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Abstract

Quantitative acoustic analysis has been used to decipher individual differences, population structure, and taxonomic diversity in numerous primate species. We previously described three distinct call types in wild Aotus nigriceps, and now assess acoustic differences in two of these call types between social groups and spatially distinct populations. Acoustic parameters for both analyzed call types exhibited significant variability between groups. Similarly, geographically distant field sites were acoustically distinct from one another. Several groups also used a variation of a common call: a triplet Ch Ch instead of a duplicate. Other groups made use of ultrasonic frequencies which have not previously been reported in Aotus. Our results suggest that Aotus nigriceps exhibits substantial acoustic variability across sites that could potentially be useful for taxonomic classification, although additional geographically distant populations still need to be sampled. The possibility of individual signatures also exists and will require recording vocalizations from known individuals.
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W. D. Helenbrook1,2, N. A. Linck3, M. A. Pardo4, and J. A. Suarez2
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Spatial variation in black-headed night monkey (Aotus nigriceps) vocalizations
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1State University of New York College of Environmental Science and Forestry (SUNY-ESF), 1 Forestry Drive,
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Syracuse, New York, United States; 2Tropical Conservation Fund, 760 Parkside Trl NW, Marietta, Georgia, USA;
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3University of Minnesota Twin-Cities, St. Paul, Minnesota, USA; 4Cornell University, 215 Tower Road, Ithaca,
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New York, USA
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Corresponding author: William Helenbrook, 760 Parkside Trl NW, Marietta, Georgia 30064
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wdhelenb@syr.edu, Phone 239-470-1200, Fax 315- 470-6934; ORCID ID: https://orcid.org/0000-0002-2706-3525
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Acknowledgements
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We would like to thank the Amazonian Conservation Association (ACA), Villa Carmen Biological Station, and
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Manu Learning Center (CREES) staff for hosting us, clearing trails, and providing valuable insight into location and
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behavior of groups. We are indebted to students and staff from the School for Field Studies who assisted with data
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collection and logistics. We carried out data collection in accordance with the legal requirements of Peru, and with
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permission of the Amazon Conservation Association and CREES.
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was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which. http://dx.doi.org/10.1101/688333doi: bioRxiv preprint first posted online Jul. 8, 2019;
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Abstract
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Quantitative acoustic analysis has been used to decipher individual differences, population structure, and taxonomic
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diversity in numerous primate species. We previously described three distinct call types in wild Aotus nigriceps, and
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now assess acoustic differences in two of these call types between social groups and spatially distinct populations.
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Acoustic parameters for both analyzed call types exhibited significant variability between groups. Similarly,
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geographically distant field sites were acoustically distinct from one another. Several groups also used a variation of
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a common call: a triplet Ch Ch instead of a duplicate. Other groups made use of ultrasonic frequencies which have
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not previously been reported in Aotus. Our results suggest that Aotus nigriceps exhibits substantial acoustic
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variability across sites that could potentially be useful for taxonomic classification, although additional
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geographically distant populations still need to be sampled. The possibility of individual signatures also exists and
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will require recording vocalizations from known individuals.
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Key words: vocalizations, Aotus nigriceps, night monkey, Peru, acoustics
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was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which. http://dx.doi.org/10.1101/688333doi: bioRxiv preprint first posted online Jul. 8, 2019;
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Introduction
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Primates use vocalizations to communicate about the presence of predators (Zuberbühler 2002; Schel et al.
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2009), location of food sources (Slocombe and Zuberbühler 2005), nesting behavior, travel intentions and group
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cohesion (Boinski 1996), territorial defense (Raemaekers and Raemaekers 1985; Cowlishaw, 1992), mate
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assessment and pair bonding (Cowlishaw 1996; Geissmann and Orgeldinger 2000). Primate acoustic signals may
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also be used for kin recognition and can convey information on age, sex, body size, and rank (Salmi and
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Hammerschmidt 2014). The diversity of these vocal signals makes acoustics a convenient tool to explore differences
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among primates at various taxonomic levels.
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Despite the importance of acoustic communication in primates, evidence of population level differences in
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primate vocalizations are relatively limited (Green 1975; Maeda and Masataka, 1987; Mitani et al., 1992; Fischer et
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al. 1998; Mitani et al. 1999; Delgrado 2007; Wich et al. 2008; de la Torre and Snowdon 2009; Wich et al. 2012).
