Food-caching chickadees with specialized spatial cognition do not use scrounging as a stable strategy when learning a spatial task
Bibliographic record
Abstract
Social animals may use alternative strategies when foraging, with producer-scrounger being one stable dichotomy of strategies. While 'producers' search and discover new food sources, 'scroungers' obtain food discovered by producers. Previous work suggests that differences in cognitive abilities may influence tendencies toward being either a producer or a scrounger, but scrounging behaviour in the context of specialized cognitive abilities is less understood. We investigated whether food-caching mountain chickadees, which rely on spatial cognition to retrieve food caches, engage in scrounging when learning a spatial task. We analysed data from seven seasons of spatial cognition testing, using arrays of radio frequency identification-enabled bird feeders, to identify and quantify potential scrounging behaviour. Chickadees rarely engaged in scrounging, scrounging was not repeatable within individuals and nearly all scrounging events occurred before the bird learned the 'producer' strategy. Scrounging was less frequent in harsher winters, but adults scrounged more than juveniles, and birds at higher elevations scrounged more than chickadees at lower elevations. There was no clear association between spatial cognitive abilities and scrounging frequency. Overall, our study suggests that food-caching species with specialized spatial cognition do not use scrounging as a stable strategy when learning a spatial task, instead relying on learning abilities.
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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 | 0.001 |
| 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 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".