The evolution of crypsis in replicating populations of web‐based prey
Bibliographic record
Abstract
To investigate the evolution of background matching (crypsis) when prey are viewed against uniform and heterogeneous (alternating) backgrounds, we conducted some web‐based experiments. Visitors to our experimental web site were rewarded for finding artificial prey, thereby providing a measure of their detectability. We first compared the “survivorship” of a range of pixilated prey phenotypes presented against both light green and dark green pixilated backgrounds and found clear evidence for a concave tradeoff curve, indicating that specialism on one or the other background would maximise their overall survivorship. We then compared the survivorship of a range of spotted prey phenotypes presented against backgrounds with small spots and large spots and found evidence for a more linear tradeoff curve, indicating that both intermediates and specialists would have approximately equal survivorship. Replicated evolutionary experiments were then conducted in which the survivors in any given system automatically reproduced themselves (subject to occasional mutation) when their population size was depleted to a critical threshold. Here close background matching readily evolved when prey were presented against a single uniform pixilated environment and when prey were presented against a single uniform spotted environment. As expected, the background matching that evolved in alternating light and dark green environments involved specialism on one or other background, and no intermediate forms evolved. By contrast, a more polymorphic range of phenotypes evolved in the alternating small and large spotted environments. To our knowledge this is the first time that automatically regenerating populations of web based prey have been set up to address evolutionary questions. Our findings have clearly shown conditions under which jack‐of‐all‐trades cryptic forms survive poorly, and, although more work is needed, it may also help explain why background matching frequently appears so specialized in natural systems.
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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".