Light quality affects chlorophyll biosynthesis and photosynthetic performance in Antarctic Chlamydomonas
Bibliographic record
Abstract
Abstract The perennially ice-covered Lake Bonney in Antarctica has been deemed a natural laboratory for studying life at the extreme. Photosynthetic algae dominate the lake food webs and are adapted to a multitude of extreme conditions including perpetual shading even at the height of the austral summer. Here we examine how the unique light environment in Lake Bonney influences the physiology of two Chlamydomonas species. Chlamydomonas priscuii is found exclusively in the deep photic zone where is receives very low light levels biased in the blue part of the spectrum (400-500 nm). In contrast, Chlamydomonas sp. ICE-MDV is represented at various depths within the water column (including the bright surface waters), and it receives a broad range of light levels and spectral wavelengths. The close phylogenetic relationship and psychrophilic character of both species makes them an ideal system to study the effects of light quality and quantity on chlorophyll biosynthesis and photosynthetic performance in extreme conditions. We show that the shade-adapted C. priscuii exhibits a decreased ability to accumulate chlorophyll and severe photoinhibition when grown under red light compared to blue light. These effects are particularly pronounced under red light of higher intensity, suggesting a loss of capability to acclimate to varied light conditions. In contrast, ICE-MDV has retained the ability to synthesize chlorophyll and maintain photosynthetic efficiency under a broader range of light conditions. Our findings provide insights into the mechanisms of photosynthesis under extreme conditions, and have implications on algal survival in changing conditions of Antarctic ice-covered lakes.
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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".