Identifying global conservation priorities for terrestrial vertebrates based on multiple dimensions of biodiversity
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework of the Convention on Biological Diversity calls for an expansion of the current protected areas (PAs) to cover at least 30% of global land and water areas by 2030 (i.e., the 30×30 target). Efficient spatial planning for PA expansion is an urgent need for global conservation practice. A spatial prioritization framework considering multiple dimensions of biodiversity is critical for improving the efficiency of the spatial planning of PAs, yet it remains a challenge. We developed an index for the identification of priority areas based on functionally rare, evolutionarily distinct, and globally endangered species (FREDGE) and applied it to 21,536 terrestrial vertebrates. We determined species distributions, conservation status (global endangerment), molecular phylogenies (evolutionary distinctiveness), and life-history traits (functional rarity). Madagascar, Central America, and the Andes were of high priority for the conservation of multiple dimensions of terrestrial vertebrate biodiversity. However, 68.8% of grid cells in these priority areas had <17% of their area covered by PAs, and these priority areas were under intense anthropogenic and climate change threats. These results highlight the difficulties of conserving multiple dimensions of biodiversity. Our global analyses of the geographical patterns of multiple dimensions of terrestrial vertebrate biodiversity demonstrate the insufficiency of the conservation of different biodiversity dimensions, and our index, based on multiple dimensions of biodiversity, provides a useful tool for guiding future spatial prioritization of PA expansion to achieve the 30×30 target under serious pressures.
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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 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| 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".