Right here t-assessment because the a test out-of differences in function among burial items, age range and you will genders were used

Normality and homogeneity of variances were checked by means of Levene’s and Shapiro–Wilk tests, respectively. Potential food sources can be grouped according to ecological, archaeological and isotopic relevance. Food resources were categorized into five food groups: terrestrial herbivores (i.e., deer, wild boar, hare), berjack), game birds (i.e., pheasants, bustard, wild goose, swan), C3 (i.e., rice, wheat, soybean), and Ccuatro (foxtail millet, broomcorn millet, barnyard millet) plants. In this study, we were not able to separate the livestock (horse, dog, cattle, pig) from the game birds due to their isotopic similarity. Thus, we combined the livestock with the game birds according to isotopic relevance. Isotopic data of three food groups (game birds, marine animals, terrestrial herbivores) derived from the Imdang burials and other archaeological sites in South Korea (Nukdo, Tongsamdong, Ando, Dongnae) 52,53,54,59 and two plant groups from archaeological and modern isotopic data in South Korea and East Asia were used 64,65,66,67,68 . We used univariate analysis of variance (ANOVA) to evaluate possible differences in isotopic values among the five food groups (C3 plant, e birds, terrestrial herbivores, C4 plant). A post hoc Turkey’ multiple test was used to evaluate differences between each food group and the Imdang humans. A MixSIAR model was used to estimate the proportional contribution of each food group to each individual, providing an estimate of the variability in the estimated proportion 35 . We incorporated the ? 13 C and ? 15 N values of the humans, the five food groups as well as trophic discrimination factors (C3 plant: ? 13 C = 5.2‰ and ? 15 N = 3.8‰; C4 plant: ? 13 C = 4.5‰ and ? 15 N = 3.8‰; animal: ? 13 C = 1‰ and ? 15 N = 3.8‰) to predict accurately the dietary proportions 33,34,69 . The level of significance was set at P ? 0.05. All statistical tests were performed using an R statistical program.

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