Behaviour and landscape contexts determine the effects of artificial light on two crepuscular bird species
Bibliographic record
Abstract
Abstract Context Artificial light at night (ALAN) is increasing worldwide, with many ecological effects. Aerial insectivores may benefit from foraging on insects congregating at light sources. However, ALAN could negatively impact them by increasing nest visibility and predation risk, especially for ground-nesting species like nightjars (Caprimulgidae). Objectives We tested predictions based on these two alternative hypotheses, potential foraging benefits vs potential predation costs of ALAN, for two nightjar species in British Columbia: Common Nighthawks (Chordeiles minor) and Common Poorwills (Phalaenoptilus nuttallii). Methods We modeled the relationship between ALAN and relative abundance using count data from the Canadian Nightjar Survey. We distinguished territorial from extra-territorial Common Nighthawks based on their wingboom behaviour. Results We found limited support for the foraging benefit hypothesis: there was an increase in relative abundance of extra-territorial Common Nighthawks in areas with higher ALAN but only in areas with little to no urban land cover. Common Nighthawks’ association with ALAN became negative in areas with 18% or more urban land cover. We found support for the nest predation hypothesis: the were strong negative associations with ALAN for both Common Poorwills and territorial Common Nighthawks. Conclusions The positive effects of ALAN on foraging nightjars may be limited to species that can forage outside their nesting territory and to non-urban areas, while the negative effects of ALAN on nesting nightjars may persist across species and landscape contexts. Reducing light pollution in breeding habitat may be important for nightjars and other bird species that nest on the ground.
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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 | 0.001 |
| 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 | 0.000 |
| 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".