Collective choice of a higher-protein food source by gregarious caterpillars occurs through differences in exploration
Bibliographic record
Abstract
Amplification, whereby an individual’s probability of exhibiting a particular behavior increases with the number of conspecifics already performing this behavior, enhances the accuracy and speed of collective decision-making by gregarious animals. However, too strong amplification can lead to the potentially suboptimal selection of the first patch encountered. In eusocial insects (e.g., ants, bees), active recruitment and differential signaling provide a mechanism to avoid this type of collective trap. We use the nomadic gregarious caterpillar Malacosoma disstria to test whether the higher quality of two food sources can be consistently selected in the absence of this type of mechanism. We show that, in this species, neither active recruitment nor differential signaling occur, but that collective choice of the higher quality of two food sources is instead made through differences in exploration. These caterpillars lack flexibility in trail laying, but a reduction in trail following behavior enables them to abandon a poor food source and initiate exploration, forming new trails that are followed by the group. The propensity to leave marked territory and initiate exploration varies with food quality, and increases with decreasing protein content in the food. Indeed, although previous work showed that caterpillar groups can become trapped on a poor-quality protein-only food, we show that they abandon a similarly poor-quality low-protein food and relocate to a more nutritionally appropriate one. This is consistent with previous work showing that protein deprivation increases exploratory behavior in individual caterpillars and suggests that food quality can influence collective foraging in ways more complex than a simple distinction between high and low quality sources. Furthermore, the latency to initiate exploration also decreases with increasing group size, as the probability that a single individual initiates exploration increases. Contrary to previously studied species, amplification is not modulated according to source quality in this species; instead it is through effects on exploration that food protein content and caterpillar group size influence collective decision-making.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".