Busy Beavers in the Big City: An Analysis of Beaver Distribution, Foraging and Non-lethal Forage Management in Urban Riverine Forests
Bibliographic record
Abstract
Where human and beaver populations overlap in cities, conflicts can arise over the beavers’ impact to woody vegetation and river valley infrastructure. One way that cities reduce beavers exploitation of limited shared resources is using non-lethal deterrents, like tree enclosures. Beaver foraging behaviour is well studied in natural systems, with decades of research describing their feeding behaviour and interactions with the woody vegetation community. In comparison, there is a poor understanding of beaver foraging behaviour in urban areas, and effectiveness of forage deterrents. My thesis helps to address this research gap. During late summer and fall of 2017, a survey of beaver lodge distribution and an inventory of riparian woody vegetation, as impacted by beavers, were completed on two reaches of the South Saskatchewan River. One river reach (24 km) passed through the City of Saskatoon where there is active beaver management; the second river reach (29 km) was the adjacent upstream conservation area where there is no beaver management. In City parks the effectiveness of a non-lethal forage deterrent use – tree enclosures - was assessed in May 2018. Results from the beaver activity surveys show that lodge complex density is 56% lower in the city reach; lodges active at the time of the inventory had a dispersed spatial distribution. The riparian woody vegetation community along the two river reaches is markedly different, with more than twice the species richness for both trees and shrubs in the city reach. Much of the enhanced plant diversity can be attributed to introduced woody species. Beaver prefer cottonwoods (Populus spp.), as evidenced by high foraging of this taxa in the unmanaged reach. But, in the managed reach, cottonwood trees are protected. Thus beavers shifted their foraging efforts to Manitoba maple and green ash. The City of Saskatoon is currently using four primary materials for construction tree enclosures. Wire-mesh in various gauges and patterns have an overall 80% effectiveness in deterring further beaver foraging, but chicken wire performs poorest as it girdles trees. Overall, this research contributes to the understanding of urban beaver foraging patterns and preferences within river valley forests. In addition, this research provides land and resource managers with evidence and suggestions regarding the appropriate use of wire-wrapping as a non-lethal beaver deterrent technique.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".