Wild bird mass mortalities in eastern Canada associated with the Highly Pathogenic Avian Influenza A(H5N1) virus, 2022
Bibliographic record
Abstract
Abstract In 2022, a severe outbreak of clade 2.3.4.4b Highly Pathogenic Avian Influenza (HPAI) H5N1 virus resulted in unprecedented mortality among wild birds in eastern Canada. Tens of thousands of birds were reported sick or dead, prompting a comprehensive assessment of mortality spanning the breeding season between April 1 and September 30, 2022. Mortality reports were collated from federal, Indigenous, provincial, and municipal agencies, the Canadian Wildlife Health Cooperative, non-governmental organizations, universities, and citizen science platforms. A scenario analysis was conducted to refine mortality estimates, accounting for potential double counts from multiple sources under a range of spatial and temporal overlap. Correcting for double counting, an estimated 40,966 wild birds were reported sick or dead in eastern Canada during the spring and summer of 2022. Seabirds and sea ducks, long-lived species that are slow to recover from perturbations, accounted for 98.7% of reported mortalities. Mortalities were greatest among Northern Gannets (Morus bassanus ; 26,193), Common Murres ( Uria aalge ; 8,133), and American Common Eiders ( Somateria mollissima dresseri; 1,945), however, these figures underestimate total mortality as they exclude unreported deaths on land and at sea. In addition to presenting mortality estimates, we compare mortalities with known population sizes and trends and make an initial assessment of whether population-level impacts are possible for the Northern Gannet, a species that has suffered significant global mortality, and two harvested species, Common Murre and American Common Eider, to support management decisions. We hypothesize that population-level impacts in eastern Canada are possible for Northern Gannets and American Common Eiders but are unlikely for Common Murres. This study underscores the urgent need for further research to understand the broader ecological ramifications of the HPAI outbreak on wild bird populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".