Data from: Effects of habitat quality and access management on the density of a recovering grizzly bear population
Bibliographic record
Abstract
Human activities have dramatic effects on the distribution and abundance of wildlife. Increased road densities and human presence in wilderness areas have elevated human-caused mortality of grizzly bears and reduced bears' use. Management agencies frequently attempt to reduce human-caused mortality by managing road density and thus human access, but the effectiveness of these actions is rarely assessed. We combined systematic, DNA-based mark–recapture techniques with spatially explicit capture–recapture models to estimate population size of a threatened grizzly bear population (Kettle–Granby), following management actions to recover this population. We tested the effects of habitat and road density on grizzly bear population density. We tested both a linear and threshold-based road density metric and investigated the effect of current access management (closing roads to the public). We documented an c. 50% increase in bear density since 1997 suggesting increased landscape and species conservation from management agencies played a significant role in that increase. However, bear density was lower where road densities exceeded 0.6 km/km2 and higher where motorised vehicle access had been restricted. The highest bear densities were in areas with large tracts of few or no roads and high habitat quality. Access management bolstered bear density in small areas by 27%. Synthesis and applications. Our spatially explicit capture–recapture analysis demonstrates that population recovery is possible in a multi-use landscape when management actions target priority areas. We suggest that road density is a useful surrogate for the negative effects of human land use on grizzly bear populations, but spatial configuration of roads must still be considered. Reducing roads will increase grizzly bear density, but restricting vehicle access can also achieve this goal. We demonstrate that a policy target of reducing human access by managing road density below 0.6 km/km2, while ensuring areas of high habitat quality have no roads, is a reasonable compromise between the need for road access and population recovery goals. Targeting closures to areas of highest habitat quality would benefit grizzly bear population recovery the most.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".