Is temporal synchrony necessary for effective Batesian mimicry?
Bibliographic record
Abstract
Batesian mimicry occurs when palatable mimics gain protection from predators by evolving a phenotypic resemblance to an aposematic model species. While common in nature, the mechanisms maintaining mimicry are not fully understood. Patterns of temporal synchrony (i.e. temporal co-occurrence) and model first occurrence have been observed in several mimicry systems, but the hypothesis that predator foraging decisions can drive the evolution of prey phenology has not been experimentally tested. Here, using phenotypically accurate butterfly replicas, we measured predation rates on the chemically defended model species Battus philenor and its imperfect Batesian mimic Limenitis arthemis astyanax under four different phenological conditions to understand the importance of temporal synchrony and model first occurrence in mimicry complexes. We predicted that protection for mimics increases when predators learn to avoid the models' aposematic signal right before encountering the mimic, and that learned avoidance breaks down over time in the model’s absence. Surprisingly, we found that asynchronous model first occurrence, even on short time scales, did not provide increased protection for mimics. Mimics were only protected under conditions of temporal synchrony, suggesting that predators rely on current information, not previously learned information, when making foraging decisions.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".