Drivers of walleye, brook trout, and lake trout abundance in Québec lakes
Bibliographic record
Abstract
Inland fisheries provide essential ecosystem services, yet they face growing threats from anthropogenic activities and climate change. To sustainably manage these fisheries under global change, understanding the main drivers of freshwater fish populations is key. Since the late 1980s, the Québec government has conducted standardized gillnet surveys to monitor game fish species and their habitats. Here, we identified the main drivers influencing the abundance of walleye, brook trout, and lake trout. Through random forest models, we analyzed 662 lakes and 38 predictor variables, revealing that fish community composition shaped the abundance of the two salmonid species. The best model performance was for brook trout ( R 2 = 0.54), followed by lake trout ( R 2 = 0.46), and walleye ( R 2 = 0.44). Brook trout populations were larger in allopatric lakes, while lake trout abundances were smaller in association with large piscivores. On the other hand, climate was more important for walleye. The dominant explanatory variables varied across species, suggesting different ecological niches. Our findings deepen understanding of fish population drivers in Québec lakes, highlighting the need for management plans to consider fish community context.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".