Bioenergetics assessment of fish and crayfish consumption by river otter (Lontra canadensis): integrating prey availability, diet, and field metabolic rate
Bibliographic record
Abstract
River otters ( Lontra canadensis ) are important predators in aquatic ecosystems, but few studies quantify their prey consumption. We trapped crayfish monthly as an index of availability and collected otter scat for diet analysis in the Ozark Mountains of northwestern Arkansas, USA. We measured otter daily energy expenditure (DEE) with the doubly labeled water method to develop a bioenergetics model for estimating monthly prey consumption. Meek’s crayfish ( Orconectes meeki ) catch-per-unit-effort was positively related to stream temperature, indicating that crayfish were more available during warmer months. The percentage frequency of occurrence for crayfish in scat samples peaked at 85.0% in summer and was lowest (42.3%) in winter. In contrast, the percentage occurrence of fish was 13.3% in summer and 57.7% in winter. Estimates of DEE averaged 4738 kJ·day–1 for an otter with a body mass of 7842 g. Total biomass consumption ranged from 35 079 to 52 653 g·month–1 (wet mass), corresponding to a high proportion of fish and crayfish in the diet, respectively. Otter consumption represents a large fraction of prey production, indicating potentially strong effects of otters on trophic dynamics in stream ecosystems.
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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 | 0.000 | 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".