Impacts of hydraulic redistribution on grass–tree competition vs facilitation in a semi‐arid savanna
Bibliographic record
Abstract
A long-standing ambition in ecosystem science has been to understand the relationship between ecosystem community composition, structure and function. Differential water use and hydraulic redistribution have been proposed as one mechanism that might allow for the coexistence of overstory woody plants and understory grasses. Here, we investigated how patterns of hydraulic redistribution influence overstory and understory ecophysiological function and how patterns vary across timescales of an individual precipitation event to an entire growing season. To this end, we linked measures of sap flux within lateral and tap roots, leaf-level photosynthesis, ecosystem-level carbon exchange and soil carbon dioxide efflux with local meteorology data. The hydraulic redistribution regime was characterized predominantly by hydraulic descent relative to hydraulic lift. We found only a competitive interaction between the overstory and understory, regardless of temporal time scale. Overstory trees used nearly all water lifted by the taproot to meet their own transpirational needs. Our work suggests that alleviating water stress is not the reason we find grasses growing in the understory of woody plants; rather, other stresses, such as excessive light and temperature, are being ameliorated. As such, both the two-layer model and stress gradient hypothesis need to be refined to account for this coexistence 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".