Two Wrongs Make a Right: High Salinity and Low Light Intensity Protects Polar Algae from Heat Stress
Bibliographic record
Abstract
The world is dominated by cold environments that include the poles, the deep ocean, and alpine regions. Polar algae support the aquatic food chain and are increasingly threatened by climate change. With a changing climate, ice cover is decreasing with increased temperatures, leading to changes in light availability and salinity. Using two closely related but geographically distant algal species, Chlamydomonas priscuii and Chlamydomonas malina, we examined how the heat stress responses changed depending on their culturing conditions. C. malina was isolated from the Beauford Sea in the Canadian Arctic and C. priscuii is from the permanently ice-covered Lake Bonney, Antarctica. This work looks at two questions; (1) how cold adapted algae responds to changing conditions and subsequent heat stress, and (2) what contributes to stress resistance? We found that algae show robust growth across a wide spectrum of light and salinity but have the fastest growth rates at low salinity and high light intensities. These fast-growing algae are the most susceptible to heat stress indicating liability during climate change. High salinity grown algae were more resilient when challenged by heat stress in the terms of maintaining photosynthetic efficiency, attenuated ROS production, and delayed cell death. At high salinity C. priscuii produces high levels of glycerol which for the first time in green algae has been shown to contribute to thermotolerance. Overall, this work provides insight into the stress resilience of psychrophiles, something that is especially pertinent in our age of rapid climate change.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".