Bait trapping of waterfowl increases the environmental contamination of avian influenza virus (AIV)
Bibliographic record
Abstract
Abstract Highly pathogenic avian influenza virus (HPAIV) H5Nx clade 2.3.4.4b has circulated in North America since late 2021, resulting in higher rates of morbidity and mortality in wild birds than observed in this region before. The objective of this study was to determine whether baiting, which is widely conducted in Canada and the United States as part of waterfowl management practices (e.g., duck banding), influences the occurrence of avian influenza virus (AIV) in wetlands. We used a quasi‐experimental design, collecting superficial sediment samples (n = 336) and fecal samples (n = 242) from paired baited (treatment) and non‐baited (control) sites at 2 wetlands in Saskatchewan, Canada, between August and September 2022. We visited sampling sites 3 times during the sampling period: prior to the commencement of baiting activities (t0), approximately 14 days after t0 (t1), and 24 days after t0 (t2). We screened samples for AIV using real‐time reverse‐transcriptase polymerase chain reaction (rRT‐PCR) targeting the matrix gene and subjected the PCR‐positive samples to next‐generation sequencing. We used a mixed‐effects logistic regression model to estimate the effect of baiting on the odds of AIV positivity in sediment samples, while controlling for clustering by wetland. At control sites, we did not detect evidence for a difference in the odds of AIV detection in sediment at t1 or t2 versus t0; however, at baited sites, the odds of AIV detection at t1 were 5.43 (95% CI = 1.99, 14.79) times the odds at t0 and at t2 the odds of AIV detection were 8.73 (95% CI = 3.29, 23.18) times the odds at t0. We detected HPAIV clade 2.3.4.4b H5N1 in sediment at 1 treatment site following baiting. There was also a trend towards increased fecal AIV positivity and increased fecal and sediment AIV diversity in baited versus non‐baited sites; however, there was insufficient power to determine if these findings were statistically significant. Overall, our results indicate that baiting is associated with localized increases in AIV environmental contamination, with baiting potentially creating concentrated areas of AIV accumulation. As such, wetland baiting activities may pose a risk to wildlife population health through the propagation of AIV in wetlands and the waterfowl using those environments and efforts to replace, refine, or reduce this activity may be warranted depending on local ecosystem contexts and cost‐benefit analyses.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".