Long-term changes of the Lake Michigan fish community following the reduction of exotic alewife (<i>Alosa pseudoharengus</i>)
Bibliographic record
Abstract
We used our long-term annual bottom trawl survey (1973–2004) in Lake Michigan to reveal the response of the native fish community to the biological control of a dominant exotic fish, alewife (Alosa pseudoharengus), as well as to changes in total phosphorus and salmonine biomass. Through nonmetric multidimensional scaling, we documented a 1970s community largely dominated by alewife, and then a shift to a community dominated by several native species during the 1980s through 1990s, when alewife remained at relatively low levels. We argue that the recovery of burbot (Lota lota), deepwater sculpin (Myoxocephalus thompsonii), and yellow perch (Perca flavescens) was partially or fully aided by the alewife reduction. We argue that changes in phosphorus or salmonines were not directly related to abundance increases of native species. An additional community shift occurred during 1999–2004, which coincided with a reduction in species richness and total fish biomass in our trawl. The mechanisms underlying this latest shift may be related to reductions in nutrients, but further research is required. The restoration of the native fish community has been incomplete, however, as emerald shiner (Notropis atherinioides), cisco (Coregonus artedii), and lake trout (Salvelinus namaycush) have yet to demonstrate recovery.
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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 | 0.000 | 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) | 0.001 | 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".