生物
形态计量学
系统基因组学
古柯
克莱德
分类学(生物学)
进化生物学
生态学
系统发育学
地理
考古
生物化学
基因
作者
Natalia A. S. Przelomska,Rudy A. Diaz,Fabio Andrés Ávila,Gustavo A. Ballen,Rocío Cortés‐B,Logan Kistler,Daniel H. Chitwood,Martha Charitonidou,Susanne S. Renner,Oscar A. Pérez‐Escobar,Alexandre Antonelli
标识
DOI:10.1093/molbev/msae114
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI