Space use of cougars at the northern edge of their range
Bibliographic record
Abstract
Abstract The space use strategies animals use to acquire resources needed for survival and reproduction reflect life history traits and individual behaviors. For large solitary carnivores, such as cougars (Puma concolor), prey, mates, and safe habitat in which to raise offspring, are resources that influence space use. Most animal home range studies investigate differences between sexes but fail to explore the space use patterns among individuals. We first used 95% minimum convex polygon (MCP), kernel density estimate (KDE), and Brownian bridge estimator (BB), to estimate the home range of 43 cougars satellite-collared in west-central Alberta, Canada, in 2016–2018. We found that adult males (MCP = 498 km2; KDE = 623 km2; BB = 547 km2) had home ranges that were more than twice the size of those of adult females (MCP = 181 km2; KDE = 273 km2; BB = 217 km2). We then used net squared displacement, path segmentation analysis, and multi-response permutation procedure, to examine the space use patterns of 27 female and 16 male cougars. We constructed a decision tree and found that 23% of cougars were dispersers (12% of females and 44% of males), 47% were residents (58% of females and 31% of males), 9% were seasonal home range shifters (12% of females and 6% of males), and 19% shifted to a new area during the study period (19% of females and 19% of males). We learned that dispersers all were subadults, whereas all residents, seasonal shifters, and shifters, were adults, except for one subadult male. Our study provides insights on animal home ranges with methods to categorize different space use strategies which could be used to help assess the dynamics of a population.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".