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Record W2990267251 · doi:10.1242/jeb.217513

Monarch butterfly caterpillars tune in to buzzing insects

2019· article· en· W2990267251 on OpenAlexaboutno aff
Kathryn Knight

Bibliographic record

VenueJournal of Experimental Biology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsDanausInsectDiggingSimple eye in invertebratesCalliphora vicinaBiologyLepidoptera genitaliaZoologyEcologyCalliphoridaeHistoryArchaeology

Abstract

fetched live from OpenAlex

Caterpillars come in all shapes, sizes and colours. Some are furry, others have stripes, some even have eye-shaped spots, and it turns out that a large number also have great hearing. ‘Over 30 different species of caterpillars have been noted to respond to a variety of sounds including the human voice, clapping, tuning forks, doors slamming, highway noise and jet aircraft’, says Jayne Yack from Carleton University, Canada. Yet, despite all their thrashing, rearing and freezing on the spot in response to sounds, it wasn't clear what caterpillars are listening out for. ‘While we are aware that many caterpillars respond to sounds, we know little about how or why they hear’, says Yack. As the caterpillars of monarch butterflies (Danaus plexippus) freeze, shorten their bodies and flick their front ends in response to a tuning fork pitched at 250 Hz, Yack and Chantel Taylor decided to find out which frequencies the yellow, white and black caterpillars tune into and how they hear.Playing short tones ranging in pitch from 50 to 1200 Hz to the caterpillars, Taylor assessed which frequencies set the insects thrashing defensively. Plotting the caterpillars’ reactions on a graph, it was clear that the insects were most sensitive to tones between 100 and 200 Hz, although their hearing ranged from 50 Hz up to 900 Hz. Then, Taylor began searching the insects' bodies for evidence of their ‘ears’. Knowing that Dwight Minnich had suggested in the 1930s that hairs on the insect's body might pick up sounds, Taylor focused on three structures: seven pairs of vibration-sensitive hairs (filiform trichoid sensillae) distributed along the caterpillar's sides, the prothoracic shields on its back and fleshy structures, known as tubercles, at two locations on its flanks. Surgically removing each structure and then testing the caterpillars’ hearing, Taylor quickly ruled out the involvement of the tubercules and shield structures, before eventually narrowing in on two sensilla on the thorax portion (near the head) of the caterpillar's body. And when she visualised the structures with an electron microscope, she could see a 0.5 mm long hair, which would be ideal for detecting sound waves carried by vibrating air molecules.But why have monarch butterflies evolved hearing when they aren't exactly known for their conversation? ‘We propose that hearing in monarch larvae functions to protect against aerial insect parasitoids and predators’, says Yack, who explains that the hefty larvae are prone to being consumed from the inside out by the larvae of parasitic insects that lay their eggs on the hapless caterpillars. Yack suggests that the caterpillars may freeze when they hear pitches that sound like an approaching insect to avoid detection, but resort to flailing around when they are at risk of an impending attack. However, Taylor also discovered that the caterpillars can be lulled into a false sense of security, losing interest in lashing out after hearing the same buzz five times, which could place the insects at more risk from predators in noisy environments, such as railways and airports. But Yack adds that forcing pest caterpillars with hearing to drop their defences by repeatedly buzzing them could form the basis of novel green forms of pest control by leaving them vulnerable to predators that might otherwise be vanquished by a quick flick of the head.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.011
GPT teacher head0.293
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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