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Steve Ousley was our friend, colleague, and collaborator. This article reflects on the impact of his work in modernizing three-dimensional cranial data collection. By building the 3Skull software program, something he is less well known for than FORDISC, he allowed generations of researchers to collect cranial and other skeletal coordinate data in a meticulous yet efficient manner. This contribution not only allowed for the creation of new knowledge, but it also served to facilitate repatriation at the Smithsonian. At the time of his death, Steve was working on the next great leap in skeletal data collection, virtual osteology. We discuss this growing component of our field, including how it is grounded in Steve’s prior work, ethical concerns, and promise for the future.

In forensic anthropology, ancestry estimation is essential in establishing the individual biological profile. The aim of this study is to present a new program—AncesTrees—developed for assessing ancestry based on metric analysis. AncesTrees relies on a machine learning ensemble algorithm, random forest, to classify the human skull. In the ensemble learning paradigm, several models are generated and co-jointly used to arrive at the final decision. The random forest algorithm creates ensembles of decision trees classifiers, a non-linear and non-parametric classification technique. The database used in AncesTrees is composed by 23 craniometric variables from 1,734 individuals, representative of six major ancestral groups and selected from the Howells’ craniometric series. The program was tested in 128 adult crania from the following collections: the African slaves’ skeletal collection of Valle da Gafaria; the Medical School Skull Collection and the Identified Skeletal Collection of 21st Century, both curated at the University of Coimbra. The first step of the test analysis was to perform ancestry estimation including all the ancestral groups of the database. The second stage of our test analysis was to conduct ancestry estimation including only the European and the African ancestral groups. In the first test analysis, 75 % of the individuals of African ancestry and 79.2 % of the individuals of European ancestry were correctly identified. The model involving only African and European ancestral groups had a better performance: 93.8 % of all individuals were correctly classified. The obtained results show that AncesTrees can be a valuable tool in forensic anthropology.

The illegal wildlife markets of Southeast Asia are bolstered by organised criminal networks and the region’s rich density of charismatic wildlife. Forensic tools identifying species and their origins are vital to combat wildlife crime. However, many require expensive technology and skilled personnel, limiting their use in rural trade hotspots. This study introduces a replicable statistical framework, using skull morphometrics, to distinguish related species with simple measurements. We developed a web-based classifier trained on clouded leopard ( Neofelis spp.) skulls from museum collections across Europe, Asia and the U.S.A., a genus often targeted in wildlife trade. Our categorical predictive model, based on two key metrics, the fronto-nasal “pit” and m1 talonid morphology achieved 97% accuracy ( p  < 0.005). A continuous predictor model, using postorbital width, achieved 80.6% accuracy for males and 85.6% for females (both p  < 0.05). These models were encoded into a free, user-friendly web app, enabling practitioners in remote areas to distinguish these two species easily. This tool not only supports anti-trafficking efforts but also enables museum curators to correctly assign provenance to clouded leopard skulls with uncertain origins.

Abstract Four species of Eurasian badgers ( Meles ) are widely distributed across Eurasia. Small-sized and dark-coloured Far Eastern M. leucurus amurensis is the most distinctive form among all Asian badgers, M. leucurus . Traditionally, Far Eastern badgers are treated as a subspecies of the M. leucurus . This study aims to revise the geographical variability of the M. leucurus sensu lato from the entire species range (324 adult skulls) and assess the position of the Far Eastern badgers. In addition, 551 adult skulls of the European badger, M. meles , the Southwest Asian badger, M. canescens , and the Japanese badger, M. anakuma , were used for comparison with Asian badgers. Meles leucurus amurensis from the Far East resembles the Japanese M. anakuma in many respects, but is very different from the M. l. leucurus of southern Siberia and Middle Asia. Analysis of palaeontological data suggests that the Far Eastern badgers could have descended from the ancestral “ leucurus -like” badger chronospecies known from China since the mid-Early Pleistocene. It then probably dispersed westwards into southern Siberia no later than the MIS9 stage (~ 0.32 Ma), where it evolved into M. leucurus . Previous molecular data suggest that the Japanese badgers are unique, whereas the Far Eastern badgers are closer to Asian badgers from the Urals, Siberia and Tibet. We propose that the Far Eastern badger retains some craniometrics features of the ancestral form of M. leucurus sensu stricto and M. anakuma and should therefore be considered a separate species, M. amurensis Schrenck, 1859 stat. rev. The presumed range of this species is in the Far East, east of the Great Khingan Range to Russian Primorye, Manchuria and the Korean Peninsula and possibly eastern China.

