用机械和形态计量学方法估算猕猴的体重:骨骼变量测试

IF 1.7 2区 生物学 Q1 ANTHROPOLOGY American Journal of Biological Anthropology Pub Date : 2024-03-06 DOI:10.1002/ajpa.24901
Cassandra M. Turcotte, Audrey M. Choi, Jeffrey K. Spear, Eva M Hernandez-Janer, Edwin Dickinson, Hannah G. Taboada, Michala K. Stock, Catalina I. Villamil, Samuel E. Bauman, Cayo Biobank Research Unit, Melween I. Martinez, Lauren J. N. Brent, Noah Snyder-Mackler, Michael J. Montague, Michael L. Platt, Scott A. Williams, Susan C. Antón, James P. Higham
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引用次数: 0

摘要

目的:通过骨骼指标估算体重可以揭示考古或化石遗骸古生物学的重要信息。标准的方法是从颅骨后构建预测方程,但有研究质疑传统测量方法的可靠性。在此,我们研究了几种骨骼特征,以评估它们在预测体重方面的准确性:使用 115 只猕猴(雄性:n = 43;雌性:n = 72)将死前体重测量结果与同一动物死后的常见骨骼尺寸进行比较。个体被分为训练样本(n = 58)和测试样本(n = 57),通过残差平方和(RSS)和AIC权重建立和评估普通最小二乘法或多元回归。采用 "留一弃一 "的方法建立最合适的多元模型,并将其与单变量模型和以前发表的猫科动物体重估计模型进行比较:结果:股骨周长代表了最佳单变量模型。最佳模型由四个变量(股骨、胫骨和腓骨周长以及肱骨长度)组成。根据RSS和AICw,猕猴数据建立的模型(RSS = 26.91,AIC = -20.66)比猫科动物模型(RSS = 65.47,AIC = 20.24)更能预测体重:结论:由肱骨长度和后肢中轴周长组成的四变量方程最能预测猕猴的体重。通过比较猕猴和猫科动物的数据建立的模型,突出了分类特异性在预测体重中的重要性。本文提供了一个宝贵的灵长类躯体和骨骼综合数据集,可用于为零散化石证据建立体重方程。
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Mechanical and morphometric approaches to body mass estimation in rhesus macaques: A test of skeletal variables

Objectives

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.

Materials and Methods

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.

Results

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).

Conclusion

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.

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