Body condition and acoustic noise modify female responses to uni- and multimodal signals emitted by a male-mimicking robot frog

Mate choice is shaped by both intrinsic and extrinsic factors, including signal conspicuity, receiver body condition, and environmental properties. Multimodal signals are generally more conspicuous than unimodal ones and are expected to elicit stronger responses. However, variation in body condition and background noise modulate receivers’ responsiveness. We investigated how signal type (uni- or multimodal) interacts with body condition and background acoustic noise to influence female responses to male displays in the stream-breeding frog Crossodactylus schmidti. In a field experiment using a male-mimicking robot frog, females were exposed to acoustic (advertisement calls), visual (toe flags), and multimodal (advertisement calls + toe flags) stimuli, while their behavioral responses, body condition, and environmental acoustic noise were recorded. Females responded preferentially to multimodal stimuli over acoustic or visual ones. Those in poorer body condition were more responsive over..., , # Body condition and acoustic noise modify female responses to uni- and multimodal signals emitted by a male-mimicking robot frog

Dataset DOI: [10.5061/dryad.k98sf7mmj](https://doi.org/10.5061/dryad.k98sf7mmj)

## Description of the data and file structure

The data were used to test whether stimulus type, female body condition, and background acoustic noise influenced female responses to the robot frog. We used model selection with generalized linear mixed models (GLMMs) to analyze the *probability of female response* and random-effect (frailty) Cox Proportional Hazards (CPH) models to evaluate *latency to female response*. Female identity was included as a random factor in all models to account for repeated exposures of the same individual to different stimuli. The two continuous predictors, body condition and background acoustic noise, were standardized (i.e., centered to zero and scaled by standard deviation). 

The response variables used in the GLMMs were: (1) the probability of ...,