Winter is not coming: evaluating impacts of changing winter conditions on coregonine reproductive phenology
Bibliographic record
Abstract
Fishes in northern latitude lakes are at risk from climate-induced warming because the seasonality in water temperature is degrading, which can change ecosystem properties and the phenology of life-history events. Temperature-dependent embryo development models were developed for a group of cold, stenothermic fishes (Salmonidae Coregoninae) to assess the potential impacts of climate-induced changes in water temperature on cisco (Coregonus artedi) from two populations in Lake Superior (Apostle Islands [USA] and Thunder Bay [Canada]) and one in Lake Ontario (USA), vendace (C. albula) in Lake Southern Konnevesi (Finland), and European whitefish (C. lavaretus) in lakes Southern Konnevesi, Constance (Germany), Geneva (France), and Annecy (France). Water temperatures for each study group were simulated and changes in reproductive phenology across historic (1900–2006) and three future climatic-warming scenarios (2007–2099) were investigated. Models predicted that increases in water temperatures are likely to cause delayed spawning, shorter embryo incubation durations, and earlier larval hatching. Relative changes increased as warming scenarios increased in severity and were higher for littoral as compared to pelagic populations. Our simulations demonstrated that slower cooling in the autumn and (or) more rapid warming in spring can translate into substantial changes in the reproductive phenology of coregonines among our study groups. We expect that the changes in reproductive phenology predicted by our models, in the absence of thermal or behavioral adaptation, will have negative implications for population sustainability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".