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1 Simultaneously vocalizing Asian barbets adopt different frequencies without
2 coordinating temporal rhythms
3 Anand Krishnan1
4
5 1- Indian Institute of Science Education and Research (IISER) Pune, Pashan Road, Pune
6 411008, India. Email: [email protected]
7 Running title: Vocal rhythms of Asian barbets
8 Keywords: Barbet, duet, frequency, sound stream segregation, vocal coordination
9
10 Abstract
11 Sound stream segregation is an important challenge faced by simultaneously vocalizing
12 animals. In duetting passerine birds, coordinating vocal timing helps minimize overlap.
13 Alternatively, in birds that do not coordinate their vocalizations, sound stream segregation may
14 involve other mechanisms. For example, birds are known to use frequency differences to
15 segregate sound streams, and vocalizing at different frequencies may enable them to remain
16 distinct from each other. Here, I present data showing that conspecific individuals of four
17 species of Asian barbets vocalize at distinctly different peak frequencies from each other.
18 Additionally, they also differ in repetition rate such that each species exhibits two peaks in
19 frequency-repetition rate space. However, conspecific individuals across species do not
20 temporally coordinate with each other during vocal interactions, maintaining independent and
21 highly stereotyped individual rhythms together with different peak frequencies. Frequency
22 differences between individuals may facilitate sound stream segregation when calls overlap in
23 time. I hypothesize that simple, uncoordinated temporal rhythms with different frequencies may
24 have given rise to the more complex coordination seen in duetting birds.
25
26 bioRxiv preprint doi: https://doi.org/10.1101/754580; this version posted September 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
27 Introduction
28 Animal choruses represent a striking natural example of the ‘cocktail party problem’, where
29 individuals must segregate relevant information from competing streams of sound[1,2].
30 Vocalizing at differing frequencies[3], or altering vocal timing[4,5] may minimize masking
31 interference from overlapping sounds. Many bird species sing complex duets, which serve to
32 communicate or cooperatively defend territories[6,7]. Duetting pairs coordinate vocal timing,
33 resulting in a definite phase relationship between simultaneously vocalizing birds[8,9]. Other
34 ‘duetters’ simply sing together without coordination, and their vocalizations drift in and out of
35 phase with each other[6,10]. As a result, simultaneously vocalizing conspecifics may overlap in
36 time. The mechanisms employed by these birds to remain distinct are relatively poorly
37 understood.
38 Here, I study the vocal strategies of four species of Asian barbet (Piciformes: Megalaimidae)[11]
39 in two regions of India. Each region possesses two species with different vocal frequencies from
40 each other[12]. I examined whether simultaneously vocalizing conspecific barbets also exhibited
41 frequency differences, as well as whether they coordinate vocal timing to minimize temporal
42 overlap. Understanding the vocal strategies employed by these non-passerine birds has
43 implications in understanding the evolution of coordinated signals such as pair-displays.
44
45 Materials and Methods
46 Recording
47 I passively recorded barbet choruses in the city of Pune in Maharashtra (Peninsular India), and
48 the village of Mandal in Uttarakhand (Western Himalayas) in March-April 2018, early in the
49 breeding season. Each site houses two species of barbet, Psilopogon viridis/P.haemacephalus
50 in Pune, and P.virens/P.asiaticus in Mandal (Figure 1A), all easily observed close to human
51 habitation. For recordings, I used Sennheiser (Wedemark, Germany) ME62 omnidirectional
52 microphones connected to a Zoom H6 (Tokyo, Japan) recorder sampling at 44.1KHz, making bioRxiv preprint doi: https://doi.org/10.1101/754580; this version posted September 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
53 note of multiple simultaneously vocalizing conspecific individuals. The recorder and
54 microphones were stationary on the ground to avoid movement noise. The overall dataset
55 consisted of approximately seven hours of barbet chorus recordings.
56
57 Analysis
58 Using Raven Pro 1.5 (Cornell Laboratory of Ornithology, Ithaca, NY, USA), I digitized
59 vocalizations of barbets from each recording (Hann window size 512 samples with overlap of
60 256 samples) and calculated peak frequencies (PF) as well as the inter-phrase interval (IPI, the
61 time gap between the end of one phrase and the beginning of the next). For each species, I
62 digitized approximately 2500 individual phrases (1-5 notes/phrase depending on the species). I
63 listened to all recordings using headphones while labeling vocalizations, differentiating
64 conspecific individuals by the differences in relative amplitude (birds that were further from the
65 microphone were softer in recordings, and distinguishable in Raven). I measured the
66 interquartile range or IQR of IPI (for 108 total instances of a vocal barbet), to quantify
67 stereotypy. Secondly, to examine whether simultaneously vocalizing conspecifics (36 instances)
68 were temporally synchronous or asynchronous with each other, I calculated the time lag
69 between the beginning of each phrase of one individual and the closest call of the other using
70 MATLAB (Mathworks Inc., Natick, MA, USA). If birds were synchronous, I predicted that the
71 distribution of time lags across a bout should show a clear peak and low coefficient of variation
72 (i.e. a stereotypical time lag between individuals)[13]. However, an asynchronous bout would
73 imply independent rhythms from each other; the two individuals would thus drift in and out of
74 phase, resulting in a uniform distribution of time lags. For each instance with 5 or more
75 measured time lags (27/36), I performed two-sample Kolmogorov-Smirnov tests against 100
76 randomly generated uniform distributions spanning the same range of values. I scored the
77 results as 0 if they did not differ significantly from uniform (P>0.05), or 1 if they did (P<0.05), and
78 measured the percentage of total 1’s (out of 100 tests) for each of the 27 instances. A score of 0 bioRxiv preprint doi: https://doi.org/10.1101/754580; this version posted September 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
79 indicated a uniform distribution of time lags consistent with independent temporal rhythms, and
80 a score of 1 indicated a constant time lag consistent with two birds singing in coordination.
