Terrestrial Vertebrate Biodiversity Loss under Future Global Land Use Change Scenarios
Bibliographic record
Abstract
Efficient forward-looking mitigation measures are needed to halt the global biodiversity decline. These require spatially explicit scenarios of expected changes in multiple indicators of biodiversity under future socio-economic and environmental conditions. Here, we link six future (2050 and 2100) global gridded maps (0.25° × 0.25° resolution) available from the land use harmonization (LUH) database, representing alternative concentration pathways (RCP) and shared socio-economic pathways (SSPs), with the countryside species–area relationship model to project the future land use change driven rates of species extinctions and phylogenetic diversity loss (in million years) for mammals, birds, and amphibians in each of the 804 terrestrial ecoregions and 176 countries and compare them with the current (1900–2015) and past (850–1900) rates of biodiversity loss. Future land-use changes are projected to commit an additional 209–818 endemic species and 1190–4402 million years of evolutionary history to extinction by 2100 depending upon the scenario. These estimates are driven by land use change only and would likely be higher once the direct effects of climate change on species are included. Among the three taxa, highest diversity loss is projected for amphibians. We found that the most aggressive climate mitigation scenario (RCP2.6 SSP-1), representing a world shifting towards a radically more sustainable path, including increasing crop yields, reduced meat production, and reduced tropical deforestation coupled with high trade, projects the lowest land use change driven global biodiversity loss. The results show that hotspots of future biodiversity loss differ depending upon the scenario, taxon, and metric considered. Future extinctions could potentially be reduced if habitat preservation is incorporated into national development plans, especially for biodiverse, low-income countries such as Indonesia, Madagascar, Tanzania, Philippines, and The Democratic Republic of Congo that are otherwise projected to suffer a high number of land use change driven extinctions under all scenarios.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 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; 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".