P R O ME T H E US P R E S S / P A L A E O N T O L O G I C A L N E T W O R K F O UN D A T I O N
(TERUEL)
2004
Journal of Taphonomy
VOLUME 2
Available online at www.journaltaphonomy.com
(ISSUE 2)
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 11794-4364
Curtis W. Marean
Institute of Human Origins, Department of Anthropology, PO Box 872402, Arizona State
University, Tempe, AZ 85287-2402 USA
Journal of Taphonomy 2 (2) (2004), 43-67.
Manuscript received 24 September 2004, revised manuscript accepted 22 November 2004.
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
butchery strategies (Binford, 1981, 1984;
Bunn, 1986; Bunn & Kroll, 1986; Conard,
1992; Conard, et. al. 1998; Klein, 1976;
Lartet & Christy, 1865-1875; Perkins &
Daly, 1968; White, 1953). With the
introduction of utility indices, Binford
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
* E-mail: [email protected]
Article JTa017. All rights reserved.
43
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, 2002) anatomical
region profile (ARP) method,
Rogers’ (2000a, 2000b) Absolute Bone
Counts by Maximum Likelihood (ABCML)
method, and Marean and Cleghorn’s
(Cleghorn & Marean, in press; Marean &
Cleghorn, 2003) 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., 2003; Marean,
1998; Marean & Frey, 1997; Marean &
Kim, 1998; Marean & Spencer, 1991;
Marean et al., 1992; Pickering et al., 2003).
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, 2001; Stiner,
1998, 2002).
44
Cleghorn & Marean
B) Radius
A) Humerus
30
End MNE
End MNE
30
20
10
0
20
10
0
0
50
100
0
Shaft MNE
End MNE
End MNE
10
8
6
4
2
0
20
25
20
15
10
5
0
0
40
20
40
Shaft MNE
Shaft MNE
F) Tibia
E) Femur
40
25
20
15
10
5
0
End MNE
End MNE
100
D) Metacarpal
C) Ulna
0
50
Shaft MNE
30
20
10
0
0
50
100
0
150
50
100
150
Shaft MNE
Shaft MNE
G) Metatarsal
End MNE
30
20
10
0
0
20
40
Shaft MNE
Figure 1. Comparisons (with linear regression lines) of long bone shaft-inclusive and end-based MNE estimates
from the 10 archaeological assemblages listed in Table 1.
45
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
10% 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
Cleghorn & Marean
Max BMD vs. FUI
6000
6000
4000
4000
FUI
MUI
Max BMD vs MUI
2000
2000
0
0
0
0.5
1
1.5
0
BMD
0.5
1
1.5
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.
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
Density Mediated Attrition and the
Archaeological Record
Developments in zooarchaeological
research warrant a re-evaluation of the
evidence for density-mediated attrition in
* Lyman (1994, p. 507) 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
Distinguishing selective transport and in situ attrition
3.06
2.06
2.10
2.57
-
-
-
-
1.42
1.20
1.60
1.83
Metacarpal
2.16
2.21
2.27
3.11
Femur
2.06
1.55
1.90
2.38
Tibia
1.34
1.20
1.37
1.83
Metatarsal
-
-
-
-
Metapodial
Table 1.
