Singing more, singing harsher: occurrence of nonlinear phenomena in a primate' song

doi:10.21203/rs.3.rs-2371019/v1

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Singing more, singing harsher: occurrence of nonlinear phenomena in a primate' song

https://doi.org/10.21203/rs.3.rs-2371019/v1

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published 17 Jul, 2023

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Nonlinear phenomena (NLP) in animal vocalizations arise from irregularities in the oscillation of the vocal folds. Various hypotheses have been put forward to explain the occurrence of NLP, from adaptive to physiological ones. Non-human primates often display NLP in their vocalizations, yet the meaning of these features is still unclear. We present here the first study of NLP occurrence in the song of a singing primate: the indri (Indri indri). We found that the more an individual sings, the higher the likelihood of emitting NLP. Our results show that NLP occurrence in indri depends on phonation, i.e., the cumulative duration of all the units emitted by an individual. Furthermore, NLP occurrence depends on the timing of the signal and increases as the singing progresses. Our findings suggest that indris may be subjected to physiological constraints during the singing process which may limit the production of harmonic sounds because of the fatigue needed to perform loud and long calls. However, indris may still benefit from emitting NLP by strengthening the loudness of their signals for better advertising their presence to the neighboring conspecific groups.

Nonlinearities

vocal folds

phonation

vocal fatigue

singing primates

Indri indri

Nonlinear phenomena (NLP) are produced by irregular vibrations of the vocal folds (Herzel et al. 1995). NLP generally occurring in mammalian emissions belong to four major groups: 1) chaotic phenomena arising from aperiodic oscillations (Fitch et al. 2002), 2) splitting of harmonic cycles due to differential vibration of the vocal folds (Wilden et al. 1998; Riede et al. 2004), 3) simultaneous emission of sounds produced by different phonatory sources within the vocal tract (Reby et al. 2016), and 4) sudden changes in the fundamental frequency (Riede et al. 2004). Non-human primates have been indicated as animals that frequently exhibit NLP in their vocalizations (Fitch et al. 2002) and recent findings show that it is the peculiar morphology of their larynx that makes them susceptible to nonlinearities (Nishimura et al. 2022). It is therefore of particular interest to investigate the occurrence of NLP in species that emit harmonic signals, such as singing primates (De Gregorio et al. 2022).

Among these taxa, there is only one lemur species, the indri (Indri indri). Indris inhabit the mountain rainforests of Madagascar, live in small family groups including a reproductive pair and its offspring (Torti et al. 2017), and they occupy territories that are defended through the emission of songs (Torti et al. 2013), which spread through the forest for kilometers (Spezie et al. 2022). Indris emit sexually dimorphic songs (Gamba et al. 2016) starting with harsh roars given by all group members, followed by harmonic frequency-modulated units, organized in phrases (Zanoli et al. 2020). Youngsters and subadults may sing with parents, in duets or choruses (Giacoma et al. 2010), and undergo changes during growth according to their age and sex (De Gregorio et al. 2021).

This study aims at understanding whether NLP occur evenly between the two sexes and between age classes, and increase as singing proceeds. Indri songs adhere to the laws of compression: they show a trade-off between the length of the signal and that of its components and a balance between signal duration and occurrence (Valente et al. 2021), suggesting the need for compensating the cost of sustained vocal emission. Furthermore, NLP have been considered as a potential indicator of vocal fatigue (Riede et al. 2004; Boucher 2008). We, therefore, hypothesized an effect of the song duration on the occurrence of NLP due to a trade-off between the need of conveying important information to conspecifics and the effort required to perform long, complex, and potentially exhausting vocal performances. Hence, we predicted that 1) the more indris sing, the greater the likelihood of emitting NLP. We also predicted that 2) NLP distribution may change depending on the timing of the signal, i.e., the initial or the final portion of the song, and increase in number over time as the singing proceeds. Moreover, we predicted 3) a higher occurrence of NLP during the reproductive season when indris sing more often (Ravaglia et al., in preparation) and may be more prone to vocal fatigue.

