Demographic effects of artificial nighttime lighting on animal populations
Bibliographic record
Abstract
Artificial lighting, especially but not exclusively through street lights, has transformed the nighttime environment in much of the world. Impacts have been identified across multiple levels of biological organization and process. The influences, however, on population dynamics, particularly through the combined effects on the key demographic rates (immigration, births, deaths, emigration) that determine where individual species occur and in what numbers, have not previously been well characterized. The majority of attention explicitly on demographic parameters to date has been placed on the attraction of organisms to lights, and thus effectively local immigration, the large numbers of individuals that can be involved, and then to some extent the mortality that can often result. Some of the most important influences of nighttime lighting, however, are likely more subtle and less immediately apparent to the human observer. Particularly significant are effects of nighttime lighting on demography that act through (i) circadian clocks and photoperiodism and thence on birth rates; (ii) time partitioning and thence on death rates; and (iii) immigration/emigration through constraining the movements of individuals amongst habitat networks, especially as a consequence of continuously lit linear features such as roads and footpaths. Good model organisms are required to enable the relative consequences of such effects to be effectively determined, and a wider consideration of the effects of artificial light at night is needed in demographic studies across a range of species.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".