Behavioural mimicry among poison frogs diverges during close-range encounters with predators
Bibliographic record
Abstract
Aposematic species signal their unpalatability to potential predators with recognizable, and frequently conspicuous, colour patterns. These visual signals are often also associated with bold behaviour and a reduced propensity to escape from approaching predators. Bold behaviours may act as an aversive signal and allow defended prey to avoid the energetic/opportunity costs that arise from fleeing predators. For Batesian mimics, non-defended species which replicate the colours of defended models, behavioural mimicry may also improve mimic fidelity and reduce energetic/opportunity costs. However, as predators may test the honesty of aposematic signals through sampling behaviour, Batesian mimics can be at high risk during close-range interactions with predators. This raises the question of whether/when Batesian mimics should deviate from behavioural mimicry and initiate more extensive escape behaviour. Here, we exposed the chemically defended poison frog Ameerega bilinguis and its (non-toxic) Batesian mimic Allobates zaparo to a simulated predator encounter. We predicted Al. zaparo would escape to a greater distance and in a more erratic manner than Am. bilinguis. Yet, contrary to our predictions, Al. zaparo did not flee far from predators. It was, however, more likely to initiate escape prior to physical contact from the predator. We suggest that bold behaviour coupled with pre-emptive movement allows Al. zaparo to retain the benefits of behavioural mimicry while reducing the likelihood that predators will test signal honesty. Our data highlight that when examining the evolution of mimicry, we must consider both morphological and behavioural traits, as well as how risk to the prey may change how they behave throughout the predation sequence.
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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".