Lake temperature and morphometry shape the thermal composition of recreational fishing catch
Bibliographic record
Abstract
Abstract Objective Managing freshwater fisheries in warming lakes is challenging because climate change impacts anglers, fish, and their interactions. Methods We integrated recent models of current and future lake temperatures with recreational fisheries catch data from 587 lakes in three north-central U.S. states (Michigan, Minnesota, and Wisconsin) to evaluate how the thermal composition of recreational fisheries catch varied as a function of temperature, ice coverage, and lake morphometry. Result We found that warmwater catch share (WCS), defined as the proportion of fish in recreational angling catch that belonged to the warmwater thermal guild (final temperature preferendum [FTP] > 25°C), increased with average annual lake surface temperature and decreased with survey ice coverage. However, we also found that WCS decreased with increased lake area and depth. Using mid-century (2040–2060) water temperature and ice projections while holding all other variables constant, we predicted that WCS will likely increase as the climate warms but that significant thermal heterogeneity will persist. Conclusion Lakes that are large (>100 ha) and deep (>10 m) and those with cooler (<3700 annual growing degree-days) predicted future temperatures will likely hold thermal refugia for coolwater (FTP = 19–25°C) and coldwater (FTP < 19°C) fish even as average lake temperatures rise, creating the potential for management actions to resist the shift from coolwater to warmwater fisheries. Managers of smaller and more rapidly warming lakes may want to consider strategies that accept or direct emerging warmwater fishing opportunities. We suggest that the most viable path to climate adaptation in landscapes of diverse lakes may be to resist warmwater shifts where possible and to accept or direct the rise of warmwater fishing opportunities where necessary.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".