Wolf-Cougar Occupancy Modelling in the Rocky Mountains of Alberta, Canada
Bibliographic record
Abstract
Cougars (Puma concolor) and wolves (Canis lupis) are top predators that influence the dynamics of an ecosystem, including prey behavior and dynamics, and interspecific competition. I am examining co-occurrence between wolves and cougars in the Central Alberta Rockies using occupancy modeling. I hypothesize that cougars will be pushed out of higher quality habitat in the presence of wolves, to higher elevations, more rugged terrain, and areas with lower NPP than the areas occupied by wolves. There is a system of 167 remote wildlife cameras in Banff, Jasper, and Yoho National Parks; I am using the 2013 data for analysis. I have separated the data into logical seasons to better understand cooccurence patterns, summer (May 1 – October 31) and winter (Nov 1 – April 30), and it is separated into seven-day increments. From naïve occupancy models, summer cougar occupancy is 0.35 with a detection probability of 0.202 and winter occupancy is 0.157 with a detection probability of 0.0674. Summer wolf occupancy is 0.625 with a detection probability of 0.209, while winter occupancy is 0.435 with a detection probability of 0.134. The larger proportional, seasonal difference for cougar occupancy is intriguing because prey density is higher during the winter, meaning cougar-wolf competition may increase during winter; wolf presence may impact cougar detection and occupancy. This will be explored in the study, as well as covariates describing cougar and wolf occupancy separately and together. This study is important because the literature about wolf-cougar cooccurence provides mixed results: mostly cougars are secondary predators to wolves, but occasionally, cougars are unaffected by wolf presence. Understanding these interactions in this specific site will add to the literature and provide insight into the study ecosystems.
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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".