Protected areas and the ranges of threatened species: Towards the EU Biodiversity Strategy 2030
Bibliographic record
Abstract
Despite the protected area expansion over the last decades, biodiversity continues to decline. The EU Biodiversity Strategy for 2030, as well as the Kunming-Montreal Global Biodiversity Framework, call for 30 % coverage by protected areas of the land and sea in order to halt and reverse biodiversity loss. Here, we use European species assessed as threatened on the IUCN Red List – the ones facing the most imminent threat of extinction - to guide the proposed expansion. We overlapped the ranges of 2290 threatened terrestrial and freshwater resident species and 127,046 terrestrial protected areas (28,130 Natura2000 sites and 98,916 nationally designated protected areas) in the EU and we found that species' EU ranges are covered on average 46.6 % by protected areas (41.5 % by Natura2000 and 34.0 % by nationally designated protected areas). We found 71 Gap0.1 species (<0.1 % coverage), most of which are invertebrates and endemic to southern EU islands. The southern EU countries have the highest number of threatened endemic species, they offer among the highest coverage to threatened species ranges and they have almost reached the 30 % land coverage by protected areas and. Therefore, the expansion of the protected area network, should not be guided solely by percentage of area targets, but instead by biodiversity needs. Although it should not be the only approach, targeting the protected area expansion towards Gap0.1 threatened species, would strengthen the EU protected area network by maximising the ability to conserve biodiversity and prevent extinctions.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".