Distinguishing Selective Transport and In Situ Attrition: A Critical Review of Analytical Approaches. Naomi Cleghorn*

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1 PROMETHEUS PRESS/PALAEONTOLOGICAL NETWORK FOUNDATION Journal of Taphonomy (TERUEL) 24 VOLUME 2 (ISSUE 2) Available online at Cleghorn & Marean Distinguishing Selective Transport and In Situ Attrition: A Critical Review of Analytical Approaches Naomi Cleghorn* Interdepartmental Doctoral Program in Anthropological Sciences, SUNY at Stony Brook, Stony Brook, NY Curtis W. Marean Institute of Human Origins, Department of Anthropology, PO Box 87242, Arizona State University, Tempe, AZ USA Journal of Taphonomy 2 (2) (24), Manuscript received 24 September 24, revised manuscript accepted 22 November 24. Skeletal element frequencies are at once enticing sources of behavioral information and thorny taphonomic dilemmas. Many archaeofaunal assemblages combine some degree of selective transport and in situ attrition, both of which affect the relative representation of elements. In addition, some analytical methods may add their own signature, further complicating the analysis of the element profile (Marean et al., this volume). Three methods have been applied to the problem of distinguishing attrition from selective transport: the Anatomical Region Profile (ARP), the Analysis of Bone Counts by Maximum Likelihood (ABCML), and the high and low survival element set model. We find that the ARP technique fails to perform as suggested. The ABCML is an innovative and promising line of inquiry, but is currently limited by methodological and theoretical shortcomings. The high and low survival set model appears to be an effective approach to analysis, but the actualistic tests of its power are still limited. We conclude that sensitivity to the issue of differential intra-element survival is key to future research into this problem. Keyords: SKELETAL ELEMENT ANALYSIS, EQUIFINALITY, BONE DENSITY, CARNIVORE RAVAGING Introduction The relative frequency of skeletal elements in archaeofaunal collections has long been recognized as a potentially rich source of information on human transport and butchery strategies (Binford, 1981, 1984; Bunn, 1986; Bunn & Kroll, 1986; Conard, 1992; Conard, et. al. 1998; Klein, 1976; Lartet & Christy, ; Perkins & Daly, 1968; White, 1953). With the introduction of utility indices, Binford Article JTa17. All rights reserved. * ncleghor@ic.sunysb.edu 43

2 Distinguishing selective transport and in situ attrition (1978) provided archaeologists with a tool to link element portion frequencies to ethnographically-based behavioral scenarios. As the importance of the skeletal element analysis increased, so did interest in the taphonomic development of the element profile, prompting extensive research into the factors affecting bone survival as well as transport. Bone structural properties, particularly density, were initially linked to element destruction by studies of carnivore ravaging (Brain, 1967, 1969). Further research indicated a weak negative relationship existed between bone utility and bone mineral density (Lyman, 1985, 1992). An investigation of the archaeological record showed that representation was often correlated with one or both of these parameters (Lyman, 1991, 1993). The implication was that destructive processes correlated with density could confound the interpretation of the relationship between bone survival and food utility, thus blurring any behavioral interpretations of the data. For at least forty years densitymediated attrition has been known to affect element frequencies, yet relatively few analysts have developed methods for extracting transport data from affected assemblages (Lyman, 1994:287). We are aware of three serious attempts to analytically distinguish in situ attrition from selective transport with the goal of accurately characterizing the latter. These are Stiner s (1991, 1994, 22) anatomical region profile (ARP) method, Rogers (2a, 2b) Absolute Bone Counts by Maximum Likelihood (ABCML) method, and Marean and Cleghorn s (Cleghorn & Marean, in press; Marean & Cleghorn, 23) high and low survival element distinction. The first and last of these analyze the interaction of two variables in an attempt to control proxies for attrition and thus emphasize any selective transport. In the case of the ARP, the two relevant variables are bone mineral density (as a proxy for bone survivability) and carcass portion representation. The high and low element model, by contrast, uses element frequencies and survivability (based on both mineral density and carnivore ravaging patterns). Rogers attempts a multivariate statistical reconstruction of depositional agents, original number of carcasses, and the intensity of ravaging using human behavioral analogues and attritional probabilities. All of these methods critically rely on skeletal element frequencies and published bone mineral density values, and are therefore affected by the accuracy of such estimates. A substantial body of evidence from actualistic research, structural analysis, and archaeological data has demonstrated that long bone mid-shaft fragments are vital to element quantification (Bartram & Marean, 1999; Blumenschine, 1988; Blumenschine & Marean, 1993; Bunn, 1983, 1986, 1991; Bunn & Kroll, 1986; Capaldo, 1995, 1998; Lam et al., 1999; Lam et al., 23; Marean, 1998; Marean & Frey, 1997; Marean & Kim, 1998; Marean & Spencer, 1991; Marean et al., 1992; Pickering et al., 23). Others have described these concerns as shaft anxiety or have downplayed the methodological significance of excluding isolated shaft fragments from analysis (Klein et al., 1999; Outram, 21; Stiner, 1998, 22). 44

