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Intra-specific variation in Bornean agile gibbon

(Hylobates agilis albibarbis) vocalisation.

By: Anthony Stephen Lusher, BSc

Submitted in part fulfilment of the University of Roehampton Degree MRes Primatology

University of Roehampton

© University of Surrey Roehampton 2006

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Abstract:

This study investigated whether or not female Bornean agile gibbons (Hylobates agilis albibarbis) in Sebangau National Park, Central Kalimantan, Indonesia displayed individuality in their vocalisation. It was also intended to investigate whether or not physical distance between individuals had an effect on the levels of this variation. Neither study had been carried out in a mixed peat swamp forest previously and no previous study had investigated the effects of distance apart on individuality of vocalisation. It was found that the female gibbons study did display significant levels of individuality in their vocalisations. However, no link between physical distance apart between individuals and the levels of variation in their vocalisation was found. This supports the previously reported theory that gibbon vocalisations are genetically inherited, and that no external factor is driving this degree of variation.

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Acknowledgments:

Firstly, I would like to thank all the staff at Roehampton University who made this project possible. To my project supervisor, Dr. Caroline Ross, many thanks. Your patience and assistance was invaluable, as was your proof reading. Much thanks also goes out to Dr. Stuart Semple who knowledge of primate communication was of great help in getting this project off the ground. A big thank you to Dr. Peter Shaw, whose assistance with statistics was excellent. Of course many thanks also to Dr. Anne MacLarnon who gave this study the green light. A massive thank you to Dr. Susan Cheyne. Without your help on the ground at Sebangau National Park my study could have sunk on more than one occasion. For teaching me all you knew of gibbon singing, I can never thank you enough. Also, for providing me with an excellent microphone when my one died in the jungle I will forever be thankful, for my study would surely ground to a halt. Many thanks to all the staff at OUTROP who accepted by proposal to conduct a study on the female gibbons at Sebangau National Park. Especially to Laura D’Arcy, with whom I was in contact prior to my study beginning. Many thanks to Mr. Suwedo Limin, director of CIMTROP, who maintain the study grid at Sebangau National Park. Without your sponsorship I would never have been allowed to conduct my study at my chosen site. To all my field assistants, there are not enough thanks in this world! At various time I had a different field assistant, in some cases for only one day, but your assistance was massive. To Claire Thompson, thank you, thank you, thank you. Your knowledge of the groups and singing was unbelievable, and for helping fill in the gaps of ‘silence’ with interesting conversation. To Ellie Monks, many thanks, and a big thanks for getting me out of the jungle alive! My Dayak field assistance were of great help too. To Hendri Sebangau Sagara, thanks. Thanks for teaching me the ways of the jungle, thanks for teaching me to speak Indonesian, thanks for being my jungle guru. Big thanks also to Yuhdi. Massive thanks to Otto and Santiano who kept me safe as we camped out deep in the jungle by ourselves. Thanks to Ibu Yanti who fed me with aplomb for three months and taught me an appreciation of Indonesian food, seafood and beans. A big thanks also for keeping me in clean clothes during my time at camp. Huge thanks to my family and friends, especially my mother and father. Without your texts and emails to receive from time to time I may well have lost my sanity in the wilds of Borneo, never to return…. ii

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And finally, and by no means least, a big thank you to Jemmalou Tissay Edmonds. Without your patience, understanding and support I would never realised my ambition to do this kind of field research in the deepest darkest. Your patience to let me run off traipsing through foreign jungles for three months will never be forgotten. Your daily text messages and weekly phone calls made the whole trip worthwhile. All my thanks from the bottom of my heart for eternity. iii

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Table of Contents Page Abstract i Acknowledgements ii Table of Contents iii - v List of Table vi List of Figures vii - ix 1. Introduction 1-29 1.1 Taxonomy and Ecology 2 1.2 Behaviour and Social Ecology 7 1.3 Vocalisations 10 1.4 Previous Individuality of Vocalisation Studies 24 1.5 Aims of Study 28 2. Methods 30-50 2.1 Study Site 31 2.1.1 Sebangau National Park 31 2.1.2 Setia Alam Field Station 35 2.2 Study Period, Study Species and Individuals 37 2.3 Data Collection Methods and Materials 41 2.4 Song Analysis 43 2.4.1 Principle Components Analysis (PCA) 48 2.4.2 Kruskal-Wallis Test 48 2.4.3 Effects of Distance on Individuality 49 3. Results 51-96 3.1 Principle Components Analysis (PCA) 56 3.1.1 PCA showing which characteristics account for most variation 56 in song individuality compared between the different female Bornean agile gibbons (H. agilis albibarbis) 3.1.2 PCA identifying degree of individuality of vocalisation for 67 individual female Bornean agile gibbons (H. agilis albibarbis) 3.2 Kruskal-Wallis 83 3.5 Effect of Physical Distance Apart of Individuals on Levels of 91 Individuality of Vocalisation 3.5.1 PCA vs Physical Distance Apart 93 3.5.2 PCA vs Euclidean Distance Apart 95 iv

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4. Discussion 97-112 4.1 Principle Components Analysis (PCA) 98 4.1.1 PCA showing which characteristics account for most variation 98 in song individuality compared between the different female Bornean agile gibbons (H. agilis albibarbis) 4.1.2 PCA identifying degree of individuality of vocalisation for 103 individual female Bornean agile gibbons (H. agilis albibarbis) 4.2 Use of Kruskal-Wallis to test for individuality of female Bornean 105 agile gibbon (H. agilis albibarbis) vocalisation 4.3 Effect of Physical Distance Apart of Individuals on Levels of 107 Individuality of Vocalisation 4.4 Suggestions for Further Studies 112 References 113-123 Appendices 124-168

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List of Tables Page

Table 1.1 Previous gibbon classification 4 Table 1.2 Hylobatid Classification 5 Table 1.3 Home ranges of different gibbon species 8 Table 2.1 Sampled Female Individuals 38

Table 2.2: 14 Song Characteristics Studied 45

Table 3.1 Showing number of analysed great calls across the 52 studied individual females. Table 3.2 Showing PCA output with total variance explained after 58 PCA reduction had been performed Table 3.3 Normal extraction method component matrix showing 63 original characteristics and their relation to PCA components. Table 3.4 Varimax rotation extraction method component matrix 64 showing original characteristics and their relation to PCA components. Table 3.5 Song Characteristics, in order of most significance, 65 explaining variation in female vocalisation Table 4.1 Normal PCA and Varimax Rotated PCA ranks of song 99 characteristics’ significance to individuality of vocalisation

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List of Figures Page

Figure 1.1 Map showing distribution of gibbons throughout 3 South East Asia Figure 1.2 Table showing gibbons place amongst the 3 taxonomy of primates Figure 1.3 Sonograms of some different primate species. 11 Figure 1.5 Sonograms of different female gibbon species great calls. 16 Figure 1.6 Phases of the female agile gibbon great call. 21 Figure 2.1 Map showing location of Setia Alam Field Station, 32 as well as the Sebangau Catchment Area. Figure 2.2 Map of study grid, also showing known groups on grid. 36

Figure 2.3 Typical Female Agile Gibbon Great Call 44 (excluding Pre-Introductory Phase). Figure 3.1 Sonogram of female individual FG5 on date 16/05/06 54

Figure 3.2 Sonogram of female great call showing juvenile female’s 55 attempt to mimic, copy or imitate her mother’s song structure. Figure 3.3 Scatterplot showing levels of individuality of each 68 female’s analysed songs Figure 3.4 Graph showing first two significant PCA components 69 displaying level of individuality of vocalisation for Group 2 Female Figure 3.5 Graph showing first two significant PCA components 70 displaying level of individuality of vocalisation for Group 3 Female Figure 3.6 Graph showing first two significant PCA components 71 displaying level of individuality of vocalisation for Group 4 Female Figure 3.7 Graph showing first two significant PCA components 72 displaying level of individuality of vocalisation for Group 5 Female Figure 3.8 Graph showing first two significant PCA components 73 displaying level of individuality of vocalisation for Group 6 Female vii

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Figure 3.9 Graph showing first two significant PCA components 74 displaying level of individuality of vocalisation for Group 7 Female Figure 3.10 Graph showing first two significant PCA components 75 displaying level of individuality of vocalisation for Group A Female Figure 3.11 Graph showing first two significant PCA components 76 displaying level of individuality of vocalisation for Group C Female Figure 3.12 Graph showing first two significant PCA components 77 displaying level of individuality of vocalisation for Group E Female Figure 3.13 Graph showing first two significant PCA components 78 displaying level of individuality of vocalisation for Group HH Female Figure 3.14 Graph showing first two significant PCA components 79 displaying level of individuality of vocalisation for female from Group K Figure 3.15 Graph showing first two significant PCA components 80 displaying level of individuality of vocalisation for Group N Female Figure 3.16 Graph showing first two significant PCA components 81 displaying level of individuality of vocalisation for Group PO Female Figure 3.17 Graph showing first two significant PCA components 82 displaying level of individuality of vocalisation for Group S Female Figure 3.18 Sonogram for individual FG2 89

Figure 3.19 Sonogram for individual FG3 89

Figure 3.20 Sonogram for individual FG4 89

Figure 3.21 Sonogram for individual FG5 89

Figure 3.22 Sonogram for individual FG6 90

Figure 3.23 Sonogram for individual FG7 90

Figure 3.24 Sonogram for individual FGA 90

Figure 3.25 Sonogram for individual FGC 90

Figure 3.26 Sonogram for individual FGE 90

Figure 3.27 Sonogram for individual FGHH 91

Figure 3.28 Sonogram for individual FGK 91

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Figure 3.29 Sonogram for individual FGN 91

Figure 3.30 Sonogram for individual FGPO 91

Figure 3.31 Sonogram for individual FGS 91

Figure 3.32 PCA plotted against Physical Distance Apart for 94 comparisons between all studied individuals Figure 3.33 PCA plotted against Euclidean Distance Apart for 96 comparisons between all studied individuals

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1. Introduction 1.1 Taxonomy and Ecology: Gibbons (Hylobatids) are small, arboreal apes which live in rainforests throughout

Southeast Asia (Carpenter 1940; Chivers 1977; Geissmann 1995; Marshall and

Sugardjito 1986), this is displayed in Figure 1.1. Geissmann (1995) believes that the

gibbon family was the earliest to diverge from the remaining four ape lineages around

11mya before present (BP) (see Figure 1.2). Their dental formula is 2.1.2.3., as it is

with all apes. Studies have shown that gibbons also share similar vision processing

that can be found in the other ape lineages (Deegan and Jacobs 2001). Another

species specific specialization displayed by gibbons is the form of locomotion known

as brachiation (Gittins 1983; Geissmann 2000a). This form of locomotion allows swift

travel above and below branches, which greatly aids feeding and territory defense.

Geissmann (2000a) believes that this gives gibbons access to an arboreal niche that

other mammals of their size cannot access, and that this adaptation is correlated to the

elongation of hands and arms that gibbons display. Initial classifications by Chivers

and Gittins (1978) of the Hylobatids were based on physical colour differences, while

further classifications (Geissmann 2002) found twelve species across four genera,

Hylobates, Bunopithecus, Namascus, Symphalangus. (see Table 1.1).

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Figure 1.1 Map showing distribution of gibbons throughout South East Asia

(from Chivers 1977) Figure 1.2 Table showing gibbons place amongst the taxonomy of primates

(from Geissmann 1995)

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Table 1.1 Previous gibbon classification

(from Geissmann 2002) More recent technological advances has allowed DNA sequencing and further

classification to place 26 species or sub-species across these four gibbon genera. This

can be seen in Table 1.2. This large divergence in individual species numbers and

wide spread gibbon distribution makes them the most widespread and radiated of all

the apes (Chivers 1977).

Bornean agile gibbons (H. agilis albibarbis) visually have a contrasting pileage. Their

fur is a dark brown on their hands and feet, as well as their skull cap and chest. Their

limbs display a lighter brown colour. Eyebrows are much lighter in colour and can

often be white (pers. obs.).

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Table 1.2 Hylobatid Classification

(from Cheyne 2004)

Geissmann (2003) identified that there are at least 29 taxa of gibbons, spread across

the twelve gibbon species. These are distributed throughout South East Asia. The

agile gibbon (Hylobates agilis) is found in Borneo, Sumatra and Peninsula Malay

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(Marsh 1987). Amoungst the agile gibbon, three sub-species are recognised: the

mountain agile gibbon (H. agilis agilis) which are located in Western Sumatra, the

lowland agile gibbon (H. agilis unko) which are located in Peninsula Malaysia and

Eastern Sumatra, and finally the Bornean agile gibbon (H. agilis albibarbis) which are

located in Kalimantan, Indonesia between Kapuas River and Barito River. All three of

these sub-species are listed as IUCN Lower Risk (Eudey, 2000).

Agile gibbons are distributed throughout northern Malaysia, southern Sumatra, in the

south of Thailand and in Central Kalimantan, Borneo (Gittins 1978, 1979; Marshall

1981; Marshall & Marshall 1976). The sub species which this study will be conducted

on, the Bornean agile gibbon (H. agilis albibarbis) is restricted to southern and central

parts of Indonesian Borneo (Kalimantan), while the rest of the island, including

Malaysian Sarawak and Sabah, consists of Mullers’ gibbons (H. Mulleri).

The different gibbon species inhabit a wide range of habitats. This varies across the

different South East Asian habitats that species live within. Gibbons have been found

to live within primary peat swamp forests, secondary peat swamp forests, mixed peat

swamp forest, low pole forests, tall pole forests and karangas forests, in which the

peat has dried up. These forests are often found surrounding a dense tall pole interior.

Agile gibbons are known to inhabit all of these habitat types across the range.

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1.2 Behaviour and Social Ecology: Gibbons form monogamous pairbonds and develop territorial family groups

(Carpenenter 1940; Chivers 1972, 1984; Gittins 1980; Tilson, 1981; Brockelman and

Srikosamatara 1984; Leighton 1987). This is considered a unique social structure

among primates (Mitani 1985a), and indeed mammals. Geissmann (2000a) argues that

only 3% of all mammal species adopt this social form. These family groups formed

are largely territorial. Monogamous groups consist of an adult male and female pair,

with up to three juvenile offspring (Chivers, 1977, 1989; Leighton 1987); Dallmann

and Geissmann 2001a). Gibbon groups defend their territories against conspecifics,

and all eating, sleeping, mating and nurturing of young is carried out within their

territory. Territory sizes range greatly across the different gibbon species. Cheyne

(2004) showed that this could range from as little as 8ha for H. Klossii in Siberut,

Indonesia, and up to 46ha for the H. agilis x H. muelleri hybrids which are found in

northern Central Kalimantan, Indonesia. Agile gibbons defend one of the larger

territory sizes across the gibbons with up to 44ha for their home ranges. Up to 88% of

this territory is defended against competitors (Gittins 1979). Table 1.3 lists the

differing home ranges for the various gibbon species.

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Table 1.3 Home ranges of different gibbon species

(from Cheyne 2004) Interestingly, some observations have been made where it could be questioned

whether or not gibbons are totally monogamous. These observations (Palombit 1992,

1994a, 1994b; Sommer and Reichard 2000) have led to some discussion about this

classification. Sommer and Reichard’s (2000) observations of lar gibbons at Khao Yai

National Park, Thailand residing in groups with sometimes two adult males and two

adult females in particular questioned the gibbon social structure. Regardless, it is

believed by the author of this thesis that such observations, being limited, are

restricted occurrences, and that as a whole, the gibbon live in monogamous family

groups as has always previously been believed. Indeed Reichard (2003) was in no

doubt when he speculated that gibbons are indeed monogamous because males defend

territorial access as well as mates. Reichard also believed that this provided males

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with guaranteed reproductive success. Gibbons formed monogamous pairings, it was

argued by Reichard, to reduce the threat of infanticide as well as reducing the threats

from predators.

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1.3 Vocalisations: Primates have three major forms of communication at their disposal. These are vocal,

visual and olfactory. Communication studies on gibbons often focus on gibbon

vocalizations. This is due to the uniqueness and complexities of gibbon vocalizations.

This study will also be focused on the vocal form of gibbon vocalization.