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Acoustic variation between populations can exist for any of several reasons: divergence through cultural drift in
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species that learn their vocalizations (i.e. inaccurate copying transmitted vertically or horizontally), genetic drift
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following reproductive isolation, or local adaptation in response to sexual selection, habitat transmission properties,
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predation pressure, or social selection pressures (Yoktan et al. 2011).
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Most evidence suggests that non-human primates are not vocal learners; however, several recent studies
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have found that primates can learn slight modifications to their vocalizations (Watson et al. 2015; Takahashi et al.
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2017). Examining patterns of geographic variation in call structure can provide some evidence for or against the
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presence of vocal learning; for example, if there is a sharp acoustic divide between two spatially contiguous areas
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that show no evidence of genetic divergence (i.e. vocal dialects), this often suggests the presence of vocal learning.
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Spatial variation in vocal characteristics within a species could be a valuable tool for addressing a variety of
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ecological questions. For example, primate calls can be used to distinguish species, populations, groups, and
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individuals (Table 1). If individuals could be distinguished from one another solely using quantitative acoustic
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analysis, population size could be estimated by combining acoustic analysis and line transect surveys (Terry et al.
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2005; Marques et al. 2013; Kalan et al. 2015). Moreover, variation in specific call characteristics could be used to
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infer group membership, or be used for taxonomic classification, supplementing morphometric or genetic data.
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Night monkeys, Aotus spp., are a useful model for investigating patterns of acoustic variation because
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nocturnal and forest-dwelling species tend to rely heavily on vocalizations to communicate with one another. We
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was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which. http://dx.doi.org/10.1101/688333doi: bioRxiv preprint first posted online Jul. 8, 2019;
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have previously reported on the vocal repertoire of wild Aotus nigriceps, describing three calls: the Squeak, Ch Ch,
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and Long Trill (Helenbrook et al. 2018). In this study we focus on quantitatively comparing acoustic variation of
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two of these calls between groups and distant populations.
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Methods
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Eleven Aotus nigriceps groups were sampled (Fig.1): eight at the Villa Carmen Biological Station in
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Pilcopata, Peru (12°53'39"S, 71°24'16"W), and three at CREES - the Manu Learning Center, on the edge of Manu
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National Park (12°47′22″S 71°23′32″W). The two field sites are separated by a low mountain range (~1143m) and
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are just over 10 km apart at their nearest borders. Villa Carmen has a long history of development, ecotourism and
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agriculture. The groups sampled near the station lived in secondary forest, often dominated by bamboo or cane,
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whereas groups sampled at CREES inhabited recovering clear-cut to primary rainforest where bamboo and cane
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were largely absent. Research groups of 3-8 observers went into the field from 5:30-7:30am and 5:30-7:30pm for a
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total of 28 days at Villa Carmen and nine days at CREES to collect acoustic data, times when A. nigriceps groups
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are known to be active near their nesting sites. Several recordings also took place during the day as part of a separate
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behavioral study.
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A Zoom H1 Handy Recorder was coupled with a RØDE NTG-2 condenser shotgun microphone and shoe
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shockmount on a micro boompole at a distance varying from 2-25m. Digital recordings were made at 48 kHz
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sampling frequency with 16 or 24-bit amplitude resolution. Acoustic analysis was conducted using Raven Pro 1.5
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sound analysis software (Cornell Lab of Ornithology Bioacoustics Research Program, Ithaca, New York). Calls
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were digitized and measured spectrographically (DFT size 512, time resolution 3.1 ms, Hann window with 50%
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overlap). Twenty-four acoustic parameters were measured for each call (Table 2).
