Phylogenetically diverse native systems are more resistant to invasive plant species on Robben Island, South Africa
Bibliographic record
Abstract
Alien invasive species are problematic both economically and ecologically, particularly on islands. As such, understanding how they interact with their environment is necessary to inform invasive species management. Here, we ask the following questions: What are the main functional traits that correlate with invasion success of alien plants on Robben Island? How does phylogenetic structure shape biotic interactions on the island? Using multiple approaches to explore these questions, we found that alien invasive species flower later during the year and for longer period, although flowering phenology was sensitive to alternative starting date. Additionally, we observed that alien invasive species are mostly abiotically pollinated and are generally hermaphroditic whilst their native counterparts rely on biotic pollinators, flower earlier, and are generally dioecious, suggesting that alien invasive and native species use different ecological niches. Furthermore, we found a facilitative interaction between an alien invasive legume and other invasive plants as predicted by the invasional meltdown hypothesis, but this does not influence the phylogenetic structure of plant communities. Finally, phylogenetically diverse set of native species are less receptive to alien invasive species. Collectively, our findings reveal how biotic interactions and phylogenetic relatedness structure alien invasive - native co-existence.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".