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Journal of Archaeological Science 39 (2012) 3284e3300

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Journal of Archaeological Science

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Swept under the rug: the problem of unacknowledged ambiguity in lithic residueidentification

Gilliane F. Monnier*, Jammi L. Ladwig, Samantha T. PorterDepartment of Anthropology, University of Minnesota, 395 Hubert Humphrey Building, 301 e 19th Avenue South, Minneapolis, MN 55455, USA

a r t i c l e i n f o

Article history:Received 7 August 2011Received in revised form22 May 2012Accepted 23 May 2012

Keywords:Lithic residue analysisMicroscopyStone toolsExperimental archaeologyScanning electron microscopy

* Corresponding author.E-mail address: monni003@umn.edu (G.F. Monnie

0305-4403/$ e see front matter � 2012 Elsevier Ltd.doi:10.1016/j.jas.2012.05.010

a b s t r a c t

Microscopic analysis of organic residues on stone tools is used to interpret prehistoric stone tool func-tions. The morphology of some residues can be difficult to interpret, yet this ambiguity is rarelyacknowledged in the literature. Our research seeks to understand the nature of this ambiguity byobjectively identifying ambiguous residues in our reference collection. We trained four archaeologists inresidue analysis using one part of our reference collection, then tested their ability to identify sixty-eightresidues in another part of the same collection. Forty-eight of the residues in the test (70%) werecorrectly identified by three or all four subjects. We considered the remaining twenty residues, whichwere correctly identified by two or fewer of the subjects, to be ambiguous. These are most often in thehide-scraping, bone-scraping, and hardwood-scraping (macerated) categories, and tend to have anatypical morphology which falls in the range of variability of another residue category. Some of theseresidues also have optical properties which make them more difficult to image than others. We explorethe potential for scanning electron microscopy (SEM) to improve residue identification in a second test.This test shows a modest improvement in identification success rates of ambiguous residues when SEMimages are included. We conclude that while images from different types of microscopes can improvereliability of identification, some residues will always be ambiguous. Rather than being ignored, theseambiguities should be brought to light, closely examined, and published as such.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

A major concern of lithic analysis is the identification of stonetool functions, for stone tools are one of the key sources of evidenceused by archaeologists to reconstruct human behavior. The field oflithic use-wear studies has fulfilled this role ever since Semenov’spioneering work in the 1960s. Use-wear analysis has maturedthroughout the past half-century from a discipline initially plaguedby subjectivity and inter-analyst disagreement (Shackley, 2008), toone inwhich great efforts have beenmade to improve accuracy andreplicability using blind tests (Odell and Odell-Vereecken, 1980;Bamforth, 1988; Newcomer et al., 1986) and new methods ofquantification (Stemp and Stemp, 2001, 2003; Lerner et al., 2007;Stevens et al., 2010; Lerner and Evans, 2011).

A newer approach to understanding stone tool functions hasemerged, and is focused on the identification of organic residuesadhering to stone tool edges. This field of study has existed for overa quarter of a century, and in the last decade has seen an explosion

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of publications using a multitude of techniques: microscopic,chemical, immunological, and others (e.g., Hardy andMoncel, 2011;Lombard and Wadley, 2007; Lombard, 2008; Koirala andRosentreter, 2009; Reuther et al., 2006; Heaton et al., 2009;Seeman et al., 2008; Evershed, 2008). Use-wear studies are some-times performed alongside residue studies, since both rely onmicroscopic examination of stone tools, and these multiple lines ofevidence are said to improve the reliability of results (Hardy andGarufi, 1998; Hardy et al., 2001; Rots and Williamson, 2004;Lombard, 2005; Hogberg et al., 2009; Dinnis et al., 2009; Rots et al.,2011). Nevertheless, microscopy- based residue analysis suffersfrom the problem that, like earlier use-wear studies, residueidentifications are based upon the interpretation of an image,which may be more or less clear depending on the optical prop-erties of the sample and the features of the instrument.

Early work on lithic residue analysis was conducted by PatriciaAnderson, who studied experimental and prehistoric stone toolsexhibiting microwear polish using SEM (Anderson, 1980). Sheidentified microscopic plant mineral components such as phyto-liths and raphides on both experimental tools and artifacts, andsuggested that elements such as silicon sometimes bond to thesurface of stone tools as a result of heat and pressure generated by

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e3300 3285

friction during use. She also identified residues resulting fromworking antler and scraping fresh bone. In each case, the stonetools were washed in hydrogen peroxide and isopropanol, effec-tively dissolving the organic residues and leaving only the mineralcomponents of plant and animal tissue. She imaged these mineralresidues and also analyzed their composition using energy-dispersive spectrometry (SEMeEDS), identifying calcium andphosphorus in antler residues, calcium in periosteum residues, andsilica and calcium in plant tissue residues.

Another pioneer in the field of residue studies, Thomas Loy,published extensively on the identification of blood on stone tools(Loy, 1983, Loy and Hardy, 1992; Loy, 1998). The methods he used toidentify animal species from hemoglobin crystals (Loy, 1983) havebeen challenged (Smith andWilson,1992), however, and importantconcerns regarding the degradation of hemoglobin were raised(Gurfinkel and Franklin, 1988; see Odell, 2001 for an excellentreview of these studies). Bruce Hardy carried out extensive repli-cation experiments in order to develop a methodology of residueanalysis (Hardy and Garufi, 1998). His studies are based upon anunderstanding of the microscopic anatomy of plant and animaltissues, and he has identified fragments of bone or antler, featherbarbules, hair, andwood, some of which he has identified to class oreven species, on stone tools from Paleolithic sites in France and theUkraine (Hardy and Garufi, 1998; Hardy et al., 2001, 2008; Hardy,2004; Hardy and Moncel, 2011).

One of the most extensively published lithic residue researchprograms to date has been carried out by Lyn Wadley, MarlizeLombard, and colleagues (Wadley et al., 2004; Wadley andLombard, 2007; Lombard, 2005, 2007; 2008). They have devel-oped a catalog of microscopic images of experimentally generatedresidues from plant and animal sources (Wadley et al., 2004). Theyhave also published several blind tests as well as analyzed artifactsfrom several sites in South Africa (e.g., Lombard, 2008; 2011). It isnotable that they published results of blind tests even when thoseresults were poor (Wadley et al., 2004), and that they used theseresults to improve and refine their methodology (Lombard andWadley, 2007; Wadley and Lombard, 2007). In particular, theymade extensive efforts to control for contamination and to refinethe identification of plant and animal residues.

Important lithic residue research has also been carried out inAustralia. Some of these research programs (e.g., Robertson et al.,2009) are similar to the Wadley and Lombard research describedabove, in that they are based upon visual comparisons of micro-scopic images of archaeological residues with experimental resi-dues. Other methods (e.g., Fullagar et al., 2006) rely upon theimaging of residues which have been mechanically extracted fromthe stone tools (via ultrasonication or removal with a scalpel orpipette). Since this paper deals with the imaging of residues as theyare preserved on the stone tools, such methods will not be dis-cussed further here.

