Gaps and opportunities for the World Heritage Convention to contribute to global wilderness conservation
Bibliographic record
Abstract
Wilderness areas are ecologically intact landscapes predominantly free of human uses, especially industrial-scale activities that result in substantial biophysical disturbance. This definition does not exclude land and resource use by local communities who depend on such areas for subsistence and bio-cultural connections. Wilderness areas are important for biodiversity conservation and sustain key ecological processes and ecosystem services that underpin planetary life-support systems. Despite these widely recognized benefits and values of wilderness, they are insufficiently protected and are consequently being rapidly eroded. There are increasing calls for multilateral environmental agreements to make a greater and more systematic contribution to wilderness conservation before it is too late. We created a global map of remaining terrestrial wilderness following the established last-of-the-wild method, which identifies the 10% of areas with the lowest human pressure within each of Earth's 62 biogeographic realms and identifies the 10 largest contiguous areas and all contiguous areas >10,000 km2. We used our map to assess wilderness coverage by the World Heritage Convention and to identify gaps in coverage. We then identified large nationally designated protected areas with good wilderness coverage within these gaps. One-quarter of natural and mixed (i.e., sites of both natural and cultural value) World Heritage Sites (WHS) contained wilderness (total of 545,307 km2), which is approximately 1.8% of the world's wilderness extent. Many WHS had excellent wilderness coverage, for example, the Okavango Delta in Botswana (11,914 km2) and the Central Suriname Nature Reserve (16,029 km2). However, 22 (35%) of the world's terrestrial biorealms had no wilderness representation within WHS. We identified 840 protected areas of >500 km2 that were predominantly wilderness (>50% of their area) and represented 18 of the 22 missing biorealms. These areas offer a starting point for assessing the potential for the designation of new WHS that could help increase wilderness representation on the World Heritage list. We urge the World Heritage Convention to ensure that the ecological integrity and outstanding universal value of existing WHS with wilderness values are preserved.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".