. 9
( 10)


animal. Their data on amount of meat removed and number of cut marks produced
per each of eighteen butchering experiments are plotted in Figure 7.8 by taxon. There
is no signi¬cant relationship between the two variables intrataxonomically for any of
the three taxa represented (goat, r = 0.62, p = 0.19; cow, r = “0.78, p = 0.065; zebra,
r = “0.48, p = 0.34). However, there is a positive relationship between carcass size and
number of cut marks when all carcasses were included and analysis is intertaxonomic
rather than intrataxonomic (Figure 7.9, r = 0.49, p = 0.039). It matters little which
analysis is correct. The varied results highlight a critical point. Target variables must
figure 7.8. Relationships between number of cut marks and the amount of ¬‚esh removed
from six hindlimbs in each of three carcass sizes. Simple best-¬t regression lines (dashed)
are shown for each carcass size. None of the relationships is statistically signi¬cant (goat,
r = 0.62, p = 0.19; cow, r = “0.78, p = 0.065; zebra, r = “0.48, p = 0.34). Data from Table 7.6.

figure 7.9. Relationship between number of cut marks and the amount of ¬‚esh removed
from eighteen hindlimbs (r = 0.49, p = 0.039). Data from Table 7.6.

quantitative paleozoology

be explicitly de¬ned, as must measured variables and the suspected relationship
between the two. Such speci¬cations will assist with determination of the appropriate
statistical tests.
With respect to quantifying cut marks, obscure target variables and poorly under-
stood relationships between target and measured variables are not the only aspects of
the quantitative data that are recorded, analyzed, and reported that likely contribute
to the lack of resolution of the debate whether early hominids hunted large game
or merely scavenged long-dead carcasses. Lupo and O™Connell (2002:102) correctly
note that analysts report tallies of cut marked specimens (and tooth marked speci-
mens) differently. Many analysts report the number of marked specimens per portion
(e.g., proximal, distal, shaft) per skeletal element (e.g., humerus, radius, femur) (e.g.,
Dom´nguez-Rodrigo 1997; Dom´nguez-Rodrigo and Pickering 2003); a few report
± ±
the number of marked specimens per skeletal element with no distinction of por-
tion of element (e.g., Dom´nguez-Rodrigo 1999a; Oliver 1994); and a few report
the number of marked specimens per portion (proximal, shaft, distal) of skeletal
element with no distinction of skeletal element (e.g., Blumenschine 1995; Capaldo
1997). It is these sorts of ambiguities that in part prompted a ¬‚urry of rebuttals
and responses regarding interpretations of the cut mark and tooth mark data (e.g.,
Dom´nguez-Rodrigo 1999b, 2003a, 2003b; Monahan 1999; O™Connell and Lupo 2003;
O™Connell et al. 2003). A major cause of the debate has been poorly developed and
weakly warranted methods that are incompletely described “ the problem identi-
¬ed by Dom´nguez-Rodrigo “ in conjunction with poorly worded and incompletely
developed theoretically informed interpretive models “ the problem identi¬ed by
O™Connell et al. (2003). In terms used throughout this volume, measured variables
are inexplicit and have at best a poorly understood relationship to target variables.


Discussions in other arenas summarize in somewhat different terms what has been
discussed in this chapter. With respect to attributes on prey remains created by preda-
tors, Kowalewski (2002:14) states that “the frequency of traces is arguably the most
important and widely used metric in quantitative analyses of the fossil record of
predation that estimates the frequency of predator“prey interactions and may serve
as a proxy for predation intensity.” But when he describes ways to tally the frequency
of traces, he in fact suggests the number of specimens with traces be tallied and thus
correctly notes that “the number of specimens with traces of predation is not synony-
mous with the total number of traces found in those specimens unless all specimens
tallying for taphonomy 297

