Can environmental flows moderate riparian invasions? The influence of seedling morphology and density on scour losses in experimental floods
Bibliographic record
Abstract
Abstract Environmental flow releases are an effective tool to meet multiple management objectives, including maintaining river conveyance, restoring naturally functioning riparian plant communities, and controlling invasive species. In this context, predicting plant mortality during floods remains a key area of uncertainty for both river managers and ecologists, particularly with respect to how flood hydraulics and sediment dynamics interact with the plants’ own traits to influence their vulnerability to scour and burial. To understand these processes better, we conducted flume experiments to quantify different plant species’ vulnerability to flooding across a range of plant sizes, patch densities, and sediment condition (equilibrium transport versus sediment deficit), using sand‐bed rivers in the U.S. southwest as our reference system. We ran 10 experimental floods in a 0.6 m wide flume using live seedlings of cottonwood and tamarisk, which have contrasting morphologies. Sediment supply, plant morphology, and patch composition all had significant impacts on plant vulnerability during floods. Floods under sediment deficit conditions, which typically occur downstream of dams, resulted in bed degradation and a 35% greater risk of plant loss compared to equilibrium sediment conditions. Plants in sparse patches dislodged five times more frequently than in dense patches. Tamarisk plants and patches had greater frontal area, larger basal diameter, longer roots, and lower crown position compared to cottonwood across all seedling heights. These traits were associated with a 75% reduction in tamarisk seedlings’ vulnerability to scour compared to cottonwood. Synthesis and applications. Tamarisk's greater survivability helps to explain its vigorous establishment and persistence on regulated rivers where flood magnitudes have been reduced. Furthermore, its documented influence on hydraulics, sediment deposition, and scour patterns in flumes is amplified at larger scales in strongly altered river channels where it has broadly invaded. Efforts to remove riparian vegetation using flow releases to maintain open floodways and/or control the spread of non‐native species will need to consider the target plants’ size, density, and species‐specific traits, in addition to the balance of sediment transport capacity and supply in the river system.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".