Interspecific Dominance Via Vocal Interactions Mediates Altitudinal Zonation in Neotropical Singing Mice
Bibliographic record
Abstract
Interspecific aggression between ecologically similar species may influence geographic limits by mediating competitive exclusion at the range edge. Advertisement signals that mediate competitive interactions within species may also provide social information that contributes to behavioral dominance and spatial segregation among species. We studied the mechanisms underlying altitudinal range limits in Neotropical singing mice (Scotinomys), a genus of muroid rodent in which males vocalize to repel rivals and attract mates. We first delineated replacement zones and described temperature regimes on three mountains in Costa Rica and Panama where Chiriquí singing mice (S. xerampelinus) abruptly replace Alston's singing mice (S. teguina). Next, we conducted interspecific behavioral trials and reciprocal removal experiments to examine if interspecific aggression mediated species replacement. Finally, we performed reciprocal playback experiments to investigate whether response to song matched competitive interactions. Behavioral trials and removal experiments suggest that S. xerampelinus is behaviorally dominant and excludes S. teguina from higher, cooler altitudes. Playback experiments indicate that subordinate S. teguina is silenced and repelled by heterospecific song, whereas S. xerampelinus responded to heterospecifics with approach and song rates comparable to responses to conspecifics. Thus, interspecific communication reflects underlying dominance and suggests that acoustic signaling contributes to altitudinal zonation of ecologically similar congeners. Our findings implicate the use of social information in structuring spatial distributions of animal communities across landscapes and provide insight into how large-scale patterns are generated by individual interactions.
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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 | 0.000 | 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".