# Spatial and orientation patterns of experimental stone tool refits

## Abstract

Freehand and bipolar experimental knapping of quartzite from Olduvai Gorge in Tanzania is used to conduct spatial analysis of artefact distributions using GIS techniques, and to investigate the orientation of refit lines using circular histograms. The aim of our study is to discern patterns that can be applied to the archaeological record in two domains, namely the identification of knapping episodes and the utility of refitting line orientations in addressing post-depositional disturbance. Our spatial analysis shows that distinctive clustering patterns can be discerned according to knapping stance, handedness and flaking technique. The circular dispersion of refit lines in the horizontal distribution of bipolar assemblages is strongly patterned, indicating that anisotropy of conjoining sets is inherent to pristine hammer-and-anvil knapping episodes.

## Keywords

Conjoining sets Quartzite Bipolar flaking GIS Circular histograms Olduvai Gorge## Introduction

The study of spatial distributions in experimental lithic scatters covers a range of topics, including knapper handedness (Bargalló et al. 2017), knapper stance (Newcomer and de Sieveking, 1980; Barton and Bergman 1982; Schick 1984; Fischer 1990; Kvamme 1997), reduction strategy and raw material (Kvamme 1997), hammer type (Newcomer and de Sieveking, 1980; Kvamme 1997) and post-depositional processes (Bowers et al. 1983; Gifford-González et al. 1985; Schick 1984; Nielsen 1991; Texier et al. 1998; Lenoble et al. 2008; Bertran et al. 2015; Driscoll et al. 2016). These studies have produced relevant observations on the spatial distribution of experimental scatters, but quantitative results based on systematic GIS analyses are yet to be done. Such lack of quantification in experimental assemblages applies both to density patterns and the refitting of the lithic sets produced.

Analysis of orientation patterns in archaeology is traditionally linked to the study of post-depositional processes (e.g. Isaac 1967; Schick 1984; Kreutzer 1988; Pope 2002; Lenoble and Bertran 2004; Boschian and Saccà, 2010; Benito-Calvo and de la Torre 2011; Benito-Calvo et al. 2009, 2011; Sánchez-Romero et al. 2016; de la Torre and Wehr 2018; McPherron 2018), and refitting is widely recognised as a powerful tool to disentangle site formation (e.g. Cahen 1980; Villa 1982, 2004; Hofman 1986; Bordes 2003; Deschamps and Zilhão, 2018). Nonetheless, most studies concerned with taphonomic processes have addressed the vertical dimension of conjoining sets and their stratigraphic implications, while the horizontal dimension has received less attention in archaeological (e.g. Austin et al. 1999; Pope 2002; Ashton et al. 2005; Sisk and Shea 2008; Santamaría et al. 2010; de la Torre et al. 2014; Deschamps and Zilhão, 2018) and experimental (e.g. Schick 1984) assemblages.

This paper aims to contribute to the literature by exploring spatial dynamics of experimental knapping episodes and conclusions that can be drawn from the orientation patterns of refits. The baseline distribution for artefact and refit orientations is often assumed to be random, with the assumption that any variation is the product of natural processes. While that is certainly the case for the orientation of the long axes of items (Toots 1965; Nagle 1967; Wendt 1995), and it is not realistic to envisage undisturbed assemblages where artefact and bone axes are preferentially oriented (de la Torre and Benito-Calvo 2013), there is no evidence to suggest this random patterning is inherent to other variables such as refit lines.

To address these issues, our work includes the study of refit orientation patterns and the spatial analysis of four knapping experiments on quartzite from Olduvai Gorge in Tanzania. The goal of these experiments was to recreate core-and-flake assemblages through freehand and bipolar knapping techniques. Our analysis involves refitting of all experimental sequences, digital mapping of the artefact distribution, spatial analysis of artefact clusters and analysis of the refit orientation patterns. Through this, the study aims to elucidate clustering patterns of experimental lithic scatters, and reflect on the validity of orientation data of refit sets for spatial analysis in Palaeolithic assemblages.

