Harlequin Ducks (<i>Histrionicus histrionicus</i>) Scavenge Sea Urchin Fragments from Foraging Sea Otters (<i>Enhydra lutris</i>)
Bibliographic record
Abstract
Foraging animals may risk association with potential predators to obtain otherwise inaccessible prey. We observed this strategy in wintering Harlequin Ducks (Histrionicus histrionicus) scavenging fragments of Red Sea Urchins (Mesocentrotus franciscanus) from foraging Sea Otters (Enhydra lutris) that were re-occupying an area from which they had been ecologically absent since about 1850. Harlequin Ducks, like other sea ducks, have not previously been reported scavenging from other birds or mammals. In British columbia, Red Sea Urchins have reached large sizes and densities since the removal of Sea Otter predators by the marine fur trade in the 18th and 19th centuries. Observations of Sea Otters and Harlequin Ducks were made in 4 areas, spanning a time gradient of Sea Otter occupation from 1 to 5 years. During 3 months of observations (December 2013 – February 2014), Harlequin Ducks were associated with foraging Sea Otters only at sites that were recently occupied by Sea Otters (≤ 2 months), where the proportion of urchins in Sea Otter diets was highest and where the ducks acquired urchin fragments from foraging Sea Otters. We suggest that Sea Otters re-occupying their historic range and consuming predominantly large Red Sea Urchins provide a temporarily available prey subsidy for Harlequin Ducks. Our observations document a novel effect of Sea Otters providing important prey supplementation to a marine bird when foraging in urchin-rich habitats, contributing to the overall role of Sea Otters as a keystone species.
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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 | 0.000 | 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) | 0.002 | 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".