Anthropogenic predation risk alters waterfowl habitat selection
Bibliographic record
Abstract
Anthropogenic disturbance, such as land conversion, recreation, and predation, can affect how wildlife select resources across the landscape. Prey species are thought to rely on both a cognitive map of risks (‘landscape of fear’) and a schedule of risks (‘schedule of fear’) to navigate their environment and make trade-offs among resources and habitats. Using a popular game species, the Canada goose ( Branta canadensis ), we aimed to expand our understanding of the landscape of fear by evaluating how resource selection and home range changed in response to predation threats during the hunting season. We used GPS receivers to track the movements of resident geese in Pennsylvania throughout two hunting seasons across two study sites. We fit resource selection functions and estimated home ranges at four different spatial and temporal scales. We found that the geese did not change their landscape use to avoid the predation threat at a coarse spatiotemporal scale but did modify their habitat use and resource selection at a finer spatiotemporal scale. Our results indicate that the geese perceived both a landscape of fear and a schedule of fear and used spatial and temporal partitioning to minimize their exposure to predation. When managing a heterogeneous landscape for both animal and human use, providing sufficient spatial refuge for prey species may help buffer the effects of predation threats.
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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".