Mapping global land conversion pressure to support conservation planning
Bibliographic record
Abstract
Proactively identifying where land conversion might occur is critical to targeted and effective conservation planning. Previous efforts to map future habitat loss have largely focused on forested systems and have been limited in their consideration of drivers of loss. We developed a 1-km resolution, global map of land conversion pressure from multiple drivers, referred to as the conversion pressure index (CPI). The CPI combines past rates of anthropogenic change, as measured by temporal human modification maps, with suitability maps for potential future expansion by large-scale development. The CPI thus offers a new way to measure a cumulative gradient of anthropogenic pressure as opposed to categorical land cover change. We find that nearly 23% of land across 200 countries have relatively high conversion pressure, potentially impacting over 460 million ha of intact natural lands. We illustrate how this information can be used to identify areas for proactive conservation to avoid future loss and ensure that national commitments under the Kunming-Montreal Global Biodiversity and Paris Agreement Climate Frameworks are upheld.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".