LIFE: A metric for mapping the impact of land-cover change on global extinctions
Bibliographic record
Abstract
Human-driven habitat loss is recognized as the greatest cause of the biodiversity crisis, yet to date we lack robust, spatially explicit metrics quantifying the impacts of anthropogenic changes in habitat extent on species’ extinctions. Existing metrics either fail to consider species identity or focus solely on recent habitat losses. The persistence score approach developed by Durán et al . (Durán et al. 2020 Methods Ecol. Evol . 11 , 910–921 (doi:10.1111/2041-210X.13427) represented an important development by combining species’ ecologies and land-cover data while considering the cumulative and non-linear impact of past habitat loss on species’ probability of extinction. However, it is computationally demanding, limiting its global use and application. Here we couple the persistence score approach with high-performance computing to generate global maps of what we term the LIFE (Land-cover change Impacts on Future Extinctions) metric for 30 875 species of terrestrial vertebrates at 1 arc-min resolution (3.4 km 2 at the equator). These maps provide quantitative estimates, for the first time, of the marginal changes in the expected number of extinctions (both increases and decreases) caused by converting remaining natural vegetation to agriculture, and restoring farmland to natural habitat. We demonstrate statistically that this approach integrates information on species richness, endemism and past habitat loss. Our resulting maps can be used at scales from 0.5–1000 km 2 and offer unprecedented opportunities to estimate the impact on extinctions of diverse actions that affect change in land cover, from individual dietary choices through to global protected area development. This article is part of the discussion meeting issue ‘Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future’.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".