ECOHYDROLOGICAL MODELING OF BEAVER DAMS
Bibliographic record
Abstract
Beavers (Castor canadensis and C. fiber) are expanding in their native range in North America and Eurasia and are expanding their range into urban environments and the Arctic tundra. Outside their natural range, they are also in Southern Patagonia because of historic releases in the fur industry. Given the broad geographical span of this expansion, it is critical to understand and predict the hydrology of beaver-dominated landscapes. Beavers build dams that modify the water balance and modulate streamflow through different flow states, which might result in drought and flood mitigation. To date, four published hydrological models have been developed to predict these impacts; however, these models were unable to represent dam variability and dynamics. In this study, a model specific to beaver dams was developed to predict the impacts of beaver dams on hydrology by including the flow state dynamics and the heterogeneity of dams and ponds. First, through the instrumentation of the montane peatland of Sibbald Fen in the Canadian Rocky Mountains, I determined that flow state changes of beaver dams are dynamic on a much shorter scale than previously documented. The shifts from one flow state to another happen regularly, have limited synchronicity within dam sequences, and can be predicted. In Sibbald, 66% to 80% of the flow state changes coincided with rainfall-runoff triggers and no changes were associated with biota using the dams. Following this flow state dynamic, I then developed an open-source model called BeaverPy in Python to simulate key features of dams and their impact on hydrology. Five single flow states and mixed combinations were included to identify their dynamics using a vector-based modeling approach, which accounted for changes in dam structures. Simulating individual and in-sequence dams from Sibbald Fen demonstrated that BeaverPy successfully models streamflow modulation by beaver dams, water storage in ponds, and flow state changes. Metrics for simulated vs. measured behavior for streamflow showed a good agreement in root mean squared error (g in beaver-dominated environments, thereby enhancing the understanding of how to incorporate beaver dams into flood mitigation and stream restoration projects and climate change initiatives.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".