Forestry, fire, and fur: factors influencing densities of endangered fishers (<i>Pekania pennanti</i>) in central British Columbia, Canada
Bibliographic record
Abstract
Abstract The isolated and endangered Columbian population of fishers (Pekania pennanti) is thought to be declining in central interior British Columbia, Canada, yet little is known about the distribution and abundance of the species in several regions. We conducted DNA‐based mark‐recapture surveys to estimate the density of fishers in 2 spatially distinct ecosystems where current data were lacking, and evaluated which factors best explained the distribution of fishers in each ecosystem. We found the density of fishers varied substantially between study areas, likely because of the level of trapping mortality during each survey season. The density of fishers in the Chilcotin study area west of Williams Lake was among the highest ever reported for the province (21.3 ± 3.8 [SE] fishers/1,000 km2), whereas density in the Enterprise study area southeast of Williams Lake was among the lowest (8.9 ± 2.7 fishers/1,000 km2). Density estimates for both study areas, however, were orders of magnitude lower than those reported for eastern North America and the western United States and thus are at levels worthy of concern. Forested stands with wet soil moisture regimes composed of older deciduous and spruce trees were most strongly related to higher densities in both study areas; these stands also were known to contain higher densities of the denning and resting structures fishers require to survive and reproduce. This study provided wildlife managers with a snapshot of the density of fishers in 2 areas of central interior British Columbia that will help refine the estimate for the endangered Columbian population. Additionally, this work suggests that reducing trapping mortality within the Columbian population of fishers and increasing the protection of high‐value denning and resting habitats may both be necessary to promote the recovery of the species over the longer term.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".