Protecting Important Sites for Biodiversity Contributes to Meeting Global Conservation Targets
Bibliographic record
Abstract
Protected areas (PAs) are a cornerstone of conservation efforts and now cover nearly 13% of the world's land surface, with the world's governments committed to expand this to 17%. However, as biodiversity continues to decline, the effectiveness of PAs in reducing the extinction risk of species remains largely untested. We analyzed PA coverage and trends in species' extinction risk at globally significant sites for conserving birds (10,993 Important Bird Areas, IBAs) and highly threatened vertebrates and conifers (588 Alliance for Zero Extinction sites, AZEs) (referred to collectively hereafter as 'important sites'). Species occurring in important sites with greater PA coverage experienced smaller increases in extinction risk over recent decades: the increase was half as large for bird species with>50% of the IBAs at which they occur completely covered by PAs, and a third lower for birds, mammals and amphibians restricted to protected AZEs (compared with unprotected or partially protected sites). Globally, half of the important sites for biodiversity conservation remain unprotected (49% of IBAs, 51% of AZEs). While PA coverage of important sites has increased over time, the proportion of PA area covering important sites, as opposed to less important land, has declined (by 0.45-1.14% annually since 1950 for IBAs and 0.79-1.49% annually for AZEs). Thus, while appropriately located PAs may slow the rate at which species are driven towards extinction, recent PA network expansion has under-represented important sites. We conclude that better targeted expansion of PA networks would help to improve biodiversity trends.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".