Seasonal diet of the northern river otter (<i>Lontra canadensis</i>): what drives prey selection?
Bibliographic record
Abstract
Diet of the northern river otter (Lontra canadensis (Schreber, 1777)) has been examined throughout much of its range and across many habitat types. Few studies have examined prey selection based on prey abundance estimates, however, and prey selection results have been inconsistent. We determined composition, seasonal variation, and prey selection of otter diet in northern Utah comprising multiple habitat types and prey communities. We evaluated the hypothesis that otters take prey according to availability and in inverse proportion to swimming ability. Fish was the primary class of prey taken by otters (96.5%), followed by crustaceans (16.9%). Among families, otter diet was composed primarily of Salmonidae and Cottidae, the two families that dominated the fish community of the main-channel habitat. Otter diet varied seasonally for nearly all classes (G[24] = 127.8, P < 0.001) and families (G[18] = 132.94, P < 0.001) of prey. In particular, fish occurrence was lower during summer than during other seasons (P ≤ 0.05), whereas crustacean (i.e., crayfish (Astacoidea)) occurrence was higher (G[3] = 71.1, P < 0.001). At the family level, occurrence of Salmonidae was greatest during fall (G[3] = 11.7, P < 0.01). Within one of our habitat types, we found otters to select for prey in proportion to abundance and in inverse proportion to swimming ability, with otters selecting for Catostomidae and Cyprinidae, against Salmonidae, and Cottidae according to its abundance. We conclude that habitat type may be the initial driver of prey selection, while factors such as abundance, agility, and life history of prey may be secondary drivers.
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 | 0.000 | 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) | 0.001 | 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".