Joseph T. Hefner

Cranial nonmetric variation and estimating ancestry

Abstract:  Historically, when predicting the ancestry of human skeletal remains, forensic anthropologists have not fully considered the variation within human populations, but instead have relied on a typological, experience‐based approach. Unfortunately, reliance on observer experience has produced a method that is as much an art as it is a science. This research focuses on the frequency distribution and inter‐trait correlations of 11 common morphoscopic traits to demonstrate that the experience‐based approach to ancestry prediction is indeed an art that is unscientific, because it is unreplicable, unreliable, and invalid. Ten of 11 traits examined had frequency distributions with significant differences (p < 0.001) between groups, but the range in variation of these traits far exceeds previous assumptions. Such within group variation clearly demonstrates that extreme trait expressions are not reliable for estimating ancestry through visual observation alone, but instead that these traits should be analyzed within a statistical framework.

Statistical Classification Methods for Estimating Ancestry Using Morphoscopic Traits

Ancestry assessments using cranial morphoscopic traits currently rely on subjective trait lists and observer experience rather than empirical support. The trait list approach, which is untested, unverified, and in many respects unrefined, is relied upon because of tradition and subjective experience. Our objective was to examine the utility of frequently cited morphoscopic traits and to explore eleven appropriate and novel methods for classifying an unknown cranium into one of several reference groups. Based on these results, artificial neural networks (aNNs), OSSA, support vector machines, and random forest models showed mean classification accuracies of at least 85%. The aNNs had the highest overall classification rate (87.8%), and random forests show the smallest difference between the highest (90.4%) and lowest (76.5%) classification accuracies. The results of this research demonstrate that morphoscopic traits can be successfully used to assess ancestry without relying only on the experience of the observer.

Ancestry Assessment Using Random Forest Modeling

A skeletal assessment of ancestry relies on morphoscopic traits and skeletal measurements. Using a sample of American Black (n = 38), American White (n = 39), and Southwest Hispanics (n = 72), the present study investigates whether these data provide similar biological information and combines both data types into a single classification using a random forest model (RFM). Our results indicate that both data types provide similar information concerning the relationships among population groups. Also, by combining both in an RFM, the correct allocation of ancestry for an unknown cranium increases. The distribution of cross‐validated grouped cases correctly classified using discriminant analyses and RFMs ranges between 75.4% (discriminant function analysis, morphoscopic data only) and 89.6% (RFM). Unlike the traditional, experience‐based approach using morphoscopic traits, the inclusion of both data types in a single analysis is a quantifiable approach accounting for more variation within and between groups, reducing misclassification rates, and capturing aspects of cranial shape, size, and morphology.

Morphoscopic Trait Expression in “Hispanic” Populations

This study evaluates population variation of eight cranial morphoscopic traits using samples of known southwest Hispanics (n = 72), Guatemalans (n = 106), American Blacks (n = 146), and American Whites (n = 218). We applied the support vector machine (SVM) method to build a prediction model based on a subsample (20%) of the data; the remainder of the data was used as a test sample. The SVM approach effectively differentiated between the four groups with correct classification rates between 72% (Guatemalan group) and 94% (American Black group). However, when the Guatemalan and southwest Hispanic samples were pooled, the same model correctly classified all groups with a higher degree of accuracy (American Black = 96%; American White = 77%; and the pooled Hispanic sample = 91%). This study also identified significant differences between the two Hispanic groups in six of the eight traits using univariate statistical tests. These results speak to the unique population histories of these samples and the current use of the term “Hispanic” within forensic anthropology. Finally, we argue that the SVM can be used as a classification model for ancestry estimation in a forensic context and as a diagnostic tool may broaden the application of morphoscopic trait data for the assessment of ancestry.

