Haoyu Wang, Jennifer A. Miller, T. Grubesic, Shalene Jha
{"title":"A Framework for Using Ensemble Species Distribution Models for Geographic Attribution in Forensic Palynology","authors":"Haoyu Wang, Jennifer A. Miller, T. Grubesic, Shalene Jha","doi":"10.1109/HST56032.2022.10025427","DOIUrl":null,"url":null,"abstract":"As a next-generation DNA sequencing technique, metabarcoding aids in identifying biotic trace materials such as pollen, fungal spores, and other environmental DNA samples. This paper aims to develop a geographic attribution framework using pollen samples associated with objects or persons of interest to reduce search space for law enforcement investigations. We use plant occurrence data from the open-source Global Biodi-versity Information Facility (GBIF) to model individual genus and species distributions which were subsequently combined to inform possible geolocations objects or persons of interest have traveled. Results indicate that the geographic attribution frame-work could potentially aid forensic investigations by eliminating geographic search areas to determine the possible location history of people and objects.","PeriodicalId":162426,"journal":{"name":"2022 IEEE International Symposium on Technologies for Homeland Security (HST)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Symposium on Technologies for Homeland Security (HST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HST56032.2022.10025427","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
As a next-generation DNA sequencing technique, metabarcoding aids in identifying biotic trace materials such as pollen, fungal spores, and other environmental DNA samples. This paper aims to develop a geographic attribution framework using pollen samples associated with objects or persons of interest to reduce search space for law enforcement investigations. We use plant occurrence data from the open-source Global Biodi-versity Information Facility (GBIF) to model individual genus and species distributions which were subsequently combined to inform possible geolocations objects or persons of interest have traveled. Results indicate that the geographic attribution frame-work could potentially aid forensic investigations by eliminating geographic search areas to determine the possible location history of people and objects.