Harnessing visitors' enthusiasm for national parks to fund cooperative large‐landscape conservation
Bibliographic record
Abstract
Abstract Spillover impacts pose challenges for the management of protected areas (PAs). The issue of external threats encroaching on PAs has long been recognized, but a corollary—that PA conservation can increase costs borne by neighboring governments or landowners—is less well appreciated. In some contexts, basic principles of fairness and cooperation suggest that PA users should help pay these costs. Several countries have developed mechanisms for distributing the costs of spillover impacts to PA users, but not the United States. Here, we investigate whether and how US park visitors could help address one type of spillover, the need for wildlife conservation efforts beyond park boundaries, using a case study of the Greater Yellowstone Ecosystem (GYE). We examine a “conservation fee” recently proposed in the Wyoming legislature, along with tax‐based alternatives. After exploring some costs of wildlife conservation in GYE, we estimate that a fee of up to $10 per vehicle could generate up to $13 million annually, and tax‐based approaches considerably more. We consider legal, political, and governance challenges, and ways to mitigate them. The GYE could serve as a demonstration site for visitor funding of cooperative, large‐landscape conservation, for potential future expansion in the US and beyond.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".