Spatiotemporal patterns in trophic niche overlap among five salmonines in Lake Michigan, USA
Bibliographic record
Abstract
Native lake trout (Salvelinus namaycush) and introduced Chinook salmon (Oncorhynchus tshawytscha), coho salmon (Oncorhynchus kisutch), steelhead (Oncorhynchus mykiss), and brown trout (Salmo trutta) are major predators in Lake Michigan’s complex ecosystem and collectively support a valuable recreational fishery, but declines in their primary prey, alewife (Alosa pseudoharengus), have raised ecological and management concerns about competition and prey allocation. We applied niche overlap analysis to evaluate competition among salmonine predators during rapid forage base change in Lake Michigan. δ13C and δ15N stable isotope ratios indicated that lake trout had a unique trophic niche from inclusion of offshore and benthic prey, with <29% lake-wide niche overlap with Chinook salmon, coho salmon, and steelhead. Brown trout had moderate overlap with other species (45%–91%), while Chinook salmon, coho salmon, and steelhead had high overlap (71%–98%). Regional differences in isotopic signatures highlighted the potential importance of subsystem differences in fish diets in large aquatic systems. The uniqueness of the lake trout niche, and broadness of brown trout and steelhead niches, suggest these species may be resilient to forage base changes. This study further demonstrates how niche overlap analysis can be applied to tease apart competitive interactions and their response to ecosystem change.
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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 | 0.001 |
| 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".