The influence of artificial light at night on algal phenol concentrations can mediate herbivore–algal interactions
Bibliographic record
Abstract
Artificial light at night (ALAN) is a growing anthropogenic stressor affecting all biological levels of complexity. Despite this, only a few studies have measured its influence on photosynthetic organisms, and even fewer its effects on macroalgae and their interaction with herbivores. Of particular interest is the potential influence of ALAN on secondary metabolites, such as phenolic compounds, that are used by macroalgae to deter herbivores. Hence, this study focused on the influence of ALAN on the production of phenolic compounds by a common red alga from the Southeast Pacific ( Mazzaella laminarioides ), and indirectly on its influence on the feeding behaviour of one of its main consumers, the black snail ( Tegula atra ). We first monitored the daily and tidal variation in phenolic compound concentrations in algal tissues. Then, we examined their changes when exposed to three light treatments: day/night conditions, continuous darkness and continuous light (ALAN). Then, algae exposed to these treatments were exposed to black snails to examine herbivore–algal interactions. The monitoring of phenolic compounds showed that the highest concentrations were reached during high tide, the period in which herbivores in the field also exhibit their highest feeding activity. When comparing the influence of light treatments, algae exposed to day/night conditions showed significantly higher phenol concentrations, whereas those exposed to ALAN showed the lowest concentration. Snail herbivory rates without a choice were significantly higher on algal fronds priorly exposed to ALAN, and lower on algae exposed to either day/night or dark conditions. These results were consistent with snail preferences, which when presented with a dual choice, always consumed more of the algae exposed to ALAN. Our results suggest that phenolic compounds are produced in lower amounts when algae are exposed to ALAN, therefore increasing algal palatability and potentially altering herbivores consumption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".