The effect of consumer pressure and abiotic stress on positive plant interactions are mediated by extreme climatic events
Bibliographic record
Abstract
Environmental extremes resulting from a changing climate can have profound implications for plant interactions in desert communities. Positive interactions can buffer plant communities from abiotic stress and consumer pressure caused by climatic extremes, but limited research has explored this empirically. We tested the hypothesis that the mechanism of shrub facilitation on an annual plant community can change with precipitation extremes in deserts. During years of extreme drought and above-average rainfall in a desert, we measured plant interactions and biomass while manipulating a soil moisture gradient and reducing consumer pressure. Shrubs facilitated the annual plant community at all levels of soil moisture through reductions in microclimatic stress in both years and herbivore protection in the wet year only. Shrub facilitation and the high rainfall year contributed to the dominance of a competitive annual species in the plant community. Precipitation patterns in deserts determine the magnitude and type of facilitation mechanisms. Moreover, shrub facilitation mediates the interspecific competition within the associated annual community between years with different rainfall amounts. Examining multiple drivers during extreme climate events is a challenging area of research, but it is a necessary consideration given forecasts predicting that these events will increase in frequency and magnitude.
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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 | 0.001 |
| 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".