Caught in the web: Spider web architecture affects prey specialization and spider–prey stoichiometric relationships
Bibliographic record
Abstract
Quantitative approaches to predator-prey interactions are central to understanding the structure of food webs and their dynamics. Different predatory strategies may influence the occurrence and strength of trophic interactions likely affecting the rates and magnitudes of energy and nutrient transfer between trophic levels and stoichiometry of predator-prey interactions. Here, we used spider-prey interactions as a model system to investigate whether different spider web architectures-orb, tangle, and sheet-tangle-affect the composition and diet breadth of spiders and whether these, in turn, influence stoichiometric relationships between spiders and their prey. Our results showed that web architecture partially affects the richness and composition of the prey captured by spiders. Tangle-web spiders were specialists, capturing a restricted subset of the prey community (primarily Diptera), whereas orb and sheet-tangle web spiders were generalists, capturing a broader range of prey types. We also observed elemental imbalances between spiders and their prey. In general, spiders had higher requirements for both nitrogen (N) and phosphorus (P) than those provided by their prey even after accounting for prey biomass. Larger P imbalances for tangle-web spiders than for orb and sheet-tangle web spiders suggest that trophic specialization may impose strong elemental constraints for these predators unless they display behavioral or physiological mechanisms to cope with nutrient limitation. Our findings suggest that integrating quantitative analysis of species interactions with elemental stoichiometry can help to better understand the occurrence of stoichiometric imbalances in predator-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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".