Distracted foragers: competitors impair foraging efficiency, accuracy and speed for eastern grey squirrels
Bibliographic record
Abstract
Social foraging can be beneficial, but it can also disrupt optimal foraging strategies. Animals possess limited capacity for attention allocation and distractions may impact their rates of food acquisition. Attention to conspecific food competitors can prevent kleptoparasitism but likely affects food intake in multiple ways. We used two field experiments to assess the impact of nearby competitors on urban eastern grey squirrel, Sciurus carolinensis, foraging in a forest setting on the University of Toronto Scarborough campus. We used a tube apparatus in the first experiment to examine foraging efficiency and a tipping container apparatus in the second experiment to examine foraging speed (handling time) and accuracy. Competing squirrels within 1 m or having just been involved in chasing or aggressive physical contact with competitors led to decreases in foraging efficiency, speed and accuracy. Competitors within 5 m of focal squirrels did not impact foraging measures. Over the course of the experiments, squirrels adapted to solving foraging apparatuses in the presence of competitor(s) and improved their efficiency and accuracy but not their speed. Vigilance rates were greater for squirrels that had competitors nearby; thus, decreases in foraging efficiency, accuracy and speed seemed to be caused by the need to allocate attention to conspecifics. These experiments show that social foraging impairs optimal food acquisition for individual eastern grey squirrels.
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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) | 0.001 | 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".