bear singular traces. When computing predation intensity we should always use the
number of prey specimens attacked (i.e., the number of specimens with traces) and
not the number of attacks (i.e., the number of traces)” (Kowalewski 2002:15). This is
because the target variable is predation intensity, implied by Kowalewski to comprise
the fraction of the prey population that has in fact been preyed on. Tallying numbers
of predation marks would thus not measure the frequency or intensity of predation
but rather how often a particular prey organism was attacked.
Kowalewski (2002) is concerned with organisms that have single element skeletons,
and so he notes that measuring predation intensity may require modi¬cation to
measurement techniques if skeletons of prey comprise multiple elements. This is so
for the same reason that the number of traces would not measure the intensity of
predation but rather the frequency of attacks (individual prey may be attacked more
than once). Counting predation traces on multiple but different skeletal elements
introduces the problem of interdependence “ has one attack been counted more than
once because multiple elements of a single organism have been tallied? Measuring
the intensity of carnivore gnawing, corrosion, burning, and butchering, however,
because of how “intensity” is (typically implicitly) de¬ned, requires tally of those
potentially interdependent specimens.
Among other approaches to mapping predation traces on the anatomy of the prey
skeleton, Kowalewski (2002:25) distinguishes a “qualitative approach” and a “sector
approach.” The ¬rst involves mapping each trace on a single standard skeletal ele-
ment. Although this approach precludes statistical comparison of data sets and is
subject to mapping error based on operator error and morphological and allometric
variation among specimens, it does reveal anatomical areas that may have tapho-
nomic or biological signi¬cance. It has been used by various taphonomists. The
sector approach involves partitioning the skeleton or skeletal elements into sectors
and tallying the number of traces in each. This approach allows statistical compar-
ison of data sets, such as χ 2 analysis and calculation of evenness and heterogeneity
indices. This approach too has been used by various taphonomists. At the risk of
being redundant, the approach chosen should be dictated by the research question.
Discussion in this chapter is not to resolve debates over whether early hominids
were scavengers, hunters, or acquired meat using both techniques. Rather, the goals of
the chapter have been two. First, methods used to quantify various sorts of damage to
bones “ weathering, corrosion, gnawing, burning, butchering “ have been described.
Second, the critically signi¬cant nature of the relationship between a measured vari-
able and a target variable and the critically signi¬cant fact that each variable must be
explicitly de¬ned have been highlighted. Precisely the same (then ambiguous) rela-
tionship underpinned debates in the 1950s through 1980s regarding the relationship
quantitative paleozoology

between NISP, MNI, biomass, and other measures of taxonomic abundances, and a
target variable of abundances of taxa exploited by people or abundances of taxa on
the landscape (Chapters 2 and 3). Those debates were more or less resolved in the
1980s as two things became clear. First, any measure of taxonomic abundance was
found to be at best ordinal scale (or to be an estimate), and second, the relationship
between a chosen measured variable (NISP, MNI, biomass) and the target variable
was a taphonomic question. Many paleobiologists came to both conclusions using
“¬delity studies,” actualistic research on the relationship between recently formed
assemblages of faunal remains and the accuracy with which they re¬‚ect taxonomic
abundances in the faunas from which the collections derive (see Chapter 2 for a for-
mal de¬nition of ¬delity studies). The success of these studies resides in unambiguous
de¬nitions of measured and target variables. Ambiguity with respect to measured
variables and target variables permeates many modern taphonomic studies. It is no
wonder that we do not understand the relationship between two variables when one
or more of them is poorly de¬ned or is simply inexplicit.
Final Thoughts

