Quantifying Transitions: Morphometric Approaches to Palaeolithic Variability and Technological Change Stephen J. Lycett

Abstract Robust assessment of lithic technological transitions requires dependable methodologies for the comparative analysis of stone tools from different localities, regions, and even continents. Many concepts of technological variability and change during the Lower–Middle Palaeolithic centre upon differences in the shape of various cores and core tools (e.g., polyhedrons, discoids, Acheulean bifaces, Levallois cores, etc.). Morphometrics is the application of the principles of geometry to the statistical analysis of shape. In palaeontology (and biology in general) powerful mathematical and statistical methods of analysis are routinely applied to detailed morphometric data sets that allow secure assessments of intra- and inter-taxonomic variability, at both regional and global levels. Conversely, Palaeolithic archaeology has been slow to adopt methods that enable the comparative morphometric analysis of lithic variability, which could potentially allow a more secure assessment of the pattern and validity of technological transitions. This paper briefly assesses the reasons why Palaeolithic archaeology has been relatively slow to adopt the morphometric approach applied in the biological sciences. Employing a new method for morphometric lithic analysis, worked examples of Palaeolithic morphometric analysis are presented. These analyses emphasize the importance of developing a ‘morphometric comparative anatomy’ of stone tools,

S.J. Lycett (*) Department of Anthropology, University of Kent, Canterbury, Kent, UK

particularly with regard to increasing our understanding of technological transitions and variability. Keywords Lithics  Homology  Morphometrics Size  Shape  Size-adjustment  Acheulean Levallois  Soanian


Lithic Morphometrics—What and Why? The study of form may be descriptive merely, or it may become analytical. We begin by describing the shape of an object in the simple words of common speech: we end by defining it in the precise language of mathematics . . . [T]he form of the earth, of a raindrop, the shape of a hanging chain, or the path of a stone thrown up into the air, may all be described, however inadequately, in common words; but when we have learned to comprehend and to define the sphere, the catenary, or the parabola, we have made a wonderful and perhaps manifold advance. The mathematical definition of a ‘form’ has a quality of precision which was quite lacking in our earlier stage of mere description; it is expressed in few words or in still briefer symbols, and these words and symbols are so pregnant with meaning that thought itself is economised . . . We are apt to think of mathematical definitions as too strict and rigid for common use, but their rigour is combined with all but endless freedom. D’Arcy Wentworth Thompson On Growth and Form (1961 [orig.1917]), p.269

Writing for biologists over ninety years ago, D’Arcy Thompson paved the way for much of the ‘revolution’ in the study of form that has taken place within biology over recent years (Adams et al. 2004; Jensen 2003; Rohlf and Marcus 1993). ‘Morphometrics’ is now seen as a major field of

M. Camps, P. Chauhan (eds.), Sourcebook of Paleolithic Transitions, DOI 10.1007/978-0-387-76487-0_5, Ó Springer ScienceþBusiness Media, LLC 2009



growth in biology and palaeontology, including physical anthropology (e.g., O’Higgins 2000). Put simply, morphometrics is the application of the principles of geometry to the study of shape. Others have pointed out that morphometrics may also usefully be termed ‘statistical shape analysis’ (e.g., Dryden and Mardia 1998). As we will see later, this requires rigorous definitions for concepts such as ‘size,’ ‘shape,’ and ‘homology.’ This chapter has three primary aims: Firstly, to provide an introduction to some of the terminology and principles of morphometrics for archaeologists. Secondly, to demonstrate some of the utility and potential of morphometrics for understanding Palaeolithic technological transitions. Lastly, and perhaps chiefly, this discussion aims to demonstrate that morphometric approaches should become more widely used in Palaeolithic archaeology, and hopes to encourage wider debate on these issues, particularly for furthering our understanding of technological transitions.

Why Has Palaeolithic Archaeology Been Slow to Adopt and Develop Modern Morphometric Methods? Many concepts of technological variability and change during the Lower–Middle Palaeolithic centre upon differences in the shape of various cores and core tools (e.g., polyhedrons, discoids, Acheulean bifaces, Levallois cores, etc.). It bears repeating that every observation about the form of a stone artefact is an exercise in the description of morphology, and constitutes the basis of all taxonomic and typological schemes. In turn, these observations should lead us toward an increased understanding of both within-assemblage and between-assemblage artefact variation, as well as the factors that lead to such variability, whether this be raw material, reduction intensity, function, cultural tradition, or cognitive and/or biomechanical differences. However, while duly acknowledging the important recent contributions of certain workers (e.g., Buchanan 2006; Carper 2005; Clarkson et al. 2006; Gowlett et al. 2001; McPherron and Dibble 1999; Nowell et al. 2003; Saragusti et al. 1998, 2005; Shott 2003; Tostevin 2003; Wynn and Tierson 1990), it

