Science-Based Approach to Adaptive Management of the TCH Corridor: Canadian Rocky Mountain Parks
Bibliographic record
Abstract
In November 1996, we began a long-term research project in Banff National Park (BNP), Alberta, Canada. Our primary study area is situated in the Bow River Valley along the Trans-Canada Highway (TCH) corridor in BNP, located approximately 100 km west of Calgary. The first 45 km of the TCH from the eastern park boundary (phase 1, 2, and 3A) is currently four lanes and is bordered on both sides by a 2.4-m-high wildlife-exclusion fence. The remaining 30 km to the western park boundary (phase 3B) is two lanes and unfenced. Between 2005 and 2007, approximately 12 km of phase 3B will be widened to four lanes with additional fencing and wildlife crossings. Twenty-two wildlife underpasses and two wildlife overpasses were constructed on the first 45 km between 1980 and 1998 to permit wildlife movement across the four-lane section of TCH. The research carried out to date has provided science-based information for mountain park transportation planners and resource managers. The results have been uniquely used in development of Golder Associates’ environmentalscreening report (environmental-impact assessment) for Parks Canada’s TCH phase 3B twinning project. Research of wildlife-crossing performance demonstrated that a longtime series of data is required to assess the function and performance of these critical cross-highway corridors accurately. Recommendations from the Golder Associates’ report for phase 3B strongly underscored the importance of continued, long-term monitoring of TCH mitigation measures in the Bow Valley. After 8 years of study, there still remain noteworthy areas of uncertainty regarding the effects or performance of the current mitigation on regional-landscape connectivity (demographic and genetic). The long-term cumulative effects (beyond 2020) of the phase 3B project and earlier twinning projects will hinge on the degree to which connectivity can be restored across the TCH. Healthy functioning ecosystems require viable wildlife populations. Thus, it is critical to know the performance of crossing structures at the population level. Although intuitively these measures should enhance population viability, to date there have been no specific studies that actually address their population-level effects. Obtaining data on individuals in a population can be problematic because wide-ranging, fragmentation-sensitive species like bears typically occur in relatively low densities and have low reproductive rates. However, modern molecular techniques now make it possible to identify individual animals, their sex, and genetic relatedness with only a few hairs. These innovations could provide a powerful, relatively inexpensive, and noninvasive way to acquire critical information regarding genetic interchange facilitated by crossings without ever having to capture or see the animal. This paper highlights: 1. Key research findings from the 8-year study 2. Mitigation myths that have been dispelled 3. Important lessons learned 4. Future research needs in the short and long term 5. Newly formed international, public-private partnership to meet many of the critical research questions needed for future management decisions Upcoming Banff research will begin empirically assessing the conservation value of wildlife crossings in restoring landscape connectivity using population-level approaches and nonintrusive DNA-based methodologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".