Projecting global land use‐driven evolutionary history loss
Bibliographic record
Abstract
Abstract Aim Recent studies have mapped the global hotspots hosting high phylogenetic diversity ( PD ), but not the regions where this diversity is under threat due to human land use. This is because, to date, it is not clear how much PD is lost as species of a given taxon go extinct. The aim of this study was to identify the global regions projected to suffer the highest PD loss due to human land use in the near future. Location Global. Methods We demonstrate a novel approach combining countryside species–area relationship, species‐specific evolutionary distinctiveness ( ED ) scores and a newly derived strong linear relationship between the cumulative ED loss and PD loss through pruning simulations on global evolutionary trees of mammals, birds, and amphibians under random species loss, to project global land use‐driven phylogenetic diversity loss in 804 terrestrial ecoregions and 175 countries. We also allocate the total projected PD loss to different land use types (agriculture, forestry, grazing or urbanization) in each region to pinpoint the major drivers. Results For the three taxa combined, we project a total loss of 9,472 million years ( MY ) of evolutionary history due to all land uses in all countries: 1,541 MY of mammal PD is at stake, 3,336 MY of bird PD and 4,595 MY of amphibian PD . Agriculture is responsible for loss of 1,579 MY ; pasture 1990 MY , forestry 5,381 and urbanization 522. Land use in Indonesia, Colombia, India, Papua New Guinea, Madagascar and Philippines is projected to cause the most loss of PD . Main conclusions Through the integrated approach, we can now project PD loss associated with species extinctions under alternative land conversion scenarios in a region. Overall, the results on hotspots and land use drivers may inform individual nations in designing regional strategies to achieve the international biodiversity and sustainability targets.
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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 | 0.000 | 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 | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".