper reports on the compilation of a new geo-referenced database of EIA dates for the regions associated with the expansion of Bantu-speaking peoples in sub-Saharan Africa. Our research project follows in the footsteps of Tom Huffman and Tim Maggs in examining the temporal evolution and spatial dispersal of cultural attributes of the African EIA. In this paper we restrict ourselves to reporting the existing contents of the database, exploring it as a source of coarse-grained evidence of ecological constraints on site location, and exploring it as a source of evidence for large-scale spatial trends in dates for first arrival and subsequent in-filling. In future we plan to integrate information about ceramic facies, model the possible population dynamics of a demic expansion, and predict first arrival times at different spatial locations (to make the assumptions of the demic model explicit and testable).

As a long-term objective, we intend to use the database to explore in more detail two sets of generalizations about the locations and vectors of spread of EIA sites in southern Africa. In KwaZulu-Natal, EIA sites are found in level valley-bottom situations with tillable (colluvial and alluvial) arable soils and proximity to rivers or lake shorelines with opportunities for grazing and for obtaining timber (Maggs 1980, 1994–95). Their overall distribution is limited, with the exception of the eastern coastal forests, to bushveld zones where summer rainfall minima are sufficient for tropical crops (sorghum, millet). In the present paper we examine the distribution of EIA sites in our more geographically extensive database to see how well their distribution is defined by this set of growing-season constraints.

Huffman (e.g. 1970, 1982, 2007) has meanwhile suggested that EIA dispersals into southern Africa followed three streams (based on ceramic style divisions): an eastern


stream, the Kwale Branch, derived from the Urewe TradiTion in east Africa and reaching coastal Mozambique and South Africa by AD 300–350; the Nkope Branch of the Urewe TradiTion entering central southern Africa at about AD 250; and a western stream, the KalUndU TradiTion, dispersing southwards from northwest Angola and reaching South Africa by AD 350. In the present paper we examine the radiocarbon dates for sites in our database to evaluate the coarse-grained evidence for the southwards extension of the EIA; a further expansion of the database will be needed to integrate it with the ceramic facies before hypotheses can be tested about the specific trajectories of these three ‘streams’.

MATERIALS AND METHODS

The aim was to collect radiocarbon determinations associated with the EIA in sub-Saharan Africa and their geographic co-ordinates. Data were collected from those countries where Bantu languages are spoken today (Vansina 1984).

Data were sourced from a combination of published articles, personal collections and institutional databases. The principal source journals for such data are Radiocarbon and the Journal of African History. Radiocarbon has been an invaluable source of geographic coordinates associated with radiocarbon determinations. It is also a key source for determining what has been dated, and therefore what a radiocarbon date might represent. For the current database, volumes from 1959 to 1997 are covered. The Journal of African History has biennial lists of radiocarbon determinations published from 1961. As the number of radiocarbon determinations increased, this Journal started to publish lists by African region. Radiocarbon determinations have been collected in the following volume years: 1961, 1963–67, 1969–74, 1976, 1977, 1979–82, 1984, 1985 and 1991.

Other journals also contain source data. Radiocarbon lists for Africa are published in the journal African Studies. The journal Azania contains site-specific reports on excavations (useful for establishing what a radiocarbon determination is associated with). The South African Archaeological Bulletin, Current Anthropology, Southern African Humanities, Nyame Akuma, Man and the Journal of Archaeological Science also provided data.

We also used the Natal Museum database, which contains a record of all recorded archaeological sites in KwaZulu-Natal, South Africa. From this database all sites that were classified as EIA were extracted. Tom Huffman of the University of the Witwatersrand provided a list of EIA sites in South Africa and their associated radiocarbon dates.

Geographic coordinates were recorded from source material where available. When no geographic coordinates are given in the source material then published site maps were used to give an estimate of geographic coordinates. In such cases this is noted on the database and a printout of the map is attached.