Besides physiological constraints, previous research provided various adaptive explanations for NLP occurrence. The emergence of NLP in mammalian vocalizations may reflect the emotional state of the emitter, e.g., stress or arousal (Blesdoe et al. 2014, Fuchs et al. 2021, Marx et al. 2021), and may be associated with the context of emission, e.g., alarm calls in the presence of predators (Townsend & Manser 2010, Karp et al. 2014, Volodin et al. 2018), or individual characteristics such as body size, health, reproductive state, or sex (Riede et al. 2007, Vannoni and McElligott 2009, Cazau et al. 2016, Weissmann et al. 2020, Anikin et al. 2021). NLP might play a role in sexual selection by conveying information on male quality to females, as reported in baboons, deer, and whales (Fischer et al. 2004, Reby & Charlton 2012, Cazau 2016). The occurrence of NLP may also trigger the listeners’ attention by adding unpredictability within singing and thus make these signals hard to ignore (Fitch et al. 2002; Townsend et al. 2010, Massenet et al. 2022). Given the sexual dimorphism of indri songs, and the different engagement of the group members in singing, we expect that NLP frequency varies according to 4) sex and 5) age class.

Subjects and recordings

Between 2010 and 2020, we recorded the spontaneous songs of 7 habituated groups of wild indris living in the Maromizaha New Protected Area (18°56′49′′S, 48°27′53′′E, Madagascar). We selected 15 advertisement songs (Torti et al. 2013; Fig. 1a) for each group (duets and choruses). Out of the 105 songs, 64 were recorded during the reproductive season (September-February, Ravaglia et al., in preparation), and 41 during the non-reproductive season (March-August, Ravaglia et al., in preparation). See S1 Supplementary Information for details on the recording procedure and equipment.

Acoustic analysis

We used Praat 6.2.07 (Boersma and Weenink 2022) to visually inspect each song spectrogram. We detected and manually selected all the nonlinearities labeling them as ‘NLP’ and consecutively numbering them through the individual contributions, i.e., the duration of the individual vocal output (De Gregorio et al. 2021; Fig. 1a, S2 Supplementary Information). We did not consider roars for our analysis as their structure prevents us from recognizing their actual emitter (Valente et al. 2021) In 246 contributions emitted by 28 individuals, we identified 1418 NLP. We measured three temporal variables (Fig. 1a): song duration, from the beginning of the first roar to the ending of the last unit; the individual contributions (contribution), from the beginning of the first to the ending of the last modulated unit (long notes and descending phrases, see Giacoma et al. 2010) emitted by a singer; the individual phonation (phonation), i.e., the cumulative duration of all the units emitted within a contribution (De Gregorio et al. 2019).

Statistical analysis

We tested whether the temporal variables (song duration, contribution, or phonation; predictors) affected the occurrence of NLP (response variable) with three Generalized Linear Mixed Models (GLMM; glmmTMB package, R Core Team 2021, version 4.1.2). For each model, we also tested the effect of sex and age (adults, i.e., ≥6yo; juveniles, i.e., non-reproductive individuals > 2yo, Rolle et al. 2021) in interaction, and season (reproductive or non-reproductive). Each model included a song code and an individual code as random factors (see S3-S5 - Supplementary Information). We selected the model showing the best goodness of fit first through a likelihood ratio test (Anova with argument test “Chisq”; Dobson 2002) to compare each model with one another, then by using a R-squared test.

We then split the selected temporal variable into duration quartiles. We counted the number of NLP occurring in each quartile to understand if the NLP distribution changed as singing proceeded. To test this hypothesis, we ran a GLMM using NLP occurrence within each quartile as the response variable (one count of NLP occurrence per every quartile of every individual contribution). The explanatory variables were the temporal variables in interaction with the quartile (as an integer variable), the interaction between sex and age, and season. The model included the song and individual codes as random factors (see S6 - Supplementary Information).

When measuring the duration of the temporal variables (S7 Supplementary Information), we found that all three predicted NLP occurrence. In particular, the three full models were significantly different from the null ones (song duration: Chisq = 35.239, df = 8, p < 0.001; contribution: Chisq = 168.659, df = 8, p < 0.001; phonation: Chisq = 234.572, df = 8, p < 0.001). Our results indicated that the longer the duration of the temporal variables, the higher the number of NLP (Fig. 1b; song duration: estimate = 0.005, z = 5.402, p < 0.001; contribution: estimate = 0.009, z = 12.845, p < 0.001; phonation: estimate = 0.033, z = 15.511, p < 0.001; S3-5 Supplementary Information). The percentage of NLP on the total number of emitted units per sex and age is shown in figure S8 (Supplementary Information).