S.J. Lycett

may be stated that Palaeolithic archaeology has been relatively slow to adopt and develop sophisticated quantitative approaches to these issues, especially when compared to the burgeoning literature that has been generated in biological morphometrics. Indeed, it might be argued that little progress has been made in this regard since the pioneering work of Roe, Bordes, and Isaac (see e.g., Isaac 1977; Roe 1968, 1994), whose system(s) for studying bifaces remain the only widely-applied morphometric methodology in Palaeolithic archaeology. Given this situation, it raises the question as to why archaeologists have seemingly been reluctant to adopt and routinely apply methodologies that have proven so effective in other disciplines, even when colleagues in physical anthropology employing these very techniques may sometimes be only a few doors away. Several possible reasons might be suggested. One possibility is simply a lack of quantitative and statistical training. It is well-noted (e.g., Shennan 1997) that archaeologists are perhaps not the most naturally inclined to mathematical procedures of analysis, although user-friendly software is making this less important than it once was (or should be). A further reason is the expense of precision digital equipment, and the difficulties of using such instruments in archaeological field conditions (McPherron and Dibble 2003). Other possibilities are potentially more pernicious, with suspicions being raised about the ‘reality’ of scientific or quantitative and statistical methods, or that such methods somehow ‘miss’ some fundamental component of the ‘technology’ that more traditional methods somehow impart to the study of stone tools. It is partly the aim of this present chapter to dispel such misconceptions, if only for the reasons that morphometric methods aim to instil a level of repeatability, objectivity, rigour, and statistical analytical potential, which many qualitative methodologies can simply never match (Hughs and Chapman 2001; Thompson 1961; Rae 2002). Fundamentally, morphometric methods aid in turning mere observation into precise numerical data, which can then be analysed statistically with an associated probability estimate of confidence in any conclusions we may draw. Elsewhere (Lycett et al. 2006) I have suggested, however, that one of the most fundamental reasons that a lithic ‘morphometric revolution’ has not yet

Quantifying Transitions: Morphometric Approaches

taken place, is that applying morphometric methods to stone artefacts imparts a level of difficulty that colleagues in other disciplines are perhaps more easily able to overcome. That is, stone artefacts do not possess a series of readily identifiable points of correspondence (or ‘homology’), which allow a series of comparable measurements or landmark configurations to be taken across a broad range of lithic morphs, as might be encountered in many Palaeolithic assemblages. Overcoming this impediment, and applying such methods to viable archaeological questions in order to demonstrate their potential, is perhaps, the real challenge facing the science of lithic morphometric analysis.

Homology: The Crux of Morphometric Analysis ‘Homology’ can be an ambiguous term, yet one of fundamental importance in morphometric analysis. Here, the term refers to points of morphological correspondence (or ‘landmarks’), which may be identified according to explicit and clearly defined rules. Confusingly, the term homology can also be used to refer to morphological features in biological organisms that have common evolutionary (i.e., genealogical) ancestry and/or common developmental pathways (see e.g., Lieberman 1999). While the issue of phylogenetic homology in stone tools has received increased consideration in recent years (e.g., O’Brien et al. 2001; Lycett 2007b), the present discussion limits its use of the term homology to that of correspondence of point(s) (or measurement) across the range of lithic forms in a given analysis. It is only armed with a concept of homology that quantitative comparative analysis of any form may proceed. Many biological objects of study (e.g., primate crania) possess a high number of easilydefined landmarks, or points of anatomical equivalence and correspondence (e.g., suture junctions, projections, foramina, etc.). In contrast, however, stone tools do not possess a series of easily-defined and precisely identifiable points of morphological homology. This lack of homologous landmarks may explain why quantitative lithic analyses tend to involve only a small number of morphometric variables, limited to particular classes of artefact.


For instance, the Bordes/Roe/Isaac system of linear biface measurements provides  11 primary variables, and is not easily adapted to analyse or incorporate a wider range of lithic core forms. Elsewhere, colleagues and I (Lycett et al. 2006) have described an instrument for the morphometric analysis of stone artefacts, which we have termed the Crossbeam Co-ordinate Caliper (CCC). Along with the associated artefact orientation protocol (Lycett et al. 2006; Lycett 2007a), the CCC can be used to both locate geometrically homologous landmarks on lithic nuclei, and measure the distances between them. Under Bookstein’s (1997) revised landmark terminology, such landmarks would be termed ‘semi-landmarks,’ since they are both geometrically and instrumentally defined. Using this methodology, two case studies are described below, which illustrate some of the potential utility of morphometric methods for understanding Palaeolithic technological variability. It should be emphasised that the analyses discussed below, and the specific methodology employed, are by no means designed to be the final word on these issues. Rather, they aim to stimulate discussion and illustrate something of the potential of such methods and their general principles. Likewise, flake tools and debitage are not discussed, and these also clearly benefit from morphometric approaches, as others have shown (e.g., Eren et al. 2005; Hiscock and Clarkson 2005; Kuhn 1990; Shea 2006; Shott et al. 2000; Shott and Weedman 2007).

