An operational framework to map Essential Life Support Areas (ELSAs) for biodiversity, climate, and sustainable development
Bibliographic record
Abstract
Abstract Almost all countries are making increasingly bold commitments to halt and reverse biodiversity loss, minimise the impacts of climate change, and transition to more sustainable development. The effective achievement of many of these commitments relies on integrated spatial planning frameworks that are adaptable to national circumstances, priorities and capabilities. This need is formally recognized by Target 1 of the Kunming-Montreal Global Biodiversity Framework (GBF), which specifies that all areas should be under such planning. Here, we describe the development and application of an operational framework for national-level integrated spatial planning: Essential Life Support Areas (ELSAs). This framework facilitates the identification of areas that - if protected, restored, or sustainably managed - can support the achievement of national commitments to biodiversity, climate, and sustainable development. The process of mapping ELSAs relies heavily on leadership by national experts and stakeholders and the integration of spatial data using systematic conservation planning tools. We showcase the ELSA process carried out for Ecuador, where the use of real-time scenario analyses enabled diverse stakeholder groups to collaborate to assess national priorities for nature, climate, and sustainable development, view trade-offs and synergies, and arrive at a spatial plan to guide national action. ELSA presented an actionable approach for Ecuador, and 12 other pilot countries, to create a spatial plan aimed at fulfilling their national and international commitments to nature, including to the GBF.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".