Biodiversity loss under future global socio-economic and climate scenarios
Bibliographic record
Abstract
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 five future (2050 and 2100) global gridded maps (0.25° × 0.25° resolution) available from the land use harmonization (LUH) database that represent alternative representative concentration and shared socio-economic pathways (RCP-SSP) 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 to 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, equivalent to 20–80% of the number committed to extinction under current (2015) land use extent. Results show that hotspots of future biodiversity loss differ depending upon the scenario, taxon and metric considered. 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 followed by RCP8.5 SSP-5, RCP6.0 SSP-4 and RCP7.0 SSP-3. Interestingly, the scenario with the second most aggressive climate target (RCP3.4 SSP-4) projected the highest biodiversity loss among the five scenarios tested. This is because it represents a world with continued high consumption in rich countries and increased land clearing for crop production in species rich, low-income countries such as Indonesia, Madagascar, Tanzania, Philippines and DR Congo. These contrasting results illustrate that the strategies to prevent climate change could simultaneously contribute to reduction in current high rates of biodiversity loss, but only if habitat preservation is incorporated into national and global sustainable development plans.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".