Overall, the model with song duration as a predictor showed a significant effect of both age and its interaction with sex, on NLP occurrence (age: estimate = -0.891, z = -2.901, p = 0.003; sex*age: estimate = 0.897, z = 2.239, p = 0.025; S3 Supplementary Information). Specifically, the post-hoc revealed that NLP occurrence was significantly higher in adult females than in juvenile ones (S4 Supplementary Information). Similarly, the model with contribution as a predictor showed a significant effect of both age and its interaction with sex overall (age: estimate = -0.763, z = -2.492, p = 0.013; sex*age: estimate = 1.263, z = 3.164, p = 0.002; S4 Supplementary Information). The post-hoc test revealed that NLP occurrence was significantly lower in juvenile females than in juvenile males (S5 Supplementary Information). The model including phonation as a predictor showed an overall significant effect of both age and its interaction with sex, but no significant post-hoc comparisons (age: estimate = -0.558, z = -2.025, p = 0.043; sex*age: estimate = 0.798, z = 2.241, p = 0.025; S5 Supplementary Information). The season did not significantly affect NLP occurrence in any of the three models. When comparing the three models, we found that the model including phonation as a predictor for NLP occurrence was the best fitting one (R²m song duration: 0.155; R²m contribution: 0.244; R²m phonation: 0.296; song duration vs contribution: Chisq = 133.420, df = 8, p < 0.001; contribution vs phonation: Chisq = 65.913, df = 8, p < 0.001; song duration vs phonation: Chisq = 199.333, df = 8, p < 0.001).

Considering that, we split phonation into duration quartiles and found that the occurrence of NLP increased with phonation (phonation: estimate = 0.044, z = 8.013, p < 0.001; quartiles: estimate = 0.451, z = 6.686, p < 0.001; S7 Supplementary Information; Fig. 1c). We did not detect a significant effect of sex, age, their interaction, and season on NLP occurrence (S7 Supplementary Information).

This study aimed to investigate whether NLP occur evenly between sex and age classes and happen more often in a particular part of indris’ song. Our findings show that the longer duration, the higher the occurrence of NLP, similar to what happens in chimpanzees’ vocalizations (Riede et al. 2004, 2007) and hyrax songs (Weissmann et al. 2020). Indeed, each temporal variable we tested was positively correlated with the emission of NLP within the song. Sex and age affect NLP occurrence in two cases. Specifically, adult females contribute significantly more to the production of NLP than juvenile females in relation to the duration of the song, and juvenile females emit significantly less NLP than juvenile males in relation to the individual contribution. Among the temporal variables we tested, phonation was the best predictor of NLP occurrence. Furthermore, we show that NLP occurrence increases over time as the singing proceeds, because NLP significantly rise as the accumulation of phonation during the song. This evidence suggests that it is the effective engagement in singing that determines how many NLP an individual emits. Our results confirm our first two predictions and seem to support the hypothesis that the more an individual sings, the higher the likelihood of emitting NLP. Overall, such findings suggest that the production of harmonic sounds may be limited by vocal fatigue in long and loud calls, and this may correspond to an increase in NLP. In agreement with previous studies on humans (Boucher 2008) and chimpanzees (Riede et al. 2004), vocal fatigue may foster the deterioration of vocal emission. Vannoni and McElligott (2009) also reported a link between NLP and vocal fatigue in the male calls of fallow bucks (Dama dama), whose NLP rates changed according to a seasonal pattern. Fallow bucks produced more NLP while singing more as the reproductive season progresses (Vannoni and McElligott 2009). In this case, NLP occurrence could be related to the accumulation of vocal fatigue over a long period rather than to the short-term effort needed to sing. Differently from fallow bucks, we did not detect an effect of the season on the occurrence of NLP, even though indris do indeed sing more often during the reproductive season (Ravaglia et al. in preparation). This finding may suggest that a certain degree of fatigue might be experienced by indris every time they emit songs, regardless of the season.

