Can genomic tools aid conservation of an arctic seabird, the northern fulmar (Fulmarus glacialis)?
Bibliographic record
Abstract
Human activities and climate change threaten Arctic ecosystems. Population genetics and genomics may help conservationists appropriately manage threatened species both by (1) determining the population genetic structure of a species, so that management can be designed to maximize conservation of genetic variation, and (2) enabling assessment of impacts on breeding populations of mortality during the nonbreeding season in migratory species. The northern fulmar (Fulmarus glacialis) is a seabird that breeds in colonies throughout the North Atlantic and Pacific Oceans. Though not currently considered at risk, several concerns, such as increased levels of toxins and ingested plastics, warrant investigation to aid conservation of northern fulmars. Up to 1% of the global population is killed annually through unintentional capture in commercial fishing activities, and northern fulmar survival appears to be negatively affected by climate change. As northern fulmars are migratory, the impact of these mortality sources on specific colonies is often unknown but may be important to inform management strategies. In this thesis, I use restriction site-associated DNA sequencing (RADseq) to provide 6614 genome-wide single nucleotide polymorphisms (SNPs) to investigate the genetic structure of the Atlantic northern fulmar, using 127 samples from six breeding colonies, one non-breeding location, and fishing activities in the Baffin Bay-Davis Strait region. I found weak genetic differentiation and suggest that Atlantic northern fulmar populations are genetically connected, experiencing high levels of gene flow. Determining the exact breeding origin of the bycatch birds was difficult due to the lack of differentiation between colonies. However, the birds appear to be from Arctic Canadian colonies, suggesting that the impact of the fisheries is on local colonies, and may be contributing to the 3% annual decline that has been observed at these locations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".