Innate and Learned Components of Defence by Flickers Against a Novel Nest Competitor, the European Starling
Bibliographic record
Abstract
Abstract Defence against predators is an important component of fitness in wild birds but the first step of defence, predator recognition, is not well understood. Anti‐predator behaviour may innate, in which case the individual responds without prior contact with that predator, and/or there may be a learned component that develops only after direct experience. In the wild, the development of anti‐predator behaviour is studied by exposing naive individuals to novel predators. I studied responses of 71 naive and experienced northern flickers Colaptes auratus , to a novel nest predator and competitor, the European starling Sturnus vulgaris that was introduced to North America. Naive individuals responded more intensely to the model starling than to the control model suggesting an innate component to recognition. However, there was also a learned component to defence because flickers nesting near to starlings reacted more aggressively than naive individuals far from starlings. Consistent with theory on life histories and optimal defence levels, no significant differences in aggression were found between the sexes or between age classes. Selection should favour more intense, and possibly innate, defence against the introduced starling. Variation in responses of naive individuals suggests that there may already be some alleles in the population associated with higher defence, but that these may not be uniform within the population.
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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) | 0.000 | 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".