Hypoxia’s impact on pelagic fish populations in Lake Erie: a tale of two planktivores
Bibliographic record
Abstract
Whether bottom hypoxia has long-lasting consequences for pelagic fish populations remains speculative for most ecosystems. We explored hypoxia’s influence on two pelagic zooplanktivores in Lake Erie that have different thermal preferences: cold-water rainbow smelt (Osmerus mordax) and warm-water emerald shiners (Notropis atherinoides). To assess acute effects, we combined predictive bioenergetics-based modeling with field collections made across the hypoxic season in central Lake Erie during 2005 and 2007. To assess chronic effects, we related fishery-independent and fishery-dependent catches with hypoxia severity and top predator (walleye, Sander vitreus) abundance during 1986–2014. As our modeling predicted, hypoxia altered rainbow smelt movement and distributions, leading to avoidance of cold, hypoxic bottom waters. In response, diets shifted from benthic to pelagic organisms, and consumption and energetic condition declined. These changes were lacking in emerald shiners. Our long-term analyses showed rainbow smelt abundance and hypoxia to be negatively related and suggested that hypoxia avoidance increases susceptibility to commercial fishing and walleye predation. Collectively, our findings indicate that hypoxia can negatively affect pelagic fish populations over the long term, especially those requiring cold water.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".