Size, Shape, and Scaling Before proceeding to the case studies, the issues of scaling, size, and shape require some discussion. In morphometrics, the concepts of ‘size’ and ‘shape’ are surprisingly more complex from a semantic point of view than may perhaps normally be considered (Bookstein 1989). In essence, the overall form (or morphology) of an object can be described as size plus shape. However, size (isometry) and shape can only be defined relative to each other, and one of the most important advances made in morphometrics in recent years has been the more explicit use of precise mathematical definitions of size in order that the two


may mathematically be disentangled (Darroch and Mosimann 1985; Jungers et al. 1995). This in turn leads us on to the issue of scaling or ‘size-adjustment.’ When a database of morphometric features is recorded for a series of objects, the major source of variation between these variables will regularly be due to raw size differences. Moreover, many linear (i.e., Euclidean) variables will be correlated with each other due to the effects of isometric scaling (Rae 2002). Hence, when attempting to analyse the shape differences between these objects, it is necessary to remove the confounding effect of size (i.e., scale) such that the artefacts may be differentiated on the basis of shape characteristics rather than overall size (Rae 2002). Given that absolute differences in the size of raw materials used for stone artefact manufacture may affect the ultimate size of an individual artefact (Chauhan 2003), size-adjustment may also potentially remove some of the confounding effects of raw material (i.e., blank form) variability. This then helps ensure that comparison of shape (e.g., ovate handaxes versus pointed handaxes) will be the major axis of comparison in an analysis rather than just size (e.g., large versus small handaxes). It is important to note that scaling data in this manner by no means implies that an investigator is automatically making the assumption that ‘size’ is an unimportant aspect of the variation between specimens. Indeed, to better understand the relationship between size differences and shape differences, it is important to have precise definitions of each. In the analyses described below, the method of geometric mean size-adjustment was applied to Euclidean distance variables1. The geometric mean is one of the Mosimann family of size variables (Mosimann 1970; Mosimann and Malley 1979), and like the arithmetic mean, provides a measure of central tendency, but is not as strongly influenced


In biological morphometrics, landmark configurations are increasingly being analysed holistically by a particular branch of morphometrics termed geometric morphometrics. Generally such methods employ ‘centroid size’ as a measure of size, which may be defined as the square root of the sum of squared Euclidean distances from each landmark to the centroid, which is simply the mean of the landmark coordinates. Geometric morphometric methods are not discussed in the present chapter, but see Lycett et al. (2006) for discussion and application of such methods in the context of lithic studies.

S.J. Lycett

by outliers or deviations from the modal data. Jungers et al. (1995) have demonstrated via experimental studies that this method (in contrast to some alternative methodologies) allows the identification of differently-sized individuals of the same shape following treatment. Size-adjustment using the geometric mean has become increasingly popular in biological morphometric analyses of shape (e.g. Ackermann 2005; Collard and Wood 2000; Dumont 2004; Klimov et al. 2004; Lycett and Collard 2005; O’Keefe and Carrano 2005; von Cramon-Taubadel et al. 2005). Note, however, that previous biological applications of such methods do not imply that such principles are only relevant in the case of biological data; the principles of size-adjustment will apply wherever there is a need to understand shape differences between objects regardless of the proximate sources of such variation. The geometric mean of a series of n variables (a1, a2, a3, . . . an) is equivalent to (a1  a2  a3  . . .  an)1/n. Simply, the geometric mean is the nth root of the product of all n variables (Jungers et al. 1995; Sokal and Rohlf 1995, 43). The method proceeds on a specimen-by-specimen basis, dividing each variable in turn by the geometric mean of the variables to be size-adjusted. The procedure effectively equalizes the volume of all specimens in a sample, creating a dimensionless scale-free variable while preserving the original shape information in the data.

Case Study 1: Acheulean Handaxe Variation The ‘Acheulean’ (or ‘Mode 2’) (Clark 1994) is an example of the type of broad terminology routinely employed to describe elements of the Palaeolithic record, in the use of which I am as guilty as anyone else (e.g., Lycett and von Cramon-Taubadel 2008). Such labels undoubtedly serve as useful terms of rapid communication, and function adequately at broad global levels of description. However, I would not be the first person (e.g., Gowlett 1998; Roe 1976; Wynn and Tierson 1990) to hint at a potential problem of generality in such terms, if I were to suggest that there is a possibility of finding meaningful patterns of variability within such