## Materials and methods

This paper involves the study of four knapping experiments (named Exp. 18, Exp. 40, Exp. 54 and Exp. 56) with quartzite (sensu Hay 1976) sourced from the Naibor Soit hills at Olduvai Gorge, Tanzania, from a repository within the Lithics Laboratory at the Institute of Archaeology, University College London. The aim of these four experiments was to produce as many flakes from individual block blanks using simple freehand and bipolar knapping techniques.

### Knapping

The other two experiments (Exp. 54 and 56) involved bipolar knapping (Fig. 1). The cores were placed on top of the anvil and stabilised with the left hand, while the right hand struck with the hammerstone at a 90° angle to the platform. Both experiments were performed kneeling to reach the anvil, and once exhausted, cores were left in their flaking position on the anvil.

### Mapping

Assemblage composition and main features of the refitted artefacts

Exp. 18 | Exp. 40 | Exp. 54 | Exp. 56 | |||
---|---|---|---|---|---|---|

Assemblage composition* | Mapped | Total plotted artefacts | 305 | 289 | 335 | 402 |

Refitted | Complete flakes | 29 | 27 | 24 | 18 | |

Flake fragments | 7 | 21 | 17 | 27 | ||

Total refitted artefacts** | 37 | 49 | 42 | 46 | ||

Distance (mm) | Product to core | Mean | 343 | 281 | 537 | 470 |

Median | 271 | 233 | 509 | 361 | ||

Min | 48 | 22 | 72 | 44 | ||

Max | 761 | 886 | 1162 | 1618 | ||

SD | 196 | 192 | 303 | 359 | ||

Between conjoining artefacts | Mean | 385 | 293 | 475 | 467 | |

Median | 269 | 258 | 480 | 398 | ||

Min | 24 | 38 | 72 | 39 | ||

Max | 1216 | 978 | 970 | 1300 | ||

SD | 280 | 196 | 242 | 347 |

The photo maps of knapping scatters were imported into ArcGIS 10 as raster images and then georeferenced. The position of the knapper in each experiment was recorded and used as an arbitrary north. Polygon shapefiles were created along the outlines for each numbered piece and, where appropriate, the anvil. The centroid of each shapefile as designated by ArcGIS was used as the *x*, *y* coordinate for each piece.

### Refitting

All material that received a unique identifier was measured (maximum length, width, thickness and weight), classified technologically and subjected to refit analysis. Refitting of Olduvai quartzite is notoriously difficult even in experimental settings (Proffitt and de la Torre 2014; Byrne et al. 2016), and thus the videos, images of the pre-modified blocks and photo maps were used to help identify the sequence position of certain pieces. Still, conjoining of freehand experiments took an average of 9 h each. Due to large amounts of crushing on the bipolar sequences, refitting was even more laborious in the case of Exp. 54 and 56, and took an average of 13 h each.

### Spatial analysis

Average nearest neighbour (ANN), Getis–Ord General G and Global Moran’s were applied to identify clustered, uniform or dispersed patterns. Ripley’s K Function was used to test if clustering changed over a range of distances and Global Moran’s I to establish the distance of maximum clustering. Identification of clusters was made using Getis–Ord Gi* statistics (Sánchez-Romero et al. 2016). The quantitative variables used in Gi* were frequency, weight and length of pieces, using the inverse Euclidean distance as the spatial relationship between artefacts. Getis–Ord and Global Moran’s statistics for frequency of artefacts followed the quadrat method, where counting of pieces was conducted in quadrats of 125 mm, which is twice the size of the mean area per piece, considering the maximum area of dispersion for artefacts of all the experiments. Artefact frequency was also analysed through kernel density maps.

### Orientation patterns

An add-in for ArcGIS was scripted to calculate distances between conjoining artefacts, and also orientation of the refit lines, based on trigonometric relationships on a Cartesian coordinate system. This script calculates both the direction and azimuth (where sequencing can be ascertained). The orientation data obtained with this ArcGIS module was then plotted into Rose diagrams with GeoRose software.

