Temporal patterns of wolverine (Gulo gulo luscus) foraging in the boreal forest
Bibliographic record
Abstract
The foraging patterns and behaviors of predators can be discerned using GPS data. We used GPS data to investigate the temporal patterns of wolverine (Gulo gulo luscus) foraging on large prey in northern Alberta. We built a predictive model of wolverine large-prey events (beaver predation or ungulate scavenging) based on the spatial and temporal patterns of wolverine GPS data at foraging sites we visited in the field in winter. We used this model to predict large-prey events throughout our entire wolverine GPS dataset in winter and summer. We then evaluated how variables related to prey availability, seasonality, competition, and territoriality affected wolverine encounter time, residency time, and return time at predicted large-prey events. We found that wolverines encountered large prey more often in the spring when there is increased beaver and ungulate availability. The total time that wolverines spent at large-prey events was greater in winter (3.11 days [95th percentile = 2.62–3.63 days]) than summer (2.08 days [95th percentile = 1.70–2.51 days]), potentially because prey availability is limited in winter or prey is easier to capture in summer. Wolverines partitioned the total time at events into multiple visits, reducing their residency time and increasing their return time with each revisit, indicating biomass decline through time. The time between visits in winter (10.12 days [95th percentile = 7.99–12.56 days]) and summer (8.39 days [95th percentile = 7.18–9.74 days]) suggests wolverines might be patrolling their home range. We also found that wolverine residency time decreased and return time increased when there were other large-prey events available. Moreover, wolverine residency time at events increased when other wolverines were in the area. Our results suggest that large-prey events are important to the energy balance of wolverines and that wolverine foraging behavior is dynamic in response to environmental change.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".