A signal-substrate match in the substrate-borne component of a multimodal courtship display
Bibliographic record
Abstract
Abstract The environment can impose strong limitations on the efficacy of signal transmission. In particular, for vibratory communication, the signaling environment is often extremely heterogeneous at small scales. Nevertheless, natural selection is expected to select for signals well-suited for effective transmission. Here, we test for substrate-dependent signal efficacy in the wolf spider Schizocosa stridulans Stratton 1991. We first explore the transmission characteristics of this important signaling modality by playing recorded substrate-borne signals through three different substrates (leaf litter, pine litter, and red clay) and measuring the propagated signal. We found that the substrate-borne signal of S. stridulans attenuates the least on leaf litter, the substrate upon which the species is naturally found. Next, by assessing mating success with artificially muted and non-muted males across different signaling substrates (leaf litter, pine litter, and sand), we explored the relationship between substrate-borne signaling and signaling substrate for mating success. We found that muted males were unsuccessful in obtaining copulations regardless of substrate, while mating success was dependent on the signaling substrate for non-muted males. For non-muted males, more males copulated on leaf litter than any other substrate. Taken together, these results confirm the importance of substrate- borne signaling in S. stridulans and suggest a match between signal properties and signal efficacy – leaf litter transmits the signal most effectively and males are most successful in obtaining copulations on leaf litter.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".