The evolution of prey-attraction strategies in spiders: the interplay between foraging and predator avoidance
Bibliographic record
Abstract
Lures and other adaptations for prey attraction are particularly interesting from an evolutionary viewpoint because they are characterized by correlational selection, involve multicomponent signals, and likely reflect a compromise between maximizing conspicuousness to prey while avoiding drawing attention of enemies and predators. Therefore, investigating the evolution of lure and prey-attraction adaptations can help us understand a larger set of traits governing interactions among organisms. We review the literature focusing on spiders (Araneae), which is the most diverse animal group using prey attraction and show that the evolution of prey-attraction strategies must be driven by a trade-off between foraging and predator avoidance. This is because increasing detectability by potential prey often also results in increased detectability by predators higher in the food chain. Thus increasing prey attraction must come at a cost of increased risk of predation. Given this trade-off, we should expect lures and other prey-attraction traits to remain suboptimal despite a potential to reach an optimal level of attractiveness. We argue that the presence of this trade-off and the multivariate nature of prey-attraction traits are two important mechanisms that might maintain the diversity of prey-attraction strategies within and between species. Overall, we aim to stimulate research on this topic and progress in our general understanding of the diversity of predator and prey interactions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".