Stable isotope analysis of western Lake Superior predatory fishes, part one: Trophic niche overlap
Bibliographic record
Abstract
Characterizing food web interactions between introduced and native fish predators is important to quantify niche overlap, assess predator–prey balance, and support fisheries management. Lake Superior provides an ideal setting to investigate potential trophic overlap between native top predators, lake trout ( Salvelinus namaycush ) and walleye ( Sander vitreus ), and a diverse assemblage of introduced salmonids. Angler-caught fish were used to measure trophic position and niche overlap between predatory fishes in the western arm of Lake Superior based on carbon and nitrogen stable isotope ratios (δ 13 C, δ 15 N). Within a species, we did not find consistent differences in either δ 13 C or δ 15 N values by sex, collection year, capture location, or length. Among species, the range in δ 15 N values was >5 ‰, indicating that these species occupy ∼2 trophic positions, with dietary reliance ranging from primarily invertebrates to primarily fish, whereas the range in δ 13 C values indicated dietary reliance on a mix of pelagic and benthic prey. Combined, the δ 13 C and δ 15 N values revealed siscowet lake trout as the apex predator having a distinct trophic niche, while walleye and lean lake trout were intermediate trophic position predators with overlapping isotopic niches, and introduced salmonids were the lowest trophic position predators with overlapping trophic niches. Overlapping trophic niches indicated similar resource reliance and habitat occupancy among native lake trout and walleye, as well as among introduced salmonids, but less so between native and introduced predators.
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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 | 0.001 |
| 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) | 0.001 | 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".