Seasonal variation in drivers of bird-window collisions on the west coast of British Columbia, Canada
Bibliographic record
Abstract
We examined the effects of façade-level building and vegetation features on bird-window collision risk, and how these effects varied across seasons at a Pacific coastal campus with mild winters, abundant evergreen vegetation, and seasonally varied bird communities. We searched for bird carcasses at 57 façades of 8 buildings at the University of British Columbia (UBC) over 155 days between January 2015 and March 2017 (total: 8835 façade surveys). Collision monitoring occurred across five equal sampling periods that represented stages of the annual cycle of the bird community, including the fall and spring migratory periods, the breeding season, and the long overwintering period. For each season, we compared logistic regression models predicting the odds of a collision from different sets of façade and vegetation characteristics expected to influence collisions: façade area, area of glass, porous surface cover (ground and shrub vegetation, soil, leaf litter), tree cover, and the number of building stories reflecting vegetation. Consistent with other studies, area of glass had a positive influence on collision probability in all seasons; however, the effect was strongest during the fall migratory period, when daily collision mortality rates peaked at UBC. The number of stories reflecting vegetation also increased collision probability, but only in the fall, indicating that the vertical extent of vegetation and reflective glass may affect collision risk differently as bird communities change across seasons. Façade area increased collision probability only in the winter (a long and lethal period for bird collisions at UBC), reflecting different risk factors associated with the species most vulnerable to collisions in this season. Our results highlight the need to measure building and vegetation effects across the longest and most lethal stages of the annual cycle of birds, both to predict the impact of proposed buildings and to prioritize mitigation strategies that will result in the greatest conservation benefits.
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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 | 0.000 | 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 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".