Cascading consequences of shifting ice phenology in an exploited lake
Bibliographic record
Abstract
Abstract Ice phenology (onset and breakup of ice cover) regulates the timings of seasonal life history events of many animal and plant species in high-latitude lakes in the Northern Hemisphere, promoting the coexistence of species that share resources like food and shelter. Increasingly warmer, more variable Earth’s climate is, however, shifting ice phenology, modifying habitat conditions, and in turn reshaping the population and interaction dynamics of many species, threatening their persistence. Applying multivariate autoregressive state-space time series modeling to extensive historical and contemporary monitoring records (spanning 36 to 113 years) of limnological and biological surveys and fishery catches, we explore how shifting ice phenology propagates through a food web over time by modulating thermal profiles and limnological properties in Lake Simcoe, a large, intensely exploited system in southern Ontario, Canada. Analysis shows that increasingly earlier ice breakups, later ice freeze-ups, earlier thermal stratification onset, and later turnover since the early 1980s, attenuated algal production. This in turn, combined with increasingly more abundant invasive species (dreissenids) that promoted greater water clarity over prolonged stratification periods, diminished zooplankton abundances. These limnological changes filtered through plankton communities further modified the population and community dynamics of higher trophic-level species like fish with differential thermal preferences, interactively with time-varying non-climate drivers. Although management interventions–reductions in nutrient loading and fishing effort in particular–in recent decades aided the recoveries of depleted cold-adapted fish populations (including lake trout, lake whitefish, and cisco), their interaction patterns and strengths with species adapted to warmer conditions were reshuffled by advanced timings of ice breakup and delayed timings of ice freeze-up. These findings reveal how complex food web dynamics can emerge from ecologically divergent responses that reshape species interactions within and among trophic levels under shifting ice phenology filtered through bottom-up processes, likely buffering destabilizing effects of a changing climate.
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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 | 0.001 | 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".