Decoding Acheulean Percussive Technology: Experimental Approach to understanding Use-Wear Traces in different raw materials

Decoding Acheulean Percussive Technology: Experimental Approach to understanding Use-Wear Traces in different raw materials | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Decoding Acheulean Percussive Technology: Experimental Approach to understanding Use-Wear Traces in different raw materials Eduardo Paixão, Tegenu Gossa, João Marreiros, Walter Gneisinger, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8784816/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The study of percussive technologies is crucial for understanding the cognitive and adaptive capacities of early hominins in all archaeological periods. Percussive technologies of early hominins testify to a large range of daily activities and serve as a source of information on raw material selection and tool-use strategies. These technologies also inform on the broader economic, social, and technical organisation of early hominin societies. While Acheulean assemblages document an increase in the variability of lithic raw materials and of types used for percussive tools compared to the Oldowan, the functional significance of such changes remains understudied. By experimentally assessing the effects of percussive actions on different raw materials, this study aims to advance our understanding and capability to identify use-wear traces related to bone percussion during the Acheulean, providing a reference that directly contributes to the study of the Melka Wakena site complex. In this paper, we present the results of a laboratory-controlled mechanical experiment combined with multi-scale analyses, including 3D scanning and multi-scale microscopy, to examine the effects of percussive actions on the natural surfaces of different raw materials. Our results show how the traces form in different raw materials, providing additional data to further characterization of traces. Such experimental data are fundamental for inferring decision-making criteria involved in the selection and use of various raw materials by early Acheulean stone toolmakers. Acheulean Use-wear Percussive technology 3D Microscopy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1. Introduction The study of early hominin behaviour aims to glean decision-making processes as reflected in technological and raw material variability. These two elements of lithic assemblages should be studied considering tool functionality. Thus, building analytical and methodological tools to identify and characterise use-wear is fundamental to getting a more complete understanding of those processes. The study of percussive tools is extremely important in the field of primatology since these elements testify to stone tool-assisted subsistence behaviours that dramatically influence the interaction of non-human primates with the environment (Braun et al., 2025 ; Carvalho et al., 2008 ; Falótico & Ottoni, 2016 ; Goldman-Neuman & Hovers, 2012 ; Luncz et al., 2019 ; Proffitt et al., 2018 ). The field of primate archaeology has proven to be a valuable source of information for in-depth exploration of percussive technologies and has also contributed methods of analysis for this type of tools (Benito-Calvo et al., 2015 ; Luncz et al., 2019 ; Proffitt et al., 2018 , 2021 ). In the context of early hominin evolution in the Early Pleistocene, a key aspect of lithic assemblages is the selection and use of different raw materials (Braun et al., 2008 ; Goldman-Neuman & Hovers, 2009 ; Lerner et al., 2007 ; Paixão et al., 2025 ), a behaviour that likely involves, beyond raw material availability also considerations of efficiency, durability, and functional performance (Bello-Alonso et al., 2020 ; Braun et al., 2009 ; Caruana & Mtshali, 2018 ; Ibáñez & Mazzucco, 2021 ; Key et al., 2018 ; Lerner et al., 2007 ; Nora et al., 2025a ; Paixão et al., 2025 ; Pedergnana, 2019 ; Pereira et al., 2017 ; Pereira et al., 2017 ; Pereira & Benedetti, 2013 ). The Acheulean techno-complex represents a critical phase in human evolution, during which hominins developed increasingly complex technological behaviours, including the use of a variety of large cutting tools and percussive implements with a large variety of shapes and raw materials (de la Torre, 2016 ; Diez-Martín et al., 2015 ; Gallotti & Mussi, 2018 ; Hovers et al., 2021 ). This could indicate an increase in the variability of resources exploited in the living environments, which might require organizational (e.g., in patterns of mobility across the landscape) and technological adaptations. The significance of pursuing the understanding of the function of percussive tools has been recognised across various areas of prehistoric research, including studies of the Oldowan and Acheulean (Arroyo & de la Torre, 2016 ; Arroyo & De La Torre, 2020 ; Goren-Inbar et al., 2015 ; Titton et al., 2018 ). Use-wear studies specially dedicated to percussive tools from Acheulean contexts are still relatively uncommon, likely of poor preservation due to post-depositional processes, which have been documented in several assemblages (Arroyo & De La Torre, 2020 , 2020 ; Benito-Calvo et al., 2018 ). The scarcity of experimental reference collections for many coarse-grain raw materials also limits archaeological use-wear analyses. However, the likelihood of success has increased over the last years with the growth and development of the use-wear analyses discipline in parallel with multiple imaging and quantitative imaging techniques and methods (including 3D surface analysis and microscopy, especially when a multi-scale approach is adopted (Arroyo & de la Torre, 2016 ; Arroyo & De La Torre, 2020 ; Benito-Calvo et al., 2018 ; Caricola et al., 2018 ; Macdonald et al., 2019 ; J. Marreiros et al., 2020 ; Paixão, 2021 ; Proffitt et al., 2021 ). Our previous study (Paixão et al., 2025 ) focused on investigating the mechanical properties of lithic raw materials at Melka Wakena, an Early Acheulean site-complex located in the Ethiopian highlands. Through controlled mechanical testing, we demonstrated that different raw materials exhibit significant variation in fracture resistance and impact absorption. These findings suggested that early hominins may have been selecting raw materials based on their structural properties. While that research phase did not directly examine the functional aspects of the tools, it yielded important information about the differential physical characteristics of the lithic raw materials involved in the present study, creating a baseline to understand how use-wear develops on different raw materials. In the current study, we focus in this study on the formation of use-wear on the percussive tools themselves, using an experimental approach. Our experiments build upon previous work (Paixão et al., 2025 ) by moving from standardised samples to the use of natural surfaces, thus incorporating variables such as cortex texture and curvature that are typically present in archaeological contexts. Specifically, we designed a controlled experiment using hammerstones for a standardised percussive movement on bone. We used a controlled and reproducible experimental setup, to characterise and compare the use-wear marks that developed in the different raw materials. Bone was selected as our contact material due to their natural surface homogeneity and due to the presence of bones in the archaeological record, making testing for bone breaking activities a research priority (Hovers et al., 2021 ). By systematically documenting surface alterations on natural surfaces (cortex) across different raw materials, and integrating them with data from the mechanical testing (Paixão et al., 2025 ), we aim to address the following key questions: How do use-wear traces develop on natural cortical surfaces of percussors across different lithic raw materials (e.g. differences in damage morphologies at different scales)? Which macro and micro-wear features emerge from bone percussion, and how do they vary by raw material? Our samples were submitted to surface analyses combining multi-scale techniques to characterise the formation of both macro and micro wear traces. This includes 3D scanning, macroscopic and microscopic imaging, and confocal microscopy to combine both qualitative and quantitative data acquisition and analysis. Case study (Melka Wakena) Melka Wakena is an Early Acheulean site complex dated to approximately 1.6 to over 0.7 million years ago. The lithic assemblages at all the localities and stratigraphic layers were attributed to the Acheulean technocomplex (Gossa & Hovers, 2022 , 2024 ; Hovers et al., 2021 ). Preliminary analysis at Melka Wakena has identified nearly 250 percussive tools from the stratified assemblages, encompassing various types of hammerstones, possible anvils, and other modified tools bearing different types of impact marks, that need to be studied further through the combination of use-wear analyses (Fig. 1 ). However, functional studies of the assemblage through use-wear analyses are still under study and could potentially increase the number of identified percussive elements. Most of the percussive tools are made of basalt and ignimbrite, with scoria, glassy ignimbrite, and pumiceous ignimbrite also present. Experimental data (Paixão et al., 2025 ) have shown that differences in raw material physical properties strongly affect resistance to percussive damage, with glassy ignimbrite and basalt displaying the highest hardness and lowest volume loss, ignimbrite and scoria showing intermediate and variable damage, and pumiceous ignimbrite exhibiting extreme fragility under identical impact conditions. Faunal remains, including large mammal bones with anthropogenic marks, have been uncovered in some of the occupation horizons at Melka Wakena, highlighting the possibility that percussive tools may have been associated with bone-processing activities. By developing dedicated experiments to study use-wear formation, we test the hypothesis for bone-braking activities while also contributing to the challenge of differentiating use-wear traces from surface alterations caused by taphonomy. Our aim with this experiment is not to test all the functional possibilities but rather to focus on testing the bone activities as a first testable hypothesis. 2. Methods and materials 2.1. Experimental Setup and Workflow The mechanical experiments were conducted with a controlled setup using a mechanical modular device (SMARTTESTER®, manufactured by Inotec AP GmbH, with adaptations made by Walter Gneisinger), see Calandra et al., 2020 ; Paixão, Pedergnana, et al., 2021 ) for details on the mechanics). This experimental rig enables high-precision impact actions, ensuring consistent force application and replicable automatic movement. In this experiment, the setup includes a sample preparation process that preserves the natural curvature of the contact point and the cortical surface. The experimental protocol consisted of controlled impact cycles on a fresh cow long bone. A total of 15 lithic samples were analysed, with three specimens representing each raw material (ignimbrite, glassy ignimbrite, pumiceous ignimbrite, basalt, scoria). 2.2. Sample Preparation Lithic samples (tools) were selected based on their raw material and natural surface, making them suitable to impact the bone (contact material) on a stable and regular impact area. The contact area of each item was not modified, and its natural state was retained, including cortical areas, to assess alterations resulting from experimental use. A cut and a groove were made in the sample’s unused area (opposed to the area of impact) to install an aluminium T-section to attach the tool to the sample holder of the machine. This sample holder enables reproducible and stable positioning, allowing for removal and precise repositioning between cycles (Fig. 2 .a). The samples were subjected to a basic cleaning protocol in a 4L-ultrasonic tank using distilled water with 1ml/Litre Plurafac LF 901 surface active agent added at a temperature of 45ºC for 10 minutes. Rinsing was carried out under running tap water, followed by a final rinse with distilled water. Drying was accelerated using compressed air from an oil-free compressor. Because these experiments were designed to replicate high-impact percussive motions, substantial surface damage was expected. For this reason, a local coordinate system was not applied to the sample surfaces (Calandra, Schunk, et al., 2019 ). Instead, alignment and surface comparison between the pre- and post-use samples were performed using visible natural landmarks located on unmodified areas of the surface. 2.3. Percussive Testing Protocol Each tool was subjected to two distinct impact cycles to assess the development wear patterns. The first cycle consisted of 0 to 100 impacts, while the second cycle extended from 100 to 200 impacts. These impacts were applied under controlled force conditions to produce directly comparable data on use-wear development, ensuring that all raw materials were used under the same conditions. Dropping distance (40cm), and weight (3.5Kg) were controlled to keep a standardized impact force. The impact force was monitored using a force sensor installed under a lithic anvil onto which a bone was positioned. The repetition of movement during the experimental cycles was automated using a sample mount on a free-fall rail system with a snail/drop cam plus follower. The drop frequency was controlled by a rotary drive connected to a central computer (Fig. 2 , see SOM2) 2.4. Analytical Protocol To characterize surface modifications, a multi-scale analytical approach was employed, integrating 3D surface analysis, macroscopic assessment, and microscopic examination to ensure a comprehensive evaluation of wear patterns and extract both qualitative and quantitative data. 2.5. 