Bibliographic record
Abstract
While predictive models of contemporary vegetation change often emphasize climate as the main driver, soil properties are increasingly recognized as critical mediators. This review synthesizes evidence on climate-soil interactions from diverse fields (e.g., paleobiology, species distribution modelling, and plant-soil feedbacks) across multiple scales. We propose a framework capturing how soil–climate correlations and species’ niches shape vegetation change, revealing several important mechanisms of climate-soil interactions. At local scales, communities restricted to unusual soils often resist thermophilization, and high soil heterogeneity can further buffer climate impacts. However, emerging soil–climate combinations may generate novel ecosystems. At biogeographical scales, soils constrain species’ range limits and migration rates, yet most evidence comes from temperate regions. For tropical-to-temperate transitions, we predict that soils might instead accelerate migration, based on greater soil fertility in the temperate zone. The distributions and climate sensitivities of soil microbes may not align with those of plants, posing challenges for predicting plant range shifts. Mechanistic species distribution models linking traits and demographic processes to soil properties offer a promising path to improve predictions. Our synthesis shows that understanding soil-vegetation-climate interactions is essential for accurate predictions of future plant distributions, community composition, and extinction risk in a changing climate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".