3 Cleghorn & Marean A) Humerus B) Radius 3 3 End MNE 2 1 End MNE Shaft MNE 5 1 Shaft MNE C) Ulna D) Metacarpal End MNE End MNE Shaft MNE Shaft MNE E) Femur F) Tibia End MNE Shaft MNE End MNE Shaft MNE G) Metatarsal End MNE Shaft MNE Figure 1. Comparisons (with linear regression lines) of long bone shaft-inclusive and end-based MNE estimates from the 1 archaeological assemblages listed in Table 1. 45

4 Distinguishing selective transport and in situ attrition In order to evaluate the magnitude of the potential error generated by depending on end-based counts, we can examine the degree to which these counts will underestimate the Minimum Number of Elements (MNE) as predicted by bone density studies, carnivore ravaging experiments, and as seen in the archaeological record. Evidence from these three datasets is presented in Table 1. Bone mineral density values are taken from Lam et al. (1998, 1999), as these are currently the most accurate estimates for intraelement density variation. The two carnivore ravaging studies (Hudson, 1993; Marean & Spencer, 1991) are the only such studies that have incorporated isolated shaft fragments into the element survival estimate and have published survival values for shafts and ends by element. The archaeological sites are those for which we are confident that isolated shafts were counted in MNE estimates, and for which we know the MNE values for element portions. Table 1 gives the ratio of highest shaft to highest end value (either bone mineral density or portion MNE) for each long bone. If we were to predict long bone survival based on density alone, we would expect shafts to exceed ends by more than 2:1 over half of the time. Carnivore ravaging results in a range of differential survival from two instances in Hudson s (1993) study in which ends exceed shafts, to the relatively high survival of femoral shafts in both Marean s (Marean & Spencer, 1991) and Hudson s experiments. The archaeological assemblages also show a wide range of differential survival. In 1% of these cases, end MNE is greater, but in 44% of cases, shaft values were more than 3 times that of ends. Some shaft counts exceeded ends by as much as 31 to 1. Further, there does not appear to be a predictable relationship between shaft and end-based long bone MNEs in the archaeological assemblages (Table 2, Figure 1). Our point is not to prove that shaft values will always exceed end values (although they do so much of the time), but to show that it is perilous to try to make a general estimate of how skewed end-based quantities might be. In practice, we would always choose to use the inclusive MNEs carefully including criteria that will sample all long bone portions equally. A reliance on long bone end values has had a direct bearing on researchers attitudes toward the problem of equifinality in skeletal element analysis. Lyman (1985, 1992) and Grayson (1989) warned that the negative relationship between bone mineral density and element utility would result in an apparent equifinality between selective transport and processes of in situ destruction. However, these earlier correlation studies only used long bone end density values. When shaft densities are compared to whole bone utility values (Meat Utility Index [MUI] and Food Utility Index [FUI] from Metcalfe & Jones, 1988), there is no correlation between density and utility (Figure 2). Thus, the use of long bone shaft densities clearly alters the relationships between the test variables, and the specific equifinality problem identified by Lyman (1985, 1991) is only an issue when isolated shaft fragments are not included in quantification. This begs the questions: does density-mediated attrition obscure selective transport? Selective transport and in situ attrition both result in the absence of 46

5 Cleghorn & Marean Max BMD vs MUI Max BMD vs. FUI 6 6 MUI 4 2 FUI BMD BMD Figure 2. Comparisons (with linear regression lines) of bone mineral density (BMD) and element utility (meat utility index [MUI] and food utility index [FUI]). Density values are from Lam et al. (1999, p.563, Table 1) and food utility values are from Metcalfe and Jones (1988:489, Table 1 and p.492, Table 2). elements from the final assemblage, and the intensity of both processes is difficult to estimate. Thus, density-mediated in situ attrition does interfere with the identification of selective transport, even though the interference is not strictly a case of equifinality*. We still face the problem of disentangling human transport patterns from all the other processes that result in incomplete carcass representation. Density Mediated Attrition and the Archaeological Record Developments in zooarchaeological research warrant a re-evaluation of the evidence for density-mediated attrition in the archaeological record. These are: 1) the availability of element frequencies calculated with the inclusion of isolated long bone shaft fragments and 2) the availability of more accurate estimates of intra-bone density gradients for long bones. Lyman s (1991, 1993) survey of 87 published assemblages (both archaeological and ethnographic) indicated that 45% had undergone some density-mediated attrition. We have three reasons for re-evaluating these results. First, the long bone density values used in the analyses were derived from end portions, which are generally not the densest part of the bone. Second, estimates of density have been improved in recent years, and the underestimates of shaft density in previous studies have been * Lyman (1994, p. 57) defines equifinality as "the property of allowing or having the same affect or result from different events" and the Oxford Dictionary of English defines equifinal as having the same end or result. If in situ attrition and selective transport could result in the same element profile, then they would fit the definition. However, with no correlation between the two processes, there is no reason to think that they could result in the same profile. 47

