A conservation assessment of Canada's boreal forest incorporating alternate climate change scenarios
Bibliographic record
Abstract
Abstract Ecologically based strategies for climate change adaptation can be constructively integrated into a terrestrial conservation assessment for Canada's boreal forest, one of Earth's largest remaining wilderness areas. Identifying solutions that minimize variability in projected vegetation productivity may represent a less risky conservation investment by reducing the amount of anticipated environmental change. In this study, we assessed hypothetical protected area networks designed for future vegetation variability under a range of different climate conditions to provide relevant recommendations of conservation requirements that support ongoing boreal conservation and land‐use planning. We constructed a boreal conservation assessment using both a conventional (Marxan) and a new probabilistic site‐selection approach (Marxan with probability) with projected 2080 vegetation variability probability (VVP) for least change (B1), business as usual (A1B) and most extreme change (A2) climate scenarios. We then assessed (1) reserve network performance (cost and area), (2) high conservation priority areas and (3) the influence and implications of VVP on reserve networks. We found that including VVP dramatically increased the relative cost and total area of reserve networks. Many low‐cost sites with high VVP values were given higher conservation priority over fewer sites with low VVP values. Reserve networks designed for A1B and A2 climate scenarios contained more sites with very high VVP values. The ratio of sites with high and very high VVP values changes dramatically for reserve networks designed for current and least change (B1) climate scenarios when under more severe A1B and A2 conditions. We conclude that introducing additional complexity and realism into national or boreal‐wide conservation assessments, that include, for example, elements of climate change, will increase the total area and cost of a reserve network. Moreover, reserve networks designed for current or least change (B1) climate scenarios will likely not achieve conservation targets when faced with more severe conditions, and will require additional sites. The adaptive strategies presented are well suited for a boreal conservation assessment and may improve long‐term effectiveness of biodiversity conservation objectives.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".