Biology Reference
In-Depth Information
that utilizing Kerley's osteon count method in the entire cross-section of the fibula produces
the best results. Support for Kerley's methodology is echoed in more recent literature such as
Robling and Stout (2008: 164)
, who stated, “Age estimates determined by averaging the esti-
mates for all Kerley predicting formulas produced the greatest accuracy and reliability for all
age classes.”
Not only are a multitude of variables considered in current bone histological methods,
multiple skeletal elements have also been of interest. These include the femur (
Kerley,
1965
; Ahlqvist and Damsten, 1969;
Singh and Gunberg, 1970; Thompson, 1979; Ericksen,
1991
), tibia (
Kerley, 1965; Singh and Gunberg, 1970
), fibula (
Kerley, 1965
), humerus
(
Thompson, 1979; Yoshino et al., 1994
), ulna (
Thompson, 1979
), clavicle (
Stout and Paine,
1992; Stout et al., 1996
), mandible (
Singh and Gunberg, 1970
), ribs (
Stout, 1986; Stout and
Paine, 1992; Stout et al., 1996; Cho et al., 2002; Kim et al., 2007
), and neurocranium (
Clarke,
1987; Cool et al., 1995; Curtis, 2003; Trammell, 2012
).
Biological Profile and Sex Estimation
It is important that the skeletal biologist consider both intrinsic and extrinsic factors that
affect age and how these may subsequently affect histological aging methods. One key vari-
able to consider is sex.
Kerley (1965)
and
Stout and Paine (1992)
indicate that differences in
sex have very little to no effect on age-at-death estimation techniques. Other researchers,
however, have noticed significant differences between male and female histomorphometrics
(
Thompson, 1980; Samson and Branigan, 1987; Ericksen, 1991
).
Ericksen (1991)
developed linear regression equations separately for males and females as
well as for the sexes combined and found that the number of secondary osteons and
secondary osteon fragments differs between males and females and, because of these find-
ings, emphasized the importance of using sex-specific analyses (
Ericksen, 1991
).
Thompson
(1980)
indicates that with increasing age, females experience a much more significant decline
in cortical thickness and bone mineral density than do males. Females also experience more
significant age-related changes in Haversian canal areas than males (
Thompson, 1980
).
Since some findings suggest that males and females undergo bone remodeling at different
rates, this lends credibility to the potential utility of histomorphometrics to estimate sex.
Though past histological analyses have not particularly focused on estimating sex as part
of the biological profile, recent research (
Trammell, 2012
) indicates the promising potential
of using the microstructure itself to classify individuals as males or females using a discrim-
inant function analysis. When histomorphometric variables (such as secondary osteon and
Haversian canal area and perimeters and minimum and maximum secondary osteon and
Haversian canal diameters) from the frontal, parietal, and temporal are considered in
a single analysis, the discriminant function is very accurate at classifying males and
females
d
90% correct classification for males and 80% correct classification for females
(
Trammell, 2012
).
Nutrition and Pathology
Microstructural analysis of bone can discern pathology as well as signs of nutritional and
metabolic stress (
Jowsey, 1977; Martin and Burr, 1989; Bell, 1990; Ericksen et al., 1994; Pfeiffer,