Radiocarbon determinations were included if they were described as EIA by the source excavator/author. All the information given in the source material about the association of a radiocarbon determination to the EIA has been included in the database. Cases where the determinations were said to be contaminated (e.g. by post-depositional burrowing) were excluded. Ceramic typology is taken as the best indicator that a site is associated with the EIA. As we expand the database we shall work on verifying the strength of associations between radiocarbon determinations and the EIA. When ceramics are


found in association with other parts of the EIA package (iron, domestic crops, domestic animals, village/homestead, grain bins), this strengthens the association between a radiocarbon determination and the EIA people whose expansion we are tracking. At sites that had multiple radiocarbon determinations for the EIA, all the determinations were included in the full database. Sites vary in the amount of archaeological research effort applied to their dating, and we need to control for the biasing effects that this may have on analyses. In cases where we want a single value for the first occupation time of each site or site phase, we might take the average of a set of dates, or the oldest date or the most recent; these methodological issues require careful consideration (Russell 2004). For the present paper the oldest EIA-associated radiocarbon determination for each site was used in all analyses requiring calibration or pooling. Radiocarbon determinations reportedly associated with cultural material other than EIA at a site containing EIA material were excluded.

There are currently 965 entries in the database. A summary list of the database fields recorded is as follows:

• Source: Journal Name, Year, Volume, Number, Pages, Author name(s), Title.• Date: Laboratory Name, Radiocarbon Date Identifier (ID), Radiocarbon

Determination (Years Before Present, uncal. (b.p.)), Standard Deviation (Years b.p. uncal.), Material Dated.

• Site: Site Name, Latitude, Longitude, Country, Site Description, Stratigraphy, Dated Material Description/Association.

• Other References to Site/Radiocarbon Determination.• Link to published sources.

The basic breakdown is shown in Table 1 (see also Figs 1 & 2).

METHODS OF ANALYSIS

Geographical Information Systems (GIS) softwareWe used DIVA-GIS 5.2 (Hijmans, Guarino et al. 2005), a freeware package that enables plotting of EIA find spots and implements the DOMAIN (Carpenter et al. 1993) distribution-modelling algorithm. Using this software we plotted EIA sites for which we had latitude and longitude co-ordinates and for which we also had radiocarbon dates. In comparing the distribution of sites in our archaeological database with the distribution of potential habitat for plant species such as sorghum, we used the WorldClim 1.4 gridded modern climate dataset (see Hijmans, Cameron et al. 2005 and http://www.worldclim.org/), as well as values for the habitat tolerance of plant species from the FAO EcoCrop database (http://ecocrop.fao.org/), in both cases as implemented in the DIVA-GIS distribution package. We chose these datasets because they provide first approximations for EIA climatic conditions and are pre-programmed into DIVA-GIS; it is therefore a very straightforward matter to use them for predictive modelling. We used the annual ≥ 400 mm and ≥ 300 mm rainfall isohyets, and the habitat suitability map for sorghum, to define the potential range for EIA sites on our base map. We asked whether the EIA sites were preferentially distributed in areas that were more suitable for sorghum, by comparing the frequencies of EIA sites located in cells at each level of sorghum suitability with the underlying frequencies of cells with those suitability ratings in the whole study region. We defined a rectangular region


bounded by the minimal set of corner co-ordinates that contains all the sites in our geo-referenced and dated sample.

We also independently used DIVA-GIS to model potential distributions of EIA sites based on the geo-referenced sites in our database, using the DOMAIN mean distance algorithm and a set of climatic variables. DOMAIN requires a distribution map of known site locations, and at least one environmental map layer that can then be used to predict occurrences of other sites. We wanted to predict the distribution of EIA farming settlements, using our database of EIA sites. We therefore used software developed by conservation biologists to predict the potential distribution of taxa, based on their known occurrence and on environmental variables. Our assumption is that EIA sites (defined by pottery associations, and constituting our cultural ‘taxon’) lay within some environmental ‘envelope’, in which climate and other variables permitted the establishment of a farming economy. EIA pottery is therefore used as an indicator of occurrence of such an economy: we estimated the potential for finding EIA pottery in locations for which we have no records, based on a comparison of the environmental characteristics of such locations with those of known find-spots.

DOMAIN uses only occurrence data, and classifies cells in a grid for their potential to include the taxon of interest based on a similarity metric (the Gower metric, Gower 1971). In this procedure, the similarity between any two cells A and B is estimated as RAB, where, for a set of p environmental variables,

TABLE 1African sub-Saharan EIA database; showing the breakdown of the database according to the number of