Craniofacial secular change in recent mexican migrants

abstract Research by economists suggests that recent Mexican migrants are better educated and have higher socioeconomic status (SES) than previous migrants. Because factors associated with higher SES and improved education can lead to positive secular changes in overall body form, secular changes in the craniofacial complex were analyzed within a recent migrant group from Mexico. The Mexican group represents individuals in the act of migration, not yet influenced by the American environment, and thus can serve as a starting point for future studies of secular change in this population group. The excavation of a historic Hispanic cemetery in Tucson, Arizona, also allows for a comparison between historic Hispanics and recent migrants to explore craniofacial trends over a broad time period, as both groups originate from Mexico. The present research addresses two main questions: (1) Are cranial secular changes evident in recent Mexican migrants? (2) Are historic Hispanics and recent Mexican migrants similar? By studying secular changes within a migrant population group, secular trends may be detected, which will be important for understanding the biological variation of the migrants themselves and will serve as a preliminary investigation of secular change within Mexican migrants. The comparison of a sample of recent Mexican migrants with a historic Hispanic sample, predominantly of Mexican origin, allows us to explore morphological similarities and differences between early and recent Mexicans within the United States. Vault and face size and a total of 82 craniofacial interlandmark distances were used to explore secular changes within the recent Mexican migrants (females, n = 38; males, n = 178) and to explore the morphological similarities between historic Hispanics (females, n = 54; males, n = 58) and recent migrants. Sexes were separated, and multivariate adaptive regression splines and basis splines (quadratic with one knot) were used to assess the direction and magnitude of secular trends for the recent Mexican migrants. Because dates of birth were unavailable for the historic sample, partial least squares discriminant analysis (PLS-DA) was used to evaluate morphological differences between historic and recent Mexican migrant samples. The data were separated into a training data set and a testing data set to ensure realistic results. Males had eight variables (four positive and four negative) and females had six variables (two positive and four negative) that demonstrated significant differences over time. In the PLS-DA, three components were identified as important in model creation and resulted in a classification accuracy of 87% when applied to a testing sample. The high classification accuracy demonstrates significant morphological differences between the two groups, with the historic Hispanic sample displaying overall larger craniofacial dimensions. While differences in cranial morphology are evident between historic Hispanics and recent Mexican migrants, relatively few positive and negative secular trends were detected within the recent migrant sample.

A brief history of biological distance analysis

Abstract Biological distance, or biodistance, analysis employs data derived from skeletal remains to reflect population relatedness (similarity/dissimilarity) through the application of multivariate statistical methods. The approaches used in biodistance studies have changed markedly over recent centuries, exploring phenotypic expressions assumed to be informative. Biodistance analysis began as the study of anomalous variants in the human skull, but the field has transformed over the centuries now seeking to incorporate skeletal morphology in the interpretation of genetic affinity, providing insight into the genetics governing trait expression, and providing understanding into the role of developmental biology on the expression of morphological variants. As methodological approaches improve, so too has the application of these analyses. We present here a brief historical overview of biodistance analysis research, focusing on meta-themes in the field, shifts in thinking among researchers in biological anthropology, and several of the outside influences that impact biodistance analysis.

Application of the Stephan et al. Chest Radiograph Comparison Method to Decomposed Human Remains

This manuscript describes the use of comparative radiography of the chest to facilitate positive identification of human remains in advanced stages of decomposition. The method reported by Stephan et al. for positive identification of dry, disarticulated skeletal elements was used on semifleshed, decomposing remains. Positive identification was established through multiple points of concordance observed in radiographs of the left and right clavicles and the C5‐T1 vertebrae. This case study demonstrates the applicability of the Stephan et al.s method in cases involving decomposing remains.

Cranial nonmetric and morphoscopic data sets

Abstract Cranial nonmetrics and morphoscopic data are typically used differently by biological anthropologists depending on the context of their research. Bioarchaeologists often use nonmetric trait data to explore patterns of gene flow and genetic isolation in regional contexts. Collectively these are referred to as biological distance studies. Forensic anthropologists usually focus on a set of nonmetric traits, now commonly described as morphoscopic traits, to estimate ancestry in unknown skeletal remains toward the goal of positive identification. In this chapter both types of cranial trait are outlined to include definitions and data collection protocols. Statistical measures of biological distance of data sets within both bioarchaeological and forensic settings are also described.

Ancestry Estimation Using Macromorphoscopic Traits

Anthropologists have a lengthy history using cranial nonmetric traits to assess biological distances between populations. These concepts were adopted by forensic anthropologists to estimate ancestry at the individual level using population-based human variation. However, this method of estimation must adhere to the Daubert guidelines to be applied in the modern court system. To that end, a set of cranial macromorphoscopic traits have been proffered to generate a methodological approach to the estimation of ancestry that can be empirically tested and verified. Additionally, various statistical frameworks, parametric and nonparametric, have been employed to calculate error rates and provide probabilistic statements of the strength of these estimations. Recent studies have further advanced the reliability and validity of macromorphoscopic trait analysis through reference datasets, validation studies, and demonstrable exploration of the complex population histories impacting human variation between and within populations worldwide.

Beyond Taphonomy: Exploring Craniometric Variation Among Anatomical Material.

Anatomical crania are occasionally encountered in forensic anthropology laboratories when that material is mistaken for forensically significant human remains. Using craniometric analyses and statistical measures of sample homogeneity, we determine whether anatomical material can be described as a single, homogenous group or as a diverse mix of populations. Twenty‐one interlandmark distances were collected from 85 anatomical preparations. Distance measures were calculated between all pairs using a pooled within‐sample variance/covariance matrix and then subjected to a Defrise‐Gussenhoven test between each paired distance to test whether each pair was drawn randomly from the same population. In the Defrise‐Gussenhoven analysis, twenty‐two percent (n = 66) of the 300 pairwise combinations were significant at the 0.05 level or below. The level of homogeneity suggests a majority of that material originated from the subcontinent of India or West Asia. Therefore, anatomical material can be viewed as a moderately homogenous group, but with a shared taphonomic history.