Quantifying Transitions: Morphometric Approaches


Table 1 The 10 Acheulean localities, sample sizes, and raw materials employed in Case Study 1 Locality n Raw material Attirampakkam, India Bezez Cave (Level C), Adlun, Lebanon Elveden, Suffolk, UK Kariandusi, Kenya Kharga Oasis (KO10c), Egypt Lewa, Kenya Olduvai Gorge (Bed II), Tanzania Morgah, Pakistan St Acheul, France Tabun Cave (Layer Ed), Israel

broad categories, or that we might look for ‘transitions within transitions.’ In order to explore the issue of variability within the Acheulean, data were collected for a series of n = 255 handaxes from 10 localities distributed throughout the Palaeolithic Old World (Table 1). No cleavers were included to ensure that the analysis was confined to a single class of artefact. In order to maximise data collection time toward obtaining samples with broad geographical coverage, a tactical decision was taken not to measure more than 30 specimens from a single given locality. Where a particular assemblage contained more than 30 total artefacts, specimens were sampled randomly from the total assemblage using the program Research Randomizer (http://www.randomizer.org). Morphometric data were collected for all 255 handaxes via use of the Crossbeam Co-ordinate Caliper (Lycett et al. 2006). Artefacts were orientated in standard fashion using a geometric protocol (Lycett 2007a). This initially provided a series of 54 variables (Table 2), previously described in detail elsewhere (Lycett et al. 2006). Variables 1–48 (Euclidean distance variables) were size-adjusted by the geometric mean method (Jungers et al. 1995; Lycett et al. 2006) in order to remove the confounding effects of isometric differences in scale between various finished artefacts and initial blank form sizes. Descriptions of six additional variables used in the analysis (i.e., variables 55–60, Table 2) may be found in Lycett (2007a). The 60 morphometric variables were subjected to a Discriminant Function Analysis. DFA is a multivariate technique that is used to provide a set of weightings (i.e., discriminant functions)

30 30 24 30 17 30 13 21 30 30

Quartzite Chert Chert Lava Chert Lava Quartz, lava Quartzite Chert Chert

that most effectively discriminate between groups that have been defined a priori (e.g., on the basis of locality). These weightings are linear combinations of the independent variables (Hair et al. 1998; Huberty 1994). The weightings determine the quantity (%) of artefacts that may be correctly assigned to their correct (predefined) group via the data inputted to the analysis. It is also possible to test the effectiveness of the discriminant functions in producing statistically significant differences between the groups via the Wilks’ Lambda statistic (Kinnear and Gray 2004). Hence, it may be predicted that if there is little information regarding morphological differences between the ten groups employed in this case study, then artefacts will be assigned with a low percentage (e.g.,  50% accuracy) to their respective locality, and that discriminant functions will be nonsignificant (a  0.05) according to the Wilks’ Lambda test. The analyses were undertaken using the software program SPSS v.12.0.1. Figure 1 shows the results of the Discriminant Function Analysis. Of the original grouped cases, 72.8% were correctly classified to their locality. Hence, this suggests that the Acheulean samples employed here contain morphometric information that allows handaxes at different sites to be identified in over 70% of cases. Moreover, the differences between centroids are significant ( p  0.0001) according to the Wilks’ Lambda statistic (Fig. 1). Such results are inconsistent with any suggestion that the Acheulean samples considered here are highly homogeneous overall. It is particularly interesting to note the distinct separation of the African localities (positively loading) from the non-African

84 Table 2 The 60 morphometric variables used in Case Studies 1 and 2. For further details see Lycett et al. (2006) and Lycett (2007) 1. Core left width at 10% of Length 2. Core left width at 20% of Length 3. Core left width at 25% of Length 4. Core left width at 30% of Length 5. Core left width at 35% of Length 6. Core left width at 40% of Length 7. Core left width at 50% of Length 8. Core left width at 60% of Length 9. Core left width at 65% of length 10. Core left width at 70% of Length 11. Core left width at 75% of Length 12. Core left width at 80% of Length 13. Core left width at 90% of Length 14. Core right width at 10% of Length 15. Core right width at 20% of Length 16. Core right width at 25% of Length 17. Core right width at 30% of Length 18. Core right width at 35% of Length 19. Core right width at 40% of Length 20. Core right width at 50% of Length 21. Core right width at 60% of Length 22. Core right width at 65% of Length 23. Core right width at 70% of Length 24. Core right width at 75% of Length 25. Core right width at 80% of Length 26. Core right width at 90% of Length 27. Core length distal at 10% of width 28. Core length distal at 20% of width 29. Core length distal at 25% of width 30. Core length distal at 30% of width 31. Core length distal at 40% of width 32. Core length distal at 50% of width 33. Core length distal at 60% of width 34. Core length distal at 70% of width 35. Core length distal at 75% of width 36. Core length distal at 80% of width 37. Core length distal at 90% of width 38. Core length proximal at 10% of width 39. Core length proximal at 20% of Width 40. Core length proximal at 25% of Width 41. Core length proximal at 30% of Width 42. Core length proximal at 40% of Width 43. Core length proximal at 50% of Width 44. Core length proximal at 60% of Width 45. Core length proximal at 70% of Width 46. Core length proximal at 75% of Width 47. Core length proximal at 80% of Width 48. Core length proximal at 90% of Width 49. Coefficient of Surface Curvature 0–1808 50. Coefficient of Surface Curvature 90–2708 51. Coefficient of Surface Curvature 45–2258 52. Coefficient of Surface Curvature 135–3158

