Demography and genome divergence of lake and stream populations of an East African cichlid fish
Bibliographic record
Abstract
Disentangling the processes and mechanisms underlying adaptive diversification is facilitated by the comparative study of replicate population pairs that have diverged along a similar environmental gradient. Such a setting is realized in a cichlid fish from southern Lake Tanganyika, Astatotilapia burtoni, which occurs within the lake proper as well as in various affluent rivers. Previously, we demonstrated that independent lake and stream populations show similar adaptations to the two habitat regimes. However, little is known about the evolutionary and demographic history of the A. burtoni populations in question and the patterns of genome divergence among them. Here, we apply restriction site-associated DNA sequencing (RADseq) to examine the evolutionary history, the population structure and genomic differentiation of lake and stream populations in A. burtoni. A phylogenetic reconstruction based on genome-wide molecular data largely resolved the evolutionary relationships among populations, allowing us to re-evaluate the independence of replicate lake-stream population clusters. Further, we detected a strong pattern of isolation by distance, with baseline genomic divergence increasing with geographic distance and decreasing with the level of gene flow between lake and stream populations. Genome divergence patterns were heterogeneous and inconsistent among lake-stream population clusters, which is explained by differences in divergence times, levels of gene flow and local selection regimes. In line with the latter, we only detected consistent outlier loci when the most divergent lake-stream population pair was excluded. Several of the thus identified candidate genes have inferred functions in immune and neuronal systems and show differences in gene expression between lake and stream 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 | 0.001 |
| 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".