Negative phototaxis in the photosymbiotic sea anemone Aiptasia as a potential strategy to protect symbionts from photodamage
Bibliographic record
Abstract
Photosymbiotic cnidarians generally seek bright environments so that their symbionts can be photosynthetically active. However, excess light may result in a breakdown of symbiosis due to the accumulation of photodamage in symbionts causing symbiont loss (bleaching). It is currently unknown if photosymbiotic cnidarians sense light only to regulate spawning time and to facilitate predation, or whether they also use their light-sensing capacities to protect their symbionts from photodamage. In this study, we examined how the sea anemone Aiptasia changes its behaviour when exposed to excess light. We reveal that Aiptasia polyps, when carrying symbionts, contract their bodies when exposed to high light intensities and subsequently migrate away in a direction perpendicular to the light source. Interestingly, this negative phototaxis was only evident under blue light and absent upon UV, green and red light exposure. Non-symbiotic Aiptasia did not exhibit this light response. Our study demonstrates that photosymbiotic Aiptasia polyps display negative phototactic behaviour in response to blue light, and that they also can perceive its direction, despite lacking specialized eye structures. We postulate that Aiptasia uses blue light, which penetrates seawater efficiently, as a general proxy for sunlight exposure to protect its symbionts from photodamage.
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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".