A geometric-morphometric assessment of three-dimensional models of experimental cut-marks using flint and quartzite flakes and handaxes

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A geometric-morphometric assessment of three-dimensional models of experimental cut-marks using flint and quartzite flakes and handaxes Gonzalo José Linares-Matása,∗, José Yravedrab,c, Miguel Ángel Maté-Gonzálezd, Lloyd A. Courtenayb,e,f, Julia Aramendib,g, Felipe Cuarteroh, Diego González-Aguilerad a

St. Hugh's College, St. Margaret's Road, OX2 6LE, Oxford, United Kingdom Department of Prehistory, Complutense University, Prof. Aranguren s/n, 28040, Madrid, Spain c C.A.I. de Ciencias de la Tierra y Arqueometría UCM, Prof. Aranguren s/n, 28040, Madrid, Spain d Department of Cartography and Terrain Engineering, Polytechnic School of Avila, University of Salamanca, Hornos Caleros 50, 05003, Avila, Spain e Àrea de Prehistòria, Universitat Rovira i Virgili (URV), Avinguda de Catalunya 35, 43002, Tarragona, Spain f Institut de Paleoecologia Humana i Evolució Social (IPHES). Zona Educacional, Campus Sescelades URV (Edifici W3) E3, 43700, Tarragona, Spain g IDEA (Institute of Evolution in Africa), Covarrubias 36, 28010, Madrid, Spain h Department of Prehistory and Archaeology, Autonomous University of Madrid, Cantoblanco Campus, 28049, Madrid, Spain b

ARTICLE INFO

ABSTRACT

Keywords: High-resolution taphonomy Experimental archaeology Palaeolithic technology Multivariate statistics Cut-marks Raw materials

Developments in methodological approaches to high-resolution morphometrical study of cut-mark morphology further our understanding of butchering activities. Identification of micro-morphological variability between different taphonomical alterations on ancient bone allows detection and comparison of bone-surface modifications and associated taphonomical agents and activities. By taking a geometrical-morphometrical approach, data from 3-D laser-scanning and micro-photogrammetrical models of experimental cut-marks enable statistical analysis to classify and distinguish between cut-marks by bifaces from those by flakes, and, in each case, between marks made by flint from those made by quartzite tools. Analysis of two tool types, each made from two raw materials as independent variables, is a methodological advance in morphometrical studies of experimental cutmarks, which hitherto have tended to focus on the respective parts played by tool types or types of raw material in morphometrical characterization of experimental and archaeological cut-marks.

1. Introduction The taphonomic cycle is intrinsically linked to the formation processes of archaeological sites and the integrity of faunal and lithic assemblages (Gifford-González, 1981; Domínguez-Rodrigo and Fernández-López, 2011). Use-wear analysis, coupled with thorough experimental frameworks for hypothesis-testing, is a relevant taphonomic approach in lithic studies (Keeley, 1980). Use-wear analysis aims to understand tool functionality by examining the presence of edge damage and polish patterns on working surfaces, with the aid of both low and high magnification devices (Semenov 1964; Van Gijn, 2014). The identification of organic residues can further contribute to assessing the nature of the activities conducted with lithic implements, as certain tasks can leave traces of animal or plant tissues on the edges of the tool (Bruier, 1976; Anderson, 1980). Attention to use-wear (“traceology”) on stone tools encouraged systematic analysis and scientific interpretation of their functionality

∗

(Hayden, 1979; Keeley, 1980). Unfortunately, visibility and identification of microscopical traces of tool use are compromised often by taphonomical processes such as trampling, polishing, adherent concretion, or chemical dissolution. High-resolution taphonomy helps to overcome some drawbacks, complementing traceological studies by enabling identification of butchery tools, and their raw materials, inferable from characteristic anthropogenic bone-surface modifications, especially cut-marks (i.e., grooves or linear marks cut by sharp edges when defleshing or disarticulating carcasses: cf., Binford, 1981; Brain, 1981; Fernández-Jalvo and Andrews, 2016, pp. 35). Early studies highlighted aspects of cut-mark shapes caused by different tools and raw materials (Walker and Long, 1977). Latterly, attention has been given to characterization and differentiation of cutmarks inflicted by different raw materials (e.g., flint, quartzite, obsidian, metal), studied by methods ranging from inspection with handlenses or dissecting microscopes (Dewbury and Russell, 2007; Olsen, 1988), to high-power binocular microscopy (Domínguez-Rodrigo et al.,

Corresponding author. E-mail address: [email protected] (G.J. Linares-Matás).

https://doi.org/10.1016/j.quaint.2019.05.010 Received 8 March 2019; Received in revised form 6 May 2019; Accepted 7 May 2019 1040-6182/ © 2019 Elsevier Ltd and INQUA. All rights reserved.