Our results do not support adaptive explanations for the occurrence of NLP. Based on our findings, NLP production in indris does not vary according to sex and does not seem to encode information for conspecifics, in disagreement with previous findings on baboons’ loud calls (Fischer et al. 2004), humpback whales’ songs (Cazau et al. 2016), and rock hyraxes’ songs (Demartsev et al. 2016). Furthermore, our results disagree with the unpredictability hypothesis that NLP may be produced more by younger individuals to make their signals harder to ignore (Fitch et al. 2002; Massenet et al. 2022). Therefore, we reject our latest two predictions. However, NLP in indris may be a mere byproduct of energetically expensive vocalizations. It has been observed that NLP may be typical of species that exhibit peculiar communication systems based on exhausting and loud calls, such as anurans (Feng et al. 2009) and whales (Cazau et al. 2016). In this perspective, although essentially depending on physiological constraints naturally emerging when performing long calls, NLP might still have some adaptive function by helping a group of indris to add unpredictability to their songs thus facilitating the reception of their signals by other groups at long distances in the dense rainforest they inhabit. Given that NLP production requires a high level of subglottal pressure (Fitch et al. 2002), in turn depending on a powerful activation of abdominal muscles (Johnstone & Scherer 1999), and that loud signals indicate physical preparation for a fight (Tsai et al. 2010), NLP might contribute to making loud calls more threatening.

In conclusion, despite NLP being common in the vocal repertoire of non-human primates (Fitch et al. 2002; Nishimura et al. 2022), the study of these features is still in its infancy. Our work investigated and quantified the occurrence of NLP in the songs of a lemur species. Addressing this topic in other primate species will be extremely important in framing the occurrence of NLP among the Strepsirrhini sub-order and among singing primates. Further studies will deepen our understanding of the occurrence of NLP across singing primates and provide new insights into the ecological role of these signals, including the differences related to different types of NLP.

Acknowledgments

We thank the local research guides working at the Maromizaha Multipurpose Center and all the assistants helping during data collection. We are grateful to the Groupe d'Étude et des Recherche sur les Primates de Madagascar (GERP) for the support during the field activities. We also wish to thank Chester Zoo, Dr Cesare Avesani Zaborra, and Dr Caterina Spiezio for the support and financial assistance.

Ethical approval

The authors collected the data under the research permits issued each year by the Ministère de l’Environnement et du Développement Durable (MEDD) and Madagascar National Parks (formerly ANGAP): No. 118/ 10/ MEF/ SG/ DGF/ DCB.SAP/ SCBSE; No. 293/ 10/ MEF/ SG/ DGF/ DCB.SAP/ SCB, N°274/11/MEF/SG/DGF/DCB.SAP/SCB, N°245/12/MEF/SG/DGF/DCB.SAP/SCB, N°066/14/MEF/SG/DGF/DCB.SAP/SCB, N°180/15/MEEMF/SG/DGF/DAPT/SCBT, N°98/16/MEEMF/SG/DGF/DAPT/SCB.Re and N°217/16/MEEMF/SG/DGF/DSAP/SCB.Re, 73/17/MEEF/SG/DGF/DSAP/SCB.RE, 91/18/MEEF/SG/DGF/DSAP/SCB.Re, 118/19/MEDD/SG/DGEF/DSAP/DGRNE and 284/19/MEDD/SG/DGEF/DSAP/DGRNE, 338/19/MEDD/SG/DGEF/DSAP/DGRNE.

Competing interests

The Authors declare that they have no competing interests.

Authors’ contributions

Conceptualization: Walter Cristiano, Marco Gamba, Teresa Raimondi; Data curation: Walter Cristiano; Methodology: Marco Gamba, Walter Cristiano, Teresa Raimondi, Daria Valente; Formal analysis and investigation: Walter Cristiano, Teresa Raimondi; Visualisation: Teresa Raimondi, Walter Cristiano; Writing - original draft preparation: Walter Cristiano, Teresa Raimondi; Supervision: Marco Gamba, Cristina Giacoma. All authors reviewed and approved the last version of the manuscript.

Funding

Not applicable.

Availability of data and materials

Data used in this study are available on request to the corresponding author and will be loaded on github once the manuscript will be accepted for publication.

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No competing interests reported.

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Singing more, singing harsher: occurrence of nonlinear phenomena in a primate' song

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