Why Do Beavers Leave Home? Lodge Abandonment in an Invasive Population in Patagonia
Bibliographic record
Abstract
Research Highlights: Lodge abandonment by beavers is apparently a common phenomenon in Patagonia, but it is still poorly understood and we ignore what drives it. In relatively slow growth Nothofagus forests, resource depletion can impact abandonment while water availability may be a major driver in the semiarid steppe. Background and Objectives: North American beaver (Castor canadensis) was introduced in 1946 on the island of Tierra del Fuego (TDF) in southern Argentina. Since then, beavers have become a major disturbance affecting not only forest but also treeless steppe landscapes. Our goal was to determine the factors affecting lodge abandonment by beavers in two habitats of TDF: forest and steppe. Materials and Methods: A total of 47 lodges were surveyed between February and March from 2012 to 2014 in both habitat types, 22 in the forest and 25 in the steppe. To explain factors involved in lodge abandonment by beavers, we measured the following variables: water level variation, stream gradient, vegetation cover adjacent to shore and forest structure. Results: We recorded 24 abandonments events, with a similar proportion of lodges abandoned in both habitats. Our results revealed that lodge abandonment was mostly linked to water level fluctuations irrespective of habitat type. The water level at the entrances of the lodge generally decreased in abandoned lodges. Variables that characterize understory cover had some influence on lodge abandonment in the forest, and no effect in the steppe. Conclusions: Water level variation was associated with lodge abandonment in both habitats, and we found some evidence of resource depletion in the forest. However, we caution that changes in water level may be not only due to extrinsic factors but rather to beaver’s own activities or to a decay in pond maintenance following abandonment.
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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".