Vocalisations in primates are not at all uncommon. Indeed in most primate species

either males, females, or both produce some form of vocalization. This is also the case

for the ape species. Orang-utans (Pongo pygmaeus) males use long calls in their

vocalization. Gorilla (Gorilla gorilla) males are known to hoot, while chimpanzees

(Pan troglodytes) are both sexes and all ages are known to pan-hoot (Geissmann

2000a). An illustration of these differing forms of vocalizations can be seen in Fig 1.3

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Figure 1.3 Sonograms of some different primate species.

a = Chlorocebus aethiops b = Lophocebus albigena c = Colobus satanas d = Trachypithecus johnii e = Hylobates hoolock f = Pan troglodytes NB: Upwards arrows = exhalation; Downward arrows = inhalation (from Geissmann 2000a) Despite vocalizations being common in primates, both male and female gibbons

produce loud and complex vocalizations, known as song bouts, which are unique

amongst primates (Cowlishaw 1992,1996; Geissmann 2002; Geissmann &

Orgeldinger 2000; Mitani 1984, 1985, 1987, 1988). These vocalisations are sexually

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dimorphic, with females producing great calls and males producing simple (start) and

complex (end) songs. These songs follow well defined parameters depending on

species and sex. These parameters are often believed to be genetically inherited

(Carpenter 1940; Marler and Tenanza 1977). Although Geissmann (1984) believed

that there was no studied evidence to suggest this. All apes and Old World Monkeys

are capable of producing some form of ‘loud call’ (Geissmann 2000a, 2002). It is

commonly believed that these gibbon songs evolved after the gibbon lineage broke off

from the other ape lineages. Each gibbon species display significantly different song

types. These songs are known to be sex-specific as well as being species specific

(Marshall and Marshall 1976; Haimoff 1983 1984a; Geissmann 1995). Geissmann

(1993) argues that despite this species variation in song types, and that gibbon songs

most likely evolved from a single ancestral template. This reasoning was based on

studies on all songs across the gibbon species which showed that there were a number

of similarities that all the Hylobatid species share.

Gibbon vocalisations are restricted in structure by the habitat that they live in. Morton

(1975) argued that the structural complexities of the dense tropical rainforests led to

the formation of “sound windows”. These sound windows restricted vocalisations in a

dense tropical rainforest to particular frequencies Morton (1975) argues. Furthermore,

Stephens and Bate (1966) suggest that higher frequencies noises are absorbed more

easily in humid air, therefore rainforest vocalisations would also have been restricted

to lower frequencies. Haimoff (1984a) agrees with these ideas and states them as the

reasons why gibbons display a “narrow bandwidth” in their vocalisations and that

their frequency range is highly restricted to between 400Hz and 1500Hz. However,

Geissmann (1993) argues that this frequency range for gibbons is more likely to be

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between 200Hz and 5000Hz. Haimoff (1984a) also states that most gibbon

vocalisations are audible over a 2km range. Despite this belief, this audible range

must vary across the different habitats that are used by the different gibbon species,

with vocalisations being less audible in range in denser rainforests. For instance,

Cheyne (pers. comm.) found while conducting triangulation and quadrangulation

studies on the Bornean agile gibbons (H. agilis albibarbis) at the Sebangau National

Park study grid that vocalisations carried only to a range of 1km.

Some amount of discussion has arisen regarding how gibbon songs are passed on

from generation to generation. Carpenter (1940), Marler and Tenanza (1977) and

Brockelman (1978) all believed that the vocal organisation and structure of all gibbon

species to be genetically inherited. Another theory is that juvenile gibbons learn their

songs from their parents. Personal field observations by the author of this thesis in

Bornean agile gibbons showed that all juveniles, especially females, mimic the calls

of their parents. In some cases, adult females will abort great calls mid-song in order

to let her juvenile daughter finish the great call. As the juveniles increase in age, so

does the precision increase in the reproduction of the parental songs. However, certain

studies have refuted this notion (Geissmann 1984, 1993, 2000a). His studies on hybrid

gibbons (Hylobates pileatus x H. lar) that were raised without the presence of their

parents. Rather than producing the calls of their parents, as would be expected from

both theories that gibbons songs are either inherited or learned from parents, these

gibbons produced songs that are typical of hybrid gibbons. This raises further

questions regarding the inheritance and learning of gibbon songs. It must be made

note of however, that these studies were on hybrids and as such, with mixed heritage,

ability to inherit may be reduced. In recent times no further studies have shed light on

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this problem. If Geissmann is indeed correct, then personal observations made in the

field in which it appears that young gibbons ‘learn’ songs from their parents, could be

interpreted that the songs were in fact genetically inherited. Although it may sound

like learning to observers, it may in fact just be song ‘practice’ that the young are

engaged in.

Geissmann (2000a) also investigated song inheritance in hybrid gibbons where

juveniles were being raised only in the presence of one parent. For instance, his

studied looked at a female hybrid from a male white-handed gibbon gibbon (H. lar)

and a female grey gibbon (H. muelleri) which was raised only in the presence of grey

gibbon great calls. It was expected that the juvenile hybrid would learn the grey

gibbon great call. Conversely, he argues, it the parentage was reversed and the hybrid

was raised only in the presence of white-handed gibbon great calls, then the hybrid

could be expected to learn the white-handed gibbon great call. However it was found

that neither was the case. Rather, as with his previous studies on hybrid mentioned

above, the juvenile hybrids developed what has now been accepted as hybrid specific

great call types that takes parts of the great calls from each parental lineage. This is

despite the fact that these hybrid juveniles were only ever exposed to great calls of

one parental species. Geissmann (2000a) used the basis of this study to state that

“gibbons do not learn their repertoire from their parents.” These hybrid studies

conducted by Geissmann have given researchers a greater understanding into the

inheritance of vocalisations in gibbons.

Gibbon song types are considered an excellent method of identifying species type of

an unknown individual (Marshall and Marshall 1976). This is due to the extent of

knowledge now garnered on the different species song templates. This was

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demonstrated with clarity by Marshall and Marshall (1976). A number of studies have

now arisen that have successfully attempted to reconstruct relationships between

individual gibbons based on voice. These studies have been based on the study of

different vocal characteristics of known individuals. These studies have also

attempted to reconstruct phylogenetic relationships between gibbon groups (Haimoff

et al. 1982, 1984; Haimoff 1983; Creel and Preuschoft 1984; Marshall, Sugardjito and

Markaya 1984; Geissmann 1993).

The differences in vocalisations between gibbon species as a whole are indeed

marked. It is also the case when comparing females to females and males to males of

different species. Taking assumptions from different phylogenetic studies based on

vocalisation, this variation could only be the result of different species evolving and

breaking off from the common ancestor at different times (as can be seen in Fig 1.4).

Figure 1.5 illustrates the different forms of female great calls within some gibbon

species.

Figure 1.4 Showing relationship between phylogeny and vocalisation in gibbons.

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(from Geissmann 2002) Figure 1.5 Sonograms of different female gibbon species great calls.

a = Hylobates agilis b = H. lar c = H. moloch d = H. meulleri e = H. pileatus f = H. klossi g = H. hoolock h = H. concolor i = H. leucogenys j = H. gabriellae k = H. syndactylus (from Geissmann 2000a)

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Most gibbon songs occur in the early hours of the morning. The Bornean agile

gibbon (H. agilis albibarbis) was known to sing in the pre-dawn hours by preference,

with singing rarely occurring after 8am in the morning (personal field observations).

While gibbon song bouts are typically 10-30 minutes in length (Dallmann and

Geissmann 2001a). This also held true with field observations made on the Bornean

agile gibbons in Sebangau National Park.

Additional to normal gibbon song, females and males combine to produce complex

duets. These duets require complex timing and combination of both the male and

female of a mated pair. These duets consist of female introductory notes, the female

great call, and terminal male notes known as ‘coda’. Within these duet bouts the

female great call varied little, while the male ‘coda’ can vary greatly (Marshall and

Marshall 1976; Gittins 1979; Haimoff 1983, 1984a, b; Cowlishaw 1992).

Interestingly, some hold the belief that stronger and older monogamous pairs display

greater organisation of their duets (Chivers, pers. comm. Cheyne). This could be a

result of many years of practice together to perfect the duet aspect of mated pairs’

relationships.

Haimoff (1984a) interestingly made note of the following facts about gibbons and

their vocalisations. He believed that the characterising features that identify gibbon

species such as a lack of sexual dimorphism, monogamy, territorial behaviour, singing

and duetting are rather rare for mammals, let alone primates. However, he states that

these gibbon features are not too dissimilar to the behaviour and characteristics of

birds. As such, earliest studies on gibbon song evolution and function were, not

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surprisingly, based on previous such studies on birds. It comes as little surprise then

that when studying bird vocalisations, and in particular, duetting in monogamous

birds, that duetting has the following functions. Firstly, the advertisement of the given

pair’s presence in their territory, and subsequent affect on rival pairs being deterred

from entering the territory. Secondly, that duetting in birds serves the purpose of

reinforcing and strengthening the pair’s bond (Thorpe 1961, 1972; Thorpe and North

1965, 1966). It is with little shock then, that these two functions of deutting in birds,

are now widely accepted as two principle driving factors in gibbon duetting between

monogamous pairs.

In most species, duets are initialled by the females (Cowlishaw, 1992). Despite this,

field observations made by this author suggest that in Bornean agile gibbons (H. agilis

albibarbis) duet bouts are initiated by male introductory notes, with females rarely

beginning duets without first being prompted by male singing. Numerous attempts of

females to produce duets were aborted due to the lack of a male introduction effort.

Alternatively females were observed continually singing her duetting ‘warm up’ notes

until her mated male obliged her.

Gibbon songs are believed contain information unique to the singer, which represents

individual advertisement to other individuals within hearing range about the singer’s

identity and territory (Haimoff 1985; Cowlishaw 1992). Today it is widely accepted

that gibbon singing, including duets, serves the following purposes:

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• Inter group spacing: Singer’s advertise their presence within a territory. Solo

bouts give information about individual. Duet bouts provide information about

mated couple and the habitation of territory.

• Resource defence: Duetting advertises presence within a given territory. This

territory contains the resources that are required for both male and female

sustenance. Listeners are forewarned of singer’s presence.

• Territory defence: Duetting promotes the hold monogamous pairs have over

their territory that they are habituating. Information is passed on to those that

can hear to warn them of the presence of a mated pair within the home range.

• Mate attraction and defence: Unmated males and unmated females pass on

information regarding their mated status to other gibbons within hearing

range. This can lead to the attraction of a mate for unmated individuals.

Alternatively, singing passes on information regarding your mated status.

Passing unmated males will know not to attempt to lure mated females within

the territory after hearing this information passed on in vocalisation.

• Strengthening and advertisement of mated pair bond (duets only): Not only

can mated pairs advertise their mated status to other gibbons within hearing

range, but this can also provide strengthening of their monogamous

relationship. It is believed (Chivers, pers. comm. Cheyne) that stronger

monogamous pairs have greater timing and synchronicity of their duets. This

process of perfecting duets may well help strengthen the pairbond due to the

coordination and dual effort required.

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(adapted from Mitani 1984, 1985, 1987; Raemaekers and Raemaekers 1984a, b;

Haimoff 1985; Leighton 1987; Cowlishaw 1992; Geissmann 1999; Geissmann and

Orgeldinger 2000)

Due to the belief that gibbon songs contain information unique to the singer, we

would expect that each song should be different. Especially when considering levels

of individuality in vocalisation when comparing two individuals that are inside each

others’ hearing range. It could therefore be argued that gibbons that are closer to each

other in terms of physical geography, and hence, inside of hearing range, should

display greater levels of individuality of vocalisation than gibbon that are not within

hearing distance of each other. At the Sebangau National Park study grid, it is

believed that 1km apart between individuals represents being outside of hearing

range.

The earliest study made on the degree of gibbon song individuality was carried out by

Marshall and Marshall (1976). This study was in fact carried out across the gibbon

species, investigating how each species displayed its own individual song type. Focal

individuals were studied either in zoos or in the wild across the gibbons’ range. Not

only did this study find that each species displayed an individual song structure, but

also that there were sex-specific song structures displayed within each species.

Studies such as Mitani and Marler’s (1989) study continued the work on gibbon

acoustic analysis that was first studied by Marshall & Marshall (1976). These studies

emphasised that sexual dimorphism in gibbon song content. For the first time

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sonogram evidence provided proof of the great variation between male and female

gibbon vocalisations.

The different phases of female agile gibbon songs were identified by Haimoff

(1984a). These were as follows: (1) the introductory notes; (2) the inflective notes; (3)

the climax note; (4) the post climax note; (5) the terminal phase. However, by

Haimoff and Gittins (1985) had reassessed these phases and had identified that the

terminal phase was not useful when attempting to identify the levels of individuality

in female great calls. This can be seen in Figure 1.6 below

Figure 1.6 Phases of the female agile gibbon great call.

(from Haimoff and Gittins, 1985) Later, Cowlishaw (1992) studied gibbon song function and tested and agreed with

their hypotheses that females sing to defend territory, while males sing to defend

mates. Here Cowlishaw drew on data from numerous previous studies. This data

provided information on behaviour of individual singer in close vicinity of singing

time. This enabled him to test the role of male and female singing, separately, and its

role in mate attraction and defence and territory defence. Cowlishaw also was able to

test the role that duetting has on pairbond advertisement and pairbond maintenance.

31

While providing evidence that females vocalise to defend territory and that males

vocalise to defend mates, Cowlishaw in this study could find no relationship between

duetting and pairbond maintenance and advertisement.

In 1996, Cowlishaw conducted another study that was based on the collection of

previously gathered data on 21 known gibbon populations across South East Asia.

This study was also conducted on numerous gibbon species. Here he suggested that

song bouts are energetically expensive and therefore singing is sensitive to energy

intake and expenditure, therefore there is a territorial defence mechanism involved in

gibbon vocalisation. This, he believes, fits in with his 1992 findings. Cowlishaw

argued that, rather than just rainfall, temperature, season (fruiting), which affect the

expense of song bouts, but that song bouts in males and females may have other

important factors. In females, the frequency of territorial encounters would affect bout

frequency and song content. In males, the density of floating (un-mated) males would

affect the frequency and duration of song bouts.

The most unique aspect of gibbon vocalisations are duets. Duets are initiated by the

female, with the male joining in, adding complex vocalisations to the song

(Cowlishaw 1992; Geissmann 2002). These duets often last between 10 to 20 minutes

long. A number of different hypotheses have proposed explanations as to why

gibbons evolved duetting.

Geissmann and Orgeldinger (2000) tested the theory that duetting served the purpose

of strengthening pairbonds. This study involved correlations of observations of duet

activity (song activity and number of songs per day) and three indicators of pairbond

32

strengthening (mutual grooming, behavioural synchronisation and mate proximity).

The data was gathered from captive populations from zoos across Europe, including

France, Germany, Switzerland, and Hungary. This was an expansion on original

observations made by Wickler and Seibt (1980). Giessmann and Orgeldinger found

that duetting was higher in pairs that groomed more, behaved similarly and that we

often close together. Unfortunately, this study was done on captive populations. A

similar study on wild populations which could then be compared to their results

would be most interesting, however, visual observations on wild gibbon individuals,

let alone pairs, would prove most difficult.

Alternative theories to the hypothesis that duetting promotes stronger pairbonds,

include the following. Mitani (1984, 1985, 1987, 1988) proposed that duetting was

used for territorial defence. Cowlishaw (1992) agreed with Mitani when concluding

that duetting was less likely to be related to pairbond strengthening and more likely

related to territorial encounters and the avoidance of aggressive encounters. This is

also linked to the hypothesis that duetting is in fact used for mate defence. The idea

being that the pair conducting a duet are in fact making either partner advertise that

they are unavailable to all un-mated individuals in the area.

33

1.4 Previous Individuality of Vocalisation Studies:

In more recent years, a field of interest in gibbon vocalisations has experienced

resurgence in interest. This area of study surrounds the individuality displayed in

gibbon calls. This was first recorded by Marshall & Marshall (1976) and further

studies from Haimoff (1984b, 1984c, 1985), Haimoff & Tilson (1985) and Mitani

(1988) in studies on black (H. concolour hainansus), Kloss’ (H. klossii) and Mullers

gibbons (H. muelleri) looked at this vocal variation. In more recent years Dallmann &

Geissmann (2001a, 2001b) continued such studies in investigating the degree of

individuality of wild silvery gibbon (H. moloch) vocalisations.

