The ethology of wolves foraging on freshwater fish in a boreal ecosystem
Bibliographic record
Abstract
Through global positioning system (GPS) collar locations, remote cameras, field observations and the first wild wolf to be GPS-collared with a camera collar, we describe when, where and how wolves fish in a freshwater ecosystem. From 2017 to 2021, we recorded more than 10 wolves ( Canis lupus ) hunting fish during the spring spawning season in northern Minnesota, USA. Wolves ambushed fish in creeks at night when spawning fish were abundant, available and vulnerable in shallow waters. We observed wolves specifically targeting sections of rivers below beaver ( Castor canadensis ) dams, suggesting that beavers may indirectly facilitate wolf fishing behaviour. Wolves also cached fish on shorelines. We documented these findings across five different social groups at four distinct waterways, suggesting that wolf fishing behaviour may be widespread in similar ecosystems but has probably remained difficult to study given its annual brevity. Spawning fish may serve as a valuable pulsed resource for packs because the spring spawning season coincides with low primary prey (deer Odocoileus virginianus) availability and abundance, and when packs have higher energetic demands owing to newly born pups. We demonstrate the flexibility and adaptability of wolf hunting and foraging behaviour, and provide insight into how wolves can survive in a myriad of ecosystems.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".