Morphometric panel regression equations for predicting body mass in immature humans

Using BayesModelS to provide Bayesian- and phylogenetically-informed primate body mass predictions

An accurate prediction of the body mass of an extinct species can greatly inform the reconstruction of that species' ecology. Therefore, paleontologists frequently predict the body mass of extinct taxa from fossilized materials, particularly dental dimensions. Body mass prediction has traditionally been performed in a frequentist statistical framework, and accounting for phylogenetic relationships while calibrating prediction models has only recently become more commonplace. In this article, we apply BayesModelS-a phylogenetically informed Bayesian prediction method-to predict body mass in a sample of 49 euarchontan species (24 strepsirrhines, 20 platyrrhines, 3 tarsiids, 1 dermopteran, and 1 scandentian) and compare this approach's body mass prediction accuracy with other commonly used techniques, namely ordinary least squares, phylogenetic generalized least squares, and phylogenetic independent contrasts (PICs). When predicting the body masses of extant euarchontans from dental and postcranial variables, BayesModelS and PICs have substantially higher predictive accuracy than ordinary least squares and phylogenetic generalized least squares. The improved performances of BayesModelS and PIC are most evident for dentally derived body mass proxies or when body mass proxies have high degrees of phylogenetic covariance. Predicted values generated by BayesModelS and PIC methods also show less variance across body mass proxies when applied to the Eocene adapiform Notharctus tenebrosus. These more explicitly phylogenetically based methods should prove useful for predicting body mass in a paleontological context, and we provide executive scripts for both BayesModelS and PIC to increase ease of application.

Allometric scaling and growth: Evaluation and applications in subadult body mass estimation

OBJECTIVES

Previously developed methods in subadult body mass estimation have not been tested in populations other than European-American or African-American. This study uses a contemporary Taiwanese sample to test these methods. Through evaluating their accuracy and bias, we addressed whether the allometric relationships between body mass and skeletal traits commonly used in subadult body mass estimation are conserved among different populations.

MATERIALS AND METHODS

Computed tomography scans of lower limbs from individuals aged 0-17 years old of both sexes were collected from National Taiwan University Hospital along with documented body weight. Polar second moment of area, distal femoral metaphyseal breadth, and maximum superior/inferior femoral head diameter were collected either directly from the scans or from reconstructed 3D models. Estimated body mass was compared with documented body mass to assess the performance of the equations.

RESULTS

Current methods provided good body mass estimates in Taiwanese individuals, with accuracy and bias similar to those reported in other validation studies. A tendency for increasing error with increasing age was observed for all methods. Reduced major axis regression showed the allometric relationships between different skeletal traits and body mass across different age categories can all be summarized using a common fitted line. A revised, maximum likelihood-based approach was proposed for all skeletal traits.

DISCUSSION

The results suggested that the allometric relationships between body mass and different skeletal traits are largely conserved among populations. The revised method provided improved applicability with strong underlying theoretical justifications, and potential for future improvements.

Predicting body mass of bonobos (Pan paniscus) with human-based morphometric equations

A primate's body mass covaries with numerous ecological, physiological, and behavioral characteristics. This versatility and potential to provide insight into an animal's life has made body mass prediction a frequent and important objective in paleoanthropology. In hominin paleontology, the most commonly employed body mass prediction equations (BMPEs) are "mechanical" and "morphometric": uni- or multivariate linear regressions incorporating dimensions of load-bearing skeletal elements and stature and living bi-iliac breadth as predictor variables, respectively. The precision and accuracy of BMPEs are contingent on multiple factors, however, one of the most notable and pervasive potential sources of error is extrapolation beyond the limits of the reference sample. In this study, we use a test sample requiring extrapolation-56 bonobos (Pan paniscus) from the Lola ya Bonobo sanctuary in Kinshasa, Democratic Republic of the Congo-to evaluate the predictive accuracy of human-based morphometric BMPEs. We first assess systemic differences in stature and bi-iliac breadth between humans and bonobos. Due to significant differences in the scaling relationships of body mass and stature between bonobos and humans, we use panel regression to generate a novel BMPE based on living bi-iliac breadth. We then compare the predictive accuracy of two previously published morphometric equations with the novel equation and find that the novel equation predicts bonobo body mass most accurately overall (41 of 56 bonobos predicted within 20% of their observed body mass). The novel BMPE is particularly accurate between 25 and 45 kg. Given differences in limb proportions, pelvic morphology, and body tissue composition between the human reference and bonobo test samples, we find these results promising and evaluate the novel BMPE's potential application to fossil hominins.