Adaptive Management and Climate Change Adaptation: Two Mutually Beneficial Areas of Practice
Bibliographic record
Abstract
Abstract Adaptive management (AM) is a rigorous approach to implementing, monitoring, and evaluating actions, so as to learn and adjust those actions. Existing AM projects are at risk from climate change, and current AM guidance does not provide adequate methods to deal with this risk. Climate change adaptation (CCA) is an approach to plan and implement actions to reduce risks from climate variability and climate change, and to exploit beneficial opportunities. AM projects could be made more resilient to extreme climate events by applying the principles and procedures of CCA. To test this idea, we analyze the effects of extreme climatic events on five existing AM projects focused on ecosystem restoration and species recovery, in the Russian, Trinity, Okanagan, Platte, and Missouri River Basins. We examine these five case studies together to generate insights on how integrating CCA principles and practices into their design and implementation could improve their sustainability, despite significant technical and institutional challenges, particularly at larger scales. Although climate change brings substantial risks to AM projects, it may also provide opportunities, including creating new habitats, increasing the ability to quickly test flow‐habitat hypotheses, stimulating improvements in watershed management and water conservation, expanding the use of real‐time tools for flow management, and catalyzing creative application of CCA principles and procedures.
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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 | 0.068 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.076 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".