Foundation plant species provide resilience and microclimatic heterogeneity in drylands
Bibliographic record
Abstract
Climate change profoundly influences plants and animals in all ecosystems including drylands such as semi-arid and arid scrublands and grasslands. At the peak of an extended megadrought in the Southwestern USA, the microclimatic refuges provided by foundation plant species and through associated vegetation were examined. Shrubs and open interstitial spaces without a canopy but with annual plants were instrumented in 2016 and the wet season of 2017 in the central drylands of California. In both years and all seasons tested, vegetation significantly mediated fine-scale near-surface air temperature and relative soil moisture content-defined here as microclimate. The foundation species with other vegetation provided the most significant thermal refuge potential capacity for other plants and animals, but there was variation by growing season. Soil moisture content was frequently increased by the direct canopy effects of shrubs. This evidence suggests that the climate many plants and animals experience, even during an extended megadrought, is mediated by the local plants in highly impacted drylands with anthropogenic disturbance and significant water-induced challenges. Foundation species such as shrubs in drylands function as a potent starting point in examining the ecological relevance of climate at scales germane to many species locally. An ecological framework for climate resilience using shrubs will improve conservation and restoration planning in drylands.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".