Cumulative Effects and Boreal Woodland Caribou: How Bow-Tie Risk Analysis Addresses a Critical Issue in Canada's Forested Landscapes
Bibliographic record
Abstract
Boreal caribou (Woodland Caribou, boreal population; Rangifer tarandus caribou) is a prominent mammal at the heart of a decades-long conflict between a growing resource sector and the associated risks to biodiversity. We employed the ISO 31010 Bow-tie Risk Assessment Tool (BRAT) to evaluate the cumulative effects of anthropogenic and natural factors that may affect the risk of self-sustainability to the boreal caribou herds of Northeastern British Columbia. We used the BRAT to produce a visual synthesis of the cumulative effects causing the growth rate of boreal caribou herds to persistently fall below a level corresponding to a 60% chance of being self-sustaining (λ < 1.025). The BRAT diagram provided the basis for a quantitative Layers of Protection Analysis (LOPA) of risk probabilities for three caribou herds. We combined threat assessments from the Species at Risk Act recovery strategy (Environment Canada 2012) with data from published landscape experiments (e.g. restoration of seismic traces, maternal penning, and wolf culls) to parameterize the LOPA in three study areas. We report the implications of a combination of mitigation options versus current risk conditions, as well as the implications of uncertainty in threat prevention. Our analysis indicates that a combination of mitigation scenarios will best facilitate caribou herd recovery, that barriers preventing predation threats could also aid in recovery success, and that compensatory predation may account for a significant proportion of both adult and juvenile female mortality across different herds. We estimated the minimum annual cost for effective mitigation and recovery to be $CDN 224K within any of the study areas. Bow-tie diagrams are a flexible and quantifiable tool that can translate resource management solutions to the diverse audience involved in conservation decision-making: scientists, land managers, policy makers, and concerned stakeholders.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".