Strong and consistent natural selection associated with armour reduction in sticklebacks
Bibliographic record
Abstract
Measuring the strength of natural selection is tremendously important in evolutionary biology, but remains a challenging task. In this work, we analyse the characteristics of selection for a morphological change (lateral-plate reduction) in the threespine stickleback Gasterosteus aculeatus. Adaptation to freshwater, leading with the reduction or loss of the bony lateral armour, has occurred in parallel on numerous occasions in this species. Completely-plated and low-plated sticklebacks were introduced into a pond, and the phenotypic changes were tracked for 20 years. Fish from the last generation were genotyped for the Ectodysplasin-A (Eda) locus, the major gene involved in armour development. We found a strong fitness advantage for the freshwater-type fish (on average, 20% fitness advantage for the freshwater morph, and 92% for the freshwater genotype). The trend is best explained by assuming that this fitness advantage is maximum at the beginning of the invasion and decreases with time. Such fitness differences provide a quantifiable example of rapid selection-driven phenotypic evolution associated with environmental change in a natural population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 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".