A Safe Operating Space for Salmon Watersheds Under Rapid Climate Change
Bibliographic record
Abstract
ABSTRACT Climate change and local pressures are eroding the health and performance of many watersheds and their freshwater ecosystems, pushing these complex social‐ecological systems to the boundaries of their safe operating space. Here, we offer a synthetic perspective on the downscaled application of the safe operating space concept to inform the stewardship of watersheds in this time of rapid climate change, with particular focus on watersheds that support coldwater migratory fishes such as Pacific salmon. First, we review the safe operating space concept as it applies to salmon watersheds as social‐ecological systems. Salmon watersheds, and the benefits they provide for diverse peoples, are under enormous cumulative pressure from climate change as well as local activities such as forestry, urbanisation, mining and agriculture. We identify four general syndromes of dual local and climate pressures. For example, local pressures, such as the removal of riparian vegetation that shades streams, can exacerbate climate warming of water temperatures. Furthermore, extractive industries can damage or destroy future habitats and thus erode adaptive capacity. As an illustrative example of how the safe operating space concept can be operationalised, we assess alternative plausible watershed futures of land use and climate change scenarios and salmon performance. Collectively, this work showcases tangible options for local management to help give salmon watersheds the time and space to cope with climate change. More broadly, while there is a global need to address climate change, local watershed management is a key component of pathways towards freshwater sustainability and their services for humanity.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".