Phylogenetic relatedness and plant invader success across two spatial scales
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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 it