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Inter-group differences were analyzed using non-parametric Kruskal-Wallis tests coupled with post-hoc
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multiple comparisons of mean ranks tests with a Bonferroni correction. A Mann-Whitney U test was used to assess
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site differences between Villa Carmen and CREES. Stepwise discriminant function analysis was used to explore
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acoustic parameters that could be used to classify social groups. For model selection, a stepwise forward method
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was used (statistic, Wilk’s Lamda) with the criteria Fto enter=3.84 and Fto remove=2.71, and a tolerance level of <0.01
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(STATISTICA). This process was repeated for both call types separately. Variables that failed a tolerance test where
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there was an almost exact linear relationship with other variables, did not enter the analysis. We used a 10-fold cross
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validation in which 90% of the calls were randomly chosen to calculate discriminant functions, while 10% was
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excluded for testing. Differences between observed and expected frequencies of duplicate versus triplicate Ch Ch
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calls was measured using Fisher’s Exact Test. All recordings were conducted non-invasively, minimized impact on
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behavior, and avoided excessive disturbance, and were therefore deemed exempt from the Institutional Animal Care
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and Use Committee approval. All applicable international, national, and/or institutional guidelines for the care and
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use of animals were followed.
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Results
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Three vocalizations have been described in wild Aotus nigriceps populations: Squeak, Ch Ch, and Trill
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(Helenbrook et al. 2018). In this study, we analyzed acoustic variability for the two most common calls, the Squeak
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(N=1302) and the Ch Ch (N=556; Fig. 2). For Squeaks we only measured the dominant harmonic since it was
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consistently found across all sampled groups. The Trill was not used because of its rarity across most groups. At
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least ten calls were analzyed from each of seven night monkey groups, ranging in size from 2-5 individuals
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(Mean=3.7). The dominant harmonic of the Squeak ranged from a mean minimum frequency of 1591 Hz (Range:
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74-3055 Hz; SD 470) to a mean maximum frequency of 2742 Hz (Range: 2010-4443; SD 252). The Ch Ch call
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ranged from a mean minimum frequency of 1698 Hz (Range: 44-9092; SD 1182) to a mean maximum frequency of
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11636 Hz (Range: 3109-23726 Hz; SD 2538).
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Acoustic measurements varied significantly between groups (Fig. 3; Table 3). Differentiation between two
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or more monkey groups was found for all forty-eight independent vocal characteristics (2 call types x 24
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measurements, p<0.001). Discriminant function analysis distinguished among groups for both call types: Squeak
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(Wilks' Lambda=0.06, F(60,6706)=81.98, p<0.0000) and Ch Ch (Wilks' Lambda=0.06, F(48,2592)=42.58,
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p<0.0000) (Table 4 and Fig. 4). Cumulative significant functions were able to explain 87.4% of variance among
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groups using only Squeak calls, and 87.8% of the variance among groups using only Ch Ch calls. Classification
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accuracy was similar for both the Squeak (87.4%) and the Ch Ch (76.4%). Duration (90%) and energy parameters
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for Squeak and Ch Ch, respectively, provided the greatest discriminatory power at the group level (Table 4).
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Twelve acoustic parameters were significantly different between Villa Carmen and CREES biological field
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stations (Table 6). Discriminant function analysis identified seven Squeak parameters that significantly distinguished
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locations: low and high frequency, bandwidth, duration (90%), delta time, IQR duration, and max time the first of
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which contributed the greatest discriminatory power; and eight Ch Ch parameters significantly distinguished
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locations: low and high frequencies, bandwidth, energy, peak frequency, Q1 frequency, frequency (5%) and center
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time the last of which contributed the greatest discriminatory power. The two sites were found to be significantly
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different based on Squeak (Wilks’ Lambda=0.77, F(13,1281)=29.09; p<0.0000) and Ch Ch (Wilks’ Lambda=0.40,
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F(13,526)=61.77; p<0.0000). Classification accuracy was 93.8% for Squeak (5 out of 13 CREES measurements and
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116 out of 116 at Villa Carmen), and 100.0% for Ch Ch. Cumulative significant functions were able to account for
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47.7% of variance between locations using the Squeak, and 77.7% using the Ch Ch call.
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Other differences were observed between groups as well. The Ch Ch was predominately found in a series
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of two (“in duplicate) (88.3% of cases); however, four groups also produced calls in triplicate (i.e. Ch Ch Ch). Out
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of 556 total Ch Ch calls, 65 were in triplicate (11.7%), with 2.8% in T2A, 1.3% in A, 50.4% in B, and 2.8% in E.
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The distribution of triplicate calls across groups differed significantly from even distribution across groups, with
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Group B exhibiting nearly four times as many triplicates as expected (p=0.0000). In addition, two groups were
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observed using ultrasonic frequencies as part of the Ch Ch call (>20kHz): group C (N=3) at Villa Carmen and T2A
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(N=1) at CREES.