The field of microscopic residue analysis has therefore emergedas a viable method of contributing to our knowledge of past func-tions of stone tools, and has also revealed the great potential thatexists for further methodological development. There is an over-arching concern with these studies, however: how reliable are theresidue identifications? Lombard and Wadley in particular claim tobe able to identify amorphous residues such as fat cells, collagen,and bone, which have no distinctive features or structure at thelevel of magnification that they use (e.g. Lombard, 2005; Lombard,2008, Lombard and Wadley, 2007). Robertson et al., (2009) makesimilar claims. Yet, Lombard andWadley have also noted difficultiesin distinguishing animal from plant tissue residues (Lombard andWadley, 2007: 156) and, in some publications claimed to be ableto identify collagen (Fig. 6i in Lombard, 2005) and bone (Fig. 14b inLombard, 2008) residues, but in others stated that this distinction

might not be possible (Fig. 4e caption in Lombard and Wadley,2007). Finally, a concern has been raised regarding the accurateidentification of starch grains by Haslam (2006), who noted thatfungal spores can be mistaken for starch grains under polarizingmicroscopes. This is a significant problem, because analysis ofstarch grains has been used to reconstruct subsistence practicesfrom the Paleolithic (e.g., Mercader et al., 2008; Henry et al., 2011)to the rise of agriculture (e.g., Piperno et al., 2000; Fullagar et al.,2006).

The difficulty of residue identification can be traced to threemain sources: 1) the inherent difficulty of microscopically identi-fying biological and plant tissues that have been crushed andsmeared through processing with stone tools; 2) the limitations oflight microscopy, which has been the dominant methodologyapplied to residue identification; and 3) the decomposition ofresidues, which, until recently, has not been systematicallyaddressed in the literature. The first problem, the maceration ofbiological tissues by stone tool use, makes it impossible to identifyresidues through morphological comparison with histologicalsections of tissues, since the former have had their structuredestroyed. Therefore, residue analysts need to develop an under-standing of the microscopic appearance of a very wide range oftissue types through experimentation, rather than being able torely upon published histological images. This problem is exacer-bated by the second, the limitations of microscopy. Because the goalis to image residues attached to stone tools, only reflected light canbe used, which greatly limits the potential of light microscopy ascompared with thin sections or slides which can be stained andimaged using transmitted light. Also, the large size of stone toolsand the unevenness of their surface sometimes preclude the use ofhigher-magnification objectives. Some authors have been able touse magnifications as high as 800�, but this is exceptional andmost published images rarely exceed 500�. Finally, higher-magnification comes at a cost: loss of depth of field, which in manyways makes identification of residues even more difficult.

The limitations of light microscopy can be compensated for bythe use of other kinds of microscopy, such as SEM. SEM yieldsgrayscale images with excellent resolution and depth of field,producing outstanding topographical detail. Although high-vacuum SEM provides the clearest images at the greatest magni-fication, it requires coating samples in carbon, gold, or anothermetal to decrease charging. Variable-Pressure Scanning ElectronMicroscopy (VP-SEM), often referred to as “environmental” SEMfrom the Philips brand of VP-SEMs, can handle uncoated speci-mens, making it more suitable for analysis of most artifacts. It yieldsexcellent images, albeit at lowermagnifications than standard SEM.Finally, one of the great advantages of SEMs is their ability to yieldelemental information, when coupled with an X-ray detector. Thistechnique, energy-dispersive spectrometry (EDS), has greatpotential to aid residue analysis, as has been demonstrated byJahren (1997) and Anderson (1980). While SEM has not been usedsystematically in residue identification, its potential has beenclearly demonstrated by several studies (Anderson, 1980; D’Errico,1988; Knutsson, 1988; Hortola, 2002, 2005; Pawlik, 2004; Pawlikand Thissen, 2011; Byrne et al., 2006; Verges and Olle, 2011).

Finally, the third major reason why residue identification hasbeen difficult is perhaps the most obvious, yet least discussed: thatof decomposition. It has recently been addressed by Langejans(2010), in a series of experiments documenting the effect of envi-ronmental conditions on the preservation of different types ofresidues. This valuable study yields results which can helparchaeologists determine the conditions most favorable to residuepreservation. Although the “preservation index” developed by theauthor is a useful summary of the stages of decomposition ofresidues, images of these partially decomposed residues are

Table 1Summary of images used in training sessions.

Residue type Tool Numberofresidues

Number ofstereomicrographs,single focus

Number ofstereomicrographs,extended depth offocus

NumberofVP-SEMimages

Hidescraping

RS1 17 39 17 8

Bonescraping

RS4 14 45 9 17

Antlerincising

RS15 12 0 31 42a

Fraxinuswhittling

RS8,RS9

43 55 28 35

Piceawhittling

RS10 16 0 34 31b

Potatoslicing

RS16 9 0 15 9

a VP-SEM images are from RS14.b VP-SEM images are from RS12.

Fig. 1. Picea-whittling experiment; all other experiments were carried out in a simi-larly controlled fashion.

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e33003286

unfortunately not provided. Such images would be extremelyuseful to researchers interested in studying the morphologicalchanges resulting from microbial decay.

Surprisingly, little attention has been paid to these difficultiesand to their effect on the reliability of residue identification. Fromour own experience, however, it is clear that some residues havequalities that make them easy to identify and relatively unambig-uous, whereas other residues are much more difficult to reliablyidentify. The morphology of some residues is equifinal, meaningthat they can easily be assigned to several different sources. Forinstance, fibrous material occurs in skin, bone, and wood, andalthough the nature of the fibers is usually distinctive, some fibermorphologies are ambiguous and overlap inmorphologywith othercategories. When there are no visible fibers and merely an amor-phous mass of tissue, identification becomes even more difficult.What is worrisome is that this ambiguity appears in experimental,fresh residues; we can only assume that artifactual residues, havingbeen exposed to diagenetic processes, are altered in morphologyand hence even more difficult to interpret, as suggested by Lange-jans’ study (2010). Nevertheless, the issue of the reliability ofresidue identification has been all but ignored. The common prac-tice of residue analysis seems to be a presentation of some of theresidues that have been identified (e.g., Lombard, 2005, 2008;Hardy, 2004; Hardy et al., 2001, 2008; Hardy and Moncel, 2011;Robertson et al., 2009; Rots and Williamson, 2004; Wadley andLombard, 2007), including in some cases, detailed maps of thelocations of these residues on the tools (e.g., Wadley and Lombard,2007; Lombard, 2011). In these studies, a few “sample” residueimages are always presented; however, the entire compendium ofresidue images is not available for viewing by parties interested inevaluating the results (the entire set of images used in this study canbe found on the lead author’s website). Furthermore, the difficultyof residue identification is never mentioned in these publications,even though there must be at least a few (and quite likely many)residues which are ambiguous and difficult, if not impossible, toidentify with any degree of confidence.

This study was designed in order to bring the issue of ambiguityin lithic residue morphology into the open by examining theproblem using our own reference collection. The first step was toidentify ambiguous images; subsequently, we could explore thefactors that make them ambiguous. Isolating these factors wouldenable us to learn to control for them, thereby reducing the numberof ambiguous cases in the future.

