DNA barcoding reveals micro-evolutionary changes and river system-level phylogeographic resolution of African silver catfish, <I>Schilbe intermedius</I> (Actinopterygii: Siluriformes: Schilbeidae) from seven populations across different African river systems
Bibliographic record
Abstract
Van der Bank H.F., Greenfield R., Daru B.H., Yessoufou K. 2012.DNA barcoding reveals micro-evolutionary changes and river system-level phylogeographic resolution of African silver catfish, Schilbe intermedius (Actinopterygii: Siluriformes: Schilbeidae) from seven populations across different African river systems.Acta Ichthyol.Piscat.42 (4): 307-320.Background.Under the tropics, less than 40% of known fishes are identified to species-level.Further, the ongoing global change poses unprecedented threat to biodiversity, and several taxa are likely to go extinct even before they could be described.Traditional ecological theory suggests that species would escape extinction risk posed by global threats (e.g., climate change) only by migrating to new environments.In this study, we hypothesise that micro-evolutionary changes (evolution within species and populations) are also important mechanisms for the survival of Schilbe intermedius in Africa, a continent subjected to uneven distribution of climate severity. Materials and methods.Using the mitochondrial cytochrome c oxidase subunit I (COI) gene, known as animal DNA barcode, we tested this hypothesis by analysing the genetic diversity and phylogenetic relations between seven populations of S. intermedius across different African river systems.Results.We reveal a clear geographical patterning in genetic variations, with three clear clusters (southern Africa, eastern Africa, and western Africa).In southern Africa, the South African population is distinct from that of Namibia and Botswana.In addition, within Nigerian populations of silver catfish, two sub-clusters emerged from two isolated river systems.We suggest that the phylogeographic pattern within African silver catfish populations mirror the past effects of selection and gene flow, and that the split within Nigerian silver catfish populations might be the result of micro-evolutionary adaptive responses to local selection pressures. Conclusion.We suggest that the strong genetic difference in African silver catfish among geographically isolated river systems might be the result of in situ micro-evolutionary adaptive responses to changing environments, and that DNA barcode has potential beyond species delimitation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".