Adaptation in temporally variable environments: stickleback armor in periodically breaching bar‐built estuaries
Bibliographic record
Abstract
The evolutionary consequences of temporal variation in selection remain hotly debated. We explored these consequences by studying threespine stickleback in a set of bar-built estuaries along the central California coast. In most years, heavy rains induce water flow strong enough to break through isolating sand bars, connecting streams to the ocean. New sand bars typically re-form within a few weeks or months, thereby re-isolating populations within the estuaries. These breaching events cause severe and often extremely rapid changes in abiotic and biotic conditions, including shifts in predator abundance. We investigated whether this strong temporal environmental variation can maintain within-population variation while eroding adaptive divergence among populations that would be caused by spatial variation in selection. We used neutral genetic markers to explore population structure and then analysed how stickleback armor traits, the associated genes Eda and Pitx1 and elemental composition (%P) varies within and among populations. Despite strong gene flow, we detected evidence for divergence in stickleback defensive traits and Eda genotypes associated with predation regime. However, this among-population variation was lower than that observed among other stickleback populations exposed to divergent predator regimes. In addition, within-population variation was very high as compared to populations from environmentally stable locations. Elemental composition was strongly associated with armor traits, Eda genotype and the presence of predators, thus suggesting that spatiotemporal variation in armor traits generates corresponding variation in elemental phenotypes. We conclude that gene flow, and especially temporal environmental variation, can maintain high levels of within-population variation while reducing, but not eliminating, among-population variation driven by spatial environmental variation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".