Please cite this article as: Gonzalo José Linares-Matás, et al., Quaternary International, https://doi.org/10.1016/j.quaint.2019.05.010

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2009; De Juana et al., 2010), 3-D microscopical reconstruction (Boschin and Crezzini, 2012), Alicona InfiniteFocus 3-D imaging microscopy (Bello and Soligo, 2008; Bello et al., 2009), scanning electron microscopy (Greenfield, 1999; 2006), and micro-photogrammetry with DAVID laser scanning (Maté-González et al., 2018a; Yravedra et al., 2017a, b). Taphonomy can distinguish between cuts made by simple flakes, retouched flakes, and handaxes in both archaeological (Bello et al., 2009; Courtenay et al., 2017; De Juana et al., 2010) and archaeological contexts (Bello et al., 2009; Shipman and Rose, 1983; Yravedra et al., 2010, 2017a, b). Grain size of artefact raw materials affects cut-mark width (Dewbury and Russell, 2007; Maté-González et al., 2018a; Yravedra et al., 2017a, b). Different tool types may affect cut-mark width or depth (Domínguez-Rodrigo et al., 2009; Greenfield, 1999, 2006; Lewis, 2008; Merritt, 2012; Moretti et al., 2015; Yravedra et al., 2017b). Freshly-knapped tools cause narrower cut-marks than do worn tools (Braun et al., 2016). Methodological advances in microscopical assessment of cut-mark morphology further our understanding of hominid activities. Regarding cut-mark identification, experimental approaches can discriminate between handaxes and other butchery tools. De Juana et al. (2009), using a multivariate quantitative method with category variables, distinguished cut-marks made with handaxes from those created with retouched flakes in > 80% of instances. An interesting aspect commented by De Juana et al. (2009) and developed by Courtenay et al. (2017) is that bifacial tools cause more “shoulder” effects in cut-marks than do other tool types. Bello et al. (2009) attempted to determine experimentally which microscopical features of handaxe-generated cut-marks can help to identify these on Middle Pleistocene specimens from Boxgrove (UK). Combining those approaches with higher-resolution techniques, integrating geometrical morphometrics and multivariate statistics in analyses of 2-D profiles and 3-D models of cut-marks, enables robust evaluation and classification of taphonomical alterations on

bone surfaces (Arriaza et al., 2017; Courtenay et al., 2017; MatéGonzález et al., 2015, 2018a, 2017a, b). Earlier analyses of cut-marks made by handaxes pay insufficient attention to detailed morphological variability of cut-marks inflicted by different raw materials. To redress the matter, we analyse 3D geometrical-morphometrical findings from experiments where cut-marks produced by different types of flint and quartzite implements afford controlled actualistic observations relevant for addressing Palaeolithic choices of raw materials in relation to tool-use. We therefore characterise and classify differences in cut-marks caused by flint or quartzite handaxes and flakes, thereby allowing an inferential discriminatory role for multivariate statistical analysis of cut-marks. 2. Methodology 2.1. Materials We analysed cut-marks caused by a professional butcher using experimental simple flakes on Capra hircus and Ovis aries long bones and handaxes on Bos taurus long bones. Carcass differences are unimportant statistically when considering cut-mark morphology on long bones where petrological granularity influences variability more than do adult bone density or cortical hardness (Maté-González et al., 2017a, this issue). We analysed 256 experimental cut marks. 81 were made with handaxes of quartzites from Burgos (Sierra de la Demanda in the Arlanzón basin, and Olmos de Atapuerca) with white, grey, or black quartz crystals, 177–250 μm in size, in tight interlocking networks. 63 were made with handaxes of El Cañaveral (Vicálvaro, Madrid) flint, 72 with flakes of Jarama (Madrid) quartzite, and 40 with flakes of Manzanares (Madrid) flint. All flint samples were nodular cherts (Knauth, 1994) with grain sizes of 0.5–20 μm.

Fig. 1. Composite image including: DAVID structured-light scanner SLS-2 technical equipment. Note the macro lens used to improve the SLS-2 resolution; microphotogrammetric protocol for modelling cut-marks (bottom right); landmark characterization of cut-marks (bottom left).