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Discussion
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The majority of acoustic parameters for both calls differed significantly between groups and geographic
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locations, though single acoustic parameters alone were not sufficient to predict group membership. Variance of
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acoustic parameters overlapped in nearby groups, making absolute classification difficult. However, there was a
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consistent pattern whereby calls from the same groups and population tended to cluster together based on similar
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acoustic measurements. Population level classification was more accurate, largely driven by acoustic parameters of
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the Ch Ch call. Quantitative analysis of acoustic traits may therefore be useful in elucidating group and population
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level differences and may provide useful insight into the underlying phylogenetic relationships between groups,
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populations and potentially species of Aotus. However, additional recordings are needed both at the group and
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population levels, preferably with more distant populations included.
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We were unable to investigate individual acoustic variability because of our inability to pair calls to
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specific individuals in a complex environment at night. Based on various other primate studies it is likely that
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individuals can be differentiated based on vocal signatures (Table 1). However, confirmation of vocal individuality
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will require either analysis in captivity or pairing video and audio recordings in wild nesting groups. If recordings
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can be attributed to specific individuals, then acoustic analysis could be used to establish whether individual
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conspecifics vary predictably in their vocalizations. Establishing the ability to vocally differentiate individuals
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would be particularly useful for a nocturnal species such as the black-headed night monkey, allowing researchers to
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study group composition solely based on vocal recordings.
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Aside from differences in acoustic parameters, two other acoustic differences were discovered among
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groups. First, a triplet Ch Ch call was found in recordings from groups T2A, A, B, E. Though relatively rare within
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the sampled populations (11.7% of cases), over half of these cases were found in Group B. The other groups at Villa
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Carmen that used the triplet call are likely of the same population since they are isolated on all but one side and in
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relative proximity to group B (<1300m at furthest extent). The prevalence of the triplet call in Group B suggests that
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this is not an aberration but rather a consistent modification of a common call. The fact that the triplet call only
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occurred in certain groups could reflect any number of possibilities including increased prevalence of a particular
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behavioral context, or a vocal innovation (genetic or learned). Alternatively, it is possible that the presence of both
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duplicate and triplet Ch Ch calls is the ancestral state and the absence of the triplet call is derived. Either way,
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additional sampling of nearby groups coupled with underlying population genetics analysis - would confirm
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whether this is a relatively unique acoustic irregularity which is independent of underlying population structure, or
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whether this call variation routinely arises and is widespread. Likewise, being able to obtain calls specific to
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individuals through video and audio pairing in nests would allow us to decipher whether all individuals within a
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particular group use the triplet call.
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It is uncertain whether the use of ultrasonic frequencies in night monkeys is rare or whether this is a
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common response to environmental pressures such as inter-species competition for lower frequencies or predator
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avoidance. Of course, other nocturnal primates (i.e., Tarsius, Galago, Microcebus, Nycticebus) and some diurnal
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neotropical primates (i.e., Callithrix and Cebuella) produce calls containing ultrasonic frequencies, though only the
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tarsiers produce calls entirely within the ultrasonic range, with the other species always producing dominant
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frequencies in the human audible range (Ramsier et al. 2012). In several species, the use of ultrasound appears to be
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context specific, often in the presence of predators, including humans (e.g. Rahlfs and Fichtel 2010; Gursky-Doyen
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2013).
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Aotus currently consists of eleven described species based on both phenotypic and genotypic evidence.
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Night monkey taxonomy has been revised considerably based on differences in karyotypes, morphology, molecular
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sequencing, malaria sensitivity, immunological responses, and geographic isolation (Menezes et al. 2010). Despite
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this, few specimens from any one study have come from Aotus nigriceps despite this species having one of the
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largest ranges of any Aotus species. Moreover, the current taxonomic classification lumps A. nigriceps populations
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from areas with considerably different elevations and from areas separated by significant river systems. Thus, the
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possibility remains that further evolutionary and conservation management units may exist. Considering the distinct
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differences in call types previously described between Aotus species and the use of quantitative acoustic sampling to
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differentiate many other primate species, we anticipate that further analysis would prove useful in differentiating
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population-level or species-level taxonomy.
