Shining the light on marine infrastructure: The use of artificial light to manipulate benthic marine communities
Bibliographic record
Abstract
Abstract In urbanised areas, marine infrastructure alters natural light regimes, creating habitats that are permanently shaded, affecting the community structure of settling and resident biota. While applications of artificial lights have been suggested to minimise impacts of shading or manipulate community composition for specific aims (e.g. carbon capture, biosecurity), their use has not been evaluated. We used two field experiments to test the effect of artificial light of low (1 × 20,000 lumen light) and high (3 × 20,000 lumen light or 1 × 66,000 lumen light) intensities (7 AM–7 PM on–off cycle) on benthic communities on marine infrastructure. Specifically, we assessed whether artificial lights (i) led to the development of communities similar to those under natural light conditions, (ii) enhanced the abundance of algae and discouraged invertebrates (functional groups that are inversely impacted by shade) and whether (iii) the extent to which artificial lights impact marine communities varies across environmental settings (high and low turbidity) where natural light availability differs. Impacts were assessed on new (two sites) and established communities (one site). We also assessed the effect of artificial light on predation/herbivory pressure using a caging experiment at the low turbidity site. For new communities developing on bare substrate in low turbidity conditions, low intensity artificial light resulted in similar communities to bare substrate exposed to natural light levels. High intensity artificial light increased algal cover beyond communities developing in natural light. Caging increased the cover of algae and invertebrates across all treatments. In turbid locations, artificial light (high and low) increased algal abundance, though to a lesser extent. The addition of artificial light had limited effects on established benthic communities. Synthesis and applications. Our experiments indicate that artificial lights can be effective in reversing effects of shading, but that targeted outcomes (e.g. increased algal or reduced invertebrate cover) are limited to bare substrates and low turbidity environments where more light is available, and algae are naturally abundant.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".