Climate and landscape drivers of a mosquito-borne pathogen in an iconic game bird in the eastern and upper midwestern USA
Bibliographic record
Abstract
The ruffed grouse (Bonasa umbellus) is a non-migratory upland game bird that inhabits young and mature forests in the USA and Canada. Population indices in some portions of its range, particularly the eastern USA, have been in decline since the arrival of West Nile virus (WNV), a mosquito-borne Flavivirus. Subsequent experimental research suggested that WNV may cause morbidity and/or mortality in up to 90% of grouse, which had similar clinicopathologic findings to naturally-infected grouse. Additionally, WNV serosurveys in Pennsylvania revealed low seroprevalence concurrent with elevated vector indices. To further elucidate aspects of WNV epidemiology in ruffed grouse, we tested hunter-collected filter paper strips for anti-WNV antibodies in 15 states during fall-winter, 2018-2022. Annual total seroprevalence ranged from 12.0% in 2019-2020 to 17.9% in 2021-2022. We assessed for associations between county-level WNV seroprevalence and large-scale climate, environmental, and landscape variables through Bayesian multilevel modeling, accounting for spatial autocorrelation. The top model suggested that WNV seroprevalence was positively correlated with summer precipitation; the second most supported model suggested similar findings of positive correlation between WNV seroprevalence and spring precipitation. Management strategies should prioritize understanding factors that influence mosquito-borne pathogen transmission in conjunction with providing more forested habitat of high quality for ruffed grouse to optimize survival in the face of WNV and other challenges.
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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 | 0.000 | 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".