There are great differences between male songs and female great calls. Therefore,

different methods of acoustic analysis of male vocalizations are required. Those

methods used by Haimoff & Gittens (1985) and Dallmann & Geissmann (2001a)

would not be suited to a study on males. Therefore, attention must be paid to Mitani’s

(1988) study of male agile gibbon (H. agilis) song variation. Temporal aspects of

songs were identified by Mitani which could be used to indicate individuality in male

vocalizations. The temporal aspects he used were: singing performance time

(minutes), song duration (seconds), number of syllables per song and singing rate

34

(songs per minute). Although it is of interest to note that male agile gibbons also

display individuality in vocalization, this will not be investigated in this study as time

would not permit it.

Other studies, such as that by Haimoff and Gittins (1985), showed that wild female

agile gibbons at Peninsula Malaysia displayed individuality in their vocalisations.

Here they made recordings of female agile gibbons’ great calls. Great calls were

collected for 8 individuals with a total of 53 great calls being analysed. Then they

were able to analyse the recording across the 4 significant phases of the great call that

they had identified (the introductory notes, the inflective note, the climax note and the

post-climax notes). They developed 12 characteristics across these 4 great call phases

which they could extract data for. They were able to conduct parametrical statistical

testing in order to gain an insight into levels of variation in individual’s vocalisations.

They concurred that this variation may have related to neighbouring mate attraction.

A similar study carried out at the same time was done by Haimoff and Tilson (1985).

This was a study of the levels of individuality in vocalisation in female Kloss’s

gibbons (H. klossii). The data for this study was collected on 4 individuals at

Kalejetan, Indonesia, with a total number of 48 great calls being analysed. This data

was then statistically analysed using the Kruskal-Wallis one-way analysis of variance

of ranks. As a result of this study, Haimoff and Tilson concluded that was statistically

significant individuality in female Kloss’s gibbon great calls.

Another similar studies were carried out by Dallmann, R. and Geissmann, T. in 2001

(Dallmann and Geissmann 2001a) on wild silvery gibbons (H. molloch – unusual

35

because males rarely sing). Here they analysed 316 great calls that were recorded at 5

different localities in Java, Indonesia. Dallmann and Geissmann developed a new

method of testing for individuality, rather than use the method developed by Haimoff

and Gittins (1985). They identified 39 different characteristics of the silvery gibbon

great call. From this a Mean Pairwise Difference (MPD) variable for the great call

could be calculated. It was their claim that such a method could be used for any

gibbon species. They found that female silvery gibbons displayed individuality in

their vocalizations. Dallmann and Geissmann suggest that this individuality in songs

has a function of individual recognition and could be helpful in mate-recognition.

They argue that this individuality should be higher among non-duetting species and

lower among deutting species. They offer that further proof of this could prove vital

in gibbon conservation as traditional methods of identification of individuals

(morphology, fur color) are not suitable for gibbon recognition.

Interestingly, Dallmann & Geissmann (2001b) also found that there was a degree of

variation within individual silvery vocalization as well. This study looked at data on

silvery gibbons (H. molloch), agile gibbons (H. agilis) and Kloss’s gibbons (H.

klossii). The data was collected on wild individual silvery gibbon from Java. This data

was then complimented with data from previous studies. They took agile gibbon data

from Haimoff and Gittins’ (1985) study, while they collected data on Kloss’s gibbons

from Haimoff and Tilson’s (1985) study. This study was aimed at identifying levels of

individuality in vocalization in female silvery gibbons, however, it was also aimed to

compare these levels of individuality between silvery, agile and Kloss’s gibbons. To

undertake this they used the Kruskal-Wallis non-parametric test, rather than the

(MPD) method they had recently developed. Here Dallmann and Geissmann

36

concluded that all three female species displayed statistically significant levels of

individuality. Further to this they discovered that female agile gibbons (H. agilis)

displayed the greatest levels of individuality, followed by female silvery gibbons (H.

molloch), with the least levels of individuality to be found in Kloss’s gibbons (H.

klossii).

The methods of acoustic analysis utilized in the studies by Haimoff & Gittens (1985)

and Dallmann & Geissmann (2001a) are to be noted. Haimoff & Gittens (1985)

named five basic identifiable phases of the female agile gibbon (H. agilis) great call

which could be analysed: “the introductory, inflective, climax, post-climax and

terminal phases.” Analysis of these phases led to the creation of their table with

twelve variables across the five phases which could be used in vocal analysis to

identify individuality in song calls. Dallmann & Geissmann (2001a) named three

identifiable phases of the female silvery gibbon (H. moloch) which could be analysed:

“pre-trill phase, trill phase and determination phase.” From this Dallmann &

Geissmann created their table containing 39 variables across the three phases which

could be used to identify individuality of female silvery gibbon vocalizations. Both of

these methods of analysis appear to hold merit, however, for the purposes of this

study, the method used by Haimoff and Gittens (1985) will be used, but developed

further. It was felt that this method has been used for a greater amount of time and by

using this method of analysis, comparisons between the two studies could be made.

37

1.5 Aims of Study:

This study is firstly aiming to investigate the levels of individuality in vocalization of

female Bornean agile gibbons (H. agilis albibarbis) within a population located in a

Mixed Peat Swamp Forest. This is the first such study to be carried out on agile

gibbons in a mixed peat swamp forest. It is understood that there should be a

significant level of individuality displayed by the individual females at the Setia Alam

Field Station, based on information gathered from previous, similar studies.

In addition to this, this study will also investigate whether or not these levels of

individuality in vocalization of female Bornean agile gibbons (H. agilis albibarbis)

within a population located in a Mixed Peat Swamp Forest are affected by physical

distance apart between individuals. Such a study also has never been attempted

before. Some previous studies have attempted to look at differences in levels of

individuality across a much greater geographical scale in terms of distance apart. For

instance, a studied carried out by Thompson (2005) investigated agile gibbons

vocalizations and levels of individuality between groups that were separated by

distances of up to and over 150km. This study will be looking at intra-population

variation and whether or not this variation is explained by physical distance apart

between individuals. This will be the first chance to see whether or not female

Bornean agile gibbons (H. agilis albibarbis) adhere to the generally accepted belief

that their songs must be different to those of neighbours within hearing range in order

38

to be able to identify each other. Alternatively, this study may show that gibbons do

not require only differences in vocalization structures and frequencies for

identification, but rather, there may be other, as yet unknown methods of identifying

neighbours within hearing distance. Further still, this study may prove that physical

distance between neighbouring individuals has no effect on levels of individuality.

This could be due to individual’s vocalizations being developed with no need to be

greatly different from another individual’s, who may happen to be within hearing

range. Individual A, may be greatly different to Individual B, who may be located

outside of hearing distance, while Individual C may be less different and may be

inside of hearing distance. If this is found to be the case and pattern, then it could be

expected that there is no influence of physical proximity to other individuals that

drives levels of individuality in female Bornean agile gibbons (H. agilis albibarbis)

vocalizations. This would then support the evidence that suggests that these gibbons

do indeed inherit their vocalizations genetically. If this was the case, then we would

expect gibbons to be unable to alter their vocalizations in order to differentiate

themselves from other individuals. Rather, gibbons are born with a set vocal template

and possess enough intelligence to identify other gibbons irrespective of great

differences between the vocalizations of other individuals.

39

2. Methods

40

2.1 Study Site:

2.1.1. Sebangau National Park

The Sebangau National Park is located in Central Kalimantan, Borneo, Indonesia. The

Sebangau N.P. encompasses the Sebangau Catchment Area. This is a large peat

covered area of land which comprising a very large part of Central Kalimantan. (see

Figure 2.1) The catchment is found between the Katingan and Kahayan rivers, and is

believed to be one of the largest remaining peat swamp forests in South East Asia at

7000ha (OUTROP Website).

41

Figure 2.1 Map showing location of Setia Alam Field Station, as well as the

Sebangau Catchment Area.

(from Page et al., 1999)

Although some of the catchment has well developed settlements (Palangkaraya) and

agriculture contained within, some 7000km2 remains undisturbed peat swamp forest

(Morrogh-Bernard et al., 2003). As can be expected, such forest types are huge,

global carbon sinks. It is believed that the Sebangau peat dome is one of the largest in

42

the world (Rieley et al. 1997, Page et al. 1999). The depth of the peat dome can be as

thin as 2m at the margins of the dome near the Sebangau River. Whereas the depth

can become as large as 14m thick toward the centre in the tall-pole forest.

Peat swamp forests can encompass different forest sub-types. This distribution varies

from the outer perimeter to the centre of the swamp forest (Rieley et al. 1997, Page et

al. 1999). The Sebangau Catchment contains three different forest sub-types. The first

sub type is ‘mixed–swamp forest’. This is found between one and seven kilometres

from the Sebangau River. This forest area has a canopy of 35m and is high in

diversity. The second forest type is located from eight to twelve kilometres from the

Sebangau River. This is known as the ‘low-pole forest’. The low pole canopy is about

20m. This area is covered in water all year round, and is low in diversity. The final

forest type found in the Sebangau Catchment is the ‘tall-pole forest’. As its name

suggest, the canopy is up to 45m high here, and a high amount of diversity can be

found here (Morrogh-Bernard et al., 2003).

A large amount of unique diversity in flora and fauna can be found in the Sebangau

Catchment area. Many of these species are endemic, protected and endangered. As a

result of lobbying for protected status, 5500ha of forest was designated as the

Sebangau National Park in 2005 by the Indonesia Forestry Ministry. There are 9

species of primates in the Sebangau Catchment. These are: orang-utan, agile gibbons,

red leaf monkeys, pig tailed macaques, long tailed macaques, proboscis monkeys,

slow loris’ and western tarsiers. There are also 55 other mammals found in the forest.

These range from clouded leopards, sun-bears, wild pigs and deer, marble cats and

civets. There are also many endemic birds and trees in the Sebangau Catchment.

43

There are also many reptiles in the Catchment ranging from snakes and lizards to

crocodiles. In all, the Sebangau National Park is an ark of endangered, endemic

species.

Today, the largest threats to the Sebangau National Park are water loss and wild fires.

Much of this area is scarred by a huge canal network. These canals were built either

by the loggers, who used to logs this area of the forest legally. Alternatively, these

canals were built as a part of the Indonesian Mega-Rice Project. This 1.5 million Ha

area of formerly pristine rain forest represents a failed rice growing scheme set up by

the federal government in 1995. Due to the presence of these canals, the peat swamp

hydrology system is losing a disturbing amount of water annually. This then has the

effect of drying out this land mass greatly. The ensuing result is the annual

phenomena of massive wildfires that spread throughout Central Kalimantan. It is

estimated that in the 1997-98 fires, up to 15% of pristine forest was lost in flames, and

that the emissions of these fires made up one third of the planets usual carbon

emissions (OUTROP/CIMTROP webpage).

To this date, it is believed that this will be the first study of this kind carried to be

carried out on agile gibbons within a mixed peat swamp forest.

44

2.1.2. Setia Alam Field Station:

The Setia Alam Field Station was originally used as logger’s accommodation, when

this area used to be designated as a part of the Central Kalimantan logging

concession. The field station is located within the Catchment area, 1km away from the

Sebangau River and can be reached quite easily as it is located 20km SW of the

capital of Central Kalimantan, Palangka Raya.

The Setia Alam Field Station is located within the Laboratorium Alam Hutan Gambut

(National Laboratory [for the study] of Peat Swamp Forest) SW of Palangka Raya.

This is a 500km2 area of forest that is run and controlled by CIMTROP (Centre for

International Co-operation in Management of Tropical Peatland), which runs out of

the University of Palangka Raya, Campus Unpar. This area is designated for research

into forest and peat structure, biodiversity, ecology, as well as concentrating on orang-

utan diversity, distribution and ecology. This work is carried out by researchers

working through the British research body OuTrop (Orang-utan Tropical Peatland

Project) and the Indonesian research body CIMTROP (Centre for International Co-

operation in Management of Tropical Peatland).

It is believed that the Sebangau catchment area is the largest continuous population of

orang-utans remaining in Borneo. One estimate is that there is up to 7000 individuals

found here (Morrogh-Bernard et al., 2003). Other estimates suggest that there may be

up to 30,000 agile gibbons located within the Sebangau National Park.

45

Legal logging was ceased in the Sebangau National Park in 1998, however illegal

logging still plagues this area. Today the National Park can be used by local villagers

to extract traditional materials from the forest, however, individuals caught logging

can expect to be fined or face fiercer penalties, such as severe fines or jail terms. As

most illegal loggers are mostly poor villagers, it is normal that a jail term is imposed

due to the inability to pay any enforced fines.

Most research that is carried out at the Setia Alam Field Station is conducted in the

research grid. This is a 3km2 study grid which is located within the mixed peat

swamp forest. The grid has 13 major longitudinal transects cutting through the forest,

with latitudinal transects cut into the forest approximately every 400m travelling away

from the Sebangau River. These transects are flagged every 25-50m to aid in

orientation. The eastern boundary of the grid is bordered by the old logging

concession railway, which gives easy access to both the Sebangau River to the north,

and deeper into the swamp forest in the south.

Figure 2.2 Map of study grid, also showing known groups on grid.

46

(developed by Cheyne, 2006)

2.2 Study Period, Study Species and Individuals:

A twelve week period between 25/03/06 and 13/06/06 was spent at the Setia Alam

Field Station, studying the agile gibbons within the study grid. About half of this time

consisted of the end of the wet season in Borneo, resulting in many days of data

collection lost to rain, due to agile gibbons’ reluctance to sing during rain, or even

after a night’s rain. Due to the differing climatic conditions that can affect gibbon

singing a total of 31 days of successful recordings were enjoyed out of a total of 81

possible study days. Of these 81 days, 40 were spent searching and/or recording for

focal individuals, while 36 days were lost to unfavourable climatic conditions or other

unforeseen circumstances.

Within the OuTrop/CIMTROP study grid, there are 16 known agile gibbon family

groups (see above grid map). These group numbers have been ascertained through

triangulation and quadrangulation studies carried out by Cheyne (2006) and Buckley

(2004). These groups vary in size across the range (2-5 individuals). These family

groups display different levels of habituation as this process is still being carried out.

The groups that are situated closer to Field Station being more habituated, while the

groups furthest away are significantly less habituated. A small number of the groups

on this grid did not yield statistically significant great calls during the study period.

As such, Group T and Group 1 were not included in this study. Furthermore, it should

be noted that Group Y shown on the grid map represented a lone male home range. In

47

an unusual display of aggression, this lone male was killed during a territorial dispute

with the mated male and eldest male offspring from Group C on the first day of the

study period. All other individual females on the grid were included in this study.

Table 2.1 Sampled Female Individuals

Group

Name

Female Name

Code for

Statistics

2 FG2 2

3 FG3 3

4 FG4 4

5 FG5 5

6 FG6 6

7 FG7 7

A FGA 8

C FGC 9

E FGE 10

HH FGHH 11

K FGK 12

N FGN 13

PO FGPO 14

S FGS 15

48

Group 2 Members: unknown

Group 3 Members: unknown

Group 4 Members: unknown

Group 5 Members: adult male, adult female, sub-adult male, unknown juvenile

Group 6 Members: unknown

Group 7 Members: unknown

Group A Members: adult male, adult female, sub-adult male

Group C Members: adult male, adult female, sub-adult male, unknown infant

Group E Members: adult male, adult female, unknown juvenile, unknown infant

Group HH Members: adult male, adult female, female sub-adult, unknown juvenile

Group K Members: adult male, adult female, sub-adult female, male juvenile

Group N Members: adult male, adult female, sub-adult male, unknown juvenile

Group PO Members: unknown

Group S Members: adult male, adult female, sub-adult female

As previously mentioned, resulting from the quadrangulation and triangulation studies

on the gibbon groups within the study grid at Sebangau National Park, maps have

been produced to show the home range territories of the agile gibbon family groups

located at the study site (see grid map). This provided a significant tool in locating the

required individuals on their focal studies days. For instance, if FGS was to be study

on a given day, that given individual could be easily located in the field by

referencing the map that was being used.

