Moose are a popular species with recreationists but understudied acoustically. We used publicly available videos to characterize and quantify the vocalizations of moose in New Hampshire separated by age/sex class. We found significant differences in peak frequency, center frequency, bandwidth, and duration across the groups. Our results provide quantification of wild moose vocalizations across age/sex classes, which is a key step for passive acoustic detection of this species and highlights public videos as a potential resource for bioacoustics research of hard-to-capture and understudied species.

Sexual dimorphism, or the discernable differences between the sexes of the same species, is observable throughout the animal kingdom (Ralls and Mesnick, 2009). Sexual dimorphism can manifest as different colors, sizes, or acoustic characteristics between the sexes. Moose (Alces alces) are a classic example of a sexually dimorphic species in that the males (“bulls”) have antlers and are much larger than the females (“cows”). Differences in acoustic characteristics are often due to differences in size of sound production anatomy. Sex-related differences in vocalizations have been described in wild (Lent, 1974) and captive moose (Bogomolova 1984), as well as in other ungulate species (Jennings and Gammell, 2013).

Moose vocalize primarily during the mating season (“rut”), which occurs in the fall (Schwartz, 1992). Males emit “burps,” “croaks,” “barks,” or “grunts,” to attract others both when traveling alone and in response to female presence (Ballenberghe and Miquelle, 1996; Bogomolova , 1984; de Vos , 1967; Dussault and Huot 1986; Lent, 1974). Females may “moan” or “wail” when approached by adult males (Dussault and Huot, 1999; Lent, 1974) and may “protest moan” when harassed by small to mid-sized males to incite male–male competition (Ballenberghe and Miquelle, 1996; Bowyer , 2011, 2020; Løvlie , 2014). Cows have been known to “grunt” to their calves, or “roar” if surprised. Lent (1974) and (Bogomolova 1984) recorded and measured female “moans” and “grunts” and male “croaks” and “barks,” noting differences in the duration and frequency of calls between sexes.

Age-related acoustic differences in cervids have also been documented. In a study of captive-farmed moose, (Bogomolova 1984) described the varied vocalizations between cows and newborn calves, finding that the duration and frequency of calf “squeaks” and “groans” appeared distinct from their mother's “hoots” and “moans.” In related red deer (Cervus elaphus), the calls of juveniles are much higher in frequency than those of adults (Volodin , 2015). Despite these sex- and age-related variations, no statistical analyses have been performed to investigate how these vocalizations differ in moose.

To fill this gap in the literature, this study aimed to quantify and compare the characteristics of wild moose vocalizations among bulls, cows, and calves using updated and newly available technologies. Because moose can be highly elusive, we relied on an underutilized resource for animal behavior—publicly available user-uploaded videos on the internet. By analyzing the videos opportunistically captured by hunters and hikers, we provide a quantification of moose vocalizations across age/sex classes. Because moose populations have declined over several decades due to winter tick infestations, habitat degradation, and climate change, effective population monitoring is a top priority for many management agencies (Ellingwood , 2020; Lenarz , 2010). Identifying key acoustic characteristics for this species is the first step toward detecting this species using passive acoustic monitoring.

Data were collected through a search for videos of moose vocalizing on the platform YouTube with the following keywords: Alces alces vocalizations, female moose vocalizations, male moose vocalizations, baby moose vocalizations. Our selection criteria were that a video must have clear visuals and auditory moose vocalizations. Regardless of nasal or oral origin, all vocalizations were considered (Volodin , 2014, 2016). Vocalizations were confirmed through corresponding movement of the mouth, throat, nares or diaphragm, neck stretching, as well as behaviors that have previously been associated with vocalization emission, such as cows fleeing from bull advances (Bowyer and Kitchen, 1987; Dussault and Huot, 1999; Volodin , 2015). We could infer identification of an individual from previous vocalizations if it moved out of view. We considered all videos with locations noted from within the United States. Videos were captured from YouTube, and the audio was converted into an mp3 file using the “getmp3” online mp3 converter available at https://getn.topsandtees.space/DeaMJJ5jWe.