EIA sites, dates, and geo-referenced sites.Country No. of

sitesNo. of sites with co-ordinates

No. of dates

No. of sites with co-or-dinates and dates

Angola 11 10 10 10Botswana 6 3 7 3Burundi 3 3 8 3Cameroon 16 16 43 16Central African Republic 2 2 3 2Republic of Congo 2 2 2 2Democratic Republic of Congo 21 13 54 12Kenya 21 12 38 12Malawi 28 7 42 8Mozambique 7 5 20 5Namibia 4 4 4 4Nigeria 3 3 7 3Rwanda 11 7 15 8South Africa 368 368 75 27Sudan1 1 1 1 1Swaziland 2 1 4 2Tanzania 15 10 36 15Uganda 6 2 14 3Zambia 50 26 141 38Zimbabwe 38 24 63 32Total 615 519 588 206


The potential of occurrence of the taxon of interest (here, the EIA site) in a given cell A is then estimated by comparison with the set of known-occurrence sites Tm based on a maximal similarity index SA

Because this procedure could give undue weight to outliers that were miscoded for location, occurrence of the taxon, or for values of the environmental parameters, it is

Fig. 2. Distribution of Early Iron Age sites in the database that have both co-ordinates and radiocarbon dates (n = 206). The rectangular bounding box contains all these sites, and defines the area of land surface for which background values were estimated for the habitat suitability parameters in Figures 3 and 4 (see below). See endnote for details of the site in the Sudan.


also possible to estimate SA, based not on the unique maximum similarity value, but on the mean of a number of similarity values RTjA. In DIVA-GIS this option is also implemented and estimates SA as the mean of all values of RTjA, which minimizes the effects of any such coding errors.

The environmental data used in predicting occurrences in this study are again the modern climatic values included in the WorldClim 1.4 gridded dataset; we used these data at a gridded 10 arc-minute resolution. Paleoclimatic data for the period during which our archaeological events took place would be preferable if a high-enough resolution dataset was available with an acceptable level of error or uncertainty, but this is not yet possible. In the meantime, modern climatic data are used for predicting site occurrence at a continental scale, as a first approximation.

Finally, for the KwaZulu-Natal EIA dataset we used the ‘Global 1 km Land Cover – Olson Global Ecosystem Legend’ dataset (version 2, 2000), which is based on 1 km Advanced Very High Resolution Radiometer (AVHRR) data spanning April 1992 through March 1993, and uses categories based on Olson’s global ecosystems database (Olson 1994a, 1994b) as a finer-grained land cover map to compare land-cover at EIA site locations with background frequencies of different land-cover classes for KwaZulu-Natal as a whole. This database is not implemented in the DIVA-GIS DOMAIN module but can be used as a base-map layer with the archaeological site-distribution map treated as an overlay, enabling counting of occurrences of EIA sites in each land cover category. This land-cover base map was downloaded from the United Nations Environment Programme’s GEO Data Portal (http://geodata.grid.unep.ch). The land cover classes were automatically classified and have limited validity at the regional scale (http://edc2.usgs.gov/glcc/globdoc2_0.php#valid), but provide a useful first approximation.

Radiocarbon calibration and analysisWe calibrated dates using Calib 5.0 (Stuiver et al. 2005) and the IntCal04 and SHCal04 calibration curves (varying according to whether or not sites were found north of the equator). In practice any inter-hemispheric differences in calibrated age are of little significance for sites in the equatorial zone, given the large statistical uncertainty associated with many of the early radiocarbon dates. We charted the summed calibrated probability distributions of dates for all sites in our sample that could be assigned to an approximately 10-degree-of-latitude bin to discern any north-south gradient.

RESULTS

Figures 3 and 4 show the sites with both co-ordinates and radiocarbon dates, plotted in relation to present-day annual rainfall and to present-day habitat suitability for growing sorghum. The sorghum habitat-suitability map was generated using the FAO EcoCrop database on habitat requirements, which include a minimum growing season length, and a set of minimum and maximum temperature and rainfall levels within which observed average climate must fall for at least the minimum growing season length if a location was to be considered suitable. We have not attempted to edit these maps manually to correct for local or regional errors of approximation in predicting habitat suitability (in Figure 4, the western Cape shows up as being marginal to very marginal for sorghum growth). The histograms show the frequencies of actual sites with dates in successive rainfall and habitat suitability classes, compared with the frequencies for all possible


Fig.

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terrestrial locations in a rectangular area bounded by the most extreme co-ordinates of these radiocarbon-dated sites in the database (see Fig. 2). If the environmental variables constrained settlement location, then we would expect to find the EIA sites biased towards certain parts of the landscape as defined by these parameters.

