Hylobates Muelleri ) Female Calls

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Hylobates Muelleri ) Female Calls Erschienen in: The Journal of the Acoustical Society of America ; 144 (2018), 2. - S. 698-708 https://dx.doi.org/10.1121/1.5049578 Understanding sources of variance and correlation among features of Bornean gibbon ( Hylobates muelleri ) female calls Dena J. Clink,1, a) Mark N. Grote,1 Margaret C. Crofoot,1, b) and Andrew J. Marshall2 1Department of Anthropology, University of California, Davis, One Shields Avenue, Davis, California 95616, USA 2Department of Anthropology, Program in the Environment, Department of Ecology and Evolutionary Biology, and School for Environment and Sustainability, University of Michigan, 101 West Hall, 1085 South University Avenue, Ann Arbor, Michigan 48109, USA Acoustic signals serve important functions in mate choice, resource defense, and species recogni- tion. Quantifying patterns and sources of variation in acoustic signals can advance understanding of the evolutionary processes that shape behavioral diversity more broadly. Animal vocalization data- sets are inherently multivariate and hierarchical, wherein multiple features are estimated from calls of many individuals across different recording locations. Patterns of variation within different hier- archical levels—notwithstanding the challenges they present for modeling and inference—can pro- vide insight into processes shaping vocal variation. The current work presents a multivariate, variance components model to investigate three levels of variance (within-female, between-female, and between-site) in Bornean gibbon calls. For six of the eight features estimated from call spectro- grams, between-female variance was the most important contributor to total variance. For one fea- ture, trill rate, there were site-level differences, which may be related to geographic isolation of certain gibbon populations. There was also a negative relationship between trill rate and duration of the introduction, suggesting trade-offs in the production of gibbon calls. Given substantial inter- individual variation in gibbon calls, it seems likely that there has been selection to confer informa- tion regarding caller identity, but mechanisms leading to site-level variation in trill rate remain to be determined. I. INTRODUCTION Sun et al. , 2013 ), and mice (e.g., singing mice, Scotinomys teguina , Campbell et al. , 2010 ). In non-human primates, evi- Quantifying the sources of variation in animal vocaliza- dence for geographic variation in vocalizations has been tions is of broad interest to ecologists and evolutionary biol- found in tarsiers ( Tarsius tarsier; Burton and Nietsch, 2010 ), ogists, due to the important role vocalizations play in a pygmy marmosets ( Cebuella pygmaea ; de la Torre and diverse set of biological and behavioral processes such as Snowdon, 2009 ), Thomas langurs ( Presbytis thomasi; Wich mate choice, resource defense, and species recognition et al. , 2008 ), gibbons ( Hylobates spp.; Dallmann and (Wilkins et al. , 2012 ). Animal vocal communication systems Geissmann, 2009 ; Heller et al. , 2010 ), orangutans ( Pongo are ideal for testing hypotheses related to the evolution and spp.; Delgado, 2007 ), and chimpanzees ( Pan troglodytes; maintenance of behavioral diversity, as acoustic data are Mitani et al. , 1999 ). Differences in ecology may lead to sub- often easier to collect that other types of behavioral data. stantial variation in acoustic parameters between geographi- Like other types of behavioral data, acoustic signals may cally distinct populations (the acoustic adaptation hypothesis; vary across sites or populations, individuals may exhibit dis- Morton, 1975 ), but such patterns can also arise through the tinct call features, and signals may vary across different tem- accumulation of random changes in call structure over time poral scales or in response to different social conditions. (i.e., behavioral drift, Yurk et al. , 2002 ). Directional and ran- Understanding how call features vary across sites, individu- dom evolutionary processes need not be mutually exclusive. It als and contexts is a crucial first step for testing hypotheses is reasonable to predict that certain aspects of calls, such as related to the evolution of call diversity. Geographic variation in vocalizations has been docu- the spectral properties or note repetition rate, will be influ- mented across a wide range of taxa including birds (greenish enced by the environment ( Boncoraglio and Saino, 2007 ). For warblers, Phylloscopus trochiloides, Irwin et al. , 2008 ; example, forest habitats have greater reverberation than open Amazon parrots, Amazona auropalliata , Wright et al. , 2001 ), habitats, and higher frequencies attenuate more quickly in for- bats (greater horseshoe bats, Rhinolophus ferrumequinum, ested