Revealing Ancient Wheat Phylogenetic Diversity: Machine Learning and Logistic Regression Identify <i>Triticum sphaerococcum</i> in Bronze Age Iberia.

ABSTRACT:

Background/objectives

Identifying archaeobotanical wheat remains is central to reconstructing the evolutionary history of cereal crops. Beyond documenting agricultural practices, such analyses provide critical evidence of phylogenetic diversity, lineage persistence, and local extinction events within the genus Triticum L. This study applies advanced computational morphometrics to reveal deep-time changes in wheat species distribution, including the disappearance of taxa now phylogeographically confined to central Asia.

Methods

We developed a machine learning framework integrating Random Forest compared with logistic regression to classify morphometric data from 848 dry and 340 experimentally carbonized modern grains representing multiple wheat taxa (genus Triticum<

SUBMITTER: Rivera D 

PROVIDER: S-EPMC12733154 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature