Bridging conservation gaps under climate change at multiple scales to protect 30% of Earth's surface by 2030
Bibliographic record
Abstract
The 30×30 commitment outlined in the Kunming-Montreal Global Biodiversity Framework (KM-GBF) offers a critical opportunity for enhancing global biodiversity conservation. However, KM-GBF's efforts to address climate change impacts remain limited. We developed 1-km-resolution hotspot maps for climate change vulnerability with the exposure-sensitivity-adaptation framework, species distribution for 4 terrestrial vertebrate taxa, and carbon stock capacity including organic and biomass carbon, for 2030. Then, we developed a systematic conservation planning approach that, beyond the 3 conservation features mentioned, also considered human activities, connectivity, and Shared Socioeconomic Pathways. The plan included the identification of conservation priorities and gaps for China and the Association of Southeast Asian Nations region (China-ASEAN) at regional, national, and biogeographical scales. We found that 6.59% of the land in China-ASEAN overlapped all 3 hotspots, primarily in Indonesia, Malaysia, and Cambodia. Across all 3 spatial scales, newly identified conservation priorities were concentrated in low-elevation areas, particularly between 10° S and 10° N at the regional scale. Currently, protected areas cover 15.49% of China-ASEAN's land, representing 7.00% of climate change vulnerability hotspots, 12.45% of species distribution potential hotspots, and 14.56% of carbon stock capacity hotspots for 2030. If the 30×30 commitment is realized at a regional scale, these percentages are expected to increase to 22.93%, 33.15%, and 34.75%, respectively. Areas of conservation priority identified with our framework were significantly affected by the scale of protection coordination, yet they remained stable across Shared Socioeconomic Pathways, indicating their effectiveness in diverse future scenarios. The biogeographical scale had the smallest average conservation gap for all 12 countries (13.14%). Financial challenges are highest for Indonesia at the regional scale and for Malaysia at the national and biogeographical scales. Precise conservation based on appropriate scales is essential to achieving the 30×30 commitment and maximizing its conservation effectiveness under climate change.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".