Does urbanization lead to taxonomic and functional homogenization in riparian forests?
Bibliographic record
Abstract
Abstract Aim In urbanized areas, exotic invasions, native extinctions, and the alteration of habitats and natural processes drive homogenization, which is a form of biotic impoverishment. This study examines whether urbanization and flooding induce homogenization of herbaceous communities in riparian forests and quantifies the relationships between taxonomic and functional β‐diversity. Location Montréal, Québec, Canada. Methods Inventories were conducted in 56 riparian forests. Taxonomic and functional β‐diversity were calculated as between‐site similarities in species or trait composition for three levels of urbanization and flooding. Differences among the disturbance levels were compared using tests for homogeneity in multivariate dispersions. We quantified the correlation between local species richness, exotic proportion, taxonomic and functional β‐diversity. We also partitioned taxonomic β‐diversity into species turnover and richness difference. Results Urbanization led to taxonomic and functional differentiation, while increased flooding led to taxonomic and functional homogenization. We found a significant correlation between taxonomic and functional β‐diversity. Changes in β‐diversity were associated with species and trait turnover among both urbanization and flood levels, and with changes in species richness. Differentiation was associated with low species richness, and homogenization with high species richness. Exotic invasions tended to favour differentiation, but only at a low urbanization level. Main Conclusions The effect of urbanization on plant diversity in riparian forests was twofold: first, it directly induced taxonomic and functional differentiation through its effect on species loss and turnover (higher β‐diversity at high urbanization level); second, differentiation was indirectly favoured through the reduction in flooding (higher β‐diversity at low flood level). Taxonomic and functional β‐diversity followed similar patterns, likely because species invasions and extinctions are not random, but are related to species traits. Our results underline the need to move our focus from exotic species to the true underlying factors of biodiversity loss and homogenization, notably land use changes and human disturbances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".