It is evident even at this very coarse spatial scale that EIA sites are not typically found in areas with very low or very high annual rainfall (Fig. 3). As Maggs (1994–95) observed, 400 mm of annual rainfall seems to be an effective minimum for EIA sites, although the database contains a few outliers located in areas where present-day rainfall is slightly below that threshold level. There is also a peak in EIA site frequency in the 700–900 mm/year rainfall band. Further analysis will reveal whether this peak reflects a climatically optimal crop-growing condition, or is simply an artefact of research effort (perhaps caused by including large numbers of closely adjacent sites in some particularly well-sampled part of the study area).

It is also evident that, as Maggs (1984, 1994–95) suggested, EIA sites are preferentially found in locations that provide suitable habitat for growing seed crops of tropical origin such as sorghum (Fig. 4). Again, there are some outliers that need further investigation, but the overall pattern is clearly one of avoidance of habitat classified in the present-day as ‘not suitable’, and selective occupation of habitat classified as ‘very suitable’ or ‘excellent’ for this crop.

This exercise suggests that it may be useful to use such climatic and ecological maps to predict EIA site locations and densities. Figure 5 shows the predictive map of habitat suitability for EIA sites based on the mean values of a large range of climate variables for all the dated and geo-referenced sites in the database, as generated in DIVA-GIS using the DOMAIN mean distance algorithm. This map is similar to the habitat suitability map for sorghum, except that it also suggests a bias towards coastal locations even in arid zones where annual rainfall appears to be insufficient for farming.

At the regional spatial scale, Figure 6 shows the distribution of undated EIA sites in the Natal Museum Database (NMD) plotted on the same DOMAIN map of predicted occurrences (which was based on the entire continental distribution of dated and geo-referenced sites). Figure 6 also shows in a histogram the frequencies of EIA sites found at locations with different levels of predictiveness for site occurrence, compared with the background distribution of values for site predictiveness in KwaZulu-Natal as a whole. There is some clustering of sites in locations where they were more predicted to occur, although the DOMAIN model does not seem to have sufficient resolution to constrain site locations to any great degree at this more limited spatial scale.

Figure 7 shows the association between the undated sites in the Natal Museum Database, and different land-cover classes. Again, this association is compared with the background distribution of abundance of different land-cover classes in KwaZulu-Natal as a whole. The comparison suggests that there is a disproportionate likelihood of finding EIA sites at locations where today there is either cultivation or population aggregation (towns, urban areas). This may well be due to an archaeological sampling or reporting bias favouring locations where population is dense and/or where the ground is recently or regularly disturbed. The relatively high percentage of EIA sites found in the ‘Other’ category is mainly from coastal locations, which the global land-cover map appears to have erroneously classified as bare desert.


Fig. 5. Predictive map of occurrences based on the geo-referenced and dated EIA sites in our database, using the DOMAIN mean distance algorithm and all the WorldClim climate variables. Darker shades of grey indicate predicted greater likelihood of finding EIA sites.

Finally, Figure 8 shows summed calibrated probability distributions for the dated and geo-referenced sites in the database, organized into four latitudinal bands from north to south. It is clear that even at this coarse level of spatial and cultural resolution we can discern some delay between the take-off of EIA sites north of 10°S, and those further south. It is less clear that there is any similar offset differentiating the arrival of the EIA in the 10°–20°S band from its arrival in latitudes further to the south, with visibility increasing markedly in both latitudinal bands at about AD 200. Further work integrating ceramic facies information into the database should reveal whether or not this apparent signature of a very rapid southwards dispersal of at least the Kwale and Nkope branches is robust, and can be resolved into a spatial gradient in first arrival times. At present, the database as analysed here includes sites from Central African Republic, the two Congos, Burundi, Uganda and Rwanda, which may represent EIA


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Fig. 7. Percentages of locations in KwaZulu-Natal with undated EIA sites in the Natal Museum database in different land cover classes from the ‘Global 1 km Land Cover – Olson Global Ecosystem Legend’ map, compared with the background distribution of abundance of different land-cover classes in KwaZulu-Natal as a whole.

sites of Western Bantu speakers, whose southward expansion preceded that of the other ‘streams’, and may be adding noise to the signal when we graph the dates in latitudinal bands that cut across the whole continent.