6 Distinguishing selective transport and in situ attrition Table 1. Site/Study Taxa Humerus Radius Ulna Metacarpal Femur Tibia Metatarsal Metapodial Bone Mineral Density Studies: Lam et al. (1999, Table 1) Horse Lam et al. (1999, Table 1) Wildebeest Lam et al. (1999, Table 1) Reindeer Lam et al. (1998, Table 1) Goat Berkeley hyena ravaging study Berkeley hyena ravaging study Sheep (Hammerstone Broken) Sheep (Unbroken) Dog ravaging study Medium duikers Carnivore Ravaging Studies: *.2* - ns 6.4* Dog ravaging study Small duikers * 11.6* Ain Dara (Syria, Iron Age) Sheep & Goat (size 2) Ain Dara (Syria, Iron Age) Bovid/Cervid Size 3-4 Die Kelders 1 (South Africa, MSA) Kobeh (Iran, MP) Sheep & Goat (size 2) Kunji (Iran, MP) Sheep & Goat (size 2) Mezmaiskaya (Caucasus, MP) Mezmaiskaya (Caucasus, MP) Bovid Size Sheep & Goat (size 2) Bovid/Cervid Size 3-4 Archaeological Assemblages: Porc Epic (Ethiopia, MSA) Size Porc Epic (Ethiopia, MSA) Size Porc Epic (Ethiopia, MSA) Size 3 &

7 Cleghorn & Marean Table 2. Outcome of regression analysis comparing MNE estimates of shaft and ends by element within archaeological assemblages R squared F Probability Humerus Radius Ulna Metacarpal Femur...97 Tibia Metatarsal identified and corrected (Lam et al., 1998, 1999, 23). Finally, long bone frequencies in the assemblages surveyed by Lyman (1991, 1993) were reported by epiphysis, a method with a demonstrated tendency to underestimate counts (see discussion and citations above). Because so few assemblages have yet been analyzed or published with both long bone shaft and end MNEs, our survey cannot replicate the scope of Lyman s research. However, we can point out some interesting trends in this fledgling data set. We first tested the hypothesis that there is a correlation between skeletal element representation and bone mineral density in the archaeological assemblages listed in Table 1. We ranked all elements represented in each assemblage and their corresponding density values, using Lam and colleagues (1999) shape-corrected data. These density values were taken from Lam et al. s (1999) Table 1, BMD 1 columns, except where an internal shape correction (BMD 2 ) was applicable (i.e., wherever a medullary cavity was present). Because the archaeological data were almost entirely from bovids and cervids, we examined density relationships using the Rangifer tarandus (reindeer) and Connochaetes taurinus (wildebeest), but not the Equus burchelli (zebra) or E. prezewalskii (Prezewalskii s horse) data. Density values from these taxa are extremely similar rank-order correlation between the reindeer and wildebeest is highly significant (r s =.966, p <.1). Because the Spearman s statistic is highly sensitive to ties, we performed an additional test that derived a correlation statistic and probability value through a bootstrap procedure that is less sensitive to ties. This process generated 1 randomly ordered permutations of the datasets, returning a correlation coefficient, the number of generated datasets (P) that had a higher correlation than the original, and a probability value based on the following equation: (P+1)/(Q+1), where Q was equal to 1. A previous sample test with higher iterations (5 to 1,) did not return appreciably different results. Table 1. Ratio of greatest shaft to greatest end value (bone mineral density or MNE) for actualistic and archaeological studies. Size classes follow Brain (1981, p.9). Dash indicates no data in this category. Bone mineral density values are taken from Lam et al. (1999:563, Table 1) and Lam et al. (1998: , Table 1). Ratios of shaft to end MNE in hyena and dog ravaging studies are derived from Marean & Spencer (1991: 651, Table 2) and Hudson (1993: 316, Table 17-4) respectively.*for some data in Hudson s study, a was changed to 1 to avoid irrational ratios. No fragments survived in one example (n.s.). 49

8 Distinguishing selective transport and in situ attrition We found significant correlations between bone mineral density and skeletal element representation in 1% of our archaeological assemblages (Table 3). Although this is a small sample, the strength and uniformity of the coefficients is a clear warning that bone mineral density may be a common determinant of the skeletal element profile even more so than predicted by Lyman (1993). Although density may not always be the primary determinant of element frequency, it is obvious that the relationship should always be examined prior to skeletal element analysis. Further, this relationship is best understood when using accurate estimates of bone representation and mineral density. Skeletal Element Analysis in the Face of Clear Density-Mediated Attrition: What Now? Anatomical Region Profile (ARP) One of the first attempts to work out an effective means of dealing with the analytical consequences of densitymediated attrition was Stiner s (1991, 1994, 22) ARP method. Stiner sought to mitigate the effect of inter-bone density gradients on element survival by grouping elements into regions. Her nine regions were each constructed such that most of the midpoints of density values (non-shape corrected data taken from Lyman, 1994) from each region were within a limited range (about.1 on Lyman s density scale). Table 3. Correlation of MAU and BMD (wildebeest density values from Lam et al., 1999:563, Table 1) within archaeological assemblages. Size classes follow Brain (1981:9). Site Ungulate Body Size Bootstrap R Probability Ain Dara Sheep & Goat (Size 2).8.1 Ain Dara Bovid/Cervid Size 3 & Die Kelders 1 Bovid Size Kobeh Sheep & Goat (Size 2).9.1 Kunji Sheep & Goat (Size 2).82.1 Mezmaiskaya MP Sheep & Goat (Size 2).74.1 Mezmaiskaya MP Bovid/Cervid Size 3 & Porc Epic Size Porc Epic Size Porc Epic Size 3 &

