Disentangling direct and indirect effects of a legume shrub on its understorey community
Bibliographic record
Abstract
Direct and indirect interactions among plants contribute to shape community composition through above‐ and belowground processes. However, we have not disentangled yet the direct and indirect soil and canopy effects of dominants on understorey species. We addressed this issue in a semi‐arid system from southeast Spain dominated by the legume shrub Retama sphaerocarpa . During a year with an exceptionally dry spring, we removed the shrub canopy to quantify aboveground effects and compared removed‐canopy plots to open plots between shrubs to quantify soil effects, both with and without watering. We added a grass removal treatment in order to separate direct from indirect shrub effects and quantified biomass, abundance, richness and composition of the forb functional group. With watering, changes in forb biomass were primarily driven by indirect shrub effects, with contrasting negative soil and positive aboveground indirect effects; changes in forb abundance and composition were more influenced by direct shrub soil effects with contrasting species composition between open and Retama patches. As community composition was different between open and Retama patches the indirect effects of Retama on forb species did not concern forbs from the open community but forbs from Retama patches. Indirect effects are, thus, important at the functional group level rather than at the species level. Without watering, there were no significant interactions. Changes in species richness between treatments were weak and seldom significant. We conclude that shrub effects on understorey forbs are primarily due to their influence on soil properties, directly affecting forb species composition but indirectly affecting the biomass of the forbs of the Retama patches, and only with sufficient water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".