Linking at-sea mortality of a pelagic shearwater to breeding colonies of origin using biogeochemical markers
Bibliographic record
Abstract
An emerging issue in seabird conservation is the ability to link at-sea mortality with observed demographic changes at breeding colonies.Applications of modelling and biochemical markers can be used to assign mortalities of unknown provenance to a colony of origin ensuring conservation actions are targeted at those colonies identified as the most affected.We analysed feathers (n = 120) from flesh-footed shearwater Puffinus carneipes collected from 5 breeding colonies throughout their range.Using stable isotopes (δ 15 N and δ 13 C) and trace element concentrations (Mn, Ni, Cu, Mo, Ag, Ba, Pb), we assigned birds recovered from fishing vessels off Australia, New Zealand, and the North Pacific to colony of origin, and investigated the rate of correct assignment at 3 spatial scales.Using quadratic discriminant analysis, samples of known origin were correctly assigned to basin, region, and breeding colonies at similar rates (92.3, 81.3, and 88.1%, respectively).Stable isotopes succeeded in assigning individuals among basins (72.8%), performing less well at the region and colony level (52.5 and 36.4%,respectively).In contrast, correct assignment was consistent at all 3 scales using only trace elements (93.2, 95.7, and 96.6%, respectively).Applying our final model based on trace elements to 116 flesh-footed shearwaters taken as bycatch in eastern Australia (n = 30), Western Australia (n = 32), New Zealand (n = 16), eastern North Pacific (n = 27) and western North Pacific (n = 11), we assigned individuals to colonies in New Zealand (35.3%),Western/South Australia (36.2%),Western Australia (27.6%), and Lord Howe Island (0.9%).Bycatch in fisheries may help explain ongoing declines in fleshfooted shearwater populations across the species' range, highlighting the utility of assignment tools to account for unobservable mortality of wildlife at-sea.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".