Pathways of human development threaten biomes’ protection and their remaining natural vegetation
Bibliographic record
Abstract
Abstract Protected areas have been one of the most commonly applied conservation tools to prevent ecosystem degradation. International conservation targets have been created to incentivize widespread expansion of protected area networks, but this call might clash with expected future land use change. Here we investigated how future land use trajectories (2015-2090), representing a wide range of plausible future scenarios would impact the remaining areas of primary vegetation under different protection levels across the world’s biomes. We then highlight areas under greater risk of conflict between conservation (highly protected) and land use expansion (high projected change), and areas where these two can better co-exist (lower protection with high projected change and/or high protection with low projected change). While the most positive pathway of development led to the least loss of primary vegetation globally, this was not observed in all biomes. Further, we found no significant correlation between existing extent of protection and average proportion of vegetation loss. Mediterranean Forests, Woodlands & Scrub had the largest projected loss occurring in the highest protected areas. Tropical Forests in Central Africa and the Boreal Forests of North Euro-Asia and Canada emerge as the areas where most projected change occurs, and existing protection is still low. Areas in India and Southeast Asia emerge as potential areas for intervention as they have significant projected loss of primary vegetation, and considerably low protection. Our results can help inform policy and decision-makers to prevent such conflicts and support the development of management actions. These policy and management actions should target conservation in areas under expected great pressure of change with high ecological value (e.g., composed mainly by primary vegetation), but still not protected. This study also opens the discussion to the future of current protected areas and to the potential to expand the existing network of protected areas.
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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 | 0.002 |
| 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".