Band recovery data illustrate spatiotemporal and taxonomic patterns of seabird collisions with anthropogenic structures
Bibliographic record
Abstract
Seabird populations are declining rapidly due to numerous anthropogenic threats, including habitat destruction, invasive species, longline fisheries bycatch, and plastic ingestion. However, few studies have investigated seabird collisions with anthropogenic structures, and those studies have primarily focused on a particular taxon or location. Little research has addressed seabird collisions with buildings and similar structures despite bird-building collisions being a major source of mortality for other groups of birds in coastal and inland areas. We conducted an analysis of seabird collisions using band recovery data spanning from 1930 to 2023 from the North American Bird Banding Program, focusing on records categorized as: “caught due to striking: stationary object other than wires or towers.” Our objective was to describe taxonomic and spatiotemporal patterns emerging from these collision records, information that can be used to identify research needs and make recommendations for monitoring seabird collisions. There were 407 records of 39 seabird species representing 13 families that were categorized as striking buildings or similar structures. Species in Laridae (gulls and terns) and Pelecanidae (pelicans) represented 80% of records; Diomedeidae (albatrosses) was the only other family representing more than 5% of records. Band recoveries of collision victims were concentrated in the eastern United States, southern Canada, and urban areas on the West Coast of the U.S. and Canada. Over half of records (n = 228) were from ocean coastlines, with fewer from inland (n = 146) and offshore locations (n = 30). Most records (~90%) had no information about the type of structure with which the bird collided, but observer remarks indicate that seabirds collided with buildings, offshore oil platforms, docks/piers, and even boats. Elucidating and mitigating impacts of collisions with structures on seabird populations requires increased collision monitoring at coastal and offshore structures, which are highly underrepresented in this dataset and are likely a significant threat to seabirds. There is also a need for formal mechanisms for reporting seabird fatalities that account for sampling and detection-related biases and that allow observers to report specific structure types.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".