An Assessment of Conservation Opportunities Within Sagebrush Ecosystems of US National Parks and Wildlife Refuges
Bibliographic record
Abstract
Strategic plans for landscape-scale conservation are preferable to ad-hoc decisions that lack evidence and cohesion. The Sagebrush Conservation Design (SCD) is a biome-wide geospatial decision-support framework for a “Defend the Core, Grow the Core” strategy. We mapped US National Parks and Refuges across the SCD to guide “defend and grow” investments. We summarized amounts of sagebrush “Core Sagebrush Areas” (CSAs) and “Growth Opportunity Areas” (GOAs) areas within Parks and Refuges and asked: 1) Where are the Parks and Refuges that contain substantial sagebrush resources and that are likely to retain these resources under future climate conditions? 2) What is the trend of loss across CSAs and GOAs within Parks and Refuges? 3) Do trends immediately surrounding Parks and Refuges correlate with those within? 4) Which Parks and Refuges contain the most CSAs and GOAs? 5) What will it cost to defend and grow CSAs in these places? Approximately 127 000 ha (313 824 ac) or 75% of CSAs was lost from Parks and 87 000 ha (214 982 ac) or 25% was lost from Refuges since 1998. Climate change is likely to reduce CSAs and GOAs in the northeastern and southwestern biome periphery and at low elevations. Similar trends of loss were observed surrounding Parks and Refuges. This underscores the ‘outside-in’ nature of changes occurring in the biome as fires, conifer encroachment, and invasive grasses move rapidly through permeable landscapes. Ten Parks and 10 Refuges contain >95% of CSAs and GOAs and exhibit climate durability under our examined future scenario, revealing how investments can be prioritized. Within this list, however, estimated costs of recommended actions (e.g., annual grass suppression) greatly exceeds plausible available amounts, emphasizing the need to use strategic prioritization within high-priority units. We examined application of the SCD for guiding “open” and “defined” investment decisions for Park and Refuge case studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".