Harbour Seal (Phoca vitulina vitulina) haulout behaviour and correction factors for aerial surveys conducted in Atlantic Canada from 2019 to 2021
Bibliographic record
Abstract
Aerial surveys were conducted between 2019-2021 to estimate the abundance and distribution of harbour seals (Phoca vitulina vitulina) throughout Atlantic Canada: coastlines of the St. Lawrence Estuary and Gulf of St. Lawrence, the Bay of Fundy, southwest Nova Scotia, Eastern Shore and Cape Breton, as well as the Newfoundland and Labrador Shelves. These surveys were conducted in favourable conditions, during the pupping period of harbour seals, except in some areas of Newfoundland and Labrador Shelves where the survey was conducted during the moulting period. To account for animals not available to be counted during aerial surveys (i.e., at sea), haulout correction factors were calculated from: 1) data collected via satellite telemetry deployments on harbour seals in the St. Lawrence Estuary; and 2) published literature values. Twelve harbour seals (combination of adults or juveniles, and pups) were instrumented with satellite transmitters in the St. Lawrence Estuary, providing data on their haulout behaviour during the survey period (May 15-June 30, 2022). Haulout periods were identified from the transmitted hourly percent dry timelines. The proportion of the population hauled out at any given time during the survey window was estimated using a bootstrap approach, which corrected for the unbalanced sex-age sample of tagged animals. This method estimated that a proportion of 0.33 (95% CI: 0.09-0.60) of the harbour seal population was hauled out on average at any given time during survey-like conditions, corresponding to a mean correction factor of 3.0 (CV: 41.7%). When combined with published pupping correction factors in the Northwest Atlantic (range: 2.30-2.58), the weighted mean of the proportion hauled out was 0.39 (95% CI: 0.27-0.52) for a correction factor of 2.55 (CV: 16.02%). For parts of Newfoundland and Labrador Shelves surveyed during the moulting period, we calculated a weighted proportion hauled out of 0.61 (95% CI: 0.50-0.71) from published literature estimates, corresponding to a correction factor of 1.64 (CV: 8.67%). The bootstrap framework developed for this analysis of haulout behaviour from satellite telemetry data provides an important way forward in capturing behavioural variability and generating correction factors for aerial surveys. Additional tagging effort in different regions and across multiple years would be required to provide correction factors for harbour seals that reflect local conditions and thereby improve estimates of abundance and the associated uncertainty.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".