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Finally, Aotus nigriceps likely produce more than the three described call types since captive Aotus species
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have exhibited larger vocal repertoires. In captive situations it is easier to record night monkeys at close distances
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and calls can be induced in different situations, which could facilitate observation of a wider variety of call types.
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We anticipate that with continued sampling these additional call types could also be recorded in the wild.
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Conflict of Interest
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The authors declare that they have no conflict of interest.
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African lesser bushbabies and their implications for taxonomic relationships. Folia Primatologica 51:87-105
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Zimmermann E (1990) Differentiation of vocalizations in bushbabies (Galaginae, Prosimiae, Primates) and the
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significance for assessing phylogenetic relationships. Journal of Zoological Systematics and Evolutionary
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Research 28:217-239. https://doi.org/10.1111/j.1439-0469.1990.tb00377.x
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Zuberbühler K (2002) A syntactic rule in forest monkey communication. Animal Behaviour 63:293-299.
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https://doi.org/10.1006/anbe.2001.1914
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12
Fig. 1 Field research conducted at Villa Carmen Biological Station (eight black-headed night monkey groups) and
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CREES Manu Learning Centre (three groups), in southeastern Peru. A mountain ridge separates the two field
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stations (10.1km at nearest points)
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Fig. 2 Spectrogram representing both calls quantitatively analyzed in this study: the Squeak (left) and the Ch Ch
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(right). Both calls have been previously described (Helenbrook et al. 2018). In this example, a duplicate Ch Ch is
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depicted. The upper range of the spectrogram is faint, partially a result of minimizing backgound noise in the 7-8
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kHz from insects
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13
Fig 3 Boxplot plate of four descriptive acoustic measurements (e.g., duration, maximum frequency, minimum
317
frequency, and bandwidth) for the Squeak and Ch Ch across all groups. Median represented by solid box, box plot is
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25-75% of call variability, and whiskers are minimum and maximum which do not signify significance, rather
319
distribution of values is depicted. Note that inter quartile ranges are depicted instead of standard error or deviation
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because of the non-parametric nature. T2A, TSH, and T9A groups are from CREES while A-H are from Villa
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Carmen. Significant differences illustrated in Table 3
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14
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Fig 4 Canonical score scatterplot. The biplot axes are the first two canonical variables. These define the two
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dimensions that provide maximum separation among groups. We used a 10-fold cross validation in which 90% of
327
the calls were randomly chosen to calculate discriminant functions. Here we present the results of the 10% excluded
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for testing
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was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
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15
Table 1 Evidence of primate acoustic variation at various taxonomic levels.
337
Primate species (N)
Group
Population
Subspecies
Species
Call
description
Study
Cebidae,
Callitrichinae (28)
-
-
-
Yes
Long call
Garbino 2018
Galago spp. (8)
-
-
-
Yes
Loud call
Zimmermann
1990
Galago alleni
-
Yes
Loud call
Ambrose 2003
Galago
crassicaudatus, G.
garnettii
-
-
-
Yes
Loud call
Masters 1991
Galago
senegalensis, G.
moholi
-
-
-
Yes
14 calls
Zimmermann
1988
Gorilla gorilla
-
-
-
-
8 calls
Salmi et al.
2014
Hylobates muelleri
-
No
-
-
Great call
Clink et al. 2017
Hylobates muelleri
-
Yes
-
-
Great call
Clink et al. 2018
Lepilemur spp. (10)
-
Yes
-
-
Loud call
Méndez-
Cárdenas et al.
2008
Microcebus spp. (3)
-
-
-
Yes
Whistle, Purr,
Chitter
Hending et al.
2017
Microcebus spp. (3)
-
-
-
Yes
Advertisement
call
Braune et al.
2008
Microcebus
murinus
-
Yes
-
-
Trill
Hafen et al.
1998
Tarsius spp. (4)
-
-
-
Yes
Loud call
(Duet call)
Nietsch 1999
Varecia variegata
-
-
Yes
-
Loud call
Macedonia and
Taylor 1985
338
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was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
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16
Table 2 Name and description of acoustic parameters measured. Not all parameters were included in discriminant
339
function analysis because of redundancy.