The circular dispersion of displacement vectors was characterised using circular histograms and circular descriptive statistics (Fisher 1995). To estimate the reference direction of the assemblages, we used the mean direction (mean Cartesian vector unit) and the modal direction (the direction of maximum concentration of data). The module of the mean vector unit (R) was used as an index of dispersion (Benito-Calvo et al. 2009, 2011), varying between 1 (when all vector all coincident) and 0 (when the dispersion is high). Since R is not a useful indicator of data spreading unless they comprise unimodal distribution, we first combined a unimodal statistical test (Rayleigh’s test) with omnibus tests (Rao’s spacing, Watson’s and Kuiper’s tests) to corroborate that the data followed mainly a unimodal distribution (Benito-Calvo and de la Torre 2011; de la Torre and Benito-Calvo 2013). Concentration was also estimated using the concentration parameter K (Fisher 1995), which measures the departure of the distribution from a perfect circle (uniform distribution). Circular histograms in 15° (Figs. 2–5) and 5° (Fig. 8) bins and descriptive statistics were also calculated weighting the vectors and axis according to the distance between artefacts and to the weight of pieces. Statistics were calculated using Oriana 3.13 and SpheriStats 3.1.

## Results

### Spatial distribution of artefacts and refit lines

The Rose diagram of Exp. 18 shows that about half of the pieces are located within a 75° interval to the southeast of the core (Fig. 2c), with the rest evenly distributed in all directions. The mean distance between products and the core is 343 mm, and the longest is 761 mm (Table 1). Analysis of the sequence (28 stages) shows that the majority of products landed to the northwest or south of the previous removal (Fig. 2e). More than 75% of the stages are less than 600 mm away from the preceding product (Fig. 6b). Refits have a mean distance of 385 mm (Table 1), and longer refit lines produce a largely elongated NW–SE trend (Fig. 2g).

Figure 3c shows that most artefacts in Exp. 40 occur in a 90° interval to the northeast of the core. More than 75% of core-product distances (Fig. 3b) are less than 400 mm, with outliers at 681 mm and 886 mm (Fig. 6a and Table 1). The knapping sequence (27 stages; Fig. 3d) shows a general trend of removals to move towards the east diagonally, and almost every other stage is to the North or South of its previous and next stage, forming a N–S trend (Fig. 3e). This is corroborated by the refit orientation circular histogram (Fig. 3g), which also indicates an additional trend from NE to SW. Many of the refits are less than 400 mm from one another (Fig. 6c), thus following the pattern of core–product distances.

In the case of Exp. 54, most pieces are within a 120° interval east from the core (Fig. 4c). Core–flake distances range between 72 and 1162 mm (Fig. 6a). There are 21 stages in the sequence, which shows an E–W trend (Fig. 4e). Most distances between successive stages are less than 600 mm, although there is a larger amount of variation in the lower end of the distribution than in freehand experiments. The Rose diagram of refits (Fig. 4g) shows a strong unimodal pattern along the E–W axis. Distances between conjoining pieces present a normal distribution, and are all ≤ 970 mm, with a mean of 475 mm (Table 1; Fig. 6c).

