Artificial light at night (ALAN) pollution alters bat lunar chronobiology: insights from broad-scale long-term acoustic monitoring
Bibliographic record
Abstract
Abstract Background The timing of behavior and habitat use of nocturnal animals can be influenced by the lunar cycle in nature. The prevalence of artificial light at night (ALAN) has been recognized as a source of environmental pollution. The interaction between ALAN and the lunar cycle on bat behavior is important for understanding anthropogenic effects on bats. We utilized a decade (2012–2022) of acoustic monitoring data collected in North Carolina, United States, to investigate the relationship between bat activity, lunar cycle, and light pollution. We examined whether the amount of lunar illumination affected species-specific nightly activity and whether hourly bat activity patterns varied between nights with different moon phases. We further investigated if the relationship between bat activity and the lunar cycle might be altered by light pollution. Results We found that seven bat species showed activity variation across nights in relation to the amount of moon illumination when ALAN was absent. In general, bats were less active on full moon nights compared to new moon nights. Light pollution interacted with the bat–lunar relationship in five of the seven species, masking the effect of the lunar cycle. We identified delayed bat activity patterns on nights with a full or waxing moon in seven species, and light pollution altered that pattern in four species. Overall, ALAN was associated with decreased bat activity independent of lunar cycle effects. Conclusions Our study demonstrated that at a broad spatial scale, ALAN negatively affected many North American temperate bat species and altered their lunar chronobiology. As light pollution is spreading to historically dark areas and habitats, ALAN might couple with other threats, such as the white-nose syndrome or climate change, to cause cascading damage in the environment that depends on ecosystem services such as pest control provided by bats. We argue that further research and conservation actions are needed to mitigate the impact of light pollution.
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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 | 0.001 | 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".