An Assessment of an Unsuccessful Restoration Project for Lake Sturgeon Using Three-Dimensional Numerical Modelling
Bibliographic record
Abstract
Abstract Despite a widespread acknowledgment that river restoration projects sometimes fail due to a poor understanding of geomorphology and hydrology, there are relatively few published case studies reporting failures, particularly for nonsalmonid species such as Lake Sturgeon Acipencer fulvescens. We used a three-dimensional hydrodynamic model to retroactively assess a restoration project in the 80-m-wide Ouareau River, Quebec which did not meet its objective of providing additional spawning habitat for Lake Sturgeon. Virtual modifications of the bathymetry allowed for the flow field to be simulated with and without instream structures (boulder weirs) constructed in 2007 for four discharges representing flow conditions during spawning. Simulated velocities and flow depths were used to determine the suitability of the site and to assess the impact of the instream structures. Results revealed that instream structures did not meet the expectation of raising water levels and had no significant impact on river velocity. Furthermore, there was sufficient good quality habitat within the study area before restoration, and artificial spawning sites were placed in locations with nonoptimal velocities for spawning Lake Sturgeon. A comparison with a successful Lake Sturgeon restoration project in the St. Clair–Detroit River system revealed marked differences in restoration strategies, which likely explain the different outcomes of these two projects. These results point to the need for (1) adaptive management protocols that include an iterative decision-making process to allow for adjustments in hypotheses and strategies to improve the management process, (2) multidisciplinary input, including hydrogeomorphology, and (3) a more systematic use of two-dimensional or three-dimensional numerical models prior to the implementation of instream structures in river restoration projects.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".