In this volume, some of the most basic issues of quantifying different kinds of paleo-
zoological data have been explored. A bit more than two decades ago, Grayson (1984)
published a book-length treatment on the same general topic, and that seemed to
resolve many of the debates over how to quantify taxonomic abundances. Arguments
over whether NISP or MNI was the better measure nearly ceased to appear in the
literature. Yet, some individuals continue to report MNI values, either as the unit of
choice for quantifying taxonomic abundances (e.g., Avery 1991 , 1992; Landon 1996),
or apparently for the sake of complete descriptive reporting (e.g., Plug 2004; Stahl
and Athens 2001). A few continue to develop innovative ways of tallying MNI (e.g.,
Vasileiadou et al. 2007). The usual reason given for use of MNI is that NISP is subject to
intertaxonomic variation in fragmentation and so gives potentially biased estimates
of taxonomic abundances. Although it is true that NISP can in¬‚uence estimates of
taxonomic abundance, those who use the differential fragmentation argument as a
warrant to determine MNI values neither empirically evaluate the truthfulness of
this warrant in their particular instances nor fail to present NISP data. Why do they
present what they take to be biased data? Why do they not determine if in fact frag-
mentation varies intertaxonomically rather than simply assert that it does? Perhaps
they do not because of a lack of mathematical and statistical sophistication. That lack
of sophistication is a major reason for this book.
Some have argued on the basis of ethnoarchaeological (Hudson 1990) or histori-
cal (Breitburg 1991 ) data that MNI provides more accurate estimates of taxonomic
abundances than NISP. That may well be so in particular cases where aggregation
and derivation of MNI is not dependent on analytical choices; we must make these
choices when dealing with prehistoric materials. Reitz and Wing (1999:199) state
that MNI is the “only way to compare mammals, birds, reptiles, amphibians, ¬shes,
and mollusks,” but the arguments in Chapter 2 identify the fallacious nature of
this statement. Given the continued use and advocacy of MNI, arguments made
quantitative paleozoology

by Richard Casteel and Donald Grayson regarding the nature of MNI and its sta-
tistical relationship with NISP, as well as their characterizations of MNI and NISP
as quantitative units, have been reiterated for a new generation of paleozoologists.
This is not to say that MNI is always the wrong quantitative unit to use. Both logic
and empirical data indicate, however, that it typically is the wrong unit to use when
some measure of taxonomic abundances is needed. It has been argued that MNE is
not as good a quantitative unit as NISP when one needs a measure of skeletal part
Other methods of quantifying taxonomic abundances, such as estimating biomass,
have also been reviewed. Some analysts continue to calculate meat weight using
Theodore White™s method (references in Dean 2005b). (Ornithologists still use the
Whitean method of multiplying the MNI of prey evident in a sample of egested
pellets by the average weight of an individual prey to determine biomass [Leonardi
and Dell™Arte 2006].) The skeletal mass allometry technique is quite popular in some
areas, and it continues to be used today (e.g., Carder et al. 2004; Lapham 2005;
Pavao-Zuckerman 2007). Chapter 3 of this volume was written with the express
purpose of highlighting some of the weaknesses of estimating biomass. Because
many of the quantitative variables paleozoologists seek to measure are dependent on
sample size, Chapter 4 summarizes the various ways that sample-size effects might be
detected and analytically controlled. Chapter 5 covers a central issue in paleozoology “
quantifying and comparing the structure and composition of prehistoric faunas, and
monitoring trends in taxonomic abundances. Chapter 6 provides detailed coverage
of a quantitative unit that has been extensively used over the past 20+ years “ MNE “
even though it has been around virtually as long as MNI. And Chapter 7 describes
ways to tally and analyze quantitative variables that concern taphonomic agents and
processes. What could possibly be left to discuss?
There is one thing that warrants comment. This concerns the fact that statisticians
have found it necessary to comment on quantitative paleozoology. This commen-
tary began with Ringrose™s (1993) detailed discussion that is still quite worthwhile
to read. Pilgram and Marshall (1995) pointed out that Ringrose apparently had little
experience with faunal remains, and so some of his comments were a bit off base.
Ringrose (1995) responded that although he did not in fact know very much about
the realities of paleozoology, he commented in kind that Pilgram and Marshall (1995;
Marshall and Pilgram 1991 ) seemed to not be as statistically sophisticated as he (at
least) hoped paleozoologists might be. It was with that discussion ¬rmly in mind
that I have included minimal discussion of statistics and focused on what simple sta-
tistical analyses might reveal about the quantitative properties of a paleozoological
¬nal thoughts 301