49

These differing levels of habituation across the study individuals did not have any

obvious adverse effects on this study. This was due to the fact that the individual

groups did not have to be within sight. In fact, decent singing data could be recording

from 250m away from the focal individual. Also, it was preferred not to get within

sighting distance of the focal individuals as this visual contact with the gibbons could

result in the cessation of current singing bouts or abortion of planned singing bouts.

This was especially the case with the un-habituated groups as their singing bouts

would definitely be affected by the presence of humans.

50

2.3 Data Collection Methods and Materials:

Individual days were spent on single individuals where possible. This was not

normally a problem due to the distance between gibbon groups at the study site,

providing the opportunity to record great calls of female individuals unfettered. At

some study sites gibbon density is far greater than that at Sebangau National Park, and

as such, it can be difficult to focus on one single study animal without recording other

groups in the background. Due to the density of the forest in the Mixed Peat Swamp

Forest at Sebangau National Park, this leads to a lower density of gibbon groups at

this study site than is found in other forest types (Cheyne, pers. comm.) The existence

of a map locating the individual family groups provided by Susan Cheyne at

Sebangau National Park made it easy to locate focal individuals.

Data collection was carried out between 4am and 10am in the morning, due to the

singing habits of agile gibbons. On two occasions it was possible to collect data

opportunistically at later times in the day. This was a result of females alarm calling at

later times, which, as can happen, led to a bout of great calling, which was able to be

recorded. As many days as necessary were spent on each individual focal female until

sufficient data had been collected. For this study, it was decided that at least 20 great

calls per female individual would suffice. When this number or more songs had been

collected, then the study would shift to the next focal individual. This process

51

continued until each of the selected focal individuals had yielded the required number

of great calls for later analysis.

Data was collected on a Marantz PMD 671 DAT digital recorder, with the use of a

Stenheiser MKH416 omni directional microphone with Rycote Wind and Dust Shield.

Data was recorded to a Lescar 2Gb ProCompact Flash Media Card. After each day,

data was downloaded onto a laptop and songs were analysed on the Raven Lite 1.0 for

Windows bioacoustics software programme.

On study days, the recording equipment would be set up within a 200m-250m range

of the study group. This could be easily done as the mated male would start singing

around 30 minutes before the duetting and female great calling would begin.

Therefore, it was possible to get within a close a range as possible, preferable 100m to

the study female. As great calls were sung, recordings could be made, with a new

track being recorded for each great call that was produced by the focal female. Here

the Marantz PMD 671 DAT proved very useful as one of its functions is a 5 second

memory record. This meant that no data need be lost trying to make recordings. This

process would continue until the focal female had stopped singing her great calls. It

was not uncommon for females to complete one to two bouts of great calling per

singing day. Therefore, the amount of time between bouts was waited out at the

recording post. Sometimes it was necessary to relocate the recording post closer to the

focal individual if it were apparent that the group had moved further away during the

interval between singing bouts. Again, this was usually not too difficult due to the

excellent transect system that has been cut at the study site.

52

In total 286 great calls were recorded across the 14 focal females in this study.

2.4. Song Analysis:

Female Agile Gibbons have a specific song structure that their songs fall within. This

structure consists of 6 stages. These segments are the pre-introductory phase, the

introductory phase, the inflective note, the climax note, the post-climax notes, and the

terminal phase (Haimoff 1983, 1984a, Haimoff and Gittins 1985). Individual females

display different levels of individuality within these different phases. For the purposes

of this study, the 14 separate characteristics that were analysed here were adapted

from only five of these phases. The terminal phase was not included in this study due

to the lack of being able to record this quietest part of the female great call. As the

terminal phase was not able to be equally recorded in terms of quality across the

different focal individuals, it was decided that this section could be disregarded for

this study. The terminal phase also contains the least amount of individuality, so it can

easily be left out of this study without affecting this individuality study. In addition to

this Dallmann and Geissmann (2001b) state that the terminal phase is so soft that only

individuals within extreme close proximity to the singer could hear this phase. As

such, they argue, the terminal phase could not contain any useful information to be

advertised due to its inaudibility.

It was decided that data would also be extracted for the pre-introductory phase of the

female’s vocalisations. This phase, which is the warm up to the female great call, was

53

included as it was of interest to see if this phase also contained levels of individuality

across the females, or if this is purely a randomly performed stage as the females

launch into their great call. As this pre-introductory phase occurs after the prompting

male ‘coda’ during duets, it did not seem unreasonable to include this phase in the

analysis.

Figure 2.3 Typical Female Agile Gibbon Great Call (excluding Pre-Introductory

Phase.

(from Haimoff and Gittins, 1985)

All female gibbon songs were analysed across 14 separate characteristics in order to

investigate levels of females’ song individuality. These characteristics were developed

from Haimoff and Gittins’ (1985) study on the individuality of wild agile gibbons

found in Peninsula Malaysia. These 14 songs characteristics were Total Duration

(seconds), Total Number of Notes, Pre-Introduction Phase Duration (seconds), Pre-

Introduction Phase Notes, Introduction Phase Duration (seconds), Introduction Phase

Notes, Great Call Duration (seconds), Great Call Notes, Inflective Note Duration

(seconds), Climax Note Duration (seconds), Post-Climax Phase Duration (seconds),

Post-Climax Phase Notes, Minimum Frequency (Hertz) and Maximum Frequency

(Hertz).

54

Table 2.2: 14 Song Characteristics Studied:

Characteristic

1 Total Duration (seconds)

2 Total Notes

3 Pre-Introduction Phase Duration (seconds)

4 Pre-Introduction Phase Notes

5 Introduction Phase Duration (seconds)

6 Introduction Phase Notes

7 Great Call Duration (seconds)

8 Great Call Notes

9 Inflective Note Duration (seconds)

10 Climax Note Duration (seconds)

11 Post-Climax Phase Duration (seconds)

12 Post-Climax Phase Notes

13 Minimum Frequency (Hertz)

14 Maximum Frequency (Hertz)

1. Total Duration (seconds) – This is the total length of the entire vocalisation.

Pre-Introduction Phase and Great Call make up the total vocalisation.

2. Total Notes – The total number of notes in the entire vocalisation.

55

3. Pre-Introduction Phase Duration (seconds) – This phase is not considered a

part of the great call. Consists of short sharp whoo notes that can vary greatly

in number inter and intra individuals.

4. Pre-Introduction Phase Notes – The number of notes that this phase is

consisted of.

5. Introduction Phase Duration (seconds) – This is the first phase of the great

call. Consists of a small number (2-5) of long flat notes that contain little

inflection.

6. Introduction Phase Notes – The number of notes that make up this phase.

7. Great Call Duration (seconds) – The great call is consisted of the Introduction

Phase, Inflective Note, Climax Notes, Post Climax Phase and the Terminal

Phase (NB: Terminal Phase will not be included in this study)

8. Great Call Notes – the number of noted that this phase is consisted of.

9. Inflective Note Duration (seconds) – This is the first note in the great call that

rises greatly in frequency. Starts at the low level of introduction phase notes

and rises near to the climax note height.

10. Climax Note Duration (seconds) – This is the note following the inflective

note. It is the note with the maximum frequency and is a flat note at that

frequency.

11. Post Climax Phase Duration (seconds) – This phase is consisted of the strong

notes that follow the climax note. These notes are descending in frequency.

12. Post Climax Phase Notes – The number of notes contained in this phase.

13. Minimum Frequency (Hertz) – This is the frequency of the lowest note sung in

the total vocalisation. Almost always the last of the post climax notes.

56

14. Maximum Frequency (Hertz) – This is the frequency of the highest note sung

in the total vocalisation. Almost always the climax note.

Once the individual songs had been recorded, it was then necessary to analyse the

individual great calls across the previously mentioned 14 song characteristics. It was

decided that the method of analysis developed by Haimoff and Gitten (1985) for

analysis of individuality of vocalisation in female agile gibbons (H. agilis) would be

the best method to use. However, an extra 2 characteristics were developed for

analysis, making the total 14 characteristics, as can be seen above. It was decided that

Dallmann and Geissmann’s (2001a) Mean Pairwise Difference (MPD) method would

not be used here as this is the only study that this method has been used on to date,

and by building on Haimoff and Gittins (1985) method, this would enable

comparisons between the two studies.

This data was extracted for each of the 286 songs across the 14 studied individuals

and put intro separate spreadsheets. This was done using Raven Lite 1.0. Here songs

could be analysed using a sonographic display. These sonograms provide spatial and

temporal information regarding the great call being analysed. This enabled duration

times and note numbers to be extracted from each great call. This data could then be

collated into separate spreadsheets for each studied individual female agile gibbon at

the study site. Another spreadsheet was constructed containing all the extracted data

for all 286 songs. This enabled statistical analysis to be easily carried out across the

individual groups, as well as an intra-population analysis as well.

57

The following statistics were used in the analysis of the aforementioned collected

data. It should be noted that for the purposes of testing for individuality of female

vocalisation, two statistical tests would be used. This would provide a second measure

of individuality and would also provide a means of comparing the two results.

Therefore one test to be used will be the Principle Components Analysis (PCA), while

the second test being the non-parametric Kruskal-Wallis statistical test.

2.4.1 Principle Components Analysis (PCA):

The PCA was run against all groups, and all 14 characteristics. This is a process

which aims at reducing the 14 main characteristics into significant factors, which can

then be plotted against each other in a scatterplot. The aim of this is to see how

individual each female’s song is, in a visual format, when plotted against all the other

focal females on the same graph. The Principle Components Analysis also shows the

characteristics which are most strongly individual. In other words, the characteristics

which could be used singularly for the identification of great call individuality. Here

the hypothesis is that the scatterplot for combined groups will visually identify that

the studied female individuals display individuality of vocalisation. This will be

rejected, and the null hypothesis accepted if no clear pattern appears.

2.4.2 Kruskal-Wallis Test:

The Kruskal-Wallis Test allows one-way analysis of variance of ranks. This non-

parametric test was used as the gathered data did not display normal distribution. The

Kruskal-Wallis Test was also used to test for individuality of female’s great calls. It

58

was felt that a Kruskal-Wallis Test could be used as a further means of testing for

individuality of vocalisation. Here the hypothesis is that all females display

individuality of vocalisation. This will be rejected if a significance level of p < 0.05 is

not achieved, and the null hypothesis accepted.

2.4.3 Effects of Distance on Individuality:

To test for the effect that physical distance apart has on levels of individuality of

vocalisation, various tests had to be developed. These are as follows:

PCA vs Physical Distance Apart; and PCA vs Euclidean Distance Apart:

Firstly it was necessary to create a spreadsheet which listed the physical distances

apart of all 14 groups. This was taken from the map provided at the study site. Using

Pythagoras’ Theorem it was possible to create a matrix of physical distance between

each pair of groups. Then it was necessary to find the difference between the Principle

Components Analysis (PCA) scores, which was done using the first two axes

(51.546% of variance, see Results section) as Cartesian co-ordinates and re-

calculating the distance matrix replacing physical space with ordination space. A third

distance matrix was calculated, the Euclidean distance between all variables entered

into the Principle Component Analysis (which will correspond to the Euclidean

distance between groups in PCA space using all 14 dimensions). Each distance matrix

was square with a leading diagonal of zeroes, and 91 off-diagonal distance (14

groups, means there are 91 comparisons available; =

13+12+11+10+9+8+7+6+5+4+3+2+1 = 91). Patterns between these 91 distances can

59

be used to seek evidence that simple proximity has an effect on female agile gibbon’s

great calls. Here the hypothesis is that distance between individuals has an effect on

the degree of individuality of vocalisation. If this is a case we should see a positive

relationship between PCA distance apart and physical distance apart. The hypothesis

will be rejected if this is not the case.

If the hypothesis is rejected in this case, this would lend support to the theory that

gibbon songs are inherited genetically and that they are not altered, or require altering,

to adapt to their surroundings. If gibbon songs were genetically inherited (Geissmann

1984, 1993, 2000a), then we would expect there to be no relationship between

distance between individuals and differences in their vocalisations. If gibbon songs

were not genetically ‘coded’, then we would expect to see a relationship when there

are greater differences in vocalisations between gibbons that are physically closer

together, than those of gibbons that are physically farther apart, or even outside of

hearing range.

60

3. Results

61

Great calls were recorded for 14 individual females. Data was extracted from 286

great calls that were recorded across the groups, and were considered to be of high

enough quality to analyse. Table 3.1 shows the breakdown of analysed great calls

across the 14 female individuals.

Table 3.1 Showing number of analysed great calls across the studied individual

females.

Group Individual

Female

No. Great

Calls

Analysed

2

3

4

5

6

7

A

C

E

HH

K

N

PO

FG2

FG3

FG4

FG5

FG6

FG7

FGA

FGC

FGE

FGHH

FGK

FGN

FGPO

20

22

12

21

16

25

21

24

31

11

21

10

26

62

S FGS 26

TOTAL = 286

It was intended to collect at least 20 songs per individual to add to the data matrix,

however, numerous factors associated with agile gibbon singing habits and

frequencies, including unfavourable climatic conditions restricted the total number of

songs recorded. Of 81 days spent at the Setia Alam Field Station, 36 days were lost to

poor weather. This was due to the fact that the wet season was nearing an end in the

first month or so of this study period.

Once songs had been recorded, data was required to be extracted from them, with this

data being collated into spreadsheets for each individual female agile gibbon. This

data extracted involved getting information on 14 key great call characteristics. This

was done by manually counting song phase notes, or measuring the duration of song

phases in seconds. Once this data was collected for each individual, intra population

comparison could be able to be made. Appendix 1 shows an example of one

individual female’s extracted data.

Extracted great call data was gathered from analysing recorded songs in the

bioacoustics software program Raven Lite 1.0. Using this method it was possible to

63

view recorded songs in sonogram format. Figure 3.1 shows an example of one such

sonogram of a recorded great call for an agile gibbon female individual.

Figure 3.1 Sonogram of female individual FG5 on date 16/05/06

During the making of great call recordings in the mixed peat swamp forest in

Sebangau National Park, the following observations were made. Both juvenile male

and female individuals belonging to family groups were practicing great calls. As

clarified in the introduction of this thesis, the current thought is that juvenile gibbons

do not ‘learn’ their great calls from their parents. Do not confuse this with ‘learning’

how to sing. Rather, this concerns the ‘learning’ of their individual songs which pass

on information about themselves and their surroundings. Rather, juvenile gibbons

appear to inherit their song structures genetically as numerous studies have displayed

(Geissmann 1984, 1993, 2000a). Despite this, while recordings were being made,

young gibbons could be heard mimicking their parents singing. Again, as previously

discussed, this could represent the juvenile gibbons practicing singing, rather than

imitating their parents and trying to ‘learn’ their parents songs structures. It cannot be

ignored, however, that juvenile female gibbons attempt to imitate their mother’s songs

exactly, as can be seen in Figure 3.2.

64

Figure 3.2 Sonogram of female great call showing juvenile female’s attempt to

mimic, copy or imitate her mother’s song structure.

Levels of Individuality Displayed by female Bornean Agile Gibbons (H. agilis

albibarbis)

In order to investigate the levels of individuality displayed, if any, by individual

female Bornean agile gibbons’ (H. agilis albibarbis) vocalisations at Sebangau

National Park two methods of statistical analysis were employed. This was done as it

was felt that two statistical tests provide a greater insight into any apparent presence,

or lack, of individuality in these vocalisations. As such, a Principle Components

Analysis was performed on the combined data for all analysed great calls across all

groups. The second statistical test performed was the non-parametric Kruskal-Wallis

one-way analysis of variance of ranks. The results of both of these performed tests

now follow.

65

3.1 Principle Components Analysis (PCA):

3.1.1 PCA showing which characteristics account for most variation in song

individuality compared between the different female Bornean agile gibbons (H.

agilis albibarbis)

The Principle Components Analysis was used as it has the ability to reduce the 14

analysed female song characteristics into a smaller number of dimensions. These,

more significant dimensions explain the most significantly individual song

characteristics, and can also be used to be plotted against each other on a scatterplot.

