Biodiversity, Urban Areas, and Agriculture: Locating Priority Ecoregions for Conservation
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| 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".