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2.2. Methods

space. Therefore, we can apply this principle to understand the standard Euclidean distance in transformed space between the different subgroups of our sample. Projection of the coordinates in Euclidean space permits multivariate statistical analysis (Rohlf, 1999; Rohlf and Corti, 2000; Slice, 2001) such as principal components analysis, PCA (Hotelling, 1933, 1936; Pearson, 1901). PCA can reduce large data sets into orthogonal dimensions in hyperspace where eigenvectors reflect structural diversity within sets, thus allowing representation of how data are distributed while retaining the original distances between constituent elements. PCA is helpful when exploring matters such as a possibility of different patterns of morphological variation within cut-mark shapes. PCA interprets eigenvectors as principal components accounting for successively smaller proportions of total sample variance (Bookstein, 1991; Hotelling, 1933, 1936; Pearson, 1901). In this study, we obtained information about shape and size by using the natural logarithm of centroid size and rescaling the data (Adams and Otárola-Castillo, 2013). Centroid size, a common measurement in geometrical morphometrical analyses (Mitteroecker et al., 2013), is a compound of the differences between all landmark configurations and their averages or centroids (cf., Pearson, 1901). Softwares used for PCA were MorphoJ (Klingenberg, 2011) and Morphologika 2.5 (O’Higgins and Johnson, 1988). From the PCA data, we assessed the significance of within-sample differences and similarities by Multiple Variance Analysis (MANOVA) using both the MASS (Venables and Ripley, 2002) and the RVAideMemoire library for biostatistics (Hervé, 2019) R packages (R Core Team, 2018) to ascertain the presence of well-defined groups. The application of multivariate statistical approaches contributes to determining those shape features that can define, and discriminate between, cut-marks made with different tools or raw materials. Canonical Variation Analysis (CVA) describes formal structures in hyperspace that are invariant with respect to the rotation of their coordinates, thus leaving many optimising properties preserved. It allows generation of a simple and meaningful distribution structure based on Mahalanobis and Procrustes distances that enhance interpretability. CVAs are useful for illustrating similarities and differences within known experimental data because it assumes internal homogeneity of the subgroup, but this assumption precludes their use for archaeological classification of unknown specimens (Albrecht, 1992). A jack-knifed linear discriminant analysis (LDA) was applied to determine whether significant differences were present through the calculation of a confusion matrix to evaluate accuracy in group classification; we used the R package caret (Kuhn, 2018).

Digitalization of flake-inflicted cut-marks required high-resolution micro-photogrammetrical (MPG) imaging with oblique photography (Maté-González et al., 2015) and computerized visualization with precise cross-sectional 3D modelling (Fig. 1). A Canon EOS 700D reflex camera with a 60 mm macro lens gave excellent high-resolution images. Shutter speed and lighting were kept constant. A millimetrical scale beside the cut-marks enabled precise measurement. Depending on bone geometry and cut-mark shape, 6–9 photographs were taken of each cutmark, followed by generation of 3D models with GRAPHOS inteGRAted PHOtogrammetric Suite (González-Aguilera et al., 2016a,b) or Agisoft PhotoScan PIX4D, and Amira 5.0 processing for landmark placement. Digitalization of handaxe-inflicted cut-marks required a structured-light 3D scanner (SLS), involving a camera, projector, and calibration marker board (Maté-González et al., 2017b, Fig. 1). During preliminary calibration both camera and projector remained fixed and stable to ensure quality of scans, after which the marker board was replaced by a bone when the structured-light SLS-2 DAVID scanner could produce a density of up to 1.2 million points, thereby affording high-resolution 3D images for Amira 5.0 for landmark placement and accurate reproduction of external bone topography. With this approach, the DAVID structuredlight scanner SLS-2 produced improvement of resolution vis-à-vis previous scanning and micro-photogrammetrical (MPG) methods (MatéGonzález et al., 2015, 2017a, b, 2018a,b; Yravedra et al., 2017a, b), not to mention the time required for reconstructing cut-marks, reduced to a few seconds with SLS-2, as against 25 min required by MPG. Results from SLS and MPG are statistically identical, thus allowing combination of SLS with MPG for studying taphonomical traces (Maté-González et al., 2017b); moreover, SLS provides better resolution when scrutinizing differences between apparently similar cut-marks. 2.3. Geometric morphometric analysis For the purposes of this study, the 3D landmark model described by Courtenay et al. (2017) was used to characterise each cut-mark. This model consists in the use of 13 homologous landmarks, located on the exterior and interior surface of each incision. These reference points were allocated using the Amira-Avizo software (Visualization Sciences Group, USA), before being edited and imported into R (R Core Team, 2018) for further multivariate analysis. Landmarks were only established for well-defined cut-marks, excluding those incomplete due to bone fractures or superposition with other Bone Surface Modifications. Descriptive studies of variation in size and shape have long given way to morphological analysis by geometrical morphometrics (Goodall and Mardia, 1993; Kendall, 1989) with visualization of resultant covariation as warping and transformation grids (cf., Rohlf and Marcus, 1993; Slice, 2005). Their basis is Generalised Procrustes Analysis (Gower, 1975) with Procrustes superpositioning that normalizes information contained in landmark data, translating, rotating, and scaling elements under study (Goodall, 1991). The resulting differences (Procrustes residuals) reflect variation and covariation patterns between elements as Procrustes distances (Monteiro et al., 2000). Procrustes distances afford statistical evaluation of real shape differences between actual cut-marks (a noteworthy advance over mere demonstration of sample differences overall); they are derived from the square roots of the summed squared distances (SSD) between corresponding points (landmarks) when two translated, scaled, and optimally-rotated elements are superimposed (Dryden and Mardia, 1998; Zollikofer and Ponce de León, 2005). In a similar vein, Mahalanobis distance is a measure of the separation between a point and a distribution, in terms of the number of standard deviations from a point to the distribution mean (Mahalanobis, 1936). If each of the corresponding axes is rescaled to unit variance, then the unitless, scale-invariant, Mahalanobis distance corresponds to standard Euclidean distance in the transformed

