Managing biodiversity in the Anthropocene: discussing the Nature Futures Framework as a tool for adaptive decision-making for nature under climate change
Bibliographic record
Abstract
Abstract Conservation approaches to social-ecological systems have largely been informed by a framing of preserving nature for its instrumental societal benefits, often ignoring the complex relationship of humans and nature and how climate change might impact these. The Nature Futures Framework (NFF) was developed by the Task Force on scenarios and models of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services as a heuristic approach that appreciates the diverse positive values of nature and its contribution to people. In this overview, we convene a group of experts to discuss the NFF as a tool to inform management in social-ecological systems facing climate change. We focus on three illustrative case studies from the global south across a range of climate change impacts at different ecological levels. We find that the NFF can facilitate the identification of trade-offs between alternative climate adaptation pathways based on different perspectives on the values of nature they emphasize. However, we also identify challenges in adopting the NFF, including how outputs can be translated into modeling frameworks. We conclude that using the NFF to unpack diverse management options under climate change is useful, but that there are still gaps where more work needs to be done to make it fully operational. A key conclusion is that a range of multiple perspectives of people’s values on nature could result in adaptive decision-making and policy that is resilient in responding to climate change impacts in social-ecological systems.
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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 | 0.044 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".