Wolverine density, survival, and population trends in the Canadian boreal forest
Bibliographic record
Abstract
Abstract There is limited information available on wolverine (Gulo gulo) population density and trends in the boreal forest of North America. We estimated wolverine density using spatial capture‐recapture methods across 2 boreal forest study areas in Red Lake, Ontario (26,568 km2) and Rainbow Lake, Alberta (19,084 km2), Canada. We also used radio‐telemetry data to estimate annual survival of adult and sub‐adult wolverines and evaluated population trends with a stage‐based matrix model. We used an array of run poles and live traps to detect wolverines. In Red Lake over 3 winter field seasons (2019–2022), we detected 56 individual wolverines (17 females, 32 males, and 7 unknown sex), and in Rainbow Lake over 2 field seasons (2014–2016), we detected 48 individuals (19 females, 18 males, and 11 of unknown sex). Average densities in Red Lake and Rainbow Lake were 3.64 and 6.74 wolverines/1,000 km2, respectively. Adults and sub‐adults occurred at equal abundance. Spring snow cover, roads, and industrial developments were not associated with spatial patterns of wolverine density. Most deaths occurred near roads; wolverines were killed in fur traps set along roads, by wolves using roads to travel, and by vehicles. The largest source of death was from incidental (n = 6 in Red Lake) or licensed fur trapping (n = 8 in Rainbow Lake) and we report 8 injuries from fur trapping sets. Red Lake survival estimates for adults (0.87) and sub‐adults (0.86) contributed to a stable population trend. Rainbow Lake survival estimates for adults (0.66) and sub‐adults (0.50) contributed to a declining population trend based on a relatively low sample of radio‐days. Red Lake and Rainbow Lake combined survival estimates for adults (0.77) and sub‐adults (0.73) also contributed to a declining population trend. Our survival and population modeling suggests that human‐caused mortality is a significant risk to these populations. Our results can be applied to wolverine status assessments and used as benchmarks for future monitoring. Wolverine population stability or growth might be achieved by reducing incidental trapping deaths or injury and hindering human access to wolverine habitats through decommissioning or limiting development of industrial roads or other anthropogenic linear features.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".