Human stature in the Near East and Europe ca. 10,000–1000 BC: its spatiotemporal development in a Bayesian errors-in-variables model

Abstract

A sample of 6098 published prehistoric skeletons consisting of long bone lengths, stature estimated from them using three different methods, as well as recalculated stature data created with other methods, was used to model tempo-spatial variance of stature in the Holocene prehistory of the Near East and Europe. Bayesian additive mixed modeling with errors-in-variables was applied, fitting a global spatiotemporal trend using a tensor product spline approach, a local random effect for the archaeological sites and corrections for mismeasurement and misclassification of covariates to obtain stature isoline maps for various time slices and diachronic stature trend curves for various regions. Models calculated for maximum long bone lengths and for stature are all largely consistent with each other, so Bayesian errors-in-variables models can be regarded as a viable means of smoothing regional and temporal variance in skeletal data as well as in estimation methods so that only robust trends become manifest. In addition to a general northwestsoutheast gradient in stature, tallest stature in Eurasia and declining stature in Iberia confirms archaeogenetic insights. Transition to farming shows stable, decreasing, or even increasing stature depending on the region and the mode of Neolithization, putting into question the common assumption of a general negative effect of Neolithic lifeways on physical health. Particularly, Northern Europe experienced a rise in stature after the 4th millennium BC. Likely caused by both genetics as well as generally improving living conditions, our findings date the origin of the modern NW-SE gradient in stature to around 3000 BC.

Genetic contributions to variation in human stature in prehistoric Europe

“…North-South trends following Allen’s (62) and Bergmann’s (63) rules are most often interpreted as environmental adaptations to the polar-equatorial climate gradient. Today, Northern Europeans are generally taller than Southern Europeans (1), a pattern which emerged between the Mesolithic and post-Neolithic (4, 7). Longitudinal variation within Europe is present during the Mesolithic (64), though these trends are difficult to interpret due to sampling bias across the time period (4).…”

“…Whether this increase was driven by selection in the ancestors of these populations remains unresolved. The geographic gradient of increasing skeletal stature is unclear in the Paleolithic, largely West-East in the Mesolithic (7, 64) and largely South-North by the Bronze Age (4, 7, 9). Latitudinal, but not longitudinal, patterns are qualitatively consistent with geographic patterns in PRS suggesting that, like temporal variation, both genetics and environment contribute to geographic variation.…”

“…The earliest anatomically modern humans in Europe, present by 42-45,000 BP (5, 6), were relatively tall (mean adult male height in the Early Upper Paleolithic was ~174 cm). Mean male stature then declined from the Paleolithic to the Mesolithic (~164 cm) before increasing to ~167 cm by the Bronze Age (4, 7). Height can respond rapidly in a developmentally plastic manner to changes in environment, as demonstrated by large increases in Europe, and worldwide, during the secular trends of the 19 th and 20 th centuries (1, 4).…”

“…It is broadly agreed that prehistoric changes were likely to have been driven by a combination of environmental (e.g. climate or diet) and genetic factors including drift, admixture and selection (4, 7–9), although the effects of these variables cannot be separated based on skeletal data alone. In this study, by combining the results of genome-wide association studies (GWAS) with ancient DNA, we directly estimate the genetic component of stature and test whether population-level skeletal changes between ~35,000 and 1,000 BP are consistent with those predicted by genetics.…”

Economic Diversification Supported the Growth of Mongolia’s Nomadic Empires

“…It was assumed that the enamel δ 13 C signal is defined by the dietary carbon mix 50 . To extrapolate the spatial distribution of per capita millet caloric intakes (dietscape) a Bayesian additive mixed model with error-in variables [83][84][85] available as an online app via the Pandora & IsoMemo initiatives was employed 86 . Dietscapes were generated for two main periods corresponding to a temporal divide defined by the intensification of millet consumption as observed from the interpretation of raw isotopic data, into Early (Bronze Age) and Late (combining the Early Iron Age; Xiongnu; Mongol periods).…”