Application of a species distribution model to identify and manage bear den habitat in central British Columbia, Canada
Bibliographic record
Abstract
Several mammals have adapted to harsh winter conditions by adopting hibernation strategies that enable them to survive periods of unfavourable environmental conditions. At northern latitudes, black and brown bears can be in a state of hibernation for up to seven months. As a result of this prolonged occupation of one small space, bears can be vulnerable to environmental and human caused disturbances. In this study, we developed a predictive model that identifies potential den habitat for black bears that can assist with management planning for industrial land development activities. We identified 40 dens (17 excavated in soil and 23 natural rock cavities) and used fine‐scale information to determine how dens were positioned in forest stands. We found that bears denned in areas on mid to upper slope positions and that soil dens were located mainly in clay‐loam soil complexes while rock cavity dens were either caves or cavities in boulder piles. Den location was distant from portions of the study area with relatively high road density. We then used resource selection functions to predict where bear dens might be located on the landscape. When applied to the GIS data, the averaged coefficients suggested that 3.1% of the study area had a high suitability ranking as den habitat while 9.1%, 14.6%, and 73.2% had mid, low, and limited suitability, respectively. In our study area, habitat for den sites is reasonably predictable and should be considered during the planning of industrial activities.
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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 | 0.000 | 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".