Distributional Patterns of Alien Plants in China: The Relative Importance of Phylogenetic History and Functional Attributes
Bibliographic record
Abstract
Distribution patterns of alien species in nonnative ranges might be driven by a combination of various mechanisms, including phylogenetic history, competition for resource, environmental filtering, and so on. Both phylogenetic and functional limitations might work synergistically to determine the distribution of alien species. In this report, by utilizing the information of provincial distribution, functional attributes, and phylogeny for 95 alien species of China, the corresponding phylogenetic and functional community structures are evaluated. The results show that introduction pathway, life form, and flowering time of alien plants of China processed significant phylogenetic clustering patterns, while both the origin of distribution and reproduction mode of alien species showed phylogenetic overdispersion patterns, as revealed by NRI/NTI indices. The phylogenetic signal tests using Pagel’s statistic and Blomberg et al.’s statistic further verified the previous patterns, even though there are some inconsistencies. Through partial Mantel test, it is found that compositional patterns of alien plant community were mainly affected by phylogenetic limitation but not functional limitation. Conclusively, phylogeny plays a more important role in structuring provincial distribution of alien plants in China.
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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".