Factors influencing autumn–winter movements of midcontinent Mallards and consequences for harvest and habitat management
Bibliographic record
Abstract
Abstract Annual phenology and distributions of migratory wildlife have been noticeably influenced by climate change, leading to concerns about sustainable populations. Recent studies exploring conditions influencing autumn migration departure have provided conflicting insights regarding factors influencing the movements of Mallards (Anas platyrhynchos), a popular game species. We determined factors affecting timing and magnitude of long‐distance movements of 97 juvenile Mallards during autumn‐winter across the midcontinent of North America marked with implanted transmitters in North and South Dakota, 2018–2019. Factors influencing variation in movement timing, along with direction and magnitudes, depended on type of movement (i.e., regional [25–310 km], initial migration, or subsequent migration movements [>310 km]). Photoperiod influenced probability of initiating all movements, although the effect was most influential for regional movements. Minimum temperature most influenced initial migration events (probability of movement increased 29% for each 1°C decrease); favorable winds also increased likelihood of initial migration events. Probability of subsequent migration events increased 80% for each 1 cm increase in depth of snow. Subsequent migration movements also were 2.0 times more likely to occur on weekend days, indicating disturbance from humans may influence movements. Migration distances increased 166 km for each 1°C reduction in minimum temperature. We also observed markedly different autumn‐winter distributions of marked birds between years. Median locations during autumn‐winter 2018–2019 were ~250 km farther north and ~300 km farther west during mid‐December–January compared to the same time in 2019–2020. Concurrently, harvest rates for marked females and males were 10% and 26% during autumn‐winter 2018–2019 and 26% and 31% during autumn‐winter 2019–2020. Climate‐related changes may result in increasingly variable autumn‐winter distributions, with implications for wildlife recreationalists, conservation planners, and harvest managers.
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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".