Strategies to restore floodplain vegetation after abandonment of human activities
Bibliographic record
Abstract
Abstract Restoring the flooding‐related disturbance regime by removing and setting back flood defenses (channel widening) is the most efficient strategy for recovering riparian plant communities in floodplains formerly impacted by human activities such as agriculture, mining, and forestry. Removing flood defenses is generally not socially accepted, and alternative recovery strategies must be explored. We assessed vegetation establishment on 33 sites in the floodplains of the Middle Ebro River and three of its tributaries (NE Spain) where restoration approaches applied in the last 20 years include channel widening, floodplain excavation, and abandonment of agriculture, gravel extraction, and hybrid poplar plantations, with and without plantation of native species. Using analysis of similarity and ordinations, we found that channel widening led to plant communities closely resembling those found on natural gravel bars, including new recruits of keystone tree species. Excavation of the floodplain as the restoration approach resulted in pioneer, non‐strictly riparian communities. Abandonment of agricultural land or clearing of poplar plantations resulted in alternative stable states predominating, regardless of time elapsed since restoration and whether poles of native species were planted. However, forest‐like communities relatively similar to mature, natural riparian references were attainable when hybrid poplars were allowed to resprout after clear‐cutting, or after the human activities were abandoned and trees were not cut. Combining channel widening and assisted revegetation where flood defenses cannot be altered may partially reproduce a mosaic of habitats typical of natural floodplains.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".