Acoustic communication in bark beetles (Scolytinae): 150 years of research
Bibliographic record
Abstract
Abstract For over a century, the role of acoustic communication in the sensory ecology of bark beetles (Scolytinae) has been recognized. However, their ‘world of sound’ remains largely unexplored. Here, we review 153 years of bark beetle bioacoustics publications to summarize current knowledge, identify gaps and suggest future research directions. Our survey identified 117 publications covering 170 species. Morphological reports revealed five stridulatory organs across 125 species, with elytro‐tergal, gular‐prosternal and vertex‐pronotum mechanisms being the most prevalent for sound production. However, confirmed sound recordings exist for only 40 species. Acoustic signalling in adults is proposed to function in avoiding enemies, pair formation, sexual selection and spacing, while in juveniles, vibratory communication is proposed for gallery spacing. However, experimental evidence supporting these functions is lacking. Acoustic sensory organs remain unidentified, and comprehension of signal transmission—whether through airborne sounds or solid‐borne vibrations (or both)—is limited. Bioacoustic technologies have emerged as tools for potential management practices and are also discussed. Based on these findings, we recommend three directions for future research: (1) characterize acoustic morphology and behaviours in more species, particularly unrepresented taxa, with recordings in various contexts, preferably under natural conditions; (2) test hypotheses to explain the functions of acoustic communication through experimental and comparative phylogenetic methods and (3) investigate how sounds or vibrations are transmitted and received through behavioural and neurophysiological experiments. Advancements in bark beetle acoustic sensing and communication research will enhance our understanding of their sensory ecology and facilitate potential control measures of these fascinating insects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".