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Morphometrics and Phylogenomics of Coca (Erythroxylum spp.) Illuminate Its Reticulate Evolution, With Implications for Taxonomy.

Natalia A S PrzelomskaRudy A DiazFabio Andrés ÁvilaGustavo A BallenRocío Cortés-BLogan KistlerDaniel H ChitwoodMartha CharitonidouSusanne S RennerOscar Alejandro Pérez-EscobarAlexandre Antonelli
Published in: Molecular biology and evolution (2024)
South American coca (Erythroxylum coca and E. novogranatense) has been a keystone crop for many Andean and Amazonian communities for at least 8,000 years. However, over the last half-century, global demand for its alkaloid cocaine has driven intensive agriculture of this plant and placed it in the center of armed conflict and deforestation. To monitor the changing landscape of coca plantations, the United Nations Office on Drugs and Crime collects annual data on their areas of cultivation. However, attempts to delineate areas in which different varieties are grown have failed due to limitations around identification. In the absence of flowers, identification relies on leaf morphology, yet the extent to which this is reflected in taxonomy is uncertain. Here, we analyze the consistency of the current naming system of coca and its four closest wild relatives (the "coca clade"), using morphometrics, phylogenomics, molecular clocks, and population genomics. We include name-bearing type specimens of coca's closest wild relatives E. gracilipes and E. cataractarum. Morphometrics of 342 digitized herbarium specimens show that leaf shape and size fail to reliably discriminate between species and varieties. However, the statistical analyses illuminate that rounder and more obovate leaves of certain varieties could be associated with the subtle domestication syndrome of coca. Our phylogenomic data indicate extensive gene flow involving E. gracilipes which, combined with morphometrics, supports E. gracilipes being retained as a single species. Establishing a robust evolutionary-taxonomic framework for the coca clade will facilitate the development of cost-effective genotyping methods to support reliable identification.
Keyphrases
  • genetic diversity
  • electronic health record
  • big data
  • single cell
  • high throughput
  • dna methylation