In order to do this, we first needed an objective means ofidentifying ambiguous images in our reference collection. Weachieved this by conducting a controlled experiment in which 4volunteers, all archaeologists, were trained exactly the same wayand then given the same residue analysis test. Our volunteers didnot have prior experience with residue analysis, but this was anasset rather than a hindrance, because they therefore had a verysimilar background. Our thinking behind this approach was thatmicroscopic residue analysis is an exercise in pattern recognition, inwhich the brain compares the morphology of a given residue witha “database” (reflecting the analyst’s previous experience) ofimages and interpretations of images already seen. Because ourvolunteer experimental subjects received intensive residue analysistraining, during which they viewed 273 images of 111 residues (of 6types), their “databases” were quite extensive. Moreover, they hadaccess to these images on the computer during the test itself. Thepoint of using these subjects, therefore, was less about their indi-vidual, than their collective, performance.

The results of this test indicatedwhich residueswere ambiguous(incorrectly identified by several subjects) as well as unambiguous(correctly identified by all or most subjects). We then explored thefactors that seem to contribute to ambiguity. Following this, we

administered a second test in order to determinewhether includingadditional types of images, such as SEM, would help diminish theambiguity. Finally, we explored whether a confidence ratingassigned to the identification of each residue would accuratelyreflect the likelihood that that identification is correct.

2. Methods

Our research subjects, three archaeology graduate students andone archaeology professor, each took part in two individual two-hour training sessions with GM, as mentioned above. During thistraining session, the subject and the experimenter went througheach of the 273 light microscope images in the reference collection(see Table 1), while the experimenter explained the structure of thevarious tissues that were scraped and the morphological charac-teristics of the resulting residue types (see descriptions below). Thetest was administered in a subsequent session. In each case, the twotraining sessions and the test were completed within the space ofone week.

The images in our reference collection were generated overa period of two years, and based upon a controlled stone-tool useexperiment carried out in our labs (see Fig.1). In this experiment,18convex side-scrapers were knapped using English ballast flint. Thescrapers were photographed and then used on six different mate-rials in order to generate residues. During the experiments, which

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e3300 3287

were carried out on a thoroughly cleaned lab bench, Nitrile gloveswere worn, and a stopwatch used to precisely time the length ofuse. Scrapers were used for a single task only, for a standardizedlength of time (15 min for the deer hide, 10 min for the othermaterials), and the experiment was repeated three times,producing three scrapers for each task, for a total of 18 utilizedscrapers. The six materials and actions include scraping reindeer(caribou) (Rangifer tarandus) hide, scraping fresh cow (Bos taurus)bone, whittling deciduous wood (an ash tree [Fraxinus] branch withbark), whittling coniferous wood (a spruce tree [Picea] branch withbark), scraping deer (Odocoileus virginianus) antler (soaked inwaterfor 24 h), and slicing potato (Solanum tuberosum). After utilization,

Fig. 2. Residue maps of the tools whose residue images were used in the training sessions.the working edge. Note also that some of the tools that were subsequently imaged by SEM hhad improved our imaging technique. RS1 was used for hide-scraping; RS4, bone-scrapinslicing. Note: RS8 (Fraxinus-whittling) was also used in the reference collection, but is not

the scrapers were rinsed with tap water to remove unusually largeadhering masses and were air-dried for 24 h. The dry scrapers werestored individually in sealed plastic bags, and occasionally removedfrom the bags and stored in a desiccator when needed (for instance,prior to SEM imaging).

The first step inmicroscopic analysis of the utilized scrapers wastheir examination using a Leica MZ16A Stereomicroscopewith 1.0�and 1.6� plan apochromatic objectives, permitting magnificationsof 10� to 185�. Once located, residues were imaged using a LeicaIC3D digital camera mounted on the stereomicroscope, and imageswere processed using the Leica Application Suite (LAS) software.The location of each residue on each scraper was recorded on

Note that the distribution of residues is not confined to the retouched edge, which wasave painted letters marking residue locations, which was no longer necessary once weg; RS9, Fraxinus-whittling; RS10, Picea-whittling; RS15, antler-scraping; RS16, potato-illustrated here.

Fig. 3. Residue maps of the tools whose residue images were used in the tests. Note that the distribution of residues is not confined to the retouched edge, which was the workingedge. Note also that some of the tools that were subsequently imaged by SEM have painted letters marking residue locations, which was no longer necessary once we had improvedour imaging technique. RS3 was used for hide-scraping; RS5, bone-scraping; RS7, Fraxinus-whittling; RS12, Picea-whittling; RS13, antler-scraping; RS17, potato-slicing.

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e33003288

a printed photograph of the tool (see Figs. 2 and 3). Each scraperwas given a number, such as RS1 for Residue Study tool #1, andeach residue documented on the scraper’s ventral (V) and dorsal(D) surfaces was assigned a lower-case letter. Thus, RS1Db is thesecond residue documented on the dorsal surface of scraper 1.These residue maps show that residues are not confined to theretouched area, which was the only part of the tool that came intodirect contact with the material, but are spread over the entire tool.This means that a great deal of cautionmust be usedwhen inferringtool use from location of residues. Images at standard magnifica-tions of 50� and 115� were obtained for all observed residues;images were also obtained at other magnifications as needed.Additionally, extended depth-of-focus (EDF) images were some-times obtained using the LAS Montage application, which

controlled a motorized stage to enable 20e30 images at differentfocal planes to be taken per residue. These images were thenblended together by a computer algorithm, eliminating out-of-focus features to create a single image of a topographically vari-able feature that is entirely in focus. The resulting images (see theimages in Figs. 4e15, most of which are EDF) are of much higherquality than most residue images currently published.

One stone tool from each use category was also imaged usingScanning Electron Microscopy, which required carbon coating.Later, additional tools were imaged using Variable-Pressure Scan-ning Electron Microscopy (VP-SEM), which does not require carboncoating. Many of the tools were also imaged using anotherstereomicroscope, the Leica MZFLIII, using 1.0 and 2.0 planapochromatic objectives. Since this microscope did not have

Fig. 4. Images of hide-scraping residues used in training sessions; a, RS1Db; b, RS1Db (different portion of the residue); c, RS1Dg; d, RS1Dj; e, RS1Vg; f, RS1Vk.

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e3300 3289

a motorized stage, extended depth of focus images were createdmanually, by taking 15e50 images at different focus planes perresidue, then processing these images using the software HeliconFocus. This software did sometimes create a “halo” at the edge ofthe image, as seen in Fig. 12d, but since it does not obscure anyfeatures it is of little consequence. While manually creating EDFimages and manually inserting a scale was time-consuming, theresult is incredibly clear images that are entirely in focus (e.g.,Fig. 4b,d, f; Fig. 5a, e; Fig. 11a, f; Fig. 12c,d), with a scale enablingviewers to interpret the features. Publishing images that are well-focused and contain a scale is absolutely essential for adequatecommunication of results.