### Cluster analysis

Spatial pattern statistics of the experimental assemblages

Exp. 18 | Exp. 40 | Exp. 54 | Exp. 56 | |
---|---|---|---|---|

Average nearest neighbour (ANN) | ||||

NN observed (mm) | 37.59 | 41.49 | 39.14 | 32.47 |

NN expected (mm) | 65.97 | 67.53 | 63.05 | 56.85 |

NN ratio | 0.57 | 0.61 | 0.62 | 0.57 |

NN | − 14.39 | − 12.61 | − 13.28 | − 16.65 |

| 0 | 0 | 0 | 0 |

Ripley’s K Function | ||||

Clustered–dispersed change (mm) | 1386 | 1384 | 1391 | 1394 |

Getis–Ord general G | ||||

Observed general G | 0.023 | 0.019 | 0.014 | 0.018 |

Expected general G | 0.003 | 0.003 | 0.003 | 0.003 |

| 19.635 | 19.900 | 19.923 | 19.854 |

| 0 | 0 | 0 | 0 |

Global Moran’s | ||||

Moran’s index | 0.705 | 0.743 | 0.756 | 0.741 |

Expected index | − 0.003 | − 0.003 | − 0.003 | − 0.003 |

| 19.643 | 19.909 | 19.935 | 19.861 |

| 0 | 0 | 0 | 0 |

Incremental global Moran’s I | ||||

Maximum peak (mm) | 380 | 464 | 464 | 464 |

Distribution | Unimodal | Unimodal | Unimodal | Unimodal |

| 29.06 | 35.63 | 43.24 | 34.77 |

Kernel density statistics of artefact maps

Exp. 18 | Exp. 40 | Exp. 54 | Exp. 56 | |
---|---|---|---|---|

Minimum | 0 | 0 | 0 | 0 |

Maximum | 0.05862 | 0.08881 | 0.05932 | 0.05393 |

Mean | 0.00186 | 0.00287 | 0.00301 | 0.00249 |

SD | 0.00548 | 0.00891 | 0.00686 | 0.00657 |

Both Exp. 18 and Exp. 40 also show an area of low artefact concentration that forms a distal arc from right to the left of the knapper. In Exp. 18, this low concentration area shows a scattered pattern, whereas in Exp. 40 the low concentration area has a ring shape and is separated from the highest concentration area by a discontinuous strip with no material. Areas of low concentration do not constitute statistically significant cold spots, according to the Gi* method. The highest concentration areas in Exp. 54 and Exp. 56 are also defined by density maps and Gi* hotspots, and are strongly patterned to the right of the knapper.

Hot spot descriptive statistics calculated for artefact frequency, size (i.e. artefact area) and weight, at 95% confidence interval

Exp. 18 | Exp. 40 | Exp. 54 | Exp. 56 | ||
---|---|---|---|---|---|

Sample size (number of artefacts) | |||||

Minimum | 6 | 6 | 5 | 7 | |

Maximum | 27 | 16 | 12 | 24 | |

Mean | 12.4 | 9.7 | 7.4 | 12.8 | |

SD | 6.7 | 3.3 | 2 | 4.5 | |

| 12 | 17 | 27 | 17 | |

Area (mm | 187,500 | 265,625 | 421,875 | 265,625 | |

Length (mm) | |||||

Minimum | 33.86 | 46.88 | 47.17 | 41.39 | |

Maximum | 67.50 | 100.74 | 98.51 | 110.14 | |

Mean | 47.65 | 68.40 | 66.33 | 63.59 | |

SD | 9.78 | 16.43 | 14.28 | 17.87 | |

| 17 | 18 | 20 | 19 | |

Weight (g) | |||||

Minimum | 367 | 1515 | 1013 | 1484 | |

Maximum | 2046 | 4290 | 7990 | 6548 | |

Mean | 943.67 | 2807.80 | 2698.40 | 2736.33 | |

SD | 543.23 | 1061.05 | 2680.38 | 1767.77 | |

| 6 | 5 | 5 | 6 |

### Circular dispersion of conjoining sets

#### Core-to-flake displacements

*p*< 0.01) in all cases. This allows to reject the uniform distribution (Table 5) and indicates the presence of preferred orientations in the four experimental assemblages. Since omnibus tests reject uniformity against unimodal and multimodal distributions, and the Rayleigh test detects only unimodal orientations, all the circular core-to-flake distributions can be essentially considered as unimodal preferred orientations.

Circular statistics of refit orientations: (A) core-to-flake displacements (polar data); (B) refit lines (axial data)

A | B | |||||||
---|---|---|---|---|---|---|---|---|

Exp. 18 | Exp. 40 | Exp. 54 | Exp. 56 | Exp. 18 | Exp. 40 | Exp. 54 | Exp. 56 | |

Variable | Azimuth | Azimuth | Azimuth | Azimuth | Axial | Axial | Axial | Axial |

Number of observations ( | 36 | 48 | 41 | 45 | 64 | 90 | 75 | 80 |

Descriptive statistics | ||||||||

No weighted data | ||||||||

Mean vector, μ (°) | 108.5 | 49.9 | 83.5 | 67.2 | 129.8 | 31.6 | 92.6 | 76.0 |

Length of mean vector, R | 0.44 | 0.47 | 0.81 | 0.73 | 0.20 | 0.11 | 0.34 | 0.21 |

Modal direction (°) | 145–150 | 50–55 | 90–95 | 80–85 | 130–135 | 50–55 | 100–105 | 65–70 |

