Multispecies conservation corridors in China: For climate change adaptation
Bibliographic record
Abstract
The development of climate-adaptive migration corridors has emerged as a key strategy for biodiversity conservation. However, most existing studies focus on the migration patterns and adaptability of a few species and barely pay attention to the design of migration corridors that address multispecies needs at a national scale under climate change. In this study, we analysed 1023 nationally protected wildlife species in China to predict their potential distributions under current climatic conditions and the SSP2-4.5 scenario using the maximum entropy model. The projections were used as a base to conduct hotspot analysis to identify areas with declining, stable or increasing habitat selection rates (HSRs), which were designated as ecological sources. These areas correspond to regions likely to experience species emigration, retention or immigration. Using circuit theory and the minimum cumulative resistance model, we employed the Linkage Mapper tool to construct climate-resilient conservation corridors and identify critical ecological nodes. We identified 49 ecological sources, including 19 ecological sources with declining HSRs, 13 ecological sources with stable HSRs and 17 ecological sources with increasing HSRs. These HSRs collectively covered over 90% of the studied species and demonstrated a strong conservation representativeness. We also mapped 108 migration corridors, including 49 supporting species movement from areas with declining HSRs and 59 enhancing connectivity and species exchange. In addition, we identified 978 ecological pinch points and 203 barrier points, which are critical priorities for future corridor planning. A novel framework for the design of multispecies conservation corridors that support climate change adaptation, which contributes to China’s efforts to achieve the Kunming–Montreal Biodiversity Framework targets and improve ecosystem connectivity.
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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 | 0.000 |
| 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".