Experimental manipulations of fertile islands and nurse plant effects in the Mojave Desert, USA
Bibliographic record
Abstract
In a mixed desert shrub community we removed and added shrub canopies to examine above- and belowground influences of 3 species of shrubs on islands of soil fertility and the survival of transplanted Ambrosia dumosa seedlings. Soils sampled under shrubs in the wet season had higher pH, water content, organic matter, and both total and mineralizable nitrogen than soils in adjacent open areas, confirming a widely established pattern in arid lands. However, we also found species differences in soil parameters. Soils under Coleogyne ramosissima had highest pH, soils under A. dumosa had highest water content and nitrogen mineralization rates, and soils under Larrea tridentata had lowest water content. Soils sampled under shrubs in the dry season, 7 months after experimental shrub removal, maintained higher organic matter and total and mineralizable nitrogen content than adjacent open soils, but pH and water were altered by shrub manipulations. Species differences persisted only in soil water levels (A. dumosa soils were driest). Over a 1-year period, transplanted A. dumosa seedlings had highest survivorship in shrub removal and open treatments and died most rapidly under control shrubs of all 3 species, suggesting that shrubs had a strong negative effect on seedling survival, even in the presence of higher organic matter, nutrients, and (initially) higher water content of fertile islands. Our results suggest that nurse plants and islands of soil fertility have the potential to facilitate growth of other species by nutrient additions, but that the net effect of nurse plants can be negative due to shading and/or root competition.
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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 | 0.001 |
| 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".