Mode percentage (%) | 11.1 | 8.3 | 12.2 | 11.1 | 7.8 | 7.8 | 10.7 | 8.0 |

Concentration, K | 0.97 | 1.08 | 2.99 | 2.21 | 0.41 | 0.22 | 0.72 | 0.43 |

Weighted by distance | ||||||||

Mean vector, μ (°) | 93.7 | 46 | 81.1 | 67 | 145.2 | 45.8 | 90.8 | 68.5 |

Length of mean vector, R | 0.40 | 0.62 | 0.86 | 0.78 | 0.32 | 0.13 | 0.38 | 0.243 |

Modal direction (°) | 145–150 | 65–70 | 90–95 | 50–55 | 130–135 | 100–105 | 100–105 | 65–70 |

Mode percentage (%) | 17.7 | 11.7 | 13.4 | 16.3 | 10.4 | 7.3 | 11.3 | 10.0 |

Concentration, K | 0.89 | 1.60 | 3.82 | 6.71 | 0.68 | 0.26 | 0.83 | 0.50 |

Weighted by weight | ||||||||

Mean vector, μ (°) | 122.8 | 62.5 | 83.9 | 64.2 | ||||

Length of mean vector, R | 0.46 | 0.70 | 0.89 | 0.92 | ||||

Modal direction (°) | 105–110 | 115–120 | 100–105 | 70–75 | ||||

Mode percentage (%) | 17.8 | 26.7 | 27.1 | 37.5 | ||||

Concentration, K | 1.04 | 2.00 | 4.88 | 2.69 | ||||

Statistical tests | ||||||||

Rayleigh test ( | 6.8 | 10.7 | 26.9 | 23.9 | 2.612 | 1.029 | 8.669 | 3.945 |

Rayleigh test ( | 8.60E−04 | 1.26E−05 | 8.11E−12 | 6.15E−11 | 0.073 | 0.358 | 1.72E−04 | 0.019 |

Kuiper’s test (uniform, V) | 2.254 | 2.59 | 4.295 | 3.699 | 134.125 | 126 | 140.8 | 125.636 |

Kuiper’s test ( | < 0.01 | < 0.01 | < 0.01 | < 0.01 | 0.50 > | 0.90 > | 0.50 > p > 0.10 | 0.90 > p > 0.50 |

Rao’s spacing test (U) | 167 | 169 | 226.317 | 194 | 0.152 | 0.073 | 0.475 | 0.23 |

Rao’s spacing test ( | < 0.01 | < 0.01 | < 0.01 | < 0.01 | 0.1 > | 0.5 > | < 0.005 | < 0.025 |

Watson’s U | 0.407 | 0.605 | 1.566 | 1.362 | 1.401 | 1.074 | 2.358 | 1.98 |

Watson’s U | < 0.005 | < 0.005 | < 0.005 | < 0.005 | > 0.15 | > 0.15 | < 0.01 | < 0.025 |

Descriptive statistics also included weighting the displacement vectors with the distance covered by each piece from the core, and the artefact weight (Fig. 8a, Table 5). Results show that the mean direction does not vary significantly from the unfiltered data. Exp. 18 is where the mean changed the most significantly, varying close to 15° from weighted to unweighted data. On the other hand, the mean direction did not change significantly in Exp. 54 and Exp. 56, which show variations of only 2–3°. The modal direction is more variable, but with no similar pattern shared by all experiments (Table 5). Regarding dispersion, weighted statistics show an increase of the concentration in all experiments, excepting in the data weighted by the distance of Exp. 18, where the concentration is reduced slightly with respect to the unweighted statistics. In general, data weighted by artefact mass shows more concentrated parameters than data weighted by the distance, excepting in Exp. 56 (Table 5).

#### Refit lines

Clear differences in the axial data of refit lines exist between freehand and bipolar experiments. Statistical results of freehand knapping experiments do not allow rejecting the null hypothesis of uniformity with a > 95% confidence interval. Minimum *p* values are obtained for Exp. 18, where Rayleigh’s test and Rao’s Spacing test only reach *p* = 0.073 (92.7% confidence interval), and Exp. 40’s statistical significance is even lower (*p* > 0.15). Therefore, no solid preferred orientation can be proposed for freehand knapping refit lines. Conversely, bipolar knapping data (Exp. 54 and Exp. 56) show high statistical significance for the Rayleigh test (*Z* > 3.94; *p* < 0.019) and demonstrate evidence of departure from uniformity in Rao’s Spacing and Watson’s omnibus tests (0.025 < *p* < 0.005; Table 5). These results suggest that bipolar refit lines show strong unimodal preferred orientations.

