Local variations in population trends and migration strategies of Brünnich’s guillemots on Svalbard
Bibliographic record
Abstract
The conservation status of seabirds is of increasing concern, with climate change identified as one of the main threats. This is especially true for Arctic seabirds, which breed in one of the fastest-warming regions on Earth. On Svalbard, the number of Brünnich’s guillemots Uria lomvia has been declining since the late 1990s, mostly due to deteriorating wintering conditions; this conclusion was based on colonies located in the western and southern part of the archipelago. Here, we used new data from northeast Svalbard to investigate whether this trend holds true for other populations of Brünnich’s guillemots. We found that numbers of guillemots in northeast Svalbard have been increasing in the last decade. These contrasting trends are associated with different migration strategies. Birds from the northeast population winter in the northern Barents Sea, while the vast majority of western and southern Svalbard birds winter between Iceland and Canada. Despite rapid climate change, the northern Barents Sea still seems to provide adequate conditions for wintering seabirds. Our study highlights the need to consider spatial variation in population trends, even at a small spatial scale, when assessing the status of a given species. In the Northeast Atlantic, most Brünnich’s guillemots breed in southeast Svalbard and in the Russian Arctic, where they have been poorly studied. Our study indicates that population trends in these regions cannot be inferred from what is observed in south and west Svalbard. Geographically representative monitoring is necessary to reliably assess the status of Brünnich’s guillemots in this part of the Arctic.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".