Patterns of plant invasion along an environmental stress gradient
Bibliographic record
Abstract
Abstract. Question: Do stressful environments facilitate plant invasion by providing refuges from intense above‐ground competition associated with productive areas, or prevent it by favouring locally adapted native species? Location: An invaded and fragmented oak savanna ecosystem structured along a landscape‐level stress gradient associated with soil depth, elevation, and canopy openness. Methods: Vegetation and environmental data were collected from 184 plots in seven savanna remnants along the gradient. Using multivariate (CCA) and post‐hoc regression analyses, we determined the relationship between environment and the richness and abundance of invasives. Results: 46 of 119 species were naturalized exotics. CCA indicated the importance of environmental variation (mostly soil depth) for community structure but not for invasion; invasive species richness was similar in all areas. However, the abundance of invasives and their impacts on native diversity appear to increase significantly in less stressful habitats. Deeper soils had lower evenness and significantly fewer native species. This result was associated with dominance by exotic perennial grasses and large increases in vegetation height, suggesting strong above‐ground competition. Conclusions: Low‐stress environments were not more invasible per se but appear to be more susceptible to invasion by species with strong competitive impacts. The causes of decreasing exotic impact with decreasing soil depth may reflect shifts in competitive intensity or an increased importance of stress tolerance, both of which may favour natives. Alternatively, this ecosystem may simply lack high‐impact invaders capable of dominating shallow soils. Conservation challenges are twofold for this endangered plant community: controlling invasives that currently dominate deeper‐soils and accounting for a diverse pool of invaders that proliferate when the current dominants are removed.
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".