Refit mean directions are different within the freehand experiments (Exp. 18 = 130°, Exp. 40 = 32°; Table 5), whereas bipolar refit lines show more consistent mean directions, located around the 90–270° axis (axis 92–272° for Exp. 54 and axis 76–256° for Exp. 56). A similar—although weaker—relationship was observed in the refit line mode, which is more consistent within bipolar experiments than within freehand experiments (Table 5). The mode percentage is higher in bipolar (8–10.7%) than in freehand knapping (7.8%) (Table 5). The concentration parameters of refit lines also indicate a very dispersed distribution for Exp. 40 (R = 0.11 and K = 0.22) and the highest concentration for Exp. 54 (R = 0.34 and K = 0.72), while Exp. 18 and Exp. 56 have similar intermediate values. However, when refit lines are weighted by the distance (Fig. 8b), concentration similarities between Exp. 18 and Exp. 56 disappear, although they are still positioned between the end values of Exp. 40 and Exp. 54.

## Discussion

### Density patterns

Newcomer and de Sieveking (1980) investigated spatial patterns associated with knapper stance (e.g., standing, seated and sitting on the floor), and observed that the greater the distance from the floor, the larger and more diffused flaking scatters became (see also Fischer 1990). Schick (1984) employed a similar perspective and concurred with Newcomer and Sieveking (1980) that standing produced the largest and most diffuse lithic scatters, whilst also producing more elongated patterns whose density declined as the distance to the knapper increased. Lithic scatters produced whilst kneeling, squatting and sitting on the ground shared similar spatial patterning. These were all more densely clustered compared to standing and produced a more circular or oval distribution. Lithic spatial patterning according to varying reduction techniques has also been studied (e.g. Kvamme 1997) although, with the exception of Schick (1984)—who touched upon the spatial differences between freehand percussion and floor knapping; a position similar, but not identical, to bipolar knapping—to our knowledge freehand versus bipolar flaking had yet to be directly investigated.

Since proximity to the floor correlates with denser concentrations (Newcomer and de Sieveking, 1980; Schick 1984), it would be expected that bipolar flaking in Exp. 54 and Exp. 56 produced more tightly clustering patterns than freehand in Exp. 18 and Exp. 40. Whilst this may be the case for the smallest debris (whose spatial distribution is not the subject of our study), such an expectation is not entirely reproduced in our results, where > 20 mm pieces have a higher dispersion in bipolar than in freehand experiments (see Fig. 7). We propose this is due to the knapper’s lack of control over the products during bipolar flaking; in freehand reduction, the detached product normally rests on the knapper’s hand. However, the hand holding a bipolar core is not in contact with the flaking surface, and products often launch from the core and may travel longer distances from the knapping area, resulting in a more dispersed pattern.

Freehand versus bipolar knapping may also inform in spatial patterns of handedness, a subject that is starting to receive attention in the literature (e.g. Bargalló et al. 2017). As shown in Fig. 7, freehand scatters are largely centred with regard to the knapper’s position, whereas the bipolar products are strongly biased towards the NE. Again, these distinctive patterns are associated with core manipulation; products in a freehand sequence are left by the knapper to drop vertically from the core, whereas in bipolar flaking the core’s handling position blocks two quadrants, and products will land in the sectors associated with the hand that is manipulating the hammerstone. In the case of Exp. 54 and Exp. 56, produced by a right-handed knapper, artefact clusters will thus be located in the NE of the flaking area (Fig. 8).

