Demographic trends for the Boreal Owl, <i>Aegolius funereus</i> , using standardized migration monitoring data in eastern North America
Bibliographic record
Abstract
Many boreal species have declined during recent decades in North America. Various indexes suggest that populations of the Boreal Owl Aegolius funereus are declining across North America, but very few long-term, standardized monitoring schemes allow for reliable assessment. We combined various datasets monitoring Boreal Owls in eastern North America to assess its population trend. Using autumn migration monitoring from 1996 to 2023 at Tadoussac (Québec, Canada) and Whitefish Point (Michigan, USA), we assessed population trends with Bayesian hierarchical generalized linear models. We also analyzed the trends in the proportion of juveniles and body condition over time. We correlated migration monitoring with participatory science observations recorded throughout the year to assess Boreal Owl population trends in eastern North America. We observed a dynamic of four-year cycles and a longer-term decline in relative abundance for both the total number of captured individuals and the number of juveniles alone. The proportion of juveniles and mean body condition both varied annually but showed stable trends over time. However, we detected a reduction in the recorded fat score over time, suggesting that conditions encountered in the boreal forest could be deteriorating. This study provides population trends for the Boreal Owl, an important bioindicator of the boreal ecosystem, and could ultimately support and orient the development of future monitoring projects during the breeding period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".