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too are saw cuts. By the latter is meant cuts made with metal saws (e.g., Lyman 1977).
Saw cuts can be tallied in various ways, most of which are the same as the ways used to
tally cut marks, percussion marks, and chopping and scraping marks. For the sake of
simplicity, discussion is limited to percussion marks and cut marks in the following.



Tallying Butchering Evidence: General Comments

A classic statement in zooarchaeology is this: “It is quite possible to butcher an
animal of any size without leaving a single [butchering] mark on any bone” (Guilday
et al. 1962:64). This claim was reiterated at least twice more in later years (Bunn
and Kroll 1988; Crader 1983), so it is perhaps not surprising that numerous analysts
subsequently suggested various reasons why a bone might not display butchering
marks despite the fact that the portion of the carcass represented by that bone had
apparently been butchered. Shipman and Rose (1983:86) suggested that “soft tissues
have an ability to shield bones from being marked by bone or stone tools.” They found
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in an experimental context that even the periosteum (a <1 mm thick, soft-tissue
covering of bone) shields bone surfaces from cut marks (Shipman and Rose 1983:70).
Gifford-Gonzalez (1989:202) made a similar observation in an ethnoarchaeological
context. Olsen and Shipman (1988:545) argued “butchering requires a light touch to
prevent crushing and dulling the tool™s edge by contact with the bone,” and a butcher™s
desire to not dull a cutting tool would result in few butchering marks. Guilday et al.
(1962:64) thought that the probability that a bone will display a butchering mark is
a function of “the skill of the [butcher]. [Further,] the more hurried or careless the
process the greater the probability that the bone will [display a butchering mark]”
(see also Maltby 1985:22). Gilbert (1979:235) echoed this when he noted that butcher-
ing marks were likely created by “the sloppiest efforts at carcass division.” Finally,
Maltby (1985) underscores the fact that it is possible that all carcasses represented in
a collection were not butchered in like manners. All of these statements presume that
although all bones are butchered (speaking metaphorically; see next paragraph),
only some of them “ for various reasons “ in a collection will sustain butchering
marks. This presumption as yet has no empirical (actualistic) basis. Nevertheless,
when seeking to measure the “intensity” of butchering by quantifying butchering
damage, a seldom acknowledged assumption is required.
Most analysts (implicitly) assume that given some set of bones X, some subset X
of those bones will be butchered, and of those butchered bones some subset X will
sustain damage in the form of butchering marks. The critically important assumption
during analysis and interpretation, then, is that some proportion of each category of
skeletal part was butchered and some lesser proportion will display butchering marks,
and those two proportions will directly and positively covary at least at an ordinal
(but likely not a ratio) scale. I am speaking metaphorically when I say that bones are
butchered because it is actually carcasses and carcass parts that are butchered, with
the notable exception of fracturing of bones for purposes of marrow extraction and
grease rendering (e.g., Noe-Nygaard 1977). I use the metaphorical shorthand form
bones are butchered here for convenience and ef¬ciency.
A ¬ctitious example will make clear the signi¬cance of the requisite analytical
assumption. Let™s say that there were ten femora and ten humeri available for
butchery (X), and all are present in the archaeological collection we are studying. Of
those, six femora and ¬ve humeri were in fact butchered (X ); say, for example, that
¬‚esh was removed from them. Of those butchered elements, for whatever reason(s),
only four femora and two humeri display butchery marks (X ). The critical statistical
relation here is that more femora than humeri were butchered, and that more femora
than humeri display (archaeologically visible) butchery marks. Sixty percent of the
observed femora and 50 percent of the observed humeri were butchered, but in fact
tallying for taphonomy 283


only 40 percent of the observed femora and 20 percent of the observed humeri display
butchering marks. Thus we could say that femora were more intensively butchered
than humeri because proportionally more of the femora than humeri display butcher-
ing marks (where intensity concerns the amount of energy invested; more butchering
marks and more butchery marked bones are thought to signify more energy). But
if in fact six of ten femora were butchered and ¬ve of ten humeri were butchered,
but only one femur displays butchering marks and three humeri display butchering
marks, then we would be wrong to conclude that humeri were more intensively
butchered than femora (discussion derived from Lyman 1992b, 1995b). Notice that
“quantifying butchering damage” was said rather than “quantify butchering marks”
or “tally butchery marked bones.” The latter two are often used as synonymous
when in fact it should be (and will become) clear that they are quite different.
The preceding discussion focuses on tallying the number of skeletal elements that
display butchering marks. This counting procedure mimics those used to quantify
burning, corrosion, gnawing, and the like. In all cases, the tallying procedure pro-
vides data that answer the question: What proportion (or percentage) of specimens
(usually identi¬able, or NISP) display a particular kind of taphonomic modi¬cation?
However, the target variable seems, based on inferences attending observations of
percentages of butchery marked specimens, to be the intensity of butchering, which
is seldom clearly de¬ned but based on published interpretations and a few explicit
statements involves the amount of energy spent (e.g., Haynes 2002). Some analysts
have therefore worried that tallying the number of butchery marked bones does not
actually measure the target variable but something else. These individuals argue that
to measure the intensity of butchering, one needs to tally the number of butchering
marks so as to have quantitative data that actually re¬‚ect the intensity of butchering
(Abe et al. 2002; Marean et al. 2001 ). This is an important observation about the rela-
tionship (or lack thereof) between a measured variable (NISP of butchery marked
bone) and a target variable (intensity of butchering).



