Behavior‐specific habitat selection by raccoons in the Prairie Pothole Region of Manitoba
Bibliographic record
Abstract
Abstract Patterns of resource selection depend upon an animal's behavioral state. Because management strategies are often crafted from an understanding of animal space use, incorporating behavior explicitly into analyses of resource selection has the potential to improve outcomes. In the Prairie Pothole Region of Manitoba, Canada, raccoons (Procyon lotor) are a nest predator of waterfowl, but it is unclear how raccoons use this landscape during the waterfowl nesting season. Our objective was to use high‐resolution global positioning system (GPS) telemetry to differentiate among behaviors by raccoons and evaluate behavior‐specific habitat selection during the waterfowl nesting season. We collected approximately 32,000 locations from 33 animals during the 2018, 2019, and 2021 nesting seasons, amounting to 632 animal‐night's worth of movement data. We used hidden Markov models (HMM) to fit 4‐state models to the movement trajectories, classified observations into discrete behaviors, and fit behavior‐specific random forest resource selection models to evaluate the relative importance of habitat features on selection. Proximity to a wetland was the most important variable contributing to selection for the resting, foraging, and slow travel states. Probability of use was as high as 95% within or immediately adjacent to a wetland for animals engaged in those behaviors, and our best HMM predicted increasing probability of switching from resting or foraging to directed travel with increasing distance from a wetland edge. Human‐use sites were also important to foraging animals, suggesting raccoons subsidize their diet with anthropogenic food resources during spring and summer. These results illuminate the complexity of habitat selection by a waterfowl predator in this patchy landscape, allowing managers to develop effective conservation strategies (e.g., wetland prioritization and conservation, elimination of anthropogenic subsidies) where raccoons are having a disproportionate effect during the waterfowl nesting season.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".