Lake characteristics drive concordant trophic responses across ecosystems in three top predator fish species
Bibliographic record
Abstract
Understanding similarities in trophic ecology of top predators is crucial given their influences on food webs. We sampled walleye ( Sander vitreus), northern pike ( Esox lucius), and muskellunge ( Esox masquinongy) from 17 Minnesota (USA) lakes and used δ13C and δ15N to estimate littoral carbon use, trophic position, and isotopic niche size of each species. All three species showed large inter-lake variability yet had concordant trophic responses across lakes, as littoral carbon use, trophic position, variability in littoral carbon and trophic position, and niche size were all positively related among species across lakes. Concordant responses were driven by a few key lake variables, with trophic position positively related to proportion littoral area and depth of hypoxic water, littoral carbon positively related to depth of hypoxic water and presence of zebra mussels ( Dreissena polymorpha), and niche size inversely related to lake area. Our results indicate that lake characteristics may influence food webs via consistent effects on multiple top predators. They also show that the amount of suitable habitat can be important for the ecosystem size hypothesis for trophic position.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".