9 Cleghorn & Marean Stiner (22) noted that the density values from the neck and axial regions fall below this range of variation, and thus these two regions were likely to suffer higher rates of density-mediated destruction than the other six regions. She concentrated, therefore, on the relative representation of head, limbs, and feet. Using the ARP method, Stiner (1991, 1994) compiled results from several modern carnivore accumulations (dens and shelter sites) and suggested analogues for patterns in the Paleolithic record. From this study, she argues that hyenas and wolves tend to produce distinctly different element profiles, the formers dominated by cranial remains. Because she grouped these profiles using ARP, which she argues corrects for most inter-element density variation, she concludes that the variation between the skeletal element patterns found in wolf and hyena assemblages is due primarily to differences in what was originally transported to each site (Stiner, 1994:25). This model of variable transport for taxa with two dominant modes of carcass acquisition (hunting and scavenging) has become an important tool for interpreting the behavior of Paleolithic hominids. The premises underlying the ARP and its application to the den/shelter data can be tested using data from actualistic studies of carnivore ravaging. The destructive effects of carnivores on the skeletal element profile have been well documented in modern observational contexts (Blumenschine, 1988; Brain, 1967, 1969; Capaldo, 1995, 1998; Carlson & Pickering, 23; Klippel et al., 1987; Marean & Spencer, 1991; Pickering et al., 23; Richardson, 198; Snyder, 1988; Stallibrass, 1984; Sutcliffe, 197). Attrition caused by carnivore ravaging is usually highly correlated with bone mineral density (Cleghorn & Marean, in press). If the ARP reduces inter-element variation in density, a non-transported, carnivore-ravaged assemblage should show a level representation (excepting the neck and axial sets) of ARP categories in a frequency distribution. Pickering and colleagues (23) applied this test using data from Snyder s wolf and Marean s hyena studies. They found that neither of these assemblages retained a level representation among ARP groups after ravaging. There are at least seven other actualistic assemblages (listed in Table 4) that can be used to test the ARP method. With the exception of Hudson s (1993) data all of these studies quantified long bone ends but not shafts. Stiner (22) argues that skeletal abundance estimates based only on articular ends provide results comparable to shaft-based calculations. Because the ARP method is based on Lyman s long bone end values, the use of end-based actualistic data should not interfere with the efficacy of the test. All of the ravaging experiments in Table 4 began with complete carcasses, so if the ARP is performing as Stiner suggests, the resulting skeletal element regions should be somewhat evenly represented in a histogram. However, we find a great deal of variation in the resulting ARP graphs (Figure 3), except when element destruction is severe enough to depress all groups to very low levels (Figure 3D). Notably, the ARP does not equalize the element frequencies among the large (> 84 kg) ungulates in Richardson s (198) study (Figure 3C), which Stiner (22) has 51

10 Distinguishing selective transport and in situ attrition Table 4. List of carnivore ravaging studies. *Two data sets from Binford and Bertram s (1977) study were used the winter and the summer experiments. Consumer taxon/taxa Domestic dog Consumed taxon/taxa Sheep (size class 1 to 2) Spotted hyena Size class 3 bovids Spotted hyena, brown hyena Fox Human, domestic dog Human, domestic dog Size class 1 & 2 bovids Sheep (size class 1 to 2) Medium duiker (18 kg) Blue duiker (5 kg) Type of study Context Original condition Naturalistic Camp refuse Cooked, uncooked, defleshed Naturalistic and experimental Naturalistic and experimental Ranches and wildlife reserves Ranches and wildlife reserves complete carcass complete carcass Experimental Farm complete carcass Naturalistic Campsites defleshed and some hammerstone broken Naturalistic Campsites defleshed and some hammerstone broken Locality Source USA Southern Africa Southern Africa England Central African Republic Central African Republic Binford & Bertram (1977)* Richardson (198) Richardson (198) Stallibrass (1984) Hudson (1993) Hudson (1993) argued are the primary size class targeted by hyenas. Stiner (1994) argued that the element patterns displayed at several modern carnivore sites are indicative of transport differences. Binford and Bertram s (1977) winter and summer sheep profiles (Figures 3A and 3B) are of particular interest in this regard. One of these profiles is head dominated and the other shows greater similarity between head and limb representation. The difference between the two is similar to that between the wolf and hyena profiles. In the Binford and Bertram studies, however, one type of carnivore (dog) produced two quite different ARP patterns in the absence of transport. For this reason, together with the fact that the ARP does not in fact level the density gradient, we find no compelling reason to think that the den/shelter studies Stiner cites show any pattern that can be specifically attributed to transport. The difference in the wolf and hyena den/shelter profiles is evident in actualistic studies beyond those discussed above (Blumenschine, 1988; Capaldo, 1998; Klippel et al., 1987; Snyder, 1988). Hyenas tend to be more destructive agents than wolves, and are therefore likely to 52