3. Results The cut-mark sample analysed (n = 256) involved 81 quartzite handaxes, 63 flint handaxes, 72 quartzite flakes, and 40 flint flakes. It does not show a normal distribution (Shapiro-Wilk normality test, W = 0.91683, p-value < 2.2e-16). Estimation of statistical relationships between the 4 subgroups by a non-parametric Pairwise Permutation MANOVA (test: Wilks) in R (using the RVAideMemoire library for biostatistics: Hervé, 2019) revealed statistically significant differences between them: all pairwise combinations gave an adjusted p-value < 0.001 (Table 1). Table 1 P-values for assessing the significance of Pairwise comparisons between subgroups using permutation MANOVAs (test: Wilks). Permutations = 999.

Quartzite_Flake Flint_Flake Quartzite_Handaxe

3

Flint_Handaxe

Quartzite_Flake

Flint_Flake

< 0.001 < 0.001 < 0.001

– < 0.001 < 0.001

– – < 0.001

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Fig. 2. Graphic representation of the first two Principal Components (52.7% variance explained), as well as warps showing extreme variation along the X–Y axes.

The PCA graph (Fig. 2) fails to fully differentiate visually the different subsamples, although it is worth stressing that a significant proportion of variance (47.27%) is not represented in this PCA plot (Table 2). Therefore, we require high-resolution classification algorithms that are able to address the full range of sample variance in order to document the statistically significant subgroup differences. In our experimental setting, a CVA graph (Fig. 3) can help illustrate the experimental inter-group differences that the MANOVA hints at but are not encompasses in the PCA graph. Here we note how both flake subsamples are clearly differentiated, both between them and for the most part also in relation to the handaxe subsamples. These results are in line with the data generated by Maté-González et al. (2018a) when they analysed cut-marks generated using flint and quartzite flakes. The handaxe subsamples are harder to discriminate visually on the basis of the first two eigenvalues alone. In morphometrics, where a few principal components effect most of the separation between groups, the first eigenvector often reflects size, while subsequent vectors reflect shape components (Campbell, 1979). The third CVA eigenvalue offers greater distinctions on the basis of raw material attribution (Fig. 4). Quantitatively, the integration of the CVA three-eigenvalue data can help us assess the overall degree of similarity or difference between subsamples on the basis of their relative Mahalanobis and Procrustes distances (Table 3). The values presented in Table 3 show how the bifaces are the most closely associated, whereas quartzite flake cut-marks are at a unit of Mahalanobis further away from the difference between the handaxe. However, the largest distances separate marks cut by flint flakes from all other subsamples. In fact, the greatest inter-sample difference in Mahalanobis distance corresponds to flint vs. quartzite flakes, thereby corroborating previous experimental separation of marks cut by flint

Table 2 Summary of the first 10 eigenvalues and % variance explained by each of them. Note that the dimensionality used was 32 throughout all calculations.

