The dark side of facilitation: native shrubs facilitate exotic annuals more strongly than native annuals
Bibliographic record
Abstract
Positive interactions enhance biodiversity and ecosystem function, but can also exacerbate biological invasions. Facilitation of exotic invaders by exotic foundation species (invasional meltdown) has been studied extensively, but facilitation of exotic invaders by native foundation species has attracted less attention. Specifically, very few studies have examined the extent that native foundation species facilitate native and exotic competitors. Understanding the processes that mediate interactions between native and exotic species can help explain, predict, and improve management of biological invasions. Here, we examined the effects of native foundation shrubs on the relative abundance of the annual plant community – including native and exotic taxa – from 2015–2018 in a desert ecosystem at Carrizo Plain National Monument, California, USA (elevation: 723 m). Shrub effects varied by year and by the identity of annual species, but shrubs consistently enhanced the abundance of the annual plant community and facilitated both native (n=17 species) and exotic (n=4 species) taxa. However, at the provenance level, exotic annuals were facilitated 2.75 times stronger in abundance than native annuals, and exotic annuals were always more abundant than natives both near and away from shrubs. Our study reaffirms facilitation as an important process in the organisation of plant communities and confirms that both native and exotic species can form positive associations with native foundation species. However, facilitation by native foundation species can exacerbate biological invasions by increasing the local abundance of exotic invaders. Thus, the force of facilitation can have a dark side relevant to ecosystem function and management.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".