DOI: 10.5061/dryad.k98sf7mmj
title: Body condition and acoustic noise modify female responses to uni- and multimodal signals emitted by a male-mimicking robot frog

description: 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 overall,
 reacting more readily to all stimulus types, indicating
 condition-dependent mate choice. High levels of acoustic noise generally
 reduced responses to visual signals, suggesting cross-modal sensory
 interference. However, when exposed to unimodal acoustic stimuli in
 noisier sites, females increased their visual signaling, possibly as
 compensatory behavior in response to auditory masking. These findings
 highlight the adaptive significance of multimodal signals in noisy
 environments and emphasize the role of female condition and background
 noise in shaping intersexual communication during mate choice.
description: # 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 at zero and scaled by
 standard deviation).  The response variables used in the GLMMs were: (1)
 the probability of emitting an acoustic signal, modeled with a binomial
 error distribution and a log-log (cloglog) link function; and (2) the
 probability of emitting visual signals, modeled with a binomial error
 distribution and a logit link function. The response variables used in the
 CPH models were: (3) latency to the first acoustic response, (4) latency
 to the first visual response, and (5) latency to move toward the robot.
 Females that did not exhibit a behavioral response during the 480-second
 trial period were treated as right-censored observations. ### Files and
 variables #### File: env_0_1_fixed.csv **Description:** File used in model
 selection of probability of (1) acoustic signaling, and (2) visual
 signalling, by stimulus type, body condition, and background noise.
 Complete results are in Supplementary Material S5. ##### Variables * ind:
 Female individual identity (numbering follows the capture–mark sequence;
 not all marked females were tested) * stimulus: Stimulus type
 (categorical; either visual, acoustic, or multimodal) * temp_raw: Air
 temperature (in ºC) * temp: Air temperature (standardized, i.e., centered
 to zero and scaled by standard deviation) * condition: Female body
 condition, estimated from the residuals from an ordinary least squares
 regression between log10-transformed body mass and log10-transformed
 snout-vent length (standardized, i.e., centered to zero and scaled by
 standard deviation) * noise_raw: Acoustic background noise (in dB) *
 noise: Acoustic background noise (standardized, i.e., centered to zero and
 scaled by standard deviation) * call01: Probability of acoustic signaling
 (1 = call emission; 0 = no call emission) * vsig01: Probability of visual
 signaling (1 = visual signal emission; 0 = no visual signal emission) ####
 File: lat_surv_females_fixed.csv **Description:** File used in model
 selection of latency to (1) acoustic signaling, (2) visual signaling, and
 (3) moving towards the robot, by stimulus type, body condition, and
 background noise. Complete results are in Supplementary Material S6. #####
 Variables * ind: Female individual identity (numbering follows the
 capture–mark sequence; not all marked females were tested) *
 stim: Stimulus type (categorical; either visual, acoustic, or multimodal)
 * temp_raw: Air temperature (in ºC) * temp: Air temperature (standardized,
 i.e., centered to zero and scaled by standard deviation) *
 condition: Female body condition, estimated from the residuals from an
 ordinary least squares regression between log10-transformed body mass and
 log10-transformed snout-vent length (standardized, i.e., centered to zero
 and scaled by standard deviation) * noise_raw: Acoustic background noise
 (in dB) * noise: Acoustic background noise (standardized, i.e., centered
 to zero and scaled by standard deviation) * lat_call: Latency to acoustic
 signaling (time, in seconds, to the first call emission) *
 call_status: Indicates whether a calling response occurred (1 =
 non-respondent females; 2 = respondent females) * lat_vs: Latency to
 visual signaling (time, in seconds, to the first visual signal emission) *
 vs_status: Indicates whether a visual signaling response occurred (1 =
 non-respondent females; 2 = respondent females) * lat_mov: Latency to
 moving towards the robot  (time, in seconds, to the first move toward the
 robot) * mov_status: Indicates whether a movement towards the robot
 occurred (1 = non-respondent females; 2 = respondent females) Regarding
 the latency data at 480 s, it should be noted that these values do not
 represent actual response times. Rather, they correspond to
 *right-censored observations* in the context of Cox Proportional Hazards
 (CPH) models, which occur when a female did not exhibit the focal behavior
 within the 480 s trial window. These cases were not treated as if the
 female responded at 480 s but instead were coded as censored and handled
 explicitly in the CPH framework, in line with standard procedures in
 survival analysis (Therneau 2020; see also Chapter 3 in Klein &
 Moeschberger 2003).  The survival function (i.e., the probability of “not
 responding” over time) and the cumulative hazard function (i.e., the
 accumulated risk of responding) are estimated using partial likelihood
 methods that incorporate both event times (respondents) and censoring
 information (non-respondents). The inclusion of censored data ensures that
 the estimation properly accounts for the fact that the exact
 time-to-response is unknown for some individuals, but that they were “at
 risk” for the entire observation window. Crucially, censored observations
 do not bias the results, since survival models treat them as incomplete
 information rather than fixed endpoints. Instead, they contribute partial
 information about the likelihood of the hazard occurring, which is
 integral to the accurate estimation of both model coefficients and
 cumulative hazard functions (see Chapter 4 in Klein & Moeschberger,
 2003). Therefore, including these non-responding females is necessary for
 an unbiased analysis of latency. #### File: env_0_1.R **Description:** R
 code of model selection of probability of (1) acoustic signaling, and (2)
 visual signalling, by stimulus type, body condition, and background noise
 #### File: lat_surv_females.R **Description:** R code of model selection
 of latency to (1) acoustic signaling, (2) visual signaling, and (3) moving
 towards the robot, by stimulus type, body condition, and background noise
 #### References Klein, J. P., & Moeschberger, M. L. (2003). *Survival
 Analysis: Techniques for Censored and Truncated Data*. Springer. Therneau,
 T. (2020). *A Package for Survival Analysis in R*. R package version
 3.5-7.