Observer error and agreement rates for craniometrics, odontometrics, and cranial and dental morphological traits have been inconsistently evaluated on three-dimensional cranial reconstructions and almost never assessed on subadult individuals. This study uses a computed tomography (CT) scan sample of 12 subadults aged between birth and 20 years from the Subadult Virtual Anthropology Database (SVAD) to evaluate intra- and inter-observer error and agreement rates associated to these four types of data on virtual crania. Forty-eight cranial landmarks, 33 standard inter-landmark distances (ILDs), 13 cranial macromorphoscopic traits, four permanent and four deciduous dental landmarks and measurements per tooth, and 21 permanent and 12 deciduous dental morphological traits were collected on each individual. Results matched or improved on published standards for dry bones, teeth, or dental casts. Technical Error of Measurement (TEM) associated with metric data ranged from 0.00 mm to 0.99 mm and relative TEM ranged from 0% to 5.76%. Cohen’s kappa coefficient values for agreement on morphological traits scores were above K = 0.5 for 90% of the traits. Type III cranial landmarks showed higher error rates than Type I and II cranial landmarks. Agreement on dental morphology scores seemed influenced by observer experience and rater agreement improved when using di- or tri-chotomized grades. Skeletal maturity did not significantly affect error rates, meaning most craniofacial and dental metrics and morphological traits can be reliably obtained from virtual subadult crania. •Virtual subadult cranial and dental metric and morphological data are evaluated.•Observer errors are below 1 mm, relative error is below 5% for metric traits.•Observer agreement rates are higher than K = 0.5 for 90% of morphological traits.•Rates are comparable or improve on published dry bone/dental cast standards.

Evidence suggests that both craniometric and cranial morphoscopic (MMS) traits elucidate information about cranial phenotypic variation and are appropriate proxies of genetic variation. Yet, the types of variation underlying the expression of craniometric and MMS traits are unknown. Recent data sets of matched skeletal metric and MMS data enable a holistic exploration into the cranial phenotype. Subsequently, the current study strived to provide a better understanding of cranial data used to measure human variation in biological anthropology. Two contemporary U.S. samples were pooled to increase sample size and diversity. Following down-sampling for balanced representation of reported biological males and females, the final sample comprised 310 individuals. Twenty-five interlandmark distances and 11 MMS traits were used in numerous analyses: polychoric correlation, mutual information, mixed factor analysis, and factor analysis of mixed data. No demographic information besides reported biological sex was retained in the analyses. The results consistently indicate that having information about one data type does not provide certainty of another data type, even when the variables are analogous (i.e., nasal breadth and nasal aperture width). Findings reassert that skeletal variables should be analyzed jointly rather than independently to best capture the cranial phenotype. The results also highlight the differential influence of biological variables, such as sexual dimorphism, on the two types of cranial data. As data availability increases and additional matched data-type comparisons can be conducted, we will continue to gain a better understanding of the complexities surrounding skeletal phenotypic variation, evolutionary theory, and population affinity

The use of cranial analysis, through metric and/or morphological data, remains a popular method within biological anthropology and its subfields to allow for the analysis of an individual. These methods increasingly use multivariate statistics to empirically measure the degree of similarities between individuals and populations. CRANID is a piece of freeware which allows the user to estimate ancestry from 29 cranial measurements. This paper utilised a previously published dataset (Lee and Gerdau 2020 [29]) of cranial measurements to simulate multiple users estimating the ancestry for a single cranium of known origin. Only 32–68 % of the generated ancestry estimations were found to match the broad geographic region of the tested cranium depending on the statistical test. This paper also highlights aspects of CRANID’s results that may make it harder for users to understand the results the program provides. •Participants’ datasets achieved a match to the closest geographic sample of the reference cranium 4.0–28.0 % of the time.•Participants’ datasets achieved a match to the major geographic region of the reference cranium 32.0–68.0 % of the time.•Highlights difficulties in CRANID’s results output that make it difficult for users to understand and compare results.

