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Abstract：
Estimation of body mass from skeletal metrics can reveal important insights into the paleobiology of archeological or fossil remains. The standard approach constructs predictive equations from postcrania, but studies have questioned the reliability of traditional measures. Here, we examine several skeletal features to assess their accuracy in predicting body mass.
Antemortem mass measurements were compared with common skeletal dimensions from the same animals postmortem, using 115 rhesus macaques (male: n = 43; female: n = 72). Individuals were divided into training (n = 58) and test samples (n = 57) to build and assess Ordinary Least Squares or multivariate regressions by residual sum of squares (RSS) and AIC weights. A leave-one-out approach was implemented to formulate the best fit multivariate models, which were compared against a univariate and a previously published catarrhine body-mass estimation model.
Femur circumference represented the best univariate model. The best model overall was composed of four variables (femur, tibia and fibula circumference and humerus length). By RSS and AICw, models built from rhesus macaque data (RSS = 26.91, AIC = -20.66) better predicted body mass than did the catarrhine model (RSS = 65.47, AIC = 20.24).
Body mass in rhesus macaques is best predicted by a 4-variable equation composed of humerus length and hind limb midshaft circumferences. Comparison of models built from the macaque versus the catarrhine data highlight the importance of taxonomic specificity in predicting body mass. This paper provides a valuable dataset of combined somatic and skeletal data in a primate, which can be used to build body mass equations for fragmentary fossil evidence.
Antemortem mass measurements were compared with common skeletal dimensions from the same animals postmortem, using 115 rhesus macaques (male: n = 43; female: n = 72). Individuals were divided into training (n = 58) and test samples (n = 57) to build and assess Ordinary Least Squares or multivariate regressions by residual sum of squares (RSS) and AIC weights. A leave-one-out approach was implemented to formulate the best fit multivariate models, which were compared against a univariate and a previously published catarrhine body-mass estimation model.
Femur circumference represented the best univariate model. The best model overall was composed of four variables (femur, tibia and fibula circumference and humerus length). By RSS and AICw, models built from rhesus macaque data (RSS = 26.91, AIC = -20.66) better predicted body mass than did the catarrhine model (RSS = 65.47, AIC = 20.24).
Body mass in rhesus macaques is best predicted by a 4-variable equation composed of humerus length and hind limb midshaft circumferences. Comparison of models built from the macaque versus the catarrhine data highlight the importance of taxonomic specificity in predicting body mass. This paper provides a valuable dataset of combined somatic and skeletal data in a primate, which can be used to build body mass equations for fragmentary fossil evidence.
摘要：
目的：根据骨骼指标估算体重可以揭示考古或化石遗迹古生物学的重要见解。标准方法从颅骨后构造预测方程，但是研究质疑传统措施的可靠性。这里,我们检查了几种骨骼特征，以评估其预测体重的准确性。
方法：将死前质量测量值与相同动物死后的常见骨骼尺寸进行比较，使用115只恒河猴（雄性：n=43；雌性：n=72）。将个体分为训练样本（n=58）和测试样本（n=57），以通过残差平方和（RSS）和AIC权重来构建和评估普通最小二乘或多元回归。实施了留一法来制定最佳拟合的多元模型，将其与单变量和先前发表的卡他林体重估计模型进行比较。
结果：股骨周长代表最佳单变量模型。最佳模型总体上由四个变量组成(股骨，胫骨和腓骨周长和肱骨长度）。通过RSS和AICW，根据恒河猴数据建立的模型（RSS=26.91，AIC=-20.66）比卡他林模型（RSS=65.47，AIC=20.24）更好地预测体重。
结论：恒河猴的体重最好通过由肱骨长度和后肢中轴周长组成的4变量方程来预测。从猕猴和卡他林数据建立的模型的比较突出了分类特异性在预测体重方面的重要性。本文提供了灵长类动物体细胞和骨骼数据的有价值的数据集，可用于建立零碎化石证据的人体质量方程。
方法：将死前质量测量值与相同动物死后的常见骨骼尺寸进行比较，使用115只恒河猴（雄性：n=43；雌性：n=72）。将个体分为训练样本（n=58）和测试样本（n=57），以通过残差平方和（RSS）和AIC权重来构建和评估普通最小二乘或多元回归。实施了留一法来制定最佳拟合的多元模型，将其与单变量和先前发表的卡他林体重估计模型进行比较。
结果：股骨周长代表最佳单变量模型。最佳模型总体上由四个变量组成(股骨，胫骨和腓骨周长和肱骨长度）。通过RSS和AICW，根据恒河猴数据建立的模型（RSS=26.91，AIC=-20.66）比卡他林模型（RSS=65.47，AIC=20.24）更好地预测体重。
结论：恒河猴的体重最好通过由肱骨长度和后肢中轴周长组成的4变量方程来预测。从猕猴和卡他林数据建立的模型的比较突出了分类特异性在预测体重方面的重要性。本文提供了灵长类动物体细胞和骨骼数据的有价值的数据集，可用于建立零碎化石证据的人体质量方程。