Moose were categorized from videos based on physical characteristics and rutting behaviors. Moose congregate during the rut; therefore, we could assume that when in the presence of others, those individuals with antlers were adult males; correspondingly, females were identified as adults with no antlers. Calves stay with their mothers until they reach one year of age, at which point, males begin to grow antlers (Lenarz , 2010). Therefore, calves were identified as any juvenile present with a mother, and we did not attempt to separate calves based on sex. If we could not determine the age/sex class, the video was not considered. To minimize the effects of low-frequency background noise, we first applied a high-pass filter at 100 Hz with a 48 dB roll-off to all recordings using Audacity (version 3.1.2). Acoustic analysis was conducted in Raven Pro (version 1.6). For each vocalization, we calculated the signal-to-noise ratio (SNRs) with the SNR NIST quick function, and only considered vocalizations with SNRs greater or equal to 15 dB. We then measured the peak frequency, center frequency, 90% bandwidth, and 90% duration of these calls (Charif , 2010). Statistical analysis was conducted in R Studio (version 2023.03.1 + 446), and we used a nested ANOVA to explore whether acoustic parameters varied across individuals and age/sex classes.

After applying the SNR criteria, our final sample size included 673 calls across 19 videos: 199 from cows, 255 from bulls, and 219 calls from calves. The mean number of vocalizations per individual by age/sex class was 9.74 calls per cow (SD = 15.52), 9.18 calls per bull (SD = 14.97), and 25.00 calls per calf (SD = 37.02). We assumed that each new video (or video clip in a new habitat for compilation videos) recorded a separate individual, resulting in the following final sample sizes: cow = 19, bull = 22, calf = 8. Representative spectrograms for call types emitted from cows, bulls, and calves are highlighted in Fig. 1.

Fig. 1.

Representative spectrograms of vocalizations from (A) cow, (B) bull, and (C) calf.

Fig. 1.

Representative spectrograms of vocalizations from (A) cow, (B) bull, and (C) calf.

Close modal

There were significant differences across age/sex classes (df = 2, p < 0.001) for all acoustic characteristics (Fig. 2). For mean peak frequency, center frequency, and bandwidth, there was a greater difference between the calls of calves and those of cows and bulls, compared to the difference between bulls and cows. The peak and center frequency of cow and bull calls differed by approximately 350 Hz, whereas calves differed from adults by 800–1200 Hz. Similarly, bandwidth between cows and bulls differed by approximately 60 Hz, whereas calves differed from adults by 1300 Hz. Cow calls were on average 0.5 s longer than calf and bull calls.

Fig. 2.

Box plots of moose call parameters according to age/sex class. All classes were significantly different (p < 0.05) when analyzed with a nested ANOVA. Bold line indicates the median. Boxes represent the interquartile range. Whiskers represent the first and fourth quartiles. Dots represent statistical outliers.

Fig. 2.

Box plots of moose call parameters according to age/sex class. All classes were significantly different (p < 0.05) when analyzed with a nested ANOVA. Bold line indicates the median. Boxes represent the interquartile range. Whiskers represent the first and fourth quartiles. Dots represent statistical outliers.

Close modal

There were significant differences across individuals (df = 46, p < 0.001) for all acoustic characteristics. For peak and center frequency, there was considerable variation between calf and cow individuals (220–265 Hz), whereas bulls only differed slightly (1–22 Hz) from one another. For bandwidth, there was a large difference between calls of calf individuals (over 500 Hz) compared with calls of cows and bulls (about 100 Hz). For duration, the difference across individuals was similar for each of the age/sex classes.

Our results show that there are distinct, quantifiable vocalization differences between cow, bull, and calf moose. Although individual variation existed within each age/sex class, there was more variation between age/sex classes, with the most pronounced differences between bulls and calves. In general, bull moose had the lowest-frequency calls, cows had mid-range frequency calls, and calves had the highest-frequency calls. Calves made calls with broader bandwidths compared to cows and bulls, and cows produced calls that were much longer in duration compared to bulls and calves.

