Natural resource management confronts the growing scale and severity of ecosystem responses to drought and wildfire
Bibliographic record
Abstract
Intensification of drought and wildfire associated with climate change has triggered widespread ecosystem stress and transformation. Natural resource managers are on the frontline of these changes, yet their perspectives on whether management actions match the scale and align with the severity of ecosystem responses to improve outcomes are not well understood. To provide new insight, a new conceptual framework that linked scale and severity was tested by conducting interviews and surveys of staff associated with natural resource management on the Colorado Plateau in the southwestern United States (U.S.), which contains the highest concentration of public lands in the contiguous U.S. Results indicate that drought was experienced more frequently than wildfire, and both stressors were happening at large scales and moderate to abrupt timeframes with a high degree of impact to ecosystems. Ecosystem responses were perceived to increase in severity under future climate change with limited capacity to recover, and a majority of resource managers expressed that they had low control to shape these trajectories. Although management strategies to address drought and wildfire were well recognized, adaptation-specific actions remained unclear or had limited financial and staffing resources to support implementation. Additional effort could help close a growing misalignment between management actions and natural resource responses, including effective science communication, refined information tailored to meet adaptation goals at management-relevant spatiotemporal scales, and opportunities for adaptive management that can proactively address intensification of drought and wildfire.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".