Walleye recruitment success is less resilient to warming water temperatures in lakes with abundant largemouth bass populations
Bibliographic record
Abstract
Lakes respond heterogeneously to climate, with implications for fisheries management. We analyzed walleye (Sander vitreus) recruitment to age-0 in 359 lakes in Wisconsin, USA, to (i) quantify the relationship between annual water temperature degree days (DD) and walleye recruitment success and (ii) identify the influence of lake characteristics — area, conductivity, largemouth bass (Micropterus salmoides) catch rates, and mean DD — on this relationship. The relationship between walleye recruitment and annual DD varied among lakes and was not distinguishable from zero overall (posterior mean = −0.11, 90% CI = −0.34, 0.15). DD effects on recruitment were negative in 198 lakes (55%) and positive in 161 (45%). The effect of annual DD was most negative in lakes with high largemouth bass densities, and, on average, the probability of recruitment was highest in large lakes with low largemouth bass densities. Conductivity and mean DD influenced neither recruitment nor the effect of annual DD. Walleye recruitment was most resilient to warming in lakes with few largemouth bass, suggesting that the effects of climate change depend on lake-specific food-web and habitat contexts.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".