Local adaptation with gene flow in a highly dispersive shark
Bibliographic record
Abstract
Abstract Adaptive divergence in response to environmental clines are expected to be common in species occupying heterogeneous environments. Despite numerous advances in techniques appropriate for non‐model species, gene–environment association studies in elasmobranchs are still scarce. The bronze whaler or copper shark (Carcharhinus brachyurus) is a large coastal shark with a wide distribution and one of the most exploited elasmobranchs in southern Africa. Here, we assessed the distribution of neutral and adaptive genomic diversity in C. brachyurus across a highly heterogeneous environment in southern Africa based on genome‐wide SNPs obtained through a restriction site‐associated DNA method (3RAD). A combination of differentiation‐based genome‐scan (outflank) and genotype–environment analyses (redundancy analysis, latent factor mixed models) identified a total of 234 differentiation‐based outlier and candidate SNPs associated with bioclimatic variables. Analysis of 26,299 putatively neutral SNPs revealed moderate and evenly distributed levels of genomic diversity across sites from the east coast of South Africa to Angola. Multivariate and clustering analyses demonstrated a high degree of gene flow with no significant population structuring among or within ocean basins. In contrast, the putatively adaptive SNPs demonstrated the presence of two clusters and deep divergence between Angola and all other individuals from Namibia and South Africa. These results provide evidence for adaptive divergence in response to a heterogeneous seascape in a large, mobile shark despite high levels of gene flow. These results are expected to inform management strategies and policy at the national and regional level for conservation of C. brachyurus populations.
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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 | 0.000 | 0.001 |
| 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".