2.16
1.77
Ulna
Horse
2.33
Radius
Wildebeest
2.06
Sheep
(Hammerstone
Broken)
1.40
-
-
1.55
2.70*
-
-
1.00
0.20*
-
-
-
-
-
-
11.60*
ns
8.42
4.09
11.60*
6.40*
1.92
2.03
-
-
1.77
1.71
2.25
1.50
-
-
Humerus
Lam et al. (1999, Table 1)
Reindeer
Berkeley hyena ravaging
study
Sheep
(Unbroken)
0.53
Taxa
Lam et al. (1999, Table 1)
Goat
Berkeley hyena ravaging
study
Medium duikers
Site/Study
Lam et al. (1999, Table 1)
Bone Mineral Density Studies:
Lam et al. (1998, Table 1)
Dog ravaging study
Small duikers
Carnivore Ravaging Studies:
Dog ravaging study
Porc Epic (Ethiopia, MSA)
Porc Epic (Ethiopia, MSA)
Mezmaiskaya (Caucasus,
MP)
Mezmaiskaya (Caucasus,
MP)
Kunji (Iran, MP)
Kobeh (Iran, MP)
Die Kelders 1 (South Africa,
MSA)
Ain Dara (Syria, Iron Age)
Ain Dara (Syria, Iron Age)
Size 3 & 4
Size 2
Size 1
Bovid/Cervid
Size 3-4
Sheep & Goat
(size 2)
Sheep & Goat
(size 2)
Sheep & Goat
(size 2)
Bovid Size 2
Bovid/Cervid
Size 3-4
Sheep & Goat
(size 2)
5.50
3.67
0.88
3.20
1.67
16.60
10.00
1.40
3.50
2.86
10.00
11.75
5.56
1.67
2.30
9.57
8.67
0.95
0.33
2.45
2.00
2.00
1.78
5.50
3.67
14.50
31.00
3.40
3.00
5.50
1.71
0.82
1.08
1.75
1.86
4.00
1.67
1.15
1.20
1.67
29.00
23.80
11.60
8.50
1.69
25.00
11.29
2.14
1.00
2.73
4.00
4.00
3.27
1.80
1.53
2.84
14.25
2.11
1.00
1.38
2.57
0.92
0.42
7.00
1.10
4.22
2.13
0.87
1.38
1.04
-
-
-
-
-
-
-
-
-
-
Archaeological Assemblages:
Porc Epic (Ethiopia, MSA)
48
Cleghorn & Marean
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, BMD1
columns, except where an internal shape
correction (BMD2) 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 (rs = 0.966, p < .001).
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 1000 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 1000. A previous sample test with higher
iterations (5000 to 10,000) did not return
appreciably different results.
Table 2. Outcome of regression analysis comparing MNE estimates of shaft and ends by element within archaeological assemblages
R squared
F
Probability
Humerus
0.01
0.05
0.83
Radius
0.03
0.27
0.62
Ulna
0.01
0.12
0.74
Metacarpal
0.36
4.46
0.07
Femur
0.00
0.00
0.97
Tibia
0.10
0.85
0.38
Metatarsal
0.05
0.45
0.52
identified and corrected (Lam et al., 1998,
1999, 2003). 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
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. (1998:351 –
353, 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 0 was changed to 1 to avoid irrational ratios. No fragments survived in one example
(n.s.).
49
Distinguishing selective transport and in situ attrition
We found significant correlations
between bone mineral density and skeletal
element representation in 100% 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,
2002) 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 0.1 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)
0.80
0.001
Ain Dara
Bovid/Cervid Size 3 & 4
0.74
0.001
Die Kelders 1
Bovid Size 2
0.61
0.002
Kobeh
Sheep & Goat (Size 2)
0.90
0.001
Kunji
Sheep & Goat (Size 2)
0.82
0.001
Mezmaiskaya MP
Sheep & Goat (Size 2)
0.74
0.001
Mezmaiskaya MP
Bovid/Cervid Size 3 & 4
0.82
0.001
Porc Epic
Size 1
0.77
0.001
Porc Epic
Size 2
0.65
0.001
Porc Epic
Size 3 & 4
0.57
0.004
50
Cleghorn & Marean
Stiner (2002) 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,
1994:250). 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, 2003; Klippel et al., 1987;
Marean & Spencer, 1991; Pickering et al.,
2003; Richardson, 1980; Snyder, 1988;
Stallibrass, 1984; Sutcliffe, 1970). 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
(2003) 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 (2002) 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 (1980) study
(Figure 3C), which Stiner (2002) has
51
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
Consumed
taxon/taxa
Domestic dog
Type of study
Context
Original
condition
Locality Source
Sheep (size Naturalistic
class 1 to 2)
Camp refuse
Cooked,
uncooked,
defleshed
USA
Spotted hyena
Size class 3
bovids
Naturalistic and
experimental
Ranches and
wildlife
reserves
complete
carcass
Southern Richardson
Africa
(1980)
Spotted hyena,
brown hyena
Size class 1
& 2 bovids
Naturalistic and
experimental
Ranches and
wildlife
reserves
complete
carcass
Southern Richardson
Africa
(1980)
Fox
Sheep (size Experimental
class 1 to 2)
Farm
complete
carcass
England
Human,
domestic dog
Medium
duiker (18
kg)
Naturalistic
Campsites
defleshed and Central Hudson
some
African (1993)
hammerstone Republic
broken
Human,
domestic dog
Blue duiker
(5 kg)
Naturalistic
Campsites
defleshed and Central Hudson
some
African (1993)
hammerstone Republic
broken
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
Binford &
Bertram
(1977)*
Stallibrass
(1984)
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
A) Binford and Bertram (1977):
dog destruction of sheep carcasses,
summer sample
20
Standardized MNE
Standardized MNE
Cleghorn & Marean
15
10
5
0
CR
NK
AX
UF
LF
UH
LH
FT
25
B) Binford and Bertram (1977):
