Optimism and Challenge for Science‐Based Conservation of Migratory Species in and out of U.S. National Parks
Bibliographic record
Abstract
Public agencies sometimes seek outside guidance when capacity to achieve their mission is limited. Through a cooperative agreement and collaborations with the U.S. National Park Service (NPS), we developed recommendations for a conservation program for migratory species. Although NPS manages ∼ 36 million hectares of land and water in 401 units, there is no centralized program to conserve wild animals reliant on NPS units that also migrate hundreds to thousands of kilometers beyond parks. Migrations are imperiled by habitat destruction, unsustainable harvest, climate change, and other impediments. A successful program to counter these challenges requires public support, national and international outreach, and flourishing migrant populations. We recommended two initial steps. First, in the short term, launch or build on a suite of projects for high-profile migratory species that can serve as proof to demonstrate the centrality of NPS units to conservation at different scales. Second, over the longer term, build new capacity to conserve migratory species. Capacity building will entail increasing the limited knowledge among park staff about how and where species or populations migrate, conditions that enable migration, and identifying species' needs and resolving them both within and beyond parks. Building capacity will also require ensuring that park superintendents and staff at all levels support conservation beyond statutory borders. Until additional diverse stakeholders and a broader American public realize what can be lost and do more to protect it and engage more with land management agencies to implement actions that facilitate conservation, long distance migrations are increasingly likely to become phenomena of the past.
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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 | 0.054 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.016 | 0.034 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".