Smart Protected Area Placement Decelerates Biodiversity Loss: A Representation‐extinction Feedback Leads Rare Species to Extinction
Bibliographic record
Abstract
Abstract Protected areas (PAs) spearhead global conservation efforts, but it has been repeatedly demonstrated that narrowly distributed species are more likely to be unrepresented in PAs. This means that where local extinctions are more likely outside PAs, a positive feedback loop could render PAs largely ineffective in decelerating extinctions, even where PAs effectively abate threats. Here we empirically test the elements of this feedback loop using distribution data for 1,572 threatened plants in Japan. Narrowly distributed species were indeed less likely to overlap PAs than widespread species, and local extinction rates for unprotected populations over 15 years were 1.5 times higher than those inside PAs. A simulation model showed that new PAs will substantially reduce extinction risk for widespread species, but not for narrowly distributed species, unless they are placed very precisely in the landscape. Our results suggest that a representation‐extinction feedback will limit the effectiveness of PAs in preventing extinctions unless PA placement is carefully targeted.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".