collection. In an effort to make revelations clear, graphs of statistical relationships are
included, along with various statistical results attending the graphed relationships.
And, in most cases, the data underpinning the graphs and the statistics are included
to allow the interested reader to replicate analyses graphically and statistically. Repli-
cation will assist comprehension of an analytical technique, and it ensures correct
implementation of the technique. Hopefully, readers will ¬nd utility in the many
graphs and tables.
Paleozoologists who read this volume may well hope for more, or less, statistical
sophistication. Not being a statistician, I can only reply: Read a statistics book. But
in saying that, I also want to make the observation that, like Ringrose (1993), other
statisticians have contributed to the discussions on quantitative paleozoology. And
it is clear that at least some of those statisticians are, like Ringrose, not aware of the
practical realities of quantitative paleozoology. Thus, MNE is (incorrectly) de¬ned as
“the NISP calculated for each skeletal part” (Baxter 2003:212) by a statistician. Such
errors are not restricted to those who are not paleozoologists. NISP has been said
by paleozoologists to be the Number of Identi¬ed Skeletal Parts, or the Number of
Identi¬ed Skeletal Portions, yet they do not de¬ne skeletal part or skeletal portion.
Such loose use of key terms is commonplace in many scienti¬c endeavors, but that
does not make it acceptable. Explicitly de¬ned terminology is critical to the success of
any research; such is all the more critical with respect to quantitative units, whether
fundamental or derived. That NISP has various de¬nitions (or at least descriptions)
in the literature re¬‚ects poor understanding of the term “specimen” and how it
compares with “skeletal element” and the generic “bone.” Using the de¬nitions in
Chapter 1 of this book, or a similar set of de¬nitions that are explicitly stated by the
researcher, would help the discipline a lot.
This is not a book about terminology. It is instead a book about how to count
faunal remains “ bones, teeth, shells, and fragments thereof. To reiterate, one should
read a statistics book to learn about statistics; read Quantitative Paleozoology to
learn about counting faunal remains. In so doing, and putting the two together, a
paleozoologist may well conceive of a unique analysis that reveals something about
the behavior of a quantitative unit or gain insights to some aspect of a collection of
broken bones. In most chapters, knowledge about the relationship (or lack thereof)
between a target variable and a measured variable has been emphasized. In many
cases, such knowledge is crucial to valid interpretation, but it may not always be
required. In some cases, exploratory data analysis may suggest further analyses are
necessary because of a particular relationship between two variables. The nature of
the relationship between variables may suggest other sorts of variables that need to
quantitative paleozoology

be measured in order to understand the relationship. As a way to conclude this book,
I outline an example.


Grayson (1979, 1984) suggested that analyses of the relationship between NISP and
MNI might prove revealing. Such analyses might reveal something about the par-
ticular collections studied, something about the nature of the relationship between
these two most basic counting units, or both. Recall that Klein and Cruz-Uribe (1984)
noted that their results were different than Casteel™s (1977, n.d.) with respect to the
statistical relationship they found between NISP and MNI. Because of that difference,
Klein and Cruz-Uribe suggested that perhaps the set of assemblages they had used to
examine the relationship comprised remains that were much more fragmented than
those remains in the assemblages that Casteel had used. This was an astute obser-
vation to make, but it was also one that Klein and Cruz-Uribe could not evaluate
empirically given a lack of appropriate data. They did not have NISP:MNE data for
the various skeletal elements because the data they used (and those used by Casteel)
were derived from literature that did not present that data (it was not a target variable
of the analysts). About the same time that Klein and Cruz-Uribe (1984) presented
their conclusion, Bobrowsky (1982) pointed out that Casteel (1977, n.d.) had lumped
numerous taxa together, and that such lumping masked the in¬‚uence of intertaxo-
nomic variation in the number of identi¬able elements per individual skeleton. That
is, Bobrowsky identi¬ed a cause for variation in the relationship between NISP“MNI
data pairs that was different than the cause identi¬ed by Klein and Cruz-Uribe.
In a clever bit of analysis, Bobrowsky (1982) chose one stratum from one site and
compared the relationship of NISP to MNI across four taxonomic groups (birds,
mammals, reptiles, ¬sh) represented by the remains from that single stratum. His
results indicate that indeed, intertaxonomic variation in the number of identi¬able
elements per individual skeleton signi¬cantly in¬‚uenced the slope of the best-¬t
regression line described by the model in Figure 2.4. Thus, the line describing the
relationship between the NISP and MNI data from remains of birds had a steeper
slope and higher plateau (it leveled at a higher MNI) than did the line for mammals.
Bobrowsky (1982) found this relationship between the two lines expectable given that
each bird skeleton tended to provide fewer taxonomically identi¬able elements than
did each mammal skeleton. This simply meant that each additional NISP of birds was
more likely to contribute a new MNI than was each additional NISP of mammals.
I can identify to the genus or species level about forty-¬ve to forty-eight kinds of
¬nal thoughts 303

