The evolution of startle displays: a case study in praying mantises
Bibliographic record
Abstract
Anti-predator defences are typically regarded as relatively static signals that conceal prey or advertise their unprofitability. However, startle displays are complex performances that deter or confuse predators and can include a spectacular array of movements, colours and sounds. Yet, we do not fully understand the mechanisms by which they function, their evolutionary correlates, or the conditions under which they are performed and evolve. Here, we present, to our knowledge, the first phylogenetically controlled comparative analyses of startle displays including behavioural data, using praying mantises as a model system. We included 58 species that provide a good representation of mantis diversity and estimated the strength of phylogenetic signal in the presence and complexity of displays. We also tested hypotheses on potential evolutionary correlates, including primary defences and body size. We found that startle displays and morphological traits were phylogenetically conserved, whereas behavioural traits were highly labile. Surprisingly, body size was not correlated with display presence or complexity in phylogenetically controlled analyses. Species-rich clades were more likely to exhibit displays, suggesting that startle displays were probably involved in lineage diversification. We suggest that to further elucidate the conditions under which startle displays evolve, future work should include quantitative descriptions of multiple display components, habitat type, and predator communities. Understanding the evolution of startle displays is critical to our overall understanding of the theory behind predator-prey dynamics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".