1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Eigenvalues

% Variance

Cumulative %

0,00307718 0,00248489 0,00172075 0,00077314 0,00050390 0,00043909 0,00022578 0,00016727 0,00014093 0,00013329

29,169 23,555 16,311 7329 4777 4162 2140 1586 1336 1263

29,169 52,723 69,034 76,363 81,140 85,302 87,442 89,028 90,363 91,627

flakes from incisions by quartzite ones (Maté-González et al., 2015, 2018a,b). Procrustes distances (Table 2) show a robust difference between the quartzite-flake cut-mark shape and shapes made with bifaces, notwithstanding slight overlap in the CVA graph. They reveal separation between marks cut by flint and quartzite flakes. Differences between the handaxe subsamples (0.0292), and between any handaxe subsample and the nearest flake subsample (0.0635), permit morphometrical differentiation of flake- and biface-inflicted cut-marks, despite their visual closeness and overlap in the PCA graph. To assess the effectiveness of landmark data for predicting category membership at this level of resolution, we used applied a pairwise Generalised (or Kernel Fisher) Linear Discriminant Analysis, LDA, (based on Fisher's linear discriminant: Fisher, 1936, 1940), widely used in statistics, pattern recognition, and machine learning to explore linear combinations of features that distinguish groups of items (McLachlan,

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Fig. 3. Graph showing the results of the CVA when plotting CV1 and CV2 (88.44% of variance explained). There is no overlap between the quartzite and the flint flakes, whereas there is an evident overlap between the two types of handaxes, both of which also partially overlap with the cut-marks generated using quartzite flakes.

Fig. 4. Graph showing the results of the CVA when plotting CV1 and CV3. There is still no overlap between the quartzite and flint flakes. Note the lower degree of overlap between the two handaxe types and the greater overlap between the two types of quartzite implements.

2004). The LDA classified correctly 73.8 ± 2.9% of the marks with relatively high 0.76 Sensitivity and 0.9 Specificity values (Table 4). The confusion matrices (error matrices) show classification errors to be greatest between the most closely related subsamples (quartzite and flint bifaces, and quartzite handaxes and quartzite flakes). Whilst it is hard to mistake handaxe-cut marks for flake-cut ones, coarse granularity and edge thickness of quartzite flakes may produce cut-marks

resembling those of quartzite handaxes It may produce false positives in attempts to infer handaxe butchery from osteoarchaeological cutmarks. Prudence recommends coupling GMM assessments with investigation of the quantitative and qualitative characteristics of cutmarks. The problem may be less when comparing marks inflicted by different flint implements: these appear relatively distinctive to judge from the more limited misclassification overlap. 5

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Table 3 Mahalanobis and Procrustes distances between the experimental subgroups. P-values for inter-group Mahalanobis and Procrustes distances were obtained by means of permutation tests (10,000 iterations).

Flint handaxe Quartzite flake Flint flake

Mahalanobis Distance Procrustes Distance Mahalanobis Distance Procrustes Distance Mahalanobis Distance Procrustes Distance