Table 8.1. Statistical summary of relationship between NISP and MNI in collections of
paleontological birds, paleontological mammals, archaeological birds, and
archaeological mammals. p < 0.0001 in all

N of N of data Y
assemblages pairs Pearson™s r r Slope intercept
Paleontological 7 265 0.8747 0.7651 0.483
Paleontological 11 360 0.8719 0.7586 0.5581
Archaeological 22 696 0.9133 0.8342 0.629
Archaeological 35 764 0.8963 0.8034 0.5561

skeletal elements (including isolated teeth, ignoring side differences and fragments)
of a typical mammal skeleton. In a detailed study of an archaeological avifauna,
Broughton (2004) identi¬ed sixteen kinds of skeletal elements across forty-six genera.
This anecdotal information suggests Bobrowsky (1982) may have been correct.
Klein and Cruz-Uribe™s (1984) concern about fragmentation, and Bobrowsky™s
(1982) concern about intertaxonomic variation in the number of identi¬able ele-
ments per skeleton both concern variables that in¬‚uence the ratio NISP:MNI. Keep-
ing these variables in mind, I compiled NISP“MNI data pairs for paleozoological
assemblages in North America (the data to which I have the easiest access). To keep the
intertaxonomic variable simple, I compiled data for only birds and mammals. I also
compiled and kept separate data for both paleontological and archaeological avian
and mammalian assemblages. My reason for doing so was that it seemed reasonable to
suppose that remains of animals from archaeological assemblages, particularly those
of mammals but perhaps not those of birds, would be more fragmented than the
faunal remains in paleontological collections. It is, after all, well known that human
butchers tend to break bones with some regularity (e.g., Noe-Nygaard 1977; Thomas
1971 ). By de¬nition a human taphonomic agent had not in¬‚uenced paleontological
Descriptive statistics for the four sets of data are summarized in Table 8.1 . There
are several things that need to be considered here. First, graphs of the relationships
between NISP and MNI in the four assemblages indicate that the log-transformed
data describe a straight line. The straight-line relationship is apparent for the paleon-
tological bird remains (Figure 8.1 ), the paleontological mammal remains (Figure 8.2),
figure 8.1. Relationship between NISP and MNI in seven paleontological assemblages
of bird remains from North America. The number of data points is 265; not all are visible
because of duplication and overlap.

figure 8.2. Relationship between NISP and MNI in eleven paleontological assemblages
of mammal remains from North America. The number of data points is 360; not all are
visible because of duplication and overlap.
¬nal thoughts 305

figure 8.3. Relationship between NISP and MNI in twenty-two archaeological assem-
blages of bird remains from North America. The number of data points is 696; not all are
visible because of duplication and overlap.

