Declines in ice cover induce light limitation in freshwater diatoms
Bibliographic record
Abstract
Abstract The rediscovery of diatom blooms embedded within and beneath Lake Erie ice cover (2007-2012) ignited an intense interest in psychrophilic adaptations and winter limnology. Subsequent studies determined ice plays a vital role in winter diatom ecophysiology, as diatoms partition to the underside of ice thereby fixing their location within the photic zone. Yet, climate change has led to widespread ice decline across the Great Lakes, with Lake Erie presenting a nearly ice-free state in several recent winters. It has been hypothesized the resultant turbid, isothermal water column will induce light limitation amongst winter diatoms, serving as a detrimental competitive disadvantage. Here, we conducted a physiochemical and metatranscriptomic survey of the winter Lake Erie water column (2019-2020) that spanned spatial, temporal, and climatic gradients to investigate this hypothesis. We determined ice-free conditions decreased diatom bloom magnitude and altered diatom community composition. Diatoms increased the expression of various photosynthetic genes and iron transporters, suggesting they are attempting to increase their quantity of photosystems and light-harvesting components (a well-defined indicator of light limitation). Notably, we identified two gene families which serve to increase diatom fitness in the turbid ice-free water column: proton-pumping rhodopsins (a second means of light-driven energy acquisition) and fasciclins (a means to “raft” together to increase buoyancy and co-locate to the surface to optimize light acquisition). With large-scale climatic changes already underway, our observations provide insight into how diatoms respond to the dynamic ice conditions of today and shed light on how they will fare in a climatically altered tomorrow.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".