Beaver dam failures: Reconciling science, perception and policy for sustainable river management in Quebec (Canada)
Bibliographic record
Abstract
Abstract Beavers ( Castor fiber , Castor canadensis ) are recognized as key ecosystem engineers, influencing river hydrology and geomorphology through dam construction. While their structures are associated with positive impacts like flood attenuation, increased biodiversity and water quality improvements, beaver dams are quickly blamed for exacerbating downstream flooding following their failure during extreme rain events. This study examines two Quebec Superior Court rulings (2008 and 2017) where beaver dam failures were considered responsible for significant property damage in the Port‐au‐Persil watershed, located in the Charlevoix region of Quebec, Canada. Using hydrological and hydraulic modelling (HEC‐HMS and HEC‐RAS), we assessed the downstream impacts of beaver dam failures during extreme rainfall events caused by Hurricane Katrina (2005) and Irene (2011). The results reveal that the failure of beaver dams had minimal impact on peak discharge and water levels downstream. For the Irene 2011 event, a 1D hydraulic model showed incremental flow increases of 11–15% and water level rises of up to 0.23 m near the area affected by the damage. A revised 2D model, incorporating a hypothetical four‐fold increase in dam retention volume, demonstrated only minor changes in water levels (0.05 m), confirming that the observed flooding would have occurred even without dam failure. The 2D simulations further highlight that dam height, rather than retention volume, controls downstream flood wave propagation. These findings challenge the negative perception of beaver dams and emphasize the importance of robust scientific assessments in flood‐related liability cases. The legal implications of Article 105 of Quebec's Municipal Powers Act, which holds municipalities liable for flood damage caused by “obstacles” in rivers, create a risk of widespread beaver dam removal. This study advocates for evidence‐based management practices and public education to recognize the ecological benefits of beaver dams while addressing concerns over their perceived flood risks.
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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".