Summer ecosystem structure in mountain lakes linked to interannual variability of lake ice, snowpack, and landscape attributes
Bibliographic record
Abstract
Abstract Mountain lakes experience interannual variability in spring snowpack and ice cover that can lead to differences in physical, chemical, and biological properties in the succeeding summer. Lake studies that capture extreme years of snow and ice would be useful to understand and anticipate effects of climate change, but such data are rare for remote mountain lakes. Monitoring of lakes in Olympic, North Cascades, and Mount Rainier National Parks from 2007 to 2018 allowed us to examine limnological differences along interannual and elevation‐driven climate gradients that included unusually high (2011–2012) and 100‐yr record low (2015) snowpack years. Years with lower spring snowpack had earlier ice‐out. Across lakes, our analysis suggested an average of 0.075°C lake warming per day of lost ice duration (0.525°C per week), giving rise to other ecosystem changes linked to temperature such as lower dissolved oxygen, higher total dissolved N, higher chlorophyll, and higher abundance of cladoceran zooplankton. Conversely, in years with higher snowpack and a shorter ice‐free season, lakes were colder and clearer (1 m deeper Secchi depth for every 1 m May snow water equivalent), with more dilute ions as well as lower algal biomass and zooplankton abundance. These results add to evidence that changes in snowpack or ice‐out dates alter mountain lake ecology through multiple processes associated with hydrology, terrestrial‐aquatic connection, water temperature, productivity, ion composition, and plankton communities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".