Discriminating plant species in a local temperate flora using the <i>rbcL</i>+<i>matK</i> DNA barcode
Bibliographic record
Abstract
Summary 1. A major goal of DNA barcoding is to identify species in local floras and ecological communities. With the consensus of a two‐locus DNA barcode ( rbcL+matK ) by the Consortium for the Barcode of Life (CBOL) Plant Working Group (2009), barcoding efforts have begun to focus on building the barcode library for land plants. 2. Here, we establish a barcoding database for a temperate flora of moderate taxonomic breadth at the Koffler Scientific Reserve, Ontario, Canada based on the rbcL+matK barcode. We evaluated the performance of this combination in comparison with three other potential supplementary regions (the coding region rpoC1 and two non‐coding intergenic spacers trnH‐psbA and atpF‐atpH ). We examined these markers singly and in combination to evaluate their discriminatory power among 436 species in 269 genera of land plants. 3. Using high‐throughput techniques, we recovered a high‐quality sequence from at least one region for 98.2% of the 513 samples screened; 55% had complete coverage across all five gene regions. Sequencing success was highest for rbcL (91.4% of samples collected) and lowest for rpoC1 (74.5%). The two coding regions rbcL and matK provided a relatively high number of high‐quality bi‐directional sequences compared with the non‐coding intergenic spacers, and in combination were able to correctly identify 93.1% of the species sampled. Marginal increases in species resolution were obtained with the inclusion of the trnH‐psbA intergenic spacer (95.3%), or by using all five gene regions combined (97.3%). 4. There was a weak relation between the number of species per genus and identification success rate using rbcL+matK ; 100% for monotypic genera (70.5% of the flora) and 83.6% for polytypic genera. Identification success using the rbcL+matK barcode was higher (100%) for gymnosperms, bryophytes, lycophytes and monilophytes (collectively representing 5% of the flora), compared with angiosperms (92.7%). 5. Our results indicate that the rbcL+matK barcode can provide an acceptably high rate of species resolution in the context of this and other local northern temperate floras. It does so in a cost‐effective manner, with relatively modest laboratory effort, and despite the presence of missing data from individual plastid regions in a subset of samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".