Phylogenetic Diversity of Urban Floras in the Central Urals
Bibliographic record
Abstract
Modern cities harbor a high diversity of plants, and urban floras are significantly different from non-urban floras especially when considering the proportion of alien species found in cities. However, it is not clear whether urban areas disproportionately select for species from relatively few evolutionary lineages or provide opportunities for species across the full spectrum of plant lineages. Here, we examined the taxonomic and phylogenetic diversity of the floras in four cities (Yekaterinburg, Kamensk-Uralsky, Krasnoufimsk, and Turinsk) in the understudied region of Central Urals (Russian Federation). We classified native species into indigenous and apophytic species, namely, those that are sensitive to anthropogenic disturbance and those that have expanded their range with human activity, respectively. Alien species were classified into archaeophytes and neophytes according to when they were introduced (i.e., before or after than 1800). Phylogenetic diversity was quantified using Faith’s index to reflect total evolutionary history in urban areas and mean phylogenetic distance (MPD) to reflect species dissimilarity. Phylogenetic diversity of native species was higher than that for alien species, and the standardized effect size (SES) of MPD for natives was positive, reflecting their general dissimilarity from one another, while it was very negative for aliens, showing that they were phylogenetically clustered. However, among natives, apophytes were significantly clustered, while indigenous species were overdispersed. For the aliens, MPD was higher for archaeophytes compared to neophytes, though both groups were significantly clustered. These results show that urbanization leads to a non-random selection of plants. Apophytes and alien plants were composed of closely related species, reflecting similar ecological traits and are likely to be pre-adapted to the environmentally altered and highly disturbed urban environment.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".