340
Parameters
Description
Low frequency (Hz) The lower frequency bound of the selection
341
High frequency (Hz) The upper frequency bound of the selection
342
Bandwidth 90% The difference between the 5% and 95% frequencies
343
Energy (dB) The total energy within the selection bounds
344
Dur90% The difference between 5% and 95% times
345
Delta frequency (Hz) The difference between the upper and lower frequency limits of the selection
346
Peak frequency The frequency at which max power occurs within the selection
347
Delta time (s) The difference between the begin and end time for the selection
348
Center frequency (Hz) The frequency that divides the selection into two frequency intervals of equal energy
349
Q1 frequency (Hz) The frequency that divides the selection into two frequency intervals containing 25% and
350
75% of the energy in the selection
351
Q3 frequency (Hz) The frequency that divides the selection into two frequency intervals containing 75% and
352
25% of the energy in the selection
353
Max power The maximum power in the selection.
354
Frequency 5% The frequency that divides the selection into two frequency intervals containing 5% and
355
95% of the energy in the selection
356
Frequency 95% The frequency that divides the selection into two frequency intervals containing 95% and
357
5% of the energy in the selection
358
Center time The point in time at which the selection is divided into two time intervals of equal energy
359
Q1 time The point in time that divides the selection into two time intervals containing 25% and
360
75% of the energy in the selection
361
Q3 time The point in time that divides the selection into two time intervals containing 75% and
362
25% of the energy in the selection
363
IQR duration (s) The difference between the 1st and 3rd quartile times
364
Time 5% The point in time that divides the selection into two time intervals containing 5% and 95%
365
of the energy in the selection
366
Time 95% The point in time that divides the selection into two time intervals containing 95% and 5%
367
of the energy in the selection
368
Max time The first time in the selection at which a spectrogram point with power equal to max
369
power occurs
370
371
Quantitative acoustic characteristics analyzed (Raven).
372
373
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17
Table 3 All Aotus nigriceps vocal measurements were found to have significant (p<0.001) inter-group differences.
374
Post-hoc analysis was conducted using multiple comparisons of mean ranks with Bonferroni adjustment. All
375
associations significant at p<0.05.
376
377
Call Measurement Group Significant Associations (p value)
378
Ch Ch
379
Dur90% A C(0.00); D(0.04)
380
B C(0.00); D(0.01)
381
C E(0.00); F(0.01); T2A(0.00)
382
High frequency A C(0.00); D(0.02); T2A(0.00)
383
B C(0.00); D(0.04); T2A(0.00)
384
C E(0.00); F(0.00); H(0.01)
385
D E(0.01); F(0.01); T9A(0.05)
386
E T2A(0.00)
387
F T2A(0.00)
388
Low frequency A B(0.00); D(0.03); T2A(0.00)
389
B C(0.00); D(0.00); E(0.00); T2A(0.00)
390
C T2A(0.02)
391
Bandwidth 90% A B(0.00); E(0.00)
392
B C(0.00); E(0.00); F(0.00); H(0.00); T2A(0.00); TSH(0.00)
393
E T2A(0.00)
394
TSH T9A(0.04)
395
396
Squeak
397
Dur90% A B(0.00); E(0.01); TSH(0.00)
398
B C(0.02); E(0.00); T2A(0.00); TSH(0.00)
399
C E(0.00); TSH(0.00)
400
E H(0.00); T2A(0.00); TSH(0.00); T9A(0.05)
401
H TSH(0.00)
402
T2A TSH(0.00)
403
High frequency A C(0.03); E(0.00); H(0.00); TSH(0.02)
404
B C(0.00); E(0.00); H(0.00); TSH(0.00)
405
C E(0.00); H(0.00); T2A (0.00)
406
D TSH(0.04)
407
E H(0.00); T2A(0.00); TSH(0.00)
408
H T2A(0.00); TSH(0.00)
409
Low frequency A B(0.00); E(0.00)
410
B C(0.00); D(0.01); E(0.00); H(0.00); T2A(0.00); TSH(0.00); T9A(0.00)
411
C T2A(0.00)
412
E T2A(0.00)
413
H T2A(0.00)
414
Bandwidth 90% A B(0.00); E(0.00); H(0.00); TSH(0.01)
415
B C(0.00); E(0.00); H(0.00); T2A(0.00)
416
C E(0.00); H(0.00); T2A(0.00)
417
E T2A(0.00); TSH(0.00)
418
H T2A(0.01); TSH(0.00)
419
420
421
422
423
424
425
426
427
428
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18
Table 4 Stepwise discriminant function analysis of all non-redundant measured acoustic parameters.
