Biodiversity, Urban Areas, and Agriculture: Locating Priority Ecoregions for Conservation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
"Urbanization and agriculture are two of the most important threats to biodiversity worldwide. The intensities of these land-use phenomena, however, as well as levels of biodiversity itself, differ widely among regions. Thus, there is a need to develop a quick but rigorous method of identifying where high levels of human threats and biodiversity coincide. These areas are clear priorities for biodiversity conservation. In this study, we combine distribution data for eight major plant and animal taxa (comprising over 20,000 species) with remotely sensed measures of urban and agricultural land use to assess conservation priorities among 76 terrestrial ecoregions in North America. We combine the species data into overall indices of richness and endemism. We then plot each of these indices against the percent cover of urban and agricultural land in each ecoregion, resulting in four separate comparisons. For each comparison, ecoregions that fall above the 66th quantile on both axes are identified as priorities for conservation. These analyses yield four 'priority sets' of 6-16 ecoregions (8-21% of the total number) where high levels of biodiversity and human land use coincide. These ecoregions tend to be concentrated in the southeastern United States, California, and, to a lesser extent, the Atlantic coast, southern Texas, and the U.S. Midwest. Importantly, several ecoregions are members of more than one priority set and two ecoregions are members of all four sets. Across all 76 ecoregions, urban cover is positively correlated with both species richness and endemism. Conservation efforts in densely populated areas therefore may be equally important (if not more so) as preserving remote parks in relatively pristine regions."
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.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 it