Direct and indirect interactions between shrubs and the flowering annual community in an arid ecosystem
Bibliographic record
Abstract
Shrubs commonly shape the plant community in arid ecosystems such as deserts. Plant interactions between shrubs and the associated species can be both negative and positive, and direct and indirect. The importance of shrub effects on reproduction is also a key component of facilitation studies in deserts. Here, we tested the hypothesis that shrubs facilitate understory plant community composition and reproduction through both direct and indirect interactions mediated by the associated plant neighbourhood. Using the shrub species Larrea tridentata , we tested the effects of facilitation on understory plant density, floral density, and species richness in the Mojave Desert, California. We found that shrubs decreased the net community-wide plant assembly measures relative to open microsites including total density and species richness at the site tested. There was also evidence that shrubs directly decreased plant fitness by reducing floral density, but this effect depended on the species. The net density and diversity of the annual plant community also influenced plant and floral density under shrubs suggesting significant indirect effects between shrubs and all abundant annual plant species. While both direct and indirect interactions between shrubs and the annual plant community were species-specific, the effects of these interactions had greater impact on plant density than floral density measures. Annual plant density and to a lesser extent floral density are thus driven by both direct and indirect interactions with shrubs.
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 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 | 0.000 |
| 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".