11 Cleghorn & Marean Standardized MNE A) Binford and Bertram (1977): dog destruction of sheep carcasses, 2 summer sample CR NK AX UF LF UH LH FT ARP Groups Standardized MNE B) Binford and Bertram (1977): dog destruction of sheep carcasses, winter sample CR NK AX UF LF UH LH FT ARP Groups % Survival C) Richardson (198): spotted hyena destruction of size 3 bovids (n=7) CR NK AX UF LF UH LH FT* ARP Groups % Survival D) Richardson (198): hyena destruction of size 1 & 2 bovids (n=14) CR NK AX UF LF UH LH FT* ARP Groups %Survival E) Stallibrass (1984): fox destruction of sheep carcasses (MNI=18) CR NK AX UF LF UH LH FT ARP groups % Survival F) Hudson (1993)*: dog and human destruction of medium duikers CR NK AX UF LF UH LH FT ARP Groups % Survival G) Hudson (1993)*: dog and human destruction of blue (small) duikers CR NK AX UF LF UH LH FT ARP Groups Figure 3. ARP profiles of the seven carnivore ravaging studies cited in Table 4. Anatomical regions follow Stiner s (22) description. ARP abbreviations as follows: CR cranial, NK neck, AX axial, UF upper forelimb, LF lower forelimb, UH upper hindlimb, LF lower hindlimb, FT foot. *Because Hudson (1993) does not distinguish metacarpals from metatarsals, LF is a maximum estimate for the medium duiker set, and LH is a maximum estimate for the small duiker set. 53

12 Distinguishing selective transport and in situ attrition leave relatively fewer post-cranial elements. The difference, however, is largely in the intensity of destruction, which may vary depending upon other factors such as predator group size. Den studies are highly problematic analogues even when undertaken with rigorous archaeological standards (Cleghorn & Marean, in press). As Stiner (1994:248-25) notes, these sites are palimpsests of multiple processes including both transport and attrition. Unlike actualistic studies in which all parameters (inputs, destructive agents, outputs) are directly observed, dens include a number of unknown variables. Processes of element destruction cannot be understood unless the analyst can accurately estimate a percentage change in the survival of elements and portions thereof. This estimate requires tight control over the original number of elements and portions exposed to the taphonomic process, as well as the number surviving the process. With den assemblages, the original number of carcasses, elements, and portions is always a mystery, making it impossible to calculate an absolute survival rate. Researchers investigating carnivore dens have no way to isolate the contributing processes, except possibly by referring to models derived from actualistic studies with greater control of parameters. Although it is possible that there was some differential transport of elements to the den and shelter studies Stiner cites, the more parsimonious explanation for the different profiles is in situ attrition coupled with the exclusion of long bone shaft fragments from some of the analyses. This brings into question the idea that these profiles are related to niche differences in carcass acquisition and further, that such profile differences might be used to characterize the niche of Paleolithic hominids. Why does the ARP method not equalize inter-portion density? Part of the reason may lie with the use of a density midpoint rather than a maximum when comparing ARP groups (Stiner, 22:982, Figure 3). Variation in grease content aside, the chance that any given element will survive is dependent on its maximum density. The average density (we assume this is what is signified by midpoint ) is of much less relevance. This is clearly indicated by the differential survival, documented in numerous carnivore ravaging studies, of dense long bone midshaft portions relative to ends. If we consider the ARP groups in terms of maximum density (using data from Lam et al., 1999), compiled without long bone shaft density values, it is readily apparent that no leveling effect should be expected (Figure 4A). In this case, we have left out the shaft density values to demonstrate how this method would work with most of the assemblages to which it has been applied. If the ARP density profile is redrawn using maximum bone density including long bone shafts, there is much less variation among groups, with the exception of neck and cranial portions (Figure 4B). The test for this form of the ARP requires that the actualistic assemblage is shaft-inclusive. For ungulate carcasses, only Marean s (Marean & Spencer, 1991, sheep to hyena) and Hudson s (1993, duiker to dog) studies qualify. Both indicate that the ARP does not work even when long bone shafts are included in the MNE (Figure 3G and Pickering et al., 23:1471, Figure 1C). These are both, however, incomplete tests 54

13 Cleghorn & Marean of the problem. Marean s study included only five of eight analytically useful ARP portions. Hudson s (1993) study, although incorporating features of a good actualistic program (i.e. close observation of input), provided an incomplete account of output. Although her excavation of the Aka sites was complete, Hudson had to deal with a problem more common to zooarchaeologists than experimental taphonomists the mixing of multiple taxa. As a result, only 55% of the fragments in her assemblage could be assigned to genus or species, and her recovery of small and medium duikers was 57% and 32% of original Minimum Number of Individuals (MNI) respectively. These conditions could have easily depressed her MNE calculations in comparison to a study in which recovered bones could be linked to particular carcasses. Thus, this particular test of the ARP against assemblages with shaft-inclusive MNEs is incomplete, though highly suggestive. There is another reason, however, to suspect that the ARP cannot function as a means to level the effects of the density gradient, even if it comes close to leveling the gradient itself. ARP depends heavily on density as a proxy for sensitivity to attrition. Carnivore ravaging usually results in a strong correlation between density and survival (Cleghorn & Marean, in press). However, this is only part of the lesson offered by these studies. Ravaging is linked to density through accessible bone grease content. Carnivores do not chew certain bones only because they are soft, but in order to extract grease, which is most accessible in the trabecular matrix of cancellous bone. Thus, any bone portion that has an appreciable trabecular content Figure 4. Maximum bone mineral density (BMD) per ARP region with (4A) and without (4B) shaft values (data from Lam et al., 1999:563, Table 1; abbreviations as in Figure 3), and maximum BMD per high survival element (4C). Graph 4A uses long bone articular end density, and graphs 4B and 4C use the highest density per region/element. will be a target, even if that portion has 55

