Keeping up with the landscapes: promoting resilience in dynamic social-ecological systems
Bibliographic record
Abstract
Forest managers working in dry forest ecosystems must contend with the costs and benefits of fire, and they are seeking forest management strategies that enhance the resilience of forests and landscapes to future disturbances in a changing climate. An interdisciplinary science team worked with resource managers and stakeholders to assess future forest ecosystem dynamics, given potential climatic changes and management strategies, across a 23,000-ha landscape in the Lake Tahoe basin of California and Nevada in support of the Lake Tahoe West Restoration Partnership. We projected forest growth and fire dynamics using a landscape change model, upon which the science team layered additional modeling to evaluate changes in wildlife habitat, water, and economics. Managers and stakeholders used the findings of this integrated modeling effort to inform the design of a landscape restoration strategy that balanced risks and benefits based on a robust scientific foundation. The results, published in this Special Feature, suggest that a continuation of status quo management would be less effective at protecting and improving desired outcomes than more active and extensive management approaches. In addition, the types of management activity also affected ecosystem outcomes. Results from across the studies in this special feature suggest that thinning and prescribed fire were complementary, although they resulted in somewhat different effects, and that low-severity use of fire had the greatest array and magnitude of ecosystem benefits. A notable exception was carbon storage, which declined with more active management and prescribed fire in particular. We highlight key findings from this Special Feature and summarize key challenges and some lessons learned in our experience of co-producing science. In short, science-management partnerships require cooperation, patience, and skill, but they are effective in increasing the capacity of land managers to navigate in an environment of rapid change and increasing uncertainty.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".