S.J. Lycett Table 2 (continued) 53. Coefficient of edge-point undulation 54. Index of Symmetry 55. Max width divided by width at orientation 56. Maximum length divided by length at orientation 57. Nuclei outline length (divided by geomean) 58. Area of largest flake scar 59. CV of complete flake scar lengths 60. CV of complete flake scar widths

localities (negatively loading) on DF 1, suggestive of some degree of regional patterning to the morphometric data. Likewise on DF 2, the centroids of the non-African samples are arranged along the discriminant function in an order suggestive of regional differentiation, with European localities loading lowest on DF 2, followed by the Asian specimens, followed by those from the Levant. Moreover, note that due to the scaling (size-adjustment) procedures employed, this distinction is not one merely of size (i.e., ‘large’ African handaxes versus ‘small’ non-African handaxes), but one of actual shape variation. This may imply that distinct regional shape preferences, or the socially transmitted techniques of manufacture which ultimately lead to shape variations, differ between broad geographic regions (Lycett and Gowlett 2008). Such regional patterning would be hard to account for on the basis of raw material, given the samples employed here (Table 1), and arguably would have been difficult to detect in the absence of the morphometric and analytical methods applied. In recent decades, it has frequently been suggested that potential morphological similarities and differences within different lithic assemblages (Acheulean or otherwise) are due to function, raw material, and/or reduction intensity (e.g., Binford and Binford 1966; Dibble 1987; McBrearty 2003; Ashton and White 2003; McPherron 2000). However, it may be premature to suggest that we are at a stage anywhere approaching a full understanding of how these factors play out at a global or regional level, or whether additional factors such as social tradition, cultural variation, random cultural drift, and/or cognitive and biomechanical abilities of different hominin species are equally—or perhaps even more—important in certain situations (e.g., Lycett

Quantifying Transitions: Morphometric Approaches

ATPKM Bezez Elveden Kariandusi Kharga Oasis Lewa Morgah Olduvai St Acheul Tabun (Ed) Group Centroid


2 Function 2

Fig. 1 Results of DFA of 60 morphometric variables for n = 255 handaxes. 72.8% of original grouped cases correctly classified to locality. Differences between centroids are highly significant (Wilks’ Lambda = 0.22, df = 504, p  0.0001) on DF 1. The eight variables most highly correlated (respectively) with DF 1 were variables 32, 43, 33, 41, 42, 31, 44, 40, and 54 (see Table 2 for descriptions). Hence, variables around the ‘tip’ and ‘butt’ midline of the handaxes appear to be most important in separating the African from the nonAfrican assemblages on DF 1. Modified after Lycett and Gowlett (2008)



Bezez Tabun (Ed) Morgah ATPKM St Acheul



Olduvai Kharga Oasis Kariandusi


–4 –2

and von Cramon-Taubadel 2008; Lycett 2008). It is contended here that a more widespread adoption and development of morphometric approaches will help us to meet the challenge of understanding such phenomena further, identifying more clearly those areas of similarity and difference that demand interpretation.

Case Study 2: Is the Soanian a Lower or Middle Palaeolithic Techno-Complex? The Soanian techno-complex from the Siwalik Hills of the Himalayan frontal range is traditionally seen as one of the major Palaeolithic techno-complexes in the Indian subcontinent (Kennedy 2000; Movius 1948, 1969; Sankalia 1974). However, the Soanian has seen only limited empirical research in recent decades, further plagued by a dearth of primary context sites (Chauhan 2003, 2005, 2007, 2008). Comparison with the Mode 1 industries of East Asia and those of northwest Europe (e.g., the Clactonian) are common (Chauhan 2003; Dennell and Hurcombe 1989; Kennedy 2000; Movius 1948). However, the chronological status and typo-technological




