Avian botulism is a primary, year-round threat to adult survival in the endangered Hawaiian Duck on Kaua‘i, Hawai‘i, USA
Bibliographic record
Abstract
Abstract Adult survival is the most important demographic parameter influencing population dynamics for many bird taxa. Thus, understanding how survival probabilities and causes of mortality vary throughout the annual cycle is critical for developing informed and effective management strategies. In this study, we used radio-telemetry data to evaluate the effects of biotic (e.g., sex, peak [September–April] vs. off-peak [May–August] nesting seasons) and abiotic factors (e.g., rainfall, year, bi-monthly interval) on adult survival, estimate annual survival probabilities, and identify primary sources of mortality for Hawaiian Ducks (Anas wyvilliana), an endangered, non-migratory dabbling duck, on the island of Kaua‘i, Hawai‘i, USA over 2013 and 2014. Additionally, we used contemporaneous Hawaiian Duck carcass recovery and surveillance data to examine temporal and climatic associations with avian botulism outbreaks. Our results suggested bi-monthly survival decreased with total rainfall during the preceding 2-month interval. Survival did not vary with sex, between peak and off-peak nesting seasons, or between the two years of this study. Annual survival probabilities (62–80%) were relatively low compared to the closely related Laysan Duck (Anas laysanensis) on Laysan Island. Primary causes of mortality included avian botulism and presumed predation by cats (Felis catus). The botulism surveillance dataset revealed support for the effect of rainfall on the number of sick and dead birds recovered (n = 216), with generally a greater number of recoveries during months with middle-range total rainfall during the concurrent and preceding months. Our study provides critical baseline demographic data for population monitoring and highlights the importance of managing botulism risk and non-native mammalian predators for the recovery of the endangered Hawaiian Duck.
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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 | 0.000 |
| 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 | 0.000 | 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".