Using the Red List of Ecosystems and the Nature-based Solutions Global Standard as an integrated process for climate change adaptation in the Andean high mountains
Bibliographic record
Abstract
Under anthropogenic pressures and climate change, most ecosystems are showing signs of reduced resilience. Unfortunately, some are more at risk of collapse and, without interventions, they may lose biodiversity, ecological integrity and ecosystem services. Here, we describe two tools that were developed under the auspices of the International Union for Conservation of Nature, the Red List of Ecosystems and the Nature-based Solutions Global Standard, and their capacity to first identify the ecosystems at risk of collapse in a nation and then develop solutions based on nature to improve their resilience. Nature-based solutions include, for example ecosystem-based adaptation, where solutions are developed to meet the needs of the local people while protecting nature to ensure greater resilience of the social-ecological system, not only the natural ecosystem. We discuss through a case study in the Andean high mountains and páramo social-ecological system how these approaches have been used in Colombia. We then discuss lessons learned and challenges that may reduce the capacity of a community to initiate such interventions, such as national policies and funding restrictions. We also discuss through another early case in Ecuador the importance to adapt these types of interventions to the geographical and cultural context of the social-ecological systems. This article is part of the theme issue 'Bringing nature into decision-making'.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".