Data from: Disentangling the effects of precipitation amount and frequency on the performance of 14 grassland species
Bibliographic record
Abstract
Climate change is causing shifts in the amount and frequency of precipitation in many regions, which is expected to have implications for plant performance. Most research has examined the impacts of the amount of precipitation on plants rather than the effects of both the amount and frequency of precipitation. To understand how climate-driven changes in precipitation can affect grassland plants, we asked: (i) How does the amount and frequency of precipitation affect plant performance? (ii) Do plant functional groups vary in their response to variable precipitation? To answer these questions we grew 14 monocot and eudicot grassland species and conducted a factorial manipulation of the amount (70 vs 90mm/month) and frequency (every 3, 15, or 30 days) of precipitation under rainout shelters. Our results show that both the amount and frequency of precipitation impact plant performance, with larger effects on eudicots than monocots. Above- and below-ground biomass were affected by the amount of precipitation and/or the interaction between the amount and frequency of precipitation. Above-ground biomass increased by 21–30% when the amount of precipitation was increased. When event frequency was decreased from 3 to 15 or 30 days, below-ground biomass generally decreased by 18–34% in the 70 mm treatment, but increased by 33–40% in the 90 mm treatment. Changes in stomatal conductance were largely driven by changes in event frequency. Our results show that it is important to consider changes in both the amount and frequency of precipitation when predicting how plant communities will respond to variable precipitation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".