The evolution of startle displays: a case study in praying mantises
Bibliographic record
Abstract
Anti-predator defences are typically regarded as static signals that conceal prey or advertise their unprofitability. However, startle displays are performed by prey when attacked and can include a spectacular array of movements, colours, and sounds. Here we present the first phylogenetically-controlled comparative analyses of startle displays including behaviour, using praying mantises as a test case. For 58 species, with a dated phylogeny, we estimate the strength of phylogenetic signal in the presence and ‘complexity’ (number of display components) of displays and their components and test hypotheses on their evolutionary correlates including primary defence and body size. We report strong phylogenetic signal in display presence and complexity, and strong lability in behavioural, but not morphological, components. Body size correlates with display presence and complexity independently of phylogeny, but not in phylogenetically-controlled analyses. Finally, species in species-rich clades are more likely to have a display, and a more complex one, suggesting support for ecological displacement via behavioural traits. To further elucidate the conditions under which startle display evolve, future work should include quantitative descriptions of display components, habitat type, and predator communities. Understanding the evolution of startle displays enriches our overall understanding of predator-prey dynamics and provides scaffolding for the development of new theory.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".