A perspective on the ecological and evolutionary consequences of phenological variability in lake ice on north-temperate lakes
Bibliographic record
Abstract
Climate change is leading to shifts in not only the average timing of phenological events, but also their variance and predictability. Increasing phenological variability creates a stochastic environment that is critically understudied, particularly in aquatic ecosystems. We provide a perspective on the possible implications for increasingly unpredictable aquatic habitats, including more frequent trophic asynchronies and altered hydrologic regimes, focusing on ice-off phenology in lakes. Increasingly frequent phenological extremes may limit the ability of organisms to optimize traits required to adapt to a warming environment. Using a unique, long-term ecological dataset on Escanaba Lake, WI, USA, as a case study, we show that the average date of ice-off is shifting earlier and becoming more variable, thus altering limnological conditions and yielding uncoupled food web responses with ramifications for fish spawn timing and recruitment success. A genes-to-ecosystems understanding of the responses of aquatic communities to increasingly variable phenology is needed. Our perspective suggests that management for diversity, at the intra- and interspecific levels, will become paramount for conserving resilient aquatic ecosystems.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".