Can migration mitigate the effects of ecosystem change? Patterns of dispersal, energy acquisition and allocation in Great Lakes lake whitefish (Coregonus clupeaformis)
Bibliographic record
Abstract
Migration can be a behavioural response to poor or declining home range habitat quality and can occur when the costs of migration are overcome by the benefi ts of encountering higher- quality resources elsewhere. Despite dramatic ecosystem-level changes in the benthic food web of the Laurentian Great Lakes since the colonization of dreissenid mussels, coincident changes in condition and growth rates among benthivorous lake whitefi sh populations have been variable. We hypothesized that this variation could be in part mitigated by differences in migratory habits among populations, where increased migration distance can result in an increased probability of encountering high-quality habitat (relative to the home range). Results from four Great Lakes populations support this hypothesis; relative growth rates increased regularly with migration distance. The population with the largest average migration distance also had the least reduction in size-at-age during a period of signifi cant ecosystem change and among the highest estimated consumption and activity rates. In comparison, the population with the greatest declines in size-at-age was among the least mobile, demonstrating only moderate rates of consumption and activity. The least mobile population of lake whitefi sh was supported by a remnant Diporeia population and has experienced only moderate temporal growth declines. Our study provides evidence for the potential role of migration in mitigating the effects of ecosystem change on lake whitefi sh populations.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".