Fig. 5. Images of bone-scraping residues used in training sessions; a, RS4Da; b, RS

2.1. Training session 1

Table 1 summarizes the residue images used in the trainingsessions, which the subjects subsequently had access to during theresidue identification tests. The following is a brief discussion of thecomposition and morphologies of these residues.

Hide-scraping residues (Fig. 4): These residues are composedprimarily of dermal tissue, whose major component is denseirregular connective tissue, which is composed of collagenousfibers arranged in a multi-directional matrix. Another majorcomponent of these residues is ground substance, a viscous,transparent, gel-like substance found in many connective tissues.

4Da (different portion of the residue); c, RS4De; d, RS4Df; e, RS4Vb; f, RS4Ve.

Fig. 6. Images of antler-scraping residues used in training sessions; a, RS15Da; b, RS15Dc; c, RS15Ve.

Fig. 7. Images of Picea-whittling residues used in training sessions; a, RS10De; b, RS10Ve; c, RS10Vd; d, RS10Vf; e, RS10Vi; f, RS10Vj.

Fig. 8. Images of Fraxinus-whittling residues used in training sessions; a, RS8Dj; b, RS8Dj (magnified); c, RS8Dh; d, RS8Va; e, RS8Ve; f, RS9Vn.

Fig. 9. Images of potato (Solanum)-slicing residues used in training sessions; a, RS16De; b, RS16Df; c, RS16Dg.

Table 2Summary of images used in the residue identification tests.

Residuetype

Tool Numberofresidues

Number ofstereomicrographs,single focus

Number ofstereomicrographs,extended depth offocus

NumberofVP-SEMimages

Hidescraping

RS3 17 38 0 20

Bonescraping

RS5 10 26 4 8

Antlerincising

RS13 5 0 28 6

Fraxinuswhittling

RS7 12 44 16 11

Piceawhittling

RS12 19 61 6 7

Potatoslicing

RS17 5 0 5 1

Note: The VP-SEM images were used only in Test 2.

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e3300 3291

Hair and adipose tissue are also present. Morphologically, theseresidues are clear, almost transparent and gel-like in nature (e.g.,Fig. 4e), with collagenous fiber bundles sometimes arranged line-arly (Fig. 4f) but more often multi-directionally (Fig. 4d). Largequantities of hair are sometimes visible (Fig. 4b).

Bone-scraping residues (Fig. 5): Since fresh bone was scraped,these residues contain periosteum as well as osseous tissue(compact bone). Periosteum is composed of dense irregularconnective tissue, like the dermal tissue described above, and isthus composed primarily of collagen fibers. Mature compact bone,on the other hand, is composed of approximately 70% bone mineral(carbonate hydroxylapatite) and 30% Type 1 collagen. Morpholog-ically, some of the residues arewhite and opaque (Fig. 5a, d, e), withoccasionally visible thick fibers which may be bone fibers. Otherresidues seem to be more viscous, gel-like, translucent, and orangein color (Fig. 5b, c, f), and may contain more periosteum thanosseous tissue. An overlap in morphology can be seen betweensome of these residues and the hide-scraping residues, such asbetween Figs. 4e and 5f, which is likely due to the overlap in residuecomposition (i.e., they both contain connective tissue and groundsubstance).

Antler-scraping residues (Fig. 6): Odocoileus antler is composedof 90% carbonate hydroxylapatite. In our experiments, we usedstone tools to scrape a shed antler that had been soaked inwater for24 h. Many of the resulting residues appear to have been depositedin a thin, translucent white layer which subsequently cracked(Fig. 6a and c). Some of these residues have a more granularappearance and seem scattered across the surface of the tool insmall clumps (Fig. 6b). Overall, the morphology of the residues ismore homogeneous than that of the bone and hide residues, whichlikely reflects the greater homogeneity of the material itself.

Piceawhittling residues (Fig. 7): In our experiments, we scrapedfresh Picea (Spruce) branches, so the residues contain bark andxylem, which is made up primarily of elongated cells calledtracheids. Morphologically, the most distinctive residues arecomposed of relatively large bundles of xylem, inwhich the parallelarrays of tracheids can be seen (Fig. 7a, c, e). These fibers aregenerally quite straight, and even when they have been mashedand smeared such that they no longer occur in parallel sheets orbundles, they retain a “spiky” appearance (Fig. 7d). Resin oftenoccurs as a clear and very shiny substance which darkens theunderlying surfaces (Fig. 7c, d, f). Some of these residues are highlymacerated and woody tissue is almost undetectable (Fig. 7b).

Fraxinus whittling residues (Fig. 8): As with the Picea whittling,the Fraxinus (Ash) whittling experiments were conducted on freshbranches, and the resulting residues contain several differentcomponents of wood. Fraxinus is a hardwood, and its structurediffers from that of Picea and other conifers. Interestingly, theFraxinus-whittling residues are, for the most part, extremelydifferent in appearance from those of Picea. Out of dozens

of microscopic images, almost none contain visible fibrouselements, such as seen in (Fig. 8c). Instead, they have an extremely“macerated” appearance (Fig. 8a, b, d), and were deposited asa yellowish to brownish, irregular, crusty film which eventuallycracked (Fig. 8e), or a thin crust with striations (Fig. 8f).

Potato (Solanum) slicing residues (Fig. 9): The purpose of thepotato-slicing experiment was to obtain starch residues. As can beseen in Fig. 9, these residues consist almost entirely of starch grains,although small bits of potato skin are sometimes visible (Fig. 9a).The morphology of Solanum starch grains is extremely distinctive.They are clear, oval globules clustered together (Fig. 9c), sometimesenveloped by thin cellulose membranes (Fig. 9b). Although cross-polarized light is the imaging technique of choice for starchgrains, revealing a characteristic extinction cross, stereoscopicmicroscopy as used here also works quite well.

2.2. First residue identification test

The images used in the residue identification tests were takenfrom tools RS3 (hide-scraping), RS5 (bone-scraping), RS7 (Fraxinuswhittling), RS12 (Picea whittling), RS13 (antler-scraping), and RS17(potato-slicing) (see Fig. 3). All of the images in our collection fromall of the residues were used, except for RS12, which had so manythat we did not include RS12Vm through RS12Vs, so as not toswamp the test with Picea residues. The sample of residues acrossthe use categories is uneven, since each tool has a different numberof residues. The images used in the tests are summarized in Table 2;note that the VP-SEM images were only included in the secondresidue identification test. The tests were conducted in a quietroom in which a computer attached to two monitors showed thereference images on one screen, and the test images on the other.

Table 3Results of residue identification test 1.