14 Distinguishing selective transport and in situ attrition some extremely dense areas. The ilium, rib, and phalanges, for example, have portions that rival long bone shafts in density (Lam et al., 1999). However, these elements survive very poorly by comparison (Marean & Spencer, 1991; Pickering, 21; Carlson & Pickering, 23). Density alone does not precisely dictate fragment survival it is only part of the equation. Finally, we can consider how the ARP works as an interpretive model. This method has been used to identify whole pattern variation (ie. head and neck versus high limb representation ) in the modern and archaeological record. We argue that simple pattern matching (of archaeological and den assemblages) is both flawed (as discussed above) and ultimately less powerful than evaluating the correspondence between element frequency and some measure of utility. If we could find assemblages free of any attrition, and examine transport using the ARP, we would have some difficulty explaining the economic mechanisms behind the pattern. The ARP groups combine elements of widely varying nutritional value (Pickering et al., 23). The intermediate limb bones (radius, ulna, and tibia) have different utility indices, both in meat and marrow content, than the metapodials with which they are grouped. There is no particular reason that these portions should be considered a single transport unit independent of the whole limb. Although the metatarsal may often be a rider associated with the meatier tibia, this is better tested than assumed. Also, there are data indicating that Hadza hunters may detach the humerus from the scapula prior to transport (Bunn et al., 1988). Grouping these elements into a single class (Upper Forelimb) further obscures transport strategies. Although we have argued that the ARP is methodologically flawed, we are equally concerned with the use of this, or any element grouping method, as a standard of data publication. We strongly advocate the full publication of at least element and element portion variables. Thus, the most significant problem with the ARP from an interpretive standpoint is that it masks differences that are highly relevant to behavioral analyses. High and Low Survival Elements Recently, Marean and Cleghorn (Cleghorn & Marean, in press; Marean & Cleghorn, 23) proposed a different approach to disentangle in situ attrition from selective transport. Like others (Pickering et al., 23), we speculated that there is a threshold of bone mineral density above which bone fragments have a much better chance of survival. Actualistic studies show that this threshold may be set not only by bone density, but also by the way ravaging carnivores destroy bone. Bones with marrow cavities are attractive to scavengers only up until trabecular portions have been deleted, marrow has been removed, and cortical portions lacking trabecular bone remain. Elements without a substantial cortical portion free of trabeculae, lack this brake on ravaging. As noted above, there are several elements that are often consumed or completely destroyed despite regions of high density. Marrow bones are less likely than other elements to be completely destroyed by carnivore ravaging. 56

15 Cleghorn & Marean We therefore view the marrow bones meaning all long bones and the mandible as a coherent group with respect to survivability. This high survival set includes all of the long bones (femur, tibia, humerus, radius-ulna, and metapodials), mandibles (these basically function like long bones due to their dense cortical bone and open medullary cavity), and crania. The cranium is included because teeth and petrosals are both extremely dense and lack nutrient value. They demonstrably survive carnivore ravaging very well. The ulna is included because, in bovids and cervids, this bone often fuses with or is tightly bound to the radius shaft, and has a countable landmark in that area. The low survival set includes all vertebrae, ribs, pelves, scapulae (which have thick cortical bone but are difficult to identify and quantify when fragmented), and all tarsals, carpals, and phalanges of size class 1 and 2 ungulates (Brain, 1981) since these tend to get swallowed by carnivores. All of these have significant proportions of trabecular bone that, because of high grease content, are especially attractive to scavengers. More importantly, low survival elements lack large areas of dense cortical bone without trabeculae. Ungulate body size and taxonomy may have an effect on the actual composition of these sets. In animals as large as bison, for example, portions of the rib near the costal angle are often comprised of dense cortical bone lacking internal trabeculae. Taphonomically, these large ribs act like long bones. Unfortunately, they lack consistent, discrete landmarks in this area. It would be quite useful if we could get an axial element into the high survival set, and it is possible that there is a methodological solution to accurately quantify these types of fragments. Precise recording using imagebased techniques (see Marean et al., 21) may hold promise. For now, ribs remain in our low survival set, and there is good evidence that they survive very poorly in small and medium ungulates. In contrast to bovids and cervids, many taxa (eg. suids, equids, hominids, pinnipeds) often have trabecular bone that extends more proximally and distally in the long bones, extending the area of the long bone subject to carnivore ravaging and consumption. Thus, element survivability should be independently evaluated in these taxa. Although high survival elements are designated as such principally on the basis of their resistance to carnivore ravaging, a survey of maximum density in this group shows gratifyingly little variation as well (Figure 4C). This is similar to the leveling effect Stiner attempted to achieve through the ARP. We can test the high survival set against the carnivore ravaging data in much the same way that we tested the ARP. For this test, however, only studies that incorporate long bone shaft fragments into estimates of element survival are appropriate. We are again limited, therefore, to Marean s hyena and Hudson s dog research. Hudson s (1993) study, as noted above, suffers from identification problems that limit its applicability. Medium duikers (ca. 18 kg), for instance, suffered significant attrition according to Hudson s estimate of element loss. However, the resulting profile shows no correlation with density. The small duikers (ca. 5 kg) were also highly affected by scavengers, but do show a significant correlation with density. This skeletal 57

