The Development of DNA Markers to Resolve Uncertainties of Seabird Bycatch Identification From Longline Fisheries in Australian Waters
Bibliographic record
Abstract
ABSTRACT Incidental mortality in fisheries is a major driver of population declines for albatrosses and petrels globally. However, accurate identification of species can be difficult due to the poor condition of bycaught birds and/or visual similarities between closely related species. We assessed three genetic markers for their ability to distinguish the 36 albatross and petrel species listed in Annex 1 to the Agreement on the Conservation of Albatrosses and Petrels (ACAP) and in Australia's Threat Abatement Plan (TAP) for the bycatch of seabirds during oceanic longline fishing operations. We generated 275 new sequences, from 29 species, to improve the coverage of reference databases for these listed species. The combined use of the selected Cytochrome b and Control Region markers enabled the identification of 31 of 36 listed seabirds to species level and four to sister species. One petrel species could not be evaluated as no reference sequences were available. We tested these markers on 59 feathers from bycaught seabirds and compared these to onboard visual identification. We successfully assigned all procellariiforms to species ( n = 58), whereas only two seabirds were correctly identified to species visually onboard, highlighting the difficulty of visual species assignment and the need for alternative methods. We assessed the utility of our two chosen markers for the assignment of all procellariiform species, with 74% of species with reference sequences identified to species or sister species level. However, a precautionary approach is needed for application beyond our listed species due to unvalidated reference sequences. The approach described here provides a streamlined framework for the molecular identification of seabird bycatch. This approach is recommended for use in fisheries within and outside Australian waters to improve the resolution of bycatch reporting and to corroborate logbook entries, observer reports and audits of images captured by electronic monitoring systems as well as help inform conservation efforts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".