81
82 Results
83 Conspecific barbets sing at different frequencies and repetition rates
84 PF-IPI probability density plots reveal bimodal distributions for each species, indicating
85 intraspecific variation in PF and IPI (Figure 1B, see supplementary videos for 3D rotations). This
86 is most pronounced in P. haemacephalus, suggesting two distinct types of song, a faster song
87 at 0.7KHz, and a slower song at about 0.95KHz. Both P.viridis and P.virens exhibit two peaks in
88 PF-IPI space, also indicating faster and slower songs. P.asiaticus also exhibits a bimodal
89 probability density distribution, but the two peaks in my dataset indicate smaller differences in
90 IPI than the other species.
91
92 Simultaneously vocalizing conspecifics sing at independent rhythms without synchronization
93 Across 36 instances of simultaneously vocalizing conspecifics (pooled data from all 4 species), I
94 consistently observed differences in frequency and repetition rate between conspecific
95 individuals (spectrograms in Figure 2A). A probability density plot of the differences between
96 two individuals exhibited a non-zero peak in both PF and IPI (Supplementary Figure 1A).
97 Conspecifics thus vocalize at different frequencies and repetition rates from each other.
98 I next sought to establish whether vocally interacting conspecific barbets adjusted their
99 repetition rates to maintain synchrony with their vocal neighbors. The time lags between the
100 calls of two individuals have very high coefficients of variation for each species
101 (P.haemacephalus: 144.13%, P.viridis: 164.52%, P.virens: 182.55%, P.asiaticus: 197.11%),
102 supporting a lack of vocal timing coordination[6]. Time lags exhibit a range of values across all 4
103 species, as opposed to a single value that would be expected if two individuals coordinated
104 vocal timing by phase-locking (Figure 2B). When compared to 100 randomized uniform bioRxiv preprint doi: https://doi.org/10.1101/754580; this version posted September 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
105 distributions (27 instances, see Methods), time lags fit a uniform distribution 92% of the time on
106 average (average score 0.08, P>0.05, Supplementary Data, Supplementary Figure 1B, also see
107 Methods). This uniform distribution of time lags, together with high CVs, thus supports barbets
108 vocalizing with independent temporal rhythms rather than synchronizing with each other.
109 Further, each barbet maintains its individual rhythm even when vocalizing with conspecifics. The
110 probability density plot of IQRs for the IPI of each individual barbet peaked at 47ms
111 (Supplementary Figure 1C), indicating <50ms jitter in vocal timing across all species (including
112 measurement-related variation). This analysis included every individual instance of a vocal
113 barbet across the dataset (solo and with other conspecifics), in spite of which I still observed a
114 sharp peak indicating a precise vocal rhythm. This further supports the idea that barbets
115 maintain stable, independent vocal rhythms regardless of whether they vocalize solo or with
116 conspecifics.
117
118 Discussion
119 Avian duets and other vocal interactions have received much study for the precisely coordinated
120 vocal timing between individuals[6,9]. However, some ‘duetters’ exhibit independent rhythms
121 with no phase-locking or coordination between simultaneously vocalizing individuals[6]. My data
122 suggests that Asian barbets fall into this latter category, and I also find that simultaneously
123 vocalizing individuals tend to differ in the peak frequencies of their vocalizations. If two barbets
124 vocalize at independent and different rhythms, their calls will drift in and out of phase with each
125 other (resulting in a uniform distribution of time lags between the two individuals, as opposed to
126 the single peak one might expect if they were coordinated with each other). It is noteworthy that
127 two of the species I studied (P.haemacephalus and P.virens) have been described in the
128 literature as duetters[6]. However, all four species studied here exhibit similar patterns of
129 frequency and repetition rate differences between individuals, without timing coordination. This
130 indicates that all Asian barbets may vocalize with independent, uncoordinated rhythms. bioRxiv preprint doi: https://doi.org/10.1101/754580; this version posted September 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
131 Dilger[14] described ‘duetting’ in P.haemacephalus involving members of a pair singing at
132 different frequencies. Across species, I find that some individuals sing together with large PF
133 and IPI differences, and others with relatively small PF and IPI differences. However, none of
134 these cases exhibit evidence of a ‘call-answer’ type of vocal coordination as seen in duetting
135 passerine birds. Instead, barbets appear to simply adopt different temporal rhythms from each
136 other, which may involve paying attention only to the start of another bird’s bout. Although
137 different repetition rates may reduce temporal overlap to some extent, some vocalizations of two
138 individuals will still overlap in time. In this case, frequency differences between individuals may
139 support sound stream segregation[3,15]. Sympatric heterospecific barbets occupy distinct
140 frequency bands[12], and my data therefore suggests that smaller frequency differences may
141 also enable segregation of conspecifics within each species’ band.