3D Surface Analysis (mesh distance) The samples were scanned with an Aicon SmartScan-HE R8 3D structured-light scanner, equipped with a field of view of 110 × 80 × 70mm and a point-to-point distance of 33 µm. The scanning aimed to generate digital 3D models of the lithic surfaces before and after the experimental activity. Each surface model was extracted and analysed individually after being exported as .ply files and ensuring accurate comparative analysis. CloudCompare V2.12.3 (Kyiv) was used to align paired meshes, using two complementary steps: the pair-picking method and the Fine Registration (ICP) (Root Mean Square parameter) between samples. To facilitate the manipulation and processing, each mesh was simplified using the Simplification Quadratic Edge Collapse decimation algorithm in MeshLab. This algorithm reduces the number of faces/triangles in a 3D mesh while trying to preserve the overall shape, volume, and appearance (Garland & Heckbert, 1997 ). After alignment, the quantification of macro damage was assessed by measuring the distance between paired meshes (before and after the experiment, cycle 0 and cycle 2). The distance between meshes was calculated and analysed using distinct and complementary algorithms of R: vertex distance (VD), Root Mean Square (RMS, using both vcgClostKDand vcgMetro), and Hausdorff (see data processing and analysis R script in SOM3 and d https://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026 SOM3). Vertex Distance (VD) captures localised surface changes and measures the nearest-point distance for every vertex of one mesh to the surface of another. Root Mean Square (RMS) distance summarises overall geometric deviation by computing the square root of the mean of squared point-to-surface distances, providing a single measure of average shape difference. Within RMS, two algorithms were tested: 1) vcgClostKD, which uses a fast k-d tree nearest-neighbour search to compute RMS distances efficiently, making it suitable for high-resolution meshes; and 2) vcgMetro, which relies on a sampling-based method (metro distance) that evaluates distances between mesh surfaces using a more uniform or Monte-Carlo sampling approach. Finally, Hausdorff distance quantifies the maximum deviation between two surfaces. Unlike RMS or VD, which represent average behaviour, the Hausdorff metric identifies the largest geometric difference and is thus sensitive to outliers, small scars, or areas of extreme modification. Data were compared between the original mesh (cycle 0, prior to use), and the modified mesh (cycle 2, after use). Low mean distances signify a close similarity between pre- and post-use surfaces, implying limited modification or wear. In contrast, high mean distances indicate developed use-wear or damage, leading to significant geometric changes to the surface. Boxplots and violin plots were used to visualise data distribution. To evaluate the testing hypotheses and detect significant differences between paired differences, statistical tests were used. ANOVA (Analysis of Variance) was used to tests whether the means of multiple groups differ significantly, assuming normally distributed data and equal variances. As an alternative, we tested Kruskal–Wallis as non-parametric test for comparing groups. Dunn Post-hoc Test was used after a significant Kruskal–Wallis result. Kolmogorov–Smirnov (KS) Test was also used to compare the entire distributions between two samples. Finally, we applied Tukey’s Honest Significant Difference (HSD) to perform all pairwise comparisons between group means. To illustrate the data distribution and variance between samples, the visualisation of the distance difference between paired meshes was done in CloudCompare using the C2M tool (cloud to mesh distance) (256 distance steps and 0.5 threshold, see (Nora et al., 2025b ; Paixão et al., 2025 ; Rausch et al., 2024 ) for details. 2.6. Multi-scale microscopy A digital, fully motorized microscope (Stereo-microscope, ZEISS Smartzoom 5), equipped with a 1.6x/0.1 objective was used for macro-scale imaging. With this equipment, we documented the active areas of the tool at 34× magnification, before, within and after each experimental cycle (see SOM xx for details on equipment and image acquisition settings). Micro use-wear analyses were conducted by combining both qualitative and quantitative imaging techniques using a Upright light microscope, Zeiss Axio Imager.Z2 Vario and Zeiss LSM 800 MAT. Qualitative analysis and brightfield imaging acquisition were carried out using the optical objectives: C Epiplan-Apochromat 5x/0,2, C Epiplan-Apochromat 10x/0,4 and Epiplan-Apochromat 20x/0,7. During the analysis, all pictures were acquired with the dedicated software ZEISS Zen Core, using the image Extended Depth of Focus (EDF) stacking module to generate in-focus images. Polished surfaces were qualitatively categorised and described following established terminology, including their microtopographic context, distribution, density, morphology in cross-section, texture, margins, opacity, brightness, and the description of associated striations(Adams, 2014 ; Dubreuil & Savage, 2014 ; Keeley, 1980 ; J. Marreiros et al., 2020 ). Quantitative surface texture analysis was carried out using a laser-scanning confocal microscope, Zeiss LSM 800 MAT. Micro surface data was acquired using a C Epiplan-Apochromat 20x/0,7 objective (see SOM 1 for more details). With this equipment, we acquired bright field (BF) imaging and applied Laser Scanning Confocal Microscopy (LSM) in the same areas, collecting high-resolution imaging to evaluate micro-polish and the development of use-wear traces, combining qualitative and quantitative data. For this study, two samples from each raw material were measured. The samples selected for confocal microscopy represent the variability within and between rock types observed in the experimental assemblage using reflected light microscopy. For both imaging techniques, after examination, 3 distinct surface areas bearing use-wear were selected from each tool, and 1 area with no use-wear (natural surface). From each area, both with use-wear and unused, 3 microsurfaces were sampled for both qualitative and quantitative documentation and analysis (Fig. 3 ). With this approach and sampling methods, we were able to sample areas within the damaged area and areas outside the active area for examination, which enables the distinction of wear patterns from natural surfaces. For the surface analyses, we selected three main parameters which best characterise the surfaces of the different rock types included in this study: Sq, Vmc and Furrows analysis. Sq captures how “rough” or “smooth” a surface is as a whole. It is known to be sensitive to raw material properties, initial surface preparation and large-scalesurface alteration (Leach, 2013 ). Sq serves as a reference parameter that captures overall surface roughness and material-controlled variability. Vmc (core material volume) quantifies the volume of material contained in the core of the surface, excluding peaks and deep valleys. Vmc captures changes in the portion of the surface and is therefore well suited to detecting use modification related to contact intensity and surface smoothing. Furrow metrics (e.g., density, depth, orientation) describe elongated, directional surface features. Furrow analysis targets directional, feature-based wear that is directly related to tool use and motion, providing behavioural information that global roughness parameters cannot capture. The results obtained from these selected parameters are consistent with the trends shown by the other parameters (see 10.5281/zenodo.18309535 for details). 3D surface data were processed in batch in ConfoMap v8.1.9286 (a derivative of MountainsMap Imaging Topography developed by Digital Surf, Besançon, France). All surface processing and analysis were done using templates adapted from (Calandra, Pedergnana, et al., 2019 ) for the surface roughness standard following ISO 25178–2 (ISO, 2005, ISO, 2012), furrow parameters, texture direction parameters, and texture isotropy parameters (see SOM4 for all used templates). All data analysis and plotting were processed in the open-source software R . The analysis workflow is detailed in SOM3 and d https://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026 . In this study, three principal surface roughness parameters were selected to best describe and interpret the surface characteristics of the different rock types: Sq (height), Vmc (functional), and furrow analysis. 3. Results Macro Mesh distance Results of the 3D mesh comparison analysis are shown in Figs. 5 , 6 and 7 , and Table 1 (see also SOM3 and https://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026 SOM3). Violin plots on Fig. 5 show the Per-Vertex Distances by raw material and sample. In both plots, each point represents a sample’s per-vertex mean distance between cycle 0 and cycle 2 meshes. The width of the violin plot corresponds to the distribution shape (e.g., variability) of the data. Data from average surface changes between cycle 0 and cycle 2 show that different raw materials display substantial overlapping ranges, except for pumiceous ignimbrite, which shows higher values. Additionally, there is variation within raw materials, well represented in the regular ignimbrite samples, scoria and pumiceous ignimbrite. This variability seems to indicate that individual samples behave slightly differently within the same rock type. Concerning RMS distance analysis, unlike VD (which measures all vertices), it focuses on global differences between surfaces. Results from both cvgClostKD and cvgMetro algorithms show similar patterns, in which pumiceous ignimbrite presents a high degree of surface damage. The width of the violin plots for scoria and regular ignimbrite is also noteworthy, as it indicates variability among samples of the same rock type. In contrast, pumiceous ignimbrite and glassy ignimbrite exhibit more consistent patterns, showing similar behaviour across samples of each rock type. In Fig. 6 the heatmap shows the adjusted p-values from Tukey’s Honest Significant Difference (HSD) post-hoc test. The test compares every pair of raw materials to check whether their mean RMS distances differ significantly after ANOVA. In the plot, dark colours correspond to high p-value (meaning no difference), while light colours = low p-value (meaning significant difference). Here, results also show a similar pattern, with low p-values (represented by the red squares in the figure) indicating significant differences between pumiceous ignimbrite and both scoria and glassy ignimbrite. Hausdorff distance identifies the single largest point of difference between the two meshes, focusing on highly localised surface changes. Here, following previous results from other mesh comparison methods, the pumiceous ignimbrite sample also stands out, showing the highest degree of damage, which contrasts with glassy ignimbrite and scoria. ​​ Table 1 Summary of the descriptive statistical analysis of the 3D mesh comparison analysis. Sample ID Number of vertices Raw Material Distance - Mean Distance - Room Mean Square Distance - Minimum Distance - Maximum DWG1S1 153005 Basalt 0,00853121 0,053101622 0 1,631797314 DWG1S2 191100 Basalt 0,009096643 0,055930348 0 1,757977128 DWG1S3 114367 Basalt 0,015483832 0,108278519 0 1,809292793 DWG3S1 168169 Scoria 0,010633531 0,044865085 0 1,000451446 DWG3S2 509311 Scoria 0,00852899 0,062332592 0 3,616731644 DWG3S3 321533 Scoria 0,011624224 0,057281806 0 1,469577909 DWG4S1 303591 Pumiceous 0,068866649 0,312350755 0 4,02556324 DWG4S2 303579 Pumiceous 0,05752459 0,262710495 0 3,554227114 DWG4S3 234846 Pumiceous 0,069557465 0,223230727 0 3,180779219 KWG1S1 360323 Glassy ignimbrite 0,025366893 0,173884076 0 4,955983639 KWG1S2 316278 Glassy ignimbrite 0,011949378 0,068917594 0 2,640054941 KWG1S3 189198 Glassy ignimbrite 0,013807118 0,051418418 0 2,211892128 MW6G2S1 257773 Regular ignimbrite 0,002675091 0,028266515 0 0,828565001 MW6G2S2 265342 Regular ignimbrite 0,030688558 0,066672718 0 2,036508083 MW6G2S3 72835 Regular ignimbrite 0,003325644 0,020620064 0 0,627258301 Stereomicroscope At the macro level of analysis, the results show how different raw materials can develop considerably different degrees of surface damage. In some cases, including pumiceous ignimbrite, basalt and ignimbrite, the use-wear damage is visible to the naked eye or at low microscopic magnification. Within this group, the pumiceous ignimbrite shows the highest development of fatigue fractures with irregular curved outlines and cortex removal as a result of the impact, both on high and low topography (see Fig. 9 ). In some samples (e.g., ignimbrite), large fractures happen rapidly during the first impacts, possibly caused by pre-existing internal fractures in the raw material (see Fig. 9 ). Besides those large fractures, it is also possible to observe under low magnification, that surface damage is characterised by high topography levelling. This appears as interconnected damaged zones (connected density) of the altered surfaces and sinuous morphology of the micro-surface at the contact point (see Fig. 9 ). Glassy ignimbrite and scoria are two of the raw materials that showed more resistance to surface damage during the experiment. Surface damage is barely visible at low magnification, especially in the case of the glassy ignimbrite. Micro-wear The qualitative microscopic analysis revealed differences in the development of micro-wear traces across the tested raw materials. Basalt, glassy ignimbrite and ignimbrite show the most consistent patterns, characterised by the presence of micro-polish with covering distributions and connected densities. In these materials, the alteration typically appears as flat micro-morphology in cross-section, combined with a rough texture and sharp or diffused margins of the micro polish. The surfaces maintain an opaque aspect with high brightness, although the degree and visibility of associated striations vary between materials. Basalt shows moderate striations, while glassy ignimbrite presents intense striation density, and ignimbrite displays scarce but distinct striations. Scoria exhibited a distinct pattern of surface modification. Wear was unevenly distributed, occurring in isolated patches rather than forming continuous altered areas. At the microscopic scale, the surface remains generally flat, but appears notably rough, with more irregular edges compared to the other raw materials. The altered areas show a dull, matte appearance, and striations are rare. For pumiceous ignimbrite, no diagnostic micro-wear features could be identified. Due to the extremely friable nature of the material and the rapid macro-scale degradation observed during the experiment, micro-scale parameters could not be reliably assessed. Overall, these results demonstrate that raw material structure significantly influences both the formation and visibility of micro-wear traces. Table 2 Qualitative description of use-wear features, following the analytical framework adapted from (Dubreuil et al., 2015 ). RM Microtopographic context Distribution Density Morphology in cross-section Texture Margins Opacity Brightness Associeted wear Basalt High covering connected flat rough diffuse opaque high moderated striations Glassy Ignimbrite High covering connected flat rough sharp opaque high intense striations Ignimbrite High covering connected flat rough sharp opaque high Scarce striations Scoria High loose separated flat rough irregular opaque Dull Scarce striations Pumiceous Ignimbrite N/A N/A N/A N/A N/A N/A N/A N/A N/A Surface texture analysis (confocal microscopy). The surface texture analysis conducted in this study focused on describing and measuring the principal micro-topographic features associated with micro-polish development on the different rock types employed in the experiment. As noted above, the aim of this study was to select two samples from each raw material for analysis using confocal microscopy. The selected samples were intended to represent the range of variability observed within and between rock types in the experimental assemblage, as identified through reflected light microscopy. Because the surfaces of pumiceous ignimbrite underwent extensive damage, these samples did not preserve identifiable microwear. Consequently, only four raw materials are included in the surface texture analysis. All surface texture parameters have been analysed and plotted (see SOM3 and https://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026 SOM3). We used three main parameters, which best characterise the surfaces of the rock types used in this study (Sq, Vmc and Furrows analysis). Figure 12 shows the Sq values organised by raw materials (basalt, glassy ignimbrite, regular ignimbrite, and scoria). Sq represents the root mean square height of the surface, i.e. a measure of overall surface roughness amplitude, sensitive to both peaks and valleys. There is a clear separation in Sq values between raw materials. scoria shows the highest Sq values, with a widespread. Basalt and regular ignimbrite show intermediate Sq values. Finally, glassy