The ever-increasing number of deaths along the U.S.-México border and the diversification in the demographic characteristics of the Latin American migrants, who perish in this region, demand new approaches to forensic anthropological casework. Current rates of positive identification must be improved so that the unknown forensic case can be re-associated with the once living person, the individual’s remains returned to the family, and the needs of social justice can be served. Studies using genetic and skeletal data alike have revealed differences in ancestry for many of the spatially-defined groups that are distributed across Latin America. Demographic research on migration networks for México have found distinctive transit pathways El Norte, whereby migrants of different origins are associated with particular destinations across the U.S.-México border. In this presentation, I will argue for the value of extending these two lines of research on the living to the identification of the dead for methodological improvement in the forensic context. Specifically, I will show how, when combined, information on geographic variation in ancestry composition and spatial trends in migration are critical to building statistical models optimized for the probabilistic determination of personal identity for migrant fatalities recovered along our southern border. By quantifying these biogeographic patterns in terms of the unknown individual’s relative estimates of continental-level ancestry, geographic location of origin, and place of death along the border, I will demonstrate that it is possible to predict the individual’s sending region. Through this discussion, I will illustrate how geospatial and machine learning methods can be used to improve traditional forensic procedures by directing case investigation, providing probable locations for finding next of kin, and reducing the pool of potential matches from missing persons lists. In conclusion, I will advocate for adopting a bio-cultural approach to identification that leverages multi-disciplinary methods to aid in this human rights work.

Learning Objectives:

1. Introduce the concept of biogeography and explain its value as an alternative biological profile parameter to single-group ancestry

2. Demonstrate how novel geospatial and machine learning methods can be used to infer place of origin for unknown remains

3. Explain how trihybrid ancestry estimates can be generated from skeletal data using admixture algorithms comparable to those employed in forensic genetics