Effects of artificial light at night on fishes: A synthesis with future research priorities
Bibliographic record
Abstract
Abstract Nearly all organisms rely on natural fluctuations of light as cues for synchronizing physiological processes and behavioural actions associated with foraging, growth, sleep and rest, reproduction, and migration. Consequently, although artificial lighting sources have provided a plethora of benefits for humans, they can lead to disruptions for wild organisms. With one quarter of the human population living within 100 km of coastlines, there is great potential for artificial light at night (ALAN) to influence the physiology, behaviour and fitness of fishes. Through a review of the literature (n = 584 publications focused on the effects of ALAN on individual organisms or ecosystems), we illustrate that most papers have concentrated on terrestrial species (59%) compared with aquatic species (20%) or a mixed approach (21%). Fishes have been underrepresented in comparison with many other taxa such as birds, insects and mammals, representing the focus of less than 8% of taxa‐specific publications. While the number of publications per year focusing on fishes has generally been increasing since the mid‐2000s, there has been a downturn in publication rate in the last few years. To understand where research related to ALAN in fishes has been focused, we partitioned studies into categories and found that publications have mostly concerned behaviour (41.0%), abundance and community structure (24.4%), and physiology (22.8%), while the longer‐term effects on fitness (6.9%) are lacking. We synthesize the research completed in fishes and outline future priorities that will help ascertain the short‐ and long‐term consequences of this relatively novel stressor for fish health and persistence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".