Molecular evidence about ancestral origin, colonization patterns, and phylogenetic diversity of Coffea arabica, with insights in Latin America
Bibliographic record
Abstract
Abstract Coffee is one of the most widely consumed agricultural products worldwide. The most well-known coffee species in the market are Coffea arabica and Coffea canephora. Currently, 42 varieties are recognized within C. arabica, but strangely, with very low levels of intraspecific genetic diversity despite its worldwide geographical distribution. In addition, little is known about its phylogeographical history. Although there exist descriptive hypotheses of worldwide colonization patterns and origin for C. arabica (by Ferreira et al. in 2019), none of them are molecularly supported. In this study, data mining and bioinformatic approaches, allowing to collect DNA sequences of the Internal Transcribed Spacer (ITS) region available up to date. All sequences were analyzed under the Relaxed-Random-Walk (RRW) methodology, aiming to estimate ancestral areas and spatiotemporal dispersion patterns, with South American insights. ITS was also used with Single Locus Species Discovery (SLSD) methodology, which allows the delimitation of putative lineages/species (cryptic diversity). RRW supported that the origin and the genetic diversity center of C. arabica species took place between Ethiopia and South Sudan. The study also found evidence of five colonization events and the dispersion of the species within Latin America. SLSD estimated two to 12 cryptic lineages/species within C. arabica depending on the algorithm used; with Latin America being home to two to six of these lineages. The results suggest that the evolutionary history and global dispersion of C. arabica is more complex than historians have registered, showing intermediary regions and ancestors unpublished nowadays, as well as a cryptic diversity being underestimated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".