Quartzite handaxe

p-value

Flint handaxe

p-value

Quartzite flake

p-value

1.7000 0.0292 2.6433 0.0946 3.5167 0.0635

< 0.0001 0.0077 < 0.0001 < 0.0001 < 0.0001 < 0.0001

2.7225 0.1035 3.0521 0.0696

< 0.0001 < 0.0001 < 0.0001 < 0.0001

3.8904 0.1022

< 0.0001 < 0.0001

(France; Viallet, 2016). Bifacial reduction sequences are rooted in three main procedures: the preparation of a peripheral striking platform, and the generation of bifacial and bilateral symmetries through face-by-face or alternate removals (Moncel et al., 2018). The recurrence of this long and complex chaîne opératoire evidences skilful planning, including an awareness of an internal mental framework of the actions to be performed before actually carrying them out (cf. Piaget, 1977; Wynn, 1989). Furthermore, bifacial tool-production and ‘core-and-flake’ technologies rely on mutually-exclusive technological procedures; thus, their co-existence evidences the cognitive skills of early Homo to develop and choose between different self-constraining chains of sequential behavioural activities (Walker, 2009, 2017: 40). Their subsequent widespread distribution across the Old World, often in considerable numbers at Acheulean sites, suggests their likely use in tasks paramount for survival of hominin societies. However, the specific functionality of these implements - i.e., just which tasks - has long been the centre of attention of scholarly debate (Binford, 1972, 1987; Isaac, 1977; O'Brien, 1981; Schick and Toth, 1993; Shipman et al., 1981; Soressi and Hays, 2003; Whittaker and McCall, 2001). Isaac (1977) considered handaxes to be specialized butchery tools, regarding large concentrations of handaxes as outcomes of butchery and food-sharing. On the other hand, several interpretations of handaxe functionality infer single-episode butchery sites, particularly at those with few bifaces. Binford (1972) proposed that “light artefact arrays” were the signature butchery kit of Early and Middle Pleistocene hominins who used scrapers and flake tools for marginal scavenging of carcasses. Pope and Roberts (2005: 94) argued that at several Middle Pleistocene Acheulean butchery sites low counts of handaxes imply these were inessential for exploiting carcasses (cf., Clark and Haynes, 1970: 409). Thus, only two bifacial tools were among 2322 lithic artefacts from the 23m2 Elephas recki carcass-processing Site 15 at Olorgesailie in Kenya (Potts, 1989). Again, relatively few handaxes were present, sometimes resharpened, in the horse-butchery GTP17 site at Boxgrove in England (Roberts and Parfitt, 1999). In Spain, the Áridos 1 and 2 sites with elephant bones bearing cut-marks (Yravedra et al., 2010) contained lithic assemblages showing evidence of transport and manufacture off-site of finished bifaces and on-site resharpening. These sites imply one-off butchery events when hominins had primary access to carcasses. Relatively rapid sedimentary deposition preserved these short-lived episodes in the archaeological record. Nonetheless, paucity of bifaces need not imply their irrelevance at short-lived butchery or single-event kill sites. No direct relationship needs to exist between tools used at such sites and those discarded there, particularly in the case of handaxes prepared by intricate sequences of reduction of noteworthy amounts of raw material. At Boxgrove GTP15 flint nodules brought from a nearby cliff were flaked into handaxes subsequently taken elsewhere, leaving behind only flakes and knapping débris (Pettitt and White, 2012: 205). At Olorgesailie Site 15 several flakes bore “complicated striking platforms and intersecting scars”, suggesting derivation from large bifacial cores (Potts, 1989: 481). Perhaps provision of flakes for butchery was another function of handaxes in addition to their plausibly direct role in processing carcasses, and the greater potential of for curation and resharpening (cf. Key and Lycett, 2017) may also help explain their frequent absence

Table 4 LDA Confusion matrix. In bold are the number of correctly classified cut-marks for each of the raw material and implement categories. Note how there is no misclassification between flint and quartzite flakes. LDA

Flint Flake

Flint Handaxe

Quartzite Flake

Quartzite Handaxe

Flint Flake Flint Handaxe Quartzite Flake Quartzite Handaxe

36 3 0 1

4 36 5 18

0 4 54 14

1 13 4 63

Our results imply that raw-material grain-size and consequent effects on edge sharpness and morphology are important factors in cutmark morphometrics, which allow distinction of marks cut by flakes from others cut by handaxes, particularly in terms of Mahalanobis and Procrustes distances (Tables 2 and 3). Although cut-marks made by flint and quartzite handaxes show some overlap in Mahalanobis and Procrustes distances (see the CV1-CV2 graph, Fig. 3), they nevertheless are statistically separate. Cut-marks made with quartzite implements (handaxes and flakes) are associated also, particularly in terms of LDA confusion matrix overlap, albeit showing noteworthy shape differences in Procrustes distances, owing mainly to thicker and shorter edges than those of flint implements, as implied by the PCA warps (Fig. 2) and the graph of CV1-CV3 variables (Bookstein, 1989; Fig. 4). Thus, the LDA supervised classification suggests that cut-marks made with quartzite flakes resemble more closely those of handaxes than do marks inflicted by flint flakes, in line with the Mahalanobis distances (Table 2). Cutmarks made by flint flakes are those most easily distinguishable from the other three subgroups. Nonetheless, the differences between all subgroups are statistically significant (see above). Therefore, our results support the application of high-resolution taphonomical research methodologies in the study and characterization of the subsistence strategies of Pleistocene societies. 4. Discussion The development of bifacial core-flaking to fashion useable tools with cutting edges and broadly bilateral symmetry, such as handaxes or cleavers, is associated chronologically with the Acheulean stone tool industry. This techno-complex appeared in Africa between 1.7 and 1.5mya, at sites such as Kokiselei in Kenya and Konso in Ethiopia and FLK-West in Olduvai, Tanzania (Asfaw et al., 1992; Roche et al., 2003; Lepre et al., 2011; Beyene et al., 2013; Diez-Martín et al., 2015). The presence of Early Pleistocene handaxes is also documented in SouthWest Asia (at Übeidiya and Nahal Zihor; Bar-Yosef and Goren-Inbar, 1993; Grosman et al., 2011) and India (at Isampur and Attirampakkan; Paddayya et al., 2002; Pappu et al., 2011). Acheulean handaxes are interesting conceptually from functional and technological standpoints. A notion of bifaces as tools or tool blanks, reflecting foresight and complexity of Acheulean reduction sequences (chaînes opératoires) is a recurrent debate in the literature (Boëda, 2013; Nicoud, 2013a, 2013b). In some instances, both dimensions are present at the same site, such as at AU 27 of Lazaret Cave 6

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from short-lived butchery sites. Whether or not ergonomic studies and unsystematic actualistic experiments are appropriate and commensurate for interpreting the palaeoanthropological record, it is nevertheless worth remarking that in his butchery experiments Jones (1980) reported that handaxes were more efficient than unretouched or retouched flakes. In a more systematic experimental butchery study, Galán and Domínguez-Rodrigo (2014) argued that small handaxes were more efficient that flakes when defleshing a carcass, although both implement categories yielded very similar processing times during disarticulation. Moreover, analyses (Solodenko et al., 2015) of fat residues on a biface and scraper from the Acheulean Revadim Quarry site (Israel), where an elephant rib bears cut-marks, imply defleshing or perhaps hide-working (vegetable residues and polish likely associated with woodworking were found on the scraper also, though not on the handaxe). Nonetheless, the African Acheulean record does indicate their use in processing vegetal fibres. Thus, phytoliths adhering to handaxes from Peninj (Tanzania) correspond morphologically to Acacia (since 2005 divided into Senegalia and Vachellia genera), which provides wood useful for efficient digging tools or spears (Domínguez-Rodrigo et al., 2001: 296); unlikely post-depositional impingements notwithstanding, the study lends support to inferring that handaxes could well have been used on wood. Moreover, evidence that early Acheulean butchery could take place without handaxes comes from the Olduvai FLK-W site where bones show not only percussion marks but also cut-marks characteristically produced by quartzite flakes (Yravedra et al., 2017b: 13–14). From Olduvai upper Bed II, the “lower floor” of the Thiongo Korongo (TK) site provides evidence of plant residues on handaxes, core-tools and light-duty pieces (Mercader et al., 2016); the presence of epidermal and woody tissues, resins, fibres, starches, and phytoliths indicates recurrent plant processing. Multi-purpose tools undoubtedly played a part in forming African Early Pleistocene industries (Schick and Toth, 1993). Application of geometrical morphometrics to taphonomical research at other African sites should contribute to understanding better the contextualized functionality of Acheulean handaxes, owing to its ability to differentiate between marks cut by flakes differing in petrology and by handaxes likewise. This high-resolution quantitative technique complements multivariate statistical approaches based on quantitative and qualitative variables (Bello and Soligo, 2008; De Juana et al., 2010). Ascertaining the characteristics of bifacial tool use in the African early Acheulean may well shed light on the likely range of uses of bifacial stone tools in the Eurasian Palaeolithic record. The Iberian Peninsula is interesting from the standpoints of both the variability in the functionality of handaxes during the Pleistocene and their early appearance in the archaeological record. Thus, a bifaciallyflaked limestone handaxe was excavated at Cueva Negra del Estrecho del Río Quípar in Murcia (southeastern Spain), together with numerous small artefacts of locally available cherts, including flakes removed by repetitive knapping on small cores (Walker et al., 2016a, b; Zack et al., 2013), in undisturbed sediments, 5 m deep, formed by gradual fluviatile alluviation (Angelucci et al., 2013) during the final Early Pleistocene, plausibly MIS-21 (Duval et al.,2019; López Jiménez et al., 2018; Scott and Gibert, 2009). Another potential Spanish example is Barranc de la Boella Pit 1 where 125 lithic artefacts accompanied remains of a Mammuthus meridionalis (Mosquera et al., 2015). During the Middle Pleistocene (particularly from MIS 11 to MIS 6), large cutting tools (LCTs) commonly were made by bifacial flaking on large- or mediumsize cores via complex reduction sequences (Sharon, 2009; Santonja and Pérez-González, 2010; Santonja et al., 2016). Locally available raw materials characterise LCTs at numerous open-air fluviatile sites, sometimes with both quartzite and flint bifaces present together (RubioJara et al., 2016). The spatial association in primary sedimentary deposits of faunal remains with several bifaces and other lithic artefacts raises the question of whether LCTs were used during carcass exploitation or whether it was carried out mainly with sharp retouched or even unmodified flakes. Therefore, characterizing the variable nature of