Tallying Percussion Damage

The morphometric criteria for identifying ¬‚ake scars and percussion damage are
spelled out in various places (e.g., Blumenschine and Selvaggio 1988, 1991 ; Capaldo
and Blumenschine 1994; Fisher 1995). Typically, zooarchaeologists have tallied the
NISP (NSP more rarely) displaying ¬‚ake scars, percussion notches, and other
percussive damage, and then calculated the proportion or percentage of NISP that dis-
plays such marks. Some individuals have tallied the number of marks (e.g., Kooyman
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2004), even though it is likely that the number of marks is at least partially a func-
tion of the number of specimens examined. Furthermore, some notches overlap
one another, and sometimes a single blow will produce a nested series of ¬‚ake scars
(Capaldo and Blumenschine 1994), although perhaps with suf¬cient training and
experience potential dif¬culties with identifying and counting ¬‚ake scars and per-
cussion marks might be minimized (Blumenschine et al. 1996). Recent experimental
work suggests that tallying percussion damage as the number of distinct marks may
be accomplished rather accurately, but the frequency of percussion marks did not
correlate with the number of hammerstone blows administered to bone specimens
in one set of experiments (Pickering and Egeland 2006). Therefore, for the present,
interpreting percussion-mark frequencies (rather than number of specimens with
percussion marks) in terms of intensity of butchering (energy invested) is precluded.
There is no empirically demonstrable relationship between the target variable and
the measured variable.
One might choose to tally the frequency of percussion-damaged specimens across
different skeletal elements of a taxon, or across a common set of skeletal elements of
several taxa. The analyst might wonder if more humeri specimens, say, display per-
cussion damage than do femora specimens of deer. Alternatively, one might wonder if
more long bones of wapiti display percussion damage than do the long bones of deer;
wapiti tend to be two to four times larger than deer. In one set of collections, I found
that deer long bones had signi¬cantly more ¬‚ake scars than did wapiti long bones, but
in another set of collections exactly the opposite situation was found (Lyman 1995b).
Why this was the case seemed to relate to the kinds of other resources that were
exploited, but lack of actualistic data linking the variables precluded straightforward
interpretation.
In sum, there are two basic ways to record percussion damage “ as the number of
damaged specimens (generally reported as %NISP that has such damage), and the
number of instances of force application manifest as individual ¬‚ake scars, percussion
notches, and the like. Explicit statement of a research question will help explicate the
target variable and an appropriate measured variable. The relationship between the
two, however, may well be unknown, and experimental work is needed in such cases
to establish that relationship.



Tallying Cut Marks and Cut Marked Specimens

The morphometric criteria for identifying cut marks are described in numerous
places (e.g., Blumenschine et al. 1996; Fisher 1995; Green¬eld 1999; Lyman 1987a;
tallying for taphonomy 285