### Orientation patterns

The use of refit lines to address orientation patterns in archaeological assemblages is still uncommon and has been applied essentially to discuss post-depositional processes (e.g. Schick 1984; Austin et al. 1999; Pope 2002; Ashton et al. 2005; Sisk and Shea 2008; Santamaría et al. 2010; de la Torre et al. 2014; de la Torre and Wehr 2018; Deschamps and Zilhão 2018). Since the preferred orientation of archaeological remains is a strong indicator of hydraulic disturbance (e.g. Toots 1965; Isaac 1967; Schick 1984; Lenoble and Bertran 2004; Benito-Calvo and de la Torre 2011), the underlying assumption in the literature has been that orientation of refit lines should—like the main axis of individual artefacts—inform on the current direction.

While we do not wish to challenge this assumption, results presented in this paper enable us to introduce a cautionary note. Our analysis of refit orientations shows that, in a pristine setting, strongly preferred orientations are to be expected for core-to-flake displacements in bipolar and freehand knapping scenarios, and for refit lines in bipolar knapping episodes. In the case of freehand knapping episodes, refit lines produce random distributions or weakly orientated patterns. Therefore, in archaeological contexts with good conditions of preservation, knapping episodes may produce circular histograms of refit lines that, albeit strongly orientated in some cases, are unrelated to post-depositional disturbance. Thus, there appears to be a degree of equifinality in the use of Rose diagrams for refit lines since they may indicate either flow direction/slope processes in water/gravity-disturbed assemblages or, quite on the contrary, the very position of the knapper in pristine sites. Therefore, results should be considered in the context of other proxies to achieve an accurate understanding of site formation processes.

Apart from acknowledging the reduced sample analysed here (limited to four experiments), it should also be stressed that our experimental models are reductionist and do not account for the near-infinite number of variables that may affect the dispersion of artefacts and therefore the refit line circular histograms. From the dynamic position of the knapper, the fragmentation of the reduction sequences, to the use life of the artefact, many factors will have an influence on the Rose diagrams of refit sets in real archaeological contexts. Despite these caveats, however, our results indicate that patterning exists in refit line orientations of some types of knapping episodes. Therefore, the analysis of circular histograms bears heuristic potential to make high-resolution interpretations of spatial dynamics, particularly in near-pristine sites (e.g. Pigeot 1990; Bodu et al. 1990; Vaquero et al. 2001; Roberts and Parfitt 1999), where preferential orientations may be used to investigate activity areas and micro-spatial patterns.

## Conclusions

The identification of single knapping episodes in the Palaeolithic record (e.g. Pigeot 1990; Fischer 1990; Bodu et al. 1990; Pope 2002) makes it relevant to model the distribution of experimental assemblages and reconstruct their spatial dynamics. With the aid of GIS techniques, we have shown in this paper that knapping scatters have clustered distributions, with high concentrations across low-density areas. These spatial dynamics seem to be patterned, and therefore it might be possible to use density models to explore aspects such as handedness and flaking methods in the archaeological record.

Refit patterns in our experiments show that most conjoins are of short distances, most within less than half a meter radius of the knapper’s position. While this is only to be expected in knapping episodes where no transport of tools is made, our analysis has also interesting results concerning the preferential orientation of refit lines. Preferential orientation of artefacts is a strong indicator of post-depositional disturbance, and circular histograms of refit connections have been used to address the extent of taphonomic processes (e.g. Austin et al. 1999; Ashton et al. 2005; de la Torre and Wehr, 2018; Deschamps and Zilhão, 2018). However, our results show that, perhaps counterintuitively, preferential orientation of refit lines is the common pattern in pristine flaking scatters associated to bipolar knapping. Although these results introduce some degree of equifinality on the interpretation of Rose diagrams for refit lines, they also highlight the potential of applying orientation analysis to refit studies, which can contribute high-resolution data on spatial dynamics of conjoining sets in Palaeolithic assemblages.

## Notes

### Acknowledgements

We are grateful to Francesca Romagnoli and Manuel Vaquero for their invitation to participate in the workshop that led to the publication of this special issue. The workshop ‘The Big Puzzle 30 years after: a multidisciplinary, shared, Palaeolithic perspective’ was kindly supported by the Wenner-Gren Foundation Ref: Gr CONF-737. We are also thankful to the volunteers of the UCL-Lithics Lab that helped in processing the experiments. T. Proffitt's contribution was funded by a British Academy Fellowship (#542133). Raw material collection at Olduvai Gorge was funded by the European Research Council (Starting Grants 283366).

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