Foraging Decision Rules and Prey Species Preferences of Northwestern Crows (<i>Corvus caurinus</i>)
Bibliographic record
Abstract
Abstract Categorization of similar prey types and the application of decision rules by dietary generalists can enhance the efficiency of foraging decisions and facilitate the inclusion of novel prey types in the diet. While considerable research attention has been directed toward investigation of these concepts in invertebrates, few have assessed categorization and decision rules used by generalist vertebrate predators. In this study, we experimentally investigated decision rules and prey preferences of northwestern crows ( Corvus caurinus ) feeding on littleneck clams ( Tapes philippinarum ) and whelks ( Nucella lamellosa ). We presented crows with three species‐size combinations: small clams (2.0–2.9 cm length) paired with large whelks (4.0–4.9 cm), small clams paired with medium whelks (3.0–3.9 cm), and large clams (4.0–4.9 cm) with large whelks. Profitability estimates based on observations of crows feeding on these prey species indicated that clams were always the more energetically profitable option; however, in prey choice trials crows consistently selected the heavier prey species, regardless of differences in profitability. These results show that crows apply a general decision rule according to which they select heavier prey items when feeding on hard‐shelled prey requiring similar handling techniques, and that while such decision rules may approximate optimal choices they may not always follow predictions based solely on prey profitability. We discuss these results in the context of behavioural flexibility of generalist predators, and predicting impacts of intertidal avian predators on prey populations.
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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 | 0.000 | 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 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".