ignimbrite consistently exhibits the lowest Sq values. This pattern is systematic and is much stronger than any difference related to the used and natural surfaces. Sq is dominated by intrinsic surface properties of the raw material, such as porosity, grain size and fracture mechanics. Figure 12 shows a clear positive relationship between Sq (arithmetical mean height) and Vmc (core material volume). As surface roughness increases, the volume of material also increases. This is an expected pattern, as rougher surfaces tend to preserve a higher volume of core material. The raw materials occupy distinct regions of the Sq-Vmc distribution, indicating material-dependent surface texture behaviour. Basalt clusters at the lowest Sq and Vmc values, reflecting relatively smooth surfaces with limited core material volume. This suggests limited polish development. Glassy ignimbrite shows slightly higher values but remains relatively compact, indicating moderate roughness and controlled surface modification. Regular ignimbrite spans a broader range of both Sq and Vmc, suggesting greater variability in surface response to use. Scoria displays the highest Sq and Vmc values, as well as the widest dispersion, reflecting its inherently rough structure and strong variability in surface modification. Worked materials (bone vs natural) tend to plot away from the original surfaces, generally toward higher Sq and Vmc values (Fig. 13 ). This indicates that use-wear increases both surface roughness and core material volume, particularly for rougher and more heterogeneous raw materials. The degree of separation between worked and unworked samples varies by raw material, suggesting that use-wear expression is strongly mediated by intrinsic rock properties. This plot reinforces the interpretation that surface texture parameters respond coherently to use, but their magnitude and variability are strongly dependent on raw material structure and mechanical properties. Figure 14 shows a clear inverse relationship between the mean depth of furrows and their mean density. Surfaces characterised by deeper furrows tend to exhibit a lower density of furrows, whereas shallower furrows are generally more densely distributed. Distinct trends are visible among the different raw materials. Basalt clusters at low furrow depths and relatively high densities, indicating the formation of numerous shallow linear features. This suggests that wear on basalt tends to be expressed through fine, closely spaced furrows rather than deep surface incisions. Glassy ignimbrite shows a similar depth range to basalt but with slightly higher densities, reflecting a surface response dominated by shallow, abundant furrows. Regular ignimbrite spans a broader range of depths and densities, indicating greater variability in how linear features develop on this material. Scoria clearly separates from the other raw materials, exhibiting much deeper but less dense furrows. This reflects its highly vesicular structure, where fewer but more pronounced linear features develop during use. Used surfaces tend to shift towards greater furrow depths and/or altered densities compared to unworked (natural) surfaces. However, the magnitude of this shift varies by raw material. In more homogeneous materials (e.g. basalt, glassy ignimbrite), the distinction between worked and natural surfaces is more subtle, whereas in scoria the effects of use are more pronounced and readily identifiable. Overall, this plot indicates that furrow-related parameters are highly sensitive to raw material properties. The inverse depth-density relationship suggests different wear mechanisms. Either the accumulation of many fine striations or the development of fewer, deeper linear features. When considered alongside roughness parameters (e.g. Sq, Vmc), the furrow analysis provides complementary insight into the directionality and mechanics of surface modification during use. Discussion The experimental results presented in this paper highlight the influence of raw material on use-wear formation (in this study, on percussive contact with fresh bone), emphasising once more how fundamental it is to conduct experiments with a variety of raw materials that represent the archaeological reality. Our results suggest that, despite the original morphology and surface texture complexity, the overall pattern remains consistent with previous durability trends, showing significant differences between the various types of rocks. Pumiceous ignimbrite, for instance, exhibited the highest degree of surface alteration. This result aligns with our previous observations and test, which correlate low Leeb rebound hardness with low resistance to impact. In contrast, glassy ignimbrite and basalt, which have scored high hardness values, once again demonstrated greater structural integrity and a low degree of damage after use. Importantly, the combined interpretation of the mesh comparison results and the confocal surface texture analyses highlights how macro-scale damage and micro-scale wear development are complementary rather than redundant proxies of percussive tool performance. Mesh distance metrics capture the extent and intensity of surface modification resulting from repeated impacts, providing a quantitative measure of material loss and structural degradation. In contrast, confocal microscopy characterises the development, continuity and texture of micro-polish and linear features that emerge on surviving surface areas and reflect contact mechanics and use intensity. When integrated, these datasets show that raw materials displaying limited macro-scale damage (e.g., basalt and glassy ignimbrite) are precisely those that preserve micro-wear signatures, while materials exhibiting extensive macro-scale degradation (e.g., pumiceous ignimbrite) fail to develop interpretable micro-wear due to rapid surface disintegration. This multi-scale aspect reinforces the interpretation that raw material properties directly mediate not only tool durability but also detectability of archaeological use-wear traces. The incorporation of natural surface curvature in the experiments revealed that fracture development can vary depending on internal weaknesses inherent to each rock type, with a stronger influence than the differences in external tool shape. Although the samples were selected to present comparable impact surfaces, ignimbrite specimens containing hidden microfractures experienced localized surface failure early in the impact cycles. This highlights how natural material heterogeneity, often invisible in standardized or schematic samples, can strongly influence damage development and must be considered when interpreting percussive tools One of the most relevant elements of this study is the variation in visible use-wear traces depending on the analytical scale. While pumiceous ignimbrite often presented macroscopic damage clearly visible with the naked eye, materials such as scoria and glassy ignimbrite displayed use-wear features only detectable through high magnification microscopy. In these raw materials, the study shows that the presence of micro-polish, micro-striations and surface texture variation was best characterized at high magnification, reinforcing the need for multi-scale analytical strategies in functional archaeology. Concerning differences between rock types, scoria shows the largest variance, high-Sq values, which likely reflects variable surface breakage during manufacture, resulting in sensitivity of Sq to deep pits and cavities. This also reinforces that Sq captures macro-roughness, not only use-related modification. With respect to the comparison between used and natural surfaces, there is a minimal separation, as data points largely overlap. This suggests that Sq is not particularly sensitive to use-wear formation in this experimental context. The lack of separation between bone and no use does not mean no wear occurred. Rather, it indicates that wear is localised, may affect directional or functional features, and its signal may be masked by initial roughness. An additional methodological aspect that deserves consideration concerns the optical objectives used for surface texture analyses. Although higher magnification objectives (e.g. 50×) may be more suitable for capturing fine-scale surface modifications and micro-polish development on some raw materials, their application was constrained in the present study by sample height and geometry. The natural morphology and thickness of the experimental tools prevented the reliable use of higher-magnification objectives without compromising image quality and focal stability. Future work should explore alternative strategies, such as the use of surface moulds or replicas, to overcome these limitations and allow systematic higher-magnification analyses of percussive wear traces. Our experimental results bear directly on the archaeological record: tools made from dense or homogeneous materials may appear functionally ambiguous if analysed only at low magnification. Therefore, the integration of high-resolution imaging, with high-power microscopy, becomes essential for accurate use-wear identification and characterisation (Arroyo & de la Torre, 2016 ; Marreiros et al., 2020 ; Paixão, Marreiros, et al., 2021 ; Pedergnana & Ollé, 2017 ). Our results also highlight qualitative differences in polish development across raw materials. On glassy ignimbrite, wear forms a continuous, high-brightness surface with intense striations. Basalt and ignimbrite also show continuous polish, but with moderate or scarce striations. In contrast, scoria develops only loose, discontinuous and dull micro-polish with scarce striations, while pumiceous ignimbrite presents no diagnostic micro-wear due to rapid surface degradation. These differences suggest that polish formation is not only a function of contact material and motion but also of the physical structure and surface morphology of the lithic raw material. Therefore, interpreting micro-polish in archaeological contexts requires an understanding of how raw materials respond differently to similar actions. The experimental results presented and discussed here correlate with preliminary archaeological observations at Melka Wakena, where percussive tools are predominantly made from basalt and ignimbrite, while tools made from pumiceous ignimbrite are rare despite its wide availability in the local landscape (Gossa & Hovers, 2022 ). When viewed together with our previous mechanical testing study (Paixão et al., 2025 ), a coherent pattern emerges: pumiceous ignimbrite consistently exhibits extremely low impact resistance, rapid surface disintegration, and a limited capacity to develop stable or diagnostic use-wear. In contrast, basalt and glassy ignimbrite show higher hardness values, greater structural integrity, slower degradation rates, and develop more continuous micro-polishes. This combination of properties makes these two raw materials far more suitable for sustained percussive activities. The convergence between experimental data, therefore, strengthens the interpretation that Acheulean toolmakers at Melka Wakena were not selecting raw materials opportunistically or solely based on abundance. Instead, the archaeological pattern is consistent with a decision-making process accounting for functional performance and/or expected tool longevity. Thus, the variability of shapes, sizes, and material properties among different raw materials at MW suggests that specific materials may have been selected for distinct intended functions. The use of glassy ignimbrite and basalt for percussive tools suggests deliberate selection based on efficiency and durability, even when alternatives were locally more available. Thus, the hominins’ ability to anticipate tool suitability for specific tasks implies a form of predictive cognition and adaptive flexibility. While the experimental dataset from our study offers a valuable reference point, it represents only one axis of investigation (focused on bone-breaking using a standardized movement). Future work should include more experiments, incorporating different contact materials and actions. Other experimental approaches should also be considered in the future, including 3rd-generation experiments (Marreiros et al., 2020 ), to address aspects of anthropogenic variation. This approach provides critical insights into early hominin behavioural complexity and contributes to a better understanding of the Acheulean behaviour. The expanded protocols would help distinguish functional traces from taphonomic damage, while also accounting for variability in task complexity. Conclusions This study contributes to the growing body of research on percussive technologies by providing an experimental reference study that contributes to the understanding of use-wear formation in Acheulean percussive tools. By combining controlled mechanical experiments with a multi-scale analytical protocol, we offer new insights into how damage forms in different raw materials as a result of bone percussion under standardised conditions. Our findings corroborate previous data indicating that the physical properties of lithic raw materials significantly influence the formation and visibility of use-wear traces. Importantly, the integration of natural surfaces into the experimental design allowed us to simulate archaeological conditions more closely and to assess the degree to which curvature heterogeneities affect surface alteration. This approach underscores the extension of experimental analyses beyond standardised samples as a necessary step to build interpretative frameworks for functional analysis. This experimental framework also serves as a foundation for future studies investigating archaeological percussive tools. This helps to refine interpretations of hominin technological strategies in the early Pleistocene and contributes to a comprehensive understanding of early hominin decision-making processes in tool selection and use. The observed variability in wear patterns and their detectability across scales reinforces the need for multi-scale analytical strategies in use-wear studies, particularly when dealing with materials that exhibit low macroscopic damage. Moreover, the experimental results align with archaeological observations at Melka Wakena, supporting the hypothesis that raw material selection was functionally based rather than opportunistic. While our experiment focused on a specific task, bone percussion, it provides a methodological foundation for future studies aiming to investigate the full functional spectrum of percussive technologies. Expanding this approach to include diverse contact materials, motion types, and user variability will be essential for refining interpretations of tool function and behavioural complexity in the early Pleistocene. More experiments will be crucial to understanding the real impact of percussive technology on the relationship of early humans with the natural resources during the Acheulean. Overall, this study demonstrates that integrating multi-scale analysis is essential for reliably interpreting percussive tool use in Acheulean assemblages. Without such multi-scale approaches, functional variability and raw material selection strategies may be underestimated or misinterpreted, particularly in assemblages dominated by heterogeneous raw materials. Declarations Author Contribution Conceptualisation: E.P., W.G., J.M.Data curation: E.P., W.G., J.M.Formal analysis: E.P., T.G., J.M., E.H.Funding acquisition: