Interactions between soil heterogeneity and freezing: Implications for grassland plant diversity and relative species abundances
Bibliographic record
Abstract
Plant stress resulting from soil freezing is expected to increase in northern temperate regions over the next century due to reductions in snow cover caused by climate change. Within plant communities, soil spatial heterogeneity can potentially buffer the effects of plant freezing stress by increasing the availability of soil microsites that function as microrefugia. Moreover, increased species richness resulting from soil heterogeneity can increase the likelihood of stress-tolerant species being present in a community. We used a field experiment to examine interactions between soil heterogeneity and increased freezing intensity (achieved via snow removal) on plant abundance and diversity in a grassland. Patches of topsoil were mixed with either sand or woodchips to create heterogeneous and homogeneous treatments, and plant community responses to snow removal were assessed over three growing seasons. Soil heterogeneity interacted significantly with snow removal, but it either buffered or exacerbated the snow removal response depending on the specific substrate (sand vs. woodchips) and plant functional group. In turn, snow removal influenced plant responses to soil heterogeneity; for example, adventive forb cover responded to increased heterogeneity under ambient snow cover, but this effect diminished with snow removal. Our results reveal that soil heterogeneity can play an important role in determining plant responses to changes in soil freezing stress resulting from global climate change. While the deliberate creation of soil microsites in ecological restoration projects as a land management practice could increase the frequency of microrefugia that mitigate plant community responses to increased freezing stress, the design of these microsites must be optimized, given that soil heterogeneity also has the potential to exacerbate freezing stress responses.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".