FORDISC® and AncesTrees:Limitations and Considerations for the Identification of Individuals From Central and South America

Abstract

Learning Overview: The goal of this presentation is to present new data on population structure and population history from individuals from Central and South America through the interpretation of population-affinity estimations obtained by two online applications (FORDISC® and AncesTrees).Impact Statement: This presentation will impact the forensic science community by providing forensic practitioners with a theoretical and practical background for the application of two popular online softwares, FORDISC® and AncesTrees, targeting issues for population-affinity estimations on human remains from Central and South American. Extreme conditions leading to rapid skeletonization in some areas of the United States-Mexico border can make identification difficult. From the demographics gathered for biological profile estimation, biogeographical origin can impact the choice of subsequent methods for sex or age-at-death assessment. AncesTrees and FORDISC® are two computerized statistically based methods for estimating population affinity. To determine the reliability of group allocation using these applications, different target samples should be tested.The aim of this study is to explore the reliability of these applications in estimating the origin of individuals from Central and South America, comprising population groups that likely form the migratory flow prevalent across the United States-Mexico border. A total of 50 adult individuals (25 males and 25 females) identified as Mexican, Mexican American, Hispanic, New Mexican, or Latino were selected from the New Mexico Decedent Image Database. Seventeen cranial measurements were collected from Computed Tomography (CT) scans. Compiled measurements followed protocols outlined by AncesTrees and FORDISC®. Parameter values were inserted into the online platforms, with the settings for the application determined as non-prior background information.For FORDISC®, both the Howells’ and the Forensic Anthropology Data Bank (FDB) datasets were tested to explore any potential differences related to the chronology of the reference samples, as well as allowing for a comparison between AncesTrees and FORDISC® historical samples. All allocations are reported in the results, with Posterior Probability (PP) and Group Membership (GM) with values ≥0.80 being considered for further interpretation of the group allocation.Results show that AncesTrees allocated individuals as European (48%) and East Asian (32%), with the remaining biogeographical population sample allocations representing less than 10%. For the FORDISC® FDB, 26% of the sample was estimated as White and 24% of the sample was allocated as Hispanic, with the third-highest number of individuals being associated to Japanese (16%), Guatemalan (12%) and Chinese (12%) samples. Regarding the FORDISC® historical dataset, 24% of the individuals were classified as European with the next highest allocation being for East Asian samples (40%). Very few individuals obtained a PP higher than the threshold for FORDISC® estimations while GM for AncesTrees was over 0.80 for around 62% of the sampleAn accurate reconstruction of the biological profile is crucial for positive identification. Most of the population allocations obtained here using two commonly used online platforms present trends that suggest individuals from Central and South American origin would fall within Hispanic and European populations as well as East Asian samples. This research provides insights on population structure and population histories, as the patterns observed may be linked to social and political history. Future research, including larger and more diverse samples, will confirm our results and provide further information on human variation and inter-population differences.A consensus on the terminology as well as a revision of the information used for grouping the samples is recommended to ensure consistency between forensic practitioners and researchers. Moreover, larger and more representative datasets are needed to increase the accuracy and reliability of identification of unknown individuals in a forensic context, especially for areas with high migration flows such as the United States-Mexico border

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