An alternative size variable for allometric investigations in subadults

• NCJ Number: 305193
• Author(s): Elaine Y. Chu; Kyra E. Stull; Adam D. Sylvester
• Date Published: September 2022
• Grant Number(s): 2015-DN-BX-K409
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)
• Award: Development of Modern Subadult Standards: Improved Age and Sex Estimation in U.S. Forensic Practice

Annotation

Effective allometry research relies on appropriate size variables; however, two of the largest obstacles in subadult (ontogenetic) allometry research is small sample sizes and unknown dimensions. This study overcomes a barrier of ontogenetic allometry research by proposing alternative size variables that do not require additional calculations for use in subadult allometry research and retain general patterns among long bones when stature is used for size.

Abstract

Diaphyseal measurements, stature, and age were collected from computed tomography (CT) and full-body radiographic images for a sample of subadults between birth and 13 years from the United States (U.S., n = 308) and South Africa (Z.A., n = 25). Nineteen alternative size variables were evaluated using reduced-major-axis regression to identify the closest one-to-one relationship to stature. The applicability across samples was then evaluated using the selected alternative size variables. Radius midshaft breadth (RMSB), femur midshaft breadth (FMSB), and the geometric mean of midshaft breadths (GM midshaft) yielded the closest isometric relationships to stature. Allometric relationships among long bones are maintained when using stature, FMSB, and GM midshaft as size variables for both the U.S. and Z.A. samples. A large, modern dataset facilitated an investigation into alternative size variables that can be used for single-bone ontogenetic allometry. Generalizability of the model suggests FMSB and GM midshaft are persistent across populations. This methodology identifies alternative size variables appropriate for other allometry research and offers a robust approach even when historically relied upon size variables are unknown. (Publisher abstract provided)