Impacts of river bank stabilization using riprap on fish habitat in two contrasting environments
Bibliographic record
Abstract
Abstract Riverbank stabilization using rock riprap is commonly used for protecting road and bridge structures from fluvial erosion. However, little is known about how streams adjust to such perturbation or how this can affect fish habitat in different fluvial environments, particularly for non‐salmonid species in small streams. The objective of this study is to assess impacts of riprap on fish habitat quantity and quality through a pairwise comparison of 27 stabilized and non‐stabilized stream reaches in two physiographic regions, the Saint Lawrence Lowlands and the Appalachian highlands of Montérégie‐Est (Quebec, Canada). Both quantitative (Hydro‐morphological Index of Diversity, HMID) and qualitative (Qualitative Habitat Evaluation Index, QHEI) fish habitat assessment techniques are applied in order to compare results between methods. For each stream reach depth and velocity were measured to calculate HMID. In‐stream cover (woody debris, overhanging vegetation, undercut banks, aquatic macrophytes) and habitat units (pools, riffles, runs, glides) were also documented and used to determine QHEI. Results show that overall bank stabilization using riprap at bridge and stream crossings alters fish habitat characteristics. Loss of in‐stream covers and riparian vegetation lower QHEI scores at stabilized reaches, especially in more pristine Appalachian streams, but has less impact on already altered straightened Lowlands streams. In this latter context, some positive alterations of fish habitat were observed in riprapped reaches due to the coarsening of the substrate and an induced increase of slope. The two metrics (HMID and QHEI) revealed similar differences between stabilized and non‐stabilized sites for Lowlands sites, but their level of agreement was much less in the Appalachian streams, suggesting caution when interpreting habitat quality results based on a single metric. Copyright © 2016 John Wiley & Sons, Ltd.
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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".