environments ( Marten and Marler, 1977 ), which may select for calls with fewer repeated elements and lower fre- quencies. Other aspects of the calls, such as note duration or sequential order of call types, may be more influenced by drift a) Also at: Bioacoustics Research Program, Cornell Laboratory of (e.g., Slabbekoorn and Smith, 2002 ). Ornithology, Cornell University, Ithaca, NY 14850, USA. Electronic mail: [email protected] In addition to population-level variation in call struc- b) Also at: Smithsonian Tropical Research Institute, Balboa, Ancon, Panama. ture, substantial between-individual variation is expected 698 Konstanzer Online-Publikations-System (KOPS) URL: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-194szlsqem7k93 when calls transmit information regarding individual identity and Schilling, 1984; Geissmann, 1984) and do not require a (Tibbetts and Dale, 2007). Vocal individuality has been substantial learning component—but see Koda et al. documented in a variety of nonhuman primates, including (2013)—which means that they may be more appropriate mouse lemurs ( Microcebus murinus ; Leliveld et al. , 2011 ), models for understanding the evolutionary sources of varia- ring-tailed lemurs (Lemur catta; Macedonia, 1986), tarsiers tion in vocal behavior, relative to vocal learners such as (Tarsius syrichta; Reh akov a-Petr u˚ et al. , 2012 ), squirrel songbirds. Similar to most studies of variation in animal monkeys ( Saimiri sciureus; Symmes et al. , 1979 ), baboons vocalizations, our data exhibited a multi-level, multivariate (Papio cynocephalus; Fischer et al. , 2002 ; Semple, 2001), structure because we estimated multiple features from each Japanese macaques ( Macaca fuscata; Ceugniet and Izumi, call and analyzed replicate calls from multiple females at 2004), gibbons ( Hylobates spp.; Haimoff and Gittins, 1985; multiple sites. To understand how variance in call features Haimoff and Tilson, 1985; Dallmann and Geissmann, 2009; was partitioned at each level, as well as to investigate the Terleph et al. , 2015 ; Clink et al. , 2017a) , orangutans ( Pongo covariance among features, we developed a Bayesian multi- pygmaeus; Lameira and Wich, 2008), gorillas ( Gorilla variate, variance components model. We estimated intraclass gorilla; Salmi et al. , 2014 ), and chimpanzees ( Pan troglo- correlation coefficients for eight features based on posterior dytes; Mitani et al. , 1996 ). Indeed, most vocal animals that samples generated by a Hamiltonian Markov-chain Monte have been examined exhibit vocal individuality, and there is Carlo method, allowing us to make inferences regarding var- evidence via playback experiments that primates can distin- iance partitioning at each level ( Merlo et al. , 2005 ). We also guish between individuals solely on the basis of their vocal- estimated correlation matrices for features at each of our izations (Cheney and Seyfarth, 1980 , 1999; Rendall et al. , three levels of analysis, which provide insight into possible 1996). Importantly, strong patterns of variation at the tradeoffs inherent to gibbon call production. The benefits of individual-level do not preclude variation at other levels, our approach include the ability to estimate variance, along such as between-population or between-site variation with uncertainty, of each call feature for each level of analy- (Tibbetts and Dale, 2007). sis, while simultaneously investigating correlations among Studies of animal vocalizations generally result in data call features across each level. with a hierarchical structure, wherein there are replicate calls from multiple individuals, often from multiple populations II. MATERIALS AND METHODS or sites (Mundry and Sommer, 2007 ). This produces nested clusters of calls that, despite being statistically non- A. Study subjects and data collection independent, can reveal important sources of variation D.J.C. collected recordings from unhabituated gibbons (Merlo et al. , 2005 ). Pairs of features (such as note duration at seven sites across Sabah, Malaysia during multiple field and frequency) within a call may also exhibit non- seasons from January 2013 to September 2016 (map shown independence, and patterns of variation and co-variation in Fig. 1; site information listed in Table I). We recorded within and among clusters may hint at underlying ecological vocalizations at a sampling rate of 44.1 kHz and 16-bit size and evolutionary processes. While vocalizations are gener- using a Marantz PMD 660 flash recorder (Marantz, ally considered to be more plastic than many other pheno- Kawasaki, Kanagawa Prefecture, Japan) equipped with a typic traits, they are still constrained by genetic variation Røde NTG-2 directional condenser microphone (Røde and mechanical limits of sound production ( Podos et al. , Microphones, Sydney, Australia).
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