Revealing Ancient Wheat Phylogenetic Diversity: Machine Learning and Logistic Regression Identify

BACKGROUND/

objectivesIdentifying 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

methodsWe 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

resultsThe analysis identified

conclusionsOur results demonstrate substantial phylogenetic turnover in wheat over the past 4000 years, marked by regional extirpations and contraction of once-widespread lineages to central Asia. This provides rare archeological evidence for the tempo and mode of cereal phylogeography, illustrating how domesticated lineages underwent extinction and range restriction akin to wild taxa. By integrating computational morphometrics with archaeobotanical evidence, this study establishes a scalable framework for tracing cryptic phylogenetic diversity, refining models of wheat domestication and assessing long-term genetic erosion in cultivated plants.