Winter ecology of specialist and generalist morphs of European whitefish, <i>Coregonus lavaretus</i>, in subarctic northern Europe
Bibliographic record
Abstract
European whitefish is a model species for adaptive radiation of fishes in temperate and subarctic lakes. In northern Europe the most commonly observed morphotypes are a generalist (LSR) morph and a pelagic specialist (DR) morph. The evolution of a pelagic specialist morph is something of an enigma, however, as this region is characterized by long, dark winters with pelagic primary production limited to a brief window in late summer. We conducted the first winter-based study of polymorphic whitefish populations to determine the winter ecology of both morphs, and we combined seasonal diet and stable isotope analysis with several proxies of condition in three polymorphic whitefish populations. The generalist LSR morph fed on benthic and pelagic prey in summer but was solely reliant on benthic prey in winter. This was associated with a noticeable but moderate reduction in condition, lipid content and stomach fullness in winter relative to summer. In contrast, the DR whitefish occupied a strict pelagic niche in both seasons. A significant reduction in pelagic prey during winter resulted in severe decrease in condition, lipid content and stomach fullness in DR whitefish in winter relative to summer, with the pelagic morph apparently approaching starvation in winter. We suggest that this divergent approach to seasonal foraging is associated with the divergent life-history traits of both morphs.
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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) | 0.000 | 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 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".