Disguise or surprise: spider antipredator adaptations as a function of the architecture of their webs
Bibliographic record
Abstract
Abstract Animals exhibit a variety of strategies to avoid predation; spiders are no exception. We explored whether web-building spiders that differ in the architecture of their webs exhibit morphologies or behaviors suggestive of antipredator strategies that trade-off with the degree of protection offered by their webs. Spiders build webs of 3 types: the more protected tangles and sheet-and-tangles, which are three-dimensional (3D), and the more exposed orbs, which are two-dimensional (2D), both with or without a refuge. We hypothesize that spiders whose webs offer greater protection—a 3D architecture or a refuge—will be less likely to be armored or brightly colored when compared to spiders without these protections. We collected data on 446 spiders and their webs in 2 lowland tropical rainforest sites. We show that 2D web builders with no refuges tended to be brightly colored (background contrasting) and spiny (spiky), whereas those with refuges tended to blend against the background of their refuges. 3D web builders, on the other hand, were neither cryptic nor brightly colored nor armored but were more likely to drop out of the web upon simulated predator contact. These results support the hypothesis that web-building spiders tend to be protected either through the architecture of their webs or their morphology and behavior, suggesting a trade-off between different types of antipredator strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".