Meta‐corridor solutions for climate‐vulnerable plant species groups in South Korea
Bibliographic record
Abstract
Summary Vulnerability assessments can provide useful information for the establishment of climate change adaptation strategies. We performed spatial vulnerability assessments for multiple plant species that incorporate potential range shifts to areas of future suitable climate. We conducted the assessments at a national level for plant species organized into vulnerable species groups. We then identified a climate meta‐corridor for each vulnerable group that could potentially be a pathway for multiple species. We estimated climate suitability for 2297 South Korean terrestrial plant species under current climate conditions and climate projections for 2050 using the Multivariate Adaptive Regression Splines multiresponse species distribution model. We classified the plants into five groups based on their current spatial distribution patterns: centrally located species, wide‐range species, coastal mountain species, montane species, and lowland species. Three vulnerability assessment components — exposure, spatial disruption, and dispersal pressure — were used to calculate the spatial vulnerability of each species. Vulnerability values were averaged by group. We identified climate meta‐corridors that would link current suitable areas to future climatically suitable areas, and tested the corridors for multi‐species accessibility. The vulnerability assessment indicates that coastal mountain, montane, and lowland species groups, comprising 37% of all modelled species, are the most vulnerable to climate change. The climate meta‐corridor for each group overlaps at least some portion of 83% or more of its species' current modelled ranges. The current and future climate‐suitable areas for the lowland species group have very little spatial overlap, suggesting a high priority should be placed on the corridor identified for these species. We found that the destinations of the climate corridors converge, raising questions about large numbers of species moving to limited areas, and that transboundary corridor modelling is needed on the Korean Peninsula. Policy implications . Each of the three meta‐corridors has unique policy implications: assisted migration for the highest elevation species for the montane; significant conservation and restoration work for the lowland; and perhaps no direct intervention but monitoring to evaluate effectiveness of the relatively intact habitats of the coastal mountain meta‐corridor. Overall, implementation policies for climate connectivity will be context‐dependent, requiring different approaches dependent on local and regional conditions and the species targeted.
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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 | 0.001 | 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) | 0.033 | 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".