Residue Actual Use ID: subject 1 Subj.1confidencelevel

ID: subject 2 Subj.2confidencelevel

ID: subject 3 Subj.3confidencelevel

ID: subject 4 Subj.4confidencelevel

Total numberof correctidentifications

Mean confidence

13Da Antler Antler 6 Fraxinus 4 Antler 3 Antler 3 3 413Vb Antler Antler 3 Picea 6 Antler 5 Antler 4 3 4.513Db Antler Antler 4 Fraxinus 5 Fraxinus 5 Antler 4 2 4.513Dc Antler Antler 6 Antler 5 Fraxinus 4 Antler 5 3 513Va Antler Antler 6 Antler 6 Antler 6 Antler 5 4 5.755Dc Bone Bone 2 Bone 4 Bone 4 Bone 3 4 3.255Vb Bone Bone 1 Bone 3 Potato 5 Potato 5 2 3.55Ve Bone Hide 3 Hide 4 Hide 4 Hide 3 0 3.55Da Bone Bone 5 Hide 3 Bone 4 Hide 4 2 45Va Bone Bone 3 Bone 5 Bone 5 Bone 3 4 45Vd Bone Bone 5 Bone 2 Bone 5 Bone 5 4 4.255Db Bone Antler 4 Bone 5 Bone 4 Bone 5 3 4.55De Bone Bone 5 Bone 4 Bone 5 Bone 4 4 4.55Vc Bone Bone 4 Bone 5 Bone 6 Hide 4 3 4.755Dd Bone Bone 6 Bone 5 Bone 6 Potato 4 3 5.257Df Fraxinus Hide 1 Bone 3 Picea 1 Bone 4 0 2.257Da Fraxinus Antler 4 Fraxinus 3 Bone 2 Bone 4 1 3.257Db Fraxinus Fraxinus 4 Antler 3 Fraxinus 4 Antler 4 2 3.757De Fraxinus Fraxinus 2 Fraxinus 5 Fraxinus 6 Fraxinus 3 4 47Dh Fraxinus Fraxinus 5 Fraxinus 5 Fraxinus 5 Fraxinus 2 4 4.257Va Fraxinus Bone 3 Fraxinus 6 Fraxinus 5 Bone 3 2 4.257Vd Fraxinus Picea 4 Picea 6 Picea 4 Picea 3 0 4.257Dg Fraxinus Fraxinus 6 Fraxinus 6 Fraxinus 4 Picea 2 3 4.57Vc Fraxinus Fraxinus 2 Fraxinus 6 Fraxinus 5 Fraxinus 5 4 4.57Ve Fraxinus Fraxinus 3 Fraxinus 5 Fraxinus 6 Fraxinus 5 4 4.757Vb Fraxinus Fraxinus 5 Fraxinus 6 Fraxinus 5 Fraxinus 3 4 4.757Dc Fraxinus Fraxinus 6 Fraxinus 6 Fraxinus 5 Bone 3 3 53Vg Hide Antler 1 Antler 2 Bone 1 Antler 2 0 1.53Dd Hide Hide 1 Fraxinus 2 Antler 2 Hide 1 2 1.53Ve Hide Bone 3 Bone 4 Bone 1 Hide 2 1 2.53Dc Hide Bone 1 Hide 2 Bone 5 Hide 2 2 2.53Vj Hide Hide 1 Hide 3 Hide 3 Bone 3 3 2.53Vk Hide Hide 3 Fraxinus 4 Hide 2 Bone 4 2 3.253Vh Hide Hide 4 Hide 4 Hide 2 Hide 3 4 3.253Vc Hide Hide 4 Fraxinus 3 Picea 4 Hide 2 2 3.253Vl Hide Fraxinus 1 Fraxinus 4 Fraxinus 5 Bone 3 0 3.253Vf Hide Antler 3 Antler 1 Antler 5 Bone 4 0 3.253Vb Hide Hide 5 Fraxinus 6 Hide 4 Bone 3 2 4.53Vd Hide Hide 5 Hide 5 Hide 5 Hide 3 4 4.53Vi Hide Hide 5 Hide 4 Hide 5 Hide 4 4 4.53Dg Hide Bone 4 Hide 4 Hide 6 Hide 5 3 4.753Db Hide Hide 4 Hide 6 Hide 5 Hide 4 4 4.753Va Hide Hide 6 Hide 6 Hide 6 Hide 6 4 63De Hide Hide 6 Hide 6 Hide 6 Hide 6 4 612Vh Picea Hide 1 Hide 2 Picea 1 Hide 2 1 1.512Vi Picea Hide 2 Hide 4 Hide 5 Hide 3 0 3.512Ve Picea Picea 1 Picea 6 Picea 5 Fraxinus 2 3 3.512Dh Picea Picea 5 Picea 1 Picea 5 Picea 5 4 412Vb Picea Picea 5 Antler 5 Picea 5 Picea 2 3 4.2512Vj Picea Hide 3 Picea 4 Picea 5 Picea 5 3 4.2512Df Picea Picea 5 Picea 4 Picea 6 Picea 3 4 4.512Da Picea Picea 3 Picea 6 Picea 6 Picea 5 4 512Vc Picea Picea 5 Picea 5 Picea 5 Picea 5 4 512Dg Picea Picea 6 Picea 6 Picea 5 Fraxinus 3 3 512Dd Picea Picea 5 Picea 6 Picea 5 Picea 5 4 5.2512Vg Picea Picea 5 Picea 6 Picea 6 Picea 5 4 5.512Dc Picea Picea 5 Picea 6 Picea 6 Picea 5 4 5.512Vf Picea Picea 6 Picea 6 Picea 6 Picea 5 4 5.7512Vd Picea Picea 6 Picea 6 Picea 6 Picea 5 4 5.7512Vk Picea Picea 5 Picea 6 Picea 6 Picea 6 4 5.7512Db Picea Picea 6 Picea 6 Picea 6 Picea 5 4 5.7512Va Picea Picea 6 Picea 6 Picea 6 Picea 5 4 5.7512De Picea Picea 6 Picea 6 Picea 6 Picea 6 4 617De Potato Potato 6 Potato 6 Potato 4 Potato 5 4 5.2517Dd Potato Potato 6 Potato 6 Potato 6 Potato 6 4 617Dc Potato Potato 6 Potato 6 Potato 6 Potato 6 4 617Va Potato Potato 6 Potato 6 Potato 6 Potato 6 4 617Dg Potato Potato 6 Potato 6 Potato 6 Potato 6 4 6

Note: Confidence level was entered on a scale of 1e6, with 1 being not confident at all, 6 being extremely confident.

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e33003292

Table 4Success rates of residue identifications by the experimental subjects (Test 1).

Subject Number of correctidentifications

Percentage of correctidentifications (N ¼ 68)

Number of correctcategory identifications(plant vs. animal)

Percentage of correctcategory identifications(N ¼ 68)

Mean confidencerating (N ¼ 68)

1 53 78% 61 90% 4.12 50 74% 56 82% 4.73 53 78% 61 90% 4.74 46 68% 59 87% 4.0

Table 5Mean number of correct identifications by residue type (Test 1).

