Using model‐based distance sampling to estimate decadal population change in Northern Gannets (<i>Morus bassanus</i>) across periods spanned by different at‐sea survey methods
Bibliographic record
Abstract
Seabirds are important sentinels of climate and ecosystem change, but many breeding populations are difficult to monitor because of the remoteness and inaccessibility of their colonies, and the sometimes cryptic nature of their nests and burrows. Large‐scale monitoring of seabird populations at sea can also be used to estimate population trends and inform conservation efforts. However, although modern survey techniques can be used to estimate absolute abundance, many older survey methodologies have recorded only relative, and possibly biased, abundance. These approaches are exemplified in the western North Atlantic, where seabirds have been surveyed at sea using modern methods (Eastern Canada Seabirds at Sea, or ECSAS) since 2006, but under the simpler PIROP (Programme intégré de recherches sur les oiseaux pélagiques) protocol from 1965 to 1992. Methodological differences between these survey types limit our understanding of long‐term trends in seabird populations, both in the western North Atlantic and elsewhere. Hence, we conducted simultaneous surveys using both methods from 2014 to 2021 and used advances in model‐based distance sampling to allow comparison across these longer‐term datasets. We validated our methodology by comparing population trends of Northern Gannets Morus bassanus using the at‐sea data and breeding colony surveys. The trend in abundance at sea (2.69% increase annually) was similar to that at breeding colonies (2.91% increase annually), suggesting that our combined approach can be used to estimate seabird population changes robustly across the period spanned by the two survey programmes. We envision that analyses using similar combined survey methods could reveal decadal population trends and changes in conservation status of many seabird species that currently lack such information because of the absence of colony counts.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".