E.P., E.H.Investigation: E.P., T.G., W.G., J.M., E.H.Methodology: E.P., T.G., W.G., J.M., E.H.Writing: E.P., T.G., W.G., J.M., E.H.All authors reviewed and approved the final manuscript. Acknowledgement We thank the Ethiopian Heritage Authority (EHA) for permission to work on this materialand are grateful to Mr. Getahun Tekle and Mr. Sahlesellasie Melaku (EHA curators) for their assistance in the field and the laboratory. We are grateful to the people of Aluba village for their hospitality and help in the field. We are especially grateful to Mr. Dawud Nure Dawwe and Mr. Abdulkadir Bariso Dube for their help.This study was supported by the Fritz Thyssen Foundation (grant 10.21.1.07AA),the Leibniz-Zentrum für Archäologie, the Hebrew University of Jerusalem, The Interdisciplinary Center for Archaeology and Evolution of Human Behaviour (ICArEHB), the Portuguese Science Foundation mobility program and by the Leakey Foundation. The corresponding author is financially supported by the Portuguese Foundation for Science and Technology (FCT) under the CEEC project “EARLYDECISIONS - Deciphering early hominin decision-making behaviour: High resolution analysis of percussive stone tools from the African Acheulian”(ref: 2022.07007.CEECIND). Data Availability Data are available in public repositories. All scripts and analytical workflows used for 3D surface comparison and surface texture analyses are publicly available at GitHub (https://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8784816","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590487701,"identity":"0c7229ff-cade-4f09-9fb6-ca47779a374b","order_by":0,"name":"Eduardo Paixão","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYLCCBCDmZ2BgPECaFskGBgaQFgnitRkcIFYLv/TZgx8ettnJGd9IfnDwC8OdOoJaJPvykiUSziQbm91IMzgsw/CMsC0GZ3gMJBIqmBO33UgwOCzBcJiwFvszPMY/EgzqEzfPSP9AnBYDHh4zoC2HEzdI5Bgc/ECMFokzfGkWCWeOG0uceVNwmMHgGSiw8QP+Ht7DN3+2Vcvxt6dvfPij4g4/QVsYGHgQTGYeUOyQpIXxBwNRWkbBKBgFo2CEAQDl8j7P3doAOgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Algarve","correspondingAuthor":true,"prefix":"","firstName":"Eduardo","middleName":"","lastName":"Paixão","suffix":""},{"id":590487702,"identity":"3e23297e-1753-40a1-83a0-5bd1057d10b9","order_by":1,"name":"Tegenu Gossa","email":"","orcid":"","institution":"Hebrew University of Jerusalem","correspondingAuthor":false,"prefix":"","firstName":"Tegenu","middleName":"","lastName":"Gossa","suffix":""},{"id":590487703,"identity":"8794e7ea-3546-4d76-9e04-ccb8395ccbef","order_by":2,"name":"João Marreiros","email":"","orcid":"","institution":"Laboratory for Traceology and ControlledExperime nts (TraCEr), MONREPOS -Archaeological Research Centre and Museum for Human Behavioural Evolution,LEIZA -Leibniz-Zentrum fur Archaologie","correspondingAuthor":false,"prefix":"","firstName":"João","middleName":"","lastName":"Marreiros","suffix":""},{"id":590487704,"identity":"9440d1af-7c7e-4898-9808-d464a8519d31","order_by":3,"name":"Walter Gneisinger","email":"","orcid":"","institution":"Laboratory for Traceology and ControlledExperime nts (TraCEr), MONREPOS -Archaeological Research Centre and Museum for Human Behavioural Evolution,LEIZA -Leibniz-Zentrum fur Archaologie","correspondingAuthor":false,"prefix":"","firstName":"Walter","middleName":"","lastName":"Gneisinger","suffix":""},{"id":590487705,"identity":"67323989-e262-492f-9bac-ce81cd82d2ce","order_by":4,"name":"Erella Hovers","email":"","orcid":"","institution":"Hebrew University of Jerusalem","correspondingAuthor":false,"prefix":"","firstName":"Erella","middleName":"","lastName":"Hovers","suffix":""}],"badges":[],"createdAt":"2026-02-04 10:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8784816/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8784816/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102783221,"identity":"35c5fb6f-63ba-495c-a519-8647550cd23f","added_by":"auto","created_at":"2026-02-16 15:32:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":760441,"visible":true,"origin":"","legend":"\u003cp\u003ea) Large artefacts lying flatly on top of a small-clast conglomerate at MW1. b) artefacts concentrations at MW5 during the excavation, c) Map of the location of MW, d) examples of the variability of percussive tools identified at MW.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/617ffb6421321bb0a8eb1c46.png"},{"id":102783222,"identity":"b91258d8-551b-4e2b-a97c-a217b3337b2c","added_by":"auto","created_at":"2026-02-16 15:32:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":771942,"visible":true,"origin":"","legend":"\u003cp\u003ea) samples showing the sample holder system, b) Experimental activity, c) SMARTTESTER\u003csup\u003e® \u003c/sup\u003ecentral computer, d,e) Examples of samples showing the different raw materials\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/d3eccbfe09be4cc1f98cc2d1.png"},{"id":102783226,"identity":"ada3b091-5d15-4193-9c82-27d51f759f63","added_by":"auto","created_at":"2026-02-16 15:32:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":353638,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the sampling strategy used for qualitative and quantitative use-wear observations and documentation.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/8b3d85b036431e47f72e6972.png"},{"id":102783228,"identity":"ad9101f7-673f-405b-bf4c-fdacbac5051c","added_by":"auto","created_at":"2026-02-16 15:32:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":405754,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological simplified workflow\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/baa3e3e5126a191e73582fc1.png"},{"id":102783236,"identity":"8ca7e998-b49e-4c76-91b7-04eedfa8cfec","added_by":"auto","created_at":"2026-02-16 15:32:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":159957,"visible":true,"origin":"","legend":"\u003cp\u003ePer-vertex distance calculation per sample, organised by raw material.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/6e75587dfd8ca1759b5ac986.png"},{"id":102962878,"identity":"83a9a18c-eb4e-461f-8b93-9b45115efba8","added_by":"auto","created_at":"2026-02-19 04:11:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":53651,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap plot of the adjusted p-values from Tukey’s Honest Significant Difference (HSD) post-hoc test for raw material comparison. Dark colours = high p-value (no difference), light colours = low p-value (potentially significant difference are marked in red and by *).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/f784bdba620ac6e1c739535c.png"},{"id":102963171,"identity":"e52e3325-70c3-4c61-97bd-b4b59e477a6e","added_by":"auto","created_at":"2026-02-19 04:14:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":41309,"visible":true,"origin":"","legend":"\u003cp\u003eHausdorff distance between cycle 0 and cycle 2, organised by raw materials.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/a5d6e78ca3323f19b5b399e8.png"},{"id":102962242,"identity":"d29a69ae-0ea2-4006-8177-9a0adecd35e8","added_by":"auto","created_at":"2026-02-19 04:06:04","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":290758,"visible":true,"origin":"","legend":"\u003cp\u003eSnapshot of the C2M comparison and analysis of an experimental sample in pumiceous ignimbrite, highlighting scan distances (before and after use).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/63d8e8e3d5ac3b09b6a8bef7.png"},{"id":102783233,"identity":"eac80e34-9d1c-4910-8d9f-94e4328658a6","added_by":"auto","created_at":"2026-02-16 15:32:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1052097,"visible":true,"origin":"","legend":"\u003cp\u003eMacroscopic views of the contact areas of percussive tools made from different raw materials before the experiment and after the first and second impact cycles. Images were acquired using a ZEISS Smartzoom 5 digital microscope equipped with a 1.6× objective (34× total magnification).\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/80542db377ff5e056f663d04.png"},{"id":102783232,"identity":"5a8bb68b-d129-4a60-86a1-5d855473c9eb","added_by":"auto","created_at":"2026-02-16 15:32:51","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":629679,"visible":true,"origin":"","legend":"\u003cp\u003eBrightfield microscopic images illustratingthe micro use-wear polish on basalt and glassy ignimbrite at different scales.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/8f694cdda80cd7ef7b42c6ef.png"},{"id":102783237,"identity":"23291172-ff89-4c3e-9c90-f708d102836d","added_by":"auto","created_at":"2026-02-16 15:32:52","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":721485,"visible":true,"origin":"","legend":"\u003cp\u003eBrightfield microscopic images illustrating of the micro use-wear on scoria and ignimbrite at different scales.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/269267221b759eacd92920af.png"},{"id":102783238,"identity":"62dad3ce-0b79-41c9-820d-7d0c0dc1a298","added_by":"auto","created_at":"2026-02-16 15:32:52","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":41154,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of the overall roughness parameter (Sq) per raw material. Area roughness - Sa (Arithmetical Mean Height) organised by raw materials.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/1d77c7c1acf19c924233b217.png"},{"id":102962227,"identity":"f02f7b47-be73-4e52-9ee2-80ab2e2379af","added_by":"auto","created_at":"2026-02-19 04:05:45","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":75597,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot using roughness, Area roughness - Sa (Arithmetical Mean Height), and volume, Vmc (Core material volume), parameters organised by raw material (colors) and worked material (circle, representing areas with use-wear on contact with bone; triangle natural surfaces, no use)\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/9685d38eaa16d18167dc7d1a.png"},{"id":102783234,"identity":"fad13706-fb64-41f9-8866-04cf511290ed","added_by":"auto","created_at":"2026-02-16 15:32:51","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":95980,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot using Mean density and Mean depth of furrows. Parameters are organised by raw material (colors) and worked material (circle, representing areas with use-wear on contact with bone; triangle natural surfaces, no use)\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/f2bc33ecef0d7c4875365c01.png"},{"id":103508876,"identity":"0f4b96bc-f7f3-48e0-a080-9a3dc8b834e0","added_by":"auto","created_at":"2026-02-26 13:54:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6854453,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/a88d4702-abdb-4310-a82f-047f44bc304c.pdf"},{"id":102783230,"identity":"2441f93b-acc3-4814-8cf8-c9c75d0951aa","added_by":"auto","created_at":"2026-02-16 15:32:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":73339,"visible":true,"origin":"","legend":"","description":"","filename":"SOM1AcquisitionSettings.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/bc87fe7becd7f858ffbdf099.pdf"},{"id":102783239,"identity":"3387bed1-8673-487c-821e-7e97cff2a493","added_by":"auto","created_at":"2026-02-16 15:32:54","extension":"mp4","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":114664632,"visible":true,"origin":"","legend":"","description":"","filename":"SOM2Paixaoetal2026.mp4","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/969311a89d550180d0434c0e.mp4"},{"id":102783224,"identity":"8dbe2465-07a5-4608-a3cb-1165c2e22d5a","added_by":"auto","created_at":"2026-02-16 15:32:51","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":671375,"visible":true,"origin":"","legend":"","description":"","filename":"SOM33Dmeshanalysis.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/9233fc1e72746bec80632d02.pdf"},{"id":103503897,"identity":"62ad91f1-f684-40e1-94cd-fdbf952e60a3","added_by":"auto","created_at":"2026-02-26 13:04:11","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1020877,"visible":true,"origin":"","legend":"","description":"","filename":"SOM4Surftextconfocal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8784816/v1/eb6217c7605160069b219d00.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decoding Acheulean Percussive Technology: Experimental Approach to understanding Use-Wear Traces in different raw materials","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe study of early hominin behaviour aims to glean decision-making processes as reflected in technological and raw material variability. These two elements of lithic assemblages should be studied considering tool functionality. Thus, building analytical and methodological tools to identify and characterise use-wear is fundamental to getting a more complete understanding of those processes.\u003c/p\u003e \u003cp\u003eThe study of percussive tools is extremely important in the field of primatology since these elements testify to stone tool-assisted subsistence behaviours that dramatically influence the interaction of non-human primates with the environment (Braun et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Carvalho et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fal\u0026oacute;tico \u0026amp; Ottoni, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Goldman-Neuman \u0026amp; Hovers, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Luncz et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Proffitt et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The field of primate archaeology has proven to be a valuable source of information for in-depth exploration of percussive technologies and has also contributed methods of analysis for this type of tools (Benito-Calvo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Luncz et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Proffitt et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of early hominin evolution in the Early Pleistocene, a key aspect of lithic assemblages is the selection and use of different raw materials (Braun et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Goldman-Neuman \u0026amp; Hovers, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lerner et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Paix\u0026atilde;o et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), a behaviour that likely involves, beyond raw material availability also considerations of efficiency, durability, and functional performance (Bello-Alonso et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Braun et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Caruana \u0026amp; Mtshali, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ib\u0026aacute;\u0026ntilde;ez \u0026amp; Mazzucco, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Key et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lerner et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Nora et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e; Paix\u0026atilde;o et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pedergnana, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pereira et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pereira et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pereira \u0026amp; Benedetti, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The Acheulean techno-complex represents a critical phase in human evolution, during which hominins developed increasingly complex technological behaviours, including the use of a variety of large cutting tools and percussive implements with a large variety of shapes and raw materials (de la