Selection for multicomponent mimicry: equal feature salience and variation in preferred traits
Bibliographic record
Abstract
When should multiple traits on Batesian mimics be selected to resemble corresponding traits on model species? Here, we explore two possibilities. First, features of equal salience to predators may be used to categorize prey, selecting for multicomponent mimicry. Second, if different predators use single yet different traits to categorize prey, multicomponent mimicry may still be selected. We studied how blue tits categorized rewarding and unrewarding artificial prey items that are differentiated by a combination of two color dimensions. Many birds used both color dimensions to make decisions, and overall, the population selected for multicomponent mimicry. However, a subset of birds used one color or another to make decisions; among this subset, multicomponent mimicry was also favored. The cost of sampling did not affect selection. Our results suggest that multicomponent warning signals may be selected when traits are equally salient to individual predators, or when communities of predators focus on different traits to discriminate between prey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".