Why do crows attack ravens? The roles of predation threat, resource competition, and social behavior
Bibliographic record
Abstract
Interspecific aggression is common in birds—Individuals regularly chase and attack members of other species. We analyzed cases of interspecific aggression between crows (American Crow [Corvus brachyrhynchos] and Northwestern Crow [C. caurinus]) and ravens (Common Raven [C. corax]) using ∼2,000 citizen scientist observations of interspecific aggression between crows and ravens from across North America. Crows and ravens may attack one another for nonadaptive reasons, because they compete with each other for food and space, or because they are nest predators of each other. We report 3 main results. First, although ravens are much larger than crows, crows chased and attacked ravens in ∼97% of observations. We observed this strong asymmetry in the direction of interspecific aggression throughout North America. Second, crow aggression toward ravens was most frequent during the crows' nesting season (∼March–May), but also occurred in other seasons. Third, crows typically attacked ravens in small groups, with relatively few observations of aggression in one-on-one contests. The pattern of seasonality in crow attacks on ravens suggests that nest predation by ravens is an important factor driving interspecific aggression from crows. Aggression also occurred outside the breeding season, particularly in the winter, indicating either that crows compete with ravens for resources at this time of year or that crows preemptively harass potential nest predators in advance of the breeding season. This study is an example of how citizen scientists can contribute to the study of behavioral interactions of birds at a continental scale.
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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 | 0.001 |
| 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 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".