Niche segregation between Arctic char (<i>Salvelinus alpinus</i>) and brown trout (<i>Salmo trutta</i>): an experimental study of mechanisms
Bibliographic record
Abstract
Interactive competition has been suggested to be an important mechanism by which brown trout (Salmo trutta) and Arctic char (Salvelinus alpinus) segregate into benthic vs. pelagic niches. According to the interactive competition hypotheses, Arctic char and brown trout should have the same preference for prey. We tested this by studying foraging performance when char and trout were offered small pelagic Daphnia longispina and (or) large epibenthic Gammarus lacustris in 10-min foraging experiments with solitary fish and with fish competing pairwise. There were obvious behavioural differences between char and trout. Trout were profoundly more aggressive than char. In comparison, char chose small pelagic daphnids and were superior daphnid foragers. Trout chose large epibenthic gammarids and were superior gammarid foragers. When competing, char and trout segregated such that rate of feeding on the chosen prey type was similar to solitary foraging fish, whereas rate of feeding on the alternative prey type was close to zero. We suggest that the observed selective differences in foraging behaviour, choice of prey, and feeding rates play an important role in niche segregation between Arctic char and brown trout. Hence, our results conform more closely with selective processes, rather than interactive processes, as the founding mechanisms for such segregation.
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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 | 0.001 |
| 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".