Active space of grasshopper mouse vocalizations (Onychomys) in relation to woody plant encroachment
Bibliographic record
Abstract
Abstract The efficacy of animal acoustic communication depends on signal transmission through an oft-cluttered environment. Anthropogenic-induced changes in vegetation may affect sound propagation and thus habitat quality, but few studies have explored this hypothesis. In the southwestern United States, fire suppression and cattle grazing have facilitated displacement of grasslands by pinyon-juniper woodlands. Northern grasshopper mice ( Onychomys leucogaster ) inhabit regions impacted by juniper encroachment and produce long-distance vocalizations to advertise their presence to conspecifics. In this study, we coupled acoustic recordings and electrophysiological measurements of hearing sensitivity from wild mice in the laboratory with sound transmission experiments of synthesized calls in the field to estimate the active space (maximum distance that stimuli are detected) of grasshopper mouse vocalizations. We found that mice can detect loud (85 dB SPL at 1 m) 11.6 kHz vocalizations at 28 dB SPL. Sound transmission experiments revealed that signal active space is approximately 50 m. However, we found no effect of woody plant encroachment on call propagation because juniper and woody plant density were inversely associated and both present barriers to a 9 cm mouse advertising at ground level. Our data indicate that woody plant encroachment does not directly impact the efficacy of grasshopper mouse communication, but vegetation shifts may negatively impact mice via alternative mechanisms. Identifying the maximum distance that vocalizations function provides an important metric to understand the ecological context of species-specific signalling and potential responses to environmental change.
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 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".