Paleodemographic analysis of age at death for a population of Black Sea Scythians: An exploration by using Bayesian methods

Abstract

Objectives: Studies of the demography of past populations involving deterministic life tables can be criticized for ignoring the errors of estimation. Bayesian methods offer an alternative, by focusing on the uncertainty of the estimates, although their results are often sensitive to the choice of prior distributions. The aim of this study is to explore a range of Bayesian methods for estimating age at death for a population of nomadic warriors-Scythians from the Black Sea region. Materials and methods: In total, skeletons of 312 individuals (93 children and 219 adults) from Glinoe (Moldova), dated to the 5th-2nd century BCE, were examined. We unified the age categories corresponding to different aging methods, allowing an application of a probabilistic assessment of the age categorization. A hierarchical Bayesian multinomial-Dirichlet-Dirichlet model was applied, with a hypothetical, subjective reference population, a real reference population, and no reference. Results: Stationary-population life expectancy was estimated as 27.7 years (95% CI: 25.1-30.3) for a newborn (e 0), and 16.4 years (14.0-19.0) for 20-year-olds (e 20), although with high uncertainty, and sensitive to the model specification. Slight differences in longevity between different social strata and between the Classical and Late chronological periods were found, although with high estimation errors. A more robust finding, confirming earlier studies, was a high probability of death in young adulthood, which could depend on Scythian lifestyle (conflicts, wars). Discussion: Our study shows a way to overcome some limitations of broad age categorization by using the Bayesian approach with alternative model specifications, allowing to assess the impact of reference populations.

Paleodemography: From archaeology and skeletal age estimation to life in the past

“…Model life tables have been used by Gowland and Chamberlain (2005) and Gowland (2007), as have historical records (Godde et al, 2020; Godde & Hens, 2021) and modern, historical, and anthropological populations characterized through Gompertz parameters (Sasaki & Kondo, 2016a). An approach that is gaining traction is the use of a combined sample of many populations regarded as representative of preindustrial societies (Caussinus & Corgeau, 2010; Łukasik et al, 2021; Séguy et al, 2008, 2013; Séguy & Buchet, 2013). What would be best as an informative prior is the age‐at‐death distribution one is striving to obtain when examining skeletons in the first place to avoid age mimicry (Hoppa & Vaupel, 2002b; Konigsberg & Frankenberg, 1992; Love & Müller, 2002).…”

Twenty‐first century bioarchaeology: Taking stock and moving forward

“…There are numerous examples of Bayesian methods and inference in bioarchaeological research, including for the purposes of estimation of individual skeletal ages and paleodemographic analyses (Boldsen et al, 2002; Coqueugniot et al, 2010; DiGangi et al, 2009; Godde & Hens, 2012, 2021; Godde et al, 2020; Gowland & Chamberlain, 2002; Konigsberg & Frankenberg, 1992; Łukasik et al, 2021; Müller et al, 2002; Nagaoka & Hirata, 2007; Sasaki & Kondo, 2016; Séguy et al, 2013); estimation of sex (Konigsberg & Hens, 1998), ancestry (Rathmann et al, 2019), and stature (Konigsberg et al, 1998); paleopathological diagnosis (Boldsen, 2007; Byers & Roberts, 2003); evaluation of antemortem tooth loss (Gilmore, 2013); and dietary reconstruction (Arcini et al, 2014; Chinique de Armas et al, 2017; Stantis et al, 2020). Although many scholars may recognize the value of the analytical rigor that Bayesian approaches represent and their advantages over frequentist approaches and classical hypothesis testing, some Workshop participants suggested that a greater number of bioarchaeologists (particularly in US contexts, as Bayesian approaches are used more commonly elsewhere) may be more strongly compelled to adopt them if more bioarchaeological studies were published that explicitly demonstrate their practical utility with respect to the questions addressed (similar to Konigsberg & Frankenberg, 2013).…”

Plant-Based Support of Respiratory Health during Viral Outbreaks

“…People’s health and well-being have always been negatively affected by low life expectancy, high child and young adult mortality, high interpersonal violence, and high burden of diseases generally recognized as infections . Mortality associated with known epidemics in human history was often selective with pre-existing frail health conditions and socioeconomic inequalities, but both wealthy and poor alike utilized a plethora of foods, plants, and local knowledge of their processing and admixture in efforts to improve overall health outcomes .…”