Shrubs indirectly increase desert seedbanks through facilitation of the plant community
Bibliographic record
Abstract
The mechanisms supporting positive ecological interactions are important. Foundation species can structure desert biodiversity by facilitating seedbanks of annual plants, but the direct and indirect mechanisms of shrub effects on seedbank have not been experimentally decoupled. We conducted the first test of shrubs increasing seedbank densities through direct effects on the seedbank (i.e. shrub seed-trapping, animal-mediated dispersal) and indirect effects by facilitating the annual plant community (i.e. seed deposition, annual seed-trapping). Two distinct desert ecosystems were used to contrast transient seedbank densities in shrub and open microsites by manipulating annual plant density and the presence of the persistent seedbank. We measured transient seedbank densities at the end of the growing season by collecting soil samples and extracting seeds from each respective treatment. Transient seedbank densities were greatest in shrub canopies and with relatively higher annual plant densities. The persistent seedbank contributed to transient seedbank densities only in one desert and in the open microsite. Shrubs indirectly increased seedbank densities by facilitation the seed production of the annual plants. Therefore, shrubs are increasing seedbank independently of the annual plant community, likely through trapping effects, and dependently by facilitating seed production of the annuals. These findings provide evidence for a previously undescribed mechanism that supports annual seedbanks and thus desert biodiversity. We also identify shrubs as being significant drivers of desert plant communities and emphasize the need to consider multiple mechanisms to improve our ability to predict the response of ecosystems to change.
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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 | 0.000 |
| 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) | 0.001 | 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".