Energy channelling, food chain length and body condition in a northern lake predator
Bibliographic record
Abstract
Abstract Identifying potential links between food web structure and animal body condition is fundamental for predicting the long‐term persistence of populations under rapidly changing environments. Northern lakes, and the cold‐water adapted species that inhabit them, are particularly vulnerable to a warming climate. We explored relationships among body condition and energy channel use in lake trout (Salvelinus namaycush) and seasonal variation in food‐chain length in the Lake of Two Rivers, Ontario, Canada. Bayesian mixing models using carbon isotope values (δ13C) of essential amino acids showed that individuals utilising multiple energy channels had higher gonad mass, suggesting that dietary diversity has reproductive fitness benefits. Amino acid nitrogen isotope (δ15N) analysis revealed seasonal variation in food chain length, with extension of the food web during winter and spring and truncation of the food web during summer. Although these findings illustrate increased omnivory of lake trout during summer, there was no clear evidence that seasonal changes in food chain length had an impact on trout condition. These findings provide new insight into potential relationships between food web structure, energy flow and consumer condition in freshwater ecosystems, thereby revealing potential responses of species to shifting ecosystem states under intensifying climate change at high latitudes.
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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 | 0.001 | 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) | 0.002 | 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".