the archaeological avian remains (Figure 8.3), and the archaeological mammal
remains (Figure 8.4). The lines plotted in these graphs should by now be familiar;
they are simple best-¬t regression lines described by the formula Y = aX b , where X
is the independent variable (log NISP), Y is the dependent variable (log MNI), a is
the Y intercept (it should be zero, given that a zero value for NISP must produce a
zero value for MNI; note that all empirically determined values are quite close to zero
[Table 8.1 ]), and b is the slope of the line. Variables a and b are constants determined
empirically for each data set. In all cases, the relationship between NISP and MNI
is statistically signi¬cant (p < 0.0001) and variation in NISP explains 76 percent or
more (= r 2 ) of the variation in MNI.
The data in Figures 8.1 “8.4 mimic the results of the data used by Bobrowsky,
Casteel, Grayson, and Hesse; a tight statistical relationship between NISP and MNI
is apparent. Clearly, MNI would seem to always increase as NISP increases. The slope
of the line (Table 8.1 ) (which measures the rate of change in MNI relative to the rate
of change in NISP) for paleontological mammals (b = 0.5581) is not signi¬cantly
different from that for archaeological mammals (b = 0.5561), but I predicted that
quantitative paleozoology

figure 8.4. Relationship between NISP and MNI in thirty-¬ve archaeological assemblages
of mammal remains from North America. The number of data points is 764; not all are visible
because of duplication and overlap.

the line for the latter would be less steep due to greater fragmentation. Perhaps
even more bizarre is the fact that the slope of the line for the paleontological birds
(b = 0.483) is less steep than that for archaeological birds (b = 0.629), suggesting that
to contribute another MNI the NISP of paleontological birds must increase more
than the NISP of archaeological birds must increase.
I am thwarted in my effort to understand why the various sets of NISP“MNI
data de¬ne the relationships that they do. This is so in part because I lack data
on fragmentation intensity and on which skeletal elements were identi¬ed for each
taxon. It is also important to note that (i) the relationship between NISP and MNI
always approximates the model described in Figure 2.4, (ii) for any given assem-
blage the relationship between NISP and MNI likely will be particularistic because
it is historically contingent (how many skeletal elements are broken and contribute
more than one NISP, and how many skeletal elements of one carcass of each taxon
are identi¬ed), and (iii) differences between the relationship of the two variables
across multiple assemblages may reveal something about the distinct nature of the
¬nal thoughts 307

assemblages. Some may be more intensively fragmented, some may have been more
thoroughly identi¬ed, and so on. Most importantly in the context of this volume, we
have learned a bit about what kinds of data are required to begin to account for how
NISP and MNI are related in any given instance.
The exploratory analysis reinforces a point I have tried to make throughout the
volume. That point simply is: be explicit in your identi¬cation of a target vari-
able, and take into account how a measured variable might, or might not, re¬‚ect
the magnitude of that target variable. Thinking about the latter likely will prompt
you to record data that you might not otherwise have recorded. In the case of
Figures 8.1 “8.4, those data might well be fragmentation (NISP:MNE ratios), deter-
mination of how many skeletal elements are identi¬able in one complete skeleton,
some other variable, or some combination of these. And that, it seems to me, is a
good reason to know about quantifying paleofaunal remains.

absolute frequency A raw tally or count of entities or phenomena (see relative
accuracy Correctness or exactness; the degree to which a measure conforms to the
true value (an estimate is less accurate than a measurement).
assemblage The entire set of faunal remains from a speci¬ed context; the context
may be arbitrarily, archaeologically, geologically, or biologically de¬ned or de¬ned
in some other way (synonym: collection).
biocoenose A living community of organisms.
closed array Quantities are given as proportions or percentages and thus must sum
to 1.0 (for proportions) or 100 percent, respectively.
community A set of organisms that live together and together form a more or less dis-
crete entity; organisms comprising a community may, or may not, be functionally
interlinked through competition or some other process (see Chapter 2).
continuous variable A variable that can take any value in a series and for which there
is yet another value intermediate between any two values.
death assemblage See thanatocoenose.
derived measurement A measurement based on multiple fundamental measure-
ments, such as a ratio of length to width.
discontinuous variable A variable for which it is possible to ¬nd two values between
which there is no intermediate value.
distal community One or more biological communities from which remains of
animals originated and which are some greater or lesser distance from the location
from which the remains were collected (after Shotwell 1955, 1958).
diversity A general term concerning any of several variables either individually or in
combination; alpha diversity, beta diversity, gamma diversity.
element See skeletal element.

