Balancing prey availability and predator consumption: a multispecies stock assessment for Lake Ontario
Bibliographic record
Abstract
Trophic interactions are drivers of ecosystem change and stability, yet are often excluded from fishery assessment models, despite their potential capacity to improve estimates of species dynamics and future fishery sustainability. In Lake Ontario, recreational salmonine fisheries, including Chinook salmon ( Oncorhynchus tshawytscha) and lake trout ( Salvelinus namaycush), depend on a single prey species, alewife ( Alosa pseudoharengus). To accommodate strong trophic interactions among species, we developed a multispecies statistical catch-at-age assessment (MSCAA) model that links the dynamics of the salmonine fisheries and alewife via prey consumption and predator growth. We found that prey availability had declined since 2015 due to decreased alewife recruitment and increased Chinook salmon biomass, leading to higher alewife mortality rates and lower predator growth rates. Forward projections of predator–prey dynamics suggest that Chinook salmon stocking reductions may improve the probability for alewife population growth, but could be counteracted by increased natural Chinook salmon recruitment. Combined with predator- and prey-monitoring efforts, multispecies assessments show promise as models of intermediate complexity to support a transition to ecosystem-based approaches to fisheries management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".