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Paleodemography is the study of human demography in antiquity and prehistory. More specifically, paleodemography looks at the changes in pre-modern populations in order to determine something about the influences on the lifespan and health of earlier peoples.
Skeletal analysis can also yield information such as an estimation of age at time of death. There are numerous methods that can be used, and it is best to field questions of further interest to an osteologist or bioarchaeologist. In addition to age estimation and sex estimation, someone versed in basic osteology can ascertain a minimum number of individuals (or MNI) in cluttered contexts—such as in mass graves or an ossuary. This is important, as it is not always obvious how many bodies compose the bones sitting in a heap as they are excavated.
Occasionally, historical disease prevalnce for illnesses such as leprosy can also be determined from bone restructuring and deterioration. Paleopathology, as these investigations are called, can be useful in accurate estimation of mortality rates.
The increasing availability of DNA sequencing since the late 1990s has allowed estimates on Paleolithic effective population sizes. Such models suggest a human effective population size of the order of 10,000 individuals for the Late Pleistocene. This includes only the breeding population that produced descendants over the long term, and the actual population may have been substantially larger (in the six digits). Sherry et al. (1997) based on Alu elements estimated a roughly constant effective population size of the order of 18,000 individuals for the population of Homo ancestral to modern humans over the past one to two million years. For the time of speciation of Homo sapiens, ca. 130,000 years ago, Sjödin et al. (2012) estimate an effective population size of the order of 10,000 to 30,000 individuals, and infer an actual "census population" of early Homo sapiens of roughly 100,000 to 300,000 individuals. The authors also note that their model disfavours the assumption of an early (pre-Out-of-Africa) population bottleneck affecting all of Homo sapiens.
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- Reich, D. E.; Goldstein, D. B. (1998). "Evolution Genetic evidence for a Paleolithic human population expansion in Africa". Proc. Natl. Acad. Sci. USA. 95 (14): 8119–23. doi:10.1073/pnas.95.14.8119. PMC . PMID 9653150.
The maximum preexpansion population size for the NorthCentral African population is 6,600, the lower bound for the postexpansion population size is 8,400, and the allowed dates are between 49,000 and 640,000 years ago
- Nikolic, Natacha; Chevalet, Claude (June 2014). "Detecting past changes of effective population size". Evol. Appl. 7 (6): 663–681. doi:10.1111/eva.12170. PMC . PMID 25067949.
- Eller, Elise; Hawks, John; Relethford, John H. (2009). "Local Extinction and Recolonization, Species Effective Population Size, and Modern Human Origins". Human Biology. 81 (5-6): 805–824. doi:10.3378/027.081.0623. PMID 20504198.
The relationship between census size and effective size is complex, but arguments based on an island model of migration show that if the effective population size reflects the number of breeding individuals and the effects of population subdivision, then an effective population size of 10,000 is inconsistent with the census size of 500,000 to 1,000,000 that has been suggested by archeological evidence. However, these models have ignored the effects of population extinction and recolonization, which increase the expected variance among demes and reduce the inbreeding effective population size. Using models developed for population extinction and recolonization, we show that a large census size consistent with the multiregional model can be reconciled with an effective population size of 10,000, but genetic variation among demes must be high, reflecting low interdeme migration rates and a colonization process that involves a small number of colonists or kin-structured colonization.
- Sherry, Stephen T.; Harpending, Henry C.; Batzer, Mark A.; Stoneking, Mark (1997). "Alu Evolution in Human Populations: Using the Coalescent to Estimate Effective Population Size". Genetics. 147 (4): 1977–82. PMC . PMID 9409852.
- Sjödin, Per; Sjöstrand, Agnès E; Jakobsson, Mattias; Blum, Michael G. B. (2012). "Resequencing data provide no evidence for a human bottleneck in Africa during the penultimate glacial period". Mol Biol Evol. 29: 1851–60. doi:10.1093/molbev/mss061. PMID 22319141.
A small human effective population size, on the order of 10,000 individuals, which is smaller than the effective population size of most great apes, has been interpreted as a result of a very long history, starting ? 2 mya, of a small population size, coined as the long-necked bottle model (Harpending et al. 1998; Hawks et al. 2000). Our findings are consistent with this hypothesis, but, depending on the mutation rate, we find either an effective population size of NA = 12,000 (95% C.I. = 9,000–15,500 when averaging over all three demographic models) using the mutation rate calibrated with the human-chimp divergence or an effective population size of NA = 32,500 individuals (95% C.I. = 27,500–34,500) using the mutation rate given by whole-genome trio analysis (The 1000 Genomes Project Consortium 2010) (supplementary figure 4 and table 6, Supplementary Material online). Not surprisingly, the estimated effective mutation rates ? = 4NAµ are comparable for the two mutation rates we considered, and are equal to 1.4 × 10?3/bp/generation (95% C.I. = (1.1–1.7) × 10?3). Relating the estimated effective population size to the census population size during the Pleistocene is a difficult task because there are many factors affecting the effective population size (Charlesworth 2009). Nevertheless, based on published estimates of the ratio between effective and census population size, a comprehensive value on the order of 10% has been found by Frankham (1995). This 10% rule roughly predicts that 120,000–325,00 individuals (depending on the assumed mutation rate)
- Sjödin et al. 2012, In contrast to the bottleneck theory, we show that a simple model without any bottleneck during the penultimate ice age has the greatest statistical support compared to bottleneck models.