Wolf–Beaver Dynamics in the Greater Voyageurs Ecosystem, Minnesota
Bibliographic record
Abstract
In the heart of the boreal forest in 1949, trappers gathered at a spring meeting in Wabowden, Manitoba, to discuss many items of business, including wolf predation on beavers. Recent debate and disagreement had broken out among the trappers regarding whether wolves actually killed beavers. One trapper stated wolves ‘harassed’ a beaver colony so extensively that he had to fell trees into the water to ensure the colony's survival. Some trappers remained sceptical and unconvinced. The debate was put to a lively and emphatic end when a trapper walked into the spring meeting and presented a bushel sack stuffed with wolf scats containing beaver fur (Nash 1951). The proof was in the poop! Surprisingly, our understanding of wolf predation on beavers has progressed relatively little since 1949. Most attempts to study wolf predation on beavers followed an approach akin to the Manitoba trappers: collecting and examining wolf scats. By doing this, researchers in many areas across North America and Eurasia concluded, like the trappers, that beavers were important prey for wolves during the ice-free season. However, wolf–beaver dynamics received little attention beyond this, largely because (1) most wolf predation research was focused on wolf–ungulate interactions and predation on smaller alternate prey was not a priority (Gable et al 2018c), and (2) rigorously studying wolf predation during spring to autumn in forested ecosystems with dense vegetation was a monumental, and often impossible, task prior to GPS collar technology. Of course, many researchers and biologists had interesting ideas or hypotheses about wolf–beaver interactions, but most were based on anecdotal observations, indirect evidence or conjecture (Gable et al 2018c). None the less, these ideas were compelling and relevant. Some suggested dense beaver populations increased wolf pup survival (Benson et al 2013) and, in turn, wolf pack and population size (Andersone 1999; Barber-Meyer et al 2016). Others posited that dense beaver populations reduced wolf predation on ungulate prey (Forbes and Theberge 1996) while some claimed it increased predation (Andersone and Ozoliņš 2004; Latham et al 2013). Still others suspected wolves changed ecosystems by altering the ecosystem engineering behaviour of beavers (Peterson et al 2014). Clearly, wolf–beaver dynamics needed to be studied in more detail.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".