Inland surface waters in protected areas globally: Current coverage and 30-year trends
Bibliographic record
Abstract
Inland waters are unique ecosystems offering services and habitat resources upon which many species depend. Despite the importance of, and threats to, inland water, global assessments of protected area (PA) coverage and trends have focused on land habitats or have assessed land and inland waters together. We here provide the first assessment of the level of protection of inland open surface waters and their trends (1984-2015) within PAs for all countries, using a globally consistent, high-resolution (30 m) and validated dataset on permanent and seasonal surface waters based on Landsat images. Globally, 15% of inland surface waters are covered by PAs with mapped boundaries. Estimated inland water protection increases to 16.4% if PAs with reported area but delineated only as points are included as circular buffers. These coverage estimates slightly exceed the comparable figure for land but fall below the 17% goal of the Convention on Biological Diversity's Aichi Target 11 for 2020. Protection levels are very uneven across countries, half of which do not yet meet the 17% target. The lowest coverage of surface water by PAs (<5%) was found in Africa and in parts of Asia. There was a global trend of permanent water losses and seasonal water gains within PAs, concomitant with an increase of both water types outside PAs. In 38% of countries, PAs lost over 5% of permanent water. Global protection targets for inland waters may well be met by 2020, but much stronger efforts are required to ensure their effective conservation, which will depend not only on sound PA governance and management but also on the sustainable use of water resources outside PAs. Given the pressures on water in a rapidly changing world, integrated management planning of water resources involving multiple sectors and entire basins is therefore necessary.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".