Risk assessment and adaptation technologies for island biodiversity conservation in China under climate change
Bibliographic record
Abstract
Islands are critically important but inherently fragile due to their isolation and limited size. Climate change poses escalating threats to island biodiversity, comprehensive and spatially explicit assessments are still limited, hindering the development of targeted adaptation strategies. Here, we integrated species distribution modeling, inundation modeling, extinction risk analysis, and spatial prioritization to assess the risks to China's island biodiversity from climate change. We applied two scenarios—SSP2-4.5 and SSP5-8.5—over 2041–2060, 2061–2080, and 2081–2100 to evaluate the risks to five key taxa, namely amphibians, birds, mammals, reptiles, and vascular plants. Future climate change might result in an extinction rate of 11.6%, 5.5%, 11.9%, 12.0% and 11.9% for amphibians, birds, mammals, reptiles, and vascular plants respectively under the SSP2-4.5 climate change scenario and 19.0%, 8.9%, 20.6%, 19.6% and 20.6% respectively under the SSP5-8.5 scenario. Additionally, 60 and 97 islands under SSP2-4.5 and SSP5-8.5, respectively, are projected to lose at least one major taxonomic group by 2081–2100. High-risk zones, such as the islands near the Pearl River Delta and the Yangtze River Delta, are likely to face greater vulnerability than other islands in China. Our species- and island-specific results provide a scientific basis for developing targeted adaptation technologies, tailored to local island characteristics and species habitat dynamics. Recommended technologies include enhancing coastal engineering and restoring coastal shelter forest for island protection, expanding protected area networks for habitat preservation, and designating target habitat islands to support species relocation for high-risk species. Advanced monitoring technologies, such as AI-driven ecological sensors, are also critical for managing data-deficient and dynamic islands.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".