marks on bone cut by different types of flint and quartzite implements can improve our understanding of raw-material selection dynamics. Another intriguing question is whether preferential selection of the raw material for handaxes was task-orientated: perhaps considerations of their functionality led to paying attention to the particular granular properties of flint, quartzite, limestone, etc. Were such a relationship to be ascertained, perhaps butchery could have been the focus of such preferential selection. This could be tested by looking at use-wear traces and high-resolution taphonomy of assemblages at those sites with handaxes of different raw materials, of which several exist in fluviatile terraces in central Spain (Rubio-Jara et al., 2016). Such research could help to determine whether, throughout the Iberian Middle Pleistocene, flint and quartzite handaxes were used selectively for specific tasks or indiscriminately for similar purposes (e.g., processing carcasses). A geometrical-morphometrical approach, coupled with multivariate statistics and/or machine learning classification using quantitative and categorical variables, is well placed to address this research question of whether handaxes were used for butchery practices during the Early and/or Middle Pleistocene. The approach has potential to document the process because marks cut by bifaces on bone are shallower and broader than those cut by unretouched or retouched flakes (Courtenay et al., 2017: 11), the variability owing mainly to petrological granularity, rather than to bone density or cortical hardness of adult bone (Maté-González et al., 2017a, 2019). This refutes previous claims that the variability is mainly a dependent function of carcass size or anatomical element (Pobiner and Braun, 2005; Merritt, 2012, 2016; Braun et al., 2016; Pante et al., 2017). It consolidates the significance of experimental studies for the characterization of cut-marks in the archaeological record. Identifying the use of handaxes in butchery practices may lead to exploration of contexts where finding such evidence might be anticipated, as well as to consideration of inferences that might be drawn about the socio-economic implications of hominin behaviour in the Early and Middle Pleistocene. 5. Conclusion Geometrical-morphometrical analysis enables characterization of 3dimensional models of cut-marks on bone, thereby permitting ready differentiation of the use of different implements and raw materials in butchery practices. With regard to cut-mark morphology, the approach successfully distinguishes cut marks by quartzite flakes from those by flint flakes, as well as distinguishing traces made by flakes from those made by handaxes. Applying geometrical morphometrics contributes to understanding better the contextualized functionality of Acheulean handaxes, although discrimination between archaeological cut-marks made by handaxes of different raw materials probably requires further research, the promising statistically-significant separation of the experimental subgroups notwithstanding. Raw material variation is the most significant variable for discriminating between flake cut-marks. However, handaxe cut-mark discrimination on the basis of raw material still remains to be fully addressed, despite the promising statistically significant separation of the subgroups. There may be other variables in addition to raw material differences, such as handaxe thickness, mode of retouch, and edge angle, which could also be significantly affecting the overall morphology of handaxe-generated cut-marks, so more future experimental research in this regard is recommended. The resolution of traditional multivariate statistics as applied in the present article achieve a relatively high level of classification success in cut-marks generated using flint and quartzite flakes and handaxes (∼74%). However, promising developments in the fields of supervised and unsupervised classification using machine learning (ML) algorithms may be able to identify the very fine-grained distinction between flint and quartzite handaxes in archaeological cut-marks even more reliably and consistently. The almost untapped potential of ML is intrinsically linked with quality and objectivity in data collection (Domínguez-Rodrigo, 2018); the proven 7

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effectiveness of GMM data to characterise morphometric differences (cf. Slice, 2001) makes PC scores of landmark data ideal for ML approaches in taphonomic research, as shown recently by Courtenay et al. (2019) in a characterization of carnivore tooth marks. The high-resolution approach complements lithic analyses of the wear patterns on implements in order to elucidate whether they were used for processing meat, plant fibres, hides, bone, etc. Analysing cutmarks advances our knowledge about hominin selection of lithic raw materials for animal butchery. It offers use-wear analysis another way to develop more specific and targeted experimental frameworks. The application of the methodological approach developed here to the study of archaeological assemblages will contribute to a better understanding of Palaeolithic raw material exploitation, tool production, and the different butchering activities at Pleistocene sites.

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