By graphing these two main significant dimensions against each other on the same

graph, using data for all 14 studied female individuals, we can get a picture of how

individual each female’s total number of analysed songs are when compared with

other females on the same graph.

The results of the PCA reduced the 14 analysed song characteristics into 5 significant

dimensions, which accounted for 90.430% of total variation across the 14

characteristics of the 286 great calls analysed. Of particular interest when performing

PCA test are the first two significant components or dimension that are produced. In

this study, the first component accounted for 30.046% of variation in the female great

calls. The second significant component accounted for 21.500% of variation. The

third, fourth and fifth significant components accounted for 17.847%, 13.194% and

7.842% of variation respectively.

66

Interestingly, when viewing the Varimax with Kaiser Normalisation method of

rotated sums of squared loadings, the five significant components produces also

account for the exact same degree of variation, 90.430%. Here the first component

accounted for 28.550% of variation, the second component accounted for 20.222% of

variation, the third component accounted for 19.571% of variation, the fourth

component accounted for 13.787& of variation, with the fifth component accounting

for 8.299% of variation in the females’ great calls.

Table 3.2 shows the five most significant components produced by the extraction

sums of square loadings and also by the rotated sums of square loadings. This can be

seen below.

67

Table 3.2 Showing PCA output with total variance explained after PCA

reduction had been performed

Total Variance Explained

Initial Eigenvalues

Extraction Sums of Squared

Loadings Rotation Sums of Squared Loadings

Compone

nt Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 4.206 30.046 30.046 4.206 30.046 30.046 3.997 28.550 28.550

2 3.010 21.500 51.546 3.010 21.500 51.546 2.831 20.222 48.772

3 2.499 17.847 69.394 2.499 17.847 69.394 2.740 19.571 68.344

4 1.847 13.194 82.588 1.847 13.194 82.588 1.930 13.787 82.131

5 1.098 7.842 90.430 1.098 7.842 90.430 1.162 8.299 90.430

6 .451 3.222 93.653

7 .357 2.551 96.204

8 .301 2.151 98.355

9 .091 .653 99.007

10 .084 .599 99.606

11 .032 .231 99.838

12 .013 .096 99.934

13 .007 .050 99.984

14 .002 .016 100.000

Extraction Method: Principal Component Analysis.

Once the PCA had produced the five significant components after data reductions, it

was then necessary to investigate what each component actually represents. In other

words, component 1 represented 30.046% of all variation across the females’ great

calls, but what original song characteristics were actually represented within this

68

30.046% of great call variation. This representation of variation will now be

addressed. Two table forms of both of these outputs can also been viewed in Tables

3.2 and 3.3 below.

When analysing the PCA component 1 an interesting pattern appears. Viewing the

normal extraction method we see that PCA component 1, which accounts for 30.046%

of total variation, comes from the following original great call characteristics: Total

Duration of Vocalisation (Component Matrix score: 0.919), Duration of Introduction

Phase (0.785), Number of Notes of the Introduction Phase (0.761), Duration of Great

Call (0.834) and Number of Notes of Great Call (0.766).

Interestingly, when we observe the Varimax with Kaiser Normalisation rotated scores,

a similar pattern appears. PCA component 1 for this method, which accounts for

28.550% of variation, consists of variation from the following original great call

characteristics: Total Duration of Vocalisation (Rotated Component Matrix score:

0.824), Duration of Introduction Phase (0.916), Number of Notes of the Introduction

Phase (0.875), Duration of Great Call (0.896) and Number of Notes of Great Call

(0.733).

Analysis of PCA component 1 across the two methods of data reduction both suggest

that the same 5 original female song characteristics provide the greatest degree of

variation between groups. These are Total Duration of Vocalisation, Duration of

Introduction Phase, Number of Notes of Introduction Phase, Duration of Great Call,

and Number of Notes of Great Call.

69

When looking at component 2 that was produced by the PCA data reduction, another

interesting pattern appears.

Analysis of the normal extraction method shows us that the PCA component 2, which

accounts for 21.500% of total variation, consists of the following original great call

characteristics: Total Number of Notes of Vocalisation (Component Matrix score:

0.589), Duration of Pre-Introduction Phase (0.611), Number of Notes of Pre-

Introductory Phase (0.568), Duration of Post Climax Phase (0.607) and Number of

Notes of Post Climax Phase (0.707).

Again, when observing the Varimax with Kaiser Normalisation rotated scores, we see

a close pattern again. The PCA component 2 accounts for 20.222% of total variation.

The following original female song characteristics account for this 20.222% of total

variation: Total Number of Notes of Vocalisation (Component Matrix score: 0.870),

Duration of Pre-Introduction Phase (0.969), and Number of Notes of Pre-Introductory

Phase (0.982).

When analysing PCA component 2 across the two methods of data reduction, both

methods agree that the variation comes from Total Number of Notes of Vocalisation,

Duration of Pre-Introduction Phase and the Number of Notes of Pre-Introductory

Phase. However, interestingly the Varimax rotation method does not agree with the

standard data reduction method when the standard method also shows that the

Duration and Total Number of Notes of the Post Climax Phase account for this level

of total variation.

70

Again, when investigating PCA component 3 across the two different methods of data

reduction used, there is a degree of difference. Using the normal extraction method

PCA component 3, which accounts for 17.847% of variation, is explained by the

following original song characteristics: Duration of Great Call (Component Matrix

score: 0.407), Number of Notes of Great Call (0.456), Duration of Post Climax Phase

(0.667) and Number of Notes of Post Climax Phase (0.667). It is of note that all these

four original song characteristics which make up the 17.847% of variation that is PCA

component 3 have already been represented by earlier components, and with greater

loadings. Duration and Number of Notes of Great Call both made up the variation

seen in PCA component 1, while Duration and Number of Notes of Post Climax

Phase both made up variation see in PCA component 2.

Looking at the results for PCA component 3 produced by the Variman rotation

method, we see a different pattern. PCA component 3 by this method accounts for

19.571% of total variation. This variation is made up by the following original song

characteristics: Number of Notes of Great Call (Rotation Component Matrix score:

0.524), Duration of Post Climax Phase (0.927) and Number of Notes of Post Climax

Phase (0.939).

Comparing these two methods of data reduction for PCA component 3 shows us

slightly different results. While the variation explained by PCA component 3 using

the normal method is consisted of four previously represented original song

characteristics, this is not the case for the results produced by the rotated method. The

Number of Notes of Great Call shows up here again, after having also been

represented in PCA component 1. However, the variation shown in PCA component 3

71

is also made up by the Duration and Number of Notes of the Post Climax Phase,

which we were both not included in component 2 (while these two were included in

component 2’s variation for the normal extraction method).

Looking at the degrees of variation accounted for in PCA component 4 across the two

different extraction methods, we see vast similarities once more. For the normal

extraction method, PCA component 4, which accounts for 13.194% of total variation,

we see the following original song characteristics which make up this variation:

Duration of the Inflective Note (Component Matrix Score: 0.799), Duration of Climax

Notes (0.690) and Maximum Frequency for Vocalisation (0.607). For the rotated

extraction method, PCA component 4, which accounts for 13.787% of total variation,

we see the very same original song characteristics which make up this variation:

Duration of the Inflective Note (Rotated Component Matrix Score: 0.837), Duration

of Climax Note (0.813) and Maximum Frequency for Vocalisation (0.571).

The final significant PCA component produced, component 5, shows similar results

between the two extraction methods once more. Using the normal extraction method

PCA component 5, which accounts for 7.842% of total variation, is explained by the

following single original song characteristics: Minimum Frequency of Vocalisation

(Component Matrix Score: 0.892). This same single original song characteristics

explains the 8.299% of total variation explained by PCA component 5, as calculated

by the Varimax rotation method (Minimum Frequency of Vocalisation {Rotated

Component Matrix Score: 0.944}).

72

Table 3.3 Normal extraction method component matrix showing original

characteristics and their relation to PCA components.

Component Matrix(a)

Component

1 2 3 4 5

Total Duration .919 .070 .065 .292 .111

Total No. Notes .671 .589 -.375 .109 -.133

Duration Pre-Intro Phase .245 .611 -.676 .242 -.009

No. Notes Pre-Intro Phase .289 .568 -.721 .223 .007

Duration of Intro Phase .785 -.564 -.067 -.047 -.075

No. Notes Intro Phase .761 -.537 -.153 -.163 -.134

Duration of Great Call .834 -.209 .407 .194 .122

No. Notes Great Call .766 .173 .456 -.141 -.265

Duration of Inflective Note -.206 -.318 .046 .799 -.111

Duration of Climax Note .107 -.477 .001 .690 .282

Duration of Post Climax .231 .607 .667 .061 .219

No.Notes Post Climax .070 .707 .639 .002 -.148

Min. Freq. .182 .108 -.072 -.261 .892

Max Freq. -.383 .271 .339 .607 .015

Extraction Method: Principal Component Analysis.

a = 5 components extracted.

73

Table 3.4 Varimax rotation extraction method component matrix showing

original characteristics and their relation to PCA components.

Rotated Component Matrix(a)

Component

1 2 3 4 5

Total Duration .824 .385 .254 .183 .159

Total No. Notes .361 .870 .217 -.178 -.032

Duration Pre-Intro Phase -.093 .969 -.024 -.015 .036

No. Notes Pre-Intro Phase -.046 .982 -.081 -.027 .061

Duration of Intro Phase .916 -.041 -.326 -.001 -.002

No. Notes Intro Phase .875 -.005 -.389 -.133 -.037

Duration of Great Call .896 -.067 .294 .203 .148

No. Notes Great Call .733 .037 .524 -.272 -.168

Duration of Inflective Note -.055 -.069 -.101 .837 -.280

Duration of Climax Note .252 -.079 -.206 .813 .152

Duration of Post Climax .085 .011 .927 -.029 .223

No.Notes Post Climax -.076 .038 .939 -.164 -.139

Min. Freq. .048 .058 .022 -.130 .944

Max Freq. -.391 -.024 .455 .571 -.133

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a = Rotation converged in 5 iterations.

When viewing these results as a whole, we see a very similar pattern appear for the

initial PCA scores. Both methods of extraction, the normal method and the Variamax

rotation with Kaiser Normalisation methods yield results that are much the same. The

5 significant PCA components produced by these two methods both account for

90.340% of total variation for all analysed female Bornean agile gibbon (H. agilis

74

albibarbis) vocalisation. When comparing the two methods of extraction they give us

a similar insight into which original song characteristics carry the greatest levels of

individuality. In other words, they can show us which characteristics explain the

greatest amount of variation in female singing. Table 3.5 compares the two methods

of extraction, showing the most significant characteristics in order of importance

when considering variation in female vocalisation.

Table 3.5 Song Characteristics, in order of most significance, explaining

variation in female vocalisation

Rank Normal PCA method Varimax Rotation method

1

Total Song Duration

(Component 1; score 0.919)

Duration of Intro Phase

(Component 1; score 0.916)

2

Duration of Great Call

(Component 1; score 0.834)

Duration of Great Call

(Component 1; score 0.896)

3

Duration of Intro Phase

(Component 1; score 0.785)

No. Notes Intro Phase

(Component 1; score 0.875)

4

No. Notes Great Call

(Component 1; score 0.766)

Total Song Duration

(Component 1; score 0.824)

5

No. Notes Intro Phase

(Component 1; score 0.761)

No. Notes Great Call

(Component 1; score 0.733)

6

No. Notes Post Climax

(Component 2; score 0.707)

No. Notes Pre-Intro Phase

(Component 2; score 0.982)

7 Duration Pre-Intro Phase Duration Pre-Intro Phase

75

(Component 2; score 0.611) (Component 2; score 0.969)

8

Duration Post Climax

(Component 2; 0.607)

Total No. Song Notes

(Component 2; score 0.870)

9

Total No. Song Notes

(Component 2; score 0.589)

No. Notes Post Climax

(Component 3; score 0.939)

10

No. Notes Pre-Intro Phase

(Component 2; score 0.568)

Duration Post Climax

(Component 3; score 0.927)

11

Duration Inflective Note

(Component 4; score 0.799)

Duration Inflective Note

(Component 4; score 0.837)

12

Duration Climax Note

(Component 4; score 0.690)

Duration Climax Note

(Component 4; score 0.813)

13

Maximum Frequency

(Component 4; score 0.607)

Maximum Frequency

(Component 4; score 0.571)

14

Minimum Frequency

(Component 5; score 0.892)

Minimum Frequency

(Component 5; score 0.944)

Although Table 3.5 shows some discrepancies as to the highest ranking song

characteristics when accounting for variation in vocalisations between females, there

is a pattern to be seen. When looking at the top 5 ranking characteristics in terms of

accounting for the most amount of variation in vocalisation both extraction methods

list the same 5 characteristics. However, these characteristics are not the same in

terms of rank order. Despite this, when looking at the bottom 5 characteristics,

accounting for the least amount of song variation, both methods shows these 5

characteristics in the same order.

76

3.1.2 PCA identifying degree of individuality of vocalisation for individual

female Bornean agile gibbons (H. agilis albibarbis)

It is possible to use the results of the Principle Components Analysis to get an insight

into how individual each female’s vocalisations are. This is a fairly simple process.

By plotting the results of the first two PCA components, which in this case

represented 51.546% of all variation in all of the analysed song data, we can get a

visual representation of these results. By graphing up the two components against

each other we get an insight into how similar each female’s analysed songs are, as

well as how individual they are when compared to the songs of other females in the

study group.

When comparing all of these groups against each other, the scatterplot appears

crowded, making it difficult to ascertain how individual each female’s songs are. This

is represented in Figure 3.3 below.

This figure shows some definite patterns in terms of individuality of vocalisation for

each individual female. Some of the females’ songs show some very significant levels

of individuality, while some others are not as pronounced in terms of individuality.

Each group will be discussed separately further on in this results section.

As a definite pattern had occurred visually for the results of PCA analysis, the

hypothesis was accepted. Female Bornean agile gibbon (H. agilis albibarbis) display

individuality of vocalisation when compared to other individuals.

77

Figure 3.3 Scatterplot showing levels of individuality of each female’s analysed

songs

2.000000.00000-2.00000

PCA Component 1 (30.05% of variation)

4.00000

3.00000

2.00000

1.00000

0.00000

-1.00000

-2.00000

PCA

Com

pone

nt 2

(20.

50%

of v

aria

tion)

SPONKHHECA765432

Group

78

Female Group 2 (FG2)

As can be seen in Figure 3.4 there is a degree of scattering about FG2’s vocalisation.

Despite this a pattern can be seen. Although the grouping is not as tight as may have

been expected, it can be argued that there is a degree of individuality displayed in the

20 analysed songs of the Group 2 female.

Figure 3.4 Graph showing first two significant PCA components displaying level

of individuality of vocalisation for Group 2 Female

1.000.500.00-0.50-1.00

PCA Component 1 (30.05% of variation)

1.00

0.50

0.00

-0.50

-1.00

-1.50

PCA

Com

pone

nt 2

(20.

50%

of v

aria

tion)

79

Female Group 3 (FG3)

As can be seen in Figure 3.5 FG3 displays a strong degree of individuality of

vocalisation. When viewing the 22 analysed songs for the female from Group 3 it can

be argued with confidence that this female does possess an individual song which

appears to be quite similar in most examples, despite there being a few songs which

do not appear within the tight cluster that most of the songs appear.

Figure 3.5 Graph showing first two significant PCA components displaying level

of individuality of vocalisation for Group 3 Female

2.001.501.000.500.00-0.50-1.00

PCA Component 1 (30.05% of variation)

0.90

0.60

0.30

0.00

-0.30

-0.60

PCA

Com

pone

nt 2

(20.

50%

of v

aria

tion)

80

Female Group 4 (FG4)

The individual referred to as FG4 does not display any great degree of clustering

when analysing the 12 recorded songs. This is certainly a pattern that was not

expected. A much tighter cluster was expected. This would suggest that the female

from Group 4 does not display a unique song each time it vocalises as can be seen in

Figure 3.6.

Figure 3.6 Graph showing first two significant PCA components displaying level

of individuality of vocalisation for Group 4 Female

-0.20-0.40-0.60-0.80-1.00-1.20

PCA Component 1 (30.05% of variation)

2.50

2.00

1.50

1.00

0.50

0.00

PCA

Com

pone

nt 2

(20.

