Study site and animals

This experiment was undertaken in a free-ranging environment at the University of Sydney, Australia, “Wolverton Farm” between June and October 2017. A herd of 18 Holstein-Friesian non-pregnant virgin heifers were recorded for this experiment. Heifers were selected to be uniform in breed, production status, age (24.5 ± 2.5 months) and weight (412.8 ± 44.7 kg) to control for their excessive influence on vocal individuality. The heifers were situated in a 4 Ha paddock containing cattle yards, where they had access to native pasture, unlimited water and were supplemented with lucerne hay (dry matter: 89.1%, crude protein: 16.4%, metabolisable energy: 8.5%) daily.

The heifers were recorded producing high-frequency open-mouth vocalisations during oestrus, two feeding contexts and two isolation contexts. Prior to the commencement of recording, heifers were adapted to the presence of human observers (between two and four people concurrently), as well as the routine of moving through the cattle yards for sorting and husbandry procedures. To assist with the identification of individual heifers during the recording contexts, heifers were assigned numbers which were spray-painted with fluoro stock-mark on either side of their flank. Spray painting was conducted with the heifers restrained in a head-bail and cattle crush in the cattle yards. Low-stress handling methods were always implemented when moving the heifers to and from the paddock and cattle yards. All procedures were approved by the University of Sydney animal ethics committee ‘IRMA’ (project number: 2016/1078), with the recording contexts only causing temporary distress to the heifers involved. All procedures were performed in accordance with the Australian code for the care and use of animals for scientific purposes44.

Audio recordings and contexts

Vocalisations were recorded using a Sennheiser K6-ME67 directional microphone (frequency response: 40 to 20000 Hz, max SPL: 125 dB at 1 kHz, Sennheiser Electronic, Wedemark, Germany) attached to a Marantz PMD-661 MK2 digital solid-state recorder (Marantz Professional, United Kingdom). The microphone was directed towards the vocalising heifer as best as possible. For shock and wind-noise reduction, the microphone was protected with a Rycote Classic Softie Windshield ®. Further, recordings were only taken when weather was permissible. Each vocal recording was saved as a separate file in the.WAV uncompressed format at 44.1 kHz sampling rate and 16-bit amplitude resolution. Vocal recordings were obtained when the same cattle were: 1) in oestrus, 2) anticipating feed, 3) denied feed access, 4) physically isolated from conspecifics, and 5) physically and visually isolated from conspecifics. Recordings were carried out during daylight hours between 08:00 and 17:00 with no recordings collected later than 17:00 h due to sound interference from the cattle feeding tractors and limited daylight. Specific details about the recording contexts are provided in the Supplementary Methods.

Inferences about emotional valence in the recording contexts

The recording contexts were classified as positive or negative, according to their putative emotional valence. We did not need precision with emotional valence classification in the present study, since we were determining whether vocal individuality could be maintained across contexts and time. Therefore we inferred emotional valence of the oestrus, feeding and isolation contexts based on the functions of emotions45,46,47,48, and knowledge of livestock behaviour31,38,49. Positive emotions are part of the pleasant-appetitive motivational system, which trigger approach towards releasing stimuli, while negative emotions are part of the unpleasant-defensive motivational system, which trigger avoidance of releasing stimuli45,46,47,48. Subsequently, oestrus was assumed to be positively valenced, as during this time cattle exhibited affiliative behaviours including approaching conspecifics, sexual behaviours including anogenital sniffing and licking, and exploratory behaviours in search of a mate47,48. At the ultimate level, oestrus functions to promote survival, allowing for the attraction of a mate and potential procreation45. Anticipation of feed was also deemed to be positively valenced since feeding should induce approach behaviour and increase fitness in the wild. Contrastingly, both physical and physical and visual social isolation were assumed to be negatively valenced, since cattle are highly gregarious and being separated from the herd could threaten fitness. Further, denial of feed access was assumed to be negatively valenced, as it could lead to frustration, lack of feed intake in the wild and an overall threat to fitness31,46. While all 18 heifers were exposed to the five recording contexts, not all heifers vocalised within each context. Nonetheless, we obtained vocalisations in at least one of the positive and one of the negative recording contexts for each heifer (Table 2).

Table 2 Number of calls analysed from each heifer including the contexts and putative valences in which they were produced. Full size table

Vocalisation selection

Cattle vocalisations are classified into two broad types, namely low-frequency and high-frequency calls which are modulated by configuration of the supra-laryngeal vocal tract2,24. During the oestrus recording context, low-frequency closed-mouth calls were seldom observed. For our acoustic analyses, we therefore only focused on the high-frequency open-mouth calls, as these were directly comparable in the heifers across the putatively positive and negative farming contexts. Calls were selected based on their high signal to noise ratio and in the absence of wind or signal saturation, resulting in a total of 333 calls analysed from 13 of the 18 heifers (Table 2). Despite the low incidence of calling from some individuals, calls were also balanced as much as possible across the putative valences. Additionally, if calls were produced as part of a sequence, we only selected them for analyses if they were more than 10 s apart in order to reduce homogeneity associated with consecutive calling. In total, 53 of the 333 vocalisations were derived from sequences of low and high-frequency vocalisations, with only two vocalisations selected from the same sequence considering they were non-consecutive.

