Abstract
Over the past forty years, stable isotope analysis of bone (and tooth) collagen and hydroxyapatite has become a mainstay of archaeological and paleoanthropological reconstructions of paleodiet and paleoenvironment. Despite this method's frequent use across anthropological subdisciplines (and beyond), the present work represents the first attempt at gauging the effects of inter-laboratory variability engendered by differences in a) sample preparation, and b) analysis (instrumentation, working standards, and data calibration). Replicate analyses of a 14C-dated ancient human bone by twenty-one archaeological and paleoecological stable isotope laboratories revealed significant inter-laboratory isotopic variation for both collagen and carbonate. For bone collagen, we found a sizeable range of 1.8‰ for δ13Ccol and 1.9‰ for δ15Ncol among laboratories, but an interpretatively insignificant average pairwise difference of 0.2‰ and 0.4‰ for δ13Ccol and δ15Ncol respectively. For bone hydroxyapatite the observed range increased to a troublingly large 3.5‰ for δ13Cap and 6.7‰ for δ18Oap, with average pairwise differences of 0.6‰ for δ13Cap and a disquieting 2.0‰ for δ18Oap. In order to assess the effects of preparation versus analysis on isotopic variability among laboratories, a subset of the samples prepared by the participating laboratories were analyzed a second time on the same instrument. Based on this duplicate analysis, it was determined that roughly half of the isotopic variability among laboratories could be attributed to differences in sample preparation, with the other half resulting from differences in analysis (instrumentation, working standards, and data calibration). These findings have serious implications for choices made in the preparation and extraction of target biomolecules, the comparison of results obtained from different laboratories, and the interpretation of small differences in bone collagen and hydroxyapatite isotope values. To address the issues arising from inter-laboratory comparisons, we devise a novel measure we term the Minimum Meaningful Difference (MMD), and demonstrate its application.