Barcoding of estuarine macrophytes and phylogenetic diversity of estuaries along the South African coastline
Bibliographic record
Abstract
Plant DNA barcoding serves as an effective approach to building community phylogenies and increasing our understanding of the factors that determine plant community assemblages. The aims of the study were to (i) barcode macrophytes with high estuarine fidelity and (ii) to determine the phylogenetic diversity (PD) of selected South African estuaries for conservation prioritisation. Three DNA barcoding gene regions (rbcLa, matK, and trnH-psbA) were assessed, and community phylogenies were constructed for 270 estuaries. Generally, the matK barcode had the greatest discrimination success rate of 67.4% (parsimony informative sites = 418). Closely related species formed clades that also represent estuarine habitat types. Estuaries with high phylogenetic diversity along the southeast coast were associated with a combination of mangrove and salt marsh habitats. Species richness was strongly and significantly correlated with PD (r = 0.93; p < 0.000). Based on mean pairwise distance (MPD), more temperate estuaries (56) showed significant phylogenetic clustering compared to subtropical estuaries (24) (p < 0.05). Similarly, based on mean nearest taxon distance (MNTD), significant phylogenetic clustering was highest in temperate estuaries (50) compared to subtropical estuaries (12) (p < 0.05). This suggests that the coexistence of plant species in estuaries is structured by both biotic and abiotic interactions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".