Eco‐morphological differentiation in Lake Magadi tilapia, an extremophile cichlid fish living in hot, alkaline and hypersaline lakes in East Africa
Bibliographic record
Abstract
Ecological diversification through divergent selection is thought to be a major force during the process of adaptive radiations. However, the large sizes and complexity of most radiations such as those of the cichlids in the African Great Lakes make it impossible to infer the exact evolutionary history of any population divergence event. The genus Alcolapia, a small cichlid lineage endemic to Lakes Magadi and Natron in East Africa, exhibits phenotypes similar to some of those found in cichlids of the radiations of the African Great Lakes. The simplicity within Alcolapia makes it an excellent model system to investigate ecological diversification and speciation. We used an integrated approach including population genomics based on RAD-seq data, geometric morphometrics and stable isotope analyses to investigate the eco-morphological diversification of tilapia in Lake Magadi and its satellite lake Little Magadi. Additionally, we reconstructed the demographic history of the species using coalescent simulations based on the joint site frequency spectrum. The population in Little Magadi has a characteristically upturned mouth--possibly an adaptation to feeding on prey from the water surface. Eco-morphological differences between populations within Lake Magadi are more subtle, but are consistent with known ecological differences between its lagoons such as high concentrations of nitrogen attributable to extensive guano deposits in Rest of Magadi relative to Fish Springs Lagoon. All populations diverged simultaneously only about 1100 generations ago. Differences in levels of gene flow between populations and the effective population sizes have likely resulted in the inferred heterogeneous patterns of genome-wide differentiation.
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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 | 0.000 | 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".