Residuetype

N Meanconfidence rating(standarddeviation)

Mean number ofcorrect identifications(maximum ¼ 4.00)(standard deviation)

Mean percentage ofcorrect identifications(mean divided by 4.00)

Antler 5 4.75 (.66) 3.00 (.707) 75%Fraxinus 12 4.13 (.76) 2.58 (1.56) 65%Bone 10 4.15 (.63) 2.90 (1.29) 73%Hide 17 3.63 (1.36) 2.41 (1.50) 60%Potato 5 5.85 (.34) 4.00 (n/a) 100%Picea 19 4.82 (1.13) 3.42 (1.12) 86%

Table 6Residues identified by two or fewer subjects (Test 1).

Residue Use Figure Number of subjectswho correctlyidentified residue

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e3300 3293

The test images were organized by residue into separate folders(because there was usually more than one image per residue) withrandomly assigned numbers replacing our classification system (forinstance, RS3Db became 248). The folders were not organized bytool, but randomized overall six tools used in the residue identifi-cation test. The test subjects studied the images in each folder,referring to the reference collection as needed; then, usinga custom data-entry program, they selected the function that theyconcluded created the residue from a list of the six use-functions ofthe experiment. They also entered their confidence level in makingthe identification, on a scale from 1 to 6, with 1 being not confidentat all, and 6 being extremely confident.

After analyzing the results from the first test, we selected all ofthe residues (see Table 6) that were identified correctly by fewerthan three of the test subjects for inclusion in the second test. Eachof these twenty residues was imaged using VP-SEM. Since one ofthe residues (RS7Da) fell off the tool, and the other could not befound under the SEM (RS3Dd), this brought the final sample ofresidues included in Test 2 down to eighteen. We used the samefolders of images that had been used in the first test, and simplyadded VP-SEM images to each folder. GM then spent approximatelyone hour training each subject, individually, in the analysis of SEMimages of residues using the 142 VP-SEM images in our referencecollection (see Table 1), immediately after which the subjectscompleted Test 2.

3Dc Hide scraping e 23Dda Hide scraping e 23Vb Hide scraping e 23Vc Hide scraping e 23Ve Hide scraping Fig. 10c 13Vf Hide scraping Fig. 10d 03Vg Hide scraping e 03Vk Hide scraping Fig. 10f 23Vl Hide scraping Fig. 10e 05Da Bone scraping Fig. 12a 25Vb Bone scraping e 25Ve Bone scraping Fig. 12f 07Daa Fraxinus bark scraping e 17Db Fraxinus bark scraping Fig. 11d 27Df Fraxinus bark scraping Fig. 11e 07Va Fraxinus bark scraping Fig. 11c 27Vd Fraxinus bark scraping Fig. 11f 012Vh Picea bark scraping Fig. 13d 112Vi Picea bark scraping Fig. 13e 013Db Antler incising Fig. 14a 2

a Not included in Test 2 because the residue was not found using the SEM.

Table 7Percentages of ambiguous residues by category (Test 1).

Residue type Ambiguousresidues

Totalresidues

Percentageambiguous

Antler 1 5 20%Fraxinus 5 12 42%Bone 3 10 30%Hide 9 17 53%Potato 0 5 0%Picea 2 20 10%

3. Results

3.1. Residue identification test 1

Table 3 presents the raw data for the first residue identificationtest (Test 1). These data are summarized in Table 4 according to testsubject. Subjects 1e3 achieved an overall success rate in identifi-cation of 74e78%. The success rate for Subject 4 was lower, at 68%.However, when larger residue categories (i.e., plant versus animal)are taken into account, Subject 4 achieved a success rate (87%) thatis within the range of the other subjects (82e90%). The averageconfidence level for residue identifications is similar across allsubjects, although it is lowest for Subject 4. It is important tonote that although individual success rates ranged from 68 to78%, the rate of agreement among the subjects on residue identi-fications is much lower than this. For instance, all four subjectsagreed on only 37 residue identifications (54%), of which 3 areincorrect (see Table 3).

When the mean number of correct identifications for eachresidue is calculated by each of the six tool-use categories (Table 5),we can see that the residue types identified correctlymost often arethe five potato-slicing residues, which were correctly identified100% of the time, followed by the Piceawhittling residues, with anaverage correct identification of 86%. The residues identifiedcorrectly least frequently are the hide-scraping residues, witha mean correct identification of 60%, and the Fraxinus whittlingresidues, with an average correct identification of 65%. Looking atthe data another way, we can ask how many of the 4 experimental

subjects correctly identified each residue. We consider residueswhich were correctly identified by 3 or 4 of the subjects as fairlyunambiguous. On the other hand, residues identified correctly byonly 2 of the subjects, or fewer, are ambiguous. The 20 ambiguousresidues are listed in Table 6. Table 7 summarizes these results

Fig. 10. Images of hide-scraping residues used in the tests; a, RS3De (4/4 correct identifications); b, RS3Vd (4/4 correct identifications); c, RS3Ve (1/4 correct identifications); d,RS3Vf (0/4 correct identifications); e, RS3Vl (0/4 correct identifications); f, RS3Vk (2/4 correct identifications).

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e33003294

according to tool-use category, and we can see, as expected, thatafter potato slicing, Piceawhittling produced the fewest ambiguousresidues (10%), while hide-scraping produced the most ambiguousresidues (53%), followed by Fraxinus-whittling (42%).

What are the causes of this ambiguity? Whenwe look closely atsome of the hide-scraping residues that are ambiguous (Fig. 10cef),compared with those that are not ambiguous (Fig. 10a, b), theformer tend to be smaller and more difficult to discern. EDF images

Fig. 11. Images of Fraxinus-whittling residues used in the tests; a, RS7Vb (4/4 correct identifiRS7Db (2/4 correct identifications); e, RS7Df (0/4 correct identifications); f, RS7Vd (0/4 cor

would be clearer and may have improved identification; however,we do not have EDF images for RS3, because it was carbon-coatedafter an initial round of light microscopy in which EDF was notused, precluding further imaging with light microscopy. On theother hand, other residue categories with few or no EDF images,such as the Picea-whittling residues (with only two EDF images),had high rates of successful identification. The real problem withthe hide residues is that they are small, translucent, and variable.

cations); b, RS7Vc (4/4 correct identifications); c, RS7Va (2/4 correct identifications); d,rect identifications).

Fig. 12. Images of bone-scraping residues used in the tests; a, RS5Da (2/4 correct identifications); b, RS5Db (3/4 correct identifications); c, RS5Dd (3/4 correct identifications); d,RS5De (4/4 correct identifications); e, RS5Vc (3/4 correct identifications); f, RS5Ve (0/4 correct identifications).

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e3300 3295

Thus, the residues in Fig. 10c and d, which lack a visible fibrousstructure, were identified as bone and antler residues respectively,by three subjects each. The residue in Fig. 10e is more opaque thanmost hide residues, which is perhaps one of the factors that led toits identification as a Fraxinus-whittling residue by three subjects.

