Data from: Colour pattern variation forms local background matching camouflage in a leaf-mimicking toad
Bibliographic record
Abstract
Optimal camouflage can, in principle, be relatively easily achieved in simple, homogeneous, environments where backgrounds always have the same color, brightness, and patterning. Natural environments are, however, rarely homogenous and species often find themselves viewed against varied backgrounds where the task of concealment is more challenging. One result of variable backgrounds is the evolution of intraspecific phenotypic variation which may either be generalized, with multiple similarly cryptic patterns, or specialized, with each discrete color form maximizing concealment against a single component of the background. We investigated the role of phenotypic variation in a highly variable population of the Neotropical toad Rhinella margaritifera using visual modeling and a computer-based detection task. We found that phenotypic variation was not divided into discrete color morphs and all toads were well camouflaged against the forest floor. However, although the whole population may appear to consist of random samples from the background, the toads were a particularly close match to the leaf litter, suggesting that they masquerade as dead leaves, which are themselves variable. Furthermore, rather than each color form being equally effective against a single background, each toad was specialized towards its own particular local surroundings, as suggested by a specialist strategy. Taken together, these data highlight the importance of background matching to a nominally masquerading species, as well as how habitat heterogeneity at multiple spatial scales may affect the evolution of camouflage and phenotypic variation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".