Sinking Rates of Dead Birds: Improving Estimates of Seabird Mortality Due to Oiling
Bibliographic record
Abstract
Seabird mortality resulting from oil spills is generally assessed based on the number of birds lost at sea. Drift blocks we often employed to determine the loss at sea and because blocks do not sink, but seabird carcasses do, it is essential to determine accurate sinking rates of seabird carcasses to correctly interpret onshore drift block recoveries. To quantify sinking rates of seabird carcasses, to determine possible differences between oiled and unoiled birds, and to investigate the importance of scavenging, I conducted an experiment in St. John's, Newfoundland, Canada, with 54 intact, but previously frozen murres (Uria spp.), the most common victims in oil spills in the Northern Hemisphere. Birds were randomly assigned to clean, lightly oiled (25% of body oiled) or heavily oiled (50% body oiled) categories. Twelve birds of each oiling category were placed into a floating three-chambered wooden-framed pen. The remaining birds were placed in a mesh-screened pen on the wharf. After nine days, randomly assigned carcasses from the floating pen and all birds on land, were opened to simulate partial scavenging. After 19 days, all flesh and muscles were removed from remaining birds to simulate complete scavenging. Changes in the buoyancy of carcasses were determined daily by measuring the amount of added mass (in 5 g increments) necessary to sink them (Burger 1991, cited in Burger and Fry 1993). On average, carcasses remained afloat 8.2 ± 1.0 d (95% C.I. 6.2 - 10.3), with no difference between oiled and unoiled, either floating or on land. Carcasses on land retained buoyancy significantly longer and only 1 carcass sank with the 24 d period. Buoyancy loss was best described by a logistic time-dependent function. When carcasses were scavenged, buoyancy loss increased exponentially. Based on these results, it appears that backwash and subsequent sinking of stranded carcasses is likely not a significant mechanism of carcasses removal from beaches. I recommend using a 10-14 d estimate for the length of time that murres and other auks remain afloat at sea, and to use a time-dependent logistic function to estimate the proportion of birds lost at sea. It appears that most estimates of seabird mortality due to spilled oil based on drift block experiments may be too low because onshore block recoveries relevant to seabird carcasses have been over-estimated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".