Reassessment of the Predatory Effects of Rainbow Smelt on Ciscoes in Lake Superior
Bibliographic record
Abstract
Abstract Evidence from small lakes suggests that predation on larval ciscoes Coregonus artedi by nonnative rainbow smelt Osmerus mordax can lead to cisco suppression or extirpation. However, evidence from larger lakes has led to equivocal conclusions. In this study, we examine the potential predation effects of rainbow smelt in two adjacent but contrasting embayments in Lake Superior (Thunder and Black bays, Ontario). During May 2006, we sampled the ichthyoplankton, pelagic fish communities, and diet composition of rainbow smelt in both bays. Using acoustics and midwater trawling, we estimated rainbow smelt densities to be 476 ± 34/ha (mean ± SE) in Thunder Bay and 3,435 ± 460/ha in Black Bay. We used a bioenergetics model to estimate the proportion of cisco larvae consumed by rainbow smelt. Our results suggest that predation by rainbow smelt accounts for 15–52% and 37–100% of the mortality of larval ciscoes in Thunder and Black bays, respectively, depending on the predator feeding rate and the scale of predator–prey overlap. We also examined the sensitivity of past conclusions (based on 1974 field collections) to assumptions of temporal overlap between rainbow smelt and larval ciscoes and estimates of rainbow smelt abundance derived from bottom trawl samples. After adjusting these parameters to reflect current understanding, we found that the previous predation estimates may have been conservative. We conclude that rainbow smelt may have been a more important contributor to the demise and slow recovery of ciscoes in Lake Superior than previously thought.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".