Evolutionary escalation: the bat–moth arms race
Bibliographic record
Abstract
Echolocation in bats and high-frequency hearing in their insect prey make bats and insects an ideal system for studying the sensory ecology and neuroethology of predator-prey interactions. Here, we review the evolutionary history of bats and eared insects, focusing on the insect order Lepidoptera, and consider the evidence for antipredator adaptations and predator counter-adaptations. Ears evolved in a remarkable number of body locations across insects, with the original selection pressure for ears differing between groups. Although cause and effect are difficult to determine, correlations between hearing and life history strategies in moths provide evidence for how these two variables influence each other. We consider life history variables such as size, sex, circadian and seasonal activity patterns, geographic range and the composition of sympatric bat communities. We also review hypotheses on the neural basis for anti-predator behaviours (such as evasive flight and sound production) in moths. It is assumed that these prey adaptations would select for counter-adaptations in predatory bats. We suggest two levels of support for classifying bat traits as counter-adaptations: traits that allow bats to eat more eared prey than expected based on their availability in the environment provide a low level of support for counter-adaptations, whereas traits that have no other plausible explanation for their origination and maintenance than capturing defended prey constitute a high level of support. Specific predator counter-adaptations include calling at frequencies outside the sensitivity range of most eared prey, changing the pattern and frequency of echolocation calls during prey pursuit, and quiet, or 'stealth', echolocation.
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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 | 0.002 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".