Mismatch between climate-based bioenergetics model of fish growth and long-term and regional-scale empirical data
Bibliographic record
Abstract
Climate-driven decreases in body size have been documented for a variety of taxa and proposed as a universal response to climate change. However, empirical support among taxa, including fishes, has been mixed, with some fishes growing larger at higher temperatures, and causal mechanisms for faster or slower growth under debate. We simulated effects of climate warming on bluegill ( Lepomis macrochirus) growth and consumption and used linear regression and boosted regression trees (BRTs) to model length-at-age for bluegill from Michigan lakes from 1945 to 2019. Bioenergetics models showed bluegill growth and consumption both increase under climate warming. In contrast, linear regression revealed that bluegill ages 1–4 decreased (–0.20 to –0.55 mm/year) in mean length-at-age and that ages 5–8 increased or did not statistically change. BRTs demonstrated that growth had a unimodal relationship with surface water temperature and degree days, peaking at intermediate values. This mismatch between simulations and empirical data may be from increased recruitment leading to increased food limitation at higher temperatures. Future research should empirically test this hypothesis and assess the consequences for ecosystem functions and services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".