dog destruction of sheep carcasses,
winter sample
20
15
10
5
0
CR
NK
C) Richardson (1980): spotted hyena
destruction of size 3 bovids (n=7)
100
80
% Survival
% Survival
100
60
40
20
0
CR NK
AX
UF
LF
UF
LF
UH
LH
FT
UH
D) Richardson (1980): hyena
destruction of size 1 & 2 bovids
(n=14)
80
60
40
20
0
LH FT*
CR
ARP Groups
NK
AX
UF
LF
UH
LH
FT*
ARP Groups
E) Stallibrass (1984): fox destruction
of sheep carcasses (MNI=18)
F) Hudson (1993)*: dog and human
destruction of medium duikers
100
100
80
80
% Survival
%Survival
AX
ARP Groups
ARP Groups
60
40
20
0
60
40
20
0
CR
NK
AX
UF
LF
UH
LH
FT
CR
ARP groups
NK
AX
UF
LF
UH
LH
FT
ARP Groups
G) Hudson (1993)*: dog and human
destruction of blue (small) duikers
% Survival
100
80
60
40
20
0
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
(2002) 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
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 (1994:248250) 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
54
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, 2002:982,
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., 2003:1471, Figure 1C).
These are both, however, incomplete tests
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
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, 2001; Carlson & Pickering,
2003). 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., 2003). 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,
2003) proposed a different approach to
disentangle in situ attrition from selective
transport. Like others (Pickering et al.,
2003), 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
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., 2001)
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
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 (2001,
Appendices 2-6).
Archaeological Assemblage:
Porc Epic, size 2
Kobeh
Kunji
Agate Basin*
Ain Dara, size 1 & 2
Mezmaiskaya MP, size 2
Clary Ranch*
Die Kelders 1, size 3 & 4
Mezmaiskaya MP, size 3 & 4
Hell Gap*
Agate Basin, Folsom comp.
(bison)*
Ain Dara, size 3 & 4
Die Kelders 1, size 2
Agate Basin, Folsom comp.
(pronghorn)*
H1
X
X
X
X
X
X
X
X
X
X
H2
X
X
X
X
X
X
X
X
X
X
X
H3
X
X
X
X
X
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., 2003:1471, Figure 1D).
By comparison, the low survival elements
of Marean’s experiment are poorly and
unevenly represented.
Carlson and Pickering (2003)
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
X
Maximum MAU
59.4
57
45.5
39
31.5
25.6
20
13.5
13.5
11
7
7
5
3
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., 2003)
indicates that the ravaging pattern follows
the ungulate model. A bar graph (Figure 5)
58
Cleghorn & Marean
Figure 5. Percentage survival of baboon bones after ravaging by leopard and hyena (data from Carlson & Pickering,
2003: 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
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,
1994:104 – 107) 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, 2003) 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
Likelihood (ABCML)
by
Maximum
Alan Rogers (Rogers, 2000a, 2000b;
Rogers & Broughton, 2002) has recently
60
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 (α0). 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
(2000b) 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 (2000b) 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 50/50
mix, both κ and β are most inaccurate when
attrition is zero.
Rogers (2000a) 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, 2000b). 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
(2000b) notes that this puts the onus upon
ethnoarchaeological studies to produce
more data (and more specific data) about
61
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
(2000a) 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 (α0 and α1) 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 (2000a) 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 (2000b) notes that bivariate
methods, which treat density and utility
separately, have a very low power of
62
Cleghorn & Marean
analysis. Thus, he argues that a bivariate
plot of density and representation may,
under some circumstances, miss substantial
attrition, and in other situations detect
attrition when none is actually present. He
explains this with reference to his sample
home base and kill site. In his kill site
assemblage, there is a positive relationship
between density and MAU even when he
sets his attrition parameter to zero. He finds
this same pattern (kill site, zero attrition,
and a high correlation between density and
representation) at the two archaeological
assemblages as well. This is a truly
remarkable and counter-intuitive result. It is
perhaps best explained by the weak
negative correlation between density and
utility in Rogers’ model. That is, if a strong
correlation exists, something is probably
driving it—even if it is not one of the
parameters labeled on the bivariate axis.