Function 1

relationship(s) of the Soanian to other Palaeolithic industries have been the subject of much debate. When first named and described (de Terra and Paterson 1939) the Soanian was considered to contain evidence of Levallois-style core reduction. Yet, in recent years, this techno-complex has been variously described as a chopper tool industry, a pebble tool and flake industry, a cobble tool industry, a core/ flake industry, or simply as ‘Mode 1’ (e.g., Chauhan 2003, 2005; Davis 1987; Gaillard 1995; Ghosh 1974; Misra 2001; Petraglia 1998, 2001). Indeed, Grahame Clark (1969, 36) explicitly included Soanian industries within Mode 1 when initially outlining his wellknown lithic taxonomic scheme. There is a long history of contrasting Soanian technology with the Acheulean of the Indian subcontinent (Chauhan 2003; Gaillard 1995; Misra 2001; Mohapatra 1990; Movius 1969; Paterson and Drummond 1962; Sankalia 1967, 1974). Some, however, have suggested that the Soanian–Acheulean distinction may simply represent the ends of a technological continuum or highly variable lithic facies (e.g., Petraglia 1998). Although Soanian material has frequently been seen as contemporary with or preceding the Acheulean in India and Pakistan (e.g., de Terra and Paterson 1939; Graziosi 1964; Mohapatra 1990), it has also been argued that


the Soanian may actually post-date the Acheulean (Chauhan 2003; Gaillard 2006; Gaillard and Mishra 2001). Indeed, Suresh et al. (2002) have recently dated (via optically stimulated luminescence) the deposition of an alluvial fan surface in the PinjoreNalagarh Dun, India, to as young as 20 Kyrs. This implies that Soanian material associated with this feature should be seen as Late Pleistocene in age rather than Middle Pleistocene or older (Chauhan 2003; Singh Soni and Singh Soni 2005). Although isolated occurrences of Acheulean technology are known in the Siwaliks (Mohapatra 1981; Chauhan 2003, 2004; Corvinus 2006), the Soanian techno-complex is more frequently compared with biface-free or Mode 1 Palaeolithic industries. It is particularly interesting to note that Movius (1948, 376) saw the Soan material as ‘one manifestation of a great complex of chopper-chopping tool found in Southern and Eastern Asia.’ Hence, the chronological and techno-typological status of the Soanian is potentially of great importance in understanding the nature and significance of the so-called ‘Movius Line,’ which is traditionally held to represent a geographic demarcation between the Mode 1 industries of East Asia and the Mode 2 (Acheulean) industries of western Eurasia and Africa (Schick 1994). The Soanian has also drawn comparison with non-bifacial industries such as the Clactonian of northwest Europe, and is thus embroiled in debates concerning the nature and significance of Lower Palaeolithic biface-free industries (e.g., Kennedy 2000; White 2000). If it could more confidently be established that at least some of the Soanian techno-complex contains a Levallois element, this would be consistent with interpretations of this industry as a Late Pleistocene phenomenon, with attendant implications regarding the relationship between the Soanian and the Acheulean, and the relevance of the Soanian in discussions of the Movius Line. In order to test the hypothesis that the Soanian techno-complex contains a Levallois (Mode 3) core element, data were collected from a series of Lower– Middle Palaeolithic Old World nuclei (n = 564 nuclei) representing 27 taxonomic units (Table 3). The taxonomic units were composed of Mode 1 nuclei (i.e., polyhedrons, choppers, discoids) (n = 157), Mode 2 handaxes (n = 255), and Mode 3 Levallois cores (n = 141). The latter had either

S.J. Lycett

previously been assigned in the literature to Levallois industries and/or conformed to commonly used qualitative morphological definitions of Levallois cores (e.g., Boe¨da 1995; Chazan 1997; Van Peer 1992). Boe¨da’s (1995) six criteria for the identification of Levallois cores were given particular emphasis here. It should be noted that 25 Mode 1 nuclei from the Soan Valley, Pakistan, were included as part of the general comparative sample (Table 3). This material represents part of the Soanian type material collected as surface finds by de Terra and Paterson (1939) from the Soan Valley during April of 1935 as part of the Yale-Cambridge expedition to India, of which modern Pakistan was then part. In addition, a sample of 11 cores was included in the analysis from de Terra and Paterson’s Soan Valley collections, which also appear to display many of the characteristics commonly used to identify Mode 3 Levallois cores (e.g., Boe¨da 1995; Chazan 1997; Van Peer 1992). This latter group of nuclei was termed ‘Soan?’ for the purposes of analysis (Table 3; taxonomic unit number 27). Hence, the following analysis essentially tests the qualitative identification of these specimens as ‘Levallois’ via a morphometric procedure. Morphometric data were again collected for all 564 nuclei via use of the Crossbeam Co-ordinate Caliper (Lycett et al. 2006), using the geometric orientation protocol (Lycett 2007a). This initially provided a series of 54 variables (Table 2), previously described in detail elsewhere (Lycett et al. 2006). Variables 1–48 (Euclidean distance variables) were again size-adjusted by the geometric mean method (Jungers et al. 1995; Lycett et al. 2006). Descriptions of six additional variables used in the analysis may be found in Lycett (2007a). In order to test the hypothesis that the Soanian sample contains Levallois cores, the morphometric variables were subjected to a Discriminant Function Analysis. For the purposes of this analysis, the lithic taxonomic units were treated as four separate groups: a Mode 1 group, a Mode 2 group, a Mode 3 group, and the ‘Soan?’ group comprised of the 11 lithic nuclei that also appear to conform to commonly employed Levallois core descriptions. Using DFA it is possible to make two specific predictions regarding how the ‘Soan?’ group should perform if the results of the analysis are to be consistent with the hypothesis that the Soanian techno-complex

