A Vulnerability-Based Strategy for Incorporating the Climate Threat in Conservation Planning: A Case Study from the British Columbia Central Interior
Bibliographic record
Abstract
We present a vulnerability-based approach for considering climate as a threat in regional conservation planning. The protocol is based on best available understanding of the climate sensitivity of species and systems of concern, has little reliance on climate or ecological change scenarios, and can be executed rapidly. This approach has advantages of (1) not being tied to environmental scenarios with high uncertainty and (2) generating ‘no regrets’ strategies for planning for climate in the context of other threats. The approach was implemented in an ecoregional assessment of the British Columbia Central Interior. Regional strategies to reduce climate vulnerability were applied to set conservation targets and goals in the site-selection process. These had a wide-ranging impact on both freshwater and terrestrial conservation assessments. Selection of high-priority areas based on climate strategies generally (1) increased the number, size, and connectivity of selected areas, (2) included and expanded on areas selected using standard protocols, (3) drew more on moderately favorable areas, and (4) showed similar outcomes for different parts of the domain, but with some selection bias to more northern areas and higher reaches of drainages. These planning outcomes adhere to the ‘no regrets’ goal—enhancing the adaptive capacity of species and systems to multiple threats while taking heed of a climate threat. The resulting plan sets the regional stage for on-the-ground climate-wise strategies by providing for larger, less fragmented, and more connected conservation sites and with restoration as a complementary strategy to reduce ecosystem vulnerability.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".