Environmental filtering and spatial processes in urban riparian forests
Bibliographic record
Abstract
Abstract Questions What are the spatial processes structuring plant trait composition in urban riparian forest communities at different spatial scales? What are the relative roles of local conditions (including historical aspects), landscape context and spatial processes in the community assembly of these forests? Location Montréal, Québec, Canada. Methods Species plant composition was inventoried in 57 riparian forests located along a gradient of urbanization. To analyse plant communities in terms of their trait composition, community‐weighted means were calculated using eight functional traits. Forests were characterized by local (physical features, hydrological regime and historical disturbances) and landscape (surrounding land use) variables. Spatial processes structuring communities were assessed using Moran's eigenvector maps and asymmetric eigenvector maps. The relative importance of these three subsets (local, landscape and spatial variables) on tree, shrub and herb functional composition was quantified by variation partitioning using redundancy analyses. Results Functional patterns in riparian forests resulted primarily from environmental filtering (local and landscape variables). Local conditions, especially flood intensity, exerted an overriding selection pressure on functional composition of riparian plant communities. Urbanization seemed to act indirectly on trait patterns through the alteration of hydrological disturbances caused by on‐going and historical land transformation. Nevertheless, dispersal along rivers was also a significant structuring force, while overland dispersal was negligible. Conclusions Our study highlights that under severe natural disturbance regimes, the effect of natural filters outweighed the negative effects of urban filters. However, the alteration of natural flooding processes by human activities is also a major mechanism influencing plant trait composition in urban riparian communities as forests subjected to reduced flooding intensity experienced a greater effect of urbanization. The effects of urbanization and of past land uses on plant communities were greater for trees than for shrubs and herbs due to the high turnover rate of the latter. Finally, our results showed the importance of dispersal along rivers for biodiversity even in fragmented urban landscapes.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".