Vocalisation analyses

Vocalisations were analysed using Praat DSP package v.6.0.3150, through both calculation off the oscillograms and spectrograms; and by using a series of custom-built scripts31,51 to automatically extract a range of acoustic features. Vocalisations were visualised as narrow-band spectrograms (FFT method, window length = 0.1 s, time steps = 1000, frequency steps = 250, Gaussian window shape, dynamic range = 60 dB) and a total of 21 vocal parameters were measured in each of the vocalisations (Table 3). Prior to running the scripts, the full duration (s) of the call was measured directly off the oscillogram. Nonlinear phenomena were widely prevalent in the calls including 80% and 93% of the putatively positive and negative calls, respectively (See Supplementary Methods for further details on prevalence). For this reason, the percentages of nonlinear phenomena relative to the full call duration were calculated off the spectrogram. Nonlinear phenomena criteria were adopted from previous vocal studies in non-human mammalian species52,53,54,55 and included deterministic chaos, biphonation sidebands, subharmonics, and frequency jumps. Example waveforms and spectrograms of the nonlinear phenomena are provided in Fig. 2.

Table 3 Description of the 21 vocal parameters measured for each vocalisation. Full size table

Figure 2 Sample oscillograms (top) and narrow-band spectrograms (bottom) of vocalisations recorded during the putatively positive and negative contexts from three different heifers, containing nonlinear phenomena including (A) biphonation sidebands during anticipation of feed, (B) deterministic chaos during denial of feed and (C) frequency jumps (FJ) during oestrus. Spectrograms were visualised in Praat v.6.0.31 (FFT method, window length = 0.1 s, time steps = 1000, frequency steps = 250, Gaussian window shape, dynamic range = 60 dB). Full size image

Before extracting the vocal parameters, a script was run to add silences of 0.1 s to each side of the 333 calls. Using custom-built scripts in Praat31,51, we then batch-processed the acoustic analyses, with output data exported to Microsoft Excel for further examination. In the script, pitch floor and ceiling settings were adapted to the individual heifer voices and these settings were maintained across calls collected during positive and negative valence for a given heifer. Specific Praat procedures are detailed in the Supplementary Methods.

Statistical analyses

Statistical analyses were performed using SPSS v.24 (IBM Corp. Released 2016). Two separate stepwise discriminant function analysis (DFA) procedures were used to quantify the extent of which individual heifers could be classified based on their calls. The DFAs were conducted both within each putative valence and across the putative positive and negative valences to establish whether individual differences in the high-frequency calls of heifers are maintained. In both DFAs, the grouping variable was heifer (1–13), the discriminant variables were the 21 vocal parameters and the selection variable was valence (positive = 1 or negative = 2). A first DFA was run with the selection variable set to 1 (positive). In this DFA, the 170 putatively positive calls were used as a ‘training set’, to firstly classify the 170 putatively positive calls to the correct individual, and secondly classify the 163 putatively negative calls in the ‘test set’ to the correct individual. Then, a second DFA was run with the selection variable set to 2 (negative). In this DFA the 163 putatively negative calls were used as a ‘training set’ to firstly classify the 163 putatively negative calls to the correct individual, and secondly classify the 170 putatively positive calls in a ‘test set’ to the correct individual. For both DFAs, we used the default settings for the F value of the model, which included an entry level of 3.84 and a removal level of 2.71. Since there was an imbalance in the number of vocalisations from each heifer across positive and negative valence, the percentage of correct classification was calculated according to the group sizes. We used the leave-one-out classification procedure to cross-validate the results and the Wilks’ lambda method to determine how strongly each of the discriminant functions contributed to the models. To confirm the accuracy of the DFA classifications, we used two-tail binomial tests to see whether correct classifications were significantly higher than chance expectations34,35,56. Graphical representations of the first two discriminant functions scores for heifer vocal individuality were additionally formulated in R Studio v.1.1.463 using the ggplot2 package57.

We also conducted two multivariate general linear models (MANOVA) using the putatively positive and negative calls separately, to determine whether there were significant differences between heifers in their 21 vocal parameters. In both MANOVAs, heifer was included as the categorical fixed factor and the 21 vocal parameters were used as the independent variables. Descriptive statistics (means ± SE) are provided in the Supplementary Materials for all vocal parameters of heifers during putative positive and negative valence.