Maximizing the effectiveness of national commitments to protected area expansion for conserving biodiversity and ecosystem carbon under climate change
Bibliographic record
Abstract
Global commitments to protected area expansion should prioritize opportunities to protect climate refugia and ecosystems which store high levels of irrecoverable carbon, as key components of an effective response to biodiversity loss and climate change. The United States and Canada are responsible for one-sixth of global greenhouse gas emissions but hold extensive natural ecosystems that store globally significant above- and below-ground carbon. Canada has initiated a process of protected area network expansion in concert with efforts at reconciliation with Indigenous Peoples, and acknowledged nature-based solutions as a key aspect of climate change mitigation. The US, although not a party to global biodiversity conventions, has recently committed to protecting 30% of its extent by 2030 and achieving the UNFCCC Paris Agreement's mitigation targets. The opportunities afforded by these dual biodiversity conservation and climate commitments require coordinated national and regional policies to ensure that new protected areas maximize biodiversity-focused adaptation and nature-based mitigation opportunities. We address how global commitments can best inform national policy initiatives which build on existing agency mandates for regional planning and species conservation. Previous analyses of global conservation priorities under climate change have been tenuously linked to policy contexts of individual nations and have lacked information on refugia due to limitations of globally available datasets. Comparison and synthesis of predictions from a range of recently developed refugia metrics allow such data to inform planning despite substantial uncertainty arising from contrasting model assumptions and inputs. A case study for endangered species planning for old-forest-associated species in the US Pacific Northwest demonstrates how regional planning can be nested hierarchically within national biodiversity-focused adaptation and nature-based mitigation strategies which integrate refugia, connectivity, and ecosystem carbon metrics to holistically evaluate the role of different land designations and where carbon mitigation and protection of biodiversity's resilience to climate change can be aligned.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".