Delineating greater ecosystems around protected areas to guide conservation
Bibliographic record
Abstract
Abstract Protected areas represent a key strategy to conserve biodiversity. However, human land use and other impacts outside protected areas boundaries significantly influence species and ecosystems within protected areas. Therefore, identifying and delineating important lands surrounding protected areas may be critical to developing conservation strategies to sustain biodiversity. Here, we identify greater ecosystems of protected areas in the contiguous United States by delineating permeable wildlands located adjacent to protected area borders using the global human footprint map as the basis for estimating permeability. We evaluated how elevating the conservation status of greater ecosystems could help achieve aspirational targets for protecting additional terrestrial land area while better representing ecological diversity. We then assessed the feasibility of elevating conservation in greater ecosystems by quantifying the composition of land ownership and existing conservation status. Greater ecosystems of different protected areas often occur as large complexes that could be used to manage protected areas and monitor their status under regional conservation strategies. Elevating the conservation status of greater ecosystems could aid in achieving international targets while increasing the representation of vegetative types within conservation reserves. The most connected and permeable lands surrounding protected areas are dominated by public land (managed by the U.S. federal and state governments), though the amount of public land within greater ecosystems decreased with distance from protected areas. Public lands may provide opportunities to elevate the conservation status of greater ecosystems surrounding protected areas through policy and management changes. We focused on the contiguous United States, but our methods could be applied globally (which we demonstrate). To achieve bold international conservation goals, identifying the greater ecosystems around protected areas and developing conservation strategies of their landscape context will ultimately benefit species and ecosystems in protected areas.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".