Potential Effects of Bigheaded Carps on Four Laurentian Great Lakes Food Webs
Bibliographic record
Abstract
Abstract Bigheaded carps (BHCs; Silver Carp Hypophthalmichthys molitrix and Bighead Carp H. nobilis) are economically and culturally important in Asia and Europe but are considered highly invasive throughout the Mississippi River watershed and pose a threat to the food web and fisheries of the Laurentian Great Lakes. We used the Ecopath with Ecosim model framework to evaluate potential risk of BHC population growth and food web effects in four Great Lakes habitats, including mesotrophic waters of Saginaw Bay (Lake Huron) and Lake Erie and the oligotrophic main basins of Lakes Michigan and Huron. We simulated BHC population growth and food web effects under different scenarios of BHC production rates, prey vulnerability to BHCs, and availability of age-0 BHCs to predation by salmonines. In the main basins of Lakes Michigan and Huron, the projected BHC population growth was low or negative, with a projected final BHC biomass of 0.5–1.1 times the initial introductory biomass (2% of total fish biomass for each BHC species), and BHCs had negligible effects on most food web groups across all scenarios. In contrast, in Saginaw Bay and Lake Erie, the projected BHC biomass was 2.5–12.5 times higher than the initial biomass across all scenarios, and the largest increases occurred under scenarios of high prey vulnerability to BHCs and high BHC production rates. High projected BHC biomass in Saginaw Bay and Lake Erie had negative effects on zooplankton and planktivorous fish groups and mixed effects on piscivores but had relatively negligible effects on most other food web groups across all scenarios. Our results are consistent with reported BHC effects on food webs in the Mississippi River and its tributaries and inform efforts to prevent BHC invasion of the 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".