Importance of scale, land‐use, and stream network properties for riparian plant communities along an urban gradient
Bibliographic record
Abstract
Abstract Riparian forests along urban streams are affected by flashy hydrological regimes and adjacent upland land‐use, and their downstream dynamics are altered by forest fragmentation and by frequent river network truncation. This can lead to lower riparian habitat quality, decreased dispersal along the river network and increased recruitment of novel plant communities including non‐native species. Compared to forested landscapes, our understanding of riparian plant community diversity and composition along urban river networks is poor. We investigated riparian plant communities and riparian habitat variables along seven river networks situated along an urban gradient in the Greater Toronto Area, Canada. Six land‐use metrics classifying the intensity of urbanisation on three spatial scales (sub‐catchment, upstream riparian buffer, site buffer) were tested to explain the variation in riparian plant communities along the river networks and to describe the differences in riparian habitat. Riparian plant diversity peaked at sites which had between 40% and 70% of built‐up land cover within sub‐catchments; however, these communities contained a large number of non‐native species compared to the forested streams. While total species richness and native species richness were more related to river network factors (catchment area), non‐native richness was also affected by land‐use within a site. Community composition was more related to local land‐use than catchment area. Geographical distance between sites (both overland and hydrological) was increasingly more important for community heterogeneity along the urbanisation gradient. Riparian habitat worsened along the urbanisation gradient, with higher disturbance and soil pH and lower soil organic content and bryophyte cover in the more urban catchments. Riparian habitat variables were also related to the position along the river networks. Our results provide important insights into how riparian plant communities and riparian habitat differ along a gradient of urbanisation. Compared to previous studies we investigated the patterns in riparian vegetation in the context of river networks, including a variety of land‐use metrics, and analysed data across different spatial scales. This revealed important trends in plant diversity, demonstrated that urban land‐use increases the occurrence of non‐native species, and showed that the land‐use at the local scale is a stronger determinant of plant assemblages compared to land‐use at broader scales. These novel findings can be applied to land management in order to mitigate the effects of urbanisation on riparian ecosystems and the functions they provide for adjacent streams and rivers.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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".