Multiple Multilocus DNA Barcodes from the Plastid Genome Discriminate Plant Species Equally Well
Bibliographic record
Abstract
A universal barcode system for land plants would be a valuable resource, with potential utility in fields as diverse as ecology, floristics, law enforcement and industry. However, the application of plant barcoding has been constrained by a lack of consensus regarding the most variable and technically practical DNA region(s). We compared eight candidate plant barcoding regions from the plastome and one from the mitochondrial genome for how well they discriminated the monophyly of 92 species in 32 diverse genera of land plants (N = 251 samples). The plastid markers comprise portions of five coding (rpoB, rpoC1, rbcL, matK and 23S rDNA) and three non-coding (trnH-psbA, atpF-atpH, and psbK-psbI) loci. Our survey included several taxonomically complex groups, and in all cases we examined multiple populations and species. The regions differed in their ability to discriminate species, and in ease of retrieval, in terms of amplification and sequencing success. Single locus resolution ranged from 7% (23S rDNA) to 59% (trnH-psbA) of species with well-supported monophyly. Sequence recovery rates were related primarily to amplification success (85-100% for plastid loci), with matK requiring the greatest effort to achieve reasonable recovery (88% using 10 primer pairs). Several loci (matK, psbK-psbI, trnH-psbA) were problematic for generating fully bidirectional sequences. Setting aside technical issues related to amplification and sequencing, combining the more variable plastid markers provided clear benefits for resolving species, although with diminishing returns, as all combinations assessed using four to seven regions had only marginally different success rates (69-71%; values that were approached by several two- and three-region combinations). This performance plateau may indicate fundamental upper limits on the precision of species discrimination that is possible with DNA barcoding systems that include moderate numbers of plastid markers. Resolution to the contentious debate on plant barcoding should therefore involve increased attention to practical issues related to the ease of sequence recovery, global alignability, and marker redundancy in multilocus plant DNA barcoding systems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".