Quantifying Transitions: Morphometric Approaches


Table 3 Samples used in Case Study 2 Locality


Raw material

Technological mode

Barnfield Pit, Kent, UK Barnham St Gregory, Suffolk, UK Lion Point, Clacton, Essex, UK Olduvai Gorge (Lower Bed II), Tanzania Olduvai Gorge (Middle/Upper Bed II), Tanzania Soan Valley, Pakistan Zhoukoudian, Locality 1, China Zhoukoudian, Locality 15, China Attirampakkam, India Bezez Cave (Level C), Adlun, Lebanon Elveden, Suffolk, UK Kariandusi, Kenya Kharga Oasis (KO10c), Egypt Lewa, Kenya Olduvai Gorge (Bed II), Tanzania Morgah, Pakistan St Acheul, France Tabun Cave (Layer Ed), Israel Baker’s Hole, Kent, UK Bezez Cave (Level B), Adlun, Lebanon El Arabah, Abydos, Egypt El Wad (Level F), Israel Fitz James, Oise, France Kamagambo, Kenya Kharga Oasis (KO6e), Egypt Muguruk, Kenya Soan Valley, Pakistan

22 30 18 11 26 25 14 11 30 30 24 30 17 30 13 21 30 30 23 28 16 27 11 13 11 12 11

Chert Chert Chert Lava, chert, quartz Lava, chert, quartz Quartzite Sandstone, quartz, limestone Sandstone, quartz Quartzite Chert Chert Lava Chert Lava Quartz, lava Quartzite Chert Chert Chert Chert Chert Chert Chert Quartzite, chert Chert Lava Quartzite

M1 M1 M1 M1 M1 M1 M1 M1 M2 M2 M2 M2 M2 M2 M2 M2 M2 M2 M3 M3 M3 M3 M3 M3 M3 M3 ?

contains a Mode 3 Levallois core component. That is, (1) the centroid of the ‘Soan?’ assemblage should be closer to the Mode 3 group centroid than that of any other group, and (2) the individual specimens within the ‘Soan?’ assemblage should overlap with variation exhibited by specimens included in the Mode 3 Levallois group. If both of these predictions are not fulfilled, the results of the analysis can be interpreted as inconsistent with the hypothesis that there is a definite Mode 3 Levallois core component to the Soanian lithic techno-complex, at least as represented by the samples examined here. Figure 2 shows the results of the DFA, plotting the discriminant scores (functions 1 and 2) for the 564 lithic nuclei used in the analysis. Cumulatively, functions 1 and 2 account for 92.5% of the variation exhibited by the specimens. The six variables most highly correlated with DF 1 were variables 49 (Coefficient of surface curvature 0–1808), 52 (Coefficient of surface curvature 135–3158), 51 (Coefficient of surface curvature 45–2258), 53 (Coefficient of edge-

point undulation), 50 (Coefficient of surface curvature 90–2708), and 59 (CV of complete flake scar lengths). The plot clearly shows that the centroid of the ‘Soan?’ assemblage is closer to the Mode 3 group centroid than that of any other group, and that the individual specimens within the ‘Soan?’ assemblage overlap closely with specimens included in the Mode 3 Levallois group, thus fulfilling the predictions of the hypothesis that the Soanian techno-complex contains a Mode 3 Levallois core component. Hence, the discriminant function analyses provide robust evidence that the type material of the Soanian technocomplex contains specimens that should taxonomically be termed Mode 3 Levallois. However, it should also be noted that some cores from the Soan Valley (i.e., taxonomic unit 6) fit comfortably within the range of Mode 1 cores examined here. These morphometric analyses have important implications for current debates regarding the typological and chronological status of the Soanian techno-complex. The presence of Mode 3 Levallois



Mode 1 Mode 2 Levallois


Soan? Group Centroid

4 Function 2

Fig. 2 DFA plot for n = 564 nuclei. Note that the centroid of the ‘Soan ?’ specimens is closest to that of the Levallois group, and that the variation of specimens within the ‘Soan ?’ group overlaps with that of the Levallois specimens. Function 1 accounts for 54.6% of variance and function 2 accounts for 37.95% of variance. The top six variables most highly correlated with the discriminant functions were 49, 52, 51, 53, 50, and 59 (see Table 2). Modified after Lycett (2007a)

S.J. Lycett




–4 –4


industries has traditionally been seen as one of the diagnostic elements of the ‘Middle Palaeolithic.’ The finding that at least some sites within the Soan Valley contain a clear Mode 3 Levallois core component is consistent with the hypothesis that the Soanian techno-complex is either late Acheulean or postAcheulean in terms of technology, and potentially Late Pleistocene in chronology (Chauhan 2003, 2007, 2008; Gaillard 2006; Gaillard and Mishra 2001). Indeed, Gaillard (2006) has recently suggested that the ‘Soanian’ may constitute two separate chronological and technological elements, comprised of distinct Lower and Middle Palaeolithic components. The clear recognition of both Mode 1 and Mode 3 technological components in the current analysis does not contradict such a hypothesis, but such assertions must await an increased chronological understanding of Palaeolithic assemblages in the Siwaliks. In any event, the firm identification of Levallois technology within the Soanian techno-complex renders scenarios suggesting that the Soanian is a precursor to the Acheulean in the Siwalik region, or simply part of a variable Mode 1–2 lithic facies, as problematic.


