Human recreational activity does not influence open cup avian nest survival in urban green spaces
Bibliographic record
Abstract
Abstract The breeding period of birds is a critical and sensitive portion of the annual cycle. Understanding how human use of urban green spaces affects nest survival can improve our understanding of conserving breeding bird populations in cities and support science-based management of urban green spaces that benefit both people and nature. We conducted a nest survival field study between April and August of 2023 in multiple green spaces in Montreal, Quebec, Canada, the country’s second-largest city. We asked whether human presence (distance to trails and amount of human activity) influences the nest survival of four common open-cup nesting bird species: American robins ( Turdus migratorius ), gray catbirds ( Dumetella carolinensis ), Northern cardinals ( Cardinalis cardinalis ), and yellow warblers ( Setophaga petechia ). We also asked if variables traditionally associated with nest survival, such as vegetation concealment and seasonality, would influence nest survival. Our analyses surprisingly revealed no significant influence of human activity, vegetation concealment, and seasonality on nest survival for our target species. We found for nests that did fail, nests established during the earlier part of the nesting period failed faster. American robin nests were the most successful of our study’s four target species, whereas Northern cardinal nests were the least successful. Within the limitations of our study system, our findings suggest that human presence on trails is not negatively impacting the nesting success for our target bird species using urban green spaces. Our study provides integrated science advice to land managers so they can support opportunities for people to connect with nature without causing trade-offs with biodiversity conservation.
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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 | 0.001 | 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) | 0.002 | 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".