Shipman and Rose 1983), and the identi¬cation of such marks is seldom questioned
these days. What is receiving the most analytical attention in the ¬rst decade of the
twenty-¬rst century is how to count cut marks. There are several ways that cut marks
have been tallied. Seldom is the proportion of butchery marked skeletal elements (not
specimens) determined (see Todd et al. [1997] for an example of tallying cut marked
elements). Sometimes, the %NISP that display cut marks is calculated, but that is
perhaps not a good procedure given the potential for variation in fragmentation
either across the different skeletal elements of a taxon or across different taxa (Abe
et al. 2002). What many analysts do is specify some speci¬c anatomical area or
portion, whether, say, the distal humerus or the greater trochanter of the femur (an
anatomical area or portion) or diaphysis fragment of the tibia, and then determine
how many of each of those portions display cut marks (e.g., Guilday et al. 1962; Lyman
1992b; Snyder and Klippel 2003). This assists with keeping track of the anatomical
distribution of cut marks. Are they all on diaphyseal pieces, or half on epiphyses and
half on diaphyses, and do proportionately more proximal femora have cut marks
than distal femora? Thus, if two of ¬ve distal humeri display cut marks, then one
would conclude that 40 percent of the distal humeri in the collection have butchering
marks. The fact that three of those ¬ve humeri are complete skeletal elements, one
consists of the distal end and distal one third of the diaphysis, and the ¬fth consists
of just the distal condyle is irrelevant to quantifying cut-mark data when they are
tallied by an anatomical location of relatively greater or lesser speci¬city (Lyman
1992b:250).
Other analysts tally the number of individual cut marks. For example, Milo
(1998:109) argued that, based on his own butchering experiments, “the relative effort
put into cutting in different areas is best re¬‚ected by the number of times the tool
scored the bone.” But Milo (1998) worried that differential representation of skeletal
parts would skew tallies of individual marks. To avoid this sort of problem, Bunn
(2001 ) tallied the total number of cut marks observed on each kind of skeletal ele-
ment (e.g., humeri, femora). He then divided each kind of skeletal element (he
was dealing solely with limb bones) into ¬ve, more-or-less equal-sized areas: prox-
imal end, proximal shaft, midshaft, distal shaft, and distal end. The underpinning
assumption to the ¬ve areas is that cut marks near the ends of long bones likely have
something to do with disarticulation whereas those on shafts result from de¬‚eshing.
Finally, Bunn determined the percentage of all cut marks per kind of skeletal element
that occurred in each of the ¬ve areas. This is indeed one way to contend with the
differential representation of skeletal parts.
There are other ways that the number of cut marks might be tallied and analyzed,
but a potentially signi¬cant problem attends any such tallying, regardless of how
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286


those tallies are mapped on anatomy or analyzed. If each individual cut mark or
single striation is to be tallied, then they must somehow be distinguished for tallying
purposes. The problem arises when cut marks overlap. If, for example, a sawing like
motion is used “ the cutting tool™s edge drags across the bone surface on both push
and pull strokes that overlay each other “ then strokes producing striae made later
in the sequence of strokes may obliterate or at least obscure striae made by earlier
strokes. There is no experimental work that evaluates this possibility, and no one
has assessed a paleozoologist™s ability to accurately count individual cut marks. But
this may be the least of our concerns. For now, however, let us assume that we can
tally the number of individual cut marks (each representing a distinct arm stroke).
How, then, might we obtain counts of cut marks as opposed to counts of cut marked
specimens or tallies of cut marked anatomical areas?



The Surface Area Solution

Recently, the argument has been made that variation in the representation of sur-
face area is relevant to tallying frequencies of cut marks. Abe et al. (2002:650) are
concerned about what they refer to as the “fragmentation dilemma.” In particular,
they are worried that “fragmentation generally decreases the number of cut marked
fragments and cut mark counts relative to total fragments” (Abe et al. 2002:649).
Fragmentation generally means breakage such that what was a single discrete object
after fragmentation comprises multiple discrete objects (Lyman 1994c:509). Frag-
mentation destroys the original integrity of a discrete object, but the material or
substance comprising that discrete object still exists and, importantly, some of the
original integrity of the discrete object may also remain. Destruction generally means
the complete loss of the original integrity of the object, such as when the specimen
is crushed into dust or into pieces that are so small that they cannot be identi¬ed
and thus are analytically invisible. Abe et al. (2002:649) state “the fragmentation pro-
cess moves fragments into the unidenti¬able category and destroys less-dense bone
altogether.” They have in mind extreme fragmentation or destruction in the sense
that the specimen is analytically invisible. This process was recognized long ago by
Watson (1972; see also Lyman and O™Brien 1987). Less extreme fragmentation, such
as when a bone specimen is broken into two or three pieces that can be identi¬ed
to skeletal portion, may increase the number of cut marked specimens if a fracture
plane truncates a cut mark such that half of the mark occurs on one specimen and
the other half occurs on another specimen. Re¬tting specimens is the only way to
correct for this.
tallying for taphonomy 287


