Low temperature and low salinity drive putatively adaptive growth differences in populations of threespine stickleback
Bibliographic record
Abstract
Colonisation can expose organisms to novel combinations of abiotic and biotic factors and drive adaptive divergence. Yet, studies investigating the interactive effects of multiple abiotic factors on the evolution of physiological traits remain rare. Here we examine the effects of low salinity, low temperature, and their interaction on the growth of three North American populations of threespine stickleback (Gasterosteus aculeatus). In north-temperate freshwater habitats, stickleback populations experience a combination of low salinity and low winter temperatures that are not experienced by the ancestral marine and anadromous populations. Here we show that both salinity and temperature, and their interaction, have stronger negative effects on marine and anadromous populations than a freshwater population. Freshwater stickleback showed only a ~20% reduction in specific growth rate when exposed to 4 °C, while marine and anadromous stickleback showed sharp declines (82% and 74% respectively) under these conditions. The modest decreases in growth in freshwater stickleback in fresh water in the cold strongly suggest that this population has the capacity for physiological compensation to offset the negative thermodynamic effects of low temperature on growth. These results are suggestive of adaptive evolution in response to the interactive effects of low salinity and low temperature during freshwater colonisation.
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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".