50%

of v

aria

tion)

81

Female Group 5 (FG5)

When analysing the 21 recorded songs for FG5 a definite pattern can be seen.

Viewing Figure 3.7 it cannot be argued that there is a definite cluster formed when

plotting the first two PCA components against each other. From this it can be

confidently said that the Group 5 female has a unique song which is similar for most

of the recorded songs.

Figure 3.7 Graph showing first two significant PCA components displaying level

of individuality of vocalisation for Group 5 Female

0.500.00-0.50-1.00-1.50-2.00

PCA Component 1 (30.05% of variation)

2.00

1.00

0.00

-1.00

PCA

Com

pone

nt 2

(20.

50%

of v

aria

tion)

82

Female Group 6 (FG6)

Like some other individual females, FG6 displays a cluster that is not as tight as might

have been expected. Regardless, when analysing the 16 recorded songs a pattern does

appear. This is suggestive that the female from Group 6 does possess an individual

song which is similar when repeated over time. The results are displayed in Figure 3.8

below.

Figure 3.8 Graph showing first two significant PCA components displaying level

of individuality of vocalisation for Group 6 Female

2.001.501.000.500.00

PCA Component 1 (30.05% of variation)

0.00

-0.50

-1.00

-1.50

PCA

Com

pone

nt 2

(20.

50%

of v

aria

tion)

83

Female Group 7 (FG7)

When comparing the 25 analysed songs after PCA reduction for FG7 we do see a

slight pattern. As with other groups, the cluster is not as not as tight as might be

expected, however a pattern does exist. As can be seen in Figure 3.9, the female from

Group 7 has an individual vocalisation which is unique for the individual over

repeated song recordings.

Figure 3.9 Graph showing first two significant PCA components displaying level

of individuality of vocalisation for Group 7 Female

3.002.502.001.501.000.500.00-0.50

PCA Component 1 (30.05% of variation)

4.00

3.00

2.00

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Female Group A (FGA)

Like many of the analysed females, FGA’s recorded songs do indeed follow a pattern,

just not as strong as is desirable. When viewing Figure 3.10 there is a loose cluster

forming for the 21 analysed songs when plotting PCA component 1 against PCA

component 2. This suggests that the female from Group A also has a unique form of

vocalisation which is repeated across the recorded songs.

Figure 3.10 Graph showing first two significant PCA components displaying

level of individuality of vocalisation for Group A Female

0.00-0.50-1.00-1.50-2.00

PCA Component 1 (30.05% of variation)

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Female Group C (FGC)

When viewing the 24 analysed songs for FGC in graph format, with PCA component

1 plotted against PCA component 2, we see a pattern appear. Like many of the

females in this study, the cluster may not be as tight as would be ideal, but regardless,

a pattern is there to be see in Figure 3.11. Again, this is suggestive that the female

from Group C also has a unique vocalisation and follows a similar pattern in the

majority of recorded songs.

Figure 3.11 Graph showing first two significant PCA components displaying

level of individuality of vocalisation for Group C Female

0.500.00-0.50-1.00-1.50

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Female Group E (FGE)

Viewing Figure 3.12 displays a very loose pattern for the vocalisations of FGE. When

the PCA component 1 is plotted against PCA component 2 the results are not as

expected. Regardless, when viewing the combined scatterplot with all of the groups

included (prev. Fig 3.3), and when viewing Figure 3.12 it can be argued that the

female from Group E does possess a unique form of vocalisation, but one which does

not follow a rigid template when analysing the 32 recorded songs for this individual.

Figure 3.12 Graph showing first two significant PCA components displaying

level of individuality of vocalisation for Group E Female

0.500.00-0.50-1.00-1.50

PCA Component 1 (30.05% of variation)

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Female Group HH (FGHH)

Only 11 vocalisations were recorded for FGHH. When viewing Figure 3.13, which

displays the PCA component 1 for this individual plotted against PCA component 2

an odd pattern appears. It could be argued that two patterns are developing. This could

be suggestive that the female from Group HH could indeed possess two unique songs.

This is not something that has been recorded before and will be discussed further in

the Discussion section of this thesis.

Figure 3.13 Graph showing first two significant PCA components displaying

level of individuality of vocalisation for Group HH Female

1.401.301.201.101.000.900.800.70

PCA Component 1 (30.05% of variation)

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Female Group K (FGK)

When viewing Figure 3.14 which plots the first two significant PCA components

against each other, another strange pattern appears. As with the female from Group

HH, it would appear that two patterns have appeared. Analysis of the 21 recorded

songs for the female from Group K it could be suggested that this female also may

have developed two unique forms of vocalisation, both of which are quite different to

each other. This possibility will be discussed further in the Discussion section.

Figure 3.14 Graph showing first two significant PCA components displaying

level of individuality of vocalisation for female from Group K

0.250.00-0.25-0.50-0.75-1.00-1.25-1.50

PCA Component 1 (30.05% of variation)

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Female Group N (FGN)

Only 10 songs were recorded and analysed for FGN. Despite this, when viewing

Figure 3.15, which plots PCA component 1 for this individual against PCA

component 2 for this individual, a tight pattern appears. This is indeed what would be

expected. Looking at this figure as well as the graph showing the all groups included

(prev. Fig 3.3), it can easily be argued that the female from Group N does indeed

posses a vocalisation that is unique to itself and that it’s vocalisations are similar for

the majority of the time.

Figure 3.15 Graph showing first two significant PCA components displaying

level of individuality of vocalisation for Group N Female

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Female Group PO (FGPO)

As has been seen with many of the females from this study, the pattern we see

forming in Figure 3.16 is not as tight as would be expected. When viewing the 20

analysed recorded songs for FGPO a weak cluster appears. Despite this is can be

argued that this female does posses a unique vocalisation, but that this individual’s

vocalisations vary somewhat in the 20 recorded songs.

Figure 3.16 Graph showing first two significant PCA components displaying

level of individuality of vocalisation for Group PO

Female

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Female Group S (FGS)

26 songs were recorded and analysed for FGS. When viewing Figure 3.17, which

plots the first two significant PCA components for this individual, a definite pattern

can be seen. Again, this cluster may not be as tight as would be considered perfect.

Despite this, it can definitely be said that the female individual from Group S does

display a unique form of vocalisation, which is adhere to loosely in the recorded

songs.

Figure 3.17 Graph showing first two significant PCA components displaying

level of individuality of vocalisation for Group S Female

0.00-0.20-0.40-0.60-0.80-1.00-1.20

PCA Component 1 (30.05% of variation)

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3.2 Kruskal-Wallis

The Kruskal-Wallis test for one-way analysis of variance of ranks was performed on

the 286 recorded female Bornean agile gibbon (H. agilis albibarbis) to test for

individuality of vocalisation. This non-parametric test was used as the data did not

display normal distribution.

The Kruskal-Wallis results will be addressed in separate sections for each of the 14

characteristics that the recorded songs were analysed across. This will follow below.

3.2.1 Duration of Vocalisation

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Duration of Total Vocalisation (chi-

squared = 156.799, P < 0.001). The raw data for this non-parametric test can be found

in Appendix 2. Visual representation of the individuality for this song characteristic

can found in the form of a Boxplot (Appendix 3) and an Error Bar Graph (Appendix

4).

3.2.2 Number of Notes of Total Vocalisation

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Number of Notes of Total Vocalisation

(chi-squared = 130.538, P < 0.001). The raw data for this non-parametric test can be

found in Appendix 5. Visual representation of the individuality for this song

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characteristic can found in the form of a Boxplot (Appendix 6) and an Error Bar

Graph (Appendix 7).

3.2.3 Duration of Pre-Introduction Phase

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Duration of the Pre-Introduction Phase

(chi-squared = 130.632, P < 0.001). The raw data for this non-parametric test can be

found in Appendix 8. Visual representation of the individuality for this song

characteristic can found in the form of a Boxplot (Appendix 9) and an Error Bar

Graph (Appendix 10).

3.3.4 Number of Notes of Pre-Introduction Phase

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Number of Notes of the Pre-Introduction

Phase (chi-squared = 138.662, P < 0.001). The raw data for this non-parametric test

can be found in Appendix 11. Visual representation of the individuality for this song

characteristic can found in the form of a Boxplot (Appendix 12) and an Error Bar

Graph (Appendix 13).

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3.3.5 Duration of Introduction Phase

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Duration of the Introduction Phase (chi-

squared = 206.718, P < 0.001). The raw data for this non-parametric test can be found

in Appendix 14. Visual representation of the individuality for this song characteristic

can found in the form of a Boxplot (Appendix 15) and an Error Bar Graph (Appendix

16).

3.3.6 Number of Notes of Introduction Phase

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Number of Notes of the Introduction

Phase (chi-squared = 223.286, P < 0.001). The raw data for this non-parametric test

can be found in Appendix 17. Visual representation of the individuality for this song

characteristic can found in the form of a Boxplot (Appendix 18) and an Error Bar

Graph (Appendix 19).

3.3.7 Duration of Great Call

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Duration of the Great Call (chi-squared =

189.892, P < 0.001). The raw data for this non-parametric test can be found in

Appendix 20. Visual representation of the individuality for this song characteristic can

found in the form of a Boxplot (Appendix 21) and an Error Bar Graph (Appendix 22).

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3.3.8 Number of Notes of Great Call

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Number of Notes of the Great Call (chi-

squared = 160.416, P < 0.001). The raw data for this non-parametric test can be found

in Appendix 23. Visual representation of the individuality for this song characteristic

can found in the form of a Boxplot (Appendix 24) and an Error Bar Graph (Appendix

25).

3.3.9 Duration of Inflective Note

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Duration of the Inflective Note (chi-

squared = 112.483, P < 0.001). The raw data for this non-parametric test can be found

in Appendix 26. Visual representation of the individuality for this song characteristic

can found in the form of a Boxplot (Appendix 27) and an Error Bar Graph (Appendix

28).

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3.3.10 Duration of Climax Note

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Duration of the Climax Note (chi-squared

= 107.182, P < 0.001). The raw data for this non-parametric test can be found in

Appendix 29. Visual representation of the individuality for this song characteristic can

found in the form of a Boxplot (Appendix 30) and an Error Bar Graph (Appendix 31).

3.3.11 Duration of Post Climax Phase

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Duration of the Post Climax Phase (chi-

squared = 162.736, P < 0.001). The raw data for this non-parametric test can be found

in Appendix 32. Visual representation of the individuality for this song characteristic

can found in the form of a Boxplot (Appendix 33) and an Error Bar Graph (Appendix

34).

3.3.12 Number of Notes of Post Climax Phase

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Number of Notes of the Post Climax

Phase (chi-squared = 166.120, P < 0.001). The raw data for this non-parametric test

can be found in Appendix 35. Visual representation of the individuality for this song

characteristic can found in the form of a Boxplot (Appendix 36) and an Error Bar

Graph (Appendix 37).

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3.3.13 Minimum Frequency of Vocalisation

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Minimum Frequency of Vocalisation

(chi-squared = 137.466, P < 0.001). The raw data for this non-parametric test can be

found in Appendix 38. Visual representation of the individuality for this song

characteristic can found in the form of a Boxplot (Appendix 39) and an Error Bar

Graph (Appendix 40).

3.3.14 Maximum Frequency of Vocalisation

Results of the Kruskal-Wallis test show that there is individuality displayed between

the 14 studied groups when considering the Maximum Frequency of Vocalisation

(chi-squared = 192.753, P < 0.001). The raw data for this non-parametric test can be

found in Appendix 41. Visual representation of the individuality for this song

characteristic can found in the form of a Boxplot (Appendix 42) and an Error Bar

Graph (Appendix 43).

As all of the 14 analysed song characteristics displayed a significance level of better

than p < 0.05, the hypothesis was accepted. Female Bornean agile gibbons (H. agilis

albibarbis) display individuality in their vocalisation when compared to other

individuals.

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3.6 Sonograms of individual females’ typical vocalisation

Below are figures illustrating each female’s typical vocalisation, showing variation in

vocalisation for the studied female Bornean agile gibbon (H. agilis albibarbis).

Figure 3.18 Sonogram for individual FG2

Figure 3.19 Sonogram for individual FG3

Figure 3.20 Sonogram for individual FG4

Figure 3.21 Sonogram for individual FG5

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Figure 3.22 Sonogram for individual FG6

Figure 3.23 Sonogram for individual FG7

Figure 3.24 Sonogram for individual FGA

Figure 3.25 Sonogram for individual FGC

Figure 3.26 Sonogram for individual FGE

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Figure 3.27 Sonogram for individual FGHH

Figure 3.28 Sonogram for individual FGK

Figure 3.29 Sonogram for individual FGN

Figure 3.30 Sonogram for individual FGPO

Figure 3.31 Sonogram for individual FGS

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3.5 Effect of Physical Distance Apart of Individuals on Levels of Individuality of

Vocalisation

As mentioned in the Method section of this thesis, two tests were developed to test

whether or not physical distance between studied individuals has an effect on the

levels of individuality in female Bornean agile gibbon (H. agilis albibarbis) singing.

It is advisable to refer back to the Methods section to refresh readers understanding of

how these two tests were developed in order to test for the effects of distance apart.

Both of these methods were correlating either physical distance apart, or Euclidean

distance apart against the difference in PCA score between individuals to check for

patterns that may be produced.

The results of the two different methods are outlined below.

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3.5.1 PCA vs Physical Distance Apart:

When plotting the differences between groups in their first two significant PCA

component (accounting for 51.546% of variation) against the physical distance apart,

we see no obvious patterns occurring. For 14 studied individuals, there were 91

comparisons to be made. If there was a relationship between physical distance apart

between individuals and the differences between their PCA scores, we would expect

to see a positive relationship developing when these two measures are plotted against

each other. As can be seen in Figure 3.32, this is clearly not the case. This clearly

shows us that there is no relationship between the physical distance apart that

individuals are in the forest and the levels of individuality in their vocalisations. This

appears to go against the logic that individuals could be expected to display greater

degrees of individuality when compared to their neighbours when considering that

vocalisations are a means of identification of individuals and their territories.

Implications of these findings will be discussed further in the Discussion section of

this thesis.

As no positive relationship was found the hypothesis was rejected. Physical distance

apart from other individuals does not affect the degree of individuality of vocalisation

between individual female Bornean agile gibbons (H. agilis albibarbis).

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Figure 3.32 PCA plotted against Physical Distance Apart for comparisons

between all studied individuals

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3.5.2 PCA vs Euclidean Distance Apart:

In order to gain another means by testing whether or not levels in individuality of

vocalisation in female agile gibbons was effected by the distance apart between

individuals, a second test was developed. As with the previous method, if

individuality of song was effected by distances between neighbours, then we could be

expected to observe a positive relationship when plotting the differences in the first

two significant PCA components (which account for 51.546% of variation) against

the Euclidean distance between the individuals. As can be seen in Figure 3.33 this

certainly is not the case. In fact, it would appear that this method for testing for the

effects of distance on individuality in singing shows us even more strongly than the

previous test that there is in fact no such relationship. Implications for these findings

will be addressed further in the Discussion section of this thesis.

As no positive relationship was found the hypothesis was rejected. Distance apart

from other individuals does not affect the degree of individuality of vocalisation

between individual female Bornean agile gibbons (H. agilis albibarbis).

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Figure 3.33 PCA plotted against Euclidean Distance Apart for comparisons

between all studied individuals

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4. Discussion

Results of statistical analysis will be discussed further in this section. This includes

discussion on degrees of individuality of vocalisation, which song phases contribute

to this individuality and also an investigation into whether or not proximity to other

individuals has an effect on levels of individuality of song.

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4.1 Principle Components Analysis (PCA)

Results of various statistical analysis have shown that the female agile gibbons at

Sebangau National Park display levels of individuality in their vocalisations. Analysis

not only showed us that there is a unique song for each individual, but also identified

which songs characteristics and phases account for the greatest amount of this

individuality. PCA analysis was used to investigate these questions.