Although prior moose studies did not directly compare the acoustic measurements of cows, bull, and calves, vocalizations have been described and followed similar trends as those in our study. Bogomolova 1984) observed a wide repertoire of calls, especially in cows. They described the relatively short, high-frequency calls of calves and the longer, lower frequency of cow calls (Bogomolova 1984). Lent (1974) also noted the long, low call of the cow and a short, low, complex grunt of the bull.

Previous studies on the vocalizations of other cervids have found variable age/sex-related differences. Like our findings in moose, male goitered gazelle, Persian deer, and European fallow deer emit a low frequency call during the rut (Frey , 2011; Stachowicz , 2014), and female red deer calls decrease in frequency as they age (Volodin , 2018). Mongolian gazelle males have comparatively higher frequency calls than moose, but the adult calls are lower in frequency than those of juveniles (Frey and Gebler, 2003; Frey and Riede, 2003). Conversely, Rocky Mountain elk male calls are higher in frequency and longer in duration compared to females and calves (Feighny, 2005), and Siberian wapiti calls do not significantly differ in frequency across age or sex classes (Volodin , 2016).

One of the most important results of this study is that we were able to quantitatively compare the vocalizations for moose, separated by age/sex class. In many North American states and provinces, moose are a species of management concern given their importance to forest habitat, recreation, and tourism (Timmermann and Rodgers, 2017). With their wide range and generally low population densities, monitoring moose can be challenging with visual or camera surveys (Moll , 2022; Newey, 2015). Passive acoustic monitoring is a viable option for determining species occupancy and density to inform land management (Campos-Cerqueria , 2016), and the vocal characteristics from our study provide key information to begin a passive acoustic monitoring framework for moose. Building off the results obtained from our public videos, we can build energy detectors to identify moose vocalizations from long-term acoustic recordings and identify age/sex classes. Future work will include networks of calibrated acoustic recorders across a landscape to develop an automated detector and determine moose population density and occupancy to inform forest management.

Because our study was constrained by the number and quality of YouTube videos available, as well as the circumstances under which videos were taken (e.g., calves were often in distress), we must acknowledge several limitations. First, we had a relatively small sample size. Second, we were unable to determine nasal vs oral calls and could not sex calves or age adults, meaning we could not account for variation in different types of calls within each age/sex class. Third, by using public videos, we could not control for variation in microphone quality and/or the distance from the source of the call. Despite these limitations, we believe our study highlights the value of public videos for behavioral research of poorly studied species. Participatory or community (formerly referred to as “citizen”) science is rapidly gaining popularity as a viable opportunity for bioacoustics research largely due to the rise of smartphone microphone quality (Jäckel , 2021; Vu , 2023). Snyder (2022) have been developing workflow and data management techniques for citizen science projects in bioacoustics that could be applied for future studies. Although audio compression affects acoustic characteristics (Heath , 2021), given the frequency characteristics of moose vocalizations and the large differences in call parameters across our age/sex classes, we predict we would obtain similar results with professional microphones. Future work with calibrated passive acoustic recorders can validate the findings from our study and continue to refine moose vocal characteristics, with the goal of an automated detector for species and forest management.

This work was funded by the University of New Hampshire Agricultural Experiment Station and was supported by the U.S. Department of Agriculture National Institute of Food and Agriculture Hatch Project 1024128 (Scientific Contribution No. 3010). The authors thank Henry Jones and Jake DeBow for feedback on moose habitat needs and management priorities. This paper was based on the undergraduate work of A. Zager, J. Brierley, and O. Boyan.

The authors have no conflicts to disclose.

This research adheres to ASA's Ethical Principles and does not require institutional approval due to the nature of passive data collection.

The data used for this project can be found in the paper's supplemental materials.

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Supplementary Material