Description of Acoustic Characters and Stridulatory <i>Pars Stridens</i> of <i>Nicrophorus</i> (Coleoptera: Silphidae): A Comparison of Eight North American Species
Bibliographic record
Abstract
Abstract Insects make use of sound in a variety of behavioral and reproductive contexts. Acoustic signals are known to serve in defense, sexual advertisement, prey location, and in cooperative activities such as offspring care and group foraging. In airborne signals produced by insects, information associated with species identification is often related to the temporal structure of the sound, while spectral quality is more closely associated with intraspecific variation. The Nicrophorine burying beetles (Insecta: Coleoptera: Silphidae) are a group known to produce sound through dorso-ventro stridulation, but the bioacoustics of this group remains understudied. Here, we examine the stridulatory sound produced by eight North American species of Nicrophorus burying beetles, testing the hypothesis that interspecific differences will be encoded in temporal characteristics of the sound, and that signal divergence will be explained by one of three mechanisms: selection as an intraspecific signal, selection for interspecific aposematism, or random divergence through drift. We digitally recorded stridulation in each species, and analyzed recordings to describe each in respect to four spectral and eight temporal acoustic characters. All species produced a low amplitude biphastic sound pulse consisting of from 58 to 126 syllables, and exhibiting weak dominant frequencies (5.8–12.7 kHz). Collapsing the 12 variables into three rotated factors using principal component analysis, we found no sex-related differences in sound, but significant interspecies divergence in respect to all three factors. We constructed a phylogeny for the group based on the morphology of the stridulatory structures and the acoustic characters, and found weak support for an intraspecific signal divergence model.
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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 | 0.000 |
| 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 | 0.000 | 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".