The role of beaver dam networks in regulating surface flows
Bibliographic record
Abstract
Beavers are ecosystem engineers that are widely recognized for their ability to modify ecosystem hydrology through dam-building activities. Beaver dams increase surface water storage through the formation of beaver ponds. When full, ponds can spill water to those downstream which has led to dam sequences being described as cascading systems. However, the fill-and-spill concept has yet to be applied to beaver-impacted systems. Additionally, existing studies have focused on the hydrological effects of individual dams or dam sequences, but very little is known about water transmission across the larger dam networks that beaver typically construct. This thesis tests the utility of the fill-and-spill concept to explain surface flow regulation through a network of dams that vary in flow state and are leaky in nature. The hydrological response of a network of 28 dams at Sibbald Fen, a mountain wetland in Kananaskis, Alberta, Canada, was examined following a 73-year, 37-year, 11-year, and 6-year rainfall event occurring in 2023. Drone surveys were conducted to measure changes in surface water extent and storage for each pond over the study period. Pond and stream hydrographs were used to assess the timing and magnitude of peak flows down the network, and observations of dam flow state were also recorded. Surface water extent and storage expanded during spring freshet and contracted during the summer. Responses in pond storage and dam flow state were also dynamic across the network in response to individual rainfall events. Hydrograph analysis for the four rainfall events indicated that the dam network did not effectively delay or appreciably reduce the magnitude of peak flows. Thus, sequential filling and spilling across the network from the upstream ponds to the downstream ponds was not observed. Instead, ponds expanded in surface area. These findings suggest the potential influence of other hydrological processes on network hydrological functioning, specifically overbank flows and flow through beaver canals, in regulating water transmission across the network. This type of flow regulation is better aligned with the variable source area concept than the fill-and-spill concept. The significant contributions of this research will scale to the cumulative impact of beaver dam networks on flow routing and their potential role as a natural flood management strategy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".