Global alternatives of natural vegetation cover
Bibliographic record
Abstract
Preserving and restoring terrestrial ecosystems is essential to preventing the decline of life on Earth. To guide global conservation efforts, we present a detailed counterfactual map showing Earth’s natural tree, short vegetation, and bare ground cover. This map accounts for environmental filtering along with realistic scenarios of fire frequency and wildlife herbivory. The most likely scenario suggests 43% (5669 ± 74 Mha) of land could support trees, 39% (5183 ± 86 Mha) shrubs and grasses, and 18% (2352 ± 59 Mha) bare ground. Adjustments in fire and herbivory could shift a minimum of 675 Mha of land, stressing the importance of considering alternative outcomes when restoring a landscape. Our findings also suggest that adjustments in fire frequency and wildlife herbivory could have a greater impact on natural vegetation than expected climate changes by 2050, highlighting decision-makers’ responsibility to guide conservation and restoration toward a sustainable and biodiverse future. Conserving and restoring ecosystems requires understanding what natural vegetation would look like without human disturbance. This study maps the most likely global cover of trees, short vegetation, and bare ground, showing that land management through fire and herbivory may influence ecosystems more than climate change alone.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".