Global human influence maps reveal clear opportunities in conserving Earth’s remaining intact terrestrial ecosystems
Bibliographic record
Abstract
Leading up to the Convention on Biological Diversity Conference of the Parties 15, there is momentum around setting bold conservation targets. Yet, it remains unclear how much of Earth's land area remains without significant human influence and where this land is located. We compare four recent global maps of human influences across Earth's land, Anthromes, Global Human Modification, Human Footprint and Low Impact Areas, to answer these questions. Despite using various methodologies and data, these different spatial assessments independently estimate similar percentages of the Earth's terrestrial surface as having very low (20%-34%) and low (48%-56%) human influence. Three out of four spatial assessments agree on 46% of the non-permanent ice- or snow-covered land as having low human influence. However, much of the very low and low influence portions of the planet are comprised of cold (e.g., boreal forests, montane grasslands and tundra) or arid (e.g., deserts) landscapes. Only four biomes (boreal forests, deserts, temperate coniferous forests and tundra) have a majority of datasets agreeing that at least half of their area has very low human influence. More concerning, <1% of temperate grasslands, tropical coniferous forests and tropical dry forests have very low human influence across most datasets, and tropical grasslands, mangroves and montane grasslands also have <1% of land identified as very low influence across all datasets. These findings suggest that about half of Earth's terrestrial surface has relatively low human influence and offers opportunities for proactive conservation actions to retain the last intact ecosystems on the planet. However, though the relative abundance of ecosystem areas with low human influence varies widely by biome, conserving these last intact areas should be a high priority before they are completely lost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".