Bias in protected‐area location and its effects on long‐term aspirations of biodiversity conventions
Bibliographic record
Abstract
To contribute to the aspirations of recent international biodiversity conventions, protected areas (PAs) must be strategically located and not simply established on economically marginal lands as they have in the past. With refined international commitments under the Convention on Biological Diversity to target protected areas in places of "importance to biodiversity," perhaps they may now be. We analyzed location biases in PAs globally over historic (pre-2004) and recent periods. Specifically, we examined whether the location of protected areas are more closely associated with high concentrations of threatened vertebrate species or with areas of low agricultural opportunity costs. We found that both old and new protected areas did not target places with high concentrations of threatened vertebrate species. Instead, they appeared to be established in locations that minimize conflict with agriculturally suitable lands. This entrenchment of past trends has substantial implications for the contributions these protected areas are making to international commitments to conserve biodiversity. If protected-area growth from 2004 to 2014 had strategically targeted unrepresented threatened vertebrates, >30 times more species (3086 or 2553 potential vs. 85 actual new species represented) would have been protected for the same area or the same cost as the actual expansion. With the land available for conservation declining, nations must urgently focus new protection on places that provide for the conservation outcomes outlined in international treaties.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".