Given their concern about the destruction of cut marks, Abe et al. (2002:650)
suggest “the likelihood of a cut mark being preserved and counted by an analyst
is a function of the amount of bone surface area studied and recorded.” This is
commonsensical “ the more surface area examined, the more cut marks will be
found. Abe et al. (2002:650) use this observation, however, to argue that (i) because
more cut marks will be found if more surface area is examined, (ii) if we determine
the density of cut marks per unit of surface area in an anatomical region, (iii) then “we
can correct the number of cut marks by the amount of examined surface area, much
as demographers standardize population size by estimating population density.” In
particular, they assume that if half (50 percent) of the potential surface area of an
anatomical region has been examined, and ten cut marks have been tallied, then were
100 percent of the surface area of that region examined, twenty cut marks would be
tallied. They are assuming that the density of cut marks on an observed sample is
the density of cut marks on the unobserved remainder of the population. They are
assuming precisely what they are trying to discover “ the original (predestruction)
frequency of cut marks (see also Lyman 2005b).
Abe et al. (2002) are trying to take advantage of the visibility of one variable “
frequencies of cut marks on observable bone surface area “ in order to measure a
variable that is invisible “ cut mark frequencies on missing or destroyed bone surfaces.
There are a plethora of problems with this procedure. The ¬rst problem is that one
must decide what comprises the sample of specimens to be examined for any given
analysis, and thus one must decide how to de¬ne an aggregate of remains. If different
specimens are included in a sample, different results are likely to attend analysis of cut
mark frequencies. The second problem concerns how to de¬ne anatomical regions
for which the amount of observed surface area will be determined. The proportion
of observed surface area is based on the maximum MNE for any given anatomical
region (e.g., proximal end, proximal shaft, mid shaft). Thus, if there is evidence of ten
distal humeri (= MNEmax), then there should be ten proximal humeri represented
even if only two are observed. But, are proximal ends and distal ends, proximal shafts
and distal shafts and mid shafts, such as proposed by Abe et al. (2002), appropriate
tallying units? That is presently unclear.
The third problem is that the analytical procedure ignores the historically con-
tingent nature of butchering episodes (Lyman 1987a, 2005b). It is easy to show that
even in experimentally controlled situations, for reasons that are unclear, there is a
tremendous range of variation in the frequency of cut marks generated in any given
butchering episode. Consider the experimental data generated by Pobiner and Braun
(2005) and summarized in Table 7.4. Those data are the number of cut marks gener-
ated during the de¬‚eshing of six goat hindlimbs (femora and tibiae). Each hindlimb
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288


Table 7.4. Frequencies of cut marks per anatomical area on six experimentally
butchered goat (Capra hircus) hindlimbs. %FR, amount of ¬‚esh removed from
femur prior to butchering. N-CM, number of cut marks; P, proximal end; PS,
proximal shaft; MS, mid shaft; DS, distal shaft; D, distal end. Data from
Pobiner and Braun (2005)

N-CM N-CM N-CM N-CM N-CM
Limb Element % FR P PS MS DS D
1 Femur 50 0 0 13 15 0
2 Femur 50 0 3 11 0 0
3 Femur 25 0 1 8 3 0
4 Femur 25 0 0 0 5 3
5 Femur 0 0 5 21 2 0
6 Femur 0 22 6 6 19 0
1 Tibia 0 0 0 0 0 0
2 Tibia 0 0 2 0 5 0
3 Tibia 0 0 2 0 0 0
4 Tibia 0 0 7 13 0 0
5 Tibia 0 0 20 3 0 0
6 Tibia 0 0 0 0 10 0




was de¬‚eshed independently of every other hindlimb, and although the amount of
¬‚esh on the femur varied when each butchery event began, nothing else did. If Abe
et al. (2002) are correct that the observed density of cut marks (frequency per unit
area) can be used to estimate the frequency of cut marks that have been destroyed,
then there should be minimal variation in the number of cut marks per anatomical
region described in Table 7.4, given that those anatomical regions are identical from
specimen to specimen in terms of surface area.
The data in Table 7.4 indicate that there is a great deal of variation in the density of
cut marks, or the number of cut marks per unit of surface area even when long bones
are treated as comprising ¬ve distinct regions (proximal and distal ends, proximal
and distal shafts, mid shaft). And this is so regardless of whether the amount of
meat on a bone was similar from case to case or was different from case to case.
Frequencies of cut marks in a given region on individual femurs range from zero to
twenty-two (proximal femur), and on individual regions of tibiae they range from
zero to twenty (proximal shaft). Given that the amount of surface area of, say, the
proximal tibia shaft does not vary signi¬cantly across the six specimens, following
Abe et al.™s (2002) suggested procedure, were only the proximal shaft of tibia ¬ve
tallying for taphonomy 289


