Urbanization and artificial light at night reduce the functional connectivity of migratory aerial habitat
Bibliographic record
Abstract
Flying animals use aerial habitats to forage, communicate and travel. However, human activities that fragment aerial habitat with built structures, noise, and chemical or light pollution, may limit the ability of wildlife to use airspace efficiently. Applying landscape connectivity theory to aerial habitats could reveal how long‐distance migrants respond to sources of aerial habitat fragmentation along their migratory routes. Artificial light at night is a major component of urbanization that fragments dark skies across North America. Attraction of nocturnal migrants to urban light is well documented, but species‐specific responses, especially throughout a full migration from breeding to wintering grounds, are not. We tested hypotheses about long‐distance migratory movements in relation to artificial light using a highly nocturnal, Nearctic‐Neotropical avian migrant (Eastern whip‐poor‐will Antrostomus vociferus ). We applied a resource selection framework at multiple spatial scales to explore whether GPS‐tracked birds (n = 10) responded to urbanization in general, or artificial light specifically, during migratory flights. We found little evidence of attraction to artificial light during nocturnal flights. Artificial light and urbanization were highly correlated and difficult to disentangle, but the birds generally avoided urban areas and selected dark‐connected skies for travel. Migratory stopovers (locations where GPS‐tracked birds (n = 20) paused for at least one night), were located almost exclusively in dark, rural areas. Our results illustrate that considering how nocturnal aerial migrants respond to both aerial and terrestrial habitat elements can improve our understanding of what may facilitate their long‐distance movements.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".