Spatial processes structuring riparian plant communities in agroecosystems: implications for restoration
Bibliographic record
Abstract
Abstract The disruption of hydrological connectivity by human activities such as flood regulation or land‐use changes strongly impacts riparian plant communities. However, landscape‐scale processes have generally been neglected in riparian restoration projects as opposed to local conditions, even though such processes might largely influence community recovery. We surveyed plant composition of field edges and riverbanks in 51 riparian zones restored by tree planting (565 1‐m2 plots) within two agricultural watersheds in southeastern Québec, Canada. Once the effects of environmental variables (hydrology, soil, agriculture, landscape, restoration) were partialled out, three models of spatial autocorrelation based on Moran's eigenvector maps and asymmetric eigenvector maps were compared to quantify the pathways and direction of the spatial processes structuring riparian communities. The ecological mechanisms underlying predominant spatial processes were then assessed by regression trees linking species response to spatial gradients to seed and morphological traits. The structure of riparian communities was predominantly related to unidirectional spatial gradients from upstream to downstream along watercourses, which contributed more to species composition than bidirectional gradients along watercourses or overland. Plant traits selected by regression trees explained 22% of species response to unidirectional upstream–downstream gradients in field edges and 24% in riverbanks, and predominantly corresponded to seed traits rather than morphological traits of the adult plants. Our study showed that even in agriculturally open landscapes, water flow remains a major force structuring spatially riparian plant communities by filtering species according to their seed traits, thereby suggesting long‐distance dispersal as a predominant process. Preserving hydrological connectivity at the watershed‐scale and restoring riparian plant communities from upstream to downstream should be encouraged to improve the ecological integrity of rivers running through agricultural landscapes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".