Larger frogs are better mimics but are more risk-averse in a nontoxic poison frog
Bibliographic record
Abstract
Abstract Aposematic species signal to potential predators with salient and recognizable coloration. Predators learn to associate these warning signals with secondary defenses (eg toxins) and will subsequently avoid attacking aposematic prey. Warning signals can therefore reduce the need to hide and/or flee and alleviate some of the energetic/opportunity costs of predator avoidance. Consequently, aposematic species are frequently active and bold in behavior. Batesian mimics replicate the colors, and often the behavior, of aposematic species and may benefit from a similar reduction in predation risk and energetic/opportunity costs. Allobates zaparo (Aromobatidae) is a nontoxic Batesian mimic of the chemically defended poison frog Ameerega bilinguis (Dendrobatidae). However, the efficacy of mimicry appears to change throughout ontogeny as Al. zaparo develops from a seemingly cryptic juvenile to the mimetic adult. We examined how morphological mimicry (ie color) and the propensity to explore a novel environment (ie boldness) changed throughout ontogeny. We predicted that mimicry would improve with increasing size and that better mimics would engage in more exploratory behavior. We found that larger mimics more closely matched their model however they were less likely to be active than were smaller frogs. These data suggest that larger size, and more accurate mimicry, do not necessarily correspond to increases in behavioral boldness. This result may arise from limitations in Batesian mimicry but factors including foraging requirements or social/reproductive behavior cannot be discounted. More research is needed to understand the relationship between behavior, color, body size, and maturity in these frogs and across Batesian mimics more widely.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".