ORIGINAL ARTICLE Assessing the vulnerability of rare plants using climate change velocity, habitat connectivity, and dispersal ability: a case study
Bibliographic record
Abstract
Abstract Climate change generally requires species to migrate northward or to higher elevation to maintain con-stant climate conditions, but migration requirement and migration capacity of individual species can vary greatly. Individual populations of species occupy different posi-tions in the landscape that determine their required range shift to maintain similar climate, and likewise the migra-tion capacity depends on habitat connectivity. Here, we demonstrate an approach to quantifying species vulnera-bilities to climate change for 419 rare vascular plants in Alberta, Canada, based on a multivariate velocity of cli-mate change metric, local habitat fragmentation, and migration capacity. Climate change velocities indicated that future migration requirements ranged from 1 to 5 km/ year in topographically complex landscapes, such as the Alberta Foothills and Rocky Mountains. In contrast, migration requirements to maintain constant climate in relatively flat Boreal Plains, Parkland, and Grassland ran-ged from 4 to 8 km/year. Habitat fragmentation was also highest in these flat regions, particularly the Parkland Natural Region. Of the 419 rare vascular plants assessed, 36 were globally threatened (G1–G3 ranking). Three globally threatened species were ranked as extremely vul-nerable and five species as highly vulnerable to the interactions among climate change velocity, habitat frag-mentation, and migration capacity. Incorporating dispersal characteristics and habitat fragmentation with local pat-terns in climate change velocity improves the assessment of climate change threats to species and may be applied to guide monitoring efforts or conservation actions.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".