Global prioritization schemes vary in their impact on the placement of protected areas
Bibliographic record
Abstract
In response to global declines in biodiversity, many global conservation prioritization schemes were developed to guide effective protected area establishment. Protected area coverage has grown dramatically since the introduction of several high-profile biodiversity prioritization schemes, but the impact of such schemes on protected area establishment has not been evaluated. We used matching methods and a Before-After Control-Impact causal analysis to evaluate the impact of two key prioritization schemes-Biodiversity Hotspots and Last of the Wild-representing examples of the reactive and proactive ends of the prioritization spectrum. We found that Last of the Wild had a positive impact on the rate of protection in its identified priority areas, but Biodiversity Hotspots did not. Because Biodiversity Hotspots are in or near human-dominated landscapes, this scheme may have been unable to overcome biases towards protecting areas with little human pressure. In contrast, Last of the Wild aligned with the tendency to protect areas far from high human use and thus with lower implementation costs, and so received greater uptake. Stronger links between large-scale prioritizations and more locally driven implementation of area-based conservation, as well as other forms of conservation action, are needed to overcome practical constraints and effectively protect biodiversity on an increasingly human-dominated planet.
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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 | 0.035 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".