Paleodemography: Age Distributions from Skeletal Samples (Cambridge Studies in Biological and Evolutionary Anthropology)
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In this book, physical anthropologists, mathematical demographers, and statisticians tackle methodological issues for reconstructing demographic structure for skeletal samples. Topics discussed include how skeletal morphology is linked to chronological age, assessment of age from the skeleton, demographic models of mortality and their interpretation, and biostatistical approaches to age structure estimation from archaeological samples. This work will be of immense importance to anyone interested in paleodemography, including biological and physical anthropologists, demographers, geographers, evolutionary biologists, and statisticians.
Museum and Departmental Specimen Collection Morphology Collection 236 Japan 990 Jikei University School of Medicine Johns Hopkins Fetal Collection 30—80s All 424 317 742 940 Cadavers from NYU Medical School, Long Island Medical College, and the Cornell Medical School General and mental hospital in Siena Southern Italian Modern Chinese Cadaver donors Body donors Istituto di Anatomia 25—80 940 44 980 205 57 Institute of Legal Medicine All 36—100 Large number 9205 101
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single-trait systems Any recapitulation of current and past research traditions in skeletal biology brings to light a longstanding and strong adherence to a select few age markers. However wide the span of possible features presenting itself today, past strategies almost exclusively focused on the fusion of cranial sutures, the process of dental wear, or the metamorphosis of the pubic Historical perspectives on age indicator methods 51 Figure 4.3. Current divergence of skeletal age markers,
(7.8). It should now be clear why we mark f (a) with a subscript zero: it represents Pr(a) only if the population’s growth rate is zero — or in the profoundly unlikely event that we are dealing with a single cohort.) As shown in Appendix 7.1, this same expression applies to all the skeletons accumulated by a stable population over some more or less protracted span of time. In principle, then, we can treat f (a) as the Pr(a) P function in our likelihood (equation (7.2)) and estimate r as an