Role of protected areas in mitigating range loss and local extinctions of terrestrial mammals
Bibliographic record
Abstract
Protected areas (PAs) are a major tool in biodiversity conservation, but the extent to which they mitigate species declines is often unclear. We evaluated the effectiveness of PAs in mitigating range contraction and local extinction for 483 terrestrial mammal species. We used expert-based species range maps from the 1970s and compared them with current distributions to estimate changes in range area and PA coverage over the last 5 decades. We used generalized boosted models and propensity score matching to assess the effectiveness of PAs in mitigating species declines in landscape units sharing environmental features but differing in protection status. On average, mammal species were extirpated across one third of their ranges from 1970 to 2015, and 86% of this range contraction occurred outside PAs. In areas protected continuously since the 1970s, extirpation rates were half that in matched landscape units that were never protected. Areas protected since the 1970s also had lower extirpation rates compared with matched areas that became protected later. Lower species extirpation and range contraction rates were also correlated with greater area encompassed by PAs. Although the proportion of species ranges encompassed by PAs seemingly increased by 12% from 1970 to 2015, this increase may be largely attributed to the loss of species range area outside PAs rather than the strategic expansion of PAs. Our results showed that PAs can mitigate range loss and local extinctions for mammals and highlighted that PAs need to be maintained continuously over long time frames to have positive effects on biodiversity. Our findings suggest that downgrading, downsizing, and degazettement of PAs can lead to biodiversity declines, whereas the strategic achievement of targets, such as protecting 30% of land area by 2030, could facilitate species persistence.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".