Fine‐scale diversity of prey detected in humpback whale feces
Bibliographic record
Abstract
Abstract Predator diets are largely influenced by prey availability and abundance. Yet, in heterogenous marine environments, identifying the prey species consumed by diving mammals remains a fundamental challenge. For rorqual whales, the energetic constraints of prey engulfment require that the whales seek areas of high prey abundance and execute discrete lunge feeding events on patches of high‐density prey. Prey occurrences in feces should therefore provide meaningful insight into the dominant taxa in food patches selected by the animal. We investigated the prey consumed by humpback whales in three regions in southern British Columbia (BC), Canada, using opportunistic fecal sampling, microscopy, and DNA metabarcoding of 14 fecal samples. Fish including Pacific herring ( Clupea pallasii ), hake ( Merluccius productus ), and eulachon ( Thaleichthys pacificus ) were the most common fish species potentially targeted by humpback whales in two regions. The krill Euphausia pacifica was the most prevalent invertebrate DNA detected in all three regions, while sergestid and mysid shrimp may also be important. High DNA read abundances from walleye pollock ( Gadus chalcogrammus ) and sablefish ( Anoplopoma fimbria ) were also recovered in one sample each, suggesting that juveniles of these semi‐pelagic species may occasionally be targeted. In general, we observed heavily digested fecal material that drove substantial dissimilarities in taxonomic resolution between polymerase chain reaction‐based and morphological analyses of the feces. Pacific herring and walleye pollock were the only prey species confirmed by both methods. Our results highlight that molecular and visual analyses of fecal samples provide a complementary approach to diet analysis, with each method providing unique insight into prey diversity.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".