Five of the twelve Fraxinus-whittling residues used in Test 1proved to be ambiguous. Fig. 11 illustrates four of these (Fig. 11c-f),

Fig. 13. Images of Picea-whittling residues used in the tests; a, RS12Va (4/4 correct identificaRS12Vh (1/4 correct identifications); e, RS12Vi (0/4 correct identifications); f, RS12Vk (4/4

as well as two of the residues which were correctly identified by allfour subjects (Fig. 11a-b). Some of the ambiguous residues areclearly anomalous when compared with the reference sample(Fig. 8), which helps explains their misidentification. The residue inFig. 11d, for instance, has the granular appearance associated withantler-scraping (e.g., see Fig. 6b), and was in fact identified as suchby two subjects. The one in Fig. 11f exhibits a large fragment of

tions); b, RS12Vc (4/4 correct identifications); c, RS12Ve (3/4 correct identifications); d,correct identifications).

Fig. 14. Images of antler-scraping residues used in the tests; a, RS13Db (2/4 correct identifications); b, RS13Va (4/4 correct identifications); c, RS13Vb (3/4 correct identifications).

Fig. 15. Images of potato (Solanum)-slicing residues used in the tests; a, RS17Db (4/4 correct identifications); b, RS17Dc (4/4 correct identifications); c, RS17Va (4/4 correctidentifications).

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e33003296

woody tissue which is unusual for the Fraxinus residues, but morecommon in the Picea whittling; it was therefore identified by allfour subjects as a Picea residue. The fact that it was correctlyidentified as wood, however, is encouraging. The residue in Fig. 11ealso has atypical morphology for Fraxinus residues, and was notcorrectly identified by any subject. The residue in Fig. 11c, whichwas misidentified by two subjects, is in fact quite typical and itsmisidentification must be attributed to lack of experience.

Three of the ten bone residues are ambiguous, and the wayin which they were misidentified is enlightening. Two of theseresidues (Fig. 12a, f) were identified by two or more subjects as

Table 8Results of Residue Identification Test 2.

Residue Actual Use ID: subject 1 Subj. 1confidencelevel

ID: subject 2 Subj. 2confidencelevel

ID: s

13Db Antler Antler 6 Antler 5 Antl5Da Bone Bone 5 Bone 6 Hide5Vb Bone Hide 4 Bone 6 Pota5Ve Bone Hide 2 Hide 4 Hide7Db Fraxinus Fraxinus 6 Fraxinus 6 Fraxi7Va Fraxinus Fraxinus 5 Fraxinus 6 Fraxi7Vd Fraxinus Fraxinus 6 Fraxinus 6 Fraxi7Df Fraxinus Fraxinus 5 Fraxinus 6 Fraxi3Vk Hide Bone 4 Hide 5 Bone3Ve Hide Antler 4 Antler 4 Bone3Vb Hide Bone 4 Hide 4 Hide3Vc Hide Hide 5 Hide 4 Hide3Dc Hide Bone 4 Antler 5 Bone3Vg Hide Bone 3 Bone 5 Bone3Vl Hide Bone 3 Hide 4 Hide3Vf Hide Bone 1 Bone 4 Bone12Vh Picea Picea 5 Bone 4 Picea12Vi Picea Picea 2 Hide 3 Hide

Note: Confidence level was entered on a scale of 1 to 6, with 1 being not confident at al

hide-scraping residues, which reflects the morphological overlapbetween the hide-scraping and bone-scraping residues. Theresidue in Fig. 12f consists of a long, translucent fiber, and isparticularly anomalous for a bone residue. The residues in Fig. 12a,identified as hide residue by two subjects, and Fig. 12e, identified ashide residue by one subject, are very similar to the hide-scrapingresidue in Fig. 4e, from the training/reference collection. All threeresidues are gelatinous and clear, and are probably composed inlarge part of ground substance. The residues illustrated in Fig. 12b,c, and d were identified correctly by three or all four of the subjects,and their morphology is, in fact, quite typical for bone residues, and

ubject 3 Subj. 3confidencelevel

ID: subject 4 Subj. 4confidencelevel

Total Numberof CorrectIdentifications

MeanConfidence

er 5 Antler 4 4 5.003 Bone 6 3 5.00

to 2 Potato 4 1 4.003 Picea 3 0 3.00

nus 6 Fraxinus 6 4 6.00nus 6 Fraxinus 6 4 5.75nus 6 Fraxinus 5 4 5.75nus 6 Fraxinus 6 4 5.75

4 Bone 3 1 4.002 Bone 3 0 3.255 Bone 5 2 4.503 Hide 3 4 3.753 Bone 5 0 4.253 Bone 5 0 4.003 Hide 4 3 3.502 Bone 4 0 2.752 Picea 4 3 3.753 Bone 5 1 3.25

l, 6 being extremely confident.

Table 9Mean number of correct identifications by residue type (Ambiguous residues only).

Residue type N Mean no. ofcorrect identificationsin Test 1(maximum ¼ 4.00)

Mean no. ofcorrect identificationsin Test 2(maximum ¼ 4.00)

Antler 1 2.00 4.00Fraxinus 4 1.00 4.00Bone 3 1.33 1.33Hide 8 1.125 1.25Picea 2 0.5 2.0

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e3300 3297

matches bone residue images published elsewhere (e.g., Lombard,2008, Fig. 14b).

The remaining categories of residues had low rates ofmisidentification. The two ambiguous Picea-scraping residues(Fig. 13d and e) are extremely anomalous and were identified ashide residues by three and four subjects, respectively. The fibers inFig. 13e are transparent and quite different from the normalappearance of Picea residues. The residue in Fig. 13d is alsoanomalous for Picea residues, although it must be said that it doesnot look like hide residue, either. The remaining residues in thefigure (Fig. 13a, b, c, and f) are unambiguous, and their morphologyis quite typical, exhibiting fragments of woody tissue, plant fibers,and resin.

Fig. 16. SEM images used in the tests; a, RS3Ve (hide); b, RS3Vc (hide); c, RS12Vh (Picea)(Fraxinus); i, RS16De.

Only one of the five antler-scraping residues in the test(Fig. 14a) is ambiguous, having been misidentified by two subjects.Since it actually looks fairly typical, its misidentification mayreflect the subjects’ relative lack of experience. Finally, none of thepotato residues (three of which are illustrated in Fig. 15) wasmisidentified.

In sum, most of the residues which fall into the “ambiguous”category of Test 1 do so because their morphology is anomalous.Some of the residue categories contain a wide range of morphol-ogies, and the morphology of the ambiguous residues tends to fallwithin the range of morphologies of another residue category.

3.2. Residue identification test 2

The aim of Test 2 was to determine whether scanning electronmicroscopy, in addition to light microscopy, enables greater accu-racy in residue identification than light microscopy alone. Theresults from this test are presented in Table 8. Table 9 summarizesthe mean numbers of correct identifications for each tool-usecategory, and compares them with the mean numbers of correctidentifications for the same residues in Test 1. The results of Test 2show that there was some improvement in residue identificationfor the categories of Fraxinus-whittling, Picea-whittling, and antlerscraping, and no improvement for identification of hide- or bone-

; d, RS5Vb (bone); e, RS5Ve (bone); f, RS13Db (antler); g, RS7Va (Fraxinus); h, RS7Df

Number of Correct Identifications (max = 4)

43210

Me

an

C

on

fid

en

ce

R

atin

g, A

ll S

ub

je

cts

C

om

bin

ed 6.00

5.755.505.255.004.754.504.254.003.753.503.252.502.251.50

Fig. 17. Boxplot diagram of mean confidence rating by number of correct identifica-tions, Test 1.

