Orchestrating Ecosystem Management: Challenges and Lessons from Sequoia National Forest
Bibliographic record
Abstract
Abstract: Numerous agencies and organizations have adopted the concept of ecosystem management as a guiding principle in natural resource management. Despite this widespread interest, no single definition of ecosystem management has been accepted, and there are no specific guidelines or standards by which to apply the concept. I examined how one federal agency, the U.S. Forest Service, is applying the principles of ecosystem management at a local level. I present a case study examining management of giant sequoia ( Sequoiadendron giganteum ) in Sequoia National Forest, California, to illuminate the challenges of practicing ecosystem management by associating on‐the‐ground management activities with ecosystem management themes, characteristics, and mechanisms identified in academic, industry, and agency literature. Experience at Sequoia National Forest suggests that the application of ecosystem management is compromised by poor relations between managers and stakeholders, multifarious policy requirements, budgetary uncertainty, and limited ecological research on which to base management decisions. To facilitate successful application of ecosystem management in the future, I recommend that managers (1) build confidence and trust in the process, (2) acknowledge bias, (3) reconcile policy and funding constraints with long‐term planning, (4) invest in scientific research, data collection, and monitoring capacity, and (5) explore the relationship between values and science. These recommendations, although based on the U.S. Forest Service experience, are relevant to natural resource management in general.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".