Torre, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Diez-Mart\u0026iacute;n et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gallotti \u0026amp; Mussi, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hovers et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This could indicate an increase in the variability of resources exploited in the living environments, which might require organizational (e.g., in patterns of mobility across the landscape) and technological adaptations. The significance of pursuing the understanding of the function of percussive tools has been recognised across various areas of prehistoric research, including studies of the Oldowan and Acheulean (Arroyo \u0026amp; de la Torre, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Arroyo \u0026amp; De La Torre, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Goren-Inbar et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Titton et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUse-wear studies specially dedicated to percussive tools from Acheulean contexts are still relatively uncommon, likely of poor preservation due to post-depositional processes, which have been documented in several assemblages (Arroyo \u0026amp; De La Torre, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Benito-Calvo et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The scarcity of experimental reference collections for many coarse-grain raw materials also limits archaeological use-wear analyses. However, the likelihood of success has increased over the last years with the growth and development of the use-wear analyses discipline in parallel with multiple imaging and quantitative imaging techniques and methods (including 3D surface analysis and microscopy, especially when a multi-scale approach is adopted (Arroyo \u0026amp; de la Torre, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Arroyo \u0026amp; De La Torre, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Benito-Calvo et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Caricola et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Macdonald et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; J. Marreiros et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Paix\u0026atilde;o, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Proffitt et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur previous study (Paix\u0026atilde;o et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) focused on investigating the mechanical properties of lithic raw materials at Melka Wakena, an Early Acheulean site-complex located in the Ethiopian highlands. Through controlled mechanical testing, we demonstrated that different raw materials exhibit significant variation in fracture resistance and impact absorption. These findings suggested that early hominins may have been selecting raw materials based on their structural properties. While that research phase did not directly examine the functional aspects of the tools, it yielded important information about the differential physical characteristics of the lithic raw materials involved in the present study, creating a baseline to understand how use-wear develops on different raw materials.\u003c/p\u003e \u003cp\u003eIn the current study, we focus in this study on the formation of use-wear on the percussive tools themselves, using an experimental approach. Our experiments build upon previous work (Paix\u0026atilde;o et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) by moving from standardised samples to the use of natural surfaces, thus incorporating variables such as cortex texture and curvature that are typically present in archaeological contexts. Specifically, we designed a controlled experiment using hammerstones for a standardised percussive movement on bone. We used a controlled and reproducible experimental setup, to characterise and compare the use-wear marks that developed in the different raw materials. Bone was selected as our contact material due to their natural surface homogeneity and due to the presence of bones in the archaeological record, making testing for bone breaking activities a research priority (Hovers et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By systematically documenting surface alterations on natural surfaces (cortex) across different raw materials, and integrating them with data from the mechanical testing (Paix\u0026atilde;o et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we aim to address the following key questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow do use-wear traces develop on natural cortical surfaces of percussors across different lithic raw materials (e.g. differences in damage morphologies at different scales)?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhich macro and micro-wear features emerge from bone percussion, and how do they vary by raw material?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eOur samples were submitted to surface analyses combining multi-scale techniques to characterise the formation of both macro and micro wear traces. This includes 3D scanning, macroscopic and microscopic imaging, and confocal microscopy to combine both qualitative and quantitative data acquisition and analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCase study (Melka Wakena)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMelka Wakena is an Early Acheulean site complex dated to approximately 1.6 to over 0.7\u0026nbsp;million years ago. The lithic assemblages at all the localities and stratigraphic layers were attributed to the Acheulean technocomplex (Gossa \u0026amp; Hovers, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hovers et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Preliminary analysis at Melka Wakena has identified nearly 250 percussive tools from the stratified assemblages, encompassing various types of hammerstones, possible anvils, and other modified tools bearing different types of impact marks, that need to be studied further through the combination of use-wear analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, functional studies of the assemblage through use-wear analyses are still under study and could potentially increase the number of identified percussive elements.\u003c/p\u003e \u003cp\u003eMost of the percussive tools are made of basalt and ignimbrite, with scoria, glassy ignimbrite, and pumiceous ignimbrite also present. Experimental data (Paix\u0026atilde;o et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) have shown that differences in raw material physical properties strongly affect resistance to percussive damage, with glassy ignimbrite and basalt displaying the highest hardness and lowest volume loss, ignimbrite and scoria showing intermediate and variable damage, and pumiceous ignimbrite exhibiting extreme fragility under identical impact conditions.\u003c/p\u003e \u003cp\u003eFaunal remains, including large mammal bones with anthropogenic marks, have been uncovered in some of the occupation horizons at Melka Wakena, highlighting the possibility that percussive tools may have been associated with bone-processing activities. By developing dedicated experiments to study use-wear formation, we test the hypothesis for bone-braking activities while also contributing to the challenge of differentiating use-wear traces from surface alterations caused by taphonomy. Our aim with this experiment is not to test all the functional possibilities but rather to focus on testing the bone activities as a first testable hypothesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Experimental Setup and Workflow\u003c/h2\u003e \u003cp\u003eThe mechanical experiments were conducted with a controlled setup using a mechanical modular device (SMARTTESTER\u0026reg;, manufactured by Inotec AP GmbH, with adaptations made by Walter Gneisinger), see Calandra et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Paix\u0026atilde;o, Pedergnana, et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for details on the mechanics). This experimental rig enables high-precision impact actions, ensuring consistent force application and replicable automatic movement. In this experiment, the setup includes a sample preparation process that preserves the natural curvature of the contact point and the cortical surface. The experimental protocol consisted of controlled impact cycles on a fresh cow long bone. A total of 15 lithic samples were analysed, with three specimens representing each raw material (ignimbrite, glassy ignimbrite, pumiceous ignimbrite, basalt, scoria).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sample Preparation\u003c/h2\u003e \u003cp\u003eLithic samples (tools) were selected based on their raw material and natural surface, making them suitable to impact the bone (contact material) on a stable and regular impact area. The contact area of each item was not modified, and its natural state was retained, including cortical areas, to assess alterations resulting from experimental use. A cut and a groove were made in the sample\u0026rsquo;s unused area (opposed to the area of impact) to install an aluminium T-section to attach the tool to the sample holder of the machine. This sample holder enables reproducible and stable positioning, allowing for removal and precise repositioning between cycles (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.a).\u003c/p\u003e \u003cp\u003eThe samples were subjected to a basic cleaning protocol in a 4L-ultrasonic tank using distilled water with 1ml/Litre Plurafac LF 901 surface active agent added at a temperature of 45\u0026ordm;C for 10 minutes. Rinsing was carried out under running tap water, followed by a final rinse with distilled water. Drying was accelerated using compressed air from an oil-free compressor.\u003c/p\u003e \u003cp\u003eBecause these experiments were designed to replicate high-impact percussive motions, substantial surface damage was expected. For this reason, a local coordinate system was not applied to the sample surfaces (Calandra, Schunk, et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Instead, alignment and surface comparison between the pre- and post-use samples were performed using visible \u003cem\u003enatural\u003c/em\u003e landmarks located on unmodified areas of the surface.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Percussive Testing Protocol\u003c/h2\u003e \u003cp\u003eEach tool was subjected to two distinct impact cycles to assess the development wear patterns. The first cycle consisted of 0 to 100 impacts, while the second cycle extended from 100 to 200 impacts. These impacts were applied under controlled force conditions to produce directly comparable data on use-wear development, ensuring that all raw materials were used under the same conditions. Dropping distance (40cm), and weight (3.5Kg) were controlled to keep a standardized impact force. The impact force was monitored using a force sensor installed under a lithic anvil onto which a bone was positioned. The repetition of movement during the experimental cycles was automated using a sample mount on a free-fall rail system with a snail/drop cam plus follower. The drop frequency was controlled by a rotary drive connected to a central computer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, see SOM2)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Analytical Protocol\u003c/h2\u003e \u003cp\u003eTo characterize surface modifications, a multi-scale analytical approach was employed, integrating 3D surface analysis, macroscopic assessment, and microscopic examination to ensure a comprehensive evaluation of wear patterns and extract both qualitative and quantitative data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. 3D Surface Analysis (mesh distance)\u003c/h2\u003e \u003cp\u003eThe samples were scanned with an Aicon SmartScan-HE R8 3D structured-light scanner, equipped with a field of view of 110 \u0026times; 80 \u0026times; 70mm and a point-to-point distance of 33 \u0026micro;m. The scanning aimed to generate digital 3D models of the lithic surfaces before and after the experimental activity. Each surface model was extracted and analysed individually after being exported as .ply files and ensuring accurate comparative analysis. \u003cem\u003eCloudCompare V2.12.3 (Kyiv)\u003c/em\u003e was used to align paired meshes, using two complementary steps: the pair-picking method and the Fine Registration (ICP) (Root Mean Square parameter) between samples. To facilitate the manipulation and processing, each mesh was simplified using the \u003cem\u003eSimplification Quadratic Edge Collapse decimation\u003c/em\u003e algorithm in MeshLab. This algorithm reduces the number of faces/triangles in a 3D mesh while trying to preserve the overall shape, volume, and appearance (Garland \u0026amp; Heckbert, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter alignment, the quantification of macro damage was assessed by measuring the distance between paired meshes (before and after the experiment, cycle 0 and cycle 2). The distance between meshes was calculated and analysed using distinct and complementary algorithms of R: vertex distance (VD), Root Mean Square (RMS, using both vcgClostKDand vcgMetro), and Hausdorff (see data processing and analysis R script in SOM3 and d \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026\u003c/span\u003e\u003cspan address=\"https://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e SOM3).\u003c/p\u003e \u003cp\u003eVertex Distance (VD) captures localised surface changes and measures the nearest-point distance for every vertex of one mesh to the surface of another. Root Mean Square (RMS) distance summarises overall geometric deviation by computing the square root of the mean of squared point-to-surface distances, providing a single measure of average shape difference. Within RMS, two algorithms were tested: 1) vcgClostKD, which uses a fast k-d tree nearest-neighbour search to compute RMS distances efficiently, making it suitable for high-resolution meshes; and 2) vcgMetro, which relies on a sampling-based method (metro distance) that evaluates distances between mesh surfaces using a more uniform or Monte-Carlo sampling approach. Finally, Hausdorff distance quantifies the maximum deviation between two surfaces. Unlike RMS or VD, which represent average behaviour, the Hausdorff metric identifies the largest geometric difference and is thus sensitive to outliers, small scars, or areas of extreme modification. Data were compared between the original mesh (cycle 0, prior to use), and the modified mesh (cycle 2, after use).\u003c/p\u003e \u003cp\u003eLow mean distances signify a close similarity between pre- and post-use surfaces, implying limited modification or wear. In contrast, high mean distances indicate developed use-wear or damage, leading to significant geometric changes to the surface.\u003c/p\u003e \u003cp\u003eBoxplots and violin plots were used to visualise data distribution. To evaluate the testing hypotheses and detect significant differences between paired differences, statistical tests were used. ANOVA (Analysis of Variance) was used to tests whether the means of multiple groups differ significantly, assuming normally distributed data and equal variances. As an alternative, we tested Kruskal\u0026ndash;Wallis as non-parametric test for comparing groups. Dunn Post-hoc Test was used after a significant Kruskal\u0026ndash;Wallis result. Kolmogorov\u0026ndash;Smirnov (KS) Test was also used to compare the entire distributions between two samples. Finally, we applied Tukey\u0026rsquo;s Honest Significant Difference (HSD) to perform all pairwise comparisons between group means.