429
430
Call Measurement Wilks’ Lambda Partial Lambda p value
431
Squeak
432
Bandwidth 90% 0.07 0.85 0.00
433
Energy 0.06 0.92 0.00
434
Dur90% 0.07 0.82 0.00
435
Frequency 95% 0.06 0.88 0.00
436
Max power 0.06 0.89 0.00
437
IQR duration 0.06 0.96 0.00
438
Center frequency 0.06 0.98 0.00
439
Q1 frequency 0.06 0.93 0.00
440
IQR bandwidth 0.06 0.90 0.00
441
Max time 0.06 0.88 0.00
442
443
Ch Ch
444
Energy 0.17 0.33 0.00
445
Dur90% 0.06 0.92 0.00
446
Bandwidth 90% 0.05 0.94 0.00
447
IQR duration 0.06 0.96 0.00
448
Frequency 95% 0.06 0.89 0.00
449
Center frequency 0.09 0.65 0.00
450
Delta frequency 0.07 0.77 0.00
451
Q1 frequency 0.07 0.79 0.00
452
453
454
455
456
Table 5 Discriminant function analysis correct classification utilizing 10-fold cross validation at group level.
457
458
Squeak alone
459
Group
% Correct
A
B
C
E
H
T2A
TSH
A
66.7
6
0
3
0
0
0
0
B
75.0
4
12
0
0
0
0
0
C
78.6
0
2
11
1
0
0
0
E
89.9
4
1
0
62
1
1
0
H
75.0
0
0
0
2
6
0
0
T2A
44.4
3
0
0
1
0
4
1
TSH
75.0
0
0
1
0
0
0
3
Ch Ch alone
460
Group
% Correct
A
B
C
E
F
H
T2A
A
100
7
0
0
0
0
0
0
B
83.3
2
10
0
0
0
0
0
C
83.3
0
1
5
0
0
0
0
E
66.7
3
1
0
10
0
0
1
F
0
1
1
0
0
0
0
0
H
0
0
0
0
1
0
0
1
T2A
90.9
0
0
0
1
0
0
10
461
462
463
464
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19
Table 6 Mann Whitney results comparing group acoustic parameters between distant locations (~10km). Sample
465
size (Ch Ch: CREES 108 and VC 441) and (Squeak: CREES 136 and VC 1150). *All associations significant at
466
p<0.05.
467
468
Call Measurement CREES Mean VC Mean U P-value
469
Squeak
470
Low Frequency 1896 1557 41532 0.00*
471
High Frequency 2905 2715 48217 0.00*
472
Bandwidth 90% 737 633 57946 0.00*
473
Energy (dB) 75 89 51471 0.00*
474
Dur90% 0.036 0.28 50231 0.00*
475
Peak Frequency 2577 2395 47752 0.00*
476
Delta Time 0.049 0.041 51588 0.00*
477
Center Frequency 2525 2370 40555 0.00*
478
Q1 Frequency 2341 2242 56039 0.00*
479
Q3 Frequency 2687 2477 37523 0.00*
480
Frequency 5% 2079 1951 71783 0.11
481
IQR Duration 0.02 0.015 51031 0.00*
482
Max Time 99 222 47045 0.00*
483
484
Ch Ch
485
Low Frequency 2504 1497 12490 0.00*
486
High Frequency 12901 11268 13405 0.00*
487
Bandwidth 90% 4991 4263 16333 0.00*
488
Energy (dB) 112 88 11554 0.00*
489
Dur90% 0.03 0.03 23617 0.89
490
Peak Frequency 5904 5596 20661 0.03*
491
Delta Time 0.04 0.04 23239 0.70
492
Center Frequency 5703 5541 23653 0.91
493
Q1 Frequency 4585 4740 17581 0.00*
494
Q3 Frequency 6810 6235 18448 0.00*
495
Center Time 376 98 8585 0.00*
496
IQR Duration (s) 0.02 0.02 23220 0.68
497
Frequency 5% 3255 3215 20142 0.01*
498
499
500
501
502
503
504
505
506
507
508
509
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