Phylogenetic relatedness and plant invader success across two spatial scales
Bibliographic record
Abstract
ABSTRACT Aim Successful invaders often possess similar ecological traits that contribute to success in new regions, and thus under niche conservatism, invader success should be phylogenetically clustered. We asked if the degree to which non‐native plant species are phylogenetically related is a predictor of invasion success at two spatial scales. Location Australia – the whole continent and Royal National Park (south‐eastern Australia). Methods We used non‐native plant species occupancy in Royal National Park, as well as estimated continental occupancy of these species from herbarium records. We then estimated phylogenetic relationships using molecular data from three gene sequences available on GenBank ( matK , rbcL and ITS1 ). We tested for phylogenetic signals in occupancy using Blomberg's K . Results Whereas most non‐native plants were relatively scarce, there was a strong phylogenetic signal for continental occupancy, driven by the clustering of successful species in Asteraceae, Caryophyllaceae, Poaceae and Solanaceae. However, we failed to detect a phylogenetic signal at the park scale. Main Conclusions Our results reveal that at a large spatial scale, invader success is phylogenetically clustered where ecological traits promoting success appear to be shared among close relatives, indicating that phylogenetic relationships can be useful predictors of invasion success at large spatial scales. At a smaller, landscape scale, there was no evidence of phylogenetic clustering of invasion success, and thus, relatedness plays a much reduced role in determining the relative success of invaders.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".