recovered, its twenty cut marks would suggest that there were twenty cut marks on
each of the other missing proximal shafts of tibia (based on an MNE of six total
recovered distal tibiae). Data in Table 7.4 indicate that such an inference is incorrect.
The fourth problem that attends determination of the number of missing cut
marks based on observable frequencies of cut marks per unit of surface area is that it
is not at all clear what the visible frequencies of cut marks are measuring. Abe et al.
(2002:657) state that a “key assumption that all zooarchaeologists make is that more
intensive cutting (more cutting actions) results in higher frequencies of cutmarks
on the bone surface.” This is indeed a key assumption. Given that creating two cut
marks requires two arm strokes, but creating one cut mark requires one arm stroke,
it is likely that what most analysts mean by intensity is number of arm strokes. The
analytical assumption in a paleozoological context, then, must be that as the number
of arm strokes or slices increases, so too does the number of cut marks created.
Unfortunately, experiments by Egeland (2003) indicate that there is no relationship
between the number of arm strokes used to butcher limbs of large mammals and the
number of cut marks that are generated.
Egeland (2003) butchered sixteen partial and complete limbs (fore and hind) of
domestic cows (Bos taurus) and domestic horses (Equus caballus). Stone tools were
used to remove ¬‚esh, arm strokes aimed at ¬‚esh removal were tallied, and the amount
of ¬‚esh removed was recorded (Table 7.5). There is neither a statistically signi¬cant
relationship between the number of arm strokes and the number of cut marks created
across the ten multiskeletal element limbs Egeland butchered (r = “0.206, p = 0.52),
nor is there a statistically signi¬cant relationship between the number of arm strokes
and the number of cut marks created across the 31 individual skeletal elements
Egeland butchered (r = “0.20, p = 0.28). These results do not change if the data are
log-transformed (Figure 7.5). This means that when we tally cut marks, we cannot
conclude that more cut marks on skeletal parts comprising the ankle joint than on
skeletal parts comprising the wrist joint means that the ankle was more intensively
butchered than the wrist. There is no actualistic research indicating the validity of the
relationship between the two variables (measured = number of cut marks; target =
number of arm strokes or intensity) and there are actualistic data (Egeland™s) which
show that at least some times there is no such relationship at all.
In sum, then, the surface area solution proposed by Abe et al. (2002), although
perhaps solving various problems that attend tallying the number of specimens that
have cut marks, introduces problems of its own. It is dependent on the aggregate of
specimens included, it is dependent on how skeletal regions are de¬ned, it ignores
the historically contingent and variable process of butchering, and one ultimately
assumes what one is trying to ascertain. The last is so because the analytical protocol
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290


Table 7.5. Frequencies of arm strokes and cut marks on sixteen limbs of cows and
horses. Number in ¬rst column identi¬es the unique butchering episode. Data
from Egeland (2003)

N of Cut Meat Removed
Limb/Element Taxon N of Strokes Marks (kg)

1 hindlimb cow 3747 11
2 forelimb/scapula horse 535 8 8.60
2 forelimb/humerus horse 877 7 7.10
2 forelimb/radius-ulna horse 525 44 2.80
3 hindlimb/tibia horse 577 8 3.40
4 hindlimb/tibia horse 582 14 2.50
5 hindlimb/tibia horse 594 1 3.80
6 hindlimb/tibia horse 202 1 0.50
7 hindlimb/femur horse 2155 3 25.7
7 hindlimb/tibia horse 420 29 4.5
8 hindlimb/femur horse 1757 0 17.5
8 hindlimb/tibia horse 650 2 2.9
11 hindlimb/femur cow 687 31 17.4
11 hindlimb/tibia cow 715 22 4.1
12 forelimb/scapula cow 395 5 6.1
12 forelimb/humerus cow 371 7 4.8
12 forelimb/radius-ulna cow 362 8 1.9
13 forelimb/scapula horse 739 26 3.1
13 forelimb/humerus horse 1124 9 4.4
13 forelimb/radius-ulna horse 586 4 2.1
14 forelimb/scapula horse 5397 13 8.4
14 forelimb/humerus horse 2265 17 5.5
14 forelimb/radius-ulna horse 2080 9 2.4
15 forelimb/scapula cow 986 0 3.0
15 forelimb/humerus cow 532 0 6.3
15 forelimb/radius-ulna cow 951 0 2.3
19 forelimb/scapula cow 148 20 1.1
19 forelimb/radius-ulna cow 178 33 0.7
21 forelimb/scapula cow 596 31 5.2
21 forelimb/radius-ulna cow 695 31 2.4
22 forelimb/scapula cow 502 107 5.8
22 forelimb/radius-ulna cow 877 29 2.3
tallying for taphonomy 291




figure 7.5. Relationship between number of arm strokes and number of cut marks on
thirty-one skeletal elements (r = “0.235, p = 0.2). Data from Table 7.5.


demands the assumption that a sample of bone surface area gives an accurate estimate
of the density of cut marks across the total (population™s) surface area of bones,
whether those bones are present, destroyed, or not collected. This might be so if

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