Landscape‐level wolf space use is correlated with prey abundance, ease of mobility, and the distribution of prey habitat
Bibliographic record
Abstract
Abstract Predator space use influences ecosystem dynamics, and a fundamental goal assumed for a foraging predator is to maximize encounter rate with prey. This can be achieved by disproportionately utilizing areas of high prey density or, where prey are mobile and therefore spatially unpredictable, utilizing patches of their prey's preferred resources. A third, potentially complementary strategy is to increase mobility by using linear features like roads and/or frozen waterways. Here, we used novel population‐level predator utilization distributions (termed “localized density distributions”) in a single‐predator (wolf), two‐prey (moose and caribou) system to evaluate these space‐use hypotheses. The study was conducted in contrasting sections of a large boreal forest area in northern Ontario, Canada, with a spatial gradient of human disturbances and predator and prey densities. Our results indicated that wolves consistently used forest stands preferred by moose, their main prey species in this part of Ontario. Direct use of prey‐rich areas was also significant but restricted to where there was a high local density of moose, whereas use of linear features was pronounced where local moose density was lower. These behaviors suggest that wolf foraging decisions, while consistently influenced by spatially anchored patches of prey forage resources, were also determined by local ecological conditions, specifically prey density. Wolves appeared to utilize prey‐rich areas when regional preferred prey density exceeded a threshold that made this profitable, whereas they disproportionately used linear features that promoted mobility when low prey density made directly tracking prey distribution unprofitable.
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 | 0.000 | 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".