\u003c/p\u003e \u003cp\u003eTo illustrate the data distribution and variance between samples, the visualisation of the distance difference between paired meshes was done in CloudCompare using the C2M tool (cloud to mesh distance) (256 distance steps and 0.5 threshold, see (Nora et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; Paix\u0026atilde;o et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Rausch et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for details.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Multi-scale microscopy\u003c/h2\u003e \u003cp\u003eA digital, fully motorized microscope (Stereo-microscope, ZEISS Smartzoom 5), equipped with a 1.6x/0.1 objective was used for macro-scale imaging. With this equipment, we documented the active areas of the tool at 34\u0026times; magnification, before, within and after each experimental cycle (see SOM xx for details on equipment and image acquisition settings). Micro use-wear analyses were conducted by combining both qualitative and quantitative imaging techniques using a Upright light microscope, Zeiss Axio Imager.Z2 Vario and Zeiss LSM 800 MAT.\u003c/p\u003e \u003cp\u003eQualitative analysis and brightfield imaging acquisition were carried out using the optical objectives: C Epiplan-Apochromat 5x/0,2, C Epiplan-Apochromat 10x/0,4 and Epiplan-Apochromat 20x/0,7. During the analysis, all pictures were acquired with the dedicated software ZEISS Zen Core, using the image Extended Depth of Focus (EDF) stacking module to generate in-focus images. Polished surfaces were qualitatively categorised and described following established terminology, including their microtopographic context, distribution, density, morphology in cross-section, texture, margins, opacity, brightness, and the description of associated striations(Adams, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dubreuil \u0026amp; Savage, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Keeley, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; J. Marreiros et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQuantitative surface texture analysis was carried out using a laser-scanning confocal microscope, Zeiss LSM 800 MAT. Micro surface data was acquired using a C Epiplan-Apochromat 20x/0,7 objective (see SOM 1 for more details). With this equipment, we acquired bright field (BF) imaging and applied Laser Scanning Confocal Microscopy (LSM) in the same areas, collecting high-resolution imaging to evaluate micro-polish and the development of use-wear traces, combining qualitative and quantitative data. For this study, two samples from each raw material were measured. The samples selected for confocal microscopy represent the variability within and between rock types observed in the experimental assemblage using reflected light microscopy.\u003c/p\u003e \u003cp\u003eFor both imaging techniques, after examination, 3 distinct surface areas bearing use-wear were selected from each tool, and 1 area with no use-wear (natural surface). From each area, both with use-wear and unused, 3 microsurfaces were sampled for both qualitative and quantitative documentation and analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). With this approach and sampling methods, we were able to sample areas within the damaged area and areas outside the active area for examination, which enables the distinction of wear patterns from natural surfaces.\u003c/p\u003e \u003cp\u003eFor the surface analyses, we selected three main parameters which best characterise the surfaces of the different rock types included in this study: Sq, Vmc and Furrows analysis. Sq captures how \u0026ldquo;rough\u0026rdquo; or \u0026ldquo;smooth\u0026rdquo; a surface is as a whole. It is known to be sensitive to raw material properties, initial surface preparation and large-scalesurface alteration (Leach, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Sq serves as a reference parameter that captures overall surface roughness and material-controlled variability. Vmc (core material volume) quantifies the volume of material contained in the core of the surface, excluding peaks and deep valleys. Vmc captures changes in the portion of the surface and is therefore well suited to detecting use modification related to contact intensity and surface smoothing. Furrow metrics (e.g., density, depth, orientation) describe elongated, directional surface features. Furrow analysis targets directional, feature-based wear that is directly related to tool use and motion, providing behavioural information that global roughness parameters cannot capture. The results obtained from these selected parameters are consistent with the trends shown by the other parameters (see \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.18309535\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.18309535\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e for details).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3D surface data were processed in batch in ConfoMap v8.1.9286 (a derivative of MountainsMap Imaging Topography developed by Digital Surf, Besan\u0026ccedil;on, France). All surface processing and analysis were done using templates adapted from (Calandra, Pedergnana, et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) for the surface roughness standard following ISO 25178\u0026ndash;2 (ISO, 2005, ISO, 2012), furrow parameters, texture direction parameters, and texture isotropy parameters (see SOM4 for all used templates). All data analysis and plotting were processed in the open-source software \u003cem\u003eR\u003c/em\u003e. The analysis workflow is detailed in SOM3 and d \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026\u003c/span\u003e\u003cspan address=\"https://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. In this study, three principal surface roughness parameters were selected to best describe and interpret the surface characteristics of the different rock types: Sq (height), Vmc (functional), and furrow analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003cem\u003eMacro\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eMesh distance\u003c/em\u003e \u003c/p\u003e \u003cp\u003eResults of the 3D mesh comparison analysis are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (see also SOM3 and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026\u003c/span\u003e\u003cspan address=\"https://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e SOM3). Violin plots on Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show the Per-Vertex Distances by raw material and sample. In both plots, each point represents a sample\u0026rsquo;s per-vertex mean distance between cycle 0 and cycle 2 meshes. The width of the violin plot corresponds to the distribution shape (e.g., variability) of the data. Data from average surface changes between cycle 0 and cycle 2 show that different raw materials display substantial overlapping ranges, except for pumiceous ignimbrite, which shows higher values. Additionally, there is variation within raw materials, well represented in the regular ignimbrite samples, scoria and pumiceous ignimbrite. This variability seems to indicate that individual samples behave slightly differently within the same rock type.\u003c/p\u003e \u003cp\u003eConcerning RMS distance analysis, unlike VD (which measures all vertices), it focuses on global differences between surfaces. Results from both cvgClostKD and cvgMetro algorithms show similar patterns, in which pumiceous ignimbrite presents a high degree of surface damage. The width of the violin plots for scoria and regular ignimbrite is also noteworthy, as it indicates variability among samples of the same rock type. In contrast, pumiceous ignimbrite and glassy ignimbrite exhibit more consistent patterns, showing similar behaviour across samples of each rock type.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e the heatmap shows the adjusted p-values from Tukey\u0026rsquo;s Honest Significant Difference (HSD) post-hoc test. The test compares every pair of raw materials to check whether their mean RMS distances differ significantly after ANOVA. In the plot, dark colours correspond to high p-value (meaning no difference), while light colours\u0026thinsp;=\u0026thinsp;low p-value (meaning significant difference). Here, results also show a similar pattern, with low p-values (represented by the red squares in the figure) indicating significant differences between pumiceous ignimbrite and both scoria and glassy ignimbrite.\u003c/p\u003e \u003cp\u003eHausdorff distance identifies the single largest point of difference between the two meshes, focusing on highly localised surface changes. Here, following previous results from other mesh comparison methods, the pumiceous ignimbrite sample also stands out, showing the highest degree of damage, which contrasts with glassy ignimbrite and scoria.\u003c/p\u003e \u003cp\u003e​​\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the descriptive statistical analysis of the 3D mesh comparison analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of vertices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaw Material\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDistance - Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDistance - Room Mean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDistance - Minimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDistance - Maximum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWG1S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBasalt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,00853121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,053101622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,631797314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWG1S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e191100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBasalt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,009096643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,055930348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,757977128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWG1S3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBasalt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,015483832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,108278519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,809292793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWG3S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScoria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,010633531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,044865085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,000451446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWG3S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e509311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScoria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,00852899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,062332592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,616731644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWG3S3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e321533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScoria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,011624224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,057281806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,469577909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWG4S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e303591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePumiceous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,068866649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,312350755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,02556324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWG4S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e303579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePumiceous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,05752459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,262710495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,554227114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWG4S3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePumiceous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,069557465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,223230727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,180779219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKWG1S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e360323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlassy ignimbrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,025366893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,173884076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,955983639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKWG1S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e316278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlassy ignimbrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,011949378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,068917594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,640054941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKWG1S3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e189198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlassy ignimbrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,013807118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,051418418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,211892128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMW6G2S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e257773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegular ignimbrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,002675091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,028266515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,828565001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMW6G2S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e265342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegular ignimbrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,030688558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,066672718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,036508083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMW6G2S3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegular ignimbrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,003325644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,020620064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,627258301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eStereomicroscope\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAt the macro level of analysis, the results show how different raw materials can develop considerably different degrees of surface damage. In some cases, including pumiceous ignimbrite, basalt and ignimbrite, the use-wear damage is visible to the naked eye or at low microscopic magnification. Within this group, the pumiceous ignimbrite shows the highest development of fatigue fractures with irregular curved outlines and cortex removal as a result of the impact, both on high and low topography (see Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). In some samples (e.g., ignimbrite), large fractures happen rapidly during the first impacts, possibly caused by pre-existing internal fractures in the raw material (see Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Besides those large fractures, it is also possible to observe under low magnification, that surface damage is characterised by high topography levelling. This appears as interconnected damaged zones (connected density) of the altered surfaces and sinuous morphology of the micro-surface at the contact point (see Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlassy ignimbrite and scoria are two of the raw materials that showed more resistance to surface damage during the experiment. Surface damage is barely visible at low magnification, especially in the case of the glassy ignimbrite.