Differential effects of anthropogenic noise and vegetation cover on the breeding phenology and success of two urban passerines
Bibliographic record
Abstract
The urban environment is associated with a multitude of challenges and stressors for populations of wild species from the surrounding natural environment. Among those, habitat fragmentation and noise pollution are suspected to have negative effects on the behavior and physiology of free-living birds in urban areas. Exposure in early life and chronic exposure to anthropogenic noise could be particularly deleterious, with short-and long-term consequences. In this study, we investigated if noise levels in city parks affect the distribution and reproductive success of two common bird species in the urban environment, the great tit (Parus major) and the blue tit (Cyanistes caeruleus) and if vegetation cover could mitigate those effects. We predicted that high noise levels might correlate with a decreased nest-box occupancy rate, a delayed laying date or a decreased clutch size, hatching, and fledging success. On the contrary, vegetation cover was expected to correlate positively with nest occupancy rate, advanced laying date, increased clutch size, hatching, and fledging success. We used data from population monitoring collected between 2012 and 2019 in parks and green public spaces in the city center and suburbs of Paris, France, and did not find any correlation between nest occupancy rates and noise levels or vegetation cover for both species. Laying date was not significantly related to anthropogenic noise in any species but was delayed with increasing vegetation cover in the great tit, while we did not find any association with clutch size. Hatching success in blue tits negatively correlated with increasing noise levels, and positively with increasing vegetation coverage. Finally, we did not find any correlation between anthropogenic noise or vegetation cover and the clutch size or fledging success in both species. In this study, two closely related species that share a common environment show a different sensibility to environmental parameters during reproduction, a key period for population maintenance. It also highlights the importance of considering multiple parameters when studying wild populations living in the urban environment.
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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 | 0.000 | 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".