estimate A description, perhaps a value, assigned to a phenomenon based on incom-
plete information (less accurate than a measurement).
estimation The act of making an estimate.
faunule An assemblage of associated animal remains recovered from one or sev-
eral contiguous strata and dominated by members of one biological community
(Tedford 1970:677).
¬at measurement A complex measurement that is conceptual or abstract and not
easily observed (synonym: proxy measurement).
¬delity studies Actualistic (experimental, ethnoarchaeological, neotaphonomic)
research aimed at determining how well a future fossil record re¬‚ects the quanti-
tative characteristics of a biological community in terms of any chosen biological
variable, including morphological classes, age classes, taxonomic richness, taxo-
nomic abundance, and trophic structure.
fundamental measurement A measurement that describes an easily observed prop-
erty or characteristic, such as length or width (see derived measurement and ¬at
identi¬ed assemblage The set of faunal remains identi¬ed to taxon and studied by
the paleozoologist, typically a fraction of the taphocoenose.
interval scale Measures greater than, less than relationships, and how much (dis-
tances between any two values are known), and has an arbitrary zero.
local fauna A set of faunal remains from one locality or several closely grouped
localities that are stratigraphically equivalent or nearly so, thus the represented
taxa are close in space and time (Tedford 1970:678).
measured variable The variable that is measured (see target variable).
measurement Writing descriptions of phenomena according to rules; speci¬cally,
the act of assigning a numerical value to an observation based on some rule(s)
of assignment (see derived measurement, ¬at measurement, fundamental mea-
surement, and proxy measurement).
MNI Minimum number of individuals (see Table 2.4).
NISP Number of identi¬ed specimens.
nominal scale Measures differences in kind, not magnitude; measurements of this
scale are sometimes referred to as qualitative attributes or discontinuous variables.
ordinal scale Measures greater than, less than relationships, but not how much.
proximal community The biological community from which remains of animals
originated and which is essentially geographically coincident with the location
from which the remains were collected (after Shotwell 1955, 1958).
proxy measurement See ¬at measurement.
quantitative variable Variables measured on interval scales and ratio scales.
glossary 311

rank order Arrangement of a set of phenomena in a series from greatest to least
magnitude, or least to greatest magnitude, but in which the distance in between
any pair of phenomena is unknown.
ratio scale Measures greater than, less than relationships, and how much (the dis-
tances between any two values are known), and has a natural zero.
relative frequency A quantity or estimate that is stated in terms of another quantity
or estimate (see absolute frequency and closed array).
reliability Replicability; repeatability; measuring something twice and obtaining the
same answer.
skeletal element A complete, discrete anatomical unit or organ, such as a bone, tooth,
or shell.
skeletalpart Same as specimen but sometimes used in this book to denote a less inclu-
sive and more restricted category, such as denoting only specimens of humerus; a
synonym used in this book is skeletal portion.
specimen A bone, tooth, or shell or fragment thereof.
taphocoenose The set of remains of organisms with a geological mode of occurrence
and found spatially and geologically associated; may be a fraction of a thanato-
taphonomy “The study of the transition (in all details) of animal remains from the
biosphere to the lithosphere” (Efremov 1940:85).
target variable The variable one is interested in and seeks to measure or estimate
(see measured variable).
thanatocoenose A set (assemblage) of dead organisms (synonym: death assemblage);
may be a fraction of a biocoenose.
validity Measurement of an attribute that re¬‚ects the concept that we wish to describe;
measuring the variable of interest rather than another variable.
variable A property or characteristic that can take on different values or magnitudes.

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