Sensory-based niche partitioning in a multiple predator–multiple prey community
Bibliographic record
Abstract
Many predators and parasites eavesdrop on the communication signals of their prey. Eavesdropping is typically studied as dyadic predator-prey species interactions; yet in nature, most predators target multiple prey species and most prey must evade multiple predator species. The impact of predator communities on prey signal evolution is not well understood. Predators could converge in their preferences for conspicuous signal properties, generating competition among predators and natural selection on particular prey signal features. Alternatively, predator species could vary in their preferences for prey signal properties, resulting in sensory-based niche partitioning of prey resources. In the Neotropics, many substrate-gleaning bats use the mate-attraction songs of male katydids to locate them as prey. We studied mechanisms of niche partitioning in four substrate-gleaning bat species and found they are similar in morphology, echolocation signal design and prey-handling ability, but each species preferred different acoustic features of male song in 12 sympatric katydid species. This divergence in predator preference probably contributes to the coexistence of many substrate-gleaning bat species in the Neotropics, and the substantial diversity in the mate-attraction signals of katydids. Our results provide insight into how multiple eavesdropping predator species might influence prey signal evolution through sensory-based niche partitioning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".