Road mitigation is a demographic filter for grizzly bears
Bibliographic record
Abstract
ABSTRACT Crossing structures (i.e., underpasses and overpasses) are becoming a widespread approach to promote movement of wildlife across roads. Studies have shown that species select for different crossing structure designs, yet little is known about intraspecific variation (i.e., differences among demographic classes) in crossing structure preference. Using data on grizzly bear ( Ursus arctos ) movement in Banff National Park (AB, Canada), we focused on selection by family groups (adult females travelling with young) and singleton (adult male or female) bears for 5 crossing structure designs distributed among 44 sites. Using data from the world's longest running monitoring program (1997–2014) on wildlife crossing structure use, we created an economic model to estimate demographic‐specific cost‐effectiveness for each crossing structure design. We found that all grizzly bears selected larger and more open structures (overpasses and open‐span bridges). Use of these structures has generally increased with time at a rate that exceeds estimates of population growth. Family groups were more selective than singletons and strongly selected overpasses. In spite of singletons’ selection for overpasses and open‐span bridges, box culverts were comparable in cost‐effectiveness. Our results suggest that structure designs targeting the selection of grizzly bear family groups are effective at restoring population connectivity, but a systematic approach to designing highway mitigation also would consider the role of lesser used structures in reducing intraspecific predation and multispecies connectivity targets. © 2017 The Wildlife Society.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".