Diversity effects on invasion vary with life history stage in marine macroalgae
Bibliographic record
Abstract
Most experimental studies of diversity effects on invasibility have reported negative relationships while observational studies have often found positive correlations between the numbers of exotic and native taxa. Nearly all of these studies have been done with terrestrial plants or aquatic invertebrates. We investigated effects of native macroalgal diversity on invasion success of the introduced macroalga Sargassum muticum (Yendo) Fensholt (Phaeophyceae: Fucales) on the west coast of Vancouver Island. We conducted both observational field surveys of the correlation between native diversity and exotic cover, and experimental manipulations of native diversity in constructed 25×25 cm communities. Field surveys found higher cover of S. muticum in plots with low native diversity, suggesting a negative relationship between diversity and invasibility at the neighbourhood scale. The experiment found initial cover of S. muticum germlings was highest in plots with greater diversity. Over the duration of the experiment cover of settled germlings increased fastest in the low diversity plots, so that there was a weak negative effect of diversity on final cover of the invader after 77 days. The slope of the relationship reversed over time, with field patterns and experimental results converging at the end of the experiment. Our results suggest native diversity has contrasting effects on different stages of invasion. Diversity facilitates invader recruitment of S. muticum but decreases growth and or survivorship.
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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 | 0.001 |
| 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 | 0.001 |
| 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".