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e33003298

scraping residues. However, the sample sizes are too small in all ofthe categories except for the Fraxinus-whittling and hide-scrapingcategories, to enable us to determine whether the improvementin identification is due to the SEM images or to chance. The FraxinusSEM images are extremely enlightening and help explain the fuzzy,macerated appearance of the Fraxinus residue morphology in thestereoscope images (Figs. 8 and 11). The electron microscopeimages reveal that thousands of raphides (calcium oxalate crystalsmanufactured by plants) are present in all of the Fraxinus whittlingresidues (Fig. 16g and h). These raphides occur not only on thewoody tissue itself, but are scattered across the surface of the tooladjacent to the residue in dense concentrations. Our Piceawhittlingresidues, on the other hand, contain very few raphides (Fig. 16c).Therefore, the presence of raphides in an SEM image for thisexperiment signaled that the residue came from Fraxinuswhittling,which explains why every Fraxinus image in Test 2 was correctlyidentified. This would seem to provide an almost foolproof meansof identifying certain species of hardwood in archaeological resi-dues. Unfortunately, raphides are rarely observed in the archaeo-logical record unless they have been burnt, in which case thecalcium oxalate has converted to calcite and preserved the originalshape of the crystal (Weiner, 2010:170).

While SEM images diminished the ambiguity of the Fraxinuswhittling residues, they unfortunately did not diminish ambiguityin the identification of hide- or bone-scraping residues. It isextremely difficult to identify bone (Fig. 16d and e) and hide resi-dues (Fig. 16a and b) using SEM images alone. In fact, it is evendifficult to distinguish Picea residues (Fig. 16c) from bone residues(Fig. 16e). On the other hand, the antler-scraping residues are quitedistinctive (Fig. 16f), as are the potato-slicing residues (Fig. 16i).

4. Discussion and conclusions

The results clearly show that certain residue types in our studyare more prone to misidentification e more ambiguous e thanothers. These are the hide-scraping, bone-scraping, and hardwood-scraping (Fraxinus) residues. Most of the ambiguous residues inthese categories have an atypical morphology, which falls in therange of variability of another residue category. In addition, thenature of some residues makes themmore difficult to image; this isespecially true of the hide residues, which are translucent. Futurework, therefore, should focus on improving microscopic imaging aswell as elucidating the nature of variability of residue morphol-ogies. Our study showed that it is particularly important to improveour ability to distinguish between hide and bone residues, whichoverlap inmorphology because they both contain some of the samecomponents, such as connective tissue. While in the end it mayprove impossible to distinguish between connective tissue from theskin and from the periosteum, when these are smeared ontoa stone tool, further research may help us identify other featureswhich may increase rates of successful identification, such as tinyfragments of bone embedded within the connective tissue.

SEM imaging is technically difficult, time-consuming, andexpensive. Nevertheless, our VP-SEM images show that this tech-nique has great potential for improving residue identification. Firstof all,we show that certain residue types, such as antler residues andstarch grains, are extremely distinctive when imaged with thistechnique (Fig.16f and i). Second, certainhardwood residues are alsodistinctive because of the large quantities of raphides revealed usingSEM, which are invisible using standard reflected-light microscopy.Although it is unlikely that raphides would be preserved archaeo-logically, the Fraxinus residues fortunately exhibit a characteristic“wrinkled” appearance which makes them morphologicallydistinctive. Nevertheless, much research still needs to be carried outto refine this imaging technique, because there is a great deal of

overlap between hide, bone, and even certain woody residuemorphologies in SEM images. In addition, while this study did notuse energy-dispersive spectroscopy (EDS), the ability of this tech-nique to reveal elemental composition of samples will undoubtedlyhelp identify residues which contain distinctive elements (such asbone and antler, which contain calcium and phosphorus).

Finally, one of the goals of this study was to determine whethera self-ascribed “confidence of identification” rating could be used asa way of determining accuracy of identification. To do so, theconfidence rating would have to correlate strongly with accuracy ofidentification. The data in Table 3 present each subject’s confidencerating, as well as the mean confidence rating across all subjects.This mean confidence rating, when correlated with number ofcorrect identifications, yields a Pearson’s two-tailed r ¼ .683,p < 0.001. In other words, there is a high correlation betweenconfidence rating and number of correct identifications in thisexperiment. Fig. 17 shows boxplots of the same data: mean confi-dence rating by number of correct identifications. Categories threeand four, representing three and four correct identifications, havea much higher confidence rating than the other ones. This strongrelationship between accuracy of identification and confidencerating supports the notion that, when identifying residues throughmicroscopy alone, analysts should publish a confidence rating inorder to document the nature and extent of potentially ambiguousresidues in their studies.

Archaeologically, we can expect the frequency of ambiguousresidues to be greater than in fresh, experimental collections, sincedecay will undoubtedly alter the morphology of residues and makethem more difficult to identify. More research is needed, therefore,into the effects of chemical weathering and microbial activity ondifferent residue types, and the resulting variability in morphol-ogies. Finally, we hope that this work will encourage others toadopt a more systematic way of reporting residues, such asreporting all residues, including contaminants, encountered duringanalysis (such as Langejans, 2010). In conclusion, if we want thediscipline of lithic residue analysis to be a scientific one, wemust 1)acknowledge ambiguity in residue morphologies, 2) fully docu-ment this ambiguity so that others can evaluate our results, and 3)systematically study the full range of residue morphological vari-ability in fresh and, to the extent possible, decayed residues, usinga wide variety of imaging and characterization techniques, in orderto reduce and better control for ambiguity in the future.

G.F. Monnier et al. / Journal of Archaeological Science 39 (2012) 3284e3300 3299

Acknowledgments

We thank the four reviewers of an earlier version of this paper,whose thoughtful comments helped to improve it considerably. Wealso thank our four test subjects, Gil Tostevin, Michele Stillinger,Kristina Golubiewski-Davis and Kia Atsales. Many others helped atvarious stages of this study, including Tracy Anderson, Gail Celio,Ellery Frahm, Matt Hunstiger, Lora Korynta, Carlos Manivel, KeithManthie, John Nelson, Mark Sanders, and John Soderberg. Thanksto Harold Dibble and Gil Tostevin for reading the second version ofthis paper and providing useful comments. This research wasfunded by a University of Minnesota Grant-in-Aid to G. Monnier.Parts of this work were carried out at the College of BiologicalSciences Imaging Center at the University of Minnesota; at theInstitute of Technology Characterization Facility at the University ofMinnesota, a member of the NSF-funded Materials Research Facil-ities Network; and at the College of Science and Engineering’sDepartment of Geology and Geophysics Microprobe Lab. The bulkof the work was carried out at the Evolutionary AnthropologyLaboratories in the Anthropology Department, College of LiberalArts, University of Minnesota.

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