Estimating regional trajectories and trends of seabirds from sparse and inconsistent colony counts: case studies from eastern Canada with Leach’s Storm-Petrel and Atlantic Puffin
Bibliographic record
Abstract
Regional seabird population monitoring is often characterized by sparse and imprecise counts from a large number of colonies, which can vary in abundance by several orders of magnitude and may show complex and concordant trajectories over time. Analysis frameworks that account for these complexities are critically needed for accurate population status assessments. Here, we developed a Bayesian hierarchical model that shares trajectory information among colonies, accounts for observation error and missing data, and estimates population trends both at the level of individual colonies and at the larger regional scale. Simulations confirmed that the model produces unbiased trend estimates even with extremely sparse and imprecise counts, and with highly non-linear population trajectories. Application of the model to empirical data for Leach’s Storm-Petrel (Hydrobates leucorhous) showed that the regional population has strongly declined over three generations in eastern Canada and that most colonies experienced similar trajectories over that period, implying they are influenced by shared large-scale environmental drivers. Conversely, the regional Atlantic Puffin (Fratercula arctica) population has likely increased in eastern Canada over three generations but individual colonies experienced highly divergent trajectories, indicating that smaller-scale colony-level processes may play a stronger role for that species. This approach provides a powerful tool for more accurate assessments of seabird population status to inform their conservation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".