4.1.1 PCA showing which characteristics account for most variation in song

individuality compared between the different female Bornean agile gibbons (H.

agilis albibarbis)

The results of the Principle Component Analysis identified which of the 14 analysed

song characteristics are the most significant in contributing to the degree of

individuality displayed by the studied females. Two methods of analysis were used to

investigate this, the normal extraction method, and the Varimax rotation method. Both

of these methods produced fairly similar results, especially when considering which

characteristics have the least effect on levels of individuality. The five significant

components produced by both of these tests both accounted for 90.430% of total

variation in individuality of vocalisation displayed in the studied females. Table 4.1

shows the different ranks each characteristic was assigned in terms of significant

impact on levels of individuality of song.

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Table 4.1 Normal PCA and Varimax Rotated PCA ranks of song characteristics’

significance to individuality of vocalisation

Characteristic

Normal PCA

Rank

Rotated PCA

Rank

Duration of Total Vocalisation 1st 4th

No. Notes of Total Vocalisation 9th 8th

Duration of Pre-Intro Phase 7th 7th

No. Notes of Pre-Intro Phase 10th 6th

Duration of Intro Phase 3rd 1st

No. Notes of Intro Phase 5th 3rd

Duration of Great Call 2nd 2nd

No. Notes of Great Call 4th 5th

Duration of Inflective Note 11th 11th

Duration of Climax Note 12th 12th

Duration of Post Climax Phase 8th 10th

No. Notes of Post Climax Phase 6th 9th

Minimum Frequency of Vocalisation 14th 14th

Maximum Frequency of Vocalisation 13th 13th

This shows us which are the most important song characteristics when considering the

impact these characteristics, or song phases, have on individuals’ song individuality.

It would appear that the length of song phases carried greater weight than the number

of notes that are contained in those song phases. If we look at the ranks produced

from the normal PCA results we see the following pattern: The duration of the entire

vocalisation (1st) has greater weight than the number of notes in this phase (9th), the

duration of the pre-introduction phase (7th) carried greater importance to levels of

individuality of song than does the number of notes in the pre-introduction phase

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(10th), the duration of the introduction phase (3rd) carried greater weight than the

number of notes of the introduction phase (5th), the duration of the great call (2nd) also

has a greater impact on individuality of song than the number of notes contained in

the great call (4th). The only phase where this pattern is not seen is in the post climax

phase. Here the number of notes (6th) is seen as having greater importance than the

duration of this phase (8th).

When analysing the ranks produced by the Varimax rotated results a similar pattern

appears. The duration of the entire vocalisation (4th) has a greater impact than the

number of notes contained in this phase (8th). The duration of the introduction phase

(1st) is seen to have a greater effect on the levels of individuality of song that does the

number of notes in the introduction phase (3rd). While the duration of the great call

(2nd) is seen to carry greater weight than that of the number of notes sung in this phase

(5th). Unlike the normal extraction method, the rotated method has the number of

noted of the pre-introduction phase (6th) ranked ahead of the duration of this phase

(7th). The rotated method also agrees with the normal method by ranking the number

of notes of the post climax phase (9th) above the duration of this phase (10th).

This suggests that the duration of song phases is more important in terms of

identifying individuality of vocalisation in female Bornean agile gibbons (H. agilis

albibarbis). It appears that when individuals are singing, and as such trying to identify

themselves to other individuals in the area, it is the length of the song phases,

especially the length of the entire vocalisation, the length of the pre-introduction

phase and the length of the great call itself. This would suggest that individuals read

110

more into the information coded into others’ songs through the length of the

vocalisation itself as well as the length of the different phases of the song as well.

When investigating which song characteristics across the two methods carry the

greatest weight in effecting levels of individuality of song we see some similarities.

Both the normal method as well as the rotated method ranks the same characteristics

in the top 5 of most importance. These song phases are as follows: duration of the

total song, duration of the introduction phase, the number of notes of the introduction

phase, the duration of the great call itself, and the number of notes of the great call

itself.

This pattern makes sense. All of these song characteristics are lengthy phases in the

total vocalisation, and as such have the ability to display greater amounts of variation

when comparing across the individuals. This shows us that the longer the duration of

the entire song or song phase, then the greater the chance of containing an individual

song characteristics for the given individual. Means of these song characteristics for

each individual can be found in Appendix 44.

When looking at the song characteristics that have the least impact on individual’s

song uniqueness, both PCA extraction methods gives us the same ranks for the bottom

4 characteristics. These are the duration of the inflective note and the duration of the

climax note, the minimum frequency for the entire song and the maximum frequency

for the entire song.

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Again, this makes sense. As can be seen looking at the song characteristics means for

each individual (see Appendix 44) both the duration and the inflective note and the

duration of the climax note are both the shortest song phases in terms of duration. As

these two characteristics are so short in time this leaves a smaller chance of displaying

great levels of individuality across the studied individuals. This means that these two

phases are unlikely to identify anything unique about the singer when considering

these two phases by themselves.

As was outlined in the Introduction of this thesis, there is a restricted frequency range

for gibbon vocalisation. Geissmann (1993) believed this range to be 200Hz as a

minimum and 5000Hz as a maximum. It must be noted that these ranges are for all

gibbon species as a whole. The data collected in the field while studying female

Borean agile gibbons (H. agilis albibarbis) it was noted that this maximum frequency

range is not as high for the gibbons studied in this project. As can be seen in

Appendix 44, the maximum mean frequency for an individual was 2060 Hz. At the

same time, the minimum mean frequency for an individual was 565 Hz. This means

that for this study, the mean frequency range for the 14 studied individuals was 565 –

2060 Hz, giving a 1495 Hz ‘window’ for variation to occur within. As this is a small

frequency range, not much variation can occur across the female individuals when

considering minimum and maximum frequencies as an important contributing factor

towards individuality of vocalisation.

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4.1.2 PCA identifying degree of individuality of vocalisation for individual

female Bornean agile gibbons (H. agilis albibarbis)

The results of the Principle Component Analysis could also be used to identify

whether or not the individuals that were studied actually displayed individual forms of

vocalisation. This was done by plotting on a scattergraph the two most significant

components produced by the standard PCA. In this case, these two components

accounted for 51.546% of all variation in vocalisation across the 14 studied

individuals, with component 1 making up 30.046% of all variation and component 2

making up 20.500% of all variation. This graph can be seen in Figure 3.4. It is shown

that these studied individuals do in fact have vocalisations that are unique to

themselves and that there is in fact individuality of vocalisation for female Bornean

agile gibbons (H. agilis albibarbis). These findings are consistent with previous

studies on individuality of vocalisation in gibbons (Haimoff 1984b, 1984c, 1985;

Haimoff and Gittins 1985; Haimoff and Tilson 1985; Dallmann and Geissmann

2001a, 2001b). Separate graphs for each individual can be located in the Results

section in Graphs 3.5 – 3.18.

One finding of great interest from the results of creating these individual scatterplots

appeared when investigating female FGK and female FGHH. Here it appears that

there are two songs that have been recorded for these given individuals. This should

not be possible. If gibbon singing serves the purpose of identifying the individual

singer, then an individual should not possess two separate songs, or this would

confuse the inter-group identification dynamics. Closer inspection of these two groups

reveals that there are sub-adult females in these groups. Therefore, the recordings may

113

in fact be of two separate individuals within the same group. If these were younger

female doing the secondary singing it would become obvious that they were juveniles

when viewing the sonograms of the songs for these groups. However, as these

females are almost of adult age they are capable of singing at similar frequencies of

other adult females, and therefore make it difficult to tell that they are not in fact the

focal females. Fortunately, however, all female gibbons develop vocalisations that are

unique to themselves. So, it is with analysis such as this that it makes it possible to

identify a sub-adult female that has been recorded by accident.

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4.2 Use of Kruskal-Wallis to test for individuality of female Bornean agile gibbon

(H. agilis albibarbis) vocalisation

To supplement the results of the Principle Components Analysis, which provide a

visual tool for viewing output, when investigation individuality of singing in he

studied females, a further test was used. This was the Kruskal-Wallis test for one-way

analysis of variance of ranks. This would not only identify if each studied female

would display individuality of vocalisation, but would also test to see if each of the 14

song characteristics would display levels of individuality across the studied females.

The Kruskal-Wallis results agreed with the results found for the PCA. As could be

expected from previous studies on individuality of vocalisation (Haimoff 1984b,

1984c, 1985; Haimoff and Gittins 1985; Haimoff and Tilson 1985; Dallmann and

Geissmann 2001a, 2001b) the studied female Bornean agile gibbons (H. agilis

albibarbis) display unique vocalisations when compared to the other studied females.

Unlike the PCA test which enabled an insight into which characteristics actually have

a greater impact on individuality, the Kruskal-Wallis tells us whether or not they have

a significant impact on this individuality. These results confirm that the females are

individual in their vocalisations and that each song characteristic display degrees of

individuality when comparing the results from the studied females. Breakdowns of the

results of the Kruskal-Wallis testing can all be found in the Appendix section of this

thesis.

So, from the results of PCA analysis and Kruskal-Wallis analysis it is clear that

female Bornean agile gibbons (H. agilis albibarbis) do possess individual songs.

115

Further to this, we get an insight into the importance, when contributing to these

levels of individuality, that each of the 14 song characteristics carries, and that each of

these characteristics are all individual when comparing across the 14 studied females

in this study.

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4.3 Effect of Physical Distance Apart of Individuals on Levels of Individuality of

Vocalisation

In order to test the hypothesis that physical distance between individuals has an effect

on levels of individuality of vocalisation, two tests were developed. As with the use of

both the PCA and Kruskal-Wallis tests when testing for individuality, it was felt that

the use of two tests would provide a means of confirming the results found.

The results of these two tests give us an insight into whether or not the theory that

gibbons inherit their songs genetically (Geissmann 1984, 1993, 2000a) holds true. If

the hypothesis is accepted, then this would lead to conclusions that perhaps gibbon

songs are not rigidly controlled by genetically inheritance, but can be altered to adapt

to their surroundings. However, if the hypothesis was rejected, and the null hypothesis

accepted, conclusions could be drawn that support the idea that these songs are

genetically inherited.

It should be understood that if female gibbons’ vocalisations contain unique

information about the singer’s identity, paired status and territory, then songs should

be individual. Female gibbons sing less than males and normally only when taking

part in duets and it has been shown that duetting serves these very purposes (Mitani

1984, 1985, 1987; Raemaekers and Raemaekers 1984a, b; Haimoff 1985; Leighton

1987; Cowlishaw 1992; Geissmann 1999; Geissmann and Orgeldinger 2000). It has

already been established that individuality for these studied females is the case.

Further to this, in order to make individuals stand out from neighbouring individuals,

or individuals within hearing range, then there should be greater levels of

individuality when compared to other individuals that are in close proximity to each

other. This would serve to differentiate between individuals for listeners.

117

Difference between all individuals’ (91 possibilities) Principle Components Analysis

scores were either correlated to physical distance apart between individuals or

Euclidean distance apart between individuals. If there was a relationship between

distance apart between individuals and the degree of individuality of vocalisation

between studied individuals, then we would expect to see a positive relationship

between these two factors when plotted against each other. In both comparisons that

were used, PCA difference Vs physical distance apart and PCA difference Vs

Euclidean distance apart, this is not the case. Rather, we see a random scattering of

the 91 possible comparisons, making it impossible to accept the hypothesis. Rather,

we can state with all confidence that individuality of vocalisation of the studied

female Bornean agile gibbons (H. agilis albibarbis) has no relationship with distances

between the individuals. Distance apart has no effect on individuals’ levels of

uniqueness of vocalisation.

What then does this suggest? Firstly, a logically, we can assume that gibbons are

intelligent enough to be able to recognise and differentiate between multiple

neighbours even if there is not massive differences in vocalisation. Yes, their

vocalisations are different, and clearly different enough to be able to differentiate

between individuals. It was expected prior to this study being carried out that there

would be a relationship between distance between individuals and degrees of

individuality of singing. It was expected that there would be less difference between

the vocalisations of individuals who could not hear each other, while there would be

greater differences in singing for individuals who could hear each other. Clearly

gibbons do not require larger differences in vocalisations to be able to identify

different individuals.

118

Perhaps then vocalisations of individuals are random. Individuals inherit there songs

genetically (Geissmann 1984, 1993, 2000a), rather than develop them as they mature

or by acquiring them from their parents. As such it could be said that each gibbon is

born with a unique song, as each human is born with a unique voice, and this song is

not related to the differences or similarities of the songs of their neighbours.

Maybe gibbons use other forms of communication to identify themselves in the

jungle. There are three ways that gibbons are able to communicate. These are vocal,

olfactory and visual. Encounters between individual groups within sight range are

fairly rare. Even so, as mentioned in the Introduction section of this paper, visually

gibbons are very similar and also show low levels of sexual dimorphism. As such

visual communication would likely prove least helpful in the identification of

individuals in their encounters. Encounters are low due to the size of their territories

and the denseness of the jungles they inhabit. Therefore, the majority of inter-group

communication is done through the means of singing. If, however, vocalisations may

not be used as the sole purpose of identification between individuals, perhaps

olfactory communication plays a part. In his 1993 PhD thesis, Geissmann investigated

all of the three forms of communication in gibbons. He concluded that gibbons

possessed a “surprisingly complex glandular system” which revolved around sternal

glands. It could therefore be argued that gibbons can identify individuals through

scent as well as by listening to other individuals.

Despite this, I believe that the majority of gibbon communication is conducted via the

vocal form of communication. This is a belief widely shared, as can be seen by the

vast amount of vocal studied on gibbons when compared to the number of olfactory

communication studies that have been conducted on gibbons. It has been shown by

119

the results of this study that gibbons do not require huge differences in the unique

songs that are possessed by individuals in comparison to other female gibbons. This

holds true with the belief that these songs are genetically inherited anyway, which

means that gibbons wouldn’t be able to alter their songs anyway in order to

differentiate themselves from other gibbons. When taking into account that this is

considered to be the primary form of communication in gibbons it suggests that

gibbons are intelligent enough to be able to identify between other individuals through

vocal communication, even if the degree of difference may not be as great as may

have been expected.

Regardless of this, these results help to gain some insight into whether or not gibbon

songs are inherited genetically. If a positive relationship had been discovered between

physical distance apart and differences in vocalisations then there is a suggestion that

these vocalisations have been or can be altered to adapt to their surroundings. This

would mean that gibbons can alter their singing in order to differentiate themselves

from their neighbours, to make themselves more readily identifiable. If no

relationship was found, then this would support the belief that gibbons vocalisations

are inherited genetically (Geissmann 1984, 1993, 2000a) and that they are not learnt

from their parents. Furthermore, this would suggest that gibbons cannot, or do not

need to, alter their singing to identify themselves in their natural habitat.

The results of this investigation into whether or not physical distance between

individuals is a driving factor behind the degree of variation in vocalisation between

individuals, has led to the following conclusion. That, as has been reported before

(Geissmann 1984, 1993, 2000a), gibbon songs are genetically inherited and that their

120

song templates either do not need changing or cannot be changed by the individual

singer.

121

4.4 Suggestions for Further Studies

I believe that the greatest problem conducting this study was that it was not possible

to always be within visual range of singing individuals. Of course this was not

possible to do with the unhabituated individuals on the study grid in the Sebangau

National Park. Unhabituated gibbons will often not sing in the presence of humans.

As well as this, mobility in the peat swamp forest is particularly low due to the

extreme density of the mixed swamp forest. Mobility is made even more difficult

when carrying recording equipment through the forest. Despite being very confident

that recordings were being made of the right individuals due to the access to an

excellent map of the grid system and gibbon group home ranges, as well as being

assisted by locals with good knowledge of the resident groups, it can never be claimed

with 100% confidence that you are recording the individual you had planned to

without actually making visual contact with that individual while singing. As well as

this gibbons have excellent mobility on this forest and can travel away (through use of

brachiation) and be a great distance away in a very short amount of time. This makes

it further difficult to always be within visual contact. Despite this, for any future

studies of this kind, I would recommend a study to be carried out on habituated

individuals and to attempt to make and keep up visual contact with singers as often as

possible.

