Countergradient variation in growth rate: compensation for length of the growing season among Atlantic silversides from different latitudes
Bibliographic record
Abstract
How do organisms adapt to the differences in temperature and length of the growing season that occur with latitude? Among Atlantic silversides (Menidia menidia) along the east coast of North America, the length of the first growing season declines by a factor of about 2.5 with increasing latitude. Yet body size at the end of the first growing season does not decline. High-latitude fish must, therefore, grow faster within the growing season than do low-latitude fish. This geographical pattern has a genetic basis. Laboratory experiments on fish from six different locations revealed a latitudinal gradient in the capacity for growth (i.e., maximum growth potential). In two subsequent experiments using fish from Nova Scotia (NS), New York (NY) and South Carolina (SC) that had been separately reared in a common environment for several generations, differences in growth rate among populations were highly significant. The rank order was NS > NY > SC, but the difference among populations depended on temperature. High-latitude fish outperformed those from low latitudes primarily at the high temperatures that low-latitude fish would be expected to experience most often in nature. These results suggest that instead of being adapted for growth at low temperatures, fish from high latitudes are adapted for rapid elevation of growth rate during the brief interval of the year when high temperatures occur. Selection on growth rate results from sizedependent winter mortality: the importance to winter survival of being large increases with latitude but the length of the growing season simultaneously decreases. The end result is countergradient variation in growth rate, a phenomenon that may be much more widespread than currently recognized.
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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 | 0.001 | 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".