Species-specific preferences drive the differential effects of lake factors on fish production
Bibliographic record
Abstract
As the global human population grows, it remains a top priority for communities, managers, policymakers, and stakeholders to maintain healthy, sustainable, and productive fisheries under continued global change. Here we used a dataset consisting of fish and lake characteristics for 536 lakes across Ontario, Canada, to test whether multiple climate, human, and biological factors differentially affect fish production (i.e., population biomass per hectare per year). We tested the hypothesis that temperature is the key driver of fisheries production by testing for the effects of multiple factors on the production of three top predatory fish species: cold-water lake trout (Salvelinus namaycush), cool-water walleye (Sander vitreus), and warm-water smallmouth bass (Micropterus dolomieu). Using boosted regression tree analyses, we found that lake trout production was most influenced by the volume of hypolimnetic habitat, walleye production was related to other climatic variables, and smallmouth bass production was most influenced by sampling day of the year followed by Secchi depth. Our results suggest that current fish production models — that only include temperature and body size — may oversimplify important ecological complexities and thus misinform management decisions because species respond differently to environmental drivers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".