DISCUSSION

This positioning paper reports an initial exploration of the database, the compilation of which is continuing (geographical co-ordinates in particular have proved hard to determine in many instances, particularly for sites excavated and published some time ago). Such databases can be very valuable for analysing large-scale patterns and processes, but they are also inevitably complicated by inconsistencies in archaeological sampling, and by evolving laboratory protocols for preparing radiocarbon samples. Thus it is likely that even after further work on the published corpus of dated EIA sites, our conclusions will include a recommendation for targeted re-analysis of excavated


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05).


material from sites at locations considered key for testing hypotheses about dispersal dynamics. Nevertheless, and given such health warnings, it seems to us that this has been a worthwhile initial exercise.

The impetus for this project is our interest in looking at large-scale population expansions using geo-referenced radiocarbon data—in this case, the spread of the first Bantu-speaking farmers southwards from an origin in West Africa. Until now this type of analysis has been confined to the peopling of the Americas (e.g. Steele & Politis 2009) and the expansion of farming across Europe (e.g. Pinhasi et al. 2005). Attempts to look at other archaeologically documented spread episodes, such as that of the fat-tailed sheep in South Africa, have been hampered by the limited amount of available data (Russell 2004). Whilst there remains vigorous debate surrounding the degree to which the spread of farming to Europe was due to demic expansion or indigenous adoption, an attraction of modelling the EIA African case with a more complete database is that the cultural data indicate demic expansion as the mode of agricultural dispersal. In addition, the radiocarbon evidence has the potential to provide a more precise chronological control on independent linguistic models that identify possible main vectors for the Bantu expansion process (e.g. Rexovó et al. 2006).

The difficulty we experienced in resolving any very obvious gradient in first-arrival dates for EIA sites south of about 10°S is a result that requires further investigation, since it may suggest that the late phase of EIA dispersal southwards was very rapid. As already noted, however, it may be that an underlying spatial trend in arrival times for the three ‘streams’ is being masked by our charts, which also contain aggregate data for the EIA expansion of Western Bantu speakers. During the preparation of this database we became aware of a parallel project in Germany reported recently by Wotzka (2006), the African ‘Metal Ages’ database. This has not yet been analysed in publications relating specifically to the southern African EIA spread dynamic, but Wotzka’s 2006 paper does include plots of grouped calibrated dates arranged by latitudinal bands similar to those published here (we have followed his approach). Wotzka’s plots suggest a north-to-south gradient in dates for five-degree latitudinal zones, but he collapses all sites located south of 16°S into a single bin representing the southernmost portion of the study area. It is this southern part of the continent (extending over some 15 further degrees of latitude) where our initial and more coarse-grained analysis has failed to disclose an obvious gradient in arrival times. The reader may find it useful to compare the two sets of results: further analysis should reveal the basis of any discrepancies.

Finally, as a methodological comment, the use of biological niche-modelling software is novel for this kind of archaeological problem but it seems to us to be promising and worthy of further study. Numerous other distribution-modelling algorithms exist and are used in conservation biology, some of which are much more complex than DOMAIN (cf. Tsoar et al. 2007). Algorithms such as GARP (Stockwell & Peters 1999), which uses genetic algorithms to characterize the niche of a taxon, have been shown to be more accurate but this is partly because they have many more degrees of freedom (for instance, a single climate variable can occur multiple times in the rule for classifying habitat suitability, with both linear and nonlinear effects). The output of such models can consequently be hard to interpret meaningfully. Although GARP has been used to predict archaeological find-spot occurrences in other studies (e.g. Banks et al. 2006, 2008), we have chosen to use DOMAIN at this initial stage of our project because its


metric is simpler and its output is easier to interpret. It is also well suited to applications where available data on site locations are sparse and incomplete.

ACKNOWLEDGEMENTS

We thank two anonymous referees for their careful and detailed comments, which have greatly improved the final version of this paper. We also thank Tom Huffman and Gavin Whitelaw for donating copies of EIA archaeological datasets, and we gratefully acknowledge financial support from the AHRC Centre for the Evolution of Cultural Diversity (University College London).

NOTE1 This site is Jebel et-Tomat, an EIA site in central Sudan with evidence of sorghum cultivation by the third

century AD. This site lies outside the current distribution of speakers of Niger-Congo languages (which include the Bantu group), and just east of the Kurdufan region of central Sudan where an enclave of Niger-Congo speakers persist in the Nuba Mountains; it may therefore relate to a separate radiation of ancestors of the current speakers of Nilo-Saharan languages. However, we have included it in the spatial analyses reported in this paper..

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