122

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Appendices

134

Appendix 1: Female Group C Song Analysis Data

135

Appendix 2: Kruskal-Wallis Data for Duration of Total Vocalisation Ranks Group N Mean Rank

2 20 128.63 3 22 151.45 4 12 128.33 5 21 96.26 6 16 191.63 7 25 198.08 8 21 43.60 9 24 115.31 10 31 202.52 11 11 218.00 12 21 90.62 13 10 214.75 14 26 224.77 15 26 50.13

Total Duration

Total 286 Test Statistics(a,b) Total Duration Chi-Square 156.799 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

136

Appendix 3: Boxplot for Duration of Total Vocalisation

15141312111098765432

Group

24.00

22.00

20.00

18.00

16.00

14.00

12.00

Tota

l Dur

atio

n (s

econ

ds)

172

208205

184

191175

226

72

137

Appendix 4: Error Bar Graph for Duration of Total Vocalisation

15141312111098765432

Group

20.00

17.50

15.00

95%

CI T

otal

Dur

atio

n (s

econ

ds)

138

Appendix 5: Kruskal-Wallis Data for Number of Notes of Total Vocalisation Ranks Group N Mean Rank

2 20 110.93 3 22 171.50 4 12 192.25 5 21 135.52 6 16 105.41 7 25 117.56 8 21 133.55 9 24 57.38 10 31 238.95 11 11 220.55 12 21 70.86 13 10 127.25 14 26 207.60 15 26 119.15

Total No. Notes

Total 286 Test Statistics(a,b)

Total No. Notes

Chi-Square 130.538 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

139

Appendix 6: Boxplot for Number of Notes of Total Duration

15141312111098765432

Group

18

15

12

Tota

l No.

Not

es (s

econ

ds)

21

79

98

140

Appendix 7: Error Bar Graph for Number of Notes of Total Vocalisation

15141312111098765432

Group

17

16

15

14

13

12

11

10

95%

CI T

otal

No.

Not

es (s

econ

ds)

141

Appendix 8: Kruskal-Wallis Data for Duration of Pre-Introduction Phase Ranks Group N Mean Rank

2 20 141.63 3 22 187.27 4 12 225.67 5 21 173.26 6 16 51.00 7 25 102.10 8 21 163.21 9 24 66.71 10 31 232.94 11 11 141.05 12 21 98.76 13 10 36.40 14 26 169.42 15 26 143.44

Duration Pre-Intro Phase

Total 286 Test Statistics(a,b)

Duration Pre-Intro Phase

Chi-Square 130.632 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

142

Appendix 9: Boxplot for Duration of Pre-Introduction Phase

15141312111098765432

Group

6.00

5.00

4.00

3.00

2.00

1.00

0.00

Dur

atio

n Pr

e-In

tro

Phas

e (s

econ

ds)

25

21

184

153

63100

143

Appendix 10: Error Bar Graph for Duration of Pre-Introduction Phase

15141312111098765432

Group

4.00

3.00

2.00

1.0095%

CI D

urat

ion

Pre-

Intr

o Ph

ase

(sec

onds

)

144

Appendix 11: Kruskal-Wallis data for Number of Notes of Pre-Introduction Phase Ranks Group N Mean Rank

2 20 157.95 3 22 185.59 4 12 236.92 5 21 185.29 6 16 49.16 7 25 99.16 8 21 147.43 9 24 62.23 10 31 226.87 11 11 165.68 12 21 106.45 13 10 47.05 14 26 172.44 15 26 121.73

No. Notes Pre-Intro Phase

Total 286 Test Statistics(a,b)

No. Notes Pre-Intro Phase

Chi-Square 138.662 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

145

Appendix 12: Boxplot for Number of Notes of Pre-Introductory Phase

15141312111098765432

Group

12

10

8

6

4

2

No.

Not

es P

re-In

tro

Phas

e (s

econ

ds)

21

79

228

227

14

280

80

179

63

67

100

98

146

Appendix 13: Error Bar Graph for Number of Notes of Pre-Introductory Phase

15141312111098765432

Group

8

7

6

5

4

3

2

95%

CI N

o. N

otes

Pre

-Intr

o Ph

ase

(sec

onds

)

147

Appendix 14: Kruskal-Wallis data for Duration of Introduction Phase Ranks Group N Mean Rank

2 20 186.70 3 22 174.16 4 12 115.21 5 21 96.26 6 16 225.03 7 25 246.60 8 21 89.60 9 24 48.33 10 31 78.98 11 11 204.77 12 21 94.76 13 10 242.15 14 26 235.50 15 26 78.04

Duration of Intro Phase

Total 286 Test Statistics(a,b)

Duration of Intro Phase

Chi-Square 206.718 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

148

Appendix 15: Boxplot for Duration of Introduction Phase

15141312111098765432

Group

12.00

10.00

8.00

6.00

4.00

2.00

0.00

Dur

atio

n of

Intr

o Ph

ase

(sec

onds

)

150

280

80

137

149

Appendix 16: Error Bar Graph for Duration of Introduction Phase

15141312111098765432

Group

9.00

8.00

7.00

6.00

5.00

4.00

3.00

95%

CI D

urat

ion

of In

tro

Phas

e (s

econ

ds)

150

Appendix 17: Kruskal-Wallis data for Number of Notes of Introduction Phase Ranks Group N Mean Rank

2 20 187.95 3 22 149.27 4 12 103.00 5 21 87.33 6 16 211.63 7 25 252.48 8 21 116.71 9 24 32.63 10 31 85.26 11 11 214.00 12 21 70.52 13 10 215.30 14 26 241.96 15 26 118.23

No. Notes Intro Phase

Total 286 Test Statistics(a,b)

No. Notes Intro Phase

Chi-Square 223.286 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

151

Appendix 18: Boxplot for Number of Notes of Introduction Phase

15141312111098765432

Group

6

4

2No.

Not

es In

tro

Phas

e (s

econ

ds)

261

91

80

118

133

146

226

228

152

Appendix 19: Error Bar Graph for Number of Notes of Introduction Phase

15141312111098765432

Group

5

4

3

295%

CI N

o. N

otes

Intr

o Ph

ase

(sec

onds

)

153

Appendix 20: Kruskal-Wallis data for Duration of Great Call Ranks Group N Mean Rank

2 20 127.38 3 22 132.18 4 12 72.96 5 21 78.98 6 16 229.19 7 25 219.96 8 21 35.21 9 24 155.50 10 31 167.26 11 11 222.09 12 21 119.48 13 10 251.25 14 26 218.60 15 26 41.58

Duration of Great Call

Total 286 Test Statistics(a,b)

Duration of Great Call

Chi-Square 189.892 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

154

Appendix 21: Boxplot for Duration of Great Call

15141312111098765432

Group

22.00

20.00

18.00

16.00

14.00

12.00

10.00

Dur

atio

n of

Gre

at C

all (

seco

nds)

196

172

226

10

214

274

191

144

141

230

155

Appendix 22: Error Bar Graph for Duration of Great Call

15141312111098765432

Group

18.00

16.00

14.00

12.0095%

CI D

urat

ion

of G

reat

Cal

l (se

cond

s)

156

Appendix 23: Kruskal-Wallis data for Number of Notes of Great Call Ranks Group N Mean Rank

2 20 72.03 3 22 137.73 4 12 64.33 5 21 65.24 6 16 229.19 7 25 184.30 8 21 124.31 9 24 110.73 10 31 192.23 11 11 241.00 12 21 52.90 13 10 237.40 14 26 197.96 15 26 140.15

No. Notes Great Call

Total 286 Test Statistics(a,b)

No. Notes Great Call

Chi-Square 160.416 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

157

Appendix 24: Boxplot for Number of Notes of Great Call

15141312111098765432

Group

12

10

8

No.

Not

es G

reat

Cal

l (se

cond

s)

14429 78

151

138

158

Appendix 25: Error Bar Graph for Number of Notes of Great Call

15141312111098765432

Group

11

10

9

8

7

95%

CI N

o. N

otes

Gre

at C

all (

seco

nds)

159

Appendix 26: Kruskal-Wallis data for Duration of Inflective Note Ranks Group N Mean Rank

2 20 119.65 3 22 173.55 4 12 121.75 5 21 144.69 6 16 147.13 7 25 160.12 8 21 108.33 9 24 218.83 10 31 79.05 11 11 13.05 12 21 240.74 13 10 86.70 14 26 137.33 15 26 167.67

Duration of Inflective Note

Total 286 Test Statistics(a,b)

Duration of Inflective Note

Chi-Square 112.483 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

160

Appendix 27: Boxplot for Duration of Inflective Note

15141312111098765432

Group

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

Dur

atio

n of

Infle

ctiv

e N

ote

(sec

onds

)

54

43

22

161

Appendix 28: Error Bar Graph for Duration on Inflective Note

15141312111098765432

Group

2.0

1.8

1.6

1.4

1.2

1.0

0.8

95%

CI D

urat

ion

of In

flect

ive

Not

e (s

econ

ds)

162

Appendix 29: Kruskal-Wallis data for Duration of Climax Note Ranks Group N Mean Rank

2 20 185.30 3 22 144.14 4 12 132.83 5 21 169.95 6 16 142.44 7 25 206.78 8 21 46.19 9 24 207.38 10 31 69.47 11 11 116.18 12 21 188.21 13 10 113.40 14 26 168.29 15 26 104.31

Duration of Climax Note

Total 286 Test Statistics(a,b)

Duration of Climax Note

Chi-Square 107.182 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

163

Appendix 30: Boxplot for Duration of Climax Note

15141312111098765432

Group

2.40

2.10

1.80

1.50

1.20

0.90

Dur

atio

n of

Clim

ax N

ote

(sec

onds

)

204

122

172

138

164

Appendix 31: Error Bar Graph for Duration of Climax Note

15141312111098765432

Group

2.00

1.80

1.60

1.40

1.20

95%

CI D

urat

ion

of C

limax

Not

e (s

econ

ds)

165

Appendix 32: Kruskal-Wallis data for Duration of Post Climax Phase Ranks Group N Mean Rank

2 20 57.25 3 22 109.55 4 12 109.38 5 21 111.86 6 16 168.44 7 25 84.06 8 21 80.14 9 24 236.60 10 31 249.18 11 11 222.09 12 21 151.71 13 10 215.40 14 26 134.52 15 26 102.37

Duration of Post Climax

Total 286 Test Statistics(a,b)

Duration of Post Climax

Chi-Square 162.736 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

166

Appendix 33: Boxplot for Duuration of Post Climax Phase

15141312111098765432

Group

10.00

8.00

6.00

4.00

2.00

0.00

Dur

atio

n of

Pos

t Clim

ax (s

econ

ds)

10929

134

172

111

101

167

Appendix 34: Error Bar Graph for Duration of Post Climax Phase

15141312111098765432

Group

8.00

7.00

6.00

5.00

4.00

3.00

2.00

95%

CI D

urat

ion

of P

ost C

limax

(sec

onds

)

168

Appendix 35: Kruskal-Wallis data for Number of Notes of Post Climax Phase Ranks Group N Mean Rank

2 20 27.00 3 22 133.66 4 12 99.42 5 21 115.60 6 16 183.63 7 25 54.98 8 21 151.45 9 24 211.42 10 31 237.82 11 11 199.50 12 21 123.81 13 10 204.80 14 26 110.75 15 26 164.56

No.Notes Post Climax

Total 286 Test Statistics(a,b)

No.Notes Post Climax

Chi-Square 166.120 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

169

Appendix 36: Boxplot for Number of Notes of Post Climax Phase

15141312111098765432

Group

7

6

5

4

3

2

1

No.

Not

es P

ost C

limax

(sec

onds

)

20569

6545

29

78

144

170

Appendix 37: Error Bar Graph for Number of Notes of Post Climax Phase

15141312111098765432

Group

5.5

5

4.5

4

3.5

3

2.5

2

95%

CI N

o.N

otes

Pos

t Clim

ax (s

econ

ds)

171

Appendix 38: Kruskal-Wallis data for Minimum Frequency of Vocalisation Ranks Group N Mean Rank

2 20 238.18 3 22 42.75 4 12 163.00 5 21 162.05 6 16 197.38 7 25 116.20 8 21 145.98 9 24 135.50 10 31 192.27 11 11 188.68 12 21 86.69 13 10 157.50 14 26 160.88 15 26 76.27

Min. Freq.

Total 286 Test Statistics(a,b) Min. Freq. Chi-Square 137.466 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

172

Appendix 39: Boxplot for Minimum Frequency of Vocalisation

15141312111098765432

Group

1000

900

800

700

600

500

400

Min

imum

Fre

quen

cy (H

z)

133 24122823

173

Appendix 40: Error Bar Graph for Minimum Frequency of Vocalisation

15141312111098765432

Group

850

800

750

700

650

600

550

500

95%

CI M

inim

um F

requ

ency

(Hz)

174

Appendix 41: Kruskal-Wallis data for Maximum Frequency of Vocalisation Ranks Group N Mean Rank

2 20 35.45 3 22 125.45 4 12 187.29 5 21 164.95 6 16 124.78 7 25 110.70 8 21 160.05 9 24 273.58 10 31 192.79 11 11 30.41 12 21 127.29 13 10 21.60 14 26 112.23 15 26 194.29

Max Freq.

Total 286 Test Statistics(a,b) Max Freq. Chi-Square 192.753 df 13 Asymp. Sig. .000

a Kruskal Wallis Test b Grouping Variable: Group

175

Appendix 42: Boxplot for Maximum Frequency of Vocalisation

15141312111098765432

Group

2200

2000

1800

1600

1400

1200

1000

Max

imum

Fre

quen

cy (H

z)

24 76

133 141

230

73

67

7

4

162

156

139

55

53

176

Appendix 43: Error Bar Graph for Maximum Frequency of Vocalisation

15141312111098765432

Group

2100

2000

1900

1800

1700

1600

1500

1400

95%

CI M

axim

um F

requ

ency

(Hz)

177

Appendix 44: Means for song characteristics across studied female individuals.

Group

Mean Total Duration (s)

Mean Total Notes

Mean Pre-Intro Duration (s)

Mean Pre-Intro Notes

Mean Intro Duration (s)

Mean Intro Notes

Mean G/C Duration (s)

Mean G/C Notes

Mean Inflective Duration (s)

Mean Climax Duration (s)

Mean Post-Climax Duration (s)

Mean Post-Climax Notes

Mean Min. Frequency (Hz)

Mean Max. Frequency (Hz)

2 16.95 12.95 2.50 5.20 6.56 3.75 14.05 7.75 1.39 1.76 2.51 2.00 769.00 1481.00

3 17.67 14.14 2.96 5.64 6.28 3.27 14.36 8.64 1.58 1.64 3.25 3.32 565.00 1691.00

4 16.95 14.67 3.60 7.00 5.10 2.75 12.92 7.67 1.41 1.60 3.16 2.92 679.00 1776.00

5 16.27 13.48 3.02 5.81 4.83 2.57 13.04 7.67 1.48 1.72 3.37 3.00 678.00 1744.00

6 18.62 12.88 1.38 3.00 7.48 4.00 16.86 9.88 1.49 1.63 4.42 3.88 704.00 1692.00

7 19.29 13.48 2.07 4.32 8.36 4.88 16.73 9.20 1.55 1.88 2.94 2.32 642.00 1685.00

A 15.08 13.43 2.74 5.00 4.68 2.90 11.96 8.43 1.38 1.32 2.90 3.57 666.00 1744.00

C 16.73 11.42 1.53 3.21 3.94 1.88 14.84 8.29 1.74 1.84 5.96 4.00 658.00 2060.00

E 19.04 16.03 3.73 6.71 4.53 2.55 15.05 9.32 1.30 1.42 6.53 4.77 700.00 1778.00

HH 19.29 15.27 2.48 5.27 6.98 4.00 16.47 10.00 1.00 1.55 5.37 4.00 703.00 1526.00

K 16.13 11.71 1.92 4.14 4.82 2.38 13.94 7.48 1.78 1.75 3.76 3.00 619.00 1705.00

N 19.30 13.30 1.21 3.00 8.12 4.10 17.68 10.30 1.31 1.54 5.21 4.20 674.00 1497.00

PO 19.59 15.20 2.89 5.60 7.50 4.45 16.33 9.60 1.42 1.65 4.12 3.15 674.00 1646.00

S 15.13 13.12 2.56 4.50 4.54 2.92 12.20 8.62 1.55 1.52 3.15 3.65 610.00 1778.00