16 Distinguishing selective transport and in situ attrition Table 5. Hypothesis support and assemblage size for rank correlation of density and representation in archaeological assemblages (after Cleghorn and Marean, in press). Hypotheses as follows: H1: significant correlation in low but not in high survival set. H2: higher, but non-significant correlation in low survival set. H3: significant correlation in high but not in low survival set. * These archaeological data are taken from Hill (21, Appendices 2-6). Archaeological Assemblage: H1 H2 H3 Maximum MAU Porc Epic, size 2 X X 59.4 Kobeh X X X 57 Kunji X X 45.5 Agate Basin* X X 39 Ain Dara, size 1 & 2 X X 31.5 Mezmaiskaya MP, size 2 X X 25.6 Clary Ranch* X X 2 Die Kelders 1, size 3 & 4 X X 13.5 Mezmaiskaya MP, size 3 & 4 X X 13.5 Hell Gap* X X 11 Agate Basin, Folsom comp. (bison)* X 7 Ain Dara, size 3 & 4 X X X 7 Die Kelders 1, size 2 X 5 Agate Basin, Folsom comp. (pronghorn)* X 3 element profile was not leveled by either the ARP grouping or in a restricted high survival set (Figure 3G). Marean s hyena data, by contrast, show remarkably similar (level) frequencies among the few representative high survival elements (Pickering et al., 23:1471, Figure 1D). By comparison, the low survival elements of Marean s experiment are poorly and unevenly represented. Carlson and Pickering (23) provide another actualistic data set that can be used to examine the premise of the high survival distinction. Baboon carcasses were fed to leopards and a spotted hyena, and bone survival was recorded. The composition of the high survival set should vary somewhat from that in a bovid or cervid. Primate metapodials, for instance, have much more in common with phalanges than with the major limb bones (ie. they are both relatively short, and have small marrow cavities). Conversely, baboon fibulae may be classed as long bones, while ungulate fibulae are more similar to tarsal bones. The preferential deletion of long bone articular ends in the baboon data (summarized in Pickering et al., 23) indicates that the ravaging pattern follows the ungulate model. A bar graph (Figure 5) 58

17 Cleghorn & Marean Figure 5. Percentage survival of baboon bones after ravaging by leopard and hyena (data from Carlson & Pickering, 23: 437, Table 2). of the baboon data shows some unevenness among the high survival set. If we had corresponding density values with full (internal and external) long bone shape correction, we might be able to better understand how much unevenness should be expected in this graph. Even so, the result confirms that long bones survive much better than other post-cranial elements. The actualistic studies provide some positive evidence supporting the leveling effect of the high survival set upon the density gradient, although a more complete test would be preferred. We can also test the effect of dividing the carcass into low and high survival elements using archaeological data. In a recent study, we examined 14 archaeological assemblages to determine how the high and low survival sets correlated with bone mineral density 59

18 Distinguishing selective transport and in situ attrition (Cleghorn & Marean, in press). We view this test as more suggestive than definitive, given that these assemblages may have undergone both selective transport and in situ attrition. The assemblages were chosen because they provided MNE estimates on both shaft and end portions, and the methods for estimating both were welldescribed and shaft-inclusive. Both MNE and MAU (minimum animal units, as defined in Lyman, 1994:14 17) were compared to density values from wildebeest and reindeer (Lam et al., 1999). Further, we used both the highest density per element and the density of the most represented portion of each element in the assemblage. Sixteen rank correlations, using the bootstrap method described above, were applied to each archaeological assemblage. In 11 of 14 assemblages we found support for our primary hypothesis that is, density correlates well with representation in the low survival, but not in the high survival set. Table 5 summarizes these results and orders the assemblages by sample size (MAU). One of the remaining assemblages showed a statistically weak pattern supporting this hypothesis, and two showed a pattern opposite to that anticipated (a significant correlation in the high but not the low survival set). The non-supportive cases were primarily from the assemblages with the smallest samples. Examination of high and low survival elements has led us to two main conclusions. First, the low survival set is likely to be correlated with bone density, as these elements are more susceptible to complete destruction by carnivores. The implication is that element frequencies in this set are unlikely to be useful for understanding selective transport. Because in situ attrition can significantly distort patterns produced by selective transport, we see little hope of using the low survival portion of the element profile toward an assessment of either process. In a previous article (Marean & Cleghorn, 23) we have speculated that there might be some taphonomic value in the low survival element set, even if no transport signature could be discerned. Under further consideration, we have come to a slightly different conclusion. Although attrition is the most parsimonious explanation for a strong correlation with density, it is not possible to rule out a role for selective transport. Thus, the relative representation of low survival elements is not a secure means to estimate the intensity of attrition. Our second conclusion is more positive. The high survival set appears to be much less susceptible to the effects of in situ attrition, and is therefore the set of elements most likely to provide evidence for differential transport. We are not suggesting that others should accept our model and begin analyzing only the high survival portion of their skeletal element profile. These relationships need to be tested within each assemblage. Additional actualistic carnivore research providing data on individual element survival are needed to fully explore the model particularly the characteristics of the high survival element set. Absolute Bone Counts by Maximum Likelihood (ABCML) Alan Rogers (Rogers, 2a, 2b; Rogers & Broughton, 22) has recently 6