Rogers (2001b:710, Figure 1) points
out the correlation between density and
utility in the beginning of his article, and it
is in fact a relationship that is well known
to zooarchaeologists (Lyman, 1984, 1985;
Grayson, 1989). However, as shown above,
this correlation is based on the density of
long bone articular ends, rather than the
maximum density per element (i.e. shaft
density). If Rogers had used the appropriate
density values in his model, it seems
unlikely that he would have found the
counter-intuitive result of a high correlation
between density and element frequency in
the absence of attrition. In another paper
(Rogers, 2000c), he argues that it makes no
difference what set of density values he
uses as long as those in the simulation fit
those in the expectation. However, this does
not take into account the fact that a
particular set of values may have a
relationship with another parameter in the
model, and thus dramatically affect the
outcome.
We agree with Rogers’ (2000a)
assessment that bivariate analyses are
somewhat limited in power. However, we
do not believe the situation is so dire that a
strong correlation between density and
representation could be produced in the
absence of attrition. It is possible that some
transport strategy might mimic the effects
of attrition (as in Rogers’ [2000a] example),
but this is not predicted by the current
models of element utility or bone transport.
The ABCML method is quite an innovative
approach to the problem of equifinality, and
may point toward a fruitful line of research.
The current drawbacks of the method,
however, go beyond the limited nature of
the models it applies. We need to be able to
distinguish transport and attrition without
forcing the results into predefined
behavioral scenarios, particularly when they
are based on such a small sample of modern
observations.
Conclusions
The ultimate goal of any skeletal element
analysis in zooarchaeological research
should be to shed light on human behavior.
Density-mediated destruction and the
closely related process of carnivore
ravaging are major stumbling blocks in this
pursuit. We suspect, given our survey of
sites for which long bone estimates include
shaft fragments, that the problem is much
more widespread than previously thought
(Lyman, 1993). To fully appreciate how
this problem shapes skeletal element
profiles, key parameters (i.e. density and
63
Distinguishing selective transport and in situ attrition
representation) must be accurately
estimated. Ignoring the importance of shaft
fragments to either of these parameters will
not “correct” for a density-mediated
pattern. Numerous studies (Bartram &
Marean, 1999; Bunn & Kroll, 1986;
Marean & Frey, 1997; Marean & Kim,
1998; Pickering et al., 2003) have shown
that estimates of long bone quantity based
on end portions alone are inadequate
measures of representation. This paper
demonstrates that the degree to which endbased MNEs are skewed can be very large,
but that the magnitude of the difference is
wholly unpredictable. Thus, one cannot
make the generalization that sites reporting
end-based MNEs (or MAUs) underestimate
long bone representation by some tractable
differential (i.e. Stiner, 2002). For the
purposes of a skeletal element analysis, the
element profiles from these sites are
proportionally inaccurate.
However, once we have applied
more reliable methods of quantification we
still have the problem of how to deal with
in situ attrition. With the exception of
forgoing the skeletal element analysis
altogether, three methodologies have been
suggested as solutions to the equifinality
problem created by overlapping attrition
and transport: the ARP, high and low
survival element sets, and the ABCML.
None of these is yet a definitive solution to
the problem. The ARP fails its primary
objective—to mitigate the effects of the
inter-element density gradient. Also,
grouping elements blurs economically
interesting differences in representation.
The ABCML is an innovative approach to
the problem of equifinality, but requires the
analyst to make prior assumptions limiting
the analytical results. All are affected by the
unique characteristics of marrow bones, and
by the fact that these elements have a
different relationship to density-mediated
destruction than other portions of the
skeleton. The distinction between the high
and low survival elements takes advantage
of this fact and, we think, is the place to
begin looking for evidence of selective
transport.
Acknowledgments
The authors thank Natalie Munro and Guy
Bar-Oz for inviting them to participate in
their symposium at the 2004 SAA
meetings, from which this paper grew. The
analysis of the Kunji and Kobeh faunal
collections was funded by NSF grant SBR9727668 to CWM, and the analysis of the
Die Kelders Cave 1 faunal collection was
funded by NSF grant SBR 9727491 to
CWM. The analysis of Mezmaiskaya was
funded by an NSF graduate fellowship, a
Fulbright Fellowship, and Wenner Gren
grant 6744 to NC.
NC thanks L.V.
Golovanova for the opportunity to work
with the Mezmaiskaya faunal assemblage.
Both authors thank Guy Bar-Oz, Lee
Lyman, Natalie Munro, and Travis
Pickering for their insightful comments,
and Charles Lockwood for developing and
sharing the bootstrap program used in the
statistical analysis.
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