Application of land-use simulation to protected area selection for efficient avoidance of biodiversity loss in Canada’s western boreal region
Bibliographic record
Abstract
Avoided ecological loss is an appropriate measure of conservation effectiveness, but challenging to measure because it requires consideration of counterfactual conditions. Land-use simulation is a well suited but underutilized tool in this regard. As a case study for the application of land-use simulation to assess the impact of protected areas, we present a scenario analysis exploring conservation options in Canada’s western boreal forest. The cumulative effect of multiple natural resource sectors, including oil and gas, forestry, and agriculture, have substantially altered the region’s ecosystems in recent decades and elevated risk to wildlife. The evolving state of the region is such that managing risks to biodiversity requires consideration of not only today’s but also tomorrow’s conditions. We simulated the long-term (50-year) outcomes of land use and protection to caribou, fisher, fish, and resource production in each of 104 watersheds in the 693,345 km2 study area. Simulated land use caused increased risk to wildlife in response to northwards expansion of resource extraction and expansion of agricultural lands. For each watershed, indicator performance with and without protection were compared to calculate the benefit (avoided ecological loss) and cost (lost opportunity for resource production) of protection. The capacity for protected areas to avoid disturbance varied substantially across watersheds, as did the potential loss of economic opportunity. Focusing protection on cost-effective watersheds made protected area expansion a more efficient strategy for reducing wildlife risk than reducing the overall rate of natural resource production. Heterogeneity in the cost-effectiveness of protection presents an opportunity to balance ecological integrity and economic growth.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".