19 Cleghorn & Marean proposed the ABCML method for dealing with the effect of in situ attrition on skeletal element analysis. He has developed a mathematical program that uses data on human behavioral patterns, sensitivity to attrition, and skeletal element frequencies to produce estimates of the original number of elements contributing to the assemblage (κ), the degree of attrition (β), and the relative proportion of the agents of deposition (α ). This method is an admirable attempt to deal with the very problem we came up against in the low survival elements: the confounding effects of attrition on selective transport. Rogers (2b) argues that the analytical problem of equifinality exists because archaeologists have been trying to interpret a multidimensional problem with a two dimensional analysis. He notes that although attrition (measured by density) may correlate weakly with transport priority (modeled on bone utility data), the two parameters result in distinct patterns. These patterns can be distinguished using a multidimensional approach sensitive to the predictive possibilities of our current models for attrition and transport. Rogers (2b) develops several test cases to illustrate how his models behave under varying degrees of attrition and in response to varying combinations of depositional agent. In this case, agent refers to the depositional context (i.e. kill or home base accumulation). In a tabulation of the parameter estimates of his simulation models, Rogers finds relatively good agreement between the actual and the estimated contribution of each agent (all home base, all kill site, and half of each). His estimate of the original contributing number of animal carcasses (κ) is somewhat better than his estimate of standard MAU (he refers to this as MNI, but it appears to lack side-specific data). His estimate of degree of attrition (β) is also somewhat variable. Both κ and β appear to have some directional variation corresponding to the other parameters. For instance, in a pure home base site, both κ and β increasingly overestimate with increasing attrition. In a pure kill site, the β estimate lags behind its increasing actual value, and κ increasingly underestimates the true parameter. Interestingly, in a 5/5 mix, both κ and β are most inaccurate when attrition is zero. Rogers (2a) applied his method to two archaeological examples: Gatecliff Shelter and Last Supper Cave. The ABCML determined that the element profiles at both of these sites are roughly similar to a Hadza kill site and totally lack attrition despite high correlations between representation and density. Rogers points out, however, that some anomalies in the residual data indicate that the model does not quite fit the observations. We will return to this counter-intuitive result. The agent (or context) of deposition is ultimately of greatest interest, while the intensity of attrition and estimates of the original number of carcasses are of secondary importance (Rogers, 2b). The ABCML method does a comparatively good job of identifying the agent in the test samples because it has perfect knowledge of each of the contributing transport behaviors. That is, the ABCML program easily recognizes probability patterns that match those in its test criteria. Rogers (2b) notes that this puts the onus upon ethnoarchaeological studies to produce more data (and more specific data) about 61

20 Distinguishing selective transport and in situ attrition transport criteria. The program is only as effective as its test criteria. Interestingly, the ABCML method appears to produce an interpretive result while bypassing an empirical description of the target assemblage. Thus, if both attrition and transport affect the skeletal element profile, the method evaluates the portion attributed to transport relative to a test model. It does not then describe the actual transport it sees, except as similar to or dissimilar from the test. This may pose a problem particularly for archaeologists dealing with behaviors that may be distinct from modern analogues. This would not only limit their interpretive options, it would almost certainly bias their conclusions. If a target assemblage deviates from the known universe of hunter-gatherer transport models (which is very small), then we want to know why and how. Rogers (2a) cites the residual analysis as useful in determining where the profile deviated from expectations. However, it is unclear how the other parameter of the analysis (attrition) might have also affected these residuals. Thus, unless there is perfect concordance between the assemblage and the analogous model, the method compromises interpretation. This problem appears to be most severe in assemblages that lie in between analogues. For instance, Rogers creates a test sample using a nearly even split of kill site and home base assemblages. The ABCML detects the division between these two very accurately. But the parameters that do so (α and α 1 ) must add up to 1. If, in actuality, there is a third or fourth agent, there is no way the ABCML can detect it. The user would have to know for certain how many different agents had contributed to the assemblage and also what those agents were. Rogers (2a) acknowledges that the effective use of the ABCML depends upon reliable model data. But his implication is that one needs good information on the traits of the agents. It may be even more important to know how many agents one is dealing with. As currently described, the ABCML divides the universe of archaeological sites into hominid kill or camp locations. This does not take into account the possibility of other scenarios such as transported scavenged remains or the independent contribution of carcasses by carnivores. These agents (or contexts) may have element transport probability sets different from the two used by the ABCML. The zooarchaeologist ultimately needs to determine the contributions of multiple agents to assemblage formation. But by taking into account only skeletal element frequencies, the transport probabilities of multiple agents may confound each other in the same way that in situ attrition can confound the transport pattern. The solution is to incorporate data that is independent of element frequency and informative on the problem of accumulating agent. Surface modification is the resource that best fits this description, although it may also be influenced by representation. It is not clear that the ABCML could incorporate more than two agents, nor that it would be able to distinguish between them if it could. The advantage of the ABCML model is its ability to analyze data along three axes: representation, density, and utility. Rogers (2b) notes that bivariate methods, which treat density and utility separately, have a very low power of 62