Long-term food web change in Lake Superior
Bibliographic record
Abstract
Restoration and rehabilitation of native species in the Laurentian Great Lakes is a priority for fisheries management agencies. Restoration efforts are increasingly incorporating a perspective that considers species within a broader food web context. We used stable isotope analysis and museum-preserved specimens to describe and quantify 100 years of food web changes in the Lake Superior fish community. We validated stable isotope analysis of museum specimens by showing a positive correlation between isotope- and diet-based estimates of trophic position. While introductions have created a more trophically diverse food web than historically found in Lake Superior, two separate metrics revealed little community-wide change in the food web. Our species-specific analysis revealed trophic niche differences between shortjaw ( Coregonus zenithicus ) and shortnose ( Coregonus reighardi ) ciscoes, two species previously argued to be indistinguishable based on morphological characteristics. By providing a historical context, our findings show the ability of the Lake Superior food web to accommodate non-native species introductions over the last century while still supporting native species populations. This long-term information about food web structure can help guide management and restoration goals in Lake Superior. Furthermore, Lake Superior can serve as a basis for comparing food web changes in other, more highly altered Great Lakes.
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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 | 0.001 |
| 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) | 0.001 | 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".