Mating signal partitioning in multi‐species assemblages: a null model test using frogs
Bibliographic record
Abstract
Abstract Competitive partitioning of ‘community’ signal space has long been suggested to underlie diversification of mating signals. Selection or competitive exclusion is expected to reduce overlap of signals, minimizing destructive interference or reducing mismating. We used null models backed by simulation of type I and II error rates to test for evidence of structuring within 11 frog advertisement call assemblages. Within three assemblages, we found significant over‐dispersion and regularity‐of‐spacing in dominant frequency and in pulse rate, consistent with a signal interference hypothesis and signal confusion hypothesis, respectively. Observed partitioning could represent signal evolution or could result from selection on assemblage composition. Most assemblages showed no acoustic partitioning possibly because: (i) partitioning is more readily apparent in female preference, calling times or sites, rather than call attributes; (ii) assemblages have not yet accommodated recently arrived species, or are compositionally unstable so that acoustic accommodation cannot occur; and (iii) evidence of partitioning is only likely where the acoustic space is densely packed.
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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 | 0.071 | 0.090 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".