Well-Connected Core Areas Retain Ecological Integrity of Sagebrush Ecosystems Amidst Overall Declines From 2001–2021
Bibliographic record
Abstract
Conservation of species’ mobility and ecological integrity is necessary for the productivity of the sagebrush biome in the western United States. Building on the recently developed Sagebrush Conservation Design (SCD) that mapped sagebrush ecological integrity (SEI)—defined as the higher cover of sagebrush and perennial grass and reduced threats due to invasive annual grass, tree encroachment, and human disturbance—we modeled the structural connectivity of sagebrush ecosystems to better incorporate the role of landscape-level processes into assessments of integrity. Because integrity can vary spatially, as well as temporally, we quantified both interannual variability and trends in variability in SEI from 2001–2021. We used the resultant map to identify areas with high structural landscape connectivity (i.e., “well-connected cores”), then determined the coincident core sagebrush areas (CSAs) that represent functioning sagebrush ecosystem with few landscape threats, and growth opportunity areas (GOAs) that represent functioning systems impacted by one or more threats as originally defined and mapped in the SCD. We found that CSAs were located in areas with higher landscape connectivity, and the biome-wide average of SEI declined by 30% from 2001 to 2021, although the structural connectivity biome-wide declined one-third less (by 20%). CSAs located in areas with high connectivity had 25% higher SEI values on average than those with low connectivity, and the trend in declining SEI values was slower. Our datasets of landscape connectivity can be combined with other SCD products to provide a broader ecosystem context—both spatially and temporally. Our results can be used to inform, refine, focus, and prioritize conservation and management efforts to those CSAs and GOAs we identified as particularly well connected and which may be more resilient to recently altered dynamics and declines—those that will serve to anchor efforts to conserve the sagebrush biome in light of changing land use and climate.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".