2 Function 1




Indeed, our understanding of hominin exploitation of the Siwalik frontal range must take greater account of this under-discussed Middle Palaeolithic element of their technological repertoire. It also suggests that using Soanian assemblages as an analogue for East Asian Mode 1 assemblages in order to understand factors that may potentially be mediating the so-called Movius Line, is inappropriate. Again, this analysis hints at the potential for morphometric analyses to provide new insights into old problems, and improve our understanding of artefact variability and assemblage composition.

Conclusions: Toward a Lithic ‘Morphometric Comparative Anatomy’ We are potentially at a new, exciting frontier of analytical capability in lithic artefact research, one that was hinted at by David Clarke several decades ago (Clarke 1968), yet never fully realised at the time because of the very real difficulties of analysing large

Quantifying Transitions: Morphometric Approaches

quantitative datasets in the absence of desktop computers. Quantitative analyses are now being used to assess the influence of raw material and reduction intensity upon Palaeolithic stone tool form (Ashton and White 2003; McPherron 2003; White 1998) and to test predictions regarding functional influences on artefact shape (Machin et al. 2007). In addition, technological lithic traditions are being analysed via novel theoretical and methodological perspectives (Bettinger and Eerkens 1999; O’Brien et al. 2001; Stout et al. 2000; Tostevin 2003; Wallace and Shea 2006; Buchanan and Collard 2007; Shott 2008). It may also now be important to compare the stone artefacts of extant primates to those of early hominins (Carvalho et al. 2008; Mercader et al. 2007; Panger et al. 2002; Schick et al. 1999; Visalberghi et al. 2007). Such new possibilities illustrate the urgency in developing more sophisticated approaches to the ‘morphometric comparative anatomy’ of lithic artefacts, in order to further our understanding of the many dynamics structuring Palaeolithic variability and technological change. Several caveats are perhaps in order, lest the overriding aims of this paper be misunderstood. Morphometrics is no panacea, nor will it address all of the problems faced by lithic specialists. The traditional matters of dating, context, function, and related issues will of course remain as important as ever. It must also be emphasised that it would be naı¨ ve to suggest that the application of morphometric methods alone leads automatically to increased understanding; we must still rely on well-founded hypotheses to derive testable predictions, whether they be drawn from observation, experiment, ethnography, primatology, ethology, or social theory (Hill 1972). It should also be stressed that morphometric methods do not automatically replace, nor are they necessarily at odds with, traditional approaches to archaeological analysis involving the assessment of technological reduction sequences, chaıˆne ope´ratoire, platform preparation, etc. Indeed, it is potentially in cases where various approaches may be combined, and the conclusions drawn from one approach tested against another, that the maximum effect of all these various methodologies is most greatly realised. The days of qualitative statements from authority, based upon vague notions that ‘technology’ is an empirical entity that can be adequately enunciated through an individual’s own


‘expert’ intuition, should, however, clearly be numbered as we delve deeper into the possibilities afforded to 21st century Palaeolithic science. Several daunting challenges undoubtedly remain, not least of which is that morphometrics is not so much a ‘technique,’ as a complex field of study with its own complications, internal debates, and controversies; and archaeologists will find little in these methods that is simply ‘plug-and-play.’ Having taken up this challenge, however, we may perhaps (sensu D’Arcy Thompson) make analyses of Palaeolithic technological variability and change increasingly ‘pregnant with meaning.’ Acknowledgments I am indebted to Marta Camps and Parth Chauhan for their invitation to present an earlier version of this paper at the session organised by them at UISPP, Lisbon, Portugal (2006), and for their invitation to contribute to this volume. Thoughtful and constructive comments from two anonymous reviewers helped to improve the clarity of this manuscript. Over several years, my work has benefited from the company and conversations held with Leslie Aiello, Parth Chauhan, Mark Collard, Chris Clarkson, Robin Dennell, Rob Foley, John Gowlett, Christopher Norton, Felix Riede and Noreen von Cramon-Taubadel. I should stress, however, that none of the aforementioned should be held responsible for the views expressed here. This research was supported by Trinity College, University of Cambridge, and the British Academy Centenary Research Project.

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