Changes in waterfowl movement behavior in response to hunting pressure
Bibliographic record
Abstract
BACKGROUND: Anthropogenic activity can modify how wildlife perceives risks and rewards across the landscape (the 'Landscape of Fear') and affect animal movement and behavior. Studying movement patterns allows researchers to infer anti-predator behaviors and their drivers that affect survival. We studied a game species, the Canada goose (Branta canadensis), to understand how hunting affects prey movement and in turn guide management decisions to maximize species abundance and hunting. METHODS: We used GPS receivers to track the movements of geese at two study sites in Pennsylvania during two hunting seasons. The hunting season in this system includes two distinct periods (hunting periods) in which hunting is permitted on alternating days (hunting days). We fitted hidden Markov models to analyze individual movement at three spatiotemporal scales and estimated how various environmental factors, such as habitat and hunting pressure, influenced the transition probabilities between behavioral states. RESULTS: We found that geese were less likely to take flight during hunting periods and on hunting days, and that geese were more likely to transition to a low ambulation state on hunting days. Overall, we found that resident Canada geese employed two nested anti-predator behaviors: individuals proactively decreased their movement during hunting, but as they neared the threat, they were more likely to take flight. CONCLUSIONS: Our study provides further evidence that animals maintain a landscape of fear and a schedule of fear. We observed decreased movement during the hunting season and hypothesize that this was due to the stationary nature of risk (hunting blinds). Based on our results, we hypothesize that goose movement may be increased during hunting with an unpredictable spatial-temporal distribution of risk.
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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".