ABSTRACT
House mice (Mus musculus) emit ultrasonic vocalizations (USVs), which are surprisingly complex and have features of bird song, but their functions are not well understood. Previous studies have reported mixed evidence on whether there are sex differences in USV emission, though vocalization rate or other features may depend upon whether potential receivers are of the same or opposite sex. We recorded the USVs of wild-derived adult house mice (F1 of wild-caught Mus musculus musculus), and we compared the vocalizations of males and females in response to a stimulus mouse of the same- or opposite-sex. To detect and quantify vocalizations, we used an algorithm that automatically detects USVs (Automatic Mouse Ultrasound Detector or A-MUD). We found high individual variation in USV emission rates (4 to 2083 elements/10 min trial) and a skewed distribution, with most mice (60%) emitting few (≤50) elements. We found no differences in the rates of calling between the sexes overall, but mice of both sexes emitted vocalizations at a higher rate and higher frequencies during opposite- compared to same-sex interactions. We also observed a trend toward higher amplitudes by males when presented with a male compared to a female stimulus. Our results suggest that mice modulate the rate and frequency of vocalizations depending upon the sex of potential receivers.
Subject(s)
Animal Communication , Sex Factors , Animals , Female , Male , MiceABSTRACT
House mice (Mus musculus) emit complex ultrasonic vocalizations (USVs) during social and sexual interactions, which have features similar to bird song (i.e., they are composed of several different types of syllables, uttered in succession over time to form a pattern of sequences). Manually processing complex vocalization data is time-consuming and potentially subjective, and therefore, we developed an algorithm that automatically detects mouse ultrasonic vocalizations (Automatic Mouse Ultrasound Detector or A-MUD). A-MUD is a script that runs on STx acoustic software (S_TOOLS-STx version 4.2.2), which is free for scientific use. This algorithm improved the efficiency of processing USV files, as it was 4-12 times faster than manual segmentation, depending upon the size of the file. We evaluated A-MUD error rates using manually segmented sound files as a 'gold standard' reference, and compared them to a commercially available program. A-MUD had lower error rates than the commercial software, as it detected significantly more correct positives, and fewer false positives and false negatives. The errors generated by A-MUD were mainly false negatives, rather than false positives. This study is the first to systematically compare error rates for automatic ultrasonic vocalization detection methods, and A-MUD and subsequent versions will be made available for the scientific community.
