Changes in lowland dry-forest native and alien plant communities on Réunion Island (Indian Ocean) over 16 years
Bibliographic record
Abstract
Native floras of oceanic islands are among the most threatened on Earth. For example, only 1% of intact dry-forest ecosystems remain on Réunion, harbouring numerous endangered native species. Alien species invasion is one of the more important threats facing these ecosystems, as it has been hypothesized that, over time, the abundance of alien species will increase more than that of native species. To explore this, we studied floristic changes over a 16 year period (1995–2011) in six dry-forest remnants. Species richness and abundance increased for both alien and native species over this period, but at a significantly higher rate for the abundance of invasive alien plants. Despite this, Jaccard’s coefficient indicates a high level of similarity between the communities of 1995 and 2011. Also, the single site that benefited from invasive species management experienced the largest increase in native species, both in terms of specific richness and abundance. This study underlines the importance of permanent plots in studying the dynamics of invaded communities and in understanding plant succession. It also suggests a potential temporal persistence of island dry-forest communities, despite the expansion of alien species, and suggests the development of a new pathway in secondary succession where native and alien species coexist.
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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 | 0.001 | 0.001 |
| 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".