Developing a Monitoring Framework to Estimate Wolf Distribution and Abundance in Southwest Alberta
Bibliographic record
Abstract
Gray wolf (Canis lupus) populations are difficult to monitor because wolves can be elusive and occur in low densities. Traditional radiotelemetry-based monitoring methods have limited application when turnover is high within the wolf population and resources to maintain long-term collaring programs are limited. We worked collaboratively with Alberta Environmental Sustainable Resource Development between 2012 and 2014 to develop techniques for monitoring gray wolf populations in the absence of radiotelemetry in southwest Alberta. We surveyed potential rendezvous sites and collected DNA samples from wolf scats for genetic analysis and surveyed hunters for wolf sightings made during the hunting seasons. We fit false-positive occupancy models to annual detection data derived from genetic results and hunter surveys with Program PRESENCE. We found percent forest cover and human density positively influenced pack occupancy whereas detection probabilities varied by survey method, sampling effort, and sampling season. The model predicted wolf pack occupancy well and distribution and abundance estimates were consistent with agency predictions. While developing the monitoring framework, questions arose regarding pack turnover and population growth under widespread human harvest. Previous studies have focused on population recovery following wolf control actions but little emphasis is put on populations that exist under regular harvest. We will use genetic data to determine how immigration contributes to wolf population trends under a long-term harvest regime and tie this into pack occupancy through colonization and local extinction probabilities. This will expand the application of our occupancy model and will further clarify how wolf populations respond to long-term regulated harvest.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".