IMPACTS OF CLIMATE CHANGE ON VEGETATION DISTRIBUTION NO. 2 - CLIMATE CHANGE INDUCED VEGETATION SHIFTS IN THE NEW WORLD
Bibliographic record
Abstract
After giving an overview of climate change induced vegetation shifts in the Palearctic region in our previous paper, in this article we review literature available in Web of Science on North and South America.We study different geographical regions such as Canada, Alaska, California, Southwestern, Eastern and Southeastern USA, the Great Lakes region, the Great Plains, intermontane basins and plateaus, Rocky Mountains and the Cascades as well as Central and South America.We summarize main results of relevant field studies, experiments and model simulations.Predicted environmental changes include temperature increases, altering precipitation patterns, droughts, permafrost thaw and ground subsidence in arctic regions, enhanced El Niño Southern Oscillation, sea level rise, increasing salinity of the vadose zone, snowpack declines and various disturbances.All vegetation types are affected by these changes, to the most important phenomena belong e.g.reduction of arctic and alpine communities, decreasing area of taiga, shrub encroachment in tundra areas, northward expansion of the tree line, reduction in wetland areas, invasion, altering forest regeneration patterns, decrease in dominance of conifer species, increased cover of salt-tolerant plant species in tidal marshes, expansion of grassland, compositional and structural changes of grasslands and forests, drying up of bogs, landward migration of mangroves, savannification of forests, expansion of chaparral as well as upward migration of species in the mountains.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".