Fish out of water: Genomic insights into persistence of rainbowfish populations in the desert
Bibliographic record
Abstract
How populations of aquatic fauna persist in extreme desert environments is an enigma. Individuals often breed and disperse during favorable conditions. Theory predicts that adaptive capacity should be low in small populations, such as in desert fishes. We integrated satellite-derived surface water data and population genomic diversity from 20,294 single-nucleotide polymorphisms across 344 individuals to understand metapopulation persistence of the desert rainbowfish (Melanotaenia splendida tatei) in central Australia. Desert rainbowfish showed very small effective population sizes, especially at peripheral populations, and low connectivity between river catchments. Yet, there was no evidence of population-level inbreeding and a signal of possible adaptive divergence associated with aridity was detected. Candidate genes for local adaptation included functions related to environmental cues and stressful conditions. Eco-evolutionary modeling showed that positive selection in refugial subpopulations combined with connectivity during flood periods can enable retention of adaptive diversity. Our study suggests that adaptive variation can be maintained in small populations and integrate with neutral metapopulation processes to allow persistence in the desert.
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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 | 0.000 |
| 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".