Biology Reference
In-Depth Information
research should have a background in multivariate statistics 1 and conduct a thorough
review of the original publications concerning the mathematical and theoretical aspects of
the methods. The availability of free statistical packages that conduct geometric morpho-
metric analyses may make it seem that little background knowledge is necessary; however,
this cannot be farther from the truth. In order to use geometric morphometrics, one must
have an in-depth understanding of the theoretical foundations for the suite of methods
including the assumptions and caveats associated with a particular method being employed.
As you utilize a geometric morphometric program, you will be asked to make choices that
require comprehensive knowledge about the analyses being conducted by the program.
Making theoretically and statistically sound choices require greater understanding than
can be acquired from a review chapter. Further, we will refer to several statistical concepts
herein that will not necessarily be fully explained. Many terms and concepts are included
in the glossary at the end of the topic, but some basic knowledge of statistics will be needed,
or you can refer to one of the statistical texts cited in the chapter.
For those interested in geometric morphometrics, we strongly encourage you to explore
well beyond the boundaries of this chapter and immerse yourself in the original literature.
You can also look for workshops offered periodically that provide intensive training in these
methods (see the “Conclusion and Additional Resources” section at the end of the chapter for
websites where such workshops are advertised). Finally, as this is a rapidly developing area,
one should conduct an exhaustive literature search to appreciate the current state of the field
and the way methods are being applied to various research questions.
TRADITIONAL MORPHOMETRICS
Any discussion of geometric morphometrics must start with at least a brief review of tradi-
tional morphometrics, defined by Marcus (1990) as the use of measurements or linear
distance data to quantify phenotypic variation analyzed with an array of exploratory and
confirmatory multivariate statistical methods. In biological anthropology, measurements of
lengths, breadths, and heights of anatomical structures or units were utilized for investi-
gating morphological variation among and within human groups and have dominated quan-
titative research for decades. In particular, multivariate morphometrics became popular in
studies of human skeletal biology as the objectively observed measurements were evaluated
with statistical methods that enabled the investigation of patterns of variation and the testing
of proposed hypotheses as well as evaluating the validity of those results (i.e., statistical
significance testing). Thus, morphometrics held great appeal to skeletal biologists who
desired to apply the rigor of the scientific method and to pursue research grounded in the
developing body of evolutionary theory as applied to phenotypic variation. Additionally,
the observation of osteological measurements is technologically simple and extensive data-
sets could be easily amassed for large series of human skeletal remains. The nondestructive
nature of linear measurements ensured the continuation of traditional morphometric
approaches after the passage of the Native American Graves Protection and Repatriation Act in
1 All bolded terms are defined in the glossary at the end of this volume.
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