Drivers of walleye recruitment in Minnesota’s large lakes
Bibliographic record
Abstract
Fish recruitment is complex and difficult to predict. Data-driven approaches show promise for predicting recruitment and understanding its drivers. We used a random forest model to infer relationships between year-class strength and 17 variables describing potential recruitment drivers across 30+ years of walleye (Sander vitreus) data from Minnesota’s nine largest inland lakes. Our model explained 20% of the variation in year-class strength overall, with predictive performance varying among lakes (–8% to 37% explained variance). Of the variables analyzed, degree-days during the first year of life and first winter severity were the most important for predicting recruitment, with relatively weak year classes predicted to occur with cold first growing seasons and severe first winters. Other thermal variables were secondarily important predictors of year-class strength. Predicted year-class strength was positively related to stock size and stocking and negatively related to the presence of invasive species; however, these variables were less important than thermal variables. Our results indicate that thermal conditions in early life can have a substantial impact on walleye recruitment and highlight the potential for differing recruitment drivers and dynamics among lakes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".