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eMicro-wear\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe qualitative microscopic analysis revealed differences in the development of micro-wear traces across the tested raw materials. Basalt, glassy ignimbrite and ignimbrite show the most consistent patterns, characterised by the presence of micro-polish with covering distributions and connected densities. In these materials, the alteration typically appears as flat micro-morphology in cross-section, combined with a rough texture and sharp or diffused margins of the micro polish. The surfaces maintain an opaque aspect with high brightness, although the degree and visibility of associated striations vary between materials. Basalt shows moderate striations, while glassy ignimbrite presents intense striation density, and ignimbrite displays scarce but distinct striations.\u003c/p\u003e \u003cp\u003eScoria exhibited a distinct pattern of surface modification. Wear was unevenly distributed, occurring in isolated patches rather than forming continuous altered areas. At the microscopic scale, the surface remains generally flat, but appears notably rough, with more irregular edges compared to the other raw materials. The altered areas show a dull, matte appearance, and striations are rare.\u003c/p\u003e \u003cp\u003eFor pumiceous ignimbrite, no diagnostic micro-wear features could be identified. Due to the extremely friable nature of the material and the rapid macro-scale degradation observed during the experiment, micro-scale parameters could not be reliably assessed.\u003c/p\u003e \u003cp\u003eOverall, these results demonstrate that raw material structure significantly influences both the formation and visibility of micro-wear traces.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQualitative description of use-wear features, following the analytical framework adapted from (Dubreuil et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMicrotopographic context\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDistribution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMorphology in cross-section\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTexture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMargins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOpacity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBrightness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAssocieted wear\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasalt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecovering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econnected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eflat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003erough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ediffuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eopaque\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003emoderated striations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlassy Ignimbrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecovering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econnected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eflat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003erough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003esharp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eopaque\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eintense striations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgnimbrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecovering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econnected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eflat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003erough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003esharp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eopaque\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eScarce striations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScoria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eloose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eseparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eflat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003erough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eirregular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eopaque\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eScarce striations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePumiceous Ignimbrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSurface texture analysis (confocal microscopy).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe surface texture analysis conducted in this study focused on describing and measuring the principal micro-topographic features associated with micro-polish development on the different rock types employed in the experiment. As noted above, the aim of this study was to select two samples from each raw material for analysis using confocal microscopy. The selected samples were intended to represent the range of variability observed within and between rock types in the experimental assemblage, as identified through reflected light microscopy. Because the surfaces of pumiceous ignimbrite underwent extensive damage, these samples did not preserve identifiable microwear. Consequently, only four raw materials are included in the surface texture analysis. All surface texture parameters have been analysed and plotted (see SOM3 and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026\u003c/span\u003e\u003cspan address=\"https://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e SOM3).\u003c/p\u003e \u003cp\u003eWe used three main parameters, which best characterise the surfaces of the rock types used in this study (Sq, Vmc and Furrows analysis).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e shows the Sq values organised by raw materials (basalt, glassy ignimbrite, regular ignimbrite, and scoria). Sq represents the root mean square height of the surface, i.e. a measure of overall surface roughness amplitude, sensitive to both peaks and valleys. There is a clear separation in Sq values between raw materials. scoria shows the highest Sq values, with a widespread. Basalt and regular ignimbrite show intermediate Sq values. Finally, glassy ignimbrite consistently exhibits the lowest Sq values. This pattern is systematic and is much stronger than any difference related to the used and natural surfaces. Sq is dominated by intrinsic surface properties of the raw material, such as porosity, grain size and fracture mechanics.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e shows a clear positive relationship between Sq (arithmetical mean height) and Vmc (core material volume). As surface roughness increases, the volume of material also increases. This is an expected pattern, as rougher surfaces tend to preserve a higher volume of core material. The raw materials occupy distinct regions of the Sq-Vmc distribution, indicating material-dependent surface texture behaviour. Basalt clusters at the lowest Sq and Vmc values, reflecting relatively smooth surfaces with limited core material volume. This suggests limited polish development. Glassy ignimbrite shows slightly higher values but remains relatively compact, indicating moderate roughness and controlled surface modification. Regular ignimbrite spans a broader range of both Sq and Vmc, suggesting greater variability in surface response to use. Scoria displays the highest Sq and Vmc values, as well as the widest dispersion, reflecting its inherently rough structure and strong variability in surface modification.\u003c/p\u003e \u003cp\u003eWorked materials (bone vs natural) tend to plot away from the original surfaces, generally toward higher Sq and Vmc values (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). This indicates that use-wear increases both surface roughness and core material volume, particularly for rougher and more heterogeneous raw materials. The degree of separation between worked and unworked samples varies by raw material, suggesting that use-wear expression is strongly mediated by intrinsic rock properties.\u003c/p\u003e \u003cp\u003eThis plot reinforces the interpretation that surface texture parameters respond coherently to use, but their magnitude and variability are strongly dependent on raw material structure and mechanical properties.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e shows a clear inverse relationship between the mean depth of furrows and their mean density. Surfaces characterised by deeper furrows tend to exhibit a lower density of furrows, whereas shallower furrows are generally more densely distributed.\u003c/p\u003e \u003cp\u003eDistinct trends are visible among the different raw materials. Basalt clusters at low furrow depths and relatively high densities, indicating the formation of numerous shallow linear features. This suggests that wear on basalt tends to be expressed through fine, closely spaced furrows rather than deep surface incisions. Glassy ignimbrite shows a similar depth range to basalt but with slightly higher densities, reflecting a surface response dominated by shallow, abundant furrows. Regular ignimbrite spans a broader range of depths and densities, indicating greater variability in how linear features develop on this material. Scoria clearly separates from the other raw materials, exhibiting much deeper but less dense furrows. This reflects its highly vesicular structure, where fewer but more pronounced linear features develop during use.\u003c/p\u003e \u003cp\u003eUsed surfaces tend to shift towards greater furrow depths and/or altered densities compared to unworked (natural) surfaces. However, the magnitude of this shift varies by raw material. In more homogeneous materials (e.g. basalt, glassy ignimbrite), the distinction between worked and natural surfaces is more subtle, whereas in scoria the effects of use are more pronounced and readily identifiable.\u003c/p\u003e \u003cp\u003eOverall, this plot indicates that furrow-related parameters are highly sensitive to raw material properties. The inverse depth-density relationship suggests different wear mechanisms. Either the accumulation of many fine striations or the development of fewer, deeper linear features. When considered alongside roughness parameters (e.g. Sq, Vmc), the furrow analysis provides complementary insight into the directionality and mechanics of surface modification during use.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe experimental results presented in this paper highlight the influence of raw material on use-wear formation (in this study, on percussive contact with fresh bone), emphasising once more how fundamental it is to conduct experiments with a variety of raw materials that represent the archaeological reality. Our results suggest that, despite the original morphology and surface texture complexity, the overall pattern remains consistent with previous durability trends, showing significant differences between the various types of rocks. Pumiceous ignimbrite, for instance, exhibited the highest degree of surface alteration. This result aligns with our previous observations and test, which correlate low Leeb rebound hardness with low resistance to impact. In contrast, glassy ignimbrite and basalt, which have scored high hardness values, once again demonstrated greater structural integrity and a low degree of damage after use.\u003c/p\u003e \u003cp\u003eImportantly, the combined interpretation of the mesh comparison results and the confocal surface texture analyses highlights how macro-scale damage and micro-scale wear development are complementary rather than redundant proxies of percussive tool performance. Mesh distance metrics capture the extent and intensity of surface modification resulting from repeated impacts, providing a quantitative measure of material loss and structural degradation. In contrast, confocal microscopy characterises the development, continuity and texture of micro-polish and linear features that emerge on surviving surface areas and reflect contact mechanics and use intensity.\u003c/p\u003e \u003cp\u003eWhen integrated, these datasets show that raw materials displaying limited macro-scale damage (e.g., basalt and glassy ignimbrite) are precisely those that preserve micro-wear signatures, while materials exhibiting extensive macro-scale degradation (e.g., pumiceous ignimbrite) fail to develop interpretable micro-wear due to rapid surface disintegration. This multi-scale aspect reinforces the interpretation that raw material properties directly mediate not only tool durability but also detectability of archaeological use-wear traces.\u003c/p\u003e \u003cp\u003eThe incorporation of natural surface curvature in the experiments revealed that fracture development can vary depending on internal weaknesses inherent to each rock type, with a stronger influence than the differences in external tool shape. Although the samples were selected to present comparable impact surfaces, ignimbrite specimens containing hidden microfractures experienced localized surface failure early in the impact cycles. This highlights how natural material heterogeneity, often invisible in standardized or schematic samples, can strongly influence damage development and must be considered when interpreting percussive tools\u003c/p\u003e \u003cp\u003eOne of the most relevant elements of this study is the variation in visible use-wear traces depending on the analytical scale. While pumiceous ignimbrite often presented macroscopic damage clearly visible with the naked eye, materials such as scoria and glassy ignimbrite displayed use-wear features only detectable through high magnification microscopy. In these raw materials, the study shows that the presence of micro-polish, micro-striations and surface texture variation was best characterized at high magnification, reinforcing the need for multi-scale analytical strategies in functional archaeology.\u003c/p\u003e \u003cp\u003eConcerning differences between rock types, scoria shows the largest variance, high-Sq values, which likely reflects variable surface breakage during manufacture, resulting in sensitivity of Sq to deep pits and cavities. This also reinforces that Sq captures macro-roughness, not only use-related modification. With respect to the comparison between used and natural surfaces, there is a minimal separation, as data points largely overlap. This suggests that Sq is not particularly sensitive to use-wear formation in this experimental context. The lack of separation between bone and no use does not mean no wear occurred. Rather, it indicates that wear is localised, may affect directional or functional features, and its signal may be masked by initial roughness.