Predator–prey interactions between gleaning bats and katydids
Bibliographic record
Abstract
Bats are voracious predators of insects, and many insects have ears sensitive to the high-frequency echolocation calls of bats. Eared insects show a variety of defences when they detect bat echolocation calls. Professor Brock Fenton was an early contributor to the field of bat–insect interactions, inspiring many students to pursue investigations that have advanced our understanding of the relationship between predators and prey. Reflecting on the integrative nature of Dr. Fenton's research, this review highlights research on the evolutionary arms race between gleaning insectivorous bats and katydid prey. Studies on this system have enhanced the field of sensory ecology by illuminating how animal auditory systems can encode and distinguish between signals that overlap in their acoustic properties but have very different consequences for the listener (sex or death). These studies also inform us about the ecological and evolutionary selection pressures on signalers and receivers that can shape mate attraction and predator avoidance behaviour. In particular, many Neotropical katydids rely on preventative instead of reactive defences against gleaning bats, likely due to the regular presence of echolocation calls from non-gleaning bats that reduce the information content of predator cues. We conclude with suggestions for future research on these fascinating animals.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".