Plant feedbacks increase the temporal heterogeneity of soil moisture
Bibliographic record
Abstract
Plant feedbacks on resource levels are well‐known, but feedbacks on resource variability have received little attention. Semi‐arid grasslands have greater temporal heterogeneity of rainfall than mesic forests, leading to the possibility that grasses further enhance this variability as a mechanism for excluding woody plants originating in habitats with less heterogeneity. Here we test the hypothesis that grasses create greater levels of temporal heterogeneity of soil resources than do woody plants. We used monocultures of five replicate species of both growth forms. Daily soil moisture measurements taken 10 and 30 cm beneath monocultures over a growing season showed that temporal heterogeneity was significantly greater under grasses than under woody plants. This occurred during a dry period when plants are most likely to compete for moisture. Differences in temporal heterogeneity between growth forms were related to differences in their abilities to reduce soil moisture: during the dry period, the net effect of vegetation on moisture 10 cm deep was greatest under grasses. Although the rate of change of soil moisture was higher under grasses, the growth forms exploited different depths of soil moisture: soils 10 cm deep were driest under grasses, but soils 30 cm deep were driest under woody species. In summary, grasses increased within‐season resource variability in a habitat already characterized by high among‐year variability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".