\u003c/p\u003e \u003cp\u003eAn additional methodological aspect that deserves consideration concerns the optical objectives used for surface texture analyses. Although higher magnification objectives (e.g. 50\u0026times;) may be more suitable for capturing fine-scale surface modifications and micro-polish development on some raw materials, their application was constrained in the present study by sample height and geometry. The natural morphology and thickness of the experimental tools prevented the reliable use of higher-magnification objectives without compromising image quality and focal stability. Future work should explore alternative strategies, such as the use of surface moulds or replicas, to overcome these limitations and allow systematic higher-magnification analyses of percussive wear traces.\u003c/p\u003e \u003cp\u003eOur experimental results bear directly on the archaeological record: tools made from dense or homogeneous materials may appear functionally ambiguous if analysed only at low magnification. Therefore, the integration of high-resolution imaging, with high-power microscopy, becomes essential for accurate use-wear identification and characterisation (Arroyo \u0026amp; de la Torre, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Marreiros et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Paix\u0026atilde;o, Marreiros, et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pedergnana \u0026amp; Oll\u0026eacute;, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results also highlight qualitative differences in polish development across raw materials. On glassy ignimbrite, wear forms a continuous, high-brightness surface with intense striations. Basalt and ignimbrite also show continuous polish, but with moderate or scarce striations. In contrast, scoria develops only loose, discontinuous and dull micro-polish with scarce striations, while pumiceous ignimbrite presents no diagnostic micro-wear due to rapid surface degradation. These differences suggest that polish formation is not only a function of contact material and motion but also of the physical structure and surface morphology of the lithic raw material. Therefore, interpreting micro-polish in archaeological contexts requires an understanding of how raw materials respond differently to similar actions.\u003c/p\u003e \u003cp\u003eThe experimental results presented and discussed here correlate with preliminary archaeological observations at Melka Wakena, where percussive tools are predominantly made from basalt and ignimbrite, while tools made from pumiceous ignimbrite are rare despite its wide availability in the local landscape (Gossa \u0026amp; Hovers, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When viewed together with our previous mechanical testing study (Paix\u0026atilde;o et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), a coherent pattern emerges: pumiceous ignimbrite consistently exhibits extremely low impact resistance, rapid surface disintegration, and a limited capacity to develop stable or diagnostic use-wear. In contrast, basalt and glassy ignimbrite show higher hardness values, greater structural integrity, slower degradation rates, and develop more continuous micro-polishes. This combination of properties makes these two raw materials far more suitable for sustained percussive activities.\u003c/p\u003e \u003cp\u003eThe convergence between experimental data, therefore, strengthens the interpretation that Acheulean toolmakers at Melka Wakena were not selecting raw materials opportunistically or solely based on abundance. Instead, the archaeological pattern is consistent with a decision-making process accounting for functional performance and/or expected tool longevity. Thus, the variability of shapes, sizes, and material properties among different raw materials at MW suggests that specific materials may have been selected for distinct intended functions. The use of glassy ignimbrite and basalt for percussive tools suggests deliberate selection based on efficiency and durability, even when alternatives were locally more available. Thus, the hominins\u0026rsquo; ability to anticipate tool suitability for specific tasks implies a form of predictive cognition and adaptive flexibility.\u003c/p\u003e \u003cp\u003eWhile the experimental dataset from our study offers a valuable reference point, it represents only one axis of investigation (focused on bone-breaking using a standardized movement). Future work should include more experiments, incorporating different contact materials and actions. Other experimental approaches should also be considered in the future, including 3rd-generation experiments (Marreiros et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), to address aspects of anthropogenic variation. This approach provides critical insights into early hominin behavioural complexity and contributes to a better understanding of the Acheulean behaviour. The expanded protocols would help distinguish functional traces from taphonomic damage, while also accounting for variability in task complexity.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study contributes to the growing body of research on percussive technologies by providing an experimental reference study that contributes to the understanding of use-wear formation in Acheulean percussive tools. By combining controlled mechanical experiments with a multi-scale analytical protocol, we offer new insights into how damage forms in different raw materials as a result of bone percussion under standardised conditions.\u003c/p\u003e \u003cp\u003eOur findings corroborate previous data indicating that the physical properties of lithic raw materials significantly influence the formation and visibility of use-wear traces. Importantly, the integration of natural surfaces into the experimental design allowed us to simulate archaeological conditions more closely and to assess the degree to which curvature heterogeneities affect surface alteration. This approach underscores the extension of experimental analyses beyond standardised samples as a necessary step to build interpretative frameworks for functional analysis.\u003c/p\u003e \u003cp\u003eThis experimental framework also serves as a foundation for future studies investigating archaeological percussive tools. This helps to refine interpretations of hominin technological strategies in the early Pleistocene and contributes to a comprehensive understanding of early hominin decision-making processes in tool selection and use.\u003c/p\u003e \u003cp\u003eThe observed variability in wear patterns and their detectability across scales reinforces the need for multi-scale analytical strategies in use-wear studies, particularly when dealing with materials that exhibit low macroscopic damage. Moreover, the experimental results align with archaeological observations at Melka Wakena, supporting the hypothesis that raw material selection was functionally based rather than opportunistic.\u003c/p\u003e \u003cp\u003eWhile our experiment focused on a specific task, bone percussion, it provides a methodological foundation for future studies aiming to investigate the full functional spectrum of percussive technologies. Expanding this approach to include diverse contact materials, motion types, and user variability will be essential for refining interpretations of tool function and behavioural complexity in the early Pleistocene. More experiments will be crucial to understanding the real impact of percussive technology on the relationship of early humans with the natural resources during the Acheulean.\u003c/p\u003e \u003cp\u003eOverall, this study demonstrates that integrating multi-scale analysis is essential for reliably interpreting percussive tool use in Acheulean assemblages. Without such multi-scale approaches, functional variability and raw material selection strategies may be underestimated or misinterpreted, particularly in assemblages dominated by heterogeneous raw materials.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualisation: E.P., W.G., J.M.Data curation: E.P., W.G., J.M.Formal analysis: E.P., T.G., J.M., E.H.Funding acquisition: E.P., E.H.Investigation: E.P., T.G., W.G., J.M., E.H.Methodology: E.P., T.G., W.G., J.M., E.H.Writing: E.P., T.G., W.G., J.M., E.H.All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the Ethiopian Heritage Authority (EHA) for permission to work on this materialand are grateful to Mr. Getahun Tekle and Mr. Sahlesellasie Melaku (EHA curators) for their assistance in the field and the laboratory. We are grateful to the people of Aluba village for their hospitality and help in the field. We are especially grateful to Mr. Dawud Nure Dawwe and Mr. Abdulkadir Bariso Dube for their help.This study was supported by the Fritz Thyssen Foundation (grant 10.21.1.07AA),the Leibniz-Zentrum f\u0026uuml;r Arch\u0026auml;ologie, the Hebrew University of Jerusalem, The Interdisciplinary Center for Archaeology and Evolution of Human Behaviour (ICArEHB), the Portuguese Science Foundation mobility program and by the Leakey Foundation. The corresponding author is financially supported by the Portuguese Foundation for Science and Technology (FCT) under the CEEC project \u0026ldquo;EARLYDECISIONS - Deciphering early hominin decision-making behaviour: High resolution analysis of percussive stone tools from the African Acheulian\u0026rdquo;(ref: 2022.07007.CEECIND).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData are available in public repositories. All scripts and analytical workflows used for 3D surface comparison and surface texture analyses are publicly available at GitHub (https://github.com/jmmarreiros/Paixao_DecodTraces_JPA2026). Processed surface texture data and analysis templates are available via Zenodo (10.5281/zenodo.18309535).Additional datasets, including full-resolution 3D surface models and microscopy images generated during the experimental programme, are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdams, J. L. (2014). Ground stone use-wear analysis: A review of terminology and experimental methods. \u003cem\u003eJournal of Archaeological Science\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e, 129\u0026ndash;138. https://doi.org/10.1016/j.jas.2013.01.030\u003c/li\u003e\n \u003cli\u003eArroyo, A., \u0026amp; de la Torre, I. (2016). 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Three-dimensional surface morphometry differentiates behaviour on primate percussive stone tools. \u003cem\u003eJournal of The Royal Society Interface\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(184), 20210576. https://doi.org/10.1098/rsif.2021.0576\u003c/li\u003e\n \u003cli\u003eRausch, H., Marreiros, J., Kullmer, O., Schunk, L., Gneisinger, W., \u0026amp; Calandra, I. (2024). An experimental approach on dynamic occlusal fingerprint analysis to simulate use-wear localisation and development on stone tools. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 20084. https://doi.org/10.1038/s41598-024-70265-1\u003c/li\u003e\n \u003cli\u003eTitton, S., Barsky, D., Bargallo, A., Verg\u0026egrave;s, J. M., Guardiola, M., Solano, J. G., Jimenez Arenas, J. M., Toro-Moyano, I., \u0026amp; Sala-Ramos, R. (2018). Active percussion tools from the Oldowan site of Barranco Le\u0026oacute;n (Orce, Andalusia, Spain): The fundamental role of pounding activities in hominin lifeways. \u003cem\u003eJournal of Archaeological Science\u003c/em\u003e, \u003cem\u003e96\u003c/em\u003e, 131\u0026ndash;147. https://doi.org/10.1016/j.jas.2018.06.004\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-paleolithic-archaeology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jpla","sideBox":"Learn more about [Journal of Paleolithic Archaeology](https://link.springer.com/journal/41982)","snPcode":"41982","submissionUrl":"https://submission.nature.com/new-submission/41982/3","title":"Journal of Paleolithic Archaeology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Acheulean, Use-wear, Percussive technology, 3D, Microscopy","lastPublishedDoi":"10.21203/rs.3.rs-8784816/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8784816/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study of percussive technologies is crucial for understanding the cognitive and adaptive capacities of early hominins in all archaeological periods. Percussive technologies of early hominins testify to a large range of daily activities and serve as a source of information on raw material selection and tool-use strategies. These technologies also inform on the broader economic, social, and technical organisation of early hominin societies. While Acheulean assemblages document an increase in the variability of lithic raw materials and of types used for percussive tools compared to the Oldowan, the functional significance of such changes remains understudied. By experimentally assessing the effects of percussive actions on different raw materials, this study aims to advance our understanding and capability to identify use-wear traces related to bone percussion during the Acheulean, providing a reference that directly contributes to the study of the Melka Wakena site complex.\u003c/p\u003e \u003cp\u003eIn this paper, we present the results of a laboratory-controlled mechanical experiment combined with multi-scale analyses, including 3D scanning and multi-scale microscopy, to examine the effects of percussive actions on the natural surfaces of different raw materials. Our results show how the traces form in different raw materials, providing additional data to further characterization of traces. Such experimental data are fundamental for inferring decision-making criteria involved in the selection and use of various raw materials by early Acheulean stone toolmakers.\u003c/p\u003e","manuscriptTitle":"Decoding Acheulean Percussive Technology: Experimental Approach to understanding Use-Wear Traces in different raw materials","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 15:32:46","doi":"10.21203/rs.3.rs-8784816/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-25T22:07:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T18:35:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T10:37:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"731115018799975438660863070021159839","date":"2026-02-12T11:11:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45337231150055545256385300908962199997","date":"2026-02-11T09:12:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-10T19:18:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-05T00:10:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-05T00:10:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Paleolithic Archaeology","date":"2026-02-04T09:27:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-paleolithic-archaeology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jpla","sideBox":"Learn more about [Journal of Paleolithic Archaeology](https://link.springer.com/journal/41982)","snPcode":"41982","submissionUrl":"https://submission.nature.com/new-submission/41982/3","title":"Journal of Paleolithic Archaeology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"166ef190-8e81-400e-8cd6-6bace9a657b9","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T11:24:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 15:32:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8784816","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8784816","identity":"rs-8784816","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}