The use of <scp>DNA</scp> barcoding as a tool for the conservation biogeography of subtropical forests in <scp>C</scp>hina
Bibliographic record
Abstract
Abstract Aim Rapid and accurate species identification is the foundation for biodiversity assessment. DNA barcoding has been shown to be an effective tool to overcome the taxonomic impediment to facilitate biodiversity conservation in temperate forests. However, this tool has rarely been considered for use in tropical forests. This study aims to investigate the utility and species resolution of DNA barcoding in a subtropical region. Location The Dinghushan National Nature Reserve (DNNR) in China. Methods A DNA barcoding database was constructed for 531 trees present in the DNNR. We used a phylogenetic method (neighbour‐joining trees) and sequence similarity (all‐to‐all BLASTn searches) to evaluate the utility and species resolution of five DNA barcode regions (rbcL, matK, ITS, ITS2 and trnH‐psbA), both singly and in combinations of two or three region. Results The combination of rbcL + matK + ITS had the highest species resolution (94.19%). However, when considering the difficulty of sequence recoverability, rbcL + ITS2 performed best (64.64%). Species resolution for large genera containing more than two species was substantially lower than that for small genera with one and two species per genus. Local small spatial scales (1‐ha quadrats) resulted in moderately improved species resolution (70.82% for rbcL + ITS2) compared to larger spatial scales (20 and 1133 ha). We document incongruent signals between nuclear and cpDNA regions and the challenges associated with barcoding large genera inherent to subtropical floras. Main conclusions This study considerably expands the global DNA barcode database for subtropical trees. Based on cost‐effectiveness and the trade‐off between sequence recovery and species resolution, we suggest that the rbcL + ITS2 barcode combination is an effective tool for documenting plant diversity in the DNNR. This study also sheds some light on the limitations and challenges for the application of barcoding to conservation biogeography in subtropical forests.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".