Cranial landmarks have been used in research and casework since the late 19th century, at which time some of the earliest landmark and measurement definitions were codified. Over the last two to three decades, however, many biological anthropologists have shifted from taking traditional caliper-derived measurements to using a three-dimensional (3D) digitizer to collect spatial coordinate landmark data. The increasing use of landmark-based data collection methods have necessitated a shift in the practitioner’s focus from understanding measurement definitions to being able to identify the location of a particular landmark in three-dimensional space. As a result, the Harris County Institute of Forensic Sciences published a Standard Operating Procedure (SOP), which provides a comprehensive list of landmark definitions for practitioners to reference while digitizing. The movement toward open access resources and digital pedagogy led to the development of a digital companion to the current SOP, entitled Landmark and Measurement-based Data Assistant (LAMbDA). LAMbDA is a free, publicly-available web interface (www.locatelambda.org) that serves as a pedagogical resource and a repository of information for those using a digitizer to collect 3D cranial coordinate data. This tool provides a series of photogrammetry models with annotated landmarks so that proper landmark placement can be viewed in 3D space following definitions provided in the Harris County SOP. Additionally, the website provides useful references, a manual with landmark placement definitions and comments on special anatomical circumstances, supplementary photographs, and the 3D models are available to download for 3D printing physical models.

•Dominican and Haitian skulls can be differentiated with a 71.8–87.5% accuracy.•Haitian individuals and males are more frequently classified correctly.•Several processes have stunted genetic and cultural homogeneity between the groups.•Population-specific data is needed to move away from the umbrella term, “Hispanic”.•CT scans are a valid substitute for dry bone; however, further research is needed. Ancestry estimation of skeletonized remains by forensic anthropologists is conducted through comparative means, and a lack of population-specific data results in possible misclassifications. This is especially germane to individuals of Latin American ancestry. Generally, each country in Latin America can trace their ancestral lineage through three main parental groups: Indigenous, European, and African. However, grouping all Latin American individuals under the broad “Hispanic” category ignores the specific genetic contributions from each parental group, which is variable and dependent on the population histories and sociocultural dynamics of each country. This study analyzes the craniometric ancestry of Hispaniola (the Dominican Republic and Haiti) using 190 cranial Computed Tomography (CT) scans (f=103; m=87), along with the island’s history, to explore similarities and differences between the two groups. MANOVA results indicate that 53.6% and 71.4% of the 28 cranial measurements differ between the ancestries and sexes, respectively; and intraobserver error analyses demonstrate that 85.7% of measurements from CT scans are good-excellent in reliability. Further, a total of 12 canonical discriminant function analyses produced cross-validated classification accuracies of 73.7–78.6% for females, 71.8–87.5% for males, and 72.0–77.8% for pooled sex. This study demonstrates that, despite sharing a small island, Dominican and Haitian individuals can be differentiated with a fair amount of statistical certainty, which is possible due to complex socio-cultural, -political, and -demographic factors that have produced and maintained genetic heterogeneity. Moreover, the discriminant functions provided here can be used by the international forensic science community to identify individuals living on Hispaniola.

•Participant variation from reference values fell between −18mm and +30mm.•Agreement among studies on which cranial measurements are error prone is lacking.•3D digital models of crania provide comparable data to physically obtained data.•Important questions are raised regarding the use of digital models in anthropology. Metric assessment of human crania can provide forensic practitioners and anthropological researchers with information on an individual’s sex and biogeographical ancestry. However, metric methods rely on the ability of users to remain consistent with themselves and others, with any error in the data rendering conclusions invalid. Digital anthropology is a growing sub-field where human remains are digitised using a growing range of methods and technologies. These models have the potential to boost research collaboration and public engagement. However, not all of these digitisation methods have been examined critically to explore the veracity of their use within a research environment. There has also been limited research into the application of digital anthropology to craniometric analysis. This study examined the intra- and inter- observer variation of seven participants taking physical measurements from a human cranial cast with an associated set of reference values. The same measurements were also taken from three digital models of the cranial cast which were created using digital photogrammetry and laser scanning. This data was then compared to the reference values and the physical measurements taken by the lead author. This study found that there was excellent statistical agreement between the reference values and the measurements taken from the cranial cast, both physical and digital. However, the participants still exhibited variation within a range of −18mm and +30mm from the reference values. MANOVA tests showed between-subject effects on nine measurements across the participant data, and 12 measurements between the digital models. However, there is little consistency between this study and the anthropological literature as to which measurements are most prone to between-subject effects. Despite the excellent agreement shown between the reference values and the digital models this study raises a number of methodological questions regarding inter-observer error and the varying levels of data processing present in different digitisation methods.