Little progress in ecoregion representation in the last decade of terrestrial and marine protected area expansion leaves substantial tasks ahead
Bibliographic record
Abstract
Adequate representation of biodiversity in protected area networks is a prerequisite for successful conservation. Aichi Target 11 of the Convention on Biological Diversity called for 17% of land area and 10% of marine area to be conserved in ecologically representative protected areas by 2020. We assess progress in protecting terrestrial and marine ecoregions for the decade 2011-2020, when the Strategic Plan for Biodiversity 2011-2020 was in effect. Using spatial analyses and the Mean Target Achievement metric, which indicates the degree to which a given representation target has been achieved, we analyze protected area coverage in nine countries from all continents, with a total of 173 terrestrial and 64 marine ecoregions. Results show that there is little evidence that the countries studied have strategically protected underrepresented ecoregions in the 2011-2020 decade. Although 170.000 km² of terrestrial and 3 million km² of marine reserves have been designated during this period in the nine countries investigated, about half of their terrestrial and marine ecoregions remain poorly protected in 2020. Our findings reinforce that targeted action is needed to adequately protect ecoregions in order for the new Kunming-Montreal target 3 to be more successful than Aichi target 11. The methodology presented allows for ongoing evaluation, identification of gaps, and monitoring of countries’ progress towards global and national targets for ecological representation and is applicable to any biodiversity surrogate beyond ecoregions and any country or region of interest.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".