142 Some species of the related African barbets (Lybiidae:Trachyphonus) appear to exhibit
143 coordination between duetting individuals[8,16], although other species may exhibit independent
144 rhythms[10]. It is possible that barbets may sometimes coordinate their rhythms over short time
145 scales, although my study does not find evidence of this. The similarly non-passerine pheasant-
146 coucal also exhibits pair-singing, with each partner vocalizing at a different frequency[17].
147 Passerine birds may have evolved temporally coordinated duets multiple times[6,13,18], and it
148 is possible that these may have arisen from uncoordinated simultaneous singing such as that
149 seen in non-passerines. Comparative study of vocal strategies may help understand the
150 ancestry of complex, temporally coordinated duets.
151
152 Acknowledgments
153 I thank Rohit Chakravarty, Zareef Khan, Baseer Baniya and Arun Varghese for assistance
154 during recordings, Raghav Rajan and his lab for inputs on the data, Samira Agnihotri and
155 Shivam Chitnis for discussions on analysis.
156 bioRxiv preprint doi: https://doi.org/10.1101/754580; this version posted September 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
157 Funding
158 My research is funded by an INSPIRE Faculty Award from the Department of Science and
159 Technology, Government of India and an Early Career Research (ECR/2017/001527) Grant
160 from the Science and Engineering Research Board (SERB), Government of India.
161
162 References
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207
208 bioRxiv preprint doi: https://doi.org/10.1101/754580; this version posted September 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
209 Figure legends
210 Figure 1: A. Spectrograms of barbet vocalizations from Peninsular India (left) and the Western
211 Himalayas (right). B. Three-dimensional probability density distributions indicating the
212 occurrence of different PF and repetition rates (IPI) for each species. For each instance of a
213 vocalizing barbet in the dataset, these plots contain the mean plus and minus one SD for PF,
214 and median, upper and lower quartile values for IPI, to represent the overall variation (both
215 inter- and intra-individual) in frequency-time space. This last is for illustrative purposes only, and
216 the quartiles have otherwise only been used to calculate the IQR for the IPI of each species.
217
218 Figure 2: A. Spectrograms of two simultaneously vocalizing P.viridis (left) and
219 P.haemacephalus (right), demonstrating intraspecific differences in frequency and repetition
220 rate. The white bars represent the time lag between two individuals; note how it changes with
221 each repetition. B. Graphs of all measured values of time lag between two individuals for each
222 species of barbet. The longer the horizontal bar, the longer the time lag; the y-axis represents
223 the number of such measurements made per species. Values vary from approximately 100ms
224 to over a second, indicating a lack of temporal synchrony between individuals.
225
226 Supplementary Figure 1: A. 3D probability density distribution for inter-individual differences in
227 PF and IPI. The peak suggests that most simultaneously vocalizing individuals have about a
228 100Hz difference in PF and a 300-500ms difference in IPI. B. Proportion of 100 Kolmogorov-
229 Smirnov tests in which the observed distribution of time lags differed from a random uniform
230 distribution (for 27 instances of two conspecific barbets vocalizing together). In 18/27 cases, the
231 observed distribution fit >90% to a uniform distribution (x-axis values <0.1, indicating
232 independent temporal rhythms without synchrony). Almost all other instances also fit well to a
233 uniform distribution (the lowest being a 59% fit to uniform, x-axis value 0.41), again supporting
234 independent vocal rhythms. C. Distribution of IQRs for the IPI of each individual instance of a bioRxiv preprint doi: https://doi.org/10.1101/754580; this version posted September 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
235 vocalizing barbet across the entire dataset (both solo and simultaneous calling, N=108). The
236 peak at 47ms indicates very low IQRs, and therefore a highly stereotyped temporal rhythm
237 regardless of whether the barbet is vocalizing solo or with conspecifics.
238 bioRxiv preprint doi: https://doi.org